Bioinformatics Platforms Market: Year 2017-2027 and its detail analysis by focusing on top key players like Illumina, Qiagen, ID Business Solutions,…

Bioinformatics Platforms Market 2020 Global Industry research report explores analysis of historical data along with size, share, growth, demand, revenue and forecast of the global Bioinformatics Platforms and estimates the future trend of market on the basis of this detailed study. The study shares market performance both in terms of volume and revenue and this factor which is useful & helpful to the business.

Bioinformatics is one of the branch of information technology which deals with the development of software solutions in order to process biological data. Some of the applications included in the bioinformatics research includes, genome annotation, modeling, molecular folding, expression profiling, and gene/protein prediction. The emergence and advancements in bioinformatics are associated with the computerized programming which are specially designed to handle large volumes of DNAs, RNAs, proteins, and metabolites.

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The report provides a detailed overview of the industry including both qualitative and quantitative information. It provides an overview and forecast of the global Bioinformatics Platforms market based on the deployment type and application. It also provides market size and forecast till 2027 for overall Bioinformatics Platforms market with respect to five major regions, namely; North America, Europe, Asia-Pacific (APAC), Middle East and Africa (MEA) and South America (SAM). The market by each region is later sub-segmented by respective countries and segments. The report covers the analysis and forecast of 18 countries globally along with the current trend and opportunities prevailing in the region.

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Bioinformatics Platforms Market Company Profiles

The List of Companies Illumina Inc., Qiagen, ID Business Solutions,, Dassault Systems, Agilent Technologies, Genologics Life Sciences Software Inc., Sophia Genetics, DNASTAR, Wuxi NextCODE,BGI

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Table Of Content

1.INTRODUCTION

2. KEY TAKEAWAYS3. RESEARCH METHODOLOGY4. BIOINFORMATICS PLATFORMS LANDSCAPE

5. BIOINFORMATICS PLATFORMS KEY MARKET DYNAMICS

6. BIOINFORMATICS PLATFORMS GLOBAL MARKET ANALYSIS

7. BIOINFORMATICS PLATFORMS REVENUE AND FORECASTS TO 2027 PLATFORM TYPE

8. BIOINFORMATICS PLATFORMS REVENUE AND FORECASTS TO 2027 APPLICATION

9. BIOINFORMATICS PLATFORMS REVENUE AND FORECASTS TO 2027 END USER

10. BIOINFORMATICS PLATFORMS REVENUE AND FORECASTS TO 2027 GEOGRAPHICAL ANALYSIS

11. INDUSTRY LANDSCAPE

12. BIOINFORMATICS PLATFORMS, KEY COMPANY PROFILES

13. APPENDIX

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Bioinformatics Platforms Market: Year 2017-2027 and its detail analysis by focusing on top key players like Illumina, Qiagen, ID Business Solutions,...

A nucleotidyltransferase toxin inhibits growth of Mycobacterium tuberculosis through inactivation of tRNA acceptor stems – Science Advances

INTRODUCTION

Toxin-antitoxin (TA) systems are widely distributed throughout prokaryotic genomes and have been shown to help bacteria to survive predation by bacteriophages, immune responses, and antibiotic treatments (15). In many cases, however, the roles of chromosomal TA systems remain largely unknown, primarily due to the lack of a phenotype associated with deletion mutants under in vitro laboratory conditions (69). TA systems are also widespread among mobile genetic elements, including plasmids, superintegrons, cryptic prophages, and conjugative transposons, where they contribute to their stability (10, 11).

TA systems encode two components, a toxic protein that targets an essential cellular process and an antagonistic antitoxin, which blocks toxin activity when cells are growing under favorable conditions. Although the processes that lead to toxin activation remain under debate, it has been proposed that under certain stress conditions, increased toxin transcription and synthesis may lead to activation (8, 12). This, in turn, reduces growth rate, which can provide a means to survive with minimal metabolic burden until favorable conditions return (13).

TA systems are divided into six types according to the nature of the toxin and antitoxin (whether they are RNA or protein) and the mechanism of toxin antagonism (3). Type II systems, in which a protein toxin is sequestered by a protein antitoxin, have been most extensively studied. They are also remarkably abundant in Mycobacterium tuberculosis, which potentially encodes more than 80 type II TA systems, and are thought to have contributed to the success of M. tuberculosis as a human pathogen (1416). Many of the putative M. tuberculosis toxins tested thus far were shown to inhibit bacterial growth, suggesting that these TA systems are functionally active and could modulate M. tuberculosis growth under certain conditions, thereby contributing to survival in the human host (15, 17). Accordingly, many M. tuberculosis TA operons were shown to be induced in response to relevant stressors, including hypoxia, the presence of antimicrobial drugs, or macrophage engulfment (14, 17). As M. tuberculosis encodes, among others, more than 50 VapBC, 10 MazEF, 3 HigBA, and 3 RelBE TA systems, it might be expected that there is redundancy between them, alongside condition-specific applications for each system. Furthermore, the highly toxic nature of some of these toxins suggests that their antibacterial mechanisms could be developed into antimicrobials (18).

This study focuses on a family of four putative toxins from M. tuberculosis, namely, Rv0078A, Rv0836c, Rv1045, and Rv2826c, which share a conserved nucleotidyltransferase (NTase)like domain annotated as domain of unknown function (DUF) 1814 (Fig. 1A). The most well-characterized example of this DUF1814 family is AbiEii from Streptococcus agalactiae, which shares 18.3% sequence identity with Rv1045, and was identified within the AbiE abortive infection bacteriophage-defense systems (19). AbiEii was shown to constitute a new type of TA system, type IV, based on the observation that no interaction could be detected between the toxin and the antitoxin proteins (20). The DUF1814 family of proteins is widespread in bacterial, archaeal, and fungal genomes (20), though not all examples are genetically linked to putative antitoxins. As putative NTases, DUF1814 proteins contain four conserved motifs. The N-terminal motifs I and II are found in DNA polymerase and are proposed to coordinate a metal ion for nucleotide binding and transfer (20). The C-terminal motif III is similar to that of tRNA NTases that add the 3-CCA motif to immature tRNAs and may be important for base stacking with substrates (21). The C-terminal motif IV is unique to DUF1814 proteins and is proposed to form a catalytic site with motif III (20).

(A) Scaled representation of the four M. tuberculosis TA systems containing NTase-like toxin genes with original and revised nomenclature (left), and corresponding toxicity and antitoxicity assays in M. smegmatis (right). For toxicity and antitoxicity assays, cotransformants of M. smegmatis mc2 155 containing pGMC-vector, -MenT1, -MenT2, -MenT3, or -MenT4 (toxins) and pLAM-vector, -MenA1, -MenA2, -MenA3, or -MenA4 (antitoxins) were plated on LB-agar in the presence or absence of anhydrotetracycline (Atc; 100 ng ml1) and acetamide (Ace; 0.2%) inducers for toxin and antitoxin expression, respectively. Plates were incubated for 3 days at 37C. T and A denote toxin and antitoxin, respectively. and + represent absence or presence of inducer, respectively. (B) M. smegmatis strain mc2 155 transformed with plasmid pGMCS-TetR-P1-RBS1-MenT3 was grown in complete 7H9 medium with Sm. At time 0, the culture was divided into two. Half was kept in the same medium (pale blue bars) and half was additionally treated with Atc (200 ng ml1) (dark blue bars). Samples were harvested at the indicated times, washed, diluted, and plated on LB-agar with Sm but without Atc. Colonies were counted after 3 days at 37C. Shown values are the average of three biological replicates with SD. CFU, colony-forming unit. (C) Samples of the same cultures as in (B) were harvested after 8 or 24 hours, labeled with the LIVE/DEAD BacLite dyes [Syto 9; propidium iodide (PI)], and analyzed by fluorescence-activated cell sorting. The percentage of PI-positive cells is shown for each sample (pale blue bars, no Atc; dark blue bars, 200 ng ml1 Atc). Shown values are the average of three biological replicates with SD. (D) M. tuberculosis wild-type (WT) H37Rv or mutant strain H37Rv (menA3-menT3)::dif6 were transformed with 100 ng of plasmids expressing either menA3, menT3, or menA3-menT3. These plasmids encode a consensus Shine-Dalgarno sequence (RBS1), except for Weak-RBS-menT3, which encodes a near-consensus sequence (RBS4) to weaken expression. After phenotypic expression, half of the transformation mix was plated on 7H11 oleic acidalbumin-dextrose-catalase (OADC) plates with Sm, and the other half was plated on 7H11 OADC Sm plates supplemented with Atc (200 ng ml1). Plates were imaged after 20 days at 37C; data are representative of three independent experiments. (E) Mutant strain H37Rv (menA3-menT3)::dif6 was transformed with 100 ng of plasmids expressing either menT3 WT or mutant alleles introducing the D80A, K189A, or D211A substitutions. After phenotypic expression, half of the transformation mix was plated on 7H11 OADC plates with Sm, and the other half was plated on 7H11 OADC Sm plates supplemented with Atc (200 ng ml1). Pictures were taken after 20 days at 37C; data are representative of three independent experiments.

In M. tuberculosis, the DUF1814 toxins are encoded downstream of a variety of putative antitoxins (Fig. 1A). The toxin gene rv0078A is paired with a short upstream open reading frame encoding a 68amino acid antitoxin, Rv0078B, related to MazE antitoxins, which is predicted to be disordered and lacking a DNA-binding domain (16). Toxin gene rv0836c lies downstream of a COG4861 gene, encoding a much larger putative antitoxin than the cognate toxin (Fig. 1A). Rv1045 and Rv2826c toxins are downstream of their cognate putative antitoxins Rv1044 and Rv2827c, respectively, both of which are COG5340 transcriptional regulator family proteins (Fig. 1A). COG5340 proteins include the S. agalactiae AbiEi antitoxin partner of AbiEii, which has previously been shown to bind to and repress the abiE promoter, similar to autoregulation observed in type II TA loci (22). An earlier transposon site hybridization study identified both the Rv1044 and Rv2827c antitoxins as essential for growth (23). Saturating transposon mutagenesis has additionally demonstrated that Rv1044 is essential, while transposon insertions in Rv2827c impart a growth defect (24). The fact that both antitoxins are important for M. tuberculosis growth strongly suggests that their putative cognate Rv1045 and Rv2826c toxins inhibit growth in M. tuberculosis.

Here, we undertook a series of microbiological, structural, genetic, and biochemical studies to investigate the DUF1814 toxins of M. tuberculosis and reveal their mode of action. We show that the Rv1045 toxin is a tRNA NTase that is active in M. tuberculosis and blocks translation through a previously undescribed mechanism involving inactivation of serine tRNAs.

We first investigated the activity of the putative TA systems containing NTase-like DUF1814 toxins in Mycobacterium smegmatis, which is closely related to M. tuberculosis and does not encode similar antitoxins (15). On the basis of the findings presented below, we renamed these putative systems as mycobacterial AbiE-like NTase toxins (MenT) and antitoxins (MenA), numbered according to their order in the M. tuberculosis genome (Fig. 1A). Toxins and antitoxins were expressed in trans, with the toxins cloned into the pGMC-integrative plasmid under the control of an anhydrotetracycline (Atc)inducible promoter and the antitoxins into the compatible pLAM plasmid under the control of an acetamide (Ace)inducible promoter (Fig. 1A). Among the four putative toxins, only MenT1 has been tested so far and was shown to be toxic in M. smegmatis when expressed without the upstream open reading frame encoding MenA1, suggesting that MenA1-MenT1 form a functional TA system (16). Accordingly, the data presented in Fig. 1A show that MenT1 toxicity was efficiently counteracted by MenA1 expressed in trans. Both MenA3-MenT3 and MenA4-MenT4 also acted as TA pairs, while MenT2 expression was not toxic (Fig. 1A). Inhibition of MenT4 toxicity could only be achieved when the putative antitoxin was expressed in the context of the menA4-menT4 operon (Fig. 1A). Expression of MenA4 alone from pLAM was toxic (fig. S1A), indicating that MenA4-MenT4 might not function as a typical TA pair under these conditions. Similar experiments performed in Escherichia coli confirmed the phenotypes observed in M. smegmatis for MenA2-MenT2, MenA3-MenT3, and MenA4-MenT4 (including MenA4 toxicity), but not for MenT1, which exhibited no detectable toxicity in E. coli (fig. S1B). Last, coexpression of the active toxins with noncognate antitoxins did not reveal any detectable cross-talk between the different TA pairs (fig. S1, A and C). Note that cross-talk assays with MenA4 antitoxin expressed from pLAM in M. smegmatis could not be performed because of its toxicity.

Ectopic expression of MenT3 in the presence of inducer showed the most robust toxicity in both M. smegmatis and E. coli when compared to the other toxins (Fig. 1A and fig. S1). In M. smegmatis, only a few MenT3 transformants were obtained, even in the absence of inducer. Ectopic expression of MenT3 in M. smegmatis induced a rapid drop of about 3-log10 in colony-forming units only 2 hours after induction with Atc (Fig. 1B). LIVE/DEAD BacLight stains have previously been used to study the effects of toxin expression on cell viability in M. tuberculosis (18). Flow cytometry analysis of M. smegmatis expressing MenT3 revealed that the proportion of propidium iodidepermeable cells was substantially higher in MenT3-induced versus noninduced cells 8 or 24 hours after induction with Atc (Fig. 1C), indicating that MenT3 strongly affects cell viability.

To investigate the impact of MenA3 and MenT3 on M. tuberculosis growth, plasmids encoding the toxin, the antitoxin, or both, were introduced into H37Rv wild-type (WT) strain. The resulting transformants were not sensitive to ectopic expression of MenT3 (Fig. 1D), presumably because endogenous MenA3 was sufficient to neutralize the sum of endogenous and ectopic MenT3. To confirm this hypothesis, we attempted to construct a strain deleted for the menA3-menT3 operon. Previous work showed that menA3 cannot be disrupted by transposon insertion (24). Accordingly, we found that deletion of the menA3-menT3 operon in M. tuberculosis H37Rv strain could not be achieved, most likely because simultaneous disruption of both genes resulted in a toxic effect from residual MenT3. To circumvent this problem, we constructed the deletion in a derivative of H37Rv carrying a second copy of menA3 constitutively expressed from a pGMC integrative plasmid. Once the menA3-menT3 operon was deleted, it was then possible to remove the ectopic copy of menA3 by pGMC plasmid replacement (fig. S2). The menA3-menT3 mutant became highly sensitive to the MenT3 toxin, even in the absence of inducer (Fig. 1D). Therefore, to finally obtain transformants, menT3 was cloned downstream of a weaker Shine-Dalgarno sequence. Using this construct, we observed inducible MenT3 toxicity, which was fully abolished by the presence of the antitoxin (Fig. 1D). Together, these data demonstrate that the MenT3 toxin inhibits growth and that MenA3-MenT3 functions as a bona fide TA pair in M. tuberculosis.

A previous amino acid sequence alignment of DUF1814 putative NTases highlighted conserved residues, a number of which were confirmed as essential for AbiEii toxicity in S. agalactiae (20). To investigate whether some of these residues were important for MenT3 toxicity, we selected and engineered three conserved residues for substitution: D80A, localized in the DNA pol superfamily motif, and K189A and D211A, both toxin-specific residues. We then tested the impact of these substitutions on M. tuberculosis growth (Fig. 1E). All three substitutions abolished MenT3 toxicity in both M. tuberculosis (Fig. 1E) and E. coli (fig. S3A).

Next, we investigated whether the MenT3 toxin and MenA3 antitoxin could interact in vivo. Since this TA pair is functional in E. coli, we performed affinity-tagged in vivo copurification experiments in E. coli using His-tagged variants of MenT3 and MenA3, which were first confirmed to be active as toxin and antitoxin, respectively (fig. S4A). In strains coexpressing both the toxin and the antitoxin (with either the toxin or the antitoxin tagged), and with tagged toxin and tagged antitoxin alone as controls, the in vivo copurification revealed that a small but significant fraction of the MenT3 toxin and the MenA3 antitoxin copurified, whether the toxin or the antitoxin was used as bait (fig. S4, C and D). Similar results were obtained with the MenA1-MenT1 pair, which encodes a much shorter, unrelated antitoxin (fig. S4, B, E, and F). Together, these data show that both TA partners can interact, but it remains to be determined whether a direct interaction between an NTase toxin and its cognate antitoxin is required for toxin inhibition.

To begin investigations into the mechanism of toxicity of MenT3, we solved its structure to 1.6 resolution by x-ray crystallography (Fig. 2A and Table 1). MenT3 is a monomeric bi-lobed globular protein, with two hemispheres connected by a short linker (Fig. 2A). This monomeric assembly matches the expected size observed by size exclusion chromatography. Surface electrostatics show a distinct electropositive surface leading to a deeper recess (Fig. 2B, left), which contains residues D80, K189, and D211 that were needed for toxicity in vivo (Fig. 1E). This potentially indicates the position of the active site, and the electropositive surface may facilitate interaction with electronegative substrates such as nucleic acids. To further characterize the DUF1814 family, we also solved the MenT4 toxin structure to 1.2 resolution (Fig. 2C and Table 1). MenT4 is also monomeric (also observed by size exclusion chromatography) and the overall architecture is similar to, but not exactly the same as, MenT3. MenT4 has a bi-lobed globular structure and distinct electropositive patches close to a similarly positioned active site region (Fig. 2, C and D). Aligning MenT3 and MenT4 by sequence gave a poor root mean square deviation (RMSD) of 13.4 ; however, this can be improved to 4.7 using sequence-independent superposition, which demonstrates similarity in overall fold (Fig. 2E). A close-up of MenT3 residues D80, K189, and D211 show them clustered at the putative active site, and when overlaid, the homologous MenT4 residues, D69, K171, and D186, respectively, take up similar positions (Fig. 2F). There was also density for a phosphoserine at MenT3 S78, but the corresponding residue in MenT4, S67, was not phosphorylated (Fig. 2F). Searches for structural homologs of MenT3 and MenT4 were performed using the DALI server (25). Among multiple hits for NTases, the best match was for JHP933 from Helicobacter pylori, a predicted NTase encoded by the jhp0933 gene (26). JHP933 aligned to MenT3 with an RMSD of 2.4 , though multiple additional helices were resolved in the MenT3 structure (Fig. 2G). An analysis of the H. pylori genome revealed that the jhp0932 gene lies just upstream of jhp0933 and partially overlaps its coding sequence. The presence of these genes in what appears to be a classic TA configuration suggests that JHP933 may belong to the MenT3/MenT4 family of NTase-like toxins.

(A) Structure of monomeric MenT3 toxin, with views from front and back, shown as cyan cartoon representations. (B) Surface electrostatics of MenT3, viewed as in (A), with red for electronegative and blue for electropositive potential. (C) Structure of monomeric MenT4, with views from front and back, shown as salmon cartoon representations. (D) Surface electrostatics of MenT4, viewed as in (C), colored as per (B). (E) Superposition of MenT4 onto MenT3, viewed and colored as per (A) and (C). (F) Tilted close-up view of the toxin active sites, as indicated by the boxed region of (E). MenT3 residues S78 (phosphorylated), D80, K189, and D211 are indicated, along with the homologous MenT4 residues S67, D69, K171, and D186. (G) Alignment of JHP933 (PDB: 4O8S) as orange cartoon representation, against MenT3 viewed and colored as per (A, left).

MenT3 is the most toxic of the four M. tuberculosis NTase-like toxins tested, both in mycobacteria and in E. coli (Fig. 1 and fig. S1). We therefore took advantage of this robust toxicity to search for E. coli genes that were able to suppress MenT3-mediated growth inhibition when overexpressed. We reasoned that identification of such suppressors might potentially shed light on the cellular processes affected by the toxin. Details of the genetic selection used are described in Materials and Methods. Among the approximately 60,000 clones of the E. coli genomic plasmid library tested in this work, we identified 18 plasmids that passed two rounds of selection and appeared to encode bona fide suppressors of MenT3 toxicity. We observed that the toxin-resistant colonies were noticeably smaller and translucent compared to noninduced cells, indicating that, although notably reduced, MenT3 toxicity is not fully suppressed. Sequencing of the genomic regions encoded by the 18 suppressor plasmids revealed that several of these candidate plasmids harbored the same genomic fragments. Six different suppressor clones encompassing two different regions of the E. coli chromosome were identified. Two of the six suppressor plasmids harbored the ydeA gene, encoding an l-arabinose (l-ara) exporter protein known to decrease l-ara levels in E. coli (27). These suppressors were discarded as YdeA overexpression would presumably decrease toxicity of many toxic proteins expressed from the araBAD promoter. The four other suppressor plasmids harbored the rph gene, encoding the phosphorolytic ribonuclease (RNase PH), involved in the 3 processing of RNAs (Fig. 3A). RNase PH removes nucleotides downstream of the 3-CCA sequence, required for aminoacylation of tRNAs, from tRNA precursors with 3 extensions. It is also involved in other RNA maturation and quality control processes, including the maturation of rRNA (28).

(A) The E. coli K-12 genomic region containing the rph gene is shown. Suppressor plasmids that counteract MenT3 toxicity encoded rph, as depicted by small arrows under the adjacent genes pyrE, yicC, and dinD. The positions in base pair of the ends of each suppressor fragment, in relation to the E. coli K-12 chromosome, are indicated between brackets. (B) Overexpression of E. coli RNase PH partially suppresses MenT3 toxicity. E. coli DLT1900 strains containing either pK6-vector () or pK6-MenT3 (+) were cotransformed with p29SEN-vector () or p29SEN-Rph (RNase PH) (+). The resulting cotransformants were serially diluted, spotted onto LB-agar plates in the presence or absence of l-ara (0.1%) and IPTG (200 M) inducers, and incubated at 37C. (C) Deletion of rph further increases MenT3 toxicity. Transformants of E. coli DLT1900 WT and rph mutant strains containing plasmid pK6-MenT3 were serially diluted, spotted onto LB-agar plates with or without l-ara (0.01%), and incubated at 37C. (D) In vitro transcription/translation reactions assessing levels of DHFR control protein produced in the absence or presence of increasing concentrations of MenT3 toxin. Samples were separated by SDSpolyacrylamide gel electrophoresis and stained with InstantBlue. (E) For in vivo assays, transformants of E. coli BL21 (DE3) containing plasmid pET-MenT3 or the empty vector were grown in M9M at 37C. Following overexpression of MenT3, tRNAs were extracted, separated, and visualized by Northern blot using specific radiolabeled probes against tRNATrp. For in vitro assays, purified MenT3 (10 M) was added to transcription/translation assays producing GatZ protein. After 2 hours at 37C, tRNAs were extracted, separated, and visualized by Northern blot as performed for the in vivo samples. All images are representative of triplicate data.

Suppression of MenT3 toxicity by RNase PH overexpression was confirmed by cloning rph alone in a lowcopy number plasmid under the control of an isopropyl--d-thiogalactopyranoside (IPTG)inducible promoter and assaying for growth in the presence of MenT3 in E. coli (Fig. 3B). We also showed that the toxicity of MenT3 was enhanced when expressed in E. coli carrying a deletion of the rph gene, even with a 10-fold decrease in inducer levels (Fig. 3C), further reinforcing the genetic link between menT3 and rph. The primary role of RNase PH in processing tRNAs suggests that DUF1814 NTase-like toxins could act directly at the site of aminoacylation at the 3-end of tRNA, thus inhibiting translation. Whether endogenous RNase PH would be sufficiently induced in response to toxin expression to help restore the functional tRNA pool in recovering M. tuberculosis cells remains to be determined.

MenT3 WT and the MenT3(D80A) and MenT3(K189A) substitutions were overexpressed and purified for biochemical characterization. When tested in an in vitro transcription/translation reaction that uses recombinant E. coli components, purified MenT3 WT reduced production of the E. coli dihydrofolate reductase (DHFR) control protein in a concentration-dependent manner (Fig. 3D). Compared to MenT3 WT, MenT3(D80A) and MenT3(K189A) had a markedly reduced impact on the production of DHFR (fig. S5A). The same trend was observed when MenT3 WT, MenT3(D80A), and MenT3(K189A) were used in in vitro reactions producing WaaF and GatZ as test proteins (fig. S5, B and C). We also expressed and purified MenT4 WT and demonstrated that this, too, prevented the production of DHFR in a concentration-dependent manner in in vitro transcription/translation assays (fig. S5D).

The fact that MenT3 inhibited protein synthesis, and that RNase PH is involved in the removal of nucleotides following the 3-CCA sequence required for tRNA aminoacylation, suggested that tRNA charging might be affected by MenT3 expression in vivo. To address this hypothesis, we first used a method developed for E. coli, which separates charged from uncharged tRNAs and allows their detection by Northern blot after extraction in vivo (29). We chose tRNATrp as a model tRNA because (i) the tryptophanyl-tRNA can be well separated from uncharged tRNATrp and (ii) there is only one tRNATrp in E. coli (29). No charged tryptophanyl-tRNATrp could be detected following overexpression of MenT3 when compared to the empty vector control (Fig. 3E and fig. S5E). tRNATrp charging levels were also investigated in vitro by adding purified MenT3 to the transcription/translation assay described above (Fig. 3E). In this case, MenT3 also affected tRNATrp charging in vitro, thus supporting the hypothesis that the toxin inhibits protein synthesis by preventing aminoacylation of tRNA.

The observation that MenT3 is related to NTases (Fig. 2G) suggests that its mode of action is to directly transfer nucleotides to tRNAs, thereby preventing aminoacylation. We performed assays using radiolabeled tRNAs to track the addition of nucleotides by MenT3 WT, MenT3(D80A), and MenT3(K189A) (Fig. 4).

(A) Radiolabeled E. coli tRNATrp was incubated with 1, 0.1, 0.01, or 0.001 g of MenT3 WT or no toxin () for 20 min at 37C in the presence of unlabeled GTP, ATP, UTP, or CTP. Extended products are indicated with arrowheads throughout all panels. (B) Radiolabeled E. coli tRNATrp was incubated with 1, 0.1, or 0.01 g of MenT3 WT or MenT3(D80A) with CTP or UTP, as per conditions in (A). (C) Incubation of radiolabeled E. coli tRNATrp with 1, 0.1, 0.01, or 0.001 g of MenT3 WT or MenT3(K189A), with CTP, UTP, or a mixture of both, as per conditions in (A). (D) Radiolabeled E. coli tRNATrp preparations, made with or without a 3-CCA motif, were incubated with 1, 0.1, or 0.01 g of either MenT3 WT, MenT3(K189A), or no toxin (), for 20 min at 37C in the presence of unlabeled UTP or CTP. Note that the () CCA lanes have been overexposed to equalize intensity to the (+) CCA lanes of the same gel. Assays of the individual WT and MenT3 substitution proteins and tRNATrp CCA substrates shown in (A) to (D) were performed between two and four times.

MenT3 WT was incubated with tRNATrp from E. coli, as a model recipient tRNA, in the presence of guanosine 5-triphosphate (GTP), adenosine 5-triphosphate (ATP), uridine 5-triphosphate (UTP), or cytidine 5-triphosphate (CTP), and nucleotide transfer was monitored as an increase in tRNA size by high-resolution polyacrylamide gel electrophoresis (PAGE; Fig. 4A). At high concentrations of the enzyme, we found that MenT3 can add two to three extra nucleotides to tRNATrp in the presence of CTP or UTP, with a slight preference for CTP, suggesting that MenT3 is a pyrimidine-specific NTase (Fig. 4A). No transfer was observed with purines ATP or GTP as substrates (Fig. 4A). MenT3(D80A), which was unable to inhibit in vitro protein synthesis (fig. S5, A to C), had no NTase activity with either UTP or CTP (Fig. 4B). MenT3(K189A), which was also inactive in the in vitro transcription/translation assay (fig. S5, A to C), only lost its NTase activity in the presence of UTP, but retained some activity (albeit less than WT) in the presence of CTP, or both nucleotides (Fig. 4C). This could imply that K189A is important for substrate nucleotide selectivity. No synergistic effect was seen when MenT3 WT was incubated with a mixture of CTP and UTP, as the pattern with both nucleotides together resembled that of CTP alone (Fig. 4C).

Canonical tRNA NTases typically add the 3-CCA motif to tRNAs lacking an encoded 3-CCA that are processed at the level of the discriminator nucleotide (nucleotide 73). They also repair this motif when 3-exoribonucleases, such as RNase PH, fail to stop at the 3-CCA motif when processing tRNA precursors containing an encoded 3-CCA, typically removing the terminal A residue. Since M. tuberculosis contains a mixture of tRNA genes encoding or lacking a 3-CCA motif, we wondered whether MenT3 had a preference for one class (or another class) of substrate. While faint NTase activity was observed when MenT3 WT and MenT3(K189A) were incubated with CTP and tRNATrp lacking a 3-CCA, the data show that MenT3 had a clear preference for tRNAs that already possessed a 3-CCA motif (Fig. 4D). This is in contrast to the normal function of tRNA NTases, which prefer tRNAs lacking an intact 3-CCA. Again, MenT3 WT modified tRNATrp using both CTP and UTP as substrate, while MenT3(K189A) could only use CTP (Fig. 4D). Addition of nucleotides to mature tRNAs by MenT3 would completely abolish the ability of these tRNAs to be charged with their cognate amino acid and take part in translation, accounting for their cellular toxicity.

Our in vivo data show that toxins MenT3, as well as MenT1 and MenT4, are significantly less toxic in E. coli than in mycobacteria (Fig. 1 and fig. S1), which suggests that these toxins may have a tRNA target preference. We therefore asked whether MenT3 would exhibit some specificity toward the different tRNAs of M. tuberculosis. We made polymerase chain reaction (PCR) templates allowing us to in vitro transcribe the 45 different tRNAs of M. tuberculosis, each with a 3-CCA motif (fig. S6). As before, each radiolabeled tRNA was incubated with MenT3 and nonradiolabeled CTP (Fig. 5A). To our surprise, MenT3 appeared to be highly specific, preferentially modifying the four M. tuberculosis tRNASer isoacceptors, along with weak modification of tRNALeu5 (Fig. 5A). Although we cannot exclude that MenT3 can modify other tRNAs in vivo, the data show that the toxin presents a high degree of specificity toward different tRNAs in vitro, which may explain the variable toxicity observed in different bacteria.

(A) Radiolabeled M. tuberculosis tRNAs were incubated with 0.1 g of MenT3 WT (+) or no toxin () for 20 min at 37C in the presence of unlabeled CTP. E. coli tRNATrp (EcTrp) was used as a positive control. The global screen of all M. tuberculosis tRNA was performed once and the effect of MenT3 tRNASer2 was confirmed twice independently. (B) Schematic diagram of the MenT3 toxin mechanism of action. MenT3 elongates the 3-CCA motif of specific tRNAs, preventing their charging by aminoacyl-tRNA synthetases (AaRS), thereby interfering with translation and inhibiting bacterial growth.

Last, we asked whether the antitoxin MenA3 inhibited the NTase activity of MenT3 directly, or whether it could simply reverse its action by removing the added nucleotides in a manner similar to the RNase PH multicopy suppressor. Addition of MenA3 strongly inhibited the NTase activity of MenT3 on the natural substrate M. tuberculosis tRNASer2 when coincubated with the toxin at a molar ratio > 2.5. However, MenA3 failed to remove the added nucleotides from tRNASer2 when added after a preincubation of tRNASer2 with MenT3, even at high concentrations (fig. S7). This suggests that the antitoxin is likely to inhibit the toxin rather than reverse the reaction on the substrate.

This study has characterized a family of TA systems from M. tuberculosis containing NTase-like DUF1814 toxins, establishing MenT3 as a potent toxin in this problematic pathogen. We have solved the structures of the homologous toxins MenT3 and MenT4 by x-ray crystallography, revealing fold similarity and conserved residues within the proposed active sites, and have observed a similar mode of toxin activity, targeting protein synthesis. We have further elucidated the mechanism of toxicity for MenT3, showing that it functions as a pyrimidine-specific NTase preferentially targeting M. tuberculosis tRNASer in vitro (Fig. 5B).

The observation that the three NTase toxins identified in this work show different levels of toxicity when expressed in the same host, and that such toxic signatures can vary when expressed in different bacterial hosts (i.e., E. coli versus mycobacteria), is intriguing (Fig. 1 and fig. S1). The most marked example is MenT1, which shows robust toxicity in M. smegmatis but no toxicity in E. coli (Fig. 1A and fig. S1B). Although we cannot exclude this being a result of improper folding or expression of the toxin in E. coli, it is also reasonable to assume that the toxin may not be able to recognize its tRNA targets due, for example, to tRNA modification, or the absence of its preferred tRNA target (30). Another possibility is that tRNA targets are expressed at higher levels in E. coli and are thus sufficiently abundant to overcome the noxious effect of the toxin in vivo. The fact that M. tuberculosis and M. smegmatis only have 45 and 46 tRNA genes, respectively, while E. coli has 86, is in line with this hypothesis (30, 31).

The apparent in vitro specificity of MenT3 for certain M. tuberculosis tRNAs, especially tRNASer, is remarkable (Fig. 5A). We did not test other tRNAs in E. coli besides tRNATrp; it may well have been fortuitous that the only tRNA we tested in this organism was detectably modified by the toxin in vitro (Fig. 4A) and in vivo, inferred from the reduced charging levels following toxin expression (Fig. 3E). We checked whether the M. tuberculosis tRNAs that were substrates of MenT3 had any distinguishing features and were struck by the fact that all serine tRNAs and several leucine tRNAs were unique among M. tuberculosis tRNAs in that they had long variable arms (fig. S8A) (32). While this is intriguing and may contribute to substrate specificity, it cannot be the only recognition element because (i) two leucine tRNAs besides tRNALeu5 have variable loops but are not MenT3 substrates in vitro and (ii) E. coli tRNATrp does not have a variable loop (fig. S8B), but can be extended by the NTase activity of the toxin. It is also intriguing in this regard that M. tuberculosis tRNATrp is not a MenT3 substrate in vitro. E. coli and M. tuberculosis tRNATrp are highly homologous but do show differences in their variable- and T-arm sequences (fig. S8C). Substrate specificity therefore appears to come from a combination of multiple sequence and structure motifs. Having identified these tRNA targets in vitro, further work is now needed to confirm targeting in vivo in M. tuberculosis.

Our observed TA interactions raise questions regarding the molecular mechanisms of antitoxicity for DUF1814 toxins (fig. S4, C to F). Typically, in type II TA systems, antitoxin function is in part driven by its strong and direct interaction with the cognate toxin (3). While we have shown interactions between cognate toxins and antitoxins (fig. S4, C to F), the antitoxin interaction in vivo appears weak. We additionally demonstrated that coincubation of the MenA3 antitoxin with MenT3 is able to neutralize the NTase activity (fig. S7). This suggests that any interaction-based antitoxicity might be a transient and labile mechanism and, due to the difference in size and sequence between antitoxins MenA1 and MenA3 (Fig. 1A), may well differ between these systems.

The DUF4433 DarT toxin from M. tuberculosis was recently identified as a single-stranded DNA NTase that specifically and reversibly adenosine 5-diphosphate (ADP)ribosylates thymidines (33). Our study identifies MenT3 as an NTase toxin from the unrelated DUF1814 protein family. In comparison to DarT, MenT3 acts via a distinct and novel mode of toxicity where the MenT3 toxin preferentially targets M. tuberculosis tRNAs in vitro, preventing their charging with cognate amino acids by adding nucleotides to the 3-CCA acceptor stem (Fig. 5B). Accordingly, antitoxin function also appears to differ between these systems. Whereas DarT is counteracted enzymatically by the cognate antitoxin DarG via target de-ADP-ribosylation (33), we found that MenA3 was unable to reverse MenT3 toxicity by removing nucleotides, suggesting that MenA3 likely inhibits the toxin activity.

Increasing numbers of toxins have been identified that target tRNAs by various mechanisms (13). The M. tuberculosis type II VapC toxins function as endoribonucleases cleaving tRNAs (34), whereas TacT from Salmonella Typhimurium and AtaT from E. coli are tRNA acetyltransferases, modifying charged tRNAs to block translation (35, 36). That MenT3 provides yet another way to inhibit tRNA activity is perhaps not unusual, given the essential nature of translation to cellular growth and survival. This likely reflects the value of possessing multiple TA systems to promote adaptability to different stressful environments via tRNA metabolism, with downstream effects ranging from stalling cell growth to potentially altering translation output (13). It remains to be seen whether this mechanism is conserved among DUF1814-toxins; while MenT4 shares structural similarities to MenT3 and inhibits protein synthesis in vitro (Fig. 2 and fig. S5D), we have not yet explored the molecular mechanism behind its toxicity. Given the continued significance of M. tuberculosis worldwide, the mechanism used by the MenA3-MenT3 TA system highlights a new way to block protein synthesis. We propose that further exploring the molecular mechanisms of both toxicity and antitoxicity will provide useful insights into the regulation of bacterial growth.

E. coli DH5 (Invitrogen), DH10B (Thermo Fisher Scientific), BL21 (DE3) (Novagen), ER2566 (New England Biolabs), W3110 [strain American Type Culture Collection (ATCC) 27325], DLT1900 (37), and M. smegmatis mc2 155 (strain ATCC 700084) are as previously described. To construct BL21 (DE3) slyD, the slyD::KmR allele from JW3311 (Keio collection) was moved into BL21 (DE3) using bacteriophage P1-mediated transduction. To construct the unmarked DLT1900 rph mutant, the rph::KmR allele from JW3618 (Keio collection) was first moved into DLT1900 using bacteriophage P1-mediated transduction and by subsequent removing of the kanamycin (Km) resistance cassette using plasmid pCP20, as previously described (38). E. coli were routinely grown at 37C in LB medium or M9 minimal (M9M) medium supplemented when necessary with Km (50 g ml1), ampicillin (Ap; 50 g ml1), chloramphenicol (Cm; 34 g ml1), streptomycin (Sm; 25 g ml1), spectinomycin (Sp; 50 g ml1), IPTG (1 mM), l-ara (0.1% w/v), or d-glucose (glu; 0.2% w/v). M. smegmatis mc2 155 strains were routinely grown at 37C in either LB or 7H9 medium (Difco). M. tuberculosis H37Rv (WT; ATCC 27294) and mutant strains were routinely grown at 37C in complete 7H9 medium (Middlebrook 7H9 medium, Difco) supplemented with 10% albumin-dextrose-catalase (ADC; Difco) and 0.05% Tween 80 (Sigma-Aldrich), or on complete 7H11 solid medium (Middlebrook 7H11 agar medium, Difco) supplemented with 10% oleic acidADC (OADC; Difco). When required, mycobacterial growth media were supplemented with Km (50 g ml1), hygromycin (Hm; 50 g ml1), Sm (25 g ml1), zeocin (Zc; 25 g ml1), Ace (0.2% w/v), or Atc (100 or 200 ng ml1).

Plasmids pMPMK6 (39), p29SEN (40), pGMCS (41), pGMCZ (42), pLAM12 (43), pETDuet-1, pET15b and pRARE (Novagen), pBAD30 (44), and pTA100 (4) have been described. Primers used for plasmid construction are described in table S1. All the plasmids constructed in this work have been verified by sequencing. The pMPMK6 derivatives expressing the toxins, namely, pK6-MenT1, pK6-MenT2, pK6-MenT3, and pK6-MenT4, were constructed as follows: menT1, menT2, menT3, and menT4 were PCR-amplified from the M. tuberculosis H37Rv genome and cloned as Eco RI/Hind III fragments (menT1 and menT2) and Mfe I/Hind III fragments (menT3 and menT4) into Eco RI/Hind IIIdigested pMPMK6.

The p29SEN plasmid derivatives encoding the antitoxins, namely, p29SEN-MenA1, p29SEN-MenA2, p29SEN-MenA3, and p29SEN-MenA4, were constructed as follows: menA1, menA2, menA3, and menA4 were PCR-amplified from the M. tuberculosis H37Rv genome and cloned either as Eco RI/Hind III fragments (menA1, menA2, and menA3) or as Mfe I/Hind III fragments (menA4) into Eco RI/Hind IIIdigested p29SEN. For p29SEN-Rph, the rph gene was PCR-amplified from the E. coli DLT1900 genome and cloned as an Eco RI/Hind III fragment into Eco RI/Hind IIIdigested p29SEN.

To construct pGMC-MenT2, pGMC-MenT3, and pGMC-MenT4, menT2, menT3, and menT4 were PCR-amplified using pK6-MenT2, pK6-MenT3, and pK6-MenT4 templates, respectively, and cloned into pGMCS using In-Fusion HD Cloning Kits (Takara Bio). Plasmid pGMC-MenT1 and pGMC-MenT1-His were obtained following PCR amplification of menT1 and menT1-His using pK6-MenT1 as a template and homologous recombination in linearized pGMCS plasmid by In-Fusion HD Cloning Kits (Takara Bio). For pGMC-*MenA4-MenT4, the menA4-menT4 operon was PCR-amplified from the H37Rv genome and cloned into linearized pGMCS plasmid by In-Fusion HD Cloning Kits (Takara Bio).

To construct plasmids pLAM-MenA2, pLAM-MenA3, and pLAM-MenA4, menA2, menA3, and menA4 were PCR-amplified using p29SEN-MenA2, p29SEN-MenA3, and p29SEN-MenA4 as templates, respectively. These were cloned as Nde I/Eco RI fragments (menA2 and menA3) and Nde I/Mfe I fragments (menA4) into Nde I/Eco RIdigested pLAM12. Plasmid p29SEN-MenA1 was used to amplify menA1 and menA1-His, which were then cloned as Nde I/Eco RI fragments into Nde I/Eco RIdigested pLAM12 to produce pLAM-MenA1 and pLAM-MenA1-His, respectively.

The pET vector derivatives used in this work were constructed as follows. To construct plasmid pET-MenT3-His, menT3-His (with an added fragment encoding a Ser-Ser-Gly-His6 C-terminal tag) was PCR-amplified from pK6-MenT3 template and cloned as an Nde I/Mfe I fragment into Nde I/Mfe Idigested pETDuet-1. Plasmid pET-MenT3-His was used as a template to construct pET-MenT3-His(D80A) and pET-MenT3-His(K189A) by QuikChange site-directed mutagenesis (Agilent) using appropriate primers. Plasmid pET-MenA3-His, encoding an N-terminal His6-tagged MenA3 antitoxin, was constructed by PCR amplification of menA3-His using p29SEN-MenA3 as a template, Nde I/Hind III digestion, and cloning into Nde I/Hind IIIdigested pET15b plasmid. To construct plasmid pET-MenT3/MenA3-His, menA3-His was first PCR-amplified from p29SEN-MenA3 template and cloned as an Nco I/Hind III fragment into Nco I/Hind IIIdigested pETDuet-1. menT3 was then PCR-amplified from pK6-MenT3, digested with Nde I/Mfe I, and cloned into Nde I/Mfe Idigested pET-MenA3-His. To construct pET-MenT3-His/MenA3, menA3 was first PCR-amplified using p29SEN-MenA3 as a template and cloned as an Nco I/Hind III fragment into Nco I/Hind IIIdigested pET-MenT3-His. To generate pET-MenT1-His (expressing MenT1 with an N-terminal His6-Ser-Ser-Gly-tag), menT1-His was PCR-amplified from pK6-MenT1 and cloned as an Nde I/Mfe I fragment into Nde I/Mfe Idigested pETDuet-1. For pET-MenA1-His (expressing MenA1 with an N-terminal His6-Ser-Ser-Gly-tag), menA1-His was PCR-amplified from p29SEN-MenA1 template and cloned as an Nco I/Bam HI fragment into Nco I/Bam HIdigested pETDuet-1. For pET-MenT1/MenA1-His, menT1 was PCR-amplified from pK6-MenA1 and cloned as an Nde I/Mfe I fragment into Nde I/Mfe Idigested pET-MenA1-His. For pET-MenT1-His/MenA1, menA1 was PCR-amplified from p29SEN-MenA1 and cloned as an Nco I/Bam HI fragment into Nco I/Bam HIdigested pET-MenT1His.

To generate MenT3 and MenT4 expression constructs for crystallization and biochemistry, overlap PCRs were performed to fuse a sentrin protease (SENP)cleavable N-terminal His6-SUMO tag, amplified from the pBAT4 derivative (45), pSAT1-LIC (this study), to either menT3 or menT4, amplified from H37Rv genomic DNA. The resulting PCR products were cloned as either Kpn I/Hind III fragments into Kpn I/Hind IIIdigested pBAD30 (menT3), producing pTRB517, or as Xma I/Hind III fragments into Xma I/Hind IIIdigested pBAD30 (menT4) to generate pTRB544.

Plasmids pPF656 and pPF657 were constructed by amplifying menA3 and menT3 from H37Rv genomic DNA and cloning as Mfe I/Xma I fragments into Eco RI/Xma Idigested pTA100 and pBAD30, respectively. To express His6-SUMO-tagged MenT3(D80A), site-directed mutagenesis was carried out using pTRB517 as a template. Briefly, nonoverlapping inverse primers were used to amplify menT3(D80A), followed by incubation with a mix of T4 DNA ligase, T4 polynucleotide kinase, and DpnI at 37C to remove template and circularize amplified DNA. This reaction was then used to transform E. coli DH5, resulting in pTRB593. Similarly, this method was used to generate MenT3(D80A), MenT3(K189A), and MenT3(D211A) for functional testing, using pPF657 as a template, resulting in pTRB591, pTRB562, and pTRB592, respectively.

Plasmid pTRB491 was generated by amplifying menA3 from H37Rv genomic DNA and cloning into pSAT1-LIC via ligation-independent cloning (LIC). The pSAT1-LIC plasmid features a LIC site that fuses an N-terminal His6-SUMO tag to the target protein. To produce MenT3(K189A) protein, the mutated gene was amplified from pTRB562 and similarly cloned into pTRB550 via LIC, resulting in pTRB577. The pTRB550 plasmid features a His6-SUMO LIC site, originally amplified from pSAT1-LIC and cloned as an Eco RI/Hind III fragment into Eco RI/Hind IIIdigested pBAD30.

To produce plasmids for use in M. tuberculosis, menA3, menT3, or both genes were amplified by PCR using PrimeSTAR GXL DNA polymerase, with M. tuberculosis H37Rv genomic DNA as template and primer pairs clo-RBS1-MenA3-attB2/clo-MenA3-attB3, clo-RBS1-MenT3-attB2/clo-MenT3-attB3, clo-RBS4-MenT3-attB2/clo-MenT3-attB3, or clo-RBS1-MenA3-attB2/clo-MenT3-attB3, respectively (tables S1 and S2). RBS1 (AGGAAGACAGGCTGCCC) and RBS4 (ACGAAGACAGGCTGCCC), corresponding to a strong or weak Shine-Dalgarno sequence, respectively, were placed upstream from the ATG translation start of MenA3 or the GTG translation start of MenT3. Plasmids pGMCS-TetR-P1-RBS1-MenA3, pGMCS-TetR-P1-RBS1-MenA3-MenT3, pGMCS-TetR-P1-RBS1-MenT3, or pGMCS-TetR-P1-RBS4-MenT3 were constructed by multisite gateway recombination (18), using plasmid pDE43-MCS as the destination vector. These plasmids are integrative vectors (insertion at the attL5 mycobacteriophage insertion site in the glyV tRNA gene) and express MenA3, MenT3, or MenA3-MenT3 under the control of P1 (Pmyc1 tetO), a tetracycline-inducible promoter (table S2) (46).

Construction of MenT3 D80A, D211A, and K189A substitutions for use in M. tuberculosis was performed as follows: Plasmid pGMCS-TetR-P1-RBS4-MenT3 was amplified by PCR with PrimeSTAR GXL DNA polymerase and the oligonucleotides pairs InFus-MenT3D80A-right/InFus-MenT3D80A-left, InFus-MenT3D211A-right/InFus-MenT3D211A-left, or InFus-MenT3K189A-right/InFus-MenT3K189A-left (table S1). The amplified linear fragments were purified on agarose gels and circularized using the In-Fusion HD Cloning Kit (Takara), as recommended by the manufacturer. Plasmids used to transform Stellar recipient cells were verified by sequencing and introduced by electroporation into M. tuberculosis (menA3-menT3)::dif6/pGMCZ (see the next paragraph).

Mutant strains of M. tuberculosis H37Rv were constructed by allelic exchange using recombineering (43), as previously described (fig. S2) (47). Two ~0.5-kb DNA fragments flanking the menA3-menT3 operon were amplified by PCR using PrimeSTAR GXL DNA polymerase (Takara), M. tuberculosis H37Rv genomic DNA, and the primer pairs MenA3Am-For/MenA3Zc-Am-Rev or MenT3Zc-Av-For/MenT3Av-Rev, respectively (table S1). A three-fragment PCR fused these two fragments to a Zc-resistance cassette flanked by two dif6 variants of the M. tuberculosis dif site and the recombination substrate was recovered by agarose gel purifications. The recipient strain for recombineering was a derivative of M. tuberculosis H37Rv carrying two plasmids: pJV53H, an Hm-resistant pJV53-derived plasmid expressing recombineering enzymes (43), and the integrative plasmid pGMCS-P1-MenA3, constitutively expressing menA3 (table S2). This strain was grown in complete 7H9 medium supplemented with Hm until mid-log phase and expression of recombineering enzymes was induced by Ace (0.2%) overnight at 37C. After induction, electrotransformation was performed with 100 ng of the linear DNA fragment for allelic exchange. After a 48-hour incubation at 37C, mycobacteria were plated onto agar supplemented with Zc. Zc-resistant clones were restreaked on the same medium, grown in complete 7H9 without antibiotic, and verified to be carrying the expected allele replacement by PCR amplification of chromosomal DNA and subsequent DNA sequencing, using primers MenA3Am-For/MenT3Av-Rev (fig. S1C and table S1). Spontaneous loss of the Zc-resistance cassette by XerCD-dependent recombination and of the pJV53H plasmid was obtained by serial rounds of culture without antibiotics and phenotypic tests for ZcS and HmS. Plasmid pGMCS-P1-MenA3 was then removed by transformation with pGMCZ, a similar integrative vector but carrying resistance to Zc, resulting in the deleted strain M. tuberculosis (menA3-menT3)::dif6/pGMCZ.

E. coli MC4100 dnaKdnaJ::KmR tig:CmR double mutant (40) was partially digested with Sau3 AI restriction enzyme and DNA fragments of about 1.5 to 4 kb in size were purified, then ligated into linearized and dephosphorylated Bam HIdigested pMPM2 (ColE1 origin) plasmid (39), and used to transform E. coli DH10B. About 25,000 independent transformants were pooled to constitute the multicopy library. This library has previously been used as a tool to identify multicopy suppressors of chaperone mutants (48).

In vivo toxicity and antitoxicity assays by cognate or noncognate antitoxins in E. coli were performed as follows. E. coli DLT1900 were cotransformed with pMPMK6-vector, pK6-MenT1, -MenT2, -MenT3, or -MenT4 (toxins), and p29SEN-vector, p29SEN-MenA1, -MenA2, -MenA3, or -MenA4 (antitoxins). Transformants were re-seeded from overnight cultures and grown at 37C to mid-log phase in LB supplemented with Km and Ap, and then serially diluted and spotted on LB-agar plates supplemented with Km and Ap, with or without l-ara (0.1%) and/or IPTG (200 M). Plates were incubated at 37C overnight and then imaged and counted. MenT3 substitutions were tested for toxicity in E. coli DH5 carrying pBAD30-vector, -MenT3 WT (pPF657), -MenT3(D80A) (pTRB591), -MenT3(K189A) (pTRB562), or -MenT3(D211A) (pTRB592). Strains were grown to mid-log phase, then serially diluted, and spotted onto M9M-agar plates supplemented with Ap, with or without l-ara (0.1%). After a 2-day incubation at 37C, plates were imaged and counted.

In vivo toxicity and rescue assays by cognate or noncognate antitoxins in M. smegmatis were performed as follows. Cultures of mc2 155 strain grown in LB at 37C were cotransformed with the integrative pGMC-vector, -MenT1, -MenT2, -MenT3, or -MenT4 (toxins), and with pLAM12-vector, pLAM-MenA1, -MenA2, -MenA3, or -MenA4 (antitoxins). Samples were selected on LB-agar plates supplemented with Km and Sm for 3 days at 37C, in the presence or absence of Atc (100 ng ml1) and Ace (0.2%) for toxin and antitoxin expression, respectively. A similar procedure was applied for pGMC-*MenA4-MenT4 carrying the menA4-menT4 operon, with the exception that no cotransformation with pLAM12 derivatives or selection on Km was needed.

Exponentially growing cultures [OD600 (optical density at 600 nm) between 0.05 and 0.2] of M. smegmatis strain mc2 155 containing plasmid pGMCS-TetR-P1-RBS1-MenT3 were divided in two: Half was left in complete 7H9 growth medium with Sm (uninduced cultures), while the other half was additionally treated with Atc (200 ng ml1) to induce expression from the P1 promoter. For labeling with LIVE/DEAD BacLight (Molecular Probes) dyes, cells were harvested 8 hours after Atc induction. Cells were centrifuged, resuspended in phosphate-buffered saline buffer, and stained as recommended by the manufacturer. Labeled cells were analyzed by fluorescence-activated cell sorting using a BD LSRFortessa X20 flow cytometer. Flow cytometry data analysis was performed using FlowJo software.

M. tuberculosis strains H37Rv or H37Rv (menA3-menT3)::dif6/pGMCZ were transformed by electroporation with 100 ng of plasmids pGMCS-TetR-P1-RBS1-MenA3, pGMCS-TetR-P1-RBS1-MenA3-MenT3, pGMCS-TetR-P1-RBS1-MenT3, pGMCS-TetR-P1-RBS4-MenT3, pGMCS-TetR-P1-RBS4-MenT3(D80A), pGMCS-TetR-P1-RBS4-MenT3(K189A), or pGMCS-TetR-P1-RBS4-MenT3(D211A). After 3 days of phenotypic expression in 7H9 ADC Tween at 37C, the transformation mix was divided into two halves. One half was plated on 7H11 OADC with Sm; the other half was plated on 7H11 OADC Sm supplemented with Atc (200 ng ml1). Plates were imaged after 20 days of incubation at 37C.

To perform in vivo copurification assays, E. coli BL21 slyD was transformed with (i) pET-MenT3-His, pET-MenA3-His, pET-MenT3/MenA3-His, or pET-MenT3-His/MenA3, or with (ii) pET-MenT1-His, pET-MenA1-His, pET-MenT1/MenA1-His, or pET-MenT1-His/MenA1, and selected on LB-agar plates supplemented with Ap and glu (20%). Transformants were grown at 37C to an OD600 of approximately 0.4 and then protein expression was induced overnight at 20C with 1 mM IPTG. Cell lysis and affinity purification of the protein complexes were performed as described below for MenT3-His purification. Elution fractions were separated on SDS-PAGE and proteins revealed using InstantBlue Protein Stain (Expedeon, catalog no. ISB1L).

To purify MenT3 for biochemistry, BL21 (DE3) slyD transformed with pET-MenT3-His, pET-MenT3-His(D80A), or pET-MenT3-His(K189A) was grown to an OD600 of approximately 0.4 at 37C. IPTG (1 mM) was then added, and the culture was incubated overnight at 20C. Under such conditions, MenT3 expression in E. coli was better tolerated and led to a reasonable amount of soluble MenT3 that could be collected for purification. Cultures were centrifuged at 5000g for 10 min at 4C, pellets were resuspended in Lysis buffer [300 mM NaCl, 50 mM tris (pH 7.5), and protease inhibitor tablet (Roche); 20 ml of buffer per 1 liter of cell culture] and incubated for 30 min on ice. Lysis was performed using the One Shot cell disrupter at 1.5 kbar (One Shot model, Constant Systems Ltd.). Lysates were centrifuged for 30 min at 30,000g in 4C, and the resulting supernatants were gently mixed at 4C for 30 min with Ninitrilotriacetic acid agarose beads (Qiagen, catalog no. 30230) preequilibrated with buffer PD [300 mM NaCl and 50 mM tris (pH 7.5)], using a 10-ml poly-prep column (Bio-Rad, catalog no. 7311550). Columns were stabilized for 10 min at 4C and washed three times with 10 ml of buffer PD plus 25 mM imidazole, and proteins were then eluted with buffer PD containing 250 mM imidazole. Elutions (500 l) were collected and PD MiniTrap G-25 columns (GE Healthcare, catalog no. 16924748) were used to exchange buffer with buffer PD supplemented with 10% glycerol. Proteins were concentrated using Vivaspin 6 columns with a 5000-Da cutoff (Sartorius, catalog no. 184501257). Proteins were stored at 80C until further use.

For additional MenT3 and MenT3(K189A) expression, either for crystallization or biochemistry, E. coli ER2566 pRARE pPF656 was transformed with either pTRB517 or pTRB577, respectively. For MenT3(D80A) expression, E. coli ER2566 pRARE was transformed with pTRB593. MenT4 was expressed in E. coli BL21 (DE3) transformed with pTRB544. MenA3 was expressed in E. coli ER2566 transformed with pTRB491. For these expressions, the same procedure was followed: Overnight cultures were re-seeded 1:100 into 2-liter flasks containing 1-liter 2 YT. Cells were grown at 175 rpm in 37C until an OD600 of 0.3 was reached and then at 22C until OD600 0.5, whereupon expression was induced by the addition of l-ara (0.1%) for toxins and IPTG (1 mM) for antitoxins. Cells were left to grow overnight at 16C, shaking at 175 rpm.

For selenomethionine incorporation, starter cultures of ER2566 pRARE pPF656 pTRB517 were grown overnight in LB at 37C with 200 rpm shaking. Cells were pelleted, washed, and resuspended in M9M, and then sub-cultured into 500 ml of M9M in 2-liter baffled flasks to a starting OD600 of 0.075. Cells were grown at 37C with 175 rpm shaking until an OD600 of 0.6, whereupon cells were centrifuged at 4200g and resuspended in fresh M9M. This sample was divided between separate 2-liter baffled flasks containing new M9M and shaken at 175 rpm for a further 1 hour at 37C. Once an OD600 of 0.7 was reached, 12 ml of nutrient mix [l-lysine hydrate (4 mg ml1), l-threonine (4 mg ml1), l-phenylalanine (4 mg ml1), l-leucine (2 mg ml1), l-isoleucine (2 mg ml1), l-valine (2 mg ml1), and 4 mM CaCl2] was added to each flask to promote feedback inhibition of methionine synthesis, followed by 250 SelenoMethionine Solution (Molecular Dimensions) to a final concentration of 40 g ml1, and cells were left to incubate for 1 hour at 20C. Last, toxin and antitoxin expression were induced by the addition of l-ara (0.1%) and IPTG (1 mM), and samples were left to grow overnight at 175 rpm in 16C.

All five proteins were purified in the same manner. Bacteria were harvested by centrifugation at 4200g, and the pellets were resuspended in buffer A500 [20 mM tris-HCl (pH 7.9), 500 mM NaCl, 5 mM imidazole, and 10% glycerol]. Cells were lysed by sonication at 40 kpsi and then centrifuged (45,000g, 4C). The clarified lysate was next passed over a HisTrap HP column (GE Healthcare), washed for 10 column volumes with A500, followed by 10 column volumes of buffer A100 [20 mM tris-HCl (pH 7.9), 100 mM NaCl, 5 mM imidazole, and 10% glycerol], and then eluted directly onto a HiTrap Q HP column (GE Healthcare) with buffer B100 [20 mM tris-HCl (pH 7.9), 100 mM NaCl, 250 mM imidazole, and 10% glycerol]. The Q HP column was transferred to an kta Pure (GE Healthcare), washed with 3 column volumes of A100, and then proteins were eluted using a gradient from 100% A100 to 100% buffer C1000 [20 mM tris-HCl (pH 7.9), 1000 mM NaCl, and 10% glycerol]. Fractions containing the protein peak were analyzed by SDS-PAGE, pooled, and incubated overnight at 4C with hSENP2 SUMO protease to cleave the His6-SUMO tag from the target protein. The following day, the samples were passed through a second HisTrap HP column and the flow-through fractions containing untagged target protein were collected. These samples were concentrated and run over a HiPrep 16/60 Sephacryl S-200 size exclusion column (GE Healthcare) in buffer S [50 mM tris-HCl (pH 7.9), 500 mM KCl, and 10% glycerol]. Peak fractions were analyzed by SDS-PAGE, pooled, and concentrated. Optimal fractions were separated and either flash-frozen in liquid N2 for storage at 80C or dialyzed overnight at 4C into buffer X [20 mM tris-HCl (pH 7.9), 150 mM NaCl, and 2.5 mM dithiothreitol (DTT)] for crystallographic studies. Crystallization samples were quantified and stored on ice and then either used immediately or flash-frozen in liquid N2 for storage at 80C. Frozen crystallization samples still formed usable crystals 15 months after storage.

Native and selenomethionine-derivatized MenT3 were concentrated to 12 mg ml1 and MenT4 was concentrated to 6 mg ml1, all in buffer X (see above). Initial crystallization screens were performed using a Mosquito Xtal3 robot (TTP Labtech) to set 200:100 nl and 100:100 nl protein:condition sitting drops. After initial screening and optimization, both MenT3 protein samples formed thick, six-sided needles in condition G5 [0.2 M calcium acetate hydrate, 0.1 M tris (pH 8.5), and 25% w/v polyethylene glycol 2000 monomethyl ether] of Clear Strategy II HT-96 (Molecular Dimensions). MenT4 formed thin, six-sided needles in the same condition as MenT3. To harvest, 20 l of condition reservoir was added to 20 l of cryo buffer [25 mM tris-HCl (pH 7.9), 187.5 mM NaCl, 3.125 mM DTT, and 80% glycerol] and mixed quickly by vortexing; an equal volume of this mixture was then added to the drop. After addition of cryo buffer, crystals were immediately extracted using a nylon loop and flash-frozen in liquid N2.

Diffraction data were collected at Diamond Light Source on beamlines I04 (MenT3 native), I03 (MenT3 selenomethionine-derivatized), and I24 (MenT4 native) (Table 1). Single 360 datasets were collected for native MenT3 and MenT4. Two 360 datasets from MenT3 selenomethionine-derivatized crystals measured at the selenium peak (0.9793 ) were merged using iSpyB (Diamond Light Source). Additional MenT3 selenomethionine-derivatized datasets were collected at selenium high remote (0.9641 ) and inflection (0.9795 ) wavelengths. Diffraction data were processed with XDS (49), and then AIMLESS from CCP4 (50) was used to corroborate the space groups (Table 1). The crystal structure of MenT3 was solved by MAD by providing the SHELX suite in CCP4 with the native and three anomalous MenT3 datasets. The solved starting model for MenT3 was built in REFMAC within CCP4. The crystal structure of MenT4 was solved ab initio using ARCIMBOLDO (51). Both models were then iteratively refined and built using PHENIX (52) and COOT (53), respectively. The quality of the final model was assessed using COOT and the wwPDB validation server. Structural figures were generated using PyMOL (Schrdinger). Comparison against models within the Protein Data Bank (PDB) was performed using DALI (25).

The following genetic procedure was developed and applied to select for E. coli genes that confer resistance to the MenT3 toxin. E. coli strain DLT1900 was first transformed with pK6-MenT3 (KmR) plasmid and transformants were selected at 37C on LB-agar plates supplemented with Km and glu (0.2%) to repress toxin expression from the araBAD promoter of pK6-MenT3. DLT1900 containing pK6-MenT3 was then grown in LB supplemented with Km and glu, transformed with the pMPMA2-based multicopy library of E. coli genes, and plated on selective LB-agar supplemented with Km, Ap, and l-ara (0.1%) to induce toxin expression. Plates were incubated for 24 hours at 37C. A control aliquot of transformants plated on nonselective plates (no l-ara) indicated that the number of transformants tested during the selection procedure was approximately 60,000. Note that under such conditions, E. coli DLT1900 pK6-MenT3 transformed with pMPMA2 empty vector did not produce any colonies on selective plates. We identified 72 toxin-resistant colonies that grew on selective plates after 24 hours, although they were smaller and translucent, indicating that growth inhibition by the toxin is not fully blocked by the suppressors identified. Of the 72 toxin-resistant colonies identified, only 41 were able to grow in culture. Plasmids were extracted from the 41 cultures, used to re-transform DLT1900 pK6-MenT3, and plated as above, to validate growth rescue in the presence of MenT3. Of 41 clones, 18 suppressors passed the second round of selection and were sequenced using the pMPMA2-For and -Rev primers (table S1).

Assays were performed as previously described (54). Briefly, template DNAs of DHFR (P0ABQ4), WaaF-Strep (P37692), and GatZ-Strep (P0C8J8) were used for in vitro transcription/translation coupled assays (PURExpress, New England Biolabs). These were performed according to the manufacturers instructions, in the presence or absence of the toxin. Following protein synthesis reactions of 2 hours at 37C, samples were separated on SDS-PAGE and visualized by InstantBlue staining (DHFR) or Western blots using anti-Strep tag antibodies (WaaF-Strep and GatZ-Strep).

Prevention of E. coli tRNATrp aminoacylation by MenT3 was monitored using a combination of two previously published methods (29, 55). E. coli BL21 (DE3) transformed with pETDuet or pET-MenT3 was grown at 37C to OD600 0.1 in M9M, whereupon expression of MenT3 was induced with 1 mM IPTG until an OD600 of about 0.4. The bacterial culture (25 ml) was then kept on ice and centrifuged for 10 min at 5000g in 4C. The pellet was resuspended in 0.5 ml of cold 0.3 M sodium acetate (pH 4.5) and 10 mM EDTA and transferred to a precooled 1.5-ml microcentrifuge tube, and 0.5 ml of phenol (equilibrated with the same buffer) was then added. After gentle pipetting, the sample was transferred into phase-lock tubes with an additional 400 l of cold chloroform. After 30 seconds shaking, the sample was first incubated on ice for 15 min and then centrifuged for 20 min at 20,000g in 4C. The aqueous phase was then transferred to a new cold 1.5-ml tube. Five hundred microliters of cold isopropanol was added and immediately mixed. RNA was precipitated for 1 hour at 20C, before the sample was centrifuged for 30 min at 20,000g in 4C (55). The supernatant was discarded and 1 ml of cold 75% ethanol was carefully added without disturbing the RNA pellet. After further centrifugation for 10 min at 20,000g in 4C, the supernatant was removed and the pellet was air-dried until no ethanol remained. The pellet was then resuspended by vigorously mixing in 20 l of cold 10 mM sodium acetate (pH 4.5) and 1 mM EDTA. Samples were stored at 80C. Samples were separated on a denaturing urea acrylamide gel for 3 hours at 100 V in 4C, as previously described (29). Northern blot and visualization with a radiolabeled DNA probe against tRNATrp was performed as previously described (56). Note that to distinguish the band of aminoacylated tRNA from its deacylated counterpart on the Northern blot, a chemically deacylated aliquot of RNA sample prepared from strain containing the empty vector was subjected to alkaline treatment. In this case, 46 l of tris-HCl (pH 9.0) was added to a 4-l aliquot of the RNA sample and incubated for 2 hours at 37C. Fifteen microliters of 0.3 M sodium acetate at pH 4.5 was added and followed by 125 l of 96% ethanol. RNA was precipitated at 20C for 1 hour, resuspended, and separated as described above.

For in vitro tRNA charging, in vitro transcription/translation assays were performed as above, using gatZ as DNA template. After a 2-hour reaction at 37C with or without MenT3 toxin (10 M), tRNA extraction, separation, and visualization were performed as described for the in vivo samples.

Labeled tRNAs were prepared by in vitro transcription of PCR templates containing an integrated T7 RNA polymerase promoter sequence. The template for E. coli tRNATrp was made by PCR amplification of chromosomal DNA from strain MG1655 with the primers CC2556 and CC2557 (CC2591 for tRNATrp without CCA) (table S1). The oligos for M. tuberculosis tRNAs are given in table S1. The T7 RNA polymerase in vitro transcription reactions were performed in 25-l total volume, with a 5-l nucleotide mix of 2.5 mM ATP, 2.5 mM CTP, 2.5 mM GTP, and 60 M UTP and 2 to 4 l of 10 mCi ml1 of radiolabeled UTP [-P32]. Template (0.1 to 0.2 g) was used per reaction with 1.5 l of rRNasin (40 U ml1) (Promega), 5 l of 5 optimized transcription buffer (Promega), 2 l of T7 RNA polymerase (20 U ml1), and 2.5 l of 100 mM DTT. Template DNA was removed by the addition of 2 l of RQ DNase (1 U ml1) (Promega). Unincorporated nucleotides were removed by G50 spin columns (GE Healthcare) according to the manufacturers instructions, in a final volume of 30 l. For E. coli tRNATrp, the transcript reaction was gel-purified on a denaturing 5% acrylamide gel and eluted in 0.3 M sodium acetate for 4 hours overnight at 4C. The supernatant was removed, ethanol-precipitated, and resuspended in 20 to 30 l of nuclease-free H2O.

MenT3 NTase activity was assayed in 10-l reaction volumes containing 50 mM tris-HCl (pH 9.5), 10 mM MgCl2, and 2.5 mM rNTPs and incubated for 20 min at 37C. Fresh, uniformly labeled tRNA (0.5 l) was used per assay, with different dilutions of the protein (1, 0.1, 0.01, and 0.001 mg ml1) in 50 mM tris-HCl (pH 7.8), 300 mM NaCl, and 10% glycerol. The 10-l reactions were mixed directly with 10 l of RNA loading dye (95% formamide, 1 mM EDTA, 0.025% SDS, xylene cyanol, and bromophenol blue), denatured at 90C, and applied to 5% polyacrylamide-urea gels. The gel was vacuum-dried at 80C and exposed to a PhosphorImager screen.

The effect of MenA3 antitoxin was assayed using in vitro-transcribed tRNASer2 as a substrate. For the coincubation assay, MenT3 (5 M) and increasing molar ratios of MenA3 were incubated with tRNASer2 and 2.5 mM CTP in 10-l reaction volumes containing 50 mM tris-HCl (pH 9.5) and 10 mM MgCl2 for 20 min at 37C. For the postincubation assay, the reactions were first incubated for 20 min at 37C with MenT3 alone in 7-l reaction volumes, then 3 l containing different concentrations of MenA3 were added, and the reactions were incubated for a further 20 min at 37C.

The tRNA screening was performed using 0.5 l of uniformly labeled M. tuberculosis tRNAs, all containing the CCA motif. The activity was tested in 50 mM tris-HCl (pH 9.5), 10 mM MgCl2, and 2.5 mM rCTP in 10-l reaction volumes and incubated for 20 min at 37C. The transcripts were incubated with 1 l of MenT3 (0.1 mg ml1), or with nuclease-free water as a control. The reaction was stopped with 10 l of RNA loading dye (95% formamide, 1 mM EDTA, 0.025% SDS, xylene cyanol, and bromophenol blue), denatured at 90C, and applied to 5% polyacrylamide-urea gels. The gel was vacuum-dried at 80C and exposed to a PhosphorImager screen.

Acknowledgments: We thank D.-J. Bigot for plasmid constructs, and P. Bordes, M.-P. Castani-Cornet, L. Falquet, L. Poljak, L. Hadjeras, and H. Akarsu for valuable advice. We also thank K. Semeijn and R. Dy for initial plasmid construction and testing, and E. Naser (Genotoul TRI-IPBS imaging facility) for help with flow cytometry analysis. Funding: This work was supported by a scholarship from the China Scholarship Council (CSC) as part of a joint international PhD program with Toulouse University Paul Sabatier (Y.C.); Springboard Award (SBF0021104) from the Academy of Medical Sciences (B.U. and T.R.B.); University of Otago Research Grant (P.C.F.), the School of Biomedical Sciences Bequest Fund, and University of Otago (P.C.F.); CNRS (UPR 9073), Universit Paris VII-Denis Diderot, the Agence Nationale de la Recherche (ARNr-QC), and the Labex (Dynamo) program (A.T. and C.C.); European Commission (contracts NEWTBVAC n241745 and TBVAC2020 n643381), Centre National de la Recherche Scientifique, Universit Paul Sabatier, Agence Nationale de la Recherche (ANR-13-BSV8-0010-01), and Fondation pour la Recherche Mdicale (DEQ20160334902) (C.G. and O.N.); and grant SNF CRSII3_160703 (P.G.). Author contributions: Conceptualization, all authors. Investigation, Y.C., B.U., C.G., A.T., and M. M. Writing, all authors. Funding acquisition, P.C.F., C.C., O.N., P.G., and T.R.B. Supervision, C.C., O.N., P.G., and T.R.B. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The crystal structures of MenT3 and MenT4 have been deposited in the Protein Data Bank under accession numbers 6Y5U and 6Y56, respectively. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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A nucleotidyltransferase toxin inhibits growth of Mycobacterium tuberculosis through inactivation of tRNA acceptor stems - Science Advances

Solve Puzzles for Science | Foldit

(This post was originally sent out on July 3 to our mailing list. You can sign up for the mailing list here to receive weekly updates about Foldit, including tips and tricks and see the top-scoring solutions to the week's puzzles. Don't forget to join our Discord as well to stay in the chat even when you're not folding!)

Hey folders!

Dev Josh here with your weekly Foldit update.

This week we saw the introduction of the Reaction Design tool. The devs are working hard on polishing it up and making it more usable! As always, thanks for your feedback and bug reports. You can submit more feedback here.

In this puzzle, I accidentally evo'ed on a broken developer build and got the top score. Whoops, sorry about that!Here are some of the solutions at the top of the leaderboards. [A note from our scientists: the top of the leaderboards doesn't always mean the most scientifically useful. These highlights are not scientific feedback and are not officially endorsed as scientifically valid designs by the Foldit team.]

Join the mailing list to see what others are folding!

This week's recipe is an oldie but a goodie from drjr. The recipe is called Reset, and it does what it says on the tin: reset to the best score, unfreeze the protein, remove all your bands, and set the CI to 1. A simple recipe, but a handy quality of life tool for when you just need to backtrack a little.

Quick shoutout to argyrw for always being a friendly voice in chat! Say hi to her in global or veteran chat.

Beginner: Are you still using Pull to draft your protein in the early game? Try making cutpoints and moving pieces around with the Move tool, it's so much easier! Don't forget to disable cutpoint bands in the Behavior tab, or they'll all come together again when you wiggle.

Intermediate: It can be really tempting mid-game to just switch to running recipes. But give some time to carefully inspect every acceptor and donor (the red and blue dots) to see what hydrogen bonds you can form, and manually mutate as needed. Not only will this lower your BUNS, but it'll help form a strong hbond network. The scientists love this, and your rank will too!

Expert: If you haven't already, read bkoep's blog on binder design metrics. DDG, SASA, and SC are going to become really important soon since we're looking to add objectives for them. So understanding and practicing these principles now can help you get a headstart on the competition! Use the protein design sandbox to try out some ideas.

Have a tip to share or a recipe to recommend? Reply with your suggestions or make a wiki page for your ideas! Reaction Design doesn't have a page yet, so if you understand this tool, help out your community by writing about it! (Since writing this post, LociOiling has graciously created the page for Reaction Design puzzles.)

Until next time, happy folding!

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Solve Puzzles for Science | Foldit

Proteasomal degradation of the intrinsically disordered protein tau at single-residue resolution – Science Advances

INTRODUCTION

Intrinsically disordered proteins (IDPs) are abundant in the human proteome and are implicated as therapeutic targets in major human diseases (1). IDPs have amino acid sequences of low complexity and lack an ordered three-dimensional (3D) structure (1). This allows IDPs to dynamically bind to diverse interaction partners and thus influence many biological processes (1). The activity of IDPs is regulated by posttranslational modifications including phosphorylation and truncation (1, 2). Because of their structural instability, IDPs are particularly sensitive to proteolytic degradation (35).

Aggregation of IDPs into insoluble deposits is the hallmark of neurodegenerative diseases (3). Aggregates of the IDP tau are linked to the progression of Alzheimers disease (AD) and are found in other age-related disorders termed tauopathies (6). The longest tau isoform in the human central nervous system comprises 441 residues (7). The N-terminal ~150 residues of tau project away from the microtubule surface and are thus termed projection domain (8). The central part of the tau sequence is formed by pseudo-repeats, which bind to microtubules (8, 9) and are essential for pathogenic aggregation and folding into cross- structure in tau amyloid fibrils (10, 11). Phosphorylated tau accumulates during the development of AD (6, 12).

The 20S proteasome forms the proteolytic core particle of the 26S proteasome holoenzyme (13). In contrast to the proteasomal degradation of most cellular proteins, IDPs can be degraded by the 20S proteasome in an ubiquitin- and adenosine triphosphate (ATP)independent process without the necessity of the 19S regulatory particle (35). Soluble tau is degraded by the 20S proteasome (14, 15), while phosphorylation and aggregation of tau inhibit its turnover by the proteasome (2, 1517). Decline of proteasomal activity and accumulation of tau have been linked to neurodegeneration (2, 18, 19): Decreased proteasomal activity results in tau accumulation, neurotoxicity, and cognitive dysfunction in cell and animal models of neurodegenerative disorders. Pharmacological activation of the 20S proteasome, direct administration of proteasome, or targeted proteasomal degradation of tau is therefore the focus of current therapeutic strategies targeting tauopathies (20, 21).

Here, we study the degradation of the IDP tau by the 20S proteasome through a residue-specific and quantitative approach that combines nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS). We provide detailed insights into the identity and properties of the proteasomal degradation products of tau, the single-residue degradation kinetics, and their specific regulation by phosphorylation in different tau domains/by different kinases.

The 20S proteasome (20S) is a barrel-shaped complex comprised by two stacked heptameric -rings that are sandwiched by two heptameric -rings (Fig. 1A) (13). The proteolytic sites, which hydrolyze the peptide bonds of substrates, are located in the subunits. IDPs thus traverse through the -rings to reach the active sites in the interior of the 20S proteasome (Fig. 1B). To study degradation of the IDP tau, we recombinantly prepared 20S from Thermoplasma acidophilum, which contains only one type of subunit and one type of subunit. This 20S particle thus has 14 identical chymotrypsin-like active sites, which are positioned at equal distances around the -rings (Fig. 1B). Electron microscopy (EM) showed intact barrel-shaped 20S complexes (Fig. 1C). The 441-residue isoform of tau (hTau40; also termed 2N4R tau; Fig. 1D) was also expressed in Escherichia coli.

(A) Schematic representation depicting the architecture of the 20S proteasome (20S) comprising 28 subunits arranged in four heptameric rings (7777). (B) The proteolytic active sites of the 20S proteasome are located in its interior, thus enabling degradation of hTau40 into short peptides once it has entered the 20S core. (C) Negatively stained EM micrograph of the 20S proteasome. (D) Domain organization of full-length hTau40 composed of 441 amino acids (aa) (UniProt ID 10636-8). N1 and N2 are the two inserts in the N-terminal projection domain, P1 and P2 correspond to the two proline-rich regions, and R1 to R are five pseudo-repeats. (E) (Left) SDS-PAGE gel showing hTau40 (1) and the degradation of (2 to 5) hTau40 by the 20S proteasome over time. The samples were incubated at 37C for 30 min (2), 90 min (3), and 150 min (4) and were subsequently put at 4C for additional 48 hours (5). After 48 hours, two well-resolved bands at ~28 and ~30 kDa (red lined box) appeared. (Right) The amino acid sequences of the upper (~30 kDa) and lower bands were identified with in-gel analysis and marked in red. Both intermediates correspond to the N-terminal domain of hTau40.

Recombinant hTau40 was incubated with the 20S proteasome, and degradation was followed by SDSpolyacrylamide gel electrophoresis (PAGE) (Fig. 1E, left). After ~150 min, a clear decrease in the intensity of the hTau40 band at ~60 kDa was apparent (lane 4 in Fig. 1E). In addition, two bands running at ~30 and 28 kDa appeared. Analysis after 48 hours of incubation confirmed the presence of the two new bands, while the full-length protein was degraded to near completion (lane 5 in Fig. 1E).

The two intermediate bands were precisely and independently excised from the gel, subjected to in-gel digestion using trypsin, which specifically cleaves at the peptide bond C terminus of lysine or arginine residues, and analyzed using liquid chromatography (LC)MS/MS. For both bands, the MS analysis confidently identified several peptides from the N-terminal domain (Fig. 1E, right). No peptides were identified in the region from 127 to 210, which contains multiple lysine and arginine residues such that trypsin digestion will produce too short sequences to be analyzed by LC-MS/MS. In the case of the upper band, the additional peptide RTPSLPTPPTR (residues 211 to 221 of hTau40) was identified (Fig. 1E, right).

We also separated the two long fragments using LC and detected their molecular weight by intact MS, giving masses of 25.782 and 22.257 kDa (fig. S1). Manual matching of the determined masses to N-terminal sequences of hTau40 showed that the long fragment contains residues 1 to 251, and the short one has residues 1 to 218. Previous studies showed that the upper band is recognized by the antibody Tau-5 (14), which binds to residues in the region from 218 to 225 (22).

To gain insight into the structural properties of the long tau fragments generated during 20S degradation, we recombinantly prepared a tau protein comprising residues 1 to 239 of hTau40. Tau(1239) contains the full epitope for the Tau-5 antibody (residues 218 to 225) and has a length in between the two long N-terminal fragments. Particle size analysis by dynamic light scattering (fig. S2A) showed that both hTau40 and Tau(1239) are more compact than the average size values for IDPs (fig. S2B) (23). hTau40, with an experimental size of 5.2 nm and an expected size for its number of residues of 5.5 nm, is 5% more compact than expected, while Tau(1239) is 18% more compact than expected with 3.3 and 4 nm as experimental and expected sizes, respectively. Despite the stronger compaction of Tau(1239), both proteins present the typical pattern of random coil conformation in circular dichroism spectra (fig. S2C).

Figure S2D shows the 1H-15N heteronuclear single-quantum coherence (HSQC) spectrum of 15N-labeled Tau(1239). The backbone cross peaks are located in the region between 7.6 and 8.6 parts per million (ppm), which is characteristic for IDPs. When compared to hTau40, chemical shift perturbation was restricted to the most C-terminal residues of Tau(1239) (fig. S2E), i.e., residues where Tau(1239), but not hTau40, ends. Analysis of the secondary structure propensities using the chemical shifts of carbonyl and C (fig. S2F) furthermore showed that both hTau40 and Tau(1239) are mainly random coil, in agreement with circular dichroism spectra (fig. S2C).

In addition, the single-residue analysis showed that Tau(1239) contains elements of transient secondary structure: residues 116 to 119 with a tendency for helical structure and two short stretches (residues 150 to 152 and 225 to 230) with extended conformation. The same transiently structured regions were detected in hTau40 (fig. S2F). TALOS+ also identified four regions with preference for extended conformation (residues 275 to 279, 306 to 310, 337 to 339, and 392 to 399) and one with helical content (residues 431 to 437) in hTau40, in agreement with previous analysis (24). The presence of extended conformations in the repeat region has previously been suggested to be responsible for the observation that the repeat region of tau, which is not present in Tau(1239), is less compact when compared to a pure random coil conformation. The combined data thus point to a compaction of the N-terminal cleavage intermediates of hTau40 (fig. S2, A and B).

To identify short tau peptides generated by 20S, we analyzed the released peptides in the supernatant after incubation of hTau40 and 20S using MS. The largest fraction of identified peptides was from hTau40s pseudo-repeat region (Fig. 2, A and B). In addition, peptides from the C-terminal domain and the residue regions 2 to 13, 84 to 103, and 167 to 192 were detected but with very low responses in MS in the supernatant (Fig. 2C). The tau peptides and their cleavage sites identified by MS are generally in good agreement with the proteasomal cleavage sites predicted by NetChop 3.1 (Fig. 2B) (25).

(A) Domain organization of hTau40. (B) Amino acid sequence of hTau40 depicting in color [color code as in (A)] the 20S-generated peptides, which were identified by LC-MS/MS. The peptides underlined with black dots were also present in the in-solution sample but with low intensities. The slashes depict all identified cleavage sites. Cleavage sites predicted by the NetChop server are marked by arrows. The bar on top of the VQIVYK sequence indicates the ability of this sequence to form amyloid-like filaments (26). (C) (Left) Histogram representation of the peak area of 20S-generated tau peptides [color code as in (A)] identified by in-solution analysis. Insert depicting the sequences of the identified peptides and the cleavage sites (marked with slashes). (Right) Histogram representing the most intense peptides in the R3 region. A.U., arbitrary units. (D) ThT fluorescence during incubation of the peptide 309VYKPVDL315. The peptide (50, 100, and 150 M) was incubated with heparin (peptide:heparin molar ratio of 4:1) in triplicates.

The peptide with the highest ion peak area was 309VYKPVDL315 (Fig. 2C, right). It partially overlaps with the hexapeptide sequence 306VQIVYK311 at the beginning of pseudo-repeat R3 (Fig. 2B).

The 306VQIVYK311 sequence is the most hydrophobic residue stretch of tau, is a major driving force for pathogenic tau aggregation, and can form amyloid-like filaments in isolation (26). We therefore tested whether the 20S-generated tau peptide 309VYKPVDL315 can aggregate into amyloid fibrils. To this end, the 309VYKPVDL315 peptide was incubated with heparin at a molar ratio of 4:1.

Figure 2D shows the results from thioflavin-T (ThT) fluorescence measurements of 309VYKPVDL315/heparin samples at three different peptide concentrations during incubation at 37C for 6 days. For all of the samples, the background-corrected ThT intensity was very low and did not increase during incubation (Fig. 2D). No increase in ThT intensity was detected even when the peptide was incubated for 6 days in the absence of heparin (fig. S3). Because ThT fluorescence intensity increases upon binding to amyloid fibrils, the data show that the 20S-generated peptide 309VYKPVDL315 is not able/has a very low propensity to form amyloid fibrils.

To gain insight into the kinetics of degradation of tau by the 20S proteasome and define its residue specificity, we used NMR spectroscopy. Figure 1A displays the 2D 1H-15N HSQC spectrum of 15N-labeled hTau40. The NMR spectrum was recorded at 5C to attenuate the exchange of amide protons with solvent and thus exchange-induced NMR signal broadening. Comparison of the HSQC spectrum of hTau40 alone with the spectra recorded after 30 min and 66 hours (red) in the presence of 20S (hTau40:20S molar ratio of 4:1) showed that after 30 min, the spectrum of hTau40 was essentially unchanged (fig. S4), but after 66 hours, additional sharp cross peaks were present. Four of the newly appearing cross peaks overlapped with signals observed in a natural abundance 1H-15N HSQC spectrum of the 309VYKPVDL315 peptide, i.e., the peptide with the highest ion peak area in MS (fig. S5). The degradation-associated cross peaks were not observed for a separate sample, which additionally contained the proteasome inhibitor oprozomib (Fig. 3A, right spectrum).

(A) Superposition of 2D 1H-15N HSQC spectra of hTau40 at 5C in the presence of the 20S proteasome after 3 hours (black) and 66 hours (red) in the absence (left) and presence (right) of the proteasome inhibitor oprozomib. (B) (Top) Evolution of relative peak intensities, I(t)/I0, in 2D 1H-15N HSQC spectra of hTau40 in the presence of 20S with increasing incubation time at 5C. I0 is the cross-peak intensity observed in the first HSQC. (Middle) Residue-specific rate constants of a first-order model of the 20S degradation kinetics of hTau40. Correlation coefficients for the fit to the first-order model are color-coded (color code bar to the right). Error bars represent SD. (Bottom) Evolution of relative peak intensities in 2D 1H-15N HSQC spectra of hTau40 in the presence of the 20S proteasome and the proteasome inhibitor oprozomib.

When tau is degraded by the proteasome into small peptides, the chemical environment of residues changes. To gain insights into the kinetics of 20S degradation, the intensity of IDP cross peaks at their location in the absence of 20S can be analyzed (27). Because a 1H-15N backbone correlation can be observed for every non-proline residue in the 2D 1H-15N HSQC, up to 397 (441 residues minus the C terminus and 43 prolines, and depending on signal overlap) sequence-specific probes for tau degradation are thus available.

The top panel in Fig. 3B displays the decrease of NMR signal intensities along the hTau40 sequence with increasing 20S incubation time. The fastest decrease occurred in the repeat domain. To derive residue-specific degradation rates, we fitted first-order decay kinetics via linear regression to the residue-specific intensity data. The highest rates occurred in repeat R3 and reached up to 0.015 hours1 at 5C (Fig. 3B, middle, and table S1). Fast degradation kinetics were also observed in the other pseudo-repeats, in agreement with similar sequence compositions. In addition, taus C terminus as well as residues ~220 to 250 at the end of the proline-rich region were rapidly affected by degradation.

Oprozomib predominantly inhibits the chymotrypsin-like activity of the 20S proteasome (28). Detailed analysis of the hTau40 spectra in the presence of both 20S and the small-molecule oprozomib showed that the cross peaks of residues in R2 and R3 decreased in intensity by up to 20% after 66 hours (Fig. 3B, bottom, and table S1). Thus, the 20S complex has residual proteolytic activity, which is not inhibited by oprozomib.

A large number of kinases can phosphorylate tau (29). These include proline-directed kinases [e.g., glycogen synthase kinase 3 (GSK3) and cyclin-dependent kinase 5 (cdk5)] that phosphorylate proline-serine/threonine motifs, notably in the proline-rich region of tau, as well as non-prolinedirected kinases [e.g., microtubule affinity-regulating kinase (MARK), protein kinase A (PKA), and Ca2+/calmodulin-dependent protein kinase II (CaMKII)], which phosphorylate the KXGS motifs in the pseudo-repeats. CaMKII phosphorylates tau at several sites (30) and colocalizes with neurofibrillary tangles (NFTs) in AD brains (31).

To gain insight into the influence of substrate phosphorylation on 20S degradation, we phosphorylated recombinant hTau40 with CaMKII in vitro. SDS-PAGE demonstrated an upfield shift in the hTau40 band, confirming successful phosphorylation (Fig. 4A). According to MS/MS analysis, CaMKII phosphorylates S131 and T135 in the projection domain, T212 and S214 in P2, S262 in R1, and S356 in R4 (30). 1H-15N NMR spectroscopy further showed that S214, S356, and S413 are fully phosphorylated in hTau40 (Fig. 4B). In addition, S262, S324, and S352 were found to be partially phosphorylated (Fig. 4B).

(A) SDS-PAGE gel demonstrating phosphorylation of hTau40 by CaMKII in the presence of calmodulin. (B) (Left) Enlarged region with phosphorylated residues taken from the first 2D 1H-15N HSQC recorded at 5C for a total duration of 3 hours on CaMKII-phosphorylated hTau40 in the presence of 20S. On top, the location of the phosphorylated residues is marked by short black lines in the context of the domain diagram of hTau40. (Right) Superposition of the first 2D 1H-15N HSQC spectrum (black; total measurement time: 3 hours) of CaMKII-phosphorylated hTau40 in the presence of 20S with the spectrum completed after 66 hours (red). (C) Relative peak intensities in 2D 1H-15N HSQC spectra of CaMKII-phosphorylated hTau40 in the presence of the 20S proteasome with increasing time of incubation at 5C (from red to blue).

We then incubated CaMKII-phosphorylated hTau40 with 20S proteasome at 5C. Even after 66 hours, no degradation peaks were observed in the 1H-15N HSQC spectrum (Fig. 4B and fig. S6). In addition, hTau40 cross-peak intensities remained largely unaffected (Fig. 4C and fig. S6). Similarly, CaMKII phosphorylation of the tau construct K18, which only contains the repeat domain, attenuated its degradation by the 20S proteasome (fig. S7). Thus, phosphorylation of tau by CaMKII interferes with the degradation of tau by the 20S proteasome.

GSK3 is ubiquitously expressed in mammalian tissue and has been implicated as a major tau kinase in AD (32). In vitro modification of hTau40 by GSK3 results in phosphorylation of S46, T175, T181, S202, T205, T212, T217, T231, S235, S396, S400, and S404 (33). NMR confirmed complete phosphorylation of S396, S400, and S404 (Fig. 5A). In contrast to CaMKII phosphorylation (Fig. 4), phosphorylation by GSK3 did not block proteasomal processing of hTau40 [Figs. 5A (red spectrum) and 6]. Analysis of cross-peak intensities at increasing 20S incubation times further showed that rapid degradation occurred in repeats R2 and R3 of hTau40 (Fig. 5B).

(A) (Left) Enlarged region with phosphorylated residues taken from the first 2D 1H-15N HSQC recorded at 5C for a total duration of 3 hours on GSK3-phosphorylated hTau40 in the presence of 20S. On top, the cartoon depicts the sites of phosphorylation of hTau40 by GSK3. (Right) Superposition of the first 2D 1H-15N HSQC spectrum (black; total measurement time: 3 hours) of GSK3-phosphorylated hTau40 in the presence of 20S with the spectrum completed after 66 hours (red). (B) Relative peak intensities in 2D 1H-15N HSQC spectra of GSK3-phosphorylated hTau40 in the presence of the 20S proteasome with increasing time of incubation at 5C (from red to blue).

(A and B) Per-residue rate constants for degradation of tau by the 20S proteasome. Residue-specific rate constants of a first-order model of the 20S degradation kinetics of hTau40 at 5C (A, top; same as in Fig. 3B), in the presence of the inhibitor oprozomib (A, bottom), of hTau40 phosphorylated by CaMKII (B, top), and of hTau40 phosphorylated by GSK3 (B, bottom). Correlation coefficients for the fit to the first-order model are color-coded (color code bars to the right). Error bars represent SD. (C) Schematic representation illustrating the phosphorylation-dependent degradation of the AD-related protein tau by the 20S proteasome: Wild-type tau (hTau40) is degraded by the 20S proteasome starting from the pseudo-repeat region and the C-terminal domain, producing short peptides (blue, pink, and orange) from those regions, followed by degradation of the N-terminal domain, which generates two long N-terminal fragments. Depending on the sites of phosphorylation, 20S degradation of tau is inhibited (CaMKII; top) or attenuated (GSK3; bottom). The color code of different hTau40 domains is described in Fig. 1.

Figure 6 (A and B) compares the residue-specific degradation rates of unmodified hTau40 in the presence of the 20S proteasome (Fig. 6A, top), unmodified hTau40 in the presence of 20S and the inhibitor oprozomib (Fig. 6A, bottom), CaMKII-phosphorylated hTau40 and 20S (Fig. 6B, top), and GSK3-phosphorylated hTau40 and 20S (Fig. 6B, bottom, and table S1). As calculated from the time-dependent decrease in cross-peak intensities, GSK3-phosphorylated hTau40 is most efficiently processed by the 20S proteasome in repeats R2 and R3. The phosphorylation of selected residues in taus C-terminal domain, however, blocks cleavage of peptide bonds in this region. In addition, the decay of NMR signals in the proline-rich region was strongly attenuated (Fig. 5B and fig. S4), in agreement with phosphorylation of T212, T217, T231, and S235 by GSK3 (33).

Within the cell, IDPs are constantly synthesized and degraded by the proteasome. Because they lack a globular structure, IDPs can directly be processed by the 20S proteasome without the need for previous ubiquitination and unfolding by the 26S proteasome (35, 34). In parallel, IDPs can be degraded in a ubiquitin-dependent manner by the 26S proteasome. Aggregates of IDPs cannot properly be degraded by the proteasome and are instead processed through autophagy (18, 19). In addition, tau aggregates might inhibit the activity of proteasomes and thereby contribute to neurodegeneration (2, 17, 18). Detailed insights into the processing of tau and other IDPs by the 20S proteasome may therefore be important for treating neurodegeneration and other human diseases (34).

Inhibition of the proteasome by small molecules results in increased amounts of tau in SH-SY5Y cells and rat brain (14, 35). In addition, the four-repeat isoform hTau43 (also termed 0NR4 tau) was shown to be degraded by the human 20S proteasome in vitro without previous ubiquitination (14). In agreement with the latter study, which used human 20S (14), we observed two relatively stable populations of long tau fragments from the N terminus when incubating hTau40 with the 20S proteasome from T. acidophilum (Fig. 1). To determine the identity of the two hTau40 fragments, we performed MS analysis and found that the long and short fragments contain residues 1 to 251 and 1 to 218, respectively (Fig. 1).

Proteasomes cleave their substrates to short peptides with mean lengths between 6 and 10 amino acids (4, 36). Longer (>50 amino acids) degradation intermediates are rarely detected, because the substrate is thought not to dissociate from the proteasome during the degradation process. The presence of two long truncated tau fragments during 20S degradation is therefore unexpected. The more than 200-residue-long tau fragments contain multiple, potential proteasomal cleavage sites (Fig. 2B). To investigate whether the generation of these fragments is the result of specific structural properties of the N-terminal domain of hTau40, we characterized this domain at a single-residue level by NMR spectroscopy. The analysis showed that Tau(1239) is more compact than hTau40 (fig. S2). We speculate that the more compact structure might interfere with 20S cleavage of the N-terminal fragments.

The short ~6- to 10-residue tau peptides generated by the 20S proteasome can further be cleaved by other proteases (2). In parallel, they might itself contain activity, which is relevant for pathological processes. Consistent with this hypothesis, the six-residue tau peptide 306VQIVYK311 can form insoluble amyloid-like filaments in vitro (26). We therefore used MS to identify the tau peptides generated by 20S degradation (Fig. 2). From the large number of different 20S-generated peptides, the tau peptide with the highest ion peak area was 309VYKPVDL315. Consistent with the high abundance of the 309VYKPVDL315 peptide generated by 20S degradation, signals corresponding to this peptide were identified in the NMR spectra of degraded tau (fig. S4). The 309VYKPVDL315 peptide lacks the first three amino acids of the filament-forming 306VQIVYK311 sequence but has four additional N-terminal residues including the two hydrophobic residues V313 and L315. Despite an overall high hydrophobicity, however, the tau peptide 309VYKPVDL315 did not aggregate into amyloid-like filaments in the presence of the aggregation enhancer heparin (Fig. 2D). Notably, all of the other 20S-generated peptides in the region from 308 to 320 also contain residue P312, i.e., a proline with known -strandbreaking property (Fig. 2C, right). Cleavage of tau by the 20S proteasome thus generates peptides that are unable to aggregate into amyloid-like filaments.

A wide range of assays have been developed to follow protein degradation. These assays often sample the degradation reaction at discrete time points using SDS-PAGE and antibody binding, autoradiography, protein staining, or Western blotting (37). In addition, proteasome activity can be analyzed through the measurement of fluorescence anisotropy of small-molecule dyes attached to substrate proteins. The identity of degradation products can furthermore be determined using MS. Here, we combined MS with NMR to (i) gain insight into the structural properties of the long degradation intermediates of tau identified by MS and (ii) quantify degradation kinetics in the IDP tau with single-residue and high temporal resolution. MS and NMR spectroscopy are thereby complementary, because MS enables large-scale identification of substrate fragments and peptides generated by proteasomal degradation, but cannot identify all released peptides, lacks single-residue resolution, and is limited in temporal resolution. NMR spectroscopy makes it possible to follow substrate degradation, while the reaction occurs in the test tube, and quantify degradation kinetics at high spatial/per-residue and temporal resolution. On the other hand, a high number of generated peptides and fragments complicate their identification by NMR especially for large IDPs, such as tau, which have many cross peaks. In addition, it has to be taken into account that the cleavage of a peptide bond can be sensed by residues that are several positions removed from the site of proteolysis (27). Because of the abovementioned aspects, we believe that the combination of MS and NMR will also be useful to investigate differences in the degradation pattern and substrate selectivity of 20S proteasomes from different organisms.

Using NMR spectroscopy, we found that the 20S degradation of many tau residues follows first-order decay kinetics (Fig. 3). The maximum degradation rate reached ~0.015 hours1 at 5C, which corresponds to a degradation half-time of ~46 hours. The reported half-life of tau in HT22 cells is 60 hours (15). The analysis further showed that the 20S proteasome from T. acidophilum preferentially cleaves tau in the pseudo-repeat region, with the fastest rates observed in repeat R3 (Fig. 3). Repeat R3 is part of the cross- structure of heparin-induced tau fibrils (38). In addition, R3 is located in the core of paired helical filaments purified from the brains of patients with AD (10). The data suggest that the 20S proteasome preferentially degrades the regions of tau, which are important for pathogenic aggregation.

SDS-PAGE analysis, in combination with antibody binding, was used to suggest that the degradation of tau by the 20S proteasome is bidirectional (14), supporting degradation models in which 20S degradation has a preference for the free NH2 or COOH terminus of a substrate (39). In contrast, we find that the proteasome degradation of tau is most efficient in the repeat domain (followed by the C-terminal domain; Fig. 3). Our results are thus in agreement with reports showing that the 20S proteasome can initiate endoproteolytic cleavage at internal sites of IDPs (5). The efficient cleavage of the pseudo-repeat region also enables the generation of the two long fragments from the N terminus of tau (Fig. 1).

The strength of the quantitative, combined MS/NMR approach was further supported by the experiments, in which we studied the influence of phosphorylation of tau on its degradation by the 20S proteasome (Figs. 4 and 5). Tau molecules found in NFTs in the brains of patients with AD are hyperphosphorylated, and dysregulation of tau phosphorylation has been linked to neuronal toxicity (6). Consistent with the hypothesis that impaired proteasomal degradation results in tau accumulation, phosphomimetic tau variants were less efficiently degraded by the proteasome in autophagy-deficient mouse embryonic fibroblasts (16).

The quantitative NMR-based degradation analysis showed that phosphorylation of tau by the non-prolinedirected serine/threonine kinase CaMKII inhibits degradation of tau by the 20S proteasome (Figs. 4 and 6). When the proteasome cannot degrade tau, autophagy becomes important, in agreement with the observation that autophagy is the primary route for clearing phosphorylated tau in neurons (16). However, using the same quantitative approach, we found that tau phosphorylated by GSK3, which phosphorylates Pro-Ser/Thr epitopes seen in NFTs in AD (32), only blocks cleavage in certain regions but does not interfere with tau cleavage in the pseudo-repeats R2 and R3 (Figs. 5 and 6). The regions of tau, which are no longer cleaved such as the C-terminal domain and the proline-rich domain, contain residues phosphorylated by GSK3 (Fig. 5). While GSK3 does not phosphorylate residues in the repeat region, CaMKII phosphorylates S262, S324, S352, and S356 and blocks degradation by the 20S proteasome (Figs. 4 and 6). Phosphorylation of S262, S324, S352, and S356 therefore appears to play an important role in the inhibition of tau degradation by the 20S proteasome. S262, S324, S352, and S356 are also phosphorylated by microtubule-associated protein/MARKs, and their phosphorylation affects tau aggregation as well as microtubule binding of tau (40). Currently, the mechanism of impaired degradation of CaMKII-phosphorylated tau is unknown but could involve (i) an impaired/restricted entry through the 20S gate formed by the first 12 amino acids of the subunit and (ii) a blocked interaction with the catalytic sites in the subunit. Our study provides the basis to quantify with single-residue resolution the degradation of tau and other IDPs, their different isoforms, and posttranslationally modified variants and thus gain mechanistic insight into disease-associated accumulation of IDPs.

Unlabeled and 15N-labeled Tau protein (hTau40, UniProt ID 10636-8, 441 residues) were expressed in E. coli strain BL21(DE3) from a pNG2 vector (a derivative of pET-3a, Merck-Novagen, Darmstadt) in the presence of an antibiotic. In case of unlabeled protein, cells were grown in 1 to 10 liters of LB and induced with 0.5 mM IPTG (isopropyl--d-thiogalactopyranoside) at OD600 (optical density at 600 nm) of 0.6 to 0.8. To obtain 15N-labeled protein, cells were grown in LB until an OD600 of 0.6 to 0.8 was reached, then centrifuged at low speed, washed with M9 salts (Na2HPO4, KH2PO4, and NaCl), and resuspended in minimal medium M9 supplemented with 15NH4Cl as the only nitrogen source and induced with 0.5 mM IPTG. After induction, the bacterial cells were harvested by centrifugation, and the cell pellets were resuspended in lysis buffer [20 mM MES (pH 6.8), 1 mM EGTA, and 2 mM dithiothreitol (DTT)] complemented with protease inhibitor mixture, 0.2 mM MgCl2, lysozyme, and deoxyribonuclease (DNase) I. Subsequently, cells were disrupted with a French pressure cell press (in ice-cold conditions to avoid protein degradation). In the next step, NaCl was added to a final concentration of 500 mM and boiled for 20 min. Denaturated proteins were removed by ultracentrifugation at 4C. The supernatant was dialyzed overnight at 4C against dialysis buffer [20 mM MES (pH 6.8), 1 mM EDTA, 2 mM DTT, 0.1 mM phenylmethylsulfonyl fluoride (PMSF), and 50 mM NaCl] to remove salt. The following day, the sample was filtered and applied onto a previously equilibrated ion-exchange chromatography column, and the weakly bound proteins were washed out with buffer A (same as the dialysis buffer). Tau protein was eluted with a linear gradient of 60% final concentration of buffer B [20 mM MES (pH 6.8), 1 M NaCl, 1 mM EDTA, 2 mM DTT, and 0.1 mM PMSF]. Protein samples were concentrated by ultrafiltration (5 kDa Vivaspin from Sartorius) and purified by gel filtration chromatography. Last, the protein was dialyzed against 50 mM sodium phosphate (NaP) (pH 6.8).

20S proteasomes from T. acidophilum were expressed from pRSETA containing the bicistronic gene including psmA and psmB. Transformed BL21 cells were induced with 0.1 mM IPTG and incubated for 18 hours at 37C. Harvested cells were resuspended in 3 ml of lysis buffer (50 mM Na2HPO4 pH 8.0, 300 mM NaCl) per 1 g of cells and lysed with the French press. The lysate was incubated at 65C for 15 min. Heat-denatured proteins were removed by centrifugation at 30,000g at 4C. Polyethylenimine (0.1%, w/v) was added to the supernatant to precipitate contaminating nucleic acids. Precipitated nucleic acids were removed by centrifugation at 100,000g for 1 hour. The supernatant was subjected to differential precipitation with polyethylene glycol 400 (PEG; number signifies the mean molecular weight of the PEG polymer). PEG400 was added to a concentration of 20% (v/v) to the supernatant under stirring at 18C and incubated for 30 min. Precipitated proteins were removed by centrifugation at 30,000g for 30 min at 4C. The supernatant was then precipitated by raising the concentration of PEG400 to 40% (v/v). The precipitate of this step contained the 20S proteasomes and was recovered by centrifugation at 30,000g for 30 min at 4C and resuspended in purification buffer (0.05 M BisTris pH 6.5, 0.05 M K(OAc), 0.01 M Mg(OAc)2, 0.01 M -Glycerophosphate) containing 5% (w/v) sucrose, 10 mM DTT, and 0.01% (w/v) lauryl maltose neopentyl glycol (LMNG) on an orbital shaker at 18C. The resuspended material was loaded on 10 to 30% (w/v) sucrose gradients in purification buffer containing 5 mM DTT, which are centrifuged at 284,000g for 16 hours at 4C. Gradients were harvested in 400 l of fractions. SDS-PAGE was used to identify fractions containing 20S proteasomes. Selected fractions were pooled and precipitated by the addition of 40% (v/v) PEG400. After centrifugation (30,000g, 20 min), the supernatant was removed and the precipitate was resuspended in purification buffer containing 5% (w/v) sucrose, 10 mM DTT, and 0.01% (w/v) LMNG. The resuspended material was loaded on linear 10 to 40% (w/v) sucrose gradients in purification buffer containing 5 mM DTT, which are centrifuged at 284,000g for 18 hours at 4C. Fractions containing 20S proteasomes are yet again identified by SDS-PAGE, precipitated and concentrated by the addition of 40% PEG400, and resuspended in purification buffer containing 5% (w/v) sucrose and 5 mM DTT, yielding the final purified protein preparation at 26 mg/ml. Protein concentrations were determined by the Bradford assay (Bio-Rad, Munich, Germany) using bovine serum albumin (BSA) as a standard.

For grid preparation, a protein stock solution (6 mg/ml) was diluted to 0.25 mg/ml with standard buffer without sucrose. Glutaraldehyde was added to the diluted protein solution to a concentration of 0.1% (v/v). After incubation for 2.5 min at room temperature, the reaction was quenched by the addition of 50 mM l-aspartate (pH 6.5). A continuous carbon foil was floated on the protein solution for 1 min at 4C. A holey carbon copper grid was used to remove the continuous carbon foil from the protein solution. Excess liquid was removed with a tissue paper. Proteins were stained by floating the grid on a saturated uranyl formate solution for 1 min at 4C. Remaining staining solution was removed with a tissue, and the grid was dried under ambient conditions. Negative-stain EM images were taken with a Philips CM200 microscope (160 kV). Images were acquired at a magnification of 66,000. The pixel size corresponds to 3.34 per pixel. The TVIPS charge-coupled device camera was used to record the micrographs.

hTau40 was phosphorylated by CaMKII (recombinant human CaMKII alpha protein from Abcam) and GSK3 [recombinant human GSK3 beta protein (active) from Abcam]. The reaction was performed by mixing 0.2 mM hTau40 with 0.02 mg/ml kinase, 2 mM DTT, 2 mM ATP, 1 mM PMSF, and 5 mM MgCl2 in 40 mM Hepes (pH 7.4). In case of CaMKII, we additionally used 2 M calmodulin (bovine calmodulin, recombinant from Sigma), 1 mM CaCl2, and, in case of GSK3, 2 mM EGTA. The samples were incubated at 30C overnight and buffer-exchanged to 50 mM NaP (pH 6.8). Protein concentrations were determined by the Bradford assay using BSA as a standard.

For detection of hTau40 degradation products/fragments generated by the 20S proteasome (hTau40:20S molar ratio of 3:1), we used a 18% separating gel [ddH2O, 30% acrylamide, 1.5 M tris (pH 8.8), 10% SDS, 10% ammonium persulfate (APS), and tetramethylethylenediamine (TEMED)] and a 4% stacking gel [ddH2O, 30% acrylamide, 1 M tris (pH 6.8), 10% SDS, 10% APS, and TEMED]. For validation of hTau40 phosphorylation, we used a 12% separating gel and a 4% stacking gel.

hTau40 was incubated with 20S proteasome for 150 min at 37C and 1 day at 4C. The resulting reaction sample was in 50 mM NaP (pH 6.8). The buffer was exchanged to MS compatible sample buffer using Amicon Ultra centrifugal filters with a molecular weight cutoff of 3000. The filter was first washed using water. The reaction sample and 300 l of sample buffer [0.1% formic acid (FA)] were then added to the filter and centrifuged at 7500g for 30 min. After removing the buffer, 300 l of sample buffer was added and centrifuged for 30 min. The buffer exchange was then repeated one more time. Last, the samples were diluted to 100 ng/l for the following MS analysis.

The intact MS experiment was performed on Q Exactive HF-X2 (Thermo Fisher Scientific) coupled to a Dionex UltiMate 3000 UHPLC system (Thermo Fisher Scientific) equipped with a PepSwift Monolithic Trap Column [200 m inside diameter (ID) 5 mm] and a ProSwift RP-4H Monolithic Nano Column (100 m ID 25 cm). The flow rate was set to 1 l/min. Mobile phase A and mobile phase B were 0.1% (v/v) FA and 80% (v/v) acetonitrile (ACN), 0.08% FA, respectively. The gradient started at 20% B and increased to 50% B in 33 min and then kept B constant at 90% for 4 min, followed by re-equilibration of the column with 5% B. MS spectra were acquired with the following settings: microscans, 1; resolution, 120,000; mass analyzer, Orbitrap; automatic gain control (AGC) target, 3 106; injection time, 100 ms; mass range, 450 to 2000 mass/charge ratio (m/z).

hTau40 samples were incubated with 20S proteasome (molar ratio of 3:1) for different times (30, 90, and 150 min at 37C and, additionally, 48 hours at 4C). The samples were then analyzed by SDS-PAGE electrophoresis as described above. The two fragments (around 25 to 30 kDa) were carefully cut from the gel and used for in-gel analysis. The in-gel digestion of the two bands was performed using trypsin (Promega) to the gels. In the next step, the extracted peptides were desalted by using stage tips. In the last step, the samples were dried (SpeedVac) and readied for further analysis.

hTau40 was incubated with 20S proteasome (molar ratio of 3:1) at 37C for 3 hours before the analysis. The samples were precipitated by acetone and put at 30C overnight. Then, the samples were centrifuged at 14,000g for 10 min, and the supernatant was collected and dried. In the next step, contaminates were removed by the sp3 method, followed by direct injection into the mass spectrometer.

In-geldigested peptides were analyzed using an Orbitrap Fusion Tribrid (Thermo Fisher Scientific) instrument. In-solution samples were analyzed using Orbitrap Fusion Lumos (Thermo Fisher Scientific). Both instruments are coupled to a Dionex UltiMate 3000 UHPLC system (Thermo Fisher Scientific) equipped with an in-housepacked C18 column (ReproSil-Pur 120 C18-AQ, 1.9 m pore size, 75 m inner diameter, 30 cm length, Dr. Maisch GmbH). Both Orbitrap Fusions (Tribrid and Lumos) were operating in data-dependent mode for MS2. Dried samples were resuspended in 5% ACN, 0.1% FA. Samples were centrifuged for 10 min at 14,000g, and the supernatants were transferred to new sample tubes. In both cases, the flow rate was set to 300 nl/min. Mobile phase A and mobile phase B were 0.1% FA (v/v) and 80% ACN, 0.08% FA (v/v), respectively. The gradient in Orbitrap Fusion Tribrid (in-gel samples) started at 10% B and increased to 42% B in 43 min and then kept B constant at 90% for 6 min, followed by re-equilibration of the column with 5% B. MS1 spectra were acquired with the following settings: resolution, 120,000; mass analyzer, Orbitrap; mass range, 380 to 1500 m/z; injection time, 50 ms; AGC target, 4 105; S-Lens radio frequency (RF) levels, 60; charge state, +2 to +7; dynamic exclusion after n time, n = 1, dynamic exclusion duration = 60 s. MS2 parameters were as follows: first mass, 120; activation type, higher-energy collisional dissociation (HCD); collision energy, 35; Orbitrap resolution, 30,000; maximum injection time, 250 ms; AGC target, 100,000. The gradient in Orbitrap Fusion Lumos (in-solution samples) increased to 30% B in 42 min and further to 40% B in 4 min and then kept B constant at 90% for 6 min, followed by re-equilibration of the column with 5% B. MS1 spectra were acquired with the following settings: resolution, 120,000; mass analyzer, Orbitrap; mass range, 350 to 1600 m/z; injection time, 50 ms; AGC target, 5 105; S-Lens RF levels, 30; charge state, +2 to +7; dynamic exclusion after n time, n = 1, dynamic exclusion duration = 30 s. MS2 parameters were as follows: first mass, 120; activation type, HCD; collision energy, 30; Orbitrap resolution, 15,000; maximum injection time, 120 ms; AGC target, 100,000.

Thermo Proteome Discoverer (2.1.0.81) was used for database searching. In Proteome Discoverer, the Sequest HT, fixed value peptide spectrum match validator, and Precursor Ions Area Detector nodes were used. Parameters for database searching were as follows: the hTau40 protein sequence (P10636-8) was downloaded from Swiss-Prot. Mass tolerance for precursors and fragment ions was set as 10 and 20 ppm, respectively. Maximal missed cleavage was 4. Dynamic modifications were set as oxidation (M) and acetylation (protein N terminus). For in-gel samples, fixed modification was carbamidomethylation (C). Trypsin was used as the enzyme, and its specificity was set as semi-specific. For in-solution sample, no enzyme was set. For precursor ions area detector, mass precision was 2 ppm. Only the peptides that were identified with high confidence were used in this study. For in-solution samples, the peak area of precursors was used for quantification of the identified peptides.

The peptide VYKPVDL was synthesized as trifluoroacetic acid salts by GenScript, and the stock solution (1 mM) was made in 25 mM Hepes (pH 7.4). To test whether the peptide can aggregate into amyloid fibrils, we used 50, 100, and 150 M of the peptide in 25 mM Hepes (pH 7.4). The stock solution of ThT (purchased from Sigma) was prepared in ddH2O, and for the binding assay, 50 M was used. When heparin (~20 kDa, Roth) was added to the sample, the molar ratio of the peptide to heparin was 4:1. ThT fluorescence was then measured with excitation at 440 nm and emission at 482 nm at 37C using a multimode microplate reader (Spark 20M, TECAN).

2D 1H-15N HSQC and 3D spectra (HNCO and HNCA) of hTau40 and Tau(1239) were acquired at 5C on a Bruker 800 MHz spectrometer equipped with triple-resonance 5-mm cryogenic probe. The protein concentration was 125 M in 50 mM NaP buffer (pH 6.8), 5% D2O, 0.1% NaN3, and 50 M dextran sulfate sodium. Spectra were processed with TopSpin 3.5 (Bruker) and analyzed using Sparky.

NMR degradation experiments with 20S proteasome involving hTau40, phosphorylated hTau40, and hTau40 in the presence of the proteasome inhibitor were acquired and processed as explained above. 2D 1H-15N HSQC spectra were recorded for 15N-labeled hTau40 and 20S proteasome in a molar ratio of 4:1 in 50 mM NaP buffer (pH 6.8) and 10% D2O. The dead time between mixing hTau40 and 20S proteasome and starting the first HSQC experiments was ~30 min.

To study the kinetics of the degradation of hTau40 by the 20S proteasome, 60-min HSQCs were measured every hour during the first 24 hours and then for 180-min HSQCs every 3 hours (for a total of 38 measurements) up to 66 hours. In case of the sample with the inhibitor as well as the phosphorylated samples, 180-min HSQCs were recorded every 3 hours for a total of 22 measurements (66 hours). For our control sample, we used the proteasome inhibitor oprozomib (ApexBio), which was incubated for 2 hours at 37C in 250 molar excess before the experiment.

Peak intensities were extracted from a series of 1H-15N HSQC datasets at predetermined time intervals. After peak assignment with the software Sparky, the peak intensities were normalized with respect to the initial peak intensity for each residue, taking into account the duration of each HSQC. A residue was excluded from plotting and further analysis if a consecutively recorded peak intensity increased to more than 115% of the relative intensity of the preceding measurement. Such an increase in peak intensity when compared to the preceding measurement can arise from more favorable relaxation properties in the generated peptides when compared to full-length tau. In addition, peak overlap can potentially cause fluctuating intensities.

The peak intensities at all recorded times of the remaining (i.e., not excluded) residues were analyzed by fitting to first-order decay kinetics via linear regression of the data with respect to the analytic solution of the normalized first-order decay model. The fitted first-order decay reaction constants were plotted for all nonexcluded residues of hTau40. The statistical uncertainty in the determined degradation rates expressed in terms of SDs of fits was estimated as follows. For each sample, we randomly excluded five (in case of samples hTau40 + 20S in molar ratios of 4:1 and 4.5:1) or three (in case of samples hTau40 + 20S + inhibitor, hTau40 + CaMKII + 20S, and hTau40 + 20S + GSK3) intensity profiles collected at the various time intervals from the fitting procedure and repeated this procedure 20 times. The selection was performed by randomly drawing five (three, respectively) numbers from a uniform distribution over all profiles measured at different time intervals, and the fitting procedure was carried out on each of these subsamples and each amino acid residue. From the 21 fits per residue obtained this way (20 undersampled plus 1 fit based on all measured profiles), we calculated the sample SD and depicted it as error bars. The plots depicting degradation rates were plotted as the full-data fit (declared here as the mean estimated value) plus/minus the SD. In addition, we determined the Pearson correlation coefficients for all respective fits, which are encoded in the color. Fits with an incorrect sign of (i.e., implying an incorrect/unphysical trend) were excluded from the plot.

Acknowledgments: We thank N. Rezaei-Ghaleh for help with NMR experiments and the Max Planck society for support. Funding: The financial support from the German Research Foundation (DFG) through the Emmy Noether Program GO 2762/1-1 (to A.G.) is acknowledged. P.F. is supported by a Manfred-Eigen-Fellowship from the Max Planck Institute for Biophysical Chemistry. M.Z. was supported by the advanced grant 787679-LLPS-NMR of the European Research Council. Author contributions: T.U.-G. performed tau phosphorylation, NMR experiments, and data analysis. P.F. and K.-T.P. performed MS and data analysis. A.I.d.O. analyzed Tau(1239) and performed NMR experiments and K18 degradation. F.H. prepared 20S proteasome. A.G. performed NMR data analysis. M.-S.C.-O. prepared Tau(1239). A.C. supervised 20S preparation. H.U. supervised MS. E.M. and M.Z. designed the study. The manuscript was written through contributions of all authors. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015349. The chemical shifts of Tau(1239) were deposited in the BMRB (identifier: 28065). All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Proteasomal degradation of the intrinsically disordered protein tau at single-residue resolution - Science Advances

What’s the Best Human Brain Alternative for Hungry Zombies? – Gizmodo UK

Lets say youre a zombie. Youre lumbering around, doing your zombie-mumble, and just ten feet ahead you see a living human being. Your first impulse, of course, is to head over there and eat their brain. And youre about to do just that, when suddenly you feel a pang of something like shame. You remember, dimly, being a human yourself. You remember how you mightve felt, if an undead weirdogot to gnawing on your skull. Youre at an impasse: at once desperate for brain meat and reluctant to kill for it. So you head to your zombie psychologist and start explaining the situation, and your zombie psychologist starts grinning, which annoys you at first I mean, youre baring your soul to this guy until he explains whats on his mind. Turns out, hes been toying with an idea a pilot program for conscience-stricken zombies. Instead of human brains, theyll be fed stuff that looks and tastes justlikebrains, thereby sparing them the obligation to kill. The only thing they need to work out is: what would be an acceptable substitute for human brains? For this weeksGiz Asks, we reached out to a number of brain experts to find out.

Associate Professor, Neurobiology, Harvard Medical School

The brain is of course composed primarily of lipids, and so it is perfectly reasonable to assume that it is brain lipids that zombies really crave. But why human brains and not, say, mouse brains? Lipidomic analysis reveals that human brains are unusually enriched in a compound called sphingomyelin (relative to brains from rodents), and so it is further reasonable to assume that what zombies want is actually lots of sphingomyelin. So where to get it? Eggs. Eggs are packed with sphingomyelin. Furthermore, eggs also have the advantage of having a white outer cortex and a lipid-rich center, just like the human brain, so they seem a reasonable substitute all around.

Chair and Professor of Neurology at the David Geffen School of Medicine at UCLA and Co-Director UCLA Broad Stem Cell Center

A food-based substitute would require a fair amount of work, because youd have to get a sort of fatty, proteinaceous slop together as a mimic for the brain. A thick macaroni and cheese might work, with a larger noodle like ziti or rigatoni and no tang, meaning a thick white cheese, as opposed to cheddar.

The brain sandwich, made from cow brains, was an unusual delicacy in St. Louis for years. When I lived there, I saw what it looked like as they fried it, and its hard to imagine any other organ meat could substitute for the real thing. Kidney and liver are too firm and too structured; most foods we eat, or could think about eating, are also too firm, and not fatty enough.

A brain from another animal might work, though it would have to be an animal with an advanced brain that is, one with the folds we see when we look at the brains surface (which are called gyri and cilici). Those are what distinguish higher mammals from lower mammals. They also make the human brain this particularly characteristic thing in terms of substance and texture and appearance. So an animal brain, to sub for a human brain, would need to have those features. That would mean anything from, say, a dog or cat on up those both have gyri and cilici, whereas rodents and rabbits, for example, do not.

Assistant Professor of Brain Science, Psychiatry and Human Behaviourat Brown University

I think my Zombie would be a vegan. The thing that I have found to be the closest in texture to the brain is tofu (not the firm kind). People are often surprised by that fact, because its really soft you can put your finger through it easily.

Broadly, I study the kind of complex planning and decision making that is localised to the front of the brain, the prefrontal cortex. This area is also one of the most likely to be injured if you hit your head, because your very soft brain bounces around inside your skull. Our lab typically does a demo for Brain Week and other events that lets people feel tofu, and then shake it around in a container and see what happens to it. Shake it around in some water (mimicking some of the protections that our brain has in the cerebro-spinal fluid that it floats in) and the tofu does much better (which is why its packaged in water!).

Unfortunately tofu doesnt mimic all the wonderful folding that it has that lets us pack so many brain cells into a tight space. A sheet of paper crumpled up is best to show that capacity, but paper is probably much less tasty than tofu (to humans anyway, I dont know about zombies!).

Professor, Systems Biology, George Mason University

My proposal is: a literal pound of flesh. Many people have too much of it; its very similar to the brain in texture; it has a lot of cholesterol, which is important, because in my opinion at least zombies would crave exactly that. Also, adipose tissue is very rich with various kinds of growth hormones and other kinds of bioactive stuff. If you could develop some kind of device that would transfer the flesh to the zombies, people might even be grateful they wouldnt have to get liposuction.

Senior Lecturer, Medical Biotechnology, Deakin University

The best thing to do would be to make small versions of a brain from stem cells, called organoids. These are almost, but not quite, brains. You grow them in an artificial 3D environment that mimics the properties of the central nervous tissue, and allow them to develop networks of neural cells in a structured way. Theyre used for research into drugs and diseases and so on, but would probably be an acceptable meat-free snack for an ethically conscious zombie plague.

Professor in Neurology and Professor of Biomedical Engineering at Duke University

If I were a vegetarian zombie, I would try to make a brain substitute using the major components of the brain carbohydrates, proteins, and cells. The major carbohydrate component is hyaluronic acid (which is found in many beauty products, and can be purchased in bulk). Though by itself it does not form a solid, only a very viscous liquid, it can be combined with other materials that do form a solid. For example, sea weed has a carbohydrate named alginate that does form gels when combined with calcium. So, a blend of hyaluronic acid and alginate with calcium can yield a material that has the mechanics of the brain. For the protein component, eggs, beans, soy, and quinoa all can be good choices. To get the texture right, the calcium can be added while stirring to generate chunks. If it is ok to eat other animals, then I would buy pig brains, which are often discarded. Pig organs are close to the same size of humans and have even been used for transplantation due to similarities in physiology/biochemistry. That would be the simplest choice.

Associate Professor, Psychology and Neuroscience, George Mason University

Whenever I eat cauliflower, I think of the cerebellum or little brain. It is tucked away behind the cerebrum, or main part of the brain. The cerebellum is small, but it is where about 80 percent of the entire brains neurons are found! Most of the cerebellums neurons, or gray matter, are found on its outer surface. They are tightly packed together in little folds called folia. The neurons in the folia are connected to each other by nerve fibres, also known as white matter. When the cerebellum is cut in half, the white matter appears as this beautiful network of branches called the arbor vitae, or tree of life. It really does look just like a head of cauliflower!

Professor, Psychology and Neuroscience, Trinity College

The brain is actually quite soft and squishy. Fortunately for us it normally floats in a pool of cerebrospinal fluid that serves as a cushiony packing material protecting the delicate brain from the hard skull. But the brain is so soft it can easily become injured without the head striking any object. If there is enough rotational or acceleration/deceleration motion for the brain to hit the skull the tips of the brain can be bruised and individual cells can be stretched or sheared from their connections. This can happen, for example, in motor vehicle accidents or shaken baby syndrome where the head is thrown very quickly forwards and then backwards.

The consistency I think the brain comes closest to is a gelatin. But I would recommend that our zombie make the gelatin with milk rather than water. This will give it a closer consistency to a brain, the color will be more opaque like a real brain, and it will provide more of the much needed protein the zombie craves. There are even commercially made gelatin molds if the zombie is able to access stores or online shopping.

Another option would be a soft tofu. This might be a great option for a zombie who is a vegetarian or vegan. There is plenty of protein but it will be much harder to mold into the right shape. Sadly, most zombies are not portrayed to have the fine motor skills needed to create a brain shape from scratch, so the tofu would just have to be eaten as is.

On a side note, if our zombie truly finds that nothing satisfies like a real brain, they could certainly consider becoming a neurosurgeon that specializes in therapeutic surgeries, like temporal lobe resections. In this case, a small portion of the temporal lobe of the brain is removed to relieve a person of intractable epilepsy. This might allow for a chance to satisfy their craving while providing benefit to the person involved.

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What's the Best Human Brain Alternative for Hungry Zombies? - Gizmodo UK

Tennessee scientists have partnered on cutting-edge discoveries in a race against COVID-19 – Knoxville News Sentinel

Scientists at Oak Ridge National Laboratory havetaken an important step in the race to understand SARS-CoV, the virus that causes COVID-19. Using sophisticated techniques, the scientists mapped out the structure of a critical protein of the coronavirus.

They'rehoping to answer this age-old scientific question that's more pressing than ever in this pandemic:How do you kill something that's not really alive?

The results of the groundbreaking work at Oak Ridge National Laboratory were published in Nature,a leading science journal.

Without this protein, the coronavirus cannot replicate. The scientists hope that by studying this structure, they will be able to find drugs that can stop it.

"The most important part is probably the fact that this protein is essential for the replication of this virus," said Dr.Daniel Kneller, a researcher at Oak Ridge National Lab andthe first author of the study. "If you inhibit this protein you're preventing the virus from assembling. Period."

While this study is focused on a very specific aspect of COVID-19, it opens a window onto the immense network of scientists and scientific organizations working on the pandemic. Tennessee is home to a state-spanning, drug development pipeline that is a microcosm of nationaland global research.

The COVID-19 protease protein is both shaped like a heart and functions as one. Without it the virus cannot grow and spread.(Photo: Andrey Kovalevsky, ORNL, U.S. Department of Energy)

Viruses are hard to treat because they arent like other things that cause diseases. Antibiotics, antifungals and antiparasitic drugs stop cells from replicating or kill them outright. But viruses arent made of cells. They are bundles of proteins and genetic material that hijack cells, forcing them to produce viral particles. Viruses cannot replicate on their own.

Theres debate among biologists as to whether viruses are actually alive because of this.

"How do you kill something that's not really alive?" said Dr. Martha S. Head, director of the Joint Institute for Biological Sciences at Oak Ridge National Lab. She oversees Oak Ridge's COVID-19 molecular design research projects. She explained that this study was part of a push to shut down viral replication at multiple stages, which is how HIV is treated.

"That combination (of drugs) shuts down so many parts of the (HIV) life cycle that you drive down viral loads to where they don't matter," Head explained.

COVID-19 Protease crystals, grown in Oak Ridge National Labs Protein Crystallization and Characterization laboratory and pictured in microscopic view.(Photo: Daniel Kneller: ORNL, U.S. Department of Energy)

To do this, the Oak Ridge team went right to the heart of the coronavirus itsproteins. When a virus infects a cell, it forces the cell to produce viral proteins. But host cells often cant create finished viral proteins, just long strands of unfinished, conjoined, protein.

Proteins are a bit like self-folding origami. Once theyre assembledthey fold into their final shape. Some proteins need to be cut by enzymes, like a protease, to get them into their final shape.Viral proteins cant do that when they're conjoined.

To make finished protein, each viral particle carries a protein enzyme called protease. The COVID-19 protease cuts unfinished viral protein, freeing it to fold itself into a final shape. If the protease cant do this, new virus cannot be made.

It is the heart of viral replication. Finding a chemical compound that can attach to and stop the heart of COVID-19 could be critical for developing a treatment. This is actually the therapeutic approach for anti-HIV drugs like Atazanavir.

But to quickly discover a drug,scientists it helps to have anaccurate map of the protease.

"If part of the protein is incorrectly modeled when you try to design drugs you may miss interactions that would otherwise form (between the drug and the protein)," explained Dr. Andrey Kovalesky, a researcher at Oak Ridge who worked on the study. He explained that without an accurate structure model, researchers might miss a potential drug or get bogged down in false positives.

To find the structure, the scientists grew large crystals made of viral protein in the lab. They took these crystals and exposed them to x-rays. When x-rays hit the protein crystal, they bend and scatter in different directions based on the shape of the protein. After they scatter they hit sensitive x-ray detectors. The scientists use the pattern of x-ray hits to ultimately figure out the shape of the protein.

This is called x-ray crystallography and was famously used to discover the structure of DNA.

The technique is like taking multiple photographs from different angles of the same object. By looking at all angles of the object you can figure out its 3-D structure.

Usually this kind of experiment is done at very cold temperatures. Thats because protein molecules tend to move more at warm temperatures. Its like photographing a moving object.

Unfortunately, getting a clearer picture can also mean missing theshape of the protein or how they move. Viruses often change shape but they cant when theyre frozen. Its like looking at frozen meat and expecting it to behave like a living muscle.

"You really have to appreciate that it's one single confirmation that you're looking at," said Dr. Paul McGonigle,director of the Drug Discovery and Development Program at Drexel University. "You hope that this is the confirmation the protein exists in most of the time, but you never know for sure."

The scientists at Oak Ridge did something special. They did this study at room temperature. Their equipment is more sensitive than the type typically used for this kind of experiment. Because of that, they could see the a fuller range of motion in the of COVID-19 protein that accuracy is very important for developing a drug.

"I think it's useful for them to have these different confirmations to target," said Dr. Ole Mortensen, associate professor of pharmacology at Drexel University. Mortensen explained that his own drug development work was made more challenging because he only had a single snapshot of his target protein.

"I'm worried that I could be missing some of the other ones. I think it makes sense what they're doing. They're opening up more possibilities." Mortensen said.

Oak Ridge might not immediately come to mind when you think medical research. But the national lab system has played a role in medical science since its inception. Sex chromosomes were discovered by pioneering geneticistLiane Russell at Oak Ridge, for example.

When Congress injected hundreds of millions of dollars into COVID-19 research through the CARES Act, The Department of Energy received $99.5 million for the national lab system. Compared to other agencies like the National Institutes of Health or Department of Defense, which got $945 million and $415 million respectively, that might not seem like a lot.

But the national lab system has unique resources that can be quickly marshaled against COVID-19. The Neutron Spallation Facility has the kind of sensitive x-ray detectors necessary to scan a protein at room temperature. The facility houses a lab capable of quickly growing large protein crystals.

"Oak Ridge is uniquely good at growing really big protein crystals," said Charles Sanders, a professor of biochemistry at Vanderbilt University. "Because they have that general expertise, it lets them do room temperature crystallography."

"They also have a network of people around the country, so if the big dogs at these agencies want stuff to happen then it can be, a wartime response, basically," Sanders said.

When the CARES act passed, it let the scientists clear their schedule and focus on COVID-19. Ordinarily, research like this takes months if not years of applying for grants and negotiating for time on equipment. This study mapped and published the protease structure in about a month.

"This is different than our usual projects," said Dr. Head. "Acrossthe Department of Energy as a whole, the speed (of organizing research) is astronomically fast."

Importantly, Oak Ridge houses the Summit supercomputer, one of the fastest supercomputers in the world. Once the structure was figured out by one team, the Summit team quickly screened it against a massive library of potential compounds, looking for potential matches.

"We broke a world record on the supercomputer," said Jeremy Smith,director of the Center for Molecular Biophysics at Oak Ridge. "We screened 1.2 billion compounds in less than a single day."

This is not the first time Dr. Smith has run a massive simulated drug test like this. Knox News covered his experiments back in March. The difference here is scale. Smiths team screened the COVID-19 protease against 1.2 billion possible drugs in a single day using Summit's whole processing system. Now the most promising candidates are being sorted out for eventual testing against live COVID-19 virus.

As impressive as Oak Ridges facilities are, they dont have the ability to do that kind of testing in house. For that, Smith turned to Dr. ColleenJonsson, a professorUniversity of Tennessee Health Sciences Center in Memphis.

Dr. Jonsson is an experienced virologist and virus hunter. She also happens to be the director of the Southeast Regional Biocontainment Laboratory, one of a small network of labs authorized by the federal government to do research on dangerous biological agents and emerging infectious diseases. She had the facility and staff to do what Oak Ridge could not:validate potential drug targets in the real world.

"A virtual screen (in a computer)is a theoretical screen." Dr. Jonsson said. "Once we find them we have to validate we're actually hitting the right thing."

Jonsson saidthat earlier in the year Smith reached out to her to test possible drugs targeting a different protein, the spike protein the coronavirus uses to attach to cells. Since then, her lab has expanded to validating other potential drugs targeting other proteins. While Dr. Jonsson hasnt yet begun to work on the COVID-19 protease, she expects to shortly.

This part of the process, where drugs are tested in live cells to see if they stop a virus from replicating, is arduous. Scientists working at the Biocontainment Lab don full biohazard suits to run their tests. Even after they validate that a potential drug works on a virus in a dish, they still need to run extensive dosage and safety testing, a process that can take months if not years.

Then they have to see if the drug actually works in a real infection. For this they need to test the drug in an infected animal that gets infected with COVID-19 like a human would. Getting an accurate animal model is a difficult process that requires its own experiments and validation.

Dr. Jonsson's team has been busy with this, and other COVID-19 work, since the start of the pandemic. They are among the very few people reporting to work at the University of Tennessee Health Sciences Center campus during the initial lockdowns. The Biocontainment team worked quickly to get everything ready for coronavirus research.

"They worked every day through the Safer at Home order with remarkable dedication," said Dr. Jonsson. "Everyone was working seven days a week to get everything ready."

In spite of its critical role in coronavirus research, the Biocontainment Lab is operating semi-independently. It has not received any funding yet through the CARES Act and is working solely on University of Tennessee funding.

None of thisscience is settled. The author of another structural study on the COVID-19 protease,Dr. Rolf Hilgenfeld of the German Center for Infection Research, was not convinced that the Oak Ridge study would amount to anything.

"I don't think this small difference, (between the shape of the proteins at different temperatures)whatever is its cause, matters for drug design," wrote Hilgenfeld in an email to Knox News.

The Oak Ridge team is planning to scan COVID-19 proteins using a higher resolution technique, neutron scattering,to get even better structures. Dr. Jonsson's team hopes to be running animal tests for possible COVID-19 drugs by the end of the year.

Read or Share this story: https://www.knoxnews.com/story/news/2020/07/14/tennessee-scientists-cutting-edge-discoveries-covid-19-race-oak-ridge-vaccine/5408399002/

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Tennessee scientists have partnered on cutting-edge discoveries in a race against COVID-19 - Knoxville News Sentinel

Setting the bar in education – The Star Online

Cheahs belief in working with the best and learning from the best also birthed the appointments of the Jeffrey Cheah distinguished professors.

Under the collaboration between Jeffrey Cheah Foundation and globally acclaimed academic institutions, eminent experts and scholars - who have contributed to solving critical global issues in health, disease and economy amongst others - are appointed to share their knowledge and expertise with Malaysian academics, students and the general public.

Among the prominent names on the list are:

Prof Jeffrey David Sachs

As a world renowned economist and director of the UN Sustainable Development Solutions Network, Prof Sachs is one of the worlds most influential experts on sustainable economic development.

A passionate leader in the fight against poverty and the special advisor to the UN secretary-general on sustainable development, he has advised heads of states and governments on economic strategy for more than a quarter century.

Appointed as an honorary Jeffrey Cheah distinguished professor of sustainable development at Sunway University this year, he is also the chairman of the Jeffrey Sachs Centre on Sustainable Development.

Prof Sir Leszek Borysiewicz

The chairman of Cancer Research United Kingdom (UK) since 2016, Prof Borysiewicz is an Honorary Jeffrey Cheah distinguished professor who is now the emeritus vice-chancellor of the University of Cambridge, after serving as its vice-chancellor from 2011 to 2017.

A founding fellow of the Academy of Medical Sciences, he has been chief executive of the UKs Medical Research Council since 2007 and was knighted in 2001 for his breakthroughs in vaccines, including developing Europes first trial of a vaccine to treat cervical cancer.

Prof Sir Alan Fersht

World leading protein scientist Prof Fersht, also an honorary Jeffrey Cheah distinguished professor and life fellow of Gonville and Caius College Cambridge, is widely regarded as one of the main pioneers of protein engineering, which is a process to analyse the structure, activity and folding of proteins.

His current research involves a fusion of protein engineering, structural biology, biophysics and chemistry to study the structure, activity, stability and folding of proteins, as well as the role of protein misfolding and instability in cancer and disease.

Prof Kay-Tee Khaw

Prof Khaw, a leading expert in the field of health and disease, is a Jeffrey Cheah professorial fellow in Gonville and Caius College, Cambridge. She is currently one of the principal UK scientists working on the European Prospective Investigation into Cancer and Nutrition, a Europe-wide project investigating the links between diet, lifestyle and cancer.

Appointed as a Commander of the order of the British Empire in 2003, Prof Khaw has been recognised for developing improved methods for collecting information on peoples diets and levels of exercise and relating this to the number of diagnosed cancer cases.

Prof Rema Hanna

A highly distinguished economist, Prof Hanna is the Jeffrey Cheah professor of South East Asia Studies and chair of the Harvard Kennedy School International Development Area, as well as the faculty director of evidence for policy design at Harvards Centre for International Development and the co-scientific director of the Abdul Latif Jameel Poverty Action Lab South East Asia office in Indonesia.

Her focus is on improving overall service delivery, understanding the impacts of corruption, bureaucratic absenteeism and discrimination against disadvantaged minority groups on delivery outcomes.

Prof Ketan J Patel

Prof Patel is a Jeffrey Cheah professorial fellow in Gonville and Caius College, Cambridge and the principal research scientist at the famous MRC Laboratory of Molecular Biology in the University of Cambridge.

His research, which focuses on the molecular basis of inherited genomic instability and the role it plays in the biology of stem cells, has been recognised through prestigious awards and prizes, including being elected as a fellow of the Royal Society of London, a member of the European Molecular Biology Organisation and a fellow of the Academy of Medical Sciences UK.

Prof John Todd

The Jeffrey Cheah fellow in medicine at Brasenose College, Oxford and professor of precision medicine, Prof Todd is a leading pioneer researcher in the fields of genetics, immunology and diabetes. His research areas include Type 1 diabetes genetics and disease mechanisms with the aim of clinical intervention.

In his former role as a professor of human genetics and a Wellcome Trust principal research fellow at Oxford, he helped pioneer genome-wide genetic studies, first in mice and then in humans.

Prof William Swadling

Prof Swadling, a Jeffrey Cheah professorial fellow, is a senior law fellow at Brasenose College, Oxford and Professor in the Law of Property in the Oxford University Law School.

An expert on the Law of Restitution, he is a contributor to Halsburys Laws of England, wrote the section on property in Burrows (ed) English Private Law and is widely cited in the British courts.

Prof William James

A Jeffrey Cheah professorial fellow emeritus and fellow in medicine at Brasenose College, Oxford, Prof James is a virologist with a background in genetics and microbiology.

As the professor of virology with the University of Oxford, he is the principal investigator at the Stem Cell Research Institute of Oxford, running a research lab studying HIV-macrophage biology using stem cell technology.

Prof Mark Wilson

Prof Wilson, the dean of Brasenose College, is a Jeffrey Cheah professorial fellow at the college and the professor of physical chemistry in the University of Oxfords physical and theoretical chemistry department.

The primary focus of his research interest is on the construction, development and application of relatively simple potential models to assess a wide range of systems with potentially unique properties.

Prof Jarlath Ronayne

Appointed in 2010 as the first Jeffrey Cheah distinguished professor, Prof Ronayne is a key member of Sunway Universitys board of directors and has played a pivotal role in establishing links between Sunway, Oxford and Cambridge.

Under his leadership, the Jeffrey Cheah Professorial Fellowships at Gonville and Caius College, Cambridge as well as Brasenose College, Oxford and the Jeffrey Cheah Scholar-in-Residence programmes in both colleges were established, alongside the prestigious Oxford University-Jeffrey Cheah Graduate Scholarship launched by the British High Commissioner in 2018. All these initiatives are in perpetuity.

Prof Sibrandes Poppema

A medical expert on Hodgkins disease, Prof Poppema has published more than 200 articles that have been cited more than 17,000 times.

The Jeffrey Cheah distinguished professor is also the co-owner of 12 patents and the founder of two biotechnology companies, as well as the advisor to the chancellor at Sunway University, especially on the establishment of a new medical school at the university.

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Setting the bar in education - The Star Online

Prayers Answered – The Herald

Story by Leann BurkePhotos by Marlena Sloss

Early afternoon sunlight shone through the stained glass windows of the new St. Mary Catholic Church building in Ireland as Pat Gress led his mother, the late Rita Gress, through the building she helped plan for and prayed about for decades.

As the pair entered the nave that late May day, Rita looked to the high altar a combination of the high altar and two side altars from the now closed St. Patrick Catholic Church in Corning and teared up.

Duane Gress takes his mother, Rita, on a tour led by his brother, Pat, not pictured, of the new St. Mary Catholic Church in Ireland on May 17. Rita was part of the original long-term planning committee for the new church and Pat was the construction manager. She had leukemia and was able to visit the new church weeks before she died.

Its just fantastic, she said of the whole church. I just cant believe it.

Im glad you like it, Pat said. You were instrumental in getting this thing going.

Pat managed construction on behalf of the church.

The congregation at St. Mary began discussing the possibility of building a new church about 40 years ago. The old building located at 2829 N. 500 W opened in 1905, and at that time, about 45 families attended the church. Today, the church is home to about 1,000 families, many of whom have been members for generations. The Gress family, for example, has been part of the congregation for about a century, and Rita was part of the early discussions about expanding.

When the first long-range planning committee formed in the 1970s, Rita joined and advocated for the construction of a new church building. Ultimately, the committee chose to expand the 1905 building and to begin saving for the construction of a new church down the line.

Then, in the early 2000s, discussion about building a new church picked up again. Rita eagerly took a role on that iteration of the long-range planning committee, as did her son-in-law, Steve Buechler. Steve attended Holy Family Catholic Church in Jasper before he married Ritas daughter, Margaret, and began attending St. Mary.

The 15-person committee spent hours talking to parishioners about what style they wanted the church to have traditional, country Catholic was the consensus and researching architects and designs. Part of their research included a structural study of the 1905 building, which revealed some issues that would have made the cost of remodeling that building about the same as building a new one. That, coupled with the knowledge that a remodel would mean losing that traditional, country look, led the committee to pursue building a new church.

We took a very serious approach, Steve said. We knew we were planning for the next 100 years of our parish.

The new church began to become a reality in 2017 when the parish launched the Building Our Future capital campaign to raise the $6 million needed for the project. As of March, the campaign had raised about $5.6 million.

Construction crews work on the pillars on the top of the new St. Mary Catholic Church in Ireland on Jan. 16. The new church was designed to look as similar as possible to the old church, seen in the background.

According to guidelines from the Catholic Diocese of Evansville, the campaign needed $4.8 million 80% of the total cost to break ground. That threshold was met by 2018, and construction crews broke ground that year. At last, the decades of research and planning were turning into something tangible.

St. Mary parishioners dedicated so many years to planning for their new church that when Evansvilles bishop, Joseph Siegel, dedicated the new building on June 28, he joked in his homily that the parishioners had been dreaming of their new church almost as long as the biblical Jews of Jesus time spent building the temple from which Jesus expelled merchants in the second chapter of The Gospel of John. According to the story which was the Gospel reading for the dedication Mass that biblical temple took 46 years to build.

The years of hard work and planning paid off, however. The final designs for the new church yielded a traditional country church that looks much like the old building, but will fit 700 per mass, with room for additional folding chairs if need be, rather than the 380 the 1905 building housed. It also includes several features the parishioners wanted, including an additional parking lot, a space inside the church building for offices, a cry/bridal room, a covered entrance and a narthex, a large open area before the nave where parishioners can gather before and after church services.

The builders did an amazing job of bringing our vision into reality, Steve said.

The wish list also included refurbishing the original nine stained-glass windows from the 1904 church for use in the new building, and the commissioning of 11 new windows. For that, the parish called on Mominee Studios of Evansville. The original windows were made according to the American Opalescent style following the technique developed by John La Farge, an artist at the forefront of the American Arts and Crafts Movement of the late 1800s, according to the Smithsonian American Art Museum.

When [St. Mary Church] was built in 1904, all the churches were using [that style], said Jules Mominee, owner of Mominee Studios.

To restore the nine old windows, Jules and members of his team spent most of a day carefully removing the century-old, intricately decorated windows some of which consisted of three glass panels and loading them into a truck for transport to the studio in Evansville. There, the team refurbished the windows to make them vibrant again.

Mominee Studios employee Nick Morgan assembles the St. Scholastica stained glass window in Evansville on Oct. 17. Mominee Studios worked on 26 windows for the new church, including designing and building 9 new windows. From the beginning of the design process to the installation of the final window, Mominee Studios spent two years on the project.

The artists also built the 11 new windows at the studio, following techniques almost identical to those used by La Farge in the late 1800s. If a single artist worked on one window from start to finish, Jules said, the process would take two months, but his team of six artists can complete a single window in two weeks, and they always work on groups of windows at the same time to make a project move along quickly.

Like the stained-glass windows, the high altar, too, took a long time to prepare for its place in the new church. Jim Buechlein, owner of Buechleins Kwik Strip & Custom Furniture & Design in Jasper, oversaw the removal of the three altars from St. Patrick in Corning three years ago and refurbished them into the ornate, gothic-style altar that is now the focal point in St. Marys nave.

It was no small task, but Jim who attends Precious Blood Catholic Church in Jasper and has built custom pieces for several local churches, including Precious Blood and St. Ferdinand was confident he could create a beautiful piece for the new church.

The process included sanding down and refinishing the altars before combining the three separate altars into a single high altar. The latter meant rebuilding some of the pieces before painting the entire structure white with milk paint, which is an architectural paint made from the milk protein casein mixed with lime, clay and earth pigments. Its a type of paint common to historic pieces.

All finished, Jim estimates the altar is worth about $1 million. He and his team installed the altar in late May.

It was like a sigh of relief, he said. It felt good to see it sitting in the church.

Jim Buechlein, left, of Buechlein's Kwik Strip & Custom Furniture & Design, hands a piece of the altar to Sam Wagner of Wagner Brothers Construction while they assemble the altar at the new St. Mary Catholic Church in Ireland on May 15. The altar was re-designed from three separate altars from a St. Patrick Parish Church in Corning that closed down.

Tom Wagner and a team of workers helped Jim install the altar. A lifelong member of St. Mary, Tom was excited to see the new church coming together and to be part of it.

It was time, Tom said of building a new church. Its going to be a lot better.

After about two years of construction, the new church building opened to parishioners on Sunday, June 28, with the Rite of Dedication led by Bishop Siegel. The pews filled with mask-clad parishioners who spread throughout the pews to socially distance and eagerly waited to anoint the halls of their new house of worship. Several local priests also attended to celebrate the event with St. Marys Father Joseph Effie Erbacher and the parish.

The dedication kicked off with the handing over of the building when representatives of the architect and general contractor presented plans for the building to Bishop Siegel. Then, Fr. Erbacher opened the doors to the nave, beginning the processional to the altar.

Enter the gates of the Lord with thanksgiving, his courts with songs of praise, Bishop Siegel said as Fr. Erbacher opened the doors.

In addition to the celebration of the eucharist, the dedication Mass also included several steps to consecrate the building and prepare it to house decades of worship. As soon as the bishop reached the altar, he performed the blessing and sprinkling of water where he blessed water before sprinkling it over the parishioners and the walls of the church. During the anointing of the altar and the walls of the church with sacred Chrism a consecrated oil used in certain sacraments the bishop spread Chrism over the Altar of Sacrifice before he and Fr. Erbacher anointed the walls of the church with the oil as well.

Pat participated in the anointing of the altar as one of two parishioners who wiped down the altar after it was anointed.

Im extremely happy to have been part of it. I almost teared up a few times, he said. Seeing people in there and knowing it is the first of hundreds of years of worshippers is an amazing feeling.

The bishop also blessed the altar with incense while deacons carried braziers with incense through the aisles to bless the parishioners and walls as well, and the candles surrounding the altar and throughout the church were lit ceremonially.

Bishop Joseph Siegel spreads holy water on The Altar of Sacrifice during the new St. Mary Catholic Church dedication in Ireland on June 28.

Toward the end of the dedication, Fr. Erbacher addressed his congregation.

It has been three years of faith, dedication and love, and I thank you all very much, he said.

Missing from the pews during the dedication was Rita. Despite the decades of prayer and intentional action she dedicated to making the new church a reality, she was not meant to attend Mass in the new building. She passed away a few weeks prior to the dedication. Her funeral Mass held June 29 was the first Mass Fr. Erbacher led in the new church.

Cindy Gress, Rita Gress' daughter-in-law, touches Rita's casket after speaking during her funeral Mass at the new St. Mary Catholic Church in Ireland on June 29.

The Gress family chose to wait to hold her funeral Mass until after the dedication because of how much the church meant to her and how active shed been in bringing the new building to fruition. In the last days of her life, the idea of having her funeral in the new building brought her peace, said her daughter, Carla Allbright of Mitchell.

Although Pat and the rest of his family had hoped Rita would live to see the new church building open, Rita didnt expect to herself, and she seemed at peace with that knowledge. As her tour of the nearly completed church came to an end that day in May, she looked around the nave with tears of joy in her eyes and a smile on her face.

When its all finished, she said, Ill see it from heaven.

Continued here:
Prayers Answered - The Herald

Whats the Best Human Brain Alternative for Hungry Zombies? – Gizmodo Australia

Lets say youre a zombie. Youre lumbering around, doing your zombie-mumble, and just ten feet ahead you see a living human being. Your first impulse, of course, is to head over there and eat their brain. And youre about to do just that, when suddenly you feel a pang of something like shame. You remember, dimly, being a human yourself. You remember how you mightve felt, if an undead weirdo got to gnawing on your skull. Youre at an impasse: at once desperate for brain meat and reluctant to kill for it. So you head to your zombie psychologist and start explaining the situation, and your zombie psychologist starts grinning, which annoys you at first I mean, youre baring your soul to this guy until he explains whats on his mind. Turns out, hes been toying with an idea a pilot program for conscience-stricken zombies. Instead of human brains, theyll be fed stuff that looks and tastes just like brains, thereby sparing them the obligation to kill. The only thing they need to work out is: what would be an acceptable substitute for human brains? For this weeks Giz Asks, we reached out to a number of brain experts to find out.

Associate Professor, Neurobiology, Harvard Medical School

The brain is of course composed primarily of lipids, and so it is perfectly reasonable to assume that it is brain lipids that zombies really crave. But why human brains and not, say, mouse brains? Lipidomic analysis reveals that human brains are unusually enriched in a compound called sphingomyelin (relative to brains from rodents), and so it is further reasonable to assume that what zombies want is actually lots of sphingomyelin. So where to get it? Eggs. Eggs are packed with sphingomyelin. Furthermore, eggs also have the advantage of having a white outer cortex and a lipid-rich centre, just like the human brain, so they seem a reasonable substitute all around.

Chair and Professor of Neurology at the David Geffen School of Medicine at UCLA and Co-Director UCLA Broad Stem Cell Centre

A food-based substitute would require a fair amount of work, because youd have to get a sort of fatty, proteinaceous slop together as a mimic for the brain. A thick macaroni and cheese might work, with a larger noodle like ziti or rigatoni and no tang, meaning a thick white cheese, as opposed to cheddar.

The brain sandwich, made from cow brains, was an unusual delicacy in St. Louis for years. When I lived there, I saw what it looked like as they fried it, and its hard to imagine any other organ meat could substitute for the real thing. Kidney and liver are too firm and too structured; most foods we eat, or could think about eating, are also too firm, and not fatty enough.

A brain from another animal might work, though it would have to be an animal with an advanced brain that is, one with the folds we see when we look at the brains surface (which are called gyri and cilici). Those are what distinguish higher mammals from lower mammals. They also make the human brain this particularly characteristic thing in terms of substance and texture and appearance. So an animal brain, to sub for a human brain, would need to have those features. That would mean anything from, say, a dog or cat on up those both have gyri and cilici, whereas rodents and rabbits, for example, do not.

Assistant Professor of Brain Science, Psychiatry and Human Behaviour at Brown University

I think my Zombie would be a vegan. The thing that I have found to be the closest in texture to the brain is tofu (not the firm kind). People are often surprised by that fact, because its really soft you can put your finger through it easily.

Broadly, I study the kind of complex planning and decision making that is localised to the front of the brain, the prefrontal cortex. This area is also one of the most likely to be injured if you hit your head, because your very soft brain bounces around inside your skull. Our lab typically does a demo for Brain Week and other events that lets people feel tofu, and then shake it around in a container and see what happens to it. Shake it around in some water (mimicking some of the protections that our brain has in the cerebro-spinal fluid that it floats in) and the tofu does much better (which is why its packaged in water!).

Unfortunately tofu doesnt mimic all the wonderful folding that it has that lets us pack so many brain cells into a tight space. A sheet of paper crumpled up is best to show that capacity, but paper is probably much less tasty than tofu (to humans anyway, I dont know about zombies!).

Professor, Systems Biology, George Mason University

My proposal is: a literal pound of flesh. Many people have too much of it; its very similar to the brain in texture; it has a lot of cholesterol, which is important, because in my opinion at least zombies would crave exactly that. Also, adipose tissue is very rich with various kinds of growth hormones and other kinds of bioactive stuff. If you could develop some kind of device that would transfer the flesh to the zombies, people might even be grateful they wouldnt have to get liposuction.

Senior Lecturer, Medical Biotechnology, Deakin University

The best thing to do would be to make small versions of a brain from stem cells, called organoids. These are almost, but not quite, brains. You grow them in an artificial 3D environment that mimics the properties of the central nervous tissue, and allow them to develop networks of neural cells in a structured way. Theyre used for research into drugs and diseases and so on, but would probably be an acceptable meat-free snack for an ethically conscious zombie plague.

Professor in Neurology and Professor of Biomedical Engineering at Duke University

If I were a vegetarian zombie, I would try to make a brain substitute using the major components of the brain carbohydrates, proteins, and cells. The major carbohydrate component is hyaluronic acid (which is found in many beauty products, and can be purchased in bulk). Though by itself it does not form a solid, only a very viscous liquid, it can be combined with other materials that do form a solid. For example, sea weed has a carbohydrate named alginate that does form gels when combined with calcium. So, a blend of hyaluronic acid and alginate with calcium can yield a material that has the mechanics of the brain. For the protein component, eggs, beans, soy, and quinoa all can be good choices. To get the texture right, the calcium can be added while stirring to generate chunks. If it is OK to eat other animals, then I would buy pig brains, which are often discarded. Pig organs are close to the same size of humans and have even been used for transplantation due to similarities in physiology/biochemistry. That would be the simplest choice.

Associate Professor, Psychology and Neuroscience, George Mason University

Whenever I eat cauliflower, I think of the cerebellum or little brain. It is tucked away behind the cerebrum, or main part of the brain. The cerebellum is small, but it is where about 80 per cent of the entire brains neurons are found! Most of the cerebellums neurons, or grey matter, are found on its outer surface. They are tightly packed together in little folds called folia. The neurons in the folia are connected to each other by nerve fibres, also known as white matter. When the cerebellum is cut in half, the white matter appears as this beautiful network of branches called the arbor vitae, or tree of life. It really does look just like a head of cauliflower!

Professor, Psychology and Neuroscience, Trinity College

The brain is actually quite soft and squishy. Fortunately for us it normally floats in a pool of cerebrospinal fluid that serves as a cushiony packing material protecting the delicate brain from the hard skull. But the brain is so soft it can easily become injured without the head striking any object. If there is enough rotational or acceleration/deceleration motion for the brain to hit the skull the tips of the brain can be bruised and individual cells can be stretched or sheared from their connections. This can happen, for example, in motor vehicle accidents or shaken baby syndrome where the head is thrown very quickly forwards and then backwards.

The consistency I think the brain comes closest to is a gelatin. But I would recommend that our zombie make the gelatin with milk rather than water. This will give it a closer consistency to a brain, the colour will be more opaque like a real brain, and it will provide more of the much needed protein the zombie craves. There are even commercially made gelatin molds if the zombie is able to access stores or online shopping.

Another option would be a soft tofu. This might be a great option for a zombie who is a vegetarian or vegan. There is plenty of protein but it will be much harder to mould into the right shape. Sadly, most zombies are not portrayed to have the fine motor skills needed to create a brain shape from scratch, so the tofu would just have to be eaten as is.

On a side note, if our zombie truly finds that nothing satisfies like a real brain, they could certainly consider becoming a neurosurgeon that specialises in therapeutic surgeries, like temporal lobe resections. In this case, a small portion of the temporal lobe of the brain is removed to relieve a person of intractable epilepsy. This might allow for a chance to satisfy their craving while providing benefit to the person involved.

Do you have a burning question for Giz Asks? Email us at [emailprotected]

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Whats the Best Human Brain Alternative for Hungry Zombies? - Gizmodo Australia

Protein Folding and Evolution: Information, Function and …

The structure/function link for proteins has for a long time served as a convenient paradigm. Increasing evidence suggests however that the order/disorder landscape in proteins is far more complex than hitherto imagined, and is an ongoing product of an exquisitely tuned evolutionary process. Furthermore, the field has been dominated by a restricted mindset resulting in neglected areas and unconventional concepts on the periphery of the main topic that we feel deserve to be addressed. Following on from the introduction of the concept of structural capacity and its inextricable origins in ribosome evolution, we wish to illustrate how transitions involving order/disorder rearrangements are involved not only in the selection of new folds and thus functions, but also play a role in the unavoidably associated increase in dysfunction and disease. The underlying theme will be the emergence of the role of information transfer between the genetic code repository and protein structure/function.

The number of potential protein folds and hence structures is immense. It is a sobering fact that the number of known folds is infinitesimally small compared to those potentially available. Current techniques, strongly influenced by main-stream structural biology approaches such as X-ray crystallography have described few of the protein structures that potentially could exist. The concept of evolutionary selection as an ongoing process, and the acceptance that proteins are dynamic structures lead to a reorganization of how the protein structure/function paradigm is viewed. The immense impact of molecular modelling, powered not only by huge computational capacities but also by machine learning and AI (Artificial Intelligence) algorithms is revolutionizing the whole field.Our aim therefore, is to collate a series of original articles that address the relationship between protein structure and function from both an evolutionary and dynamic point of view. We aim to create a platform for launching new models based on original, innovative ideas or experimental approaches. The final goal is thus to seek and exploit hitherto unexplored facets of protein structure/function with the practical aim of facilitating protein engineering and expanding our knowledge of the origins and direction of life processes, with important impact on health issues.

Within the broad scope of protein evolution, we are seeking contributions on the following topics although this list is neither exhaustive nor exclusive:

The evolution of protein folds and function;The role of order disorder transitions in protein evolution;The consequences of ribosomal evolution;Information theory, protein structure/function and evolution;Evolution, protein folding and disease;The inevitable link between evolved function and disease;The evolution of information transfer: from the genetic code to protein structure;Moonlighting proteins: disorder and order in multi-functional proteins;Protein Engineering and design: how to harness disorder?

Articles may be either data based novel observations, innovative technologies which grant new insights into protein dynamics or theoretical approaches that provide new testable models. We will be particularly attentive to fringe ideas independent of how unconventional they may be and welcome contributions from young unestablished scientists.

Dr. Ashley Buckle is founder of the structural biology and protein engineering company PTNG Consulting, and holds patents in the field. All other Topic Editors declare no competing interests.

Keywords:protein evolution, protein folding, protein engineering, intrinsically disordered proteins, protein folding evolution and disease

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The structure/function link for proteins has for a long time served as a convenient paradigm. Increasing evidence suggests however that the order/disorder landscape in proteins is far more complex than hitherto imagined, and is an ongoing product of an exquisitely tuned evolutionary process. Furthermore, the field has been dominated by a restricted mindset resulting in neglected areas and unconventional concepts on the periphery of the main topic that we feel deserve to be addressed. Following on from the introduction of the concept of structural capacity and its inextricable origins in ribosome evolution, we wish to illustrate how transitions involving order/disorder rearrangements are involved not only in the selection of new folds and thus functions, but also play a role in the unavoidably associated increase in dysfunction and disease. The underlying theme will be the emergence of the role of information transfer between the genetic code repository and protein structure/function.

The number of potential protein folds and hence structures is immense. It is a sobering fact that the number of known folds is infinitesimally small compared to those potentially available. Current techniques, strongly influenced by main-stream structural biology approaches such as X-ray crystallography have described few of the protein structures that potentially could exist. The concept of evolutionary selection as an ongoing process, and the acceptance that proteins are dynamic structures lead to a reorganization of how the protein structure/function paradigm is viewed. The immense impact of molecular modelling, powered not only by huge computational capacities but also by machine learning and AI (Artificial Intelligence) algorithms is revolutionizing the whole field.Our aim therefore, is to collate a series of original articles that address the relationship between protein structure and function from both an evolutionary and dynamic point of view. We aim to create a platform for launching new models based on original, innovative ideas or experimental approaches. The final goal is thus to seek and exploit hitherto unexplored facets of protein structure/function with the practical aim of facilitating protein engineering and expanding our knowledge of the origins and direction of life processes, with important impact on health issues.

Within the broad scope of protein evolution, we are seeking contributions on the following topics although this list is neither exhaustive nor exclusive:

The evolution of protein folds and function;The role of order disorder transitions in protein evolution;The consequences of ribosomal evolution;Information theory, protein structure/function and evolution;Evolution, protein folding and disease;The inevitable link between evolved function and disease;The evolution of information transfer: from the genetic code to protein structure;Moonlighting proteins: disorder and order in multi-functional proteins;Protein Engineering and design: how to harness disorder?

Articles may be either data based novel observations, innovative technologies which grant new insights into protein dynamics or theoretical approaches that provide new testable models. We will be particularly attentive to fringe ideas independent of how unconventional they may be and welcome contributions from young unestablished scientists.

Dr. Ashley Buckle is founder of the structural biology and protein engineering company PTNG Consulting, and holds patents in the field. All other Topic Editors declare no competing interests.

Keywords:protein evolution, protein folding, protein engineering, intrinsically disordered proteins, protein folding evolution and disease

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Protein Folding and Evolution: Information, Function and ...

Rewriting the Rules of Vaccine Design With DNA Origami – Technology Networks

By folding DNA into a virus-like structure, MIT researchers have designed HIV-like particles that provoke a strong immune response from human immune cells grown in a lab dish. Such particles might eventually be used as an HIV vaccine.

The DNA particles, which closely mimic the size and shape of viruses, are coated with HIV proteins, or antigens, arranged in precise patterns designed to provoke a strong immune response. The researchers are now working on adapting this approach to develop a potential vaccine for SARS-CoV-2, and they anticipate it could work for a wide variety of viral diseases.

The rough design rules that are starting to come out of this work should be generically applicable across disease antigens and diseases, says Darrell Irvine, who is the Underwood-Prescott Professor with appointments in the departments of Biological Engineering and Materials Science and Engineering; an associate director of MITs Koch Institute for Integrative Cancer Research; and a member of the Ragon Institute of MGH, MIT, and Harvard.

Irvine and Mark Bathe, an MIT professor of biological engineering and an associate member of the Broad Institute of MIT and Harvard, are the senior authors of the study, which appears today inNature Nanotechnology. The papers lead authors are former MIT postdocs Rmi Veneziano and Tyson Moyer.

DNA design

Because DNA molecules are highly programmable, scientists have been working since the 1980s on methods to design DNA molecules that could be used for drug delivery and many other applications, most recently using a technique called DNA origami that was invented in 2006 by Paul Rothemund of Caltech.

In 2016, Bathes lab developed an algorithm that can automatically design and build arbitrary three-dimensionalvirus-like shapesusing DNA origami. This method offers precise control over the structure of synthetic DNA, allowing researchers to attach a variety of molecules, such as viral antigens, at specific locations.

The DNA structure is like a pegboard where the antigens can be attached at any position, Bathe says. These virus-like particles have now enabled us to reveal fundamental molecular principles of immune cell recognition for the first time.

Natural viruses are nanoparticles with antigens arrayed on the particle surface, and it is thought that the immune system (especially B cells) has evolved to efficiently recognize such particulate antigens. Vaccines are now being developed to mimic natural viral structures, and such nanoparticle vaccines are believed to be very effective at producing a B cell immune response because they are the right size to be carried to the lymphatic vessels, which send them directly to B cells waiting in the lymph nodes. The particles are also the right size to interact with B cells and can present a dense array of viral particles.

However, determining the right particle size, spacing between antigens, and number of antigens per particle to optimally stimulate B cells (which bind to target antigens through their B cell receptors) has been a challenge. Bathe and Irvine set out to use these DNA scaffolds to mimic such viral and vaccine particle structures, in hopes of discovering the best particle designs for B cell activation.

There is a lot of interest in the use of virus-like particle structures, where you take a vaccine antigen and array it on the surface of a particle, to drive optimal B-cell responses, Irvine says. However, the rules for how to design that display are really not well-understood.

Other researchers have tried to create subunit vaccines using other kinds of synthetic particles, such as polymers, liposomes, or self-assembling proteins, but with those materials, it is not possible to control the placement of viral proteins as precisely as with DNA origami.

For this study, the researchers designed icosahedral particles with a similar size and shape as a typical virus. They attached an engineered HIV antigen related to the gp120 protein to the scaffold at a variety of distances and densities. To their surprise, they found that the vaccines that produced the strongest response B cell responses were not necessarily those that packed the antigens as closely as possible on the scaffold surface.

It is often assumed that the higher the antigen density, the better, with the idea that bringing B cell receptors as close together as possible is what drives signaling. However, the experimental result, which was very clear, was that actually the closest possible spacing we could make was not the best. And, and as you widen the distance between two antigens, signaling increased, Irvine says.

The findings from this study have the potential to guide HIV vaccine development, as the HIV antigen used in these studies is currently being tested in a clinical trial in humans, using a protein nanoparticle scaffold.

Based on their data, the MIT researchers worked with Jayajit Das, a professor of immunology and microbiology at Ohio State University, to develop a model to explain why greater distances between antigens produce better results. When antigens bind to receptors on the surface of B cells, the activated receptors crosslink with each other inside the cell, enhancing their response. However, the model suggests that if the antigens are too close together, this response is diminished.

Beyond HIV

In recent months, Bathes lab has created a variant of this vaccine with the Aaron Schmidt and Daniel Lingwood labs at the Ragon Institute, in which they swapped out the HIV antigens for a protein found on the surface of the SARS-CoV-2 virus. They are now testing whether this vaccine will produce an effective response against the coronavirus SARS-CoV-2 in isolated B cells, and in mice.

Our platform technology allows you to easily swap out different subunit antigens and peptides from different types of viruses to test whether they may potentially be functional as vaccines, Bathe says.

Because this approach allows for antigens from different viruses to be carried on the same DNA scaffold, it could be possible to design variants that target multiple types of coronaviruses, including past and potentially future variants that may emerge, the researchers say.

Reference: Veneziano et al. (2020). Role of nanoscale antigen organization on B-cell activation probed using DNA origami. Nature Nanotechnology. DOI: 10.1038/s41565-020-0719-0.

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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Rewriting the Rules of Vaccine Design With DNA Origami - Technology Networks

Games, not con-calls, may help build strong remote teams – Livemint

In the book, Gamification By Design, Gabe Zichermann writes that gamification is 75% psychology and 25% technology." Simply put, gamification is incorporating game elements like points, badges, leaderboard and competition into other activities to encourage engagement. And the popularity of gaming is increasing, especially during the lockdown. A study by MarketsandMarkets states the gamification market is projected to grow in size from $9.1 billion in 2020 to $30.7 billion by 2025, at a compound annual growth rate (CAGR) of 27.4% during the forecast period.

If you are considering introducing gamification at work, there are three essential factors that must be maintained, as Ethan Mollick and Nancy Rothbard suggest in their paper, Mandatory Fun: Consent, Gamification And The Impact of Games at Work. First, consent. Employees need to be looped in and made aware of the fact that they are playing a game. Second, legitimation. They must understand the rules of the game. Third, a sense of individual agency: They need to believe the game is fair.

Gamers often attract myths of being slackers and non-serious as a community. Hence, a linkage of games with a core business function with an older and gender-balanced workforce is not seen as a fit.

In her TED talk, Gaming Can Make a Better World", game designer Jane McGonigal focuses on World Of Witchcrafts highly motivated gamers who spend 22 hours a week on an average, playing the game of strategy and problem-solving. She also draws focus on the nuances of motivation and feelings that games can arouse: sense of urgency, fear, competitiveness and a sense of deep, undivided focus. She goes on to explain the larger implications of this, where at the Institute for the Future, she alongside colleagues develop games like The World Without Oil. The players sign up and are provided real-life information, data feeds about real-time oil prices, food supplies, simulated riot situations to set up the game universe.

With the pilot rolling in 2007 with 1,700 players, most players , she claimed, adopted the practices they imbibed in the video game in their real life as well, to conserve oil.

As covid-19 renders certain work practices redundant, it may do good to rethink and explore the world of gamification, as it can help ensure cohesiveness, productivity and sustainability measures for the long-term.

SAP, for example, used a gamification app to motivate sales professionals. The app simulated client meetings and incorporated real examples and data on customer needs. While playing the game, sales professionals had to answer client questions accurately. They earned badges and competed against each other, and hence were better prepared to tackle complex sales meetings with clients. It also provided sales professionals with a better understanding of what to expect and helped them succeed in their meetings.

Gamification could also lead to community-as-a-service (CaaS) being utilized in the now virtual workspace. The post-covid-19 world should not merely want success as the fulcrum, its priority instead has to be on cooperation and collaboration.

Gaming environments should be able to gauge the level of skill that the employee holds at the moment to be able to assign the perfect task" to test their skills while also levelling up just at the verge so that they can exceed and improve themselves in a slightly difficult terrain. All this while playing as a team and helping out the groups collective progress.

The University of Washington tried submitting one of its projects to the powers of the collective brain. A team of highly qualified scientists worked on a technique, called protein folding, as part of a research effort for nearly a decade to understand, prevent and treat diseases like HIV/AIDS, cancer and Alzheimers. They, however, could not attain much progress as they wanted to and decided to try incorporating gamification.

In 2011, they created a puzzle that allowed gamers to fold proteins called Foldit, and invited the general public to play the game online. About 47,000 people volunteered for this challenge and solved the problem within a record time of 10 days.

As with any form of engagement, there are ethics to be followed in gamification as well. The games should work on nudges rather than manipulation. Employees should be prodded and not coerced to choose one form of working over another. The social architecture should try to push for collective good rather than drive for a profiteering venture which exploits goodwill of the employees by keeping some part of the agenda covert.

Maintaining full transparency and ensuring the employees opt-in explicitly to the game with full knowledge about its data management and consent procedures is the most desirable and sustainable form of boosting employee morale and performance at the workplace. Write to us at businessoflife@livemint.com

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Flourishing Biopharmaceutical Industry to Boost Demand for Protein a Resins, High Adoption Expected in North America and Europe – Press Release -…

The protein A resin market players are on the innovation and approval spree with regards to manufacturing biotherapeutics, so as to have an edge over their counterparts. These innovations and approvals are expected to help the protein A resin market in the good stead in the years to come

This press release was orginally distributed by SBWire

Valley Cottage, NY -- (SBWIRE) -- 07/06/2020 -- According to a recently published report by Future Market Insights (FMI), the global protein A resins market size is expected to reach approximately US$ 800 Mn by 2025, and register an annual average growth rate of 8%. This growth is largely dependent on factors such as rising government funding for cancer treatment study, increasing research on monoclonal antibodies, and rising pipeline of monoclonal antibodies to be commercialized.

Market players are targeted towards offering advanced protein A resin products such as next-generation resins for clinical trials, which is expected to cater to evolving demand from biotechnological and pharmaceutical industries. However, widespread availability of alternative purification methods such as crystallization, ultrafiltration, capillary electrophoresis, and high pressure folding is expected to create a hindrance in the market growth.

"Mounting demand for affinity chromatography processes is the direct result of significant growth of biotech and meditech industries, especially in the development of enzymes, antibodies, and protein-based drugs & therapies. Furthermore, rising number of procedures for separating biochemical mixtures along with the increasing demand for affinity chromatography remain the primary growth-driving factors of the protein A resin market," says FMI analyst.

Download a Sample Report with Table of Contents and Figures: https://www.futuremarketinsights.com/reports/sample/rep-gb-1720

Key Takeaways - Protein A Resin Market Study

As per the FMI's study, natural protein A resin accounted for approximately 65% of the total market revenue share in 2019. On the other hand, demand for recombinant protein A resins is expected to witness a significant CAGR over the forecast period.

The latest advancements in recombinant technology emerge as key market growth influencer, due to customization and greater yields of protein A resin depending on specific demand of the customers.

Adoption of agarose-based matrix for protein A resin accounts for around 85% of the total market value, and is expected to grow at an impressive CAGR through 2029.

Demand for glass or silica-based matrix will continue to move on an upward swing, as it offers numerous benefits including high thermal and chemical stability, less toxicity, neutral pH, and high surface area, in addition to easy availability. These advantages are poised to create lucrative growth opportunities for market players.

Adoption of glass or silica-based protein in biomedical and pharmaceutical industries will contribute to positive growth prospects of the market.

Rising incidences of chronic diseases such as cancer and enhanced productivity in biopharmaceutical industry along with expanding testing services in clinics are factors boosting the demand for antibody purification in protein A resins market.

According to FMI, adoption of antibody purification holds major revenue share of the protein A resins market. Additionally, demand from clinical research laboratories is expected to grow at a significant pace and result in increased market share.Opportunities Abound in Developed Markets

North America and Europe continue to maintain their lead in the global protein A resins market, while high opportunities are expected in developing countries of Asia Pacific. The FMI study finds that China and South Korea are the major contributors, owing to increasing number of market players, growing ecological research, and high expenditure on R&D activities. Moreover, improving pharmaceutical industry along with rising government spending will further accelerate the regional market growth.

For information on the research approach used in the report, request methodology@ https://www.futuremarketinsights.com/askus/rep-gb-1720

More Valuable Insights on Protein A Resin Market

FMI's research study on the protein A resin market is segmented into:

Product (Natural and Recombinant)Application (Immunoprecipitation (IP) and Antibody Purification)End Users (Biopharmaceutical Manufacturers, Clinical Research Laboratories, and Academic Institutes)Matrix (Glass or Silica Based, Agarose-based, and Organic Polymer Based)Region (North America, Latin America, Western Europe, Eastern Europe, Asia Pacific excluding Japan, South Korea, and Japan, Middle East & Africa, Japan, South Korea, and China)

For more information on this press release visit: http://www.sbwire.com/press-releases/flourishing-biopharmaceutical-industry-to-boost-demand-for-protein-a-resins-high-adoption-expected-in-north-america-and-europe-1294793.htm

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Flourishing Biopharmaceutical Industry to Boost Demand for Protein a Resins, High Adoption Expected in North America and Europe - Press Release -...

Is The Goal-Driven Systems Pattern The Key To Artificial General Intelligence (AGI)? – Forbes

Goal-driven systems

Since the beginnings of artificial intelligence, researchers have long sought to test the intelligence of machine systems by having them play games against humans. It is often thought that one of the hallmarks of human intelligence is the ability to think creatively, consider various possibilities, and keep a long-term goal in mind while making short-term decisions. If computers can play difficult games just as well as humans then surely they can handle even more complicated tasks. From early checkers-playing bots developed in the 1950s to todays deep learning-powered bots that can beat even the best players in the world at games like chess, Go and DOTA, the idea of machines that can find solutions to puzzles is as old as AI itself, if not older.

As such, it makes sense that one of the core patterns of AI that organizations develop is the goal-driven systems pattern. Like the other patterns of AI, we see this form of artificial intelligence used to solve a common set of problems that would otherwise require human cognitive power. In this particular pattern, the challenge that machines address is the need to find the optimal solution to a problem. The problem might be finding a path through a maze, optimizing a supply chain, or optimize driving routes and idle time. Regardless of the specific need, the power that were looking for here is the idea of learning through trial-and-error, and determining the best way to solve something, even if its not the most obvious.

Reinforcement learning and learning through trial-and-error

One of the most intriguing, but least used, forms of machine learning is reinforcement learning. As opposed to supervised learning approaches in which machines learn by being trained by humans with well-labeled data, or unsupervised learning approaches in which machines try to learn through discovery of clusters of information and other groupings, reinforcement learning attempts to learn through trial-and-error, using environmental feedback and general goals to iterate towards success.

Without the use of AI, organizations depend on humans to create programs and rules-based systems that guide software and hardware systems on how to operate. Where programs and rules can be somewhat effective in managing money, employees, time and other resources, they suffer from brittleness and rigidity. The systems are only as strong as the rules that a human creates, and the machine isnt really learning at all. Rather, its the human intelligence incorporated into rules that makes the system work.

Goal-learning AI systems on the other hand are given very few rules, and need to learn how the system works on their own through iteration. In this way, AI can wholly optimize the entire system and not depend on human-set, brittle rules. Goal-driven driven systems have proved their worth to show the uncanny ability for systems to find the hidden rules that solve challenging problems. It isnt surprising just how useful goal-driven systems are in areas where resource optimization is a must.

AI can be efficiently used in scenario simulation and resource optimization. By applying this generalized approach to learning, AI-enabled systems can be set to optimize a particular goal or scenario and find many solutions to getting there, some not even obvious to their more-creative human counterparts. In this way, while the goal-driven systems pattern hasnt seen as much implementation as other patterns such as the recognition, predictive analytics, or conversational patterns, the potential is just as enormous across a wide range of industries.

Reinforcement-learning based goal-driven systems are being utilized in the financial sector for such use cases as robo-advising which uses learning to identify savings and investment plans catered to the specific needs of individuals. Other applications of the goal-driven systems pattern are in use in the control of traffic light systems, finding the best way to control traffic lights without causing disruptions. Other uses are in the supply chain and logistics industries, finding the best way to package and deliver goods. Further uses include helping to train physical robots, creating mechanisms and algorithms by which robots can run and jump.

Goal-driven systems are even being used in e-commerce and advertising, finding optimal prices for goods and automating bids on advertising space. Goal-driven systems are even used in the pharmaceutical industry to perform protein folding and discover new and innovative treatments for illnesses. These systems are capable of selecting the best reagent and reaction parameters in order to achieve the intended product, making it an asset during the complex and delicate drug or therapeutic making process.

Is the goal-driven systems pattern the key to Artificial General Intelligence (AGI)?

The idea of learning through trial-and-error is a potent one, and possibly can be applied to any problem. Notably, DeepMind, the organization that brought to reality the machine that could solve the once-thought unsolvable problem of a machine beating a human Go player, believes that reinforcement learning-based goal-driven systems could be the key to unlocking the ultimate goal of a machine that can learn anything and accomplish any task. The concept of a general intelligence is one that is like our human brain. Rather than being focused on a narrow, single learning task, as is the case with all real-world AI systems today, an artificial general intelligence (AGI) can learn any task and apply learning from one domain to another, without requiring extensive retraining.

DeepMind, established in the United Kingdom and acquired by Google in 2014, is aiming to solve some of the most complicated problems for machine intelligence by pushing the boundaries of what is capable with goal-driven systems and other patterns of AI. Starting with AlphaGo, which was purpose-built to learn how to play the game Go against a human opponent, the company rapidly branched out with AlphaZero, which could learn from scratch any game by playing itself. What had previously taken AlphaGo months to learn, AlphaZero could now do in a matter of days using reinforced learning. From scratch, with the only goal of increasing its win rate, AlphaZero triumphed over AlphaGo in all 100 test games. AlphaZero had achieved this by simply playing games against itself and learning by trial & error. It is by this simple method that general-learning systems are able to not only create patterns but essentially devise optimal conditions and outcomes for any input given to it. This predictably became the crowning glory of DeepMind and the holy grail of the AI industry.

Naturally, as those in the tech industry have often done with new technology, they turned their minds towards possible real-world applications. AlphaZero was created with the best techniques available at the time such as machine learning and applying other domains such as neuroscience and research in behavioral psychology. These techniques are channelled into the development of powerful general-purpose learning algorithms, and perhaps we might be only years away from a real breakthrough in research in AGI.

The AI industry is a bit of a crossroads with regards to research in machine learning. The most widely used algorithms today are solving important, but relatively simple problems. While machines have proven their ability to recognize images, understand speech, find patterns, spot anomalies, and make predictions, they depend on training data and narrow learning tasks to be able to achieve their tasks with any level of accuracy. In these situations, machine learning is very data and compute hungry. If you have a sufficiently complicated learning task, you might need petabytes or more of training data, hundreds of thousands of dollars of GPU-intensive computing, and months of training. Clearly, the solution to AGI is not achievable through just brute force approaches.

The goal-driven systems pattern, while today being one of the least implemented of the seven patterns, might hold a key to learning that isnt so data and compute intensive. Goal-driven systems are increasingly being implemented into projects with real-life use-cases. It is therefore one of the most interesting patterns to look into due to its potential promise.

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Is The Goal-Driven Systems Pattern The Key To Artificial General Intelligence (AGI)? - Forbes

Case Medical Awarded Patent for Multi Enzymatic Solution for Cleaning Medical Devices and Food Industry Utensils and Surfaces Exposed to Brain Wasting…

Patent is a significant step toward commercializing cleaning products to effectively inactivate and degrade prions

Case Medical today announced that it was awarded U.S. patent number 10,699,513 B2, by the U.S. Patent and Trademark office for "compositions and methods for handling potential prion contamination." The patent is a significant step for the company toward commercializing cleaning products that will enable prion contaminated devices and surfaces to be processed without resorting to the extraordinary methods required today.

Prions are a type of protein that can cause unfolding in normal prion proteins most commonly found in the brain, but also in the spine, eye, spleen, and lymphoid tissues. Prion diseases are described by the CDC as "a family of rare progressive neurodegenerative disorders that affect both humans and animals. They are distinguished by long incubation periods, characteristic spongiform changes associated with neuronal loss, and a failure to induce inflammatory response." The CDC also indicates that "the abnormal folding of the prion proteins leads to brain damage... Prion diseases are usually rapidly progressive and always fatal."

Prions are transmitted by eating of meat infected with prions, but also in healthcare settings from blood transfusions and from medical devices, especially from surgical instruments, even from apparently cleaned devices, having residual prion contamination.

"The challenge with prions is that they are almost impossible to detect before a fatal occurrence of the disease and they are also extremely hard to remove from contaminated devices and surfaces," said Marcia Frieze, CEO of Case Medical. "The logical solution would be to make prion decontamination a standard part of medical device processing but the current options are extremely time consuming and so harsh that they significantly reduce the useful life of the devices themselves."

Currently, prion contaminated materials are either incinerated or pre-treated with sodium hypochlorite, sterilization, oxidizing agents, peracetic acid, or pre-treatment at temperatures above 100C for extended periods of time. These methods and materials are environmentally unfriendly and excessively corrosive to the materials being cleaned. The cleaning solution patented by Case Medical uses a multi enzymatic formulation to achieve a safer, more thorough result and requires much less time and effort, suggesting a feasible process for healthcare settings and the food processing industry.

In brief, Case Medicals formulation uses specific enzymes combined with a surfactant. The enzymes effectively digest or inactivate prions rendering them ineffective and the surfactant lowers the level of friction to allow easy rinsing. The process is easy, biodegradable, and environmentally preferred.

"While prion diseases are currently rare and a much bigger issue in Europe than in the U.S., the coronavirus pandemic has hopefully taught us the value of being prepared," said Frieze. "We still have many regulatory steps before we can fully commercialize this product and process, but we are continuing to work as fast as we can."

Testing and validation were performed in conjunction with the U.S. Geological Service (USGS) through their National Wildlife Health Center at the Class III prion lab in Madison, Wisc.

About Case Medical

Case Medical is a FDA registered, ISO certified manufacturer of validated, sustainable, and cost effective products for instrument processing. Our reusable sterilization containers and instrument chemistries meet the highest standards for patient safety and environmental preference. Case Medical was an inaugural recipient of the U.S. EPA Safer Choice Partner of the Year award. Visit http://www.casemed.com for more information.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200616005865/en/

Contacts

Lisa Forsell, Director of MarketingPhone: 201-313-1999 x302Email: lforsell@casemed.com Web: http://www.casemed.com

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Site-specific glycan analysis of the SARS-CoV-2 spike – Science Magazine

SARS-CoV-2 spike protein, elaborated

Vaccine development for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is focused on the trimeric spike protein that initiates infection. Each protomer in the trimeric spike has 22 glycosylation sites. How these sites are glycosylated may affect which cells the virus can infect and could shield some epitopes from antibody neutralization. Watanabe et al. expressed and purified recombinant glycosylated spike trimers, proteolysed them to yield glycopeptides containing a single glycan, and determined the composition of the glycan sites by mass spectrometry. The analysis provides a benchmark that can be used to measure antigen quality as vaccines and antibody tests are developed.

Science this issue p. 330

The emergence of the betacoronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), represents a considerable threat to global human health. Vaccine development is focused on the principal target of the humoral immune response, the spike (S) glycoprotein, which mediates cell entry and membrane fusion. The SARS-CoV-2 S gene encodes 22 N-linked glycan sequons per protomer, which likely play a role in protein folding and immune evasion. Here, using a site-specific mass spectrometric approach, we reveal the glycan structures on a recombinant SARS-CoV-2 S immunogen. This analysis enables mapping of the glycan-processing states across the trimeric viral spike. We show how SARS-CoV-2 S glycans differ from typical host glycan processing, which may have implications in viral pathobiology and vaccine design.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative pathogen of coronavirus 2019 (COVID-19) (1, 2), induces fever, severe respiratory illness, and pneumonia. SARS-CoV-2 uses an extensively glycosylated spike (S) protein that protrudes from the viral surface to bind to angiotensin-converting enzyme 2 (ACE2) to mediate host-cell entry (3). The S protein is a trimeric class I fusion protein, composed of two functional subunits, responsible for receptor binding (S1 subunit) and membrane fusion (S2 subunit) (4, 5). The surface of the envelope spike is dominated by host-derived glycans, with each trimer displaying 66 N-linked glycosylation sites. The S protein is a key target in vaccine design efforts (6), and understanding the glycosylation of recombinant viral spikes can reveal fundamental features of viral biology and guide vaccine design strategies (7, 8).

Viral glycosylation has wide-ranging roles in viral pathobiology, including mediating protein folding and stability and shaping viral tropism (9). Glycosylation sites are under selective pressure as they facilitate immune evasion by shielding specific epitopes from antibody neutralization. However, we note the low mutation rate of SARS-CoV-2 and that as yet, there have been no observed mutations to N-linked glycosylation sites (10). Surfaces with an unusually high density of glycans can also enable immune recognition (9, 11, 12). The role of glycosylation in camouflaging immunogenic protein epitopes has been studied for other coronaviruses (10, 13, 14). Coronaviruses form virions by budding into the lumen of endoplasmic reticulumGolgi intermediate compartments (15, 16). However, observations of complex-type glycans on virally derived material suggests that the viral glycoproteins are subjected to Golgi-resident processing enzymes (13, 17).

High viral glycan density and local protein architecture can sterically impair the glycan maturation pathway. Impaired glycan maturation resulting in the presence of oligomannose-type glycans can be a sensitive reporter of native-like protein architecture (8), and site-specific glycan analysis can be used to compare different immunogens and monitor manufacturing processes (18). Additionally, glycosylation can influence the trafficking of recombinant immunogen to germinal centers (19).

To resolve the site-specific glycosylation of the SARS-CoV-2 S protein and visualize the distribution of glycoforms across the protein surface, we expressed and purified three biological replicates of recombinant soluble material in an identical manner to that which was used to obtain the high-resolution cryoelectron microscopy (cryo-EM) structure, albeit without a glycan-processing blockade using kifunensine (4). This variant of the S protein contains all 22 glycans on the SARS-CoV-2 S protein (Fig. 1A). Stabilization of the trimeric prefusion structure was achieved by using the 2P stabilizing mutations (20) at residues 986 and 987, a GSAS (Gly-Ser-Ala-Ser) substitution at the furin cleavage site (residues 682 to 685), and a C-terminal trimerization motif. This helps to maintain quaternary architecture during glycan processing. Before analysis, supernatant containing the recombinant SARS-CoV-2 S was purified by size exclusion chromatography to ensure that only native-like trimeric protein was analyzed (Fig. 1B and fig. S1). The trimeric conformation of the purified material was validated by using negative-stain EM (Fig. 1C).

(A) Schematic representation of the SARS-CoV-2 S glycoprotein. The positions of N-linked glycosylation sequons (N-X-S/T, where X P) are shown as branches (N, Asn; X, any residue; S, Ser; T, Thr; P, Pro). Protein domains are illustrated: N-terminal domain (NTD), receptor binding domain (RBD), fusion peptide (FP), heptad repeat 1 (HR1), central helix (CH), connector domain (CD), and transmembrane domain (TM). (B) SDSpolyacrylamide gel electrophoresis analysis of the SARS-CoV-2 S protein (indicated by the arrowhead) expressed in human embryonic kidney (HEK) 293F cells. Lane 1: filtered supernatant from transfected cells; lane 2: flow-through from StrepTactin resin; lane 3: wash from StrepTactin resin; lane 4: elution from StrepTactin resin. (C) Negative-stain EM 2D class averages of the SARS-CoV-2 S protein. 2D class averages of the SARS-CoV-2 S protein are shown, confirming that the protein adopts the trimeric prefusion conformation matching the material used to determine the structure (4).

To determine the site-specific glycosylation of SARS-CoV-2 S, we used trypsin, chymotrypsin, and -lytic protease to generate three glycopeptide samples. These proteases were selected to generate glycopeptides that contain a single N-linked glycan sequon. The glycopeptides were analyzed by liquid chromatographymass spectrometry, and the glycan compositions were determined for all 22 N-linked glycan sites (Fig. 2). To convey the main processing features at each site, the abundances of each glycan are summed into oligomannose-type, hybrid-type, and categories of complex-type glycosylation based on branching and fucosylation. The detailed, expanded graphs showing the diverse range of glycan compositions are presented in table S1 and fig. S2.

The schematic illustrates the color code for the principal glycan types that can arise along the maturation pathway from oligomannose- to hybrid- to complex-type glycans. The graphs summarize quantitative mass spectrometric analysis of the glycan population present at individual N-linked glycosylation sites simplified into categories of glycans. The oligomannose-type glycan series (M9 to M5; Man9GlcNAc2 to Man5GlcNAc2) is colored green, afucosylated and fucosylated hybrid-type glycans (hybrid and F hybrid) are dashed pink, and complex glycans are grouped according to the number of antennae and presence of core fucosylation (A1 to FA4) and are colored pink. Unoccupancy of an N-linked glycan site is represented in gray. The pie charts summarize the quantification of these glycans. Glycan sites are colored according to oligomannose-type glycan content, with the glycan sites labeled in green (80 to 100%), orange (30 to 79%), and pink (0 to 29%). An extended version of the site-specific analysis showing the heterogeneity within each category can be found in table S1 and fig. S2. The bar graphs represent the mean quantities of three biological replicates, with error bars representing the standard error of the mean.

Two sites on SARS-CoV-2 S are principally oligomannose-type: N234 and N709. The predominant oligomannose-type glycan structure observed across the protein, with the exception of N234, is Man5GlcNAc2 (Man, mannose; GlcNAc, N-acetylglucosamine), which demonstrates that these sites are largely accessible to -1,2-mannosidases but are poor substrates for GlcNAcT-I, which is the gateway enzyme in the formation of hybrid- and complex-type glycans in the Golgi apparatus. The stage at which processing is impeded is a signature related to the density and presentation of glycans on the viral spike. For example, the more densely glycosylated spikes of HIV-1 Env and Lassa virus (LASV) GPC exhibit numerous sites dominated by Man9GlcNAc2 (2124).

A mixture of oligomannose- and complex-type glycans can be found at sites N61, N122, N603, N717, N801, and N1074 (Fig. 2). Of the 22 sites on the S protein, 8 contain substantial populations of oligomannose-type glycans, highlighting how the processing of the SARS-CoV-2 S glycans is divergent from host glycoproteins (25). The remaining 14 sites are dominated by processed, complex-type glycans.

Although unoccupied glycosylation sites were detected on SARS-CoV-2 S, when quantified they were revealed to form a very minor component of the total peptide pool (table S2). In HIV-1 immunogen research, the holes generated by unoccupied glycan sites have been shown to be immunogenic and potentially give rise to distracting epitopes (26). The high occupancy of N-linked glycan sequons of SARS-CoV-2 S indicates that recombinant immunogens will not require further optimization to enhance site occupancy.

Using the cryo-EM structure of the trimeric SARS-CoV-2 S protein [Protein Data Bank (PDB) ID 6VSB] (4), we mapped the glycosylation status of the coronavirus spike mimetic onto the experimentally determined three-dimensional (3D) structure (Fig. 3). This combined mass spectrometric and cryo-EM analysis reveals how the N-linked glycans occlude distinct regions across the surface of the SARS-CoV-2 spike.

Representative glycans are modeled onto the prefusion structure of the trimeric SARS-CoV-2 S glycoprotein (PDB ID 6VSB) (4), with one RBD in the up conformation and the other two RBDs in the down conformation. The glycans are colored according to oligomannose content as defined by the key. ACE2 receptor binding sites are highlighted in light blue. The S1 and S2 subunits are rendered with translucent surface representation, colored light and dark gray, respectively. The flexible loops on which the N74 and N149 glycan sites reside are represented as gray dashed lines, with glycan sites on the loops mapped at their approximate regions.

Shielding of the receptor binding sites on the SARS-CoV-2 spike by proximal glycosylation sites (N165, N234, N343) can be observed, especially when the receptor binding domain is in the down conformation. The shielding of receptor binding sites by glycans is a common feature of viral glycoproteins, as observed on SARS-CoV-1 S (10, 13), HIV-1 Env (27), influenza hemagglutinin (28, 29), and LASV GPC (24). Given the functional constraints of receptor binding sites and the resulting low mutation rates of these residues, there is likely selective pressure to use N-linked glycans to camouflage one of the most conserved and potentially vulnerable areas of their respective glycoproteins (30, 31).

We note the dispersion of oligomannose-type glycans across both the S1 and S2 subunits. This is in contrast to other viral glycoproteins; for example, the dense glycan clusters in several strains of HIV-1 Env induce oligomannose-type glycans that are recognized by antibodies (32, 33). In SARS-CoV-2 S, the oligomannose-type structures are likely protected by the protein component, as exemplified by the N234 glycan, which is partially sandwiched between the N-terminal and receptor binding domains (Fig. 3).

We characterized the N-linked glycans on extended flexible loop structures (N74 and N149) and at the membrane-proximal C terminus (N1158, N1173, N1194) that were not resolved in the cryo-EM maps (4). These were determined to be complex-type glycans, consistent with steric accessibility of these residues.

Whereas the oligomannose-type glycan content (28%) (table S2) is above that observed on typical host glycoproteins, it is lower than other viral glycoproteins. For example, one of the most densely glycosylated viral spike proteins is HIV-1 Env, which exhibits ~60% oligomannose-type glycans (21, 34). This suggests that the SARS-CoV-2 S protein is less densely glycosylated and that the glycans form less of a shield compared with other viral glycoproteins, including HIV-1 Env and LASV GPC, which may be beneficial for the elicitation of neutralizing antibodies.

Additionally, the processing of complex-type glycans is an important consideration in immunogen engineering, especially considering that epitopes of neutralizing antibodies against SARS-CoV-2 S can contain fucosylated glycans at N343 (35). Across the 22 N-linked glycosylation sites, 52% are fucosylated and 15% of the glycans contain at least one sialic acid residue (table S2 and fig. S3). Our analysis reveals that N343 is highly fucosylated with 98% of detected glycans bearing fucose residues. Glycan modifications can be heavily influenced by the cellular expression system used. We have previously demonstrated for HIV-1 Env glycosylation that the processing of complex-type glycans is driven by the producer cell but that the levels of oligomannose-type glycans were largely independent of the expression system and are much more closely related to the protein structure and glycan density (36).

Highly dense glycan shields, such as those observed on LASV GPC and HIV-1 Env, feature so-called mannose clusters (22, 24) on the protein surface (Fig. 4). Whereas small mannose-type clusters have been characterized on the S1 subunit of Middle East respiratory syndrome (MERS)CoV S (10), no such phenomenon has been observed for the SARS-CoV-1 or SARS-CoV-2 S proteins. The site-specific glycosylation analysis reported here suggests that the glycan shield of SARS-CoV-2 S is consistent with other coronaviruses and similarly exhibits numerous vulnerabilities throughout the glycan shield (10). Last, we detected trace levels of O-linked glycosylation at Thr323/Ser325 (T323/S325), with over 99% of these sites unmodified (fig. S4), suggesting that O-linked glycosylation of this region is minimal when the structure is native-like.

From left to right, MERS-CoV S (10), SARS-CoV-1 S (10), SARS-CoV-2 S, LASV GPC (24), and HIV-1 Env (8, 21). Site-specific N-linked glycan oligomannose quantifications are colored according to the key. All glycoproteins were expressed as soluble trimers in HEK 293F cells apart from LASV GPC, which was derived from virus-like particles from Madin-Darby canine kidney II cells.

Our glycosylation analysis of SARS-CoV-2 offers a detailed benchmark of site-specific glycan signatures characteristic of a natively folded trimeric spike. As an increasing number of glycoprotein-based vaccine candidates are being developed, their detailed glycan analysis offers a route for comparing immunogen integrity and will also be important to monitor as manufacturing processes are scaled for clinical use. Glycan profiling will therefore also be an important measure of antigen quality in the manufacture of serological testing kits. Last, with the advent of nucleotide-based vaccines, it will be important to understand how those delivery mechanisms affect immunogen processing and presentation.

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Site-specific glycan analysis of the SARS-CoV-2 spike - Science Magazine

After this COVID winter comes an AI spring – VentureBeat

During boom times, companies focus on growth. In tough times, they seek to improve efficiency. History shows us that after every major economic downturn since the 1980s, businesses relied on digital technology and, specifically, innovations in software technology to return to full productivity with fewer repetitive jobs and less bloat.

The years Ive spent as a VC have convinced me that this is the best time to start an AI-first enterprise, not despite the recession, but because of it. The next economic recovery will both be driven by artificial intelligence and accelerate its adoption.

While the Great Recession is often thought of as a jobless recovery, economists at the National Bureau of Economic Research (NBER) found that the downturn accelerated the shift from repetitive to non-routine jobs at both the high and low ends of the spectrum. So, yes, existing tasks were automated, but companies empowered their employees with data and analytics to augment their judgment to improve productivity and quality, in a virtuous cycle of data and judgment that both increased profitability and created more rewarding work.

Indeed, the highest levels of unemployment during the Great Recession were followed by a surge in enrollment in post-secondary education in analytics and data science as people sought out opportunities to upskill. And the period was followed by a recovery in which despite increased automation unemployment fell to historic lows.

Through no fault of our own, were again thrust into the cycle of recession and recovery. Industries already expect to benefit from improved AI and machine learning in the next recovery. That expectation will create new opportunities for AI entrepreneurs.

Every economic recovery is defined by an emerging software technology and set of applications.

The companies that grew in the lackluster economy of the early 1980s staged the first software IPOs when the economy rebounded in the middle of that decade: Lotus, Microsoft, Oracle, Adobe, Autodesk and Borland.

Packaged software signified a unique turning point in the history of commercial enterprise; the category required little in the way of either CAPEX or personnel costs. Software companies had gross margins of 80% or more, which gave them amazing resilience to grow or shrink without endangering their existence. If entrepreneurs were willing to work for lower wages, software companies could be started quickly with minimal to no outside investment, and if they could find early product-market fit, they could often bootstrap and grow organically.

Those new software companies were perfectly adapted to foster innovation when recessions hit, because high-quality people were available and less expensive, and office space was abundant. At the same time, established companies put new product development on hold while they tried to service and keep existing customers.

I started working as a VC in 1990 for the first venture firm that focused purely on investing in software, Hummer Winblad. While it took hard work and tenacity for John Hummer and Ann Winblad to raise that first fund, their timing as investors turned out to be perfect. A recession began in the second quarter of that year and lasted through Q1 1991.

The software companies coming out of that recession pioneered cost-effective client-server computing. Sybase, which established this trend with its Open Client-Server Interfaces went public in 1991, after growing 54% in the previous year.

By then, universities had graduated many programmers, creating a talent pool for startups. New software developer platforms made those programmers more productive. The 1990s became the first golden era for enterprise computing. One Hummer Winblad company, Arbor Software, invented the category of Online Analytical Processing (OLAP). Another, Powersoft, became the dominant no-code client server development platform. It was the industrys first billion-dollar software acquisition.

The first CRM companies, spawned in that recession, held successful IPOs from 1993 to 1999. This class included Remedy a company that BusinessWeek breathlessly called Americas Number One Top Hot Growth Company in 1996. Scopus, Vantive, and Clarify all grew rapidly and went public or were acquired in this period or shortly thereafter.

That exuberance ended with the dot-com bust in March 2000.

At that time, Salesforce had existed for only a year. Concur was a relatively new company, forced to reinvent itself when its packaged software business collapsed. Many people would have thought their timing was terrible, but they were unhindered by the obligation to service an installed base during the 2001 recession that followed the bust. That left them free to innovate, and they became two of the very first SaaS businesses.

Salesforce went public in 2004, and now has a market cap of about $135 billion. In 2013, Concur sold to SAP for $8.3 billion. Amazon Web Services was also conceived during that recession and launched in July 2002. SaaS and cloud computing leveraged each other for the rest of the decade.

When the sub-prime mortgage crisis brought the entire economy down, companies had to retain customers and improve efficiency goals that are often at odds with each other. The idea of a big data future had already taken root, and forward-thinking executives suspected that the solution was already in their data, if they could only find it. But at the same time, established software companies also cut R&D spending. That opened up fertile ground for newer and more agile analytics companies.

Most software companies saw no growth in 2009, but Omniture, a leader in web analytics, grew more than 80% that year, prompting its acquisition by Adobe for $1.9 billion. Tableau had been founded back in 2003, but it grew slowly until the recession. From 2008-2010, it grew from $13 million to $34 million in sales. Over the same period Splunk went from $9 million to $35 million. Ayasdi, Cloudera, Mapr and Datameer were all launched in the depths of the Great Recession.

Of course, none of those companies could have flourished without data scientists. Just as universities accelerated the creation of software developers in the early 1990s, they again accelerated the creation of analytics experts and data scientists during the Great Recession, which again helped to spur the recovery and drive a decade of economic expansion, job growth, and the longest bull market in American history.

Even before the pandemic, many economists and corporate CFOs felt there was at least a 50% chance of recession in 2020.

Over a year ago, The Parliament the policy magazine published by the EU Parliament predicted that the next recession would usher in a wave of AI. The magazine quoted Mirko Draca, of the London School of Economics as saying, We expect to see another technology surge in the next 10 to 15 years, based on AI and robotics technology.

Those who predicted a mere recession were, to say the least, insufficiently pessimistic. Companies have reduced their labor costs more aggressively than ever to match the suddenness and seriousness of the situation. Once again, theyll rely on automation to boost production when the recovery begins.

The Atlantic Council surveyed over 100 technology experts on the impact that COVID-19 would have on global innovation. Even in the midst of the pandemic, those experts felt that over the next two to five years, data and AI would have more impact than medical bioengineering. The two are not mutually exclusive; Googles Deepmind Technologies recently used its AlphaFold tool to predict complex protein folding patterns, useful in the search for a vaccine.

Companies emerging from this recession will adapt processes to vaccinate their systems against the next pandemic. In response to supply-chain disruptions, Volkswagen is considering increasing its 3D printing capabilities in Germany, which would give the automaker a redundant parts source. The government-run Development Bank of Japan will subsidize the costs of companies that move production back to Japan.

Bringing production back onshore while controlling costs will require significant investment in robotics and AI. Even companies that dont have their own production capacity, such as online retailers, plan to use AI to improve the reliability of complex global supply chains. So a surge in demand for AI talent is inevitable.

In 2018, several major universities announced initiatives to develop that talent. MIT announced the largest-ever commitment to AI from a university: a $1 billion initiative to create a College of Computing. Carnegie-Mellon created the first bachelor of science in artificial intelligence degree program. UCBerkeleyannounced a new division of data science. And Stanford announced ahuman-centered AI initiative.

Dozens more schools have followed suit. Machine learning has moved from obscurity to ubiquity, just as software development did 30 years ago and data science did 10 years ago.

Back in 2017, a couple of my colleagues wrote about the AI risk curve, arguing that the adoption of AI is held back not by technology but by managers perception of the risks involved in replacing a worker (whose performance is known) with an unfamiliar software process.

Recessions increase the pressure on managers to reduce labor costs, and thus increase their tolerance for the risks associated with adopting new technology. Over the next year or two, companies will be more willing to take risks and integrate new technologies into their infrastructure. But the challenges of surviving in the recession will mean that AI-first companies must deliver measurable improvements in quality and productivity.

One relatively new risk that managers must tolerate pertains to data. Even companies that are not yet exploiting their data effectively now recognize it as a valuable resource. As startups deploy AI software systems that prove more accurate and cost-effective than human beings, their early-adopter customers must be more willing to trust them with proprietary data. That will allow AI companies to train new products and make them even smarter. And in return for taking this risk, companies must make their models more transparent, more easily reproducible, and more explainable to their customers, auditors, and regulators.

In the area of food and agriculture, AI will help us to understand and adapt to a changing climate. In infrastructure and security, machine learning models will improve the efficiency, reliability and performance of cloud infrastructure. Better and more dynamic risk models will help companies and the entire financial market handle the next crisis.

A host of new applied-AI companies will be needed in order accomplish all this and, especially, AI-enabling companies creating better developer tools and infrastructure, continuous optimization systems, and products that help disciplines improve data quality, security, and privacy.

Boom times favor established companies. They have the cash flow to fund skunkworks and conduct pure research. But its a truism that R&D spending is one of the first things big companies cut in a recession. As an entrepreneur, the idea of starting a company now of all times might be scary, but that retrenchment by established competitors leaves fresh ground open for you to seed with new ideas.

The first sign of AI spring will come when companies again forecast increased demand and seek to improve productivity. The only way to be there when that opportunity presents itself is to start now.

The best part is you wont just profit from the recovery, youll help to create it.

[VentureBeats Transform 2020 event in July will feature a host of disruptive new AI technologies and companies.]

Mark Gorenberg is founder and managing director at Zetta Venture Partners.

Continued here:
After this COVID winter comes an AI spring - VentureBeat

Disordered proteins follow diverse transition paths as they fold and bind to a partner – Science Magazine

Shedding light on disordered proteins

Disordered proteins often fold as they bind to a partner protein. There could be many different molecular trajectories between the unbound proteins and the bound complex. Most methods to measure transition paths rely on monitoring a single distance, making it difficult to resolve complex pathways. Kim and Chung used fast three-color single-molecule Foster resonance energy transfer (FRET) to simultaneously probe distance changes between the two ends of an unfolded protein and between each end and a probe on the partner protein. They show that binding can be initiated by diverse conformations and that the molecules are held together by non-native interactions as the disordered protein folds. This allows the association to be diffusion limited because most collisions lead to binding.

Science, this issue p. 1253

Transition paths of macromolecular conformational changes such as protein folding are predicted to be heterogeneous. However, experimental characterization of the diversity of transition paths is extremely challenging because it requires measuring more than one distance during individual transitions. In this work, we used fast three-color single-molecule Frster resonance energy transfer spectroscopy to obtain the distribution of binding transition paths of a disordered protein. About half of the transitions follow a path involving strong non-native electrostatic interactions, resulting in a transition time of 300 to 800 microseconds. The remaining half follow more diverse paths characterized by weaker electrostatic interactions and more than 10 times shorter transition path times. The chain flexibility and non-native interactions make diverse binding pathways possible, allowing disordered proteins to bind faster than folded proteins.

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Disordered proteins follow diverse transition paths as they fold and bind to a partner - Science Magazine

Coupling chromatin structure and dynamics by live super-resolution imaging – Science Advances

INTRODUCTION

The three-dimensional organization of the eukaryotic genome plays a central role in gene regulation (1). Its spatial organization has been prominently characterized by molecular and cellular approaches including high-throughput chromosome conformation capture (Hi-C) (2) and fluorescent in situ hybridization (3). Topologically associated domains (TADs), genomic regions that display a high degree of interaction, were revealed and found to be a key architectural feature (4). Direct three-dimensional localization microscopy of the chromatin fiber at the nanoscale (5) confirmed the presence of TADs in single cells but also, among others, revealed great structural variation of chromatin architecture (3). To comprehensively resolve the spatial heterogeneity of chromatin, super-resolution microscopy must be used. Previous work showed that nucleosomes are distributed as segregated, nanometer-sized accumulations throughout the nucleus (68) and that the epigenetic state of a locus has a large impact on its folding (9, 10). However, to resolve the fine structure of chromatin, high labeling densities, long acquisition times, and, often, cell fixation are required. This precludes capturing dynamic processes of chromatin in single live cells, yet chromatin moves at different spatial and temporal scales.

The first efforts to relate chromatin organization and its dynamics were made using a combination of photoactivated localization microscopy (PALM) and tracking of single nucleosomes (11). It could be shown that nucleosomes mostly move coherently with their underlying domains, in accordance with conventional microscopy data (12); however, a quantitative link between the observed dynamics and the surrounding chromatin structure could not yet be established in real time. Although it is becoming increasingly clear that chromatin motion and long-range interactions are key to genome organization and gene regulation (13), tools to detect and to define bulk chromatin motion simultaneously at divergent spatiotemporal scales and high resolution are still missing.

Here, we apply deep learningbased PALM (Deep-PALM) for temporally resolved super-resolution imaging of chromatin in vivo. Deep-PALM acquires a single resolved image in a few hundred milliseconds with a spatial resolution of ~60 nm. We observed elongated ~45- to 90-nm-wide chromatin domain blobs. Using a computational chromosome model, we inferred that blobs are highly dynamic entities, which dynamically assemble and disassemble. Consisting of chromatin in close physical and genomic proximity, our chromosome model indicates that blobs, nevertheless, adopt TAD-like interaction patterns when chromatin configurations are averaged over time. Using a combination of Deep-PALM and high-resolution dense motion reconstruction (14), we simultaneously analyzed both structural and dynamic properties of chromatin. Our analysis emphasizes the presence of spatiotemporal cross-correlations between chromatin structure and dynamics, extending several micrometers in space and tens of seconds in time. Furthermore, extraction and statistical mapping of multiple parameters from the dynamic behavior of chromatin blobs show that chromatin density regulates local chromatin dynamics.

Super-resolution imaging of complex and compact macromolecules such as chromatin requires dense labeling of the chromatin fiber to resolve fine features. We use Deep-STORM, a method that uses a deep convolutional neural network (CNN) to predict super-resolution images from stochastically blinking emitters (Fig. 1A; see Materials and Methods) (15). The CNN was trained to specific labeling densities for live-cell chromatin imaging using a photoactivated fluorophore (PATagRFP); we therefore refer to the method as Deep-PALM. We chose three labeling densities 4, 6, and 9 emitters/m2 per frame in the ON-state to test on the basis of the comparison of simulated and experimental wide-field images (fig. S1A). The CNN trained with 9 emitters/m2 performed significantly worse than the other CNNs and was thus excluded from further analysis (fig. S1B; see Materials and Methods). We applied Deep-PALM to reconstruct an image set of labeled histone protein (H2B-PATagRFP) in human bone osteosarcoma (U2OS) cells using the networks trained on 4 and 6 emitters/m2 per frame (see Materials and Methods). A varying number of predictions by the CNN of each frame of the input series were summed to reconstruct a temporal series of super-resolved images (fig. S1C). The predictions made by the CNN trained with 4 emitters/m2 show large spaces devoid of signal intensity, especially at the nuclear periphery, making this CNN inadequate for live-cell super-resolution imaging of chromatin. While collecting photons from long acquisitions for super-resolution imaging is desirable in fixed cells, Deep-PALM is a live imaging approach. Summing over many individual predictions leads to considerable motion blur and thus loss in resolution. Quantitatively, the Nyquist criterion states that the image resolution R=2/ depends on , the localization density per second, and the time resolution (16). In contrast, motion blur strictly depends on the diffusion constant D of the underlying structure R=4D. There is thus an optimum resolution due to the trade-off between increased emitter sampling and the avoidance of motion blur, which was at a time resolution of 360 ms for our experiments (Fig. 1B and fig. S1D).

(A) Wide-field images of U2OS nuclei expressing H2B-PATagRFP are input to a trained CNN, and predictions from multiple input frames are summed to construct a super-resolved image of chromatin in vivo. (B) The resolution trade-off between the prolonged acquisition of emitter localizations (green line) and motion blur due to diffusion of the underlying diffusion processes (purple line). For our experimental data, the localization density per second is = (2.4 0.1) m2s1, the diffusion constant is D = (3.4 0.8) 103 m2s1 (see fig. S8B), and the acquisition time per frame is = 30 ms. The spatial resolution assumes a minimum (69 5 nm) at a time resolution of 360 ms. (C) Super-resolution images of a single nucleus at time intervals of about 10 s. Scale bars, 2 m. (D) Magnification of segregated accumulations of H2B within a chromatin-rich region. Scale bar, 200 nm. (E) Magnification of a stable but dynamic structure (arrows) over three consecutive images. Scale bars, 500 nm. (F) Fourier ring correlation (FRC) for super-resolved images resulting in a spatial resolution of 63 2 nm. FRC was conducted on the basis of 332 consecutive super-resolved images from two cells. a.u. arbitrary units.

Super-resolution imaging of H2B-PATagRFP in live cells at this temporal resolution shows a pronounced nuclear periphery, while fluorescent signals in the interior vary in intensity (Fig. 1C). This likely corresponds to chromatin-rich and chromatin-poor regions (8). These regions rearrange over time, reflecting the dynamic behavior of bulk chromatin. Chromatin-rich and chromatin-poor regions were visible not only at the scale of the whole nucleus but also at the resolution of a few hundred nanometers (Fig. 1D). Within chromatin-rich regions, the intensity distribution was not uniform but exhibited spatially segregated accumulations of labeled histones of variable shape and size, reminiscent of nucleosome clutches (6), nanodomains (9, 11), or TADs (17). At the nuclear periphery, prominent structures arise. Certain chromatin structures could be observed for ~1 s, which underwent conformational changes during this period (Fig. 1E). The spatial resolution at which structural elements can be observed (see Materials and Methods) in time-resolved super-resolution data of chromatin was 63 2 nm (Fig. 1E), slightly more optimistic than the theoretical prediction (Fig. 1B) (18).

We compared images of H2B reconstructed from 12 frames (super-resolved images) by Deep-PALM in living cells to super-resolution images reconstructed by 8000 frames of H2B in fixed cells (fig. S2, A and B). Overall, the contrast in the fixed sample appears higher, and the nuclear periphery appears more prominent than in images from living cells. However, in accordance with the previous super-resolution images of chromatin in fixed cells (6, 8, 9, 11, 17) and Deep-PALM images, we observe segregated accumulations of signal throughout the nucleus. Thus, Deep-PALM identifies spatially heterogeneous coverage of chromatin, as previously reported (6, 8, 9, 11, 17). We further monitor chromatin temporally at the nanometer scale in living cells.

To quantitatively assess the spatial distribution of H2B, we developed an image segmentation scheme (see Materials and Methods; fig. S3), which allowed us to segment spatially separated accumulations of H2B signal with high fidelity (note S1 and figs. S4 and S5). Applying our segmentation scheme, ~10,000 separable elements, blob-like structures were observed for each super-resolved image (166 resolved images per movie; Fig. 2A). The experimental resolution does not enable us to elucidate their origin and formation because tracking of blobs in three dimensions would be necessary to do so (see Discussion). We therefore turned to a transferable computational model introduced by Qi and Zhang (19), which is based on one-dimensional genomics and epigenomics data, including histone modification profiles and binding sites of CTCF (CCCTC-binding factor). To compare our data to the simulations, super-resolution images were generated from the modeled chromosomes. Within these images, we could identify and characterize chromatin blobs analogously to those derived from experimental data (see Materials and Methods; Fig. 2B).

(A) Super-resolved images show blobs of chromatin (left). These blobs are segmented (see Materials and Methods and note S1) and individually labeled by random color (right). Magnifications of the boxed regions are shown. Scale bars, 2 m (whole nucleus); magnifications, 200 nm. (B) Generation of super-resolution images and blob identification and characterization for a 25million base pair (Mbp) segment of chromosome 1 from GM12878 cells, as simulated in Qi and Zhang (19). Beads (5-kb genomic length) of a simulated polymer configuration within a 200-nm-thick slab are projected to the imaging plane, resembling experimental super-resolved images of live chromatin. Blobs are identified as on experimental data. (C) From the centroid positions, the NND distributions are computed for up to 40 nearest neighbors (blue to red). The envelope of the k-NND distributions (black line) shows peaks at approximately 95, 235, 335, and 450 nm (red dots). (D) k-NND distributions as in (B) for simulated data. (E) Area distribution of experimental and simulated blobs. The distribution is, in both cases, well described by a lognormal distribution with parameters (3.3 2.8) 103 m2 for experimental blobs and (3.1 3.2) 103 m2 for simulated blobs (means SD). PDF, probability density function. (F) Eccentricity distribution for experimental and simulated chromatin blobs. Selected eccentricity values are illustrated by ellipses with the corresponding eccentricity. Eccentricity values range from 0, describing a circle, to 1, describing a line. Prominent peaks arise because of the discretization of chromatin blobs in pixels. The data are based on 332 consecutive super-resolved images from two cells, in each of with ~10,000 blobs were identified.

For imaged (in living and fixed cells) and modeled chromatin, we first computed the kth nearest-neighbor distance (NND; centroid-to-centroid) distributions, taking into account the nearest 1st to 40th neighbors (Fig. 2C and fig. S2, C and D, blue to red). Centroids of the nearest neighbors are (95 30) nm (means SD) apart, consistent with previous and our own super-resolution images of chromatin in fixed cells (9) and slightly further than what was found for clutches of nucleosomes (6). The envelope of all NND distributions (Fig. 2C, black line) shows several weak maxima at ~95, 235, 335, and 450 nm, which roughly coincide with the peaks of the 1st, 7th, 14th, and 25th nearest neighbors, respectively (Fig. 2C, red dots). In contrast, simulated data exhibit a prominent first nearest-neighbor peak at a slightly smaller distance, and higher-order NND distribution decay quickly and appear washed out (Fig. 2D). This hints toward greater levels of spatial organization of chromatin in vivo, which is not readily recapitulated in the used state-of-the-art chromosome model.

Next, we were interested in the typical size of chromatin blobs. Their area distribution (Fig. 2E) fit a log-normal distribution with parameters (3.3 2.8) 103 m2 (means SD), which is in line with the area distribution derived from fixed samples (fig. S2E) and modeled chromosomes. Notably, blob areas vary considerably, as indicated by the high SD and the prominent tail of the area distribution toward large values. Following this, we calculated the eccentricity of each blob to resolve their shape (Fig. 2F and fig. S2F). The eccentricity is a measure of the elongation of a region reflecting the ratio of the longest chord of the shape and the shortest chord perpendicular to it (Fig. 2F; illustrated shapes at selected eccentricity values). The distribution of eccentricity values shows an accumulation of values close to 1, with a peak value of ~0.9, which shows that most blobs have an elongated, fiber-like shape and are not circular. In particular, the eccentricity value of 0.9 corresponds to a ratio between the short and long axes of the ellipse of 1:2 (see Materials and Methods), which results, considering the typical area of blobs in experimental and simulated data, in roughly 92-nm-long and 46-nm-wide blobs on average. A highly similar value was found in fixed cells (fig. S2F). The length coincides with the value found for the typical NND [Fig. 2C; (95 30) nm]. However, because of the segregation of chromatin into blobs, their elongated shape, and their random orientation (Fig. 2A), the blobs cannot be closely packed throughout the nucleus. We find that chromatin has a spatially heterogeneous density, occupying 5 to 60% of the nuclear area (fig. S6, A and B), which is supported by a previous electron microscopy study (20).

Blob dimensions derived from live-cell super-resolution imaging using Deep-PALM are consistent with those found in fixed cells, thereby further validating our method, and in agreement with previously determined size ranges (6, 9). A previously published chromosome model based on Hi-C data (and thus not tuned to display blob-like structures per se) also displays blobs with dimensions comparable to those found here, in living cells. Together, these data strongly suggest the existence of spatially segregated chromatin structures in the sub100-nm range.

The simulations offer to track each monomer (chromatin locus) unambiguously, which is currently not possible to do from experimental data. Since the simulations show blobs comparable to those found in experiment (Fig. 2), simulations help to indicate possible mechanisms leading to the observation of chromatin blobs. For instance, because of the projection of the nuclear volume onto the imaging plane, the observed blobs could simply be overlays of distant, along the one-dimensional genome, noninteracting genomic loci. To examine this possibility, we analyzed the gap length between beads belonging to the same blob along the simulated chromosome. Beads constitute the monomers of the simulated chromosome, and each bead represents roughly 5 kb (19).

The analysis showed that the blobs are mostly made of consecutive beads along the genome, thus implying an underlying domain-like structure, similar to TADs (Fig. 3A). Using the affiliation of each bead to an intrinsic chromatin state of the model (Fig. 3B), it became apparent that blobs along the simulated chromosome consisting mostly of active chromatin are significantly larger than those formed by inactive and repressive chromatin (Fig. 3C). These findings are in line with experimental results (10) and results from the simulations directly (19), thereby validating the projection and segmentation process.

(A) Gap length between beads belonging to the same blob. An exemplary blob with small gap length is shown. The blob is mostly made of consecutive beads being in close spatial proximity. (B) A representative polymer configuration is colored according to chromatin states (red, active; green, inactive; and blue, repressive). (C) The cumulative distribution function (CDF) of clusters within active, inactive, and repressive chromatin. Inset: Mean area of clusters within the three types of chromatin. The distributions are all significantly different from each other, as determined by a two-sample Kolmogorov-Smirnov test (P < 1050). (D) Distribution of the continuous residence time of any monomer within a cluster (0.5 0.3 s; means SD). Inset: Continuous residence time of any monomer within a slab of 200-nm thickness (1.5 1.6 s; means SD). (E) The blob association strength between any two beads is measured as the frequency at which any two beads are found in one blob. The association map is averaged over all simulated configurations (upper triangular matrix; from simulations), and experimental Hi-C counts are shown for the same chromosome segment [lower triangular matrix; from Rao et al. (40)]. The association and Hi-C maps are strongly correlated [Pearsons correlation coefficient (PCC) = 0.76]. (F) Close-up views around the diagonal of Hi-Clike matrices. The association strength is shown together with the inverse distance between beads (top; PCC = 0.85) and with experimental Hi-C counts [bottom; as in (E)]. The data are based on 20,000 polymer configurations.

Since chromatin is dynamic in vivo and in computer simulations, each bead can diffuse in and out of the imaging volume from frame to frame. We estimated that, on average, each bead spent approximately 1.5 s continuously within a slab of 200-nm thickness (Fig. 3D). Furthermore, a bead is, on average, found only 0.55 0.33 s continuously within a blob, which corresponds to one to two experimental super-resolved images (Fig. 3D). These results suggest that chromatin blobs are highly dynamic entities, which usually form and dissemble within less than 1 s. We thus constructed a time-averaged association map for the modeled chromosomes, quantifying the frequency at which each locus is found with any other locus within one blob. The association map is comparable to interaction maps derived from Hi-C (Fig. 3E). Notably, interlocus association and Hi-C maps are strongly correlated, and the association map shows similar patterns as those identified as TADs in Hi-C maps, even for relatively distant genomic loci [>1 million base pairs (Mbp)]. A similar TAD-like organization is also apparent when the average inverse distance between loci is considered (Fig. 3F, top), suggesting that blobs could be identified in super-resolved images because of the proximity of loci within blobs in physical space. The computational chromosome model indicates that chromatin blobs identified by Deep-PALM are mostly made of continuous regions along the genome and cannot be attributed to artifacts originating from the projection of the three-dimensional genome structure to the imaging plane. The simulations further indicate that the blobs associate and dissociate within less than 1 s, but loci within blobs are likely to belong to the same TAD. Their average genomic content is 75 kb, only a fraction of typical TAD lengths in mammalian cells (average size, 880 kb) (4), suggesting that blobs likely correspond to sub-TADs or TAD nanocompartments (17).

To quantify the experimentally observed chromatin dynamics at the nanoscale, down to the size of one pixel (13.5 nm), we used a dense reconstruction of flow fields, optical flow (Fig. 4A; see Materials and Methods), which was previously used to analyze images taken on confocal (12, 14), and structured illumination microscopes (8). We examined the suitability of optical flow for super-resolution on the basis of single-molecule localization images using simulations. We find that the accuracy of optical flow is slightly enhanced on super-resolved images compared to conventional fluorescence microscopy images (note S2 and fig. S7, A to C). Experimental super-resolution flow fields are illustrated on the basis of two subsequent images, between which the dynamics of structural features are apparent to the eye (fig. S7, D and E). On the nuclear periphery, connected regions spanning up to ~500 nm can be observed [fig. S7D (i and ii), marked by arrows]. These structures are stable for at least 360 ms but move from frame to frame. The flow field is shown on top of an overlay of the two super-resolved images and color-coded [fig. S7D (iii); the intensity in frame 1 is shown in green, the intensity in frame 2 is shown in purple, and colocalization of both is white]. Displacement vectors closely follow the redistribution of intensity from frame to frame (roughly from green to purple). Similarly, structures within the nuclear interior (fig. S7E) can be followed by eye, thus further validating and justifying the use of a dense motion reconstruction as a quantification tool of super-resolved chromatin motion.

(A) A time series of super-resolution images (left) is subject to optical flow (right). (B) Blobs of a representative nucleus (see movie S1) are labeled by their NND (left), area (middle), and flow magnitude (right). Colors denote the corresponding parameter magnitude. (C) The average blob area, (D) NND, (E) density, and (F) flow magnitude are shown versus the normalized distance from the nuclear periphery (lower x axis; 0 is on the periphery and 1 is at the center of the nucleus) and versus the absolute distance (upper x axis). Line and shaded area denote the means SE from 322 super-resolved images of two cells. Scale bar, (A) and (B): 3 m.

Using optical flow fields, we linked the spatial appearance of chromatin to their dynamics. Effectively, the blobs were characterized with two structural parameters (NND and area) and their flow magnitude (Fig. 4B). Movie S1 shows the time evolution of those parameters for an exemplary nucleus. Blobs at the nuclear periphery showed a distinct behavior from those in the nuclear interior. In particular, the periphery exhibits a lower density of blobs, but those appear slightly larger and are less mobile than in the nuclear interior (Fig. 4, C to F), in line with previous findings using conventional microscopy (14). The peripheral blobs are reminiscent of dense and relatively immobile heterochromatin and lamina-associated domains (21), which extend only up to 0.5 m inside the nuclear interior. In contrast, blob dynamics increase gradually within 1 to 2 m from the nuclear rim.

To further elucidate the relationship between chromatin structure and dynamics, we analyzed the correlation between each pair of parameters in space and time. Therefore, we computed the auto- and cross-correlation of parameter maps with a given time lag across the entire nucleus (in space) (Fig. 5A). In general, a positive correlation denotes a low-low or a high-high relationship (a variable de-/increases when another variable de-/increases), while, analogously, a negative correlation denotes a high-low relationship. The autocorrelation of NND maps [Fig. 5A (i)] shows a positive correlation; thus, regions exist spanning 2 to 4 m, in which chromatin is either closely packed (low-low) or widely dispersed (high-high). Likewise, blobs of similar size tend to be in spatial proximity [Fig. 5A (iii)]. These regions are not stable over time but rearrange continuously, an observation bolstered by the fact that the autocorrelation diminishes with increasing time lag. The cross-correlation between NND and area [Fig. 5A (ii)] shows a negative correlation for short time lags, suggesting that large blobs appear with a high local density while small ones are more isolated. The correlation becomes slightly positive for time lags 20 s, indicating that big blobs are present in regions that were sparsely populated before and small blobs tend to accumulate in previously densely populated regions. This is in line with dynamic reorganization and reshaping of chromatin domains on a global scale, as observed in snapshots of the Deep-PALM image series (Fig. 1A).

(A) The spatial auto- and cross-correlation between parameters were computed for different time lags. The graphs depict the correlation over space lag for each parameter pair, and different colors denote the time lag (increasing from blue to red). (B) Illustration of the instantaneous relationship between local chromatin density and dynamics. The blob density is shown in blue; the magnitude of chromatin dynamics is shown by red arrows. The consistent negative correlation between NND and flow magnitude is expressed by increased dynamics in regions of high local blob density. Data represent the average over two cells. The cells behave similarly such that error bars are omitted for the sake of clarity.

The flow magnitude is positively correlated for all time lags, while the correlation displays a slight increase for time lags 20 s [Fig. 5A (vi)], which has also been observed previously (8, 12, 22). The spatial autocorrelation of dynamic and structural properties of chromatin are in stark contrast. While structural parameters are highly correlated at short but not at long time scales, chromatin motion is still correlated at a time scale exceeding 30 s. At very short time scales (<100 ms), stochastic fluctuations determine the local motion of the chromatin fiber, while coherent motion becomes apparent at longer times (22). However, there exists a strong cross-correlation between structural and dynamic parameters: The cross-correlation between the NND and flow magnitude shows notable negative correlation at all time lags [Fig. 5A (iv)], strongly suggesting that sparsely distributed blobs appear less mobile than densely packed ones. The area seems to play a negligible role for short time lags, but there is a modest tendency that regions with large blobs tend to exhibit increased dynamics at later time points [10 s; Fig. 5A (v)], likely due to the strong relationship between area and NND.

In general, parameter pairs involving chromatin dynamics exhibit an extended spatial auto- or cross-correlation (up to ~6 m; the lower row of Fig. 5A) compared to correlation curves including solely structural parameters (up to 3 to 4 m). Furthermore, the cross-correlation of flow magnitude and NND does not considerably change for increasing time lag, suggesting that the coupling between those parameters is characterized by an unexpectedly resilient memory, lasting for at least tens of seconds (23). Concomitantly, the spatial correlation of time-averaged NND maps and maps of the local diffusion constant of chromatin for the entire acquisition time enforces their negative correlation at the time scale of ~1 min (fig. S8). Such resilient memory was also proposed by a computational study that observed that interphase nuclei behave similar to concentrated solutions of unentangled ring polymers (24). Our data support the view that chromatin is mostly unentangled since entanglement would influence the anomalous exponent of genomic loci in regions of varying chromatin density (24). However, our data do not reveal a correlation between the anomalous exponent and the time-averaged chromatin density (fig. S8), in line with our previous results using conventional microscopy (14).

Overall, the spatial cross-correlation between chromatin structure and dynamics indicates that the NND between blobs and their mobility stand in a strong mutual, negative relationship. This relationship, however, concerns chromatin density variations at the nanoscale, but not global spatial density variations such as in euchromatin or heterochromatin (14). These results support a model in which regions with high local chromatin density, i.e., larger blobs are more prevalent and are mobile, while small blobs are sparsely distributed and less mobile (Fig. 5B). Blob density and dynamics in the long-time limit are, to an unexpectedly large extent, influenced by preceding chromatin conformations.

The spatial correlations above were only evaluated pairwise, while the behavior of every blob is likely determined by a multitude of factors in the complex energy landscape of chromatin (19, 22). Here, we aim to take a wider range of available information into account to reveal the principle parameters, driving the observed chromatin structure and dynamics. Using a microscopy-based approach, we have access to a total of six relevant structural, dynamic, and global parameters, which potentially shape the chromatin landscape in space and time (Fig. 6A). In addition to the parameters used above, we included the confinement level as a relative measure, allowing the quantification of transient confinement (see Materials and Methods). We further included the bare signal intensity of super-resolved images and, as the only static parameter, the distance from the periphery since it was shown that dynamic and structural parameters show some dependence on this parameter (Fig. 4). We then used t-distributed stochastic neighbor embedding (t-SNE) (25), a state-of-the-art dimensionality reduction technique, to map the six-dimensional chromatin features (the six input parameters) into two dimensions (Fig. 6A and see note S3). The t-SNE algorithm projects data points such that neighbors in high-dimensional space likely stay neighbors in two-dimensional space (25). Visually apparent grouping of points (Fig. 6B) implies that grouped points exhibit great similarity with respect to all input features, and it is of interest to reveal which subset of the input features can explain the similarity among chromatin blobs best. It is likely that points appear grouped because their value of a certain input feature is considerably higher or lower than the corresponding value of other data points. We hence labeled points in t-SNE maps which are smaller than the first quartile point or larger than the third quartile point. Data points falling in either of the low/high partition of one input feature are colored accordingly for visualization (Fig. 6D; blue/red points, respectively). We then assigned a rank to each of the input features according to their nearest-neighbor fraction (n-n fraction): Since the t-SNE algorithm conserves nearest neighbors, we described the extent of grouping in t-SNE maps by the fraction of nearest neighbors, which fall in either one of the subpopulations of low or high points (illustrated in fig. S9). A high n-n fraction (Fig. 6C) therefore indicates that many points marked as low/high are indeed grouped by t-SNE and are therefore similar. The ranking (from low to high n-n fraction) reflects the potency of a given parameter to induce similar behavior between chromatin blobs with respect to all input features.

(A) The six-dimensional parameter space is input to the t-SNE algorithm and projected to two dimensions. (B) The two-dimensional embedding of an exemplary dataset is shown and colored according to the magnitude of each input feature (blue to red; the parameter average is shown in beige). (C) Points below the first (blue) and above the third (red) quartile points of the corresponding parameter are marked, and the parameters are ranked according to the fraction of nearest neighbors that fall in one of the marked regions. (D) Data points marked below the first or above the third quartile points are labeled according to the feature in which they were marked. Priority is given to the feature with the higher n-n fraction if necessary. (E) t-SNE analysis is carried out for each nucleus over the whole time series, and it is counted how often a parameter ranked first. The results are visualized as a pie chart. The NND predominantly ranks first in about two-thirds of all cases. (F) Marked points in (C) and (D) are mapped back onto the corresponding nuclei, and the CDF over space is shown (means SE). Pie chart and CDF computations are based on 322 super-resolved images from two cells.

The relative frequency at which each parameter ranked first provides an intuitive feeling for the most influential parameters in the dataset (Fig. 6E). The signal intensity plays a negligible role, suggesting that our data are free of potential artifacts related to the bare signal intensity. Furthermore, the blob area and the distance from the periphery likewise do not considerably shape chromatin blobs. In contrast, the NND between blobs was found to be the main factor inducing the observed characteristics in 67% of all-time frames across all nuclei. The flow magnitude and confinement level together rank first in 26% of all cases (11 and 17%, respectively). These numbers suggest that the local chromatin density is a universal key regulator of instantaneous chromatin dynamics. Note that no temporal dependency is included in the t-SNE analysis and, thus, the feature extraction concerns only short-term (360 ms) relationships. The characteristics of roughly one-fourth of all blobs at each time point are mainly determined by similar dynamical features. Mapping chromatin blobs as marked in Fig. 6 (C and D) back to their respective positions inside the nucleus (Fig. 6F) shows that blobs with low/high flow magnitude or confinement level markedly also grouped in physical space, which is highly reminiscent of coherent motion of chromatin (12). In contrast, blobs with extraordinary low or high NND were found interspersed throughout the nucleus, in line with spatial correlation analysis between structural and dynamic features (Fig. 5). Our results point toward a large influence of the local chromatin density on the dynamics of chromatin at the scale of a few hundred nanometers and within a few hundred milliseconds. At longer time and length scales, however, previous results suggest that this relationship is lost (14).

With Deep-PALM, we present temporally resolved super-resolution images of chromatin in living cells. Our technique identified chromatin nanodomains, named blobs, which mostly have an elongated shape, consistent with the curvilinear arrangement of chromatin, as revealed by structured illumination microscopy (8) with typical axes lengths of 45 to 90 nm. A previous study reported ~30-nm-wide clutches of nucleosomes in fixed mammalian cells using STORM nanoscopy (6), while the larger value obtained using Deep-PALM may be attributed to the motion blurring effect in live-cell imaging. However, histone acetylation and methylation marks were shown to form nanodomains of diameter 60 to 140 nm, respectively (9), which includes the computed dimensions for histone H2B using Deep-PALM.

To elucidate the origin of chromatin blobs, we turned to a simulated chromosome model, which displays chromatin blobs similar to our experimental data when seen in a super-resolution reconstruction. The simulations suggest that chromatin blobs consist of continuous genomic regions with an average length of 75 kb, assembling and disassembling dynamically within less than 1 s. Monomers within blobs display a distinct TAD-like association pattern in the long-time limit, suggesting that the identified blobs represent sub-TADs. Transient formation is consistent with recent findings that TADs are not stable structural elements but exhibit extensive heterogeneity and dynamics (3, 5). To experimentally probe the transient assembly of chromatin blobs, it would be interesting to track individual blobs over time. However, this is a nontrivial task. While the size (area/volume) or shape of blobs could be used to establish correspondences between blobs in subsequent frames, the framework needs to be flexible enough to allow for blob deformations since blobs likely arise stochastically and are not rigid bodies. Achieving an even shorter acquisition time per frame in the future could help minimize the influence of blob deformations and make tracking feasible. The second challenge is to distinguish between disassembly and out-of-focus diffusion of a blob. The three-dimensional imaging at sufficient spatial and temporal resolution will be helpful in the future to overcome this hurdle.

Using an optical flow approach to determine the blob dynamics instead, we found that structural and dynamic parameters exhibit extended spatial and temporal (cross-) correlations. Structural parameters such as the local chromatin density (expressed as the NND between blobs) and area lose their correlation after 3 to 4 m and roughly 40 s in the spatial and temporal dimension, respectively. In contrast, chromatin mobility correlations extend over ~6 m and persist during the whole acquisition period (40 s). Extensive spatiotemporal correlation of chromatin dynamics has been presented previously, both experimentally (12) and in simulations (22), but was not linked to the spatiotemporal behavior of the underlying chromatin structure until now. We found that the chromatin dynamics are closely linked to the instantaneous but also to past local structural characterization of chromatin. In other words, the instantaneous local chromatin density influences chromatin dynamics in the future and vice versa. On the basis of these findings, we suggest that chromatin dynamics exhibit an extraordinary long memory. This strong temporal relationship might be established by the fact that stress propagation is affected by the folded chromosome organization (26). Fiber displacements cause structural reconfiguration, ultimately leading to a local amplification of chromatin motion in local high-density environments. This observation is also supported by the fact that increased nucleosome mobility grants chromatin accessibility even within regions of high nucleosome density (27).

Given the persistence at which correlations of chromatin structure and, foremost, dynamics occur in a spatiotemporal manner, we speculate that the interplay of chromatin structure and dynamics could involve a functional relationship (28): Transcriptional activity is closely linked to chromatin accessibility and the epigenomic state (29). Because chromatin structure and dynamics are related, dynamics could also correlate with transcriptional activity (14, 30, 31). However, it is currently unknown whether the structure-dynamics relationship revealed here is strictly mutual or whether it may be causal. Simulations hint that chromatin dynamics follows from structure (22, 23); this question will be exciting to answer experimentally and in the light of active chromatin remodelers to elucidate a potential functional relationship to transcription. Chromatin regions that are switched from inactive to actively transcribing, for instance, undergo a structural reorganization accompanied by epigenetic modifications (32). The mechanisms driving recruitment of enzymes inducing histone modifications such as histone acetyltransferases, deacetylases, or methyltransferases are largely unknown but often involve the association to proteins (33). Their accessibility to the chromatin fiber is inter alia determined by local dynamics (27). Such a structure-dynamics feedback loop would constitute a quick and flexible way to transiently alter gene expression patterns upon reaction to external stimuli or to coregulate distant genes (1). Future work will study how structure-dynamics correlations differ in regions of different transcriptional activity and/or epigenomic states. Furthermore, probing the interactions between key transcriptional machines such as RNA polymerases with the local chromatin structure and recording their (possibly collective) dynamics could shed light into the target search and binding mechanisms of RNA polymerases with respect to the local chromatin structure. Deep-PALM in combination with optical flow paves the way to answer these questions by enabling the analysis of time-resolved super-resolution images of chromatin in living cells.

Human osteosarcoma U2OS expressing H2B-PATagRFP cells were a gift from S. Huet (CNRS, UMR 6290, Institut Gntique et Dveloppement de Rennes, Rennes, France); the histone H2B was cloned, as described previously (34). U2OS cells were cultured in Dulbeccos modified Eagles medium [with glucose (4.5 g/liter)] supplemented with 10% fetal bovine serum (FBS), 2 mM glutamine, penicillin (100 g/ml), and streptomycin (100 U/ml) in 5% CO2 at 37C. Cells were plated 24 hours before imaging on 35-mm petri dishes with a no. 1.5 coverslip-like bottom (ibidi, Biovalley) with a density of 2 105 cells per dish. Just before imaging, the growth medium was replaced by Leibovitzs L-15 medium (Life Technologies) supplemented with 20% FBS, 2 mM glutamine, penicillin (100 g/ml), and streptomycin (100 U/ml).

Imaging of H2B-PAtagRFP in living U2OS cells was carried out on a fully automated Nikon Ti-E/B PALM (Nikon Instruments) microscope. The microscope is equipped with a full incubator enclosure with gas regulation to maintain a temperature of ~37C for normal cell growth during live-cell imaging. Image sequences of 2000 frames were recorded with an exposure time of 30 ms per frame (33.3 frames/s). For Deep-PALM imaging, a relatively low power (~50 W/cm2 at the sample) was applied for H2B-PATagRFP excitation at 561 nm and then combined with the 405 nm (~2 W/cm2 at the sample) to photoactivate the molecules between the states. Note that for Deep-PALM imaging, switched fluorophores are not required to stay as long in the dark state as for conventional PALM imaging. We used oblique illumination microscopy (11) combined with total internal reflection fluorescence (TIRF) mode to illuminate a thin layer of 200 nm (axial resolution) across the nucleus. The reconstruction of super-resolved images improves the axial resolution only marginally (fig. S1, E and F). Laser beam powers were controlled by acoustic optic-modulators (AA Opto-Electronic). Both wavelengths were united into an oil immersion 1.49-NA (numerical aperture) TIRF objective (100; Nikon). An oblique illumination was applied to acquire image series with a high signal-to-noise ratio. The fluorescence emission signal was collected by using the same objective and spectrally filtered by a Quad-Band beam splitter (ZT405/488/561/647rpc-UF2, Chroma Technology) with a Quad-Band emission filter (ZET405/488/561/647m-TRF, Chroma Technology). The signal was recorded on an electron-multiplying charge-coupled device camera (Andor iXon X3 DU-897, Andor Technology) with a pixel size of 108 nm. For axial correction, Perfect Focus System was applied to correct for defocusing. NIS-Elements software was used for acquiring the images.

The same cell line (U2OS expressing H2B-PAtagRFP), as in live-cell imaging, was used for conventional PALM imaging. Before fixation, cells were washed with phosphate-buffered saline (PBS) (three times for 5 min each) and then fixed with 4% paraformaldehyde (Sigma-Aldrich) diluted in PBS for 15 min at room temperature. A movie of 8000 frames was acquired with an exposure time of 30 ms per frame (33.3 frames/s). In comparison to Deep-PALM imaging, a relatively higher excitation laser of 561 nm (~60 W/cm2 at the sample) was applied to photobleach H2B-PATagRFP and then combined with the 405 nm (~2.5 W/cm2 at the sample) for photoactivating the molecules. We used the same oblique illumination microscopy combined with TIRF system, as applied in live-cell imaging.

PALM images from fixed cells were analyzed using ThunderSTORM (35). Super-resolution images were constructed by binning emitter localizations into 13.5 13.5 nm pixels and blurred by a Gaussian to match Deep-PALM images. The image segmentation was carried out as on images from living cells (see below).

The CNN was trained using simulated data following Nehme et al. (15) for three labeling densities (4, 6, and 9 emitters/m2 per frame). Raw imaging data were checked for drift, as previously described (12). The detected drift in raw images is in the range of <10 nm and therefore negligible. The accuracy of the trained net was evaluated by constructing ground truth images from the simulated emitter positions. The structural similarity index is computed to assess the similarity between reconstructed and ground truth images (36)SSIM=x,y(2xx+C1)(2xy+C2)(x2+y2+C1)(x2+y2+C2)(1)where x and y are windows of the predicted and ground truth images, respectively, and denote their local means and SD, respectively, and xy denotes their cross-variance. C1 = (0.01L)2 and C2 = (0.03L)2 are regularization constants, where L is the dynamic range of the input images. The second quantity to assess CNN accuracy is the root mean square error between the ground truth G and reconstructed image RRMSE=1NN(RG)2(2)where N is the number of pixels in the images. After training, sequences of all experimental images were compared to the trained network, and predictions of single Deep-PALM images were summed to obtain a final super-resolved image. An up-sampling factor of 8 was used, resulting in an effective pixel size of 108 nm/8 = 13.5 nm. A blind/referenceless image spatial quality evaluator (37) was used to determine the optimal number of predictions to be summed. For visualization, super-resolved images were convolved with a Gaussian kernel ( = 1 pixel) and represented using a false red, green, and blue colormap. The parameters of the three trained networks are available at https://github.com/romanbarth/DeepPALM-trained-models.

Fourier ring correlation (FRC) is an unbiased method to estimate the spatial resolution in microscopy images. We follow an approach similar to the one described by Nieuwenhuizen et al. (38). For localization-based super-resolution techniques, the set of localizations is divided into two statistically independent subsets, and two images from these subsets are generated. The FRC is computed as the statistical correlation of the Fourier transforms of both subimages over the perimeter of circles of constant frequency in the frequency domain. Deep-PALM, however, does not result in a list of localizations, but in predicted images directly. The set of 12 predictions from Deep-PALM were thus split into two statistically independent subsets, and the method described by Nieuwenhuizen et al. (38) was applied.

The super-resolved images displayed isolated regions of accumulated emitter density. To quantitatively assess the structural information implied by this accumulation of emitters in the focal plane, we developed a segmentation scheme that aims to identify individual blobs (fig. S3). A marker-assisted watershed segmentation was adapted to accurately determine blob boundaries. For this purpose, we use the raw predictions from the deep CNN without convolution (fig. S3A). The foreground in this image is marked by regional maxima and pixels with very high density (i.e., those with I > 0.99 Imax; fig. S3B). Since blobs are characterized by surrounding pixels of considerably less density, the Euclidian distance transform is computed on the binary foreground markers. Background pixels (i.e., those pixels not belonging to any blobs) are expected to lie far away from any blob center, and thus, a good estimate for background markers are those pixels being furthest from any foreground pixel. We hence compute the watershed transform on the distance transform of foreground markers, and the resulting watershed lines depict background pixels (fig. S3C). Equipped with fore- and background markers (fig. S3D), we apply a marker-controlled watershed transform on the gradient of the input image (fig. S3E). The marker-controlled watershed imposes minima on marker pixels, preventing the formation of watershed lines across marker pixels. Therefore, the marker-controlled watershed accurately detects boundaries and blobs that might not have been previously marked as foreground (fig. S3F). Last, spurious blobs whose median- or mean intensity is below 10% of the maximum intensity are discarded, and each blob is assigned a unique label for further correspondence (fig. S3G). The area and centroid position are computed for each identified blob for further analysis. This automated segmentation scheme performs considerably better than other state-of-the-art algorithms for image segmentation because of the reliable identification of fore- and background markers accompanied by the watershed transform (note S1).

Centroid position, area, and eccentricity were computed. The eccentricity is computed by describing the blobs as an ellipseE=1a2/b2(3)where a and b are the short and long axes of the ellipse, respectively.

We chose to use a computational chromatin model, recently introduced by Qi and Zhang (19), to elucidate the origin of experimentally determined chromatin blobs. Each bead of the model covers a sequence length of 5 kb and is assigned 1 of 15 chromatin states to distinguish promoters, enhancers, quiescent chromatin, etc. Starting from the simulated polymer configurations, we consider monomers within a 200-nm-thick slab through the center of the simulated chromosome. To generate super-resolved images as those from Deep-PALM analysis, fluorescence intensity is ascribed to each monomer. Monomer positions are subsequently discretized on a grid with 13.5-nm spacing and convolved with a narrow point-spread function, which results in images closely resembling experimental Deep-PALM images of chromatin. Chromatin blobs were then be identified and characterized as on experimental data (Fig. 2, A and B). Mapping back the association of each bead to a blob (if any) allows us to analyze principles of blob formation and maintenance using the distance and the association strength between each pair of monomers, averaged over all 20,000 simulated polymer configurations.

The radial distribution function g(r) (also pair correlation function) is calculated (in two dimensions) by counting the number of blobs in an annulus of radius r and thickness dr. The result is normalized by the bulk density = n/A, with the total number of blobs n and, A, the area of the nucleus, and the area of the annulus, 2r drdn(r)=g(r)2rdr(4)

Super-resolved images of chromatin showed spatially distributed blobs of varying size, but the resolved structure is too dense for state-of-the-art single-particle tracking methods to track. Furthermore are highly dynamic structures, assembling and dissembling within one to two super-resolved frames (Fig. 3D), which makes a single-particle tracking approach unsuitable. Instead, we used a method for dynamics reconstruction of bulk macromolecules with dense labeling, optical flow. Optical flow builds on the computation of flow fields between two successive frames of an image series. The integration of these flow fields from super-resolution images results in trajectories displaying the local motion of bulk chromatin with temporal and high spatial resolution. Further, the trajectories are classified into various diffusion models, and parameters describing the underlying motion are computed (14). Here, we use the effective diffusion coefficient D (in units of m2/s), which reflects the magnitude of displacements between successive frames (the velocity of particles or monomers in the continuous limit) and the anomalous exponent (14). The anomalous exponent reflects whether the diffusion is free ( = 1, e.g., for noninteracting particles in solution), directed ( > 1, e.g., as the result from active processes), or hindered ( < 1, e.g., because of obstacles or an effective back-driving force). Furthermore, we compute the length of constraint Lc, which is defined as the SD of the trajectory positions with respect to its time-averaged position. Denoting R(t; R0), the trajectory at time t originating from R0, the expression reads Lc(R0) = var(R(t; R0))1/2, where var denotes the variance. The length of constraint is a measure of the length scale explored of the monomer during the observation period. A complementary measure is the confinement level (39), which computes the inverse of the variance of displacements within a sliding window of length : C / var(R(t; R0)), where the sliding window length is set to four frames (1.44 s). Larger values of C denote a more confined state than small ones.

The NND and the area, as well as the flow magnitude, were calculated and assigned to the blobs centroid position. To calculate the spatial correlation between parameters, the parameters were interpolated from the scattered centroid positions onto a regular grid spanning the entire nucleus. Because not every pixel in the original super-resolved images is assigned a parameter value, we chose an effective grid spacing of five pixels (67.5 nm) for the interpolated parameter maps. After interpolation, the spatial correlation was computed between parameter pairs: Let r = (x, y)T denote a position on a regular two-dimensional grid and f(r, t) and g(r, t) two scalar fields with mean zero and variance one, at time t on that grid. The time series of parameter fields consist of N time points. The spatial cross-correlation between the fields f and g, which lie a lag time apart, is then calculated asC(,)=1Ntx,yf(r,t)g(r+,t+)x,yf(r,t)g(r,t+)(5)where the space lag is a two-dimensional vector = (x, y)T. The sums in the numerator and denominator are taken over the spatial dimensions; the first sum is taken over time. The average is thus taken over all time points that are compliant with time lag . Subsequently, the radial average in space is taken over the correlation, thus effectively calculating the spatial correlation C(, ) over the space lag =x2+y2. If f = g, then the spatial autocorrelation is computed.

We denote as global parameters those that reflect the structural and dynamic behavior of chromatin spatially resolved in a time-averaged manner. Examples involve the diffusion constant, the anomalous exponent, the length of constraint, but also time-averaged NND maps, etc. (fig. S8). Those parameters are useful to determine time-universal characteristics. The spatial correlation between those parameters is equivalent to the expression given for temporally varying parameters when the temporal dimension is omitted, effectively resulting in a correlation curve C().

The distance from the periphery, intensity, their NND, area, flow magnitude, and confinement level of each identified blob form the six-dimensionalinput feature space for t-SNE analysis. The parameters for each blob (n = 3,260,232; divided into subsets of approximately 10,000) were z-transformed before the t-SNE analysis. The t-SNE analysis was performed using MATLAB and the Statistics and Machine Learning Toolbox (Release 2017b; The MathWorks Inc., Natick, MA, USA) with the Barnes-Hut approximation. The algorithm was tested using different distance metrics and perplexity values and showed robust results within the examined ranges (note S3 and fig. S10).

Acknowledgments: We acknowledge support from the Ple Scientifique de Modlisation Numrique, ENS de Lyon for providing computational resources. We thank B. Zhang (Massachusetts Institute of Technology) for providing data of simulated chromosomes and S. Kocanova (LBME, CBI-CNRS; University of Toulouse) for providing PALM videos for fixed cells. We thank H. Babcock (Harvard University), A. Seeber (Harvard University), and M. Tamm (Moscow State University) for valuable feedback on the manuscript. Funding: This publication is based upon work from COST Action CA18127, supported by COST (European Cooperation in Science and Technology). This work is supported by Agence Nationale de la Recherche (ANR) ANDY and Sinfonie grants. Author contributions: H.A.S. designed and supervised the project. R.B. designed the data analysis and wrote the code. H.A.S. carried out experimental work. R.B. carried out the data analysis. H.A.S. and R.B. interpreted results. H.A.S., R.B., and K.B. wrote the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors.

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Coupling chromatin structure and dynamics by live super-resolution imaging - Science Advances

Why the buzz around DeepMind is dissipating as it transitions from games to science – CNBC

Google Deepmind head Demis Hassabis speaks during a press conference ahead of the Google DeepMind Challenge Match in Seoul on March 8, 2016.

Jung Yeon-Je | AFP |Getty Images | Getty Images

In 2016, DeepMind, an Alphabet-owned AI unit headquartered in London, was riding a wave of publicity thanks to AlphaGo, its computer program that took on the best player in the world at the ancient Asian board game Go and won.

Photos of DeepMind's leader, Demis Hassabis, were splashed across the front pages of newspapers and websites, and Netflix even went on to make a documentary about the five-game Go match between AlphaGo and world champion Lee SeDol. Fast-forward four years, and things have gone surprisingly quiet about DeepMind.

"DeepMind has done some of the most exciting things in AI in recent years. It would be virtually impossible for any company to sustain that level of excitement indefinitely," said William Tunstall-Pedoe, a British entrepreneur who sold his AI start-up Evi to Amazon for a reported $26 million. "I expect them to do further very exciting things."

AI pioneer Stuart Russell, a professor at the University of California, Berkeley, agreed it was inevitable that excitement around DeepMind would tail off after AlphaGo.

"Go was a recognized milestone in AI, something that some commentators said would take another 100 years," he said. "In Asia in particular, top-level Go is considered the pinnacle of human intellectual powers. It's hard to see what else DeepMind could do in the near term to match that."

DeepMind's army of 1,000 plus people, which includes hundreds of highly-paid PhD graduates, continues to pump out academic paper after academic paper, but only a smattering of the work gets picked up by the mainstream media. The research lab has churned out over 1,000 papers and 13 of them have been published by Nature or Science, which are widely seen as the world's most prestigious academic journals. Nick Bostrom, the author of Superintelligence and the director of the University of Oxford's Future of Humanity Institute described DeepMind's team as world-class, large, and diverse.

"Their protein folding work was super impressive," said Neil Lawrence, a professor of machine learning at the University of Cambridge, whose role is funded by DeepMind. He's referring to a competition-winning DeepMind algorithm that can predict the structure of a protein based on its genetic makeup. Understanding the structure of proteins is important as it could make it easier to understand diseases and create new drugs in the future.

The World's top human Go player, 19-year-old Ke Jie (L) competes against AI program AlphaGo, which was developed by DeepMind, the artificial intelligence arm of Google's parent Alphabet. Machine won the three-game match against man in 2017. The AI didn't lose a single game.

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DeepMind is keen to move away from developing relatively "narrow" so-called "AI agents," that can do one thing well, such as master a game. Instead, the company is trying to develop more general AI systems that can do multiple things well, and have real world impact.

It's particularly keen to use its AI to leverage breakthroughs in other areas of science including healthcare, physics and climate change.

But the company's scientific work seems to be of less interest to the media.In 2016, DeepMind was mentioned in 1,842 articles, according to media tracker LexisNexis. By 2019, that number had fallen to 1,363.

One ex-DeepMinder said the buzz around the company is now more in line with what it should be. "The whole AlphaGo period was nuts," they said. "I think they've probably got another few milestones ahead, but progress should be more low key. It's a marathon not a sprint, so to speak."

DeepMind denied that excitement surrounding the company has tailed off since AlphaGo, pointing to the fact that it has had more papers in Nature and Science in recent years.

"We have created a unique environment where ambitious AI research can flourish. Our unusually interdisciplinary approach has been core to our progress, with 13 major papers in Nature and Science including 3 so far this year," a DeepMind spokesperson said. "Our scientists and engineers have built agents that can learn to cooperate, devise new strategies to play world-class chess and Go, diagnose eye disease, generate realistic speech now used in Google products around the world, and much more."

"More recently, we've been excited to see early signs of how we could use our progress in fundamental AI research to understand the world around us in a much deeper way. Our protein folding work is our first significant milestone applying artificial intelligence to a core question in science, and this is just the start of the exciting advances we hope to see more of over the next decade, creating systems that could provide extraordinary benefits to society."

The company, which competes with Facebook AI Research and OpenAI, did a good job of building up hype around what it was doing in the early days.

Hassabis and Mustafa Suleyman, the intellectual co-founders who have been friends since school, gave inspiring speeches where they would explain how they were on a mission to "solve intelligence" and use that to solve everything else.

There was also plenty of talk of developing "artificial general intelligence" or AGI, which has been referred to as the holy grail in AI and is widely viewed as the point when machine intelligence passes human intelligence.

But the speeches have become less frequent (partly because Suleyman left Deepmind and works for Google now), and AGI doesn't get mentioned anywhere near as much as it used to.

Larry Page, left, and Sergey Brin, co-founders of Google Inc.

JB Reed | Bloomberg | Getty Images

Google co-founders Larry Page and Sergey Brin were huge proponents of DeepMind and its lofty ambitions, but they left the company last year and its less obvious how Google CEO Sundar Pichai feels about DeepMind and AGI.

It's also unclear how much free reign Pichai will give the company, which cost Alphabet $571 million in 2018. Just one year earlier, the company had losses of $368 million.

"As far as I know, DeepMind is still working on the AGI problem and believes it is making progress," Russell said. "I suspect the parent company (Google/Alphabet) got tired of the media turning every story about Google and AI into the Terminator scenario, complete with scary pictures."

One academic who is particularly skeptical about DeepMind's achievements is AI entrepreneur Gary Marcus, who sold a machine-learning start-up to Uber in 2016 for an undisclosed sum.

"I think they realize the gulf between what they're doing and what they aspire to do," he said. "In their early years they thought that the techniques they were using would carry us all the way to AGI. And some of us saw immediately that that wasn't going to work. It took them longer to realize but I think they've realized it now."

Marcus said he's heard that DeepMind employees refer to him as the "anti-Christ" because he has questioned how far the "deep learning" AI technique that DeepMind has focused on can go.

"There are major figures now that recognize that the current techniques are not enough," he said. "It's very different from two years ago. It's a radical shift."

He added that while DeepMind's work on games and biology had been impressive, it's had relatively little impact.

"They haven't used their stuff much in the real world," he said. "The work that they're doing requires an enormous amount of data and an enormous amount of compute, and a very stable world. The techniques that they're using are very, very data greedy and real-world problems often don't supply that level of data."

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Why the buzz around DeepMind is dissipating as it transitions from games to science - CNBC