Category Archives: Genome

The Global Whole Genome Synthesis Market To Witness A Noteworthy Progression Over 2020-2026 byZion Market Research(ZMR) is a thorough assessment of the global Whole Genome Synthesis Market entailing the numerous factors applicable to market dynamics and growth. The report, by covering all the vital data and facts about the globalWhole Genome Synthesis Marketfor the forecast period of 2020 to 2026, will serve as a valued document for clients seeking guidance in decision-making intending to enter the market or strengthen their current market position. The report will put forth widespread data separated into different units that can further make the market understanding simpler.

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Key Aspects and Trends in the Market: The study presents the market overview entailing definition, synopsis, classifications, and applications. It further includes the in-depth evaluation of numerous factors that can possibly drive or obstruct the growth of the global Whole Genome Synthesis Market. Additionally, it entails the opportunities and risks for the global market during the projected timeline. The report also comprises the latest innovation, technological advancements, and key events in the market on a regional and global level together with the likely trends influencing the expansion of global Whole Genome Synthesis Market.

Segmentation and Regional Analysis:The study segregates the global Whole Genome Synthesis Market based on diverse factors such as Product, Applications, End-Users, and Major Regions. It also categorizes the market on the basis of key regions such as [North America, Europe, Asia Pacific, the Middle East & Africa, and Latin America]. The segmentation and regional analysis include all the vital factors contributing to the growth of each segment and region in terms of revenues, market share, value, and so on.

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Global Whole Genome Synthesis Market To Witness A Noteworthy Progression Over 2020-2026 - The Courier

Horizon Europe is to allocate 5 million for projects aimed at understanding the benefits and risks of genome editing technologies in agriculture over the next two years, according to a leaked draft work programme.

Science|Business is publishing here all the draft Horizon Europe work programmes available to us. You can read them here. Or, if you have additional ones, you can send them to [emailprotected] (anonymously, if you wish.)

The move is in support of the Farm to Fork plan to reduce the use of fertilisers by 30 per cent and turn 25 per cent of agricultural land over to organic farming. To reach these objectives, the Commission says the EU needs to enable major advances in the life sciences and biotechnology, in new genomic techniques, such as gene/genome editing.

Plans for the 5 million call come after EU agriculture ministers called on the Commission last October to enable the use of new innovative ingredients and techniques to boost sustainable food production, once they are shown to be safe for humans, animals and the environment. The headline figure for the call is only indicative, and the Commission could fund proposals that go beyond this figure.

Also last October, French scientist Emmanuelle Charpentier, director at the Max Planck Institute for Infection Biology in Berlin, and her collaboration partner Jennifer Doudna, were awarded the Nobel prize in chemistry for the development of a method for genome editing.

But as things stand, precision breeding of plants with gene editing technologies cannot be used in the EU, following a 2018 ruling by the European Court of Justice (ECJ), which founds genome editing is subject to the 2001 EU directive banning genetically modified organisms (GMOs).

In an early post-Brexit move, the UK last month launched an industry consultation on gene editing, as it seeks to move away from EU regulations on genetically modified organisms (GMOs). Depending on the outcome, there will be a second consultation on changing the definition of a GMO. The UK government view is that organisms produced by gene editing or by other genetic technologies, should not be regulated as GMOs if they could have been produced by traditional breeding methods.

The proposed 5 million for genome editing research is a small part of a total of 1.83 billion that is to be spent in 2021 and 2022 on Horizon Europes sixth cluster on food, bioeconomy natural resources, agriculture and environment, the draft work programme from December 2020 has revealed.

The European Commission is expected to publish the official work programmes with final funding figures and deadlines for application by the end of April. However, many research stakeholders have had access to draft versions of the documents posted online. Science|Business has published a trove of such documents, which offer researchers a detailed insight of how the 95.5 billion funding programme will be organised.

Call to lift gene editing ban

Research stakeholders have been calling on the EU to lift restrictions on genetically modified crops, to allow the use of genome editing, which need not involve the introduction of foreign genes. In 2020, in a report by the European Federation of Academies of Sciences and Humanities, researchers in 120 institutions across Europe appealed to the Commission to help reverse the ECJ ruling.

According to the report, the policy change would help Europe develop more productive, climate-friendly, and resilient crops, and bring the EU up to date with recent scientific developments. These new technologies may contribute to a reduction of the environmental footprint of agriculture, the researchers said.

While agriculture ministers expect the Commission to complete a study of the status of novel genomic techniques under EU legislation by April, the Horizon call is still asking researchers to align their proposals with existing EU laws, including the infamous ECJ ruling of 2018.

Proposals are expected to advance new genomic techniques in bio-based innovation and to assess potential critical impacts and bottlenecks with respect to the EU and international governance frameworks.

Other priorities

According to the draft work programme, the Commission is planning to allocate 404 million over the next two years for research projects supporting its Farm to Fork strategy.

The Commission is also looking for proposals to explore the evolution and spread of microbiomes in the wild and their relationship with biodiversity loss and the growing risk of epidemics.

A 15 million call will be reserved for projects developing innovative digital tools tailored to the needs of small- and medium-sized farms. The Commission wants farmers to increase their uptake of digital technologies and prevent an increased digital divide between small and large farms.

The Commission is also planning to allocate 230 million over the next two years on projects addressing the EUs push for a circular economy, by significantly reducing waste and promoting continuous recycling of natural resources.

The projects are expected to improve material selection and product design, but also to promote new value chains and business models focused on the upgrade, refurbishment and remanufacturing, of products to reduce waste.

Some calls will be dedicated to projects that seek to make EUs industry more sustainable and reduce its dependence on resources, by lowering the use of primary non-renewable raw materials.

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Horizon Europe to fund research on genome editing in agriculture - Science Business

Abstract

An R loop is a unique triple-stranded structure that participates in multiple key biological processes and is relevant to human diseases. Accurate and comprehensive R loop profiling is a prerequisite for R loops studies. However, current R loop mapping methods generate large discrepancies, therefore an independent method is in urgent need. Here, we establish an independent R loop CUT&Tag (Tn5-based cleavage under targets and tagmentation) method by combining CUT&Tag and GST-His6-2HBD (glutathione S-transferasehexahistidine2 hybrid-binding domain), an artificial DNA-RNA hybrid sensor that specifically recognizes the DNA-RNA hybrids. We demonstrate that the R loop CUT&Tag is sensitive, reproducible, and convenient for native R loop mapping with high resolution, and find that the capture strategies, instead of the specificity of sensors, largely contribute to the disparities among different methods. Together, we provide an independent strategy for genomic profiling of native R loops and help resolve discrepancies among multiple R loop mapping methods.

An R loop is a special triple-stranded nucleic acid structure formed when nascent RNA invades double-stranded DNA (dsDNA) during transcription, resulting in a DNA-RNA hybrid and a displaced single-stranded DNA (ssDNA). R loops are widely present from bacteria to mammals (1). Although R loops have been considered as mere by-products of transcription, growing evidence shows that they participate in various key biological processes, such as genome stability maintenance (1), transcriptional regulation (2, 3), DNA damage repair (4, 5), and regulation of chromatin landscape (6, 7). Recently, dysfunction in regulation of R loops has been shown to associate with multiple human diseases, including cancers, neurological diseases, and immune disorders (8, 9).

Studying the functions and regulation of R loops in physiological and pathological processes relies on accurate and comprehensive profiling of R loops in the human genome (10). During the past decade, several genome-wide R loop mapping methods were developed, using either the S9.6 monoclonal antibody (mAb) or catalytically inactive ribonuclease (RNase) H1 for specific DNA-RNA hybrid (a defining feature of R loops) binding and capturing (11). Currently, the predominant strategy for genome-wide profiling of R loops is the DNA-RNA immunoprecipitation sequencing (DRIP-seq), which captures DNA-containing R loop fragments using the S9.6 antibody before sequencing (1217). DRIPc-seq is derived from DRIP-seq and specifically sequences the RNA component of the hybrid (18). However, the specificity of the S9.6 antibody has been questioned recently for accurate quantification and mapping of genuine R loops (11, 19, 20). Moreover, the digestion efficiency and bias in chromatin fragmentation by restriction enzymes could potentially compromise R loop mapping resolution in DRIP-related approaches (21).

R loop chromatin immunoprecipitation (R-ChIP) takes advantage of the natural affinity of RNase H1 to DNA-RNA hybrids (22). An exogenous catalytically inactive RNase H1 was expressed intracellularly to bind DNA-RNA hybrids without resolving them. The DNA (23, 24) or the RNA component (RR-ChIP) (25) of the hybrids was sequenced by ChIP assays with V5-tagged RNase H1. R-ChIP/RR-ChIP provides a new perspective of R loop mapping. However, intracellular expression of exogenous catalytically inactive RNase H1 is time consuming, and this mutant RNase H1 could compete with endogenous enzymes for DNA-RNA hybrid binding, which may affect the native R loop status. Alternatively, catalytically inactive RNase H1 was combined with affinity pulldown assays [DNA-RNA in vitro enrichment coupled to sequencing (DRIVE-seq)] or cleavage under targets and release using nuclease (MapR) to map the R loops (12, 26). Nevertheless, DRIVE-seq is less sensitive than DRIP-seq, and its application is limited (8, 12).

R loops participate in multiple biological processes, especially transcription elongation. R loops have been shown to impair transcription elongation by functioning as roadblocks, and R loops are also correlated with transcription pausing near gene promoters (24, 27, 28). Since dysfunction of R loops and transcription elongation control are implicated in human diseases (29), precise and comprehensive mapping of R loops is crucial for studying the functions and mechanisms of R loops in these diseases. Different R loop mapping methodologies have been shown to generate large discrepancies in R loop profiling, especially the genomic distribution of R loops (8, 9, 11, 23). For example, R-ChIP and MapR (26) both use catalytically inactive RNase H1 and show that R loops are condensed at promoters and almost absent at the 3 end of genes, whereas DRIP-seq and its derivatives show appreciable signals starting at 2-kb downstream of gene promoters, and much higher signals in the gene body and the 3 end of genes. The disparities may be caused by the different specificities of RNase H1 and S9.6 to R loops or the different R loop capture and sequencing strategies, such as R loop capture in situ or ex vivo. Moreover, the recombinant catalytically inactive full-length RNase H1 is not very efficient in affinity pulldown (8, 12), which is the principle of DRIP-related R loop mapping methods. Therefore, R loop mapping methodology independent of S9.6 or catalytically inactive RNase H1 is urgently needed to clarify the controversies.

The N-terminal hybrid-binding domain (HBD) of RNase H1 is a short-protein domain, composed of a three-stranded antiparallel sheet and two short helices. HBD mediates the specific recognition of DNA-RNA hybrids in a nonsequence-specific manner (22), which highlights the domain itself as a potential sensor for R loop mapping. Recently, Tn5 transposase was reported to randomly bind DNA-RNA hybrids and transpose adapters onto both strands of the DNA-RNA hybrids (30, 31). Besides, the transposed products could have the strand displaced and be directly used for sequencing library preparation, which save many time-consuming steps (30, 31). Using the Tn5 system for DNA and DNA-RNA hybrids tagmentation would potentially avoid fragmentation bias caused by restriction enzyme digestion.

To test the possibility of using the HBD and Tn5 transposase for R loop profiling, we constructed two glutathione S-transferase (GST)tagged and hexahistidine (His6)tagged artificial sensor proteins (GST-His6-HBD and GST-His6-2HBD) with tandem repeats of HBD and established an independent system for native R loop mapping. First, we found that the HBD-containing sensor proteins exhibit high specificity to DNA-RNA hybrids compared to other nucleic acid structures in vitro and GST-His6-2HBD protein behaves similarly to the S9.6 antibody in DRIPc-seq. Furthermore, we combined the R loop sensor protein GST-His6-2HBD or S9.6 with the recently developed Tn5-based cleavage under targets and tagmentation (CUT&Tag) technology (32, 33) and established a native R loop mapping method called R loop CUT&Tag. Compared to conventional R loop mapping methods, R loop CUT&Tag provides superior and highly specific R loop signals at the promoters and is able to detect transient R loops at the gene body and enhancer regions. Together, our study clarifies the controversies among different R loop mapping methods, by providing an independent methodology that accurately and comprehensively profiles the native R loops across the genome.

In an attempt to overcome the limits of the S9.6 antibody and catalytically inactive full-length RNase H1, we took advantage of the DNA-RNA hybrid binding proprieties of the N-terminal HBD domain of RNase H1 (22), which contains a three-stranded antiparallel sheet and two short helices (Fig. 1A). For specific DNA-RNA hybrid recognition in vitro, we designed two GST- and His6-tagged sensor proteins (GST-His6-HBD and GST-His6-2HBD) with tandem repeats of HBD separated by a flexible 5Glycine linker (Fig. 1A). These proteins were expressed in T7 Express lysY/Iq bacteria cells and were affinity-purified by Ninitrilotriacetic acid (NTA) agarose beads (Fig. 1B and fig. S1A). To measure the interaction of recombinant sensor proteins with different forms of nucleic acid structures, including ssDNA, dsDNA, single-stranded RNA (ssRNA), double-stranded RNA (dsRNA), and DNA-RNA hybrid, we tested the affinity of recombinant GST-His6-HBD (fig. S1, B to F) and GST-His6-2HBD (Fig. 1, C to G) by electrophoretic mobility shift assay (EMSA) analysis using the fluorescently labeled 25-mer probes and the purified sensor proteins.

(A) Schematic depiction of the domain structure of RNase H1 protein. The HBD domain of RNase H1 is responsible for the specific recognition of the DNA-RNA hybrids (22). GST-His6-HBD and GST-His6-2HBD expression constructs are shown below. (B) Analysis of the purified GST-His6-HBD and GST-His6-2HBD proteins by SDSpolyacrylamide gel electrophoresis (PAGE) and Coomassie blue staining. (C to G) EMSAs showing GST-His6-2HBD prefers the DNA-RNA hybrid (C), compared to ssDNA (D), dsDNA (E), ssRNA (F), and dsRNA (G). Fluorescent probes (30 nM) were incubated with increasing concentrations of GST-His6-2HBD (2HBD) as the indicator for binding. The complexes were resolved with a 6% native polyacrylamide gel and were imaged with a Typhoon FLA-9500. GST-His6-2HBD: DNA-RNA hybrid complexes are indicated by a bracket. (H) Biolayer interferometry assay of DNA-RNA hybrid and GST-His6-2HBD. Biotinylated DNA-RNA hybrid was immobilized on streptavidin biosensors and incubated with a range of GST-His6-2HBD (from 6.25 to 200 nM) to measure the response in an Octet Red96 instrument. (I) EMSAs analysis of GST-His6-2HBD with probes of different GC contents.

EMSA analysis showed that both GST-His6-HBD (fig. S1, B to F) and GST-His6-2HBD (Fig. 1, C to G) had the highest affinity for DNA-RNA hybrid, yet little affinity for ssDNA, dsDNA, ssRNA, and dsRNA. dsRNA showed weak interaction with GST-His6-2HBD at high concentrations (Fig. 1G). The results demonstrate that the recombinant GST-His6-HBD and GST-His6-2HBD proteins preferentially interact with the DNA-RNA hybrid. Quantitative analysis of recombinant proteins with biotin-labeled DNA-RNA hybrid probes by biolayer interferometry revealed a 10.4 nM dissociation constant Kd for the complexes of GST-His6-2HBD:DNA-RNA hybrid (Fig. 1H), and 16.5 nM for GST-His6-HBD:DNA-RNA hybrid complexes (fig. S1G), respectively. Furthermore, we checked the sequence specificity of GST-His6-2HBD with different probes of various guanine-cytosine (GC) contents and found that GST-His6-2HBD did not have obvious GC preference in DNA-RNA hybrids (Fig. 1I). Further quantification of three biological replicates of EMSA experiments using free probes showed that 0% GC substrate had a slightly weaker binding with 1600 nM GST-His6-2HBD than other probes (fig. S1H). We also confirmed that GST-His6-2HBD could bind the three-stranded R loop structure (fig. S1I). Testing GST-His6-2HBD with probes of different lengths in EMSA (fig. S1, J to L) showed that the 25-mer probe had a slightly stronger interaction with GST-His6-2HBD than the 12-mer probe, while the 48- and 25-mer probe had similar binding to GST-His6-2HBD. Together, these data suggest that the recombinant HBD sensor proteins can be potentially used in R loop profiling as specific DNA-RNA hybrid recognition modules. We chose the GST-His6-2HBD for the rest of the studies, because of its slightly higher affinity with the DNA-RNA hybrid.

As GST-His6-2HBD specifically bound DNA-RNA hybrid and R loop in vitro, we then investigated whether it could replace the S9.6 antibody in the DRIPc-seq assay, in which DNA-RNA hybrids are immunoprecipitated with the antiDNA-RNA hybrid S9.6 antibody and the associated RNA molecules are sequenced in a stranded manner to map R loops (18). We established a similar DRIPc-seq method with GST-His6-2HBD using the same restriction enzymes, R loop enrichment strategy, and RNA-based library preparation as the S9.6-based DRIPc-seq (Fig. 2A) (18). First, we optimized the restriction digestion, amount of GST-His6-2HBD for DNA-RNA hybrid immunoprecipitation, and library preparation for GST-His6-2HBDbased DRIPc-seq (fig. S2, A to C). After optimization, we successfully profiled the genome-wide R loop signals with GST-His6-2HBD as shown in Fig. 2 (B and C) and fig. S2D. Since R loop formation requires transcription elongation of RNA polymerase II (Pol II), we also performed precision nuclear run-on sequencing (PRO-seq) and transient transcriptome sequencing (TT-seq) for genome-wide mapping of the elongating Pol II in human embryonic kidney (HEK) 293T cells (Fig. 2, B and C, and fig. S2D). University of California, Santa Cruz (UCSC) genome browser tracks of our 2HBD-DRIPc-seq data revealed similar profiles as the published S9.6-based DRIPc-seq (GSE102474) (34) at individual genomic loci (Fig. 2, B and C, and fig. S2D).

(A) Schematic presentation of DRIPc-seq with GST-His6-2HBD protein. (B and C) UCSC genome browser tracks of 2HBD-DRIPc-seq, DRIPc-seq (GSE102474) (34), PRO-seq, and TT-seq reads density at the NCK2, UXS1 (B), MRPS9, and POU3F3 (C) loci. Read density was normalized by reads per million (r.p.m.). (D) Heatmap and metagene plots of 2HBD-DRIPc-seq, the published DRIPc-seq, PRO-seq, and TT-seq signals in the plus and minus strands. (E) Scatter plot of the 2HBD-DRIPc-seq counts and S9.6DRIPc-seq counts with all of the protein-coding genes. The Pearson correlation coefficient is shown. (F) The genome-wide Pearson correlation heatmap of 2HBD-DRIPc-seq, S9.6-DRIPc-seq, and TT-seq showing densities within all protein-coding genes.

To compare 2HBD-DRIPc-seq and S9.6-DRIPc-seq in the genome-wide scale, we plotted the heatmap and metagene plots at all of the protein-coding genes on both minus and plus strands. The results revealed that 2HBD-DRIPc-seq had well-correlated profiles as S9.6-DRIPc-seq, and the R loop signals mostly localize at the downstream of promoter, gene body, and transcription end site (TES) (Fig. 2D). Quantitative analysis of 2HBD-DRIPc-seq and S9.6-DRIPc-seq also revealed a high Pearson correlation coefficient of 0.799 (Fig. 2E). Besides, the DRIPc-seq signals generated from both GST-His6-2HBD and S9.6 were positively correlated with elongating Pol II as represented by TT-seq and PRO-seq densities (Fig. 2F and fig. S2, E and F). Since R loops are cotranscriptionally generated, we isolated and sequenced the RNA tethered with chromatin to identify the nascent RNA transcripts (fig. S3A). Genome browser track examples showed that the chromatin-associated RNA sequencing (RNA-seq) profile was similar to GST-His6-2HBD and S9.6-based DRIPc-seq (fig. S3B), and genome-wide analysis of these signals indicated that they were well correlated (fig. S3, C and D). Together, these results demonstrate that the GST-His6-2HBD sensor protein could be used in affinity pulldown assays and exhibits similar profiles as the S9.6 antibody in the DRIPc-seq assays.

The DRIP-based R loop mapping techniques use a combination of restriction enzymes to digest genomic DNA before immunoprecipitation and subsequent next-generation sequencing. The restriction enzymes were reported to be biased and could not fragment the genome uniformly, leading to decreased resolution of DRIP-based R loop profiling (21). To avoid potential interference of genomic DNA fragmentation and check the possibility of using GST-His6-2HBD for native R loop profiling, we tested the GST-His6-2HBD sensor protein and the S9.6 antibody with CUT&Tag (32, 33) for in situ and fragmentation-free R loop mapping. First, we constructed and purified a protein Atethering Tn5 transposase (pA-Tn5) with Ni-NTA affinity purification and performed the pA-Tn5 transposome assembly with adapters (see Materials and Methods). Next, we designed the CUT&Tag workflow for native R loop mapping (Fig. 3A), which used Bst 2.0 WarmStart DNA polymerase for strand displacement followed by library amplification (Fig. 3B). In the CUT&Tag analysis, we designed three different approaches to mapping the native R loops: The first approach used the GST-His6-2HBD and an anti-GST antibody for GST-tagged protein recognition; the second approach also relied on GST-His6-2HBD but required the binding of an anti-HisTag antibody; and the last approach used the S9.6 antibody for R loop detection. The Tn5-based chromatin profiling methods raised concerns of potential tagmentation of accessible DNA, which may generate assay for transposase-accessible chromatin sequencing such as signals (35). We used RNase A to evaluate this potential artifact, since the DNA-RNA hybrids are resistant to RNase A digestion at high salt concentrations (>300 mM) and become highly sensitive to RNase A with decreasing salt concentrations. We evaluated all these three approaches with digesting the RNA and DNA-RNA hybrids using RNase A (Fig. 3C).

(A) Overview of the R loop CUT&Tag workflow. Cells were immobilized on concanavalin A (ConA)coated magnetic beads, followed by cell permeabilization. GST-His6-2HBD or S9.6 is used to recognize the R loops in the presence or absence of RNase A. Anti-GST, anti-HisTag, or secondary antibodies were applied to enhance the tethering of pA-Tn5 transposome at the GST-His6-2HBD or S9.6-bound sites. After extensive wash, the pA-Tn5 transposome is activated to integrate the adapters on the chromatin. (B) CUT&Tag library preparation with Bst 2.0 WarmStart and Q5 polymerase. Strand displacement was performed with Bst 2.0, followed by library amplification with Q5 DNA polymerase. (C) Three different approaches for R loop CUT&Tag analysis. (D) LabChip analysis of R loop CUT&Tag library demonstrating the library size ranges from 220 to 700 bp with an average size of 405 bp. UM, upper marker; LM, lower marker. (E) Alignment rates of R loop CUT&Tag reads to the human hg38 and E. coli spiked-in genomes. RNase A treatment markedly decreases the alignment rates of CUT&Tag reads to the human genome, suggesting the specificity of GST-His6-2HBD and S9.6 on R loop recognition. (F) UCSC genome browser tracks of CUT&Tag signals at the NPM1 and YY1AP1 loci. The tracks were normalized by reads per million, and the RNase Atreated groups were further normalized with the E. coli spike-in control.

First, we optimized the Tn5-mediated tagmentation in S9.6-based CUT&Tag with different additives such as 0.85 mM adenosine 5-triphosphate (ATP), 10% N,N-dimethylformamide (DMF), and 9% polyethylene glycol, molecular weight 8000, as suggested by a recent Tn5-based DNA-RNA hybrid tagmentation study (30). Supplementing 10% DMF markedly enhanced the tagmentation, while the addition of 0.85 mM ATP further improved the effect (fig. S3E). With the improved tagmentation, we generated CUT&Tag sequencing libraries ranging from 220 to 700 base pairs (bp), with the average size of around 405 bp (Fig. 3D). Because of the concomitant Escherichia coli genomic DNA during pA-Tn5 transposase protein production, we used the DNA derived from the E. coli genome for spike-in normalization, as reported by a previous study (32). After alignment of R loop CUT&Tag reads to the human hg38 and the E. coli genomes, we noticed that RNase A treatment markedly decreased the alignment rates of CUT&Tag reads to the human genome and increased the percentages of E. coli reads with all three different approaches (Fig. 3E). As shown in Fig. 3F and fig. S3F, we detected the R loop signals at the transcription start site (TSS) of the representative genes NPM1 and YY1AP1, as well as R loop signals in the gene body (fig. S3F), while RNase A digestion led to marked reduction of signals at these loci, suggesting that the R loop CUT&Tag signals are not artifacts due to the DNA accessibility.

To comprehensively compare the three aforementioned approaches for R loop CUT&Tag, we performed peak calling and analyzed the distribution of R loop signals over the human genome. The results revealed a high degree of similarity as shown in the heatmap and metaplot analysis (Fig. 4A). Heatmap profiles of R loop CUT&Tag demonstrated that these R loop signals are highly sensitive to RNase A treatment (Fig. 4B). Most of R loop signals within all three approaches localize at the promoter, while some of these signals could distribute in the gene body and intergenic region (Fig. 4C and table S1). Compared with S9.6, GST-His6-2HBD exhibited narrower peak width in CUT&Tag analysis, indicating a slightly better resolution of GST-His6-2HBD for R loop mapping (Fig. 4D). Moreover, the second approach using the GST-His6-2HBD and an anti-HisTag antibody showed the strongest R loop signals (Fig. 4B) and appeared be the most sensitive approach to RNase A digestion (Fig. 4, E and F). Overall, R loop signals generated from all three different approaches were very similar, and their densities were highly and positively correlated with each other (Fig. 4, G to I). Together, these data demonstrate that both S9.6 and GST-His6-2HBD can be used in R loop CUT&Tag analysis and they generate highly similar native R loop profiles.

(A) Analysis of R loop CUT&Tag signals at all of the peaks from GST-His6-2HBD and S9.6 CUT&Tag. RNase A digestion markedly reduced the CUT&Tag signals at those peaks, suggesting great specificity of GST-His6-2HBD and S9.6 with R loops. (B) Heatmap profiles of CUT&Tag signals with or without RNase A treatment. (C) Annotation of CUT&Tag peaks showing the localization of the majority of R loops at the promoter regions. The genomic features are shown on the right. UTR, untranslated region. (D) Violin plot of CUT&Tag peak width with three different approaches. Wilcoxon test was used to test the statistical differences. CUT&Tag analysis with anti-HisTag antibody and GST-His6-2HBD provides a superior resolution of R loop mapping. (E and F) Scatter plots of the log2 fold changes of R loop signals detected by anti- HisTag with RNase A (+/) versus the log2 fold changes of anti-GST and RNase A (+/) (E) or log2 fold changes of S9.6 and RNase A (+/) (F). CUT&Tag analysis with anti-HisTag antibody and GST-His6-2HBD is the most specific approach for R loop mapping. (G to I) Scatter plots of CUT&Tag signals from three different approaches. Pearson correlation was performed, and the r values are shown.

Furthermore, we tested the sensitivity of R loop CUT&Tag signals to RNase H, which digests DNA-RNA hybrid. As shown in Fig. 5 (A to D), RNase H treatment markedly decreased the R loop signals at individual genes such as NPM1, YY1AP1, FUS, and RPL13A. We also found that RNase H treatment decreased the alignment rates of CUT&Tag reads to the human genome and increased the percentages of E. coli reads in both GST-His6-2HBD and S9.6 antibodybased R loop CUT&Tag (Fig. 5E), which had a similar trend as RNase Atreated R loop CUT&Tag (Fig. 3E). Heatmap and metaplot analysis of R loop CUT&Tag signals at all of the peaks from GST-His6-2HBD and S9.6 CUT&Tag confirmed the substantial reduction of R loop signals after RNase H treatment (Fig. 5, F and G), which demonstrates specificity of GST-His6-2HBD and S9.6 in R loop CUT&Tag. We also noticed that RNase H treatment did not completely abolish the CUT&Tag signals, which is consistent with a recent study (36) showing that a subset of DNA-RNA hybrids with high GC skew are partially resistant to RNase H. This result may be attributed to the digestion efficiency of commercial RNase H (18, 36) or the potential protection of DNA-RNA hybrids by some R loop binding proteins. Moreover, to measure the reproducibility of R loop CUT&Tag analysis, we performed independent studies at different days and found high reproducible results across independent biological replicates with the R loop CUT&Tag methods (Fig. 5, H to J). Together, these data suggest that GST-His6-2HBD and S9.6 are specific and reproducible for native R loop mapping in CUT&Tag analysis.

(A to D) UCSC genome browser tracks of CUT&Tag signals at the NPM1, RPL13A, YY1AP1, and FUS loci. The tracks were normalized by reads per million and the RNase Htreated groups were further normalized with the E. coli spike-in control. (E) Alignment rates of R loop CUT&Tag reads to the human hg38 and E. coli spiked-in genomes. Four-hour RNase H treatment markedly reduces the alignment rates of CUT&Tag reads to the human genome and increases the alignment rates of reads to E. coli spiked-in genomes. (F and G) Heatmap and metaplot analysis of R loop CUT&Tag signals at all of the peaks from GST-His6-2HBD and S9.6 CUT&Tag. RNase H digestion markedly decreases the CUT&Tag signals at those peaks, demonstrating great specificity of GST-His6-2HBD and S9.6 on R loop recognition. (H to J) Reproducibility of R loop CUT&Tag methods. Biological replicates were performed, and the Pearson correlation was calculated with the reads per million at R loop peaks.

To systematically compare the R loop CUT&Tag methods with conventional R loop mapping methods, we downloaded the raw data of R loop profiles generated by MapR (26), R-ChIP (24), and DRIPc-seq (34) and realigned them to the human genome hg38. As illustrated in Fig. 6 (A and B), R loop CUT&Tag with GST-His6-2HBD or S9.6 had similar patterns as R-ChIP and MapR, showing concentrated signals at TSS sites, whereas R loop CUT&Tag had much higher overall signal densities. R loop CUT&Tag was different from DRIPc-seq, which distributes highly across the gene body and TES regions. Principal components analysis (PCA) and correlation analysis of these data confirmed that R loop CUT&Tag clustered together with R-ChIP and MapR, and they were distinctly away from DRIPc-seq, PRO-seq, and TT-seq (Fig. 6C and fig. S4A). Moreover, the fingerprint plot of R loop CUT&Tag, R-ChIP, and MapR showed that R loop CUT&Tag had the highest signal-to-noise ratio in R loop profiling (Fig. 6D).

(A and B) Track examples of HEK293T PRO-seq, GST-His6-2HBD CUT&Tag, S9.6 CUT&Tag, MapR (26), R-ChIP (24), DRIPc-seq (34), and TT-seq signals at the HSPD1 (A) and GRK6 (B) loci. The reads were aligned to the human hg38 genome, and the signals were normalized by reads per million. (C) PCA plot showing R loop CUT&Tag, MapR, and R-ChIP clustered together. (D) Fingerprint plots of R loop CUT&Tag, MapR, and R-ChIP. w.r.t., with respect to. (E and F) Metaplots of signals detected by different R loop mapping methods, PRO-seq, and TT-seq around the 2-kb window of the TSSs and TESs. Strand-specific signals from PRO-seq, TT-seq, DRIPc-seq, and R-ChIP were used for plotting. (G) Heatmap analysis of PRO-seq, TT-seq, and R loop mapping methods at the TSS of transcriptionally active genes (the reads per million of PRO-seq signals at TSS, >1; n = 13,220). The heatmaps are sorted by the GST-His6-2HBD CUT&Tag signals. R loop CUT&Tag assays, MapR, and R-ChIP have enrichment at the TSS, while DRIPc-seq does not show this trend. (H and I) Scatter plots of R loop CUT&Tag and MapR reads per kilobase, per million mapped reads (RPKM) values (H) or R-ChIP RPKM values (I) at TSS. The r values were calculated by Pearson correlation.

To compare the genome-wide R loop signals among different methods, we selected all of the expressed genes presenting PRO-seq signals at the TSS sites [the reads per million of PRO-seq signals at TSS, >1; n = 13,220] in HEK293T cells. Metaplot, metagene plot, and heatmap analysis with these genes showed that CUT&Tag, MapR, and R-ChIP signals were highly concentrated at the TSS sites (Fig. 6, E to G, and fig. S4, B and C). The overall enrichment of R loop signals by CUT&Tag was much higher than other R loop mapping methods including MapR and R-ChIP. In agreement with previous studies (15, 23), we noticed the benefits of R-ChIP for strand-specific R loop mapping, while DRIPc-seq signals distributed across the gene body and TES regions (Fig. 6F and fig. S4, B and C). Besides, we found that the R loop densities at the promoter region generated by CUT&Tag, R-ChIP, and MapR were positively correlated (Fig. 6, H and I). Together, these results demonstrate that R loop CUT&Tag with either GST-His6-2HBD or S9.6 behaves similarly to R-ChIP and MapR but shows much higher R loop enrichment than R-ChIP and MapR at promoter regions. Since these R loop mapping methods use different R loop capture strategies, our data suggest that the capture strategies may substantially affect the R loop profiling.

As shown in Fig. 6 (A and B), R loop CUT&Tag signals were distributed at both the promoter and gene body regions. Next, we performed genome-wide assessment of the R loop signals by calculating the densities [reads per kilobase, per million mapped reads (RPKM)] of R loop at the TSS and gene body regions (Fig. 7A). We successfully calculated 13,181 genes with R loop signals at the TSS sites of the 13,200 expressed genes. Box plots of RPKM from the 13,181 transcriptional active genes showed that TSS densities generated from CUT&Tag, R-ChIP, and MapR were higher than densities at the gene body (Fig. 7B), which is consistent with the metagene analysis (fig. S4B). Furthermore, we noticed the variability of CUT&Tag densities at the gene body regions, suggesting high heterogeneity of densities at gene body among transcriptional active genes as measured by R loop CUT&Tag (Fig. 7B). Therefore, we further plotted the R loop CUT&Tag signals at the TSS and gene body and found that densities at the gene body had a bimodal (two peaks) distribution pattern (Fig. 7C), whereas this bimodal distribution pattern was not observed in R-ChIP or MapR (Fig. 7, D and E). These observations indicate that R loop CUT&Tag is capable of genome-wide detection of R loop. Together, these results suggest that R loop CUT&Tag is more sensitive than R-ChIP and MapR for R loop detection at the gene body regions.

(A) Scheme of calculation of signals at TSS and gene body. The signals were normalized by RPKM. (B) Box plots of RPKM values at the TSS and gene body from 13,181 transcriptional active genes. (C) Scatter plot of R loop CUT&Tag signals at the TSS and gene body showing that R loop CUT&Tag is capable of genome wide detecting the R loop in the gene body. (D and E) Scatter plots of MapR (D) and R-ChIP (E) RPKM signals at the TSS and gene body. (F) Heatmap plots of the 3769 genes with R loop signals at gene body (G and H) The R loop signals at gene body were negatively correlated with gene lengths. (I and J) The gene body R loop signals positively correlate with PRO-seq (I) and H3K36me3 (J) signals at the gene body. (K) Gene ontology (GO) analysis of the 3769 genes indicates that R loop may be involved in the regulation of various key biological processes. (L) Track examples of HEK293T PRO-seq, GST-His6-2HBD CUT&Tag, and S9.6 CUT&Tag signals at the YY1 and ZNF557 genomic loci. The reads were normalized by reads per million, and the enhancers are indicated. (M) Heatmap analysis of R loop CUT&Tag signals at 3830 intergenic regions. The heatmaps were sorted by the GST-His6-2HBD CUT&Tag signals, and the H3K27ac signals in HEK293T are shown. H3K27ac, histone 3 lysine 27 acetylation.

With the aforementioned strategy, we identified 3769 genes (Q1) with R loop densities at the gene body using the cutoffs indicated in Fig. 7C (table S2). Heatmap plots of these 3769 genes confirmed R loop signals at the gene body detected by R loop CUT&Tag (Fig. 7F). We further analyzed the genomic features of the 3769 genes and found that these genes were generally clustered together (Fig. 6B) and associated with short gene lengths (Fig. 7, G and H). Since elongating Pol II is required for R loop formation at gene body, we calculated the PRO-seq and histone 3 lysine 36 trimethylation (H3K36me3) (GSE145160) (37) signals at the gene body and found that R loop densities were positively correlated with PRO-seq signals and H3K36me3 densities at gene body (Fig. 7, I and J), suggesting the authenticity of R loop signals at gene body and that these R loops may associate with transcription elongation. Gene ontology analysis of the 3769 genes showed that they were enriched in multiple cellular processes (Fig. 7K) including cellular component organization or biogenesis, developmental process, metabolic process, and cell growth, indicating that R loops at gene body may participate in the regulation of various key biological processes.

Because of the short half-lives of enhancer RNAs and low transcriptional outputs of enhancers, R loops formed at enhancers are likely to be transient and dynamic. However, we still observed R loop CUT&Tag signals at enhancer regions as shown in Fig. 7L. Annotation of R loop CUT&Tag peaks revealed a subset of R loop peaks localized at the intergenic regions (Fig. 4C and table S1). Genome-wide analysis of R loop CUT&Tag peaks identified 3830 intergenic regions, and the heatmap analysis also confirmed that R loop CUT&Tag signals could distribute at these intergenic regions (Fig. 7M). Together, these data indicate that R loop CUT&Tag is sensitive in detecting the transient and dynamic R loops at enhancers.

As shown in Fig. 6 and fig. S4, the ex vivo or in situ capture strategies may substantially affect the R loop profiling in the genome. To systematically compare the difference between the ex vivo and in situ capture strategies, we established a modified DRIPc-seq method with random fragmentation by the New England Biolabs (NEB) dsDNA fragmentase to avoid the bias of restriction digestion (Fig. 8A) (21). We tested both the GST-His6-2HBD and S9.6 in the modified DRIPc-seq and compared them to R loop CUT&Tag analysis. Genome browser snapshots at the FUS and RPL13A loci showed that DRIPc-seq signals appeared downstream of the TSS sites and distributed mainly at the gene body and TES regions (Fig. 8B). Instead, R loop CUT&Tag used the in situ capture strategy and could capture the high signals at the promoter regions and some signals at the gene body and TES. The promoter-associated R loop signals most likely come from the paused RNA polymerases, which localize at the downstream of TSS sites. Genome-wide analysis of R loop signals by the GST-His6-2HBD and S9.6 in DRIPc-seq and CUT&Tag (Fig. 8, C to F) confirmed the global profiling differences between the ex vivo and in situ capture strategies.

(A) Workflow of DRIPc-seq with GST-His6-2HBD or S9.6 combined with random fragmentation of genomic DNA by NEB dsDNA fragmentase. IP, immunoprecipitation. (B) Genome browser tracks of DRIPc-seq and R loop CUT&Tag coverage at the FUS and RPL13A loci detected by GST-His6-2HBD and S9.6. Signals were normalized by reads per million. (C and D) Heatmap analysis of DRIPc-seq (ex vivo) and R loop CUT&Tag (native, in situ) at all the protein-coding genes by GST-His6-2HBD (C) or S9.6 (D). (E and F) Metagene plots of DRIPc-seq and R loop CUT&Tag by GST-His6-2HBD (E) or S9.6 (F).

Both GST-His6-2HBD and S9.6 showed CUT&Tag signals upstream of the TSS sites. Compared to the DRIPc-seq method, which detects the RNA moiety of DNA-RNA hybrid, the CUT&Tag used the proximal tagmentation strategy and the pA-Tn5 at the binding sites of sensor protein could access the nearby regions and add the adaptors to the nearby DNA and DNA-RNA hybrids. The library size distribution analysis (Fig. 3D) showed that the CUT&Tag libraries had an average size of 405 bp. After deduction of the sequencing adaptors, the insertion fragments are around 269 bp and could cover the regions upstream of TSS and even the downstream of Pol II pausing sites. Another possible explanation for these R loop signals upstream of TSS is the bidirectional transcription at the promoter regions, which has been reported in R-ChIP (23), RR-ChIP (25) and DRIPc-seq (15). We also noticed the difference between GST-His6-2HBD and S9.6 at the TES regions, suggesting that the R loops at TES may have unique characteristics that could be preferentially recognized by the S9.6 antibody in DRIPc-seq. Together, these data showed that the same R loop sensor proteins generated distinct R loop genomic profiling with the ex vivo and in situ capture strategies.

In this study, we constructed specific R loop sensor proteins using the HBD domain of RNase H1 and showed that these sensors bound specifically in vitro to DNA-RNA hybrids and R loops (Fig. 1 and fig. S1), indicating their potential usefulness as new R loop mapping sensors. With GST-His6-2HBD, we established a sensor proteinbased DRIPc-seq method that obtained similar R loop profiling to S9.6-based DRIPc-seq (Fig. 2 and fig. S2), showing that GST-His6-2HBD is superior to the catalytically inactive RNase H1 in affinity pulldown assays (8, 12). To eliminate concerns with the bias of fragmentation by restriction enzymes (21), we developed alternative strategies for native R loop mapping through establishing a genome-wide R loop CUT&Tag. We compared our system with other conventional R loop methods and investigated the effects of capture strategies on R loop profiling.

R loop CUT&Tag is a sensitive, reproducible, and convenient method to map genome-wide native R loops with high resolution. We tested R loop CUT&Tag with three different approaches and found that the approach combining anti-HisTag antibody and GST-His6-2HBD protein generated a superior resolution and the highest signals in R loop CUT&Tag analysis (Fig. 4). We also systematically compared the R loop signals detected by CUT&Tag methods using MapR, R-ChIP, and DRIPc-seq (Fig. 6 and fig. S4). Our results showed that CUT&Tag, MapR, and R-ChIP signals were concentrated at the TSS sites, while DRIPc-seq did not show this pattern of distribution (Fig. 6). We also found that the overall enrichment of R loop signals by CUT&Tag was much higher than other R loop mapping methods (Fig. 6, E and G). Furthermore, we optimized the DRIPc-seq method with random fragmentation of genomic DNA by dsDNA fragmentase and compared the ex vivo and in situ R loop detection strategies (Fig. 8). Ex vivo DRIPc-seq detected signals predominantly at the gene body and TES regions. This result may be explained by the relatively short and unstable R loops formed at the promoter due to the DNA sequence and topology (24, 38), while the R loops at the gene body and TES may be longer and relatively stable. Besides, RNase H1 was shown to be recruited to the promoter regions, which may degrade R loops and make the R loops at promoter regions highly dynamic (24). Moreover, the genomic DNA purification and fragmentation steps during ex vivo R loop capturing may damage the R loops at promoters. All of these factors could lead to the loss of R loop signals at the TSS and result in an enriched R loop profile at gene body and TES regions in DRIPc-seq (Fig. 8). Besides, since the RNA strands of R loops near the promoter are relatively shorter, it is also possible that the less efficiency of complementary DNA conversion and amplification of short RNA molecules caused the lack of signals at TSS in DRIPc-seq. In contrast, R loop CUT&Tag takes advantage of the in situ capture strategy and is able to capture the prominent signals at the promoter regions and some signals at the gene body and TES. In summary, we revealed that even the same R loop sensor proteins could generate distinct R loop profiles in the ex vivo DRIPc-seq and in situ CUT&Tag analysis, suggesting that the R loop capture strategies, instead of the specificity of S9.6 and RNase H1, are the major contributing factors to the discrepancies within different R loop mapping methodologies.

Although R loop CUT&Tag could detect a subset of genes with R loop signals at the gene body, neither R-ChIP nor MapR showed a similar bimodal distribution pattern, suggesting that R loop CUT&Tag is more sensitive than R-ChIP and MapR in R loop detection at the gene body regions (Fig. 7). The enriched key cellular processes (Fig. 7K) indicate that R loops at their gene body regions may participate in the regulation of various key biological processes. These genes were positively correlated with PRO-seq and H3K36me3 signals (Fig. 7, I and J), suggesting that these R loops may associate with transcription elongation. R loops have been shown to impair transcription elongation through functioning as roadblocks for RNA polymerases (27, 28). Besides, R loop induction is correlated with transcriptional pausing and elevated Pol II pausing at TSS, which allows for increased R loop formation (24). The misregulation of R loops and transcription elongation has been implicated in cancer and other human diseases. Mechanistic understanding of transcription elongation and R loop regulation is therapeutically relevant (29). Although the R loops at enhancers are transient due to the short-lived nature of enhancer RNAs (39) and the low transcriptional output of these cis-elements, the R loop CUT&Tag is capable of detecting the R loops at the enhancers (Fig. 7, L and M). R loops at enhancers were reported to promote antisense long noncoding RNA generation, modulate enhancer activities, and regulate cell differentiation and preprogramming (25, 3941). Our R loop mapping method could facilitate the mechanistic studies of enhancer R loops in gene transcription, DNA replication, DNA mutagenesis, enhanceropathies (42), as well as lineage specification and pluripotency.

R loop CUT&Tag is relatively easier and more straightforward than other enrichment-based R loop mapping methods such as DRIPc-seq and R-ChIP. It does not need fixation, sonication, restriction digestion, or generation of stable transgenic cell lines, and it only takes less than a day from cell collection to library preparation. The CUT&Tag analysis starts with half a million cells, which is far less than the minimal requirement of DRIPc-seq and R-ChIP methods, and it allows further optimization with even fewer cells. Thus, our method provides possibilities of genome-wide mapping of the native R loops with limited materials. Moreover, the unique characteristics of Tn5 transposase provide great specificity for R loop CUT&Tag analysis. Tn5 has been widely used in sequencing library preparation for rapid processing and low sample input requirement of dsDNA. In addition to its canonical function in dsDNA tagmentation, Tn5 transposase was recently shown to bind and effectively transpose both strands of DNA-RNA hybrids, which can be amplified for library preparation after strand displacement, avoiding many time-consuming steps (30, 31). Since Tn5 transposase does not react with ssRNA or ssDNA, there are no concerns with the possible RNA contamination or the off-target effect of HBD or S9.6 at loci with ssDNA. Tn5 has not been reported to transpose dsRNA, and even Tn5 could potentially react with dsRNA and ligate the adapters to dsRNA, the strands of the products are prevented from being displaced and amplified by Bst 2.0 and Q5 DNA polymerases. RNase A digestion of RNA and DNA-RNA hybrids markedly abolished the R loop CUT&Tag signals (Fig. 4), demonstrating that R loop CUT&Tag signals were not contamination from tagmentation of accessible DNA (35). Using RNase H to digest DNA-RNA hybrids during the antibody binding process substantially reduced R loop signals (Fig. 5), indicating the specificity of GST-His6-2HBD and S9.6 in R loop CUT&Tag analysis. Although the current form of R loop CUT&Tag does not provide strand information about R loops, modification of CUT&Tag library preparation to sequence both DNA and RNA strands of R loops is likely to provide useful strand information and better resolution.

HEK293T cells were obtained from American Type Culture Collection and maintained in Dulbeccos modified Eagles media (Life Technologies) supplemented with 10% fetal bovine serum (LONSERA) and 1 penicillin-streptomycin (Life Technologies) at 5% CO2 at 37C. Drosophila S2 cells were maintained in Schneiders medium at 25C. The cells were routinely tested for mycoplasma contamination with the MycoBlue mycoplasma detector (Vazyme). The HBD coding region RNase H1 was amplified by the Phanta Max Super-Fidelity DNA polymerase (Vazyme) using the ppyCAG-RNASEH1-D210N vector (Addgene #111904) and then cloned into the pET16b vector to produce the pET16b-His6-HBD plasmid by Gibson assembly. The His6-HBD coding region was further amplified and cloned into the pGEX-2 T vector (Sigma-Aldrich) to generate the pGEX-GST-His6-HBD plasmid. A 5Glycine linker and another copy of the HBD coding region were cloned into the pGEX-GST-His6-HBD vector to produce the pGEX-GST-His6-2HBD plasmid. The N-terminal 3Flag-tagged protein A and Tn5 transposase coding sequence was amplified from the 3Flag-pA-Tn5-Fl plasmid (Addgene #124601) and subsequently cloned into the pET16b vector to create the His6-pA-3Flag-Tn5 expression plasmid.

The pGEX-GST-His6-HBD and pGEX-GST-His6-2HBD plasmids were individually transformed into the T7 Express lysY/Iq competent E. coli cells (NEB, C3013). The transformed colonies were picked and cultured in the 2YT medium containing ampicillin (100 g/ml) at 200 rpm in a 37C shaker. Protein expression was induced by 0.5 mM isopropyl--d-thiogalactopyranoside (IPTG) when the optical density at 600 nm (OD600) reached 0.6, and the culture was grown for an additional 5 hours at 200 rpm. Bacterial cell pellets were collected and lysed in 40 ml of HEX buffer [20 mM Hepes-NaOH (pH 7.5), 0.8 M NaCl, 10% glycerol, and 0.2% Triton X-100] supplemented with 1 cOmplete, EDTA-free protease inhibitor cocktails (Roche, 04693132001). Homogenization of lysates was performed with a high-pressure homogenizer at 5.5 MPa for 5 min. The supernatant was collected by centrifuging at 12,000 rpm and 4C for 30 min. The GST-His6-HBD and GST-His6-2HBD proteins were purified with the Ni-NTA beads 6FF (Smart Life Sciences) and eluted with the HEX buffer containing 250 mM imidazole. The eluted proteins were dialyzed against the HEX buffer and were concentrated with an Amicon Ultra-15 centrifugal filter unit (Millipore, UFC901008; 10-kDa cutoff). The GST-His6-HBD and GST-His6-2HBD proteins were further analyzed by SDSpolyacrylamide gel electrophoresis (PAGE) and Coomassie blue staining.

Alexa Fluor 488labeled DNA oligonucleotides, Cy3-labeled RNA oligonucleotides, and their reverse complement DNA and RNA oligos were synthesized by Sangon Biotech (table S3). Fluorescent probes were generated by annealing the fluorescently labeled oligos with or without their reverse complement oligos. To perform the binding assays, 30 nM probes were incubated with the recombinant GST-His6-HBD or GST-His6-2HBD proteins at 25C for 30 min in the binding buffer [20 mM Hepes-NaCl (pH 7.0), 100 mM NaCl, 5% glycerol, 10 mM dithiothreitol (DTT), and 0.5 mM EDTA] supplemented with sheared salmon sperm DNA (5 g/ml; Thermo Fisher Scientific, AM9680). The probe and protein probe were resolved in a 6% native polyacrylamide gel in 0.5 tris-borate-EDTA buffer (pH 9.5). The polyacrylamide gels were scanned with a Typhoon FLA-9500 (GE Healthcare) to detect the probe signals. Densitometry of bands was performed using ImageJ.

Biolayer interferometry assays were performed using an Octet Red96 with streptavidin biosensors (ForteBio, 18-5019). The DNA oligonucleotides were synthesized and labeled with biotin at the 5 end (5-[biotin]AGC GTG CCG TGC AAC AAC ATT ACA C-3). Biotin-labeled DNA oligos were annealed with the reverse complement RNA oligonucleotides RNA-25 to generate a biotin-labeled DNA-RNA hybrid. Kinetic titration series were performed in the interaction buffer [20 mM Hepes-NaOH (pH 7.5) and 200 mM NaCl]. The streptavidin biosensors were hydrated in the interaction buffer for 10 min at 25C. Following the initial 120-s baseline, the streptavidin biosensors were loaded with the biotin-labeled DNA-RNA hybrid for 300 s. Redundant probes were removed by a 200-s baseline adjustment. To measure the interaction between recombinant proteins and DNA-RNA hybrid, the duration time of association and dissociation was set to 600 s. GST-His6-2HBD proteins were serially diluted from 200 to 6.25 nM and loaded in parallel to measure the binding kinetics with DNA-RNA hybrid. Sensorgrams and sensor signals were analyzed by the ForteBio data analysis software (version 7.1).

The His6-pA-3Flag-Tn5 plasmid was chemically transformed into BL21 (DE3) competent bacteria cells. A single colony was picked and inoculated with 5 ml of LB medium containing ampicillin (100 g/ml). After overnight culture at 37C and 200 rpm, 5 ml of culture was transferred to 1 liter of LB medium supplemented with ampicillin (100 g/ml) and cultured at 37C and 200 rpm. Once the OD600 of the culture reached 0.6, 0.2 mM IPTG was added, and the culture was further induced at 23C and 200 rpm for 5 hours to induce the pA-Tn5 expression. BL21 E. coli was collected and resuspended in 20 ml of HXG buffer [20 mM Hepes-KOH (pH 7.2), 0.8 M NaCl, 10% glycerol, and 0.2% Triton X-100] supplemented with 1 EDTA-free protease inhibitor cocktails and subjected to high-pressure homogenization at 4.1 MPa for 5 min. The supernatant was harvested by centrifuging at 12,000 rpm and 4C for 30 min, and 0.1 ml of 10% poly(ethyleneimine) solution (Sigma-Aldrich, P3143) was added to precipitate bacterial DNA, which was further removed by centrifugation at 12,000 rpm and 4C for 10 min. The His-tagged pA-Tn5 was purified with the Ni-NTA beads 6FF (Smart Life Sciences) and eluted with the HXG buffer containing 250 mM imidazole. The eluted pA-Tn5 was dialyzed against 2 dialysis buffer [100 mM Hepes-KOH (pH 7.2), 0.2 M NaCl, 0.2 mM EDTA, 2 mM DTT, 0.2% Triton X-100, and 20% glycerol]. The His-tagged pA-Tn5 was concentrated with an Amicon Ultra-15 centrifugal filter unit (Millipore, UFC903008; 30-kDa cutoff) and diluted 1:1 with 100% glycerol. The purified pA-Tn5 protein was analyzed by SDS-PAGE and Coomassie blue staining and quantified by bicinchoninic acid protein assays.

The pA-Tn5 transposome assembly was performed as described previously (33). Briefly, the Tn5MErev (5-[phos]CTG TCT CTT ATA CAC ATC T-3), Tn5ME-A (5-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA CAG-3), and Tn5ME-B (5-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG ACA G-3) oligonucleotides were diluted with TE buffer [10 mM tris-HCl (pH 8.0) and 1 mM EDTA] to 400 mM. The mosaic end double-stranded oligonucleotides (Tn5MEDS-A/Tn5MEDS-B) were prepared by mixing equal volume of Tn5MErev with Tn5ME-A or Tn5ME-B and were annealed with a Bio-Rad T100 thermal cycler. To generate pA-Tn5 transposome complex, Tn5MEDS-A, Tn5MEDS-B, and purified pA-Tn5 were mixed at 1:1:1 with the final concentration of 37.5 M, individually. The mixture was incubated on a three-dimensional rotator at room temperature for 1 hour. The activity of pA-Tn5 transposome was confirmed by tagmentation of plasmid DNA in N-[Tris(hydroxymethyl)methyl]-3-aminopropanesulfonic acid (TAPS)-DMF buffer [10 mM TAPS-KOH (pH 8.3), 5 mM MgCl2, and 10% DMF].

CUT&Tag assays were performed as described previously (32, 33) with some modifications. Briefly, 5 105 cells were washed twice in 1.0 ml of wash buffer [20 mM Hepes (pH 7.5), 150 mM NaCl, 0.5 mM spermidine, and 1 protease inhibitors] by gentle pipetting. Ten microliters of concanavalin Acoated magnetic beads (Smart Life Sciences) were activated and then added to 5 105 cells with incubation at room temperature for 10 min. The supernatant was removed, and bead-bound cells were resuspended in 100 l of antibody buffer [20 mM Hepes (pH 7.5), 150 mM NaCl, 0.5 mM spermidine, 1 protease inhibitors, 0.05% digitonin, 0.01% NP-40, and 2 mM EDTA]. Two micrograms of recombinant GST-His6-2HBD protein or S9.6 (antiDNA-RNA hybrid antibody, clone S9.6; Millipore, MABE1095) was added to incubate with the bead-bound cells by rotating overnight at 4C. As controls, 10 g of RNase A (Takara) or 20 U of RNase H (NEB, M0297S) was added during the antibody incubation stage. After brief wash with dig-wash buffer [20 mM Hepes (pH 7.5), 150 mM NaCl, 0.5 mM spermidine, 1 protease inhibitors, 0.05% digitonin, and 0.01% NP-40] twice, the GST-His6-2HBDtreated groups were incubated with anti-HisTag mAb (ABclonal, AE003; 1:100 dilution) or antiGST-Tag mAb (ABclonal, AE006; 1:100 dilution), individually. The S9.6-treated groups were incubated with rabbit anti-mouse immunoglobulin G (IgG) antibody (final concentration, 10 g/ml). The antibody incubation was performed in 100 l of antibody buffer and was incubated at room temperature for 1 hour. Bead-bound cells were briefly washed three times with 200-l dig-wash buffer to remove the unbound antibodies. The mouse anti-rabbit IgG antibody or rabbit anti-mouse IgG antibody was diluted 100 (final concentration, 10 g/ml) and incubated with the cells for another 1 hour, followed by washing with 200 l of dig-wash buffer three times.

A 1:250 dilution of pA-Tn5 adapter complex (~15 M) was prepared in dig-300 buffer [20 mM Hepes (pH 7.5), 300 mM NaCl, 0.5 mM spermidine, 1 protease inhibitors, and 0.05% digitonin]. One hundred microliters of diluted pA-Tn5 complex was mixed with the bead-bound cells and was rotated at room temperature for 1 hour. Bead-bound cells were washed three times in 200 l of dig-300 buffer to remove unbound pA-Tn5 protein. Next, cells were resuspended in 40 l of tagmentation buffer [10 mM TAPS-NaOH (pH 8.5), 10 mM MgCl2, 10% DMF, and 0.85 mM ATP] and incubated at 37C for 1 hour. To stop the tagmentation reaction, 2.25 l of 0.5 M EDTA, 2.75 l of 10% SDS, and 0.5 l of proteinase K (20 mg/ml; Roche) were added and were further incubated at 55C for 60 min. The tagmentation products were purified with 1 Sera-Mag carboxylatemodified magnetic beads (Sigma-Aldrich, GE24152105050350) and were eluted in 10 l of 0.1% Tween 20. The eluent was mixed with 10 U of Bst 2.0 WarmStart DNA polymerase (NEB, M0538) in 1 Q5 polymerase reaction buffer and was incubated at 65C for 30 min to perform the strand displacement reaction. The reaction was then stopped by incubation at 80C for 20 min. To generate the sequencing libraries, the mixture was mixed with a universal i5 primer and a uniquely barcoded i7 primer and then amplified with the Q5 high-fidelity master mix (NEB, M0492). The libraries were size-selected with 0.56 to 0.85 Sera-Mag carboxylatemodified magnetic beads and subjected to LabChip DNA analysis and Illumina sequencing.

CUT&Tag reads were aligned to the human genome (UCSC hg38) with Bowtie version 1.1.2, allowing only uniquely mapping reads with up to two mismatches (43). The aligned reads were normalized to total reads aligned (reads per million). The track files were made with the bamCoverage command from deepTools 3.3.0 (44). For spike-in normalization, the reads were also aligned to the E. coli genome by Bowtie2 with the options (--end-to-end --very-sensitive --no-overlap --no-dovetail --no-mixed --no-discordant --phred 33 -I 10 -X 700) (43). The RNase A and RNase Htreated groups were normalized to their nontreated counterparts by scale factors. CUT&Tag peaks were called using MACS (model-based analysis of ChIP-seq) version 2.1.2 using default parameters and q value cutoff of 1 104 (45). The distribution of CUT&Tag peaks was annotated with the R package ChIPseeker. Heatmaps, metaplots, and metagene plots were made for the indicated windows using the average coverage (reads per million) (46).

TT-seq was performed as described (47, 48) with modifications. A total of 1 107 cells were labeled with 400 M 4-thiouridine (4sU; Sigma-Aldrich, T4509) in a CO2 incubator at 37C for 10 min and were quickly lysed with 4 ml of TRIzol (Invitrogen, 15596018). Total RNA was purified with chloroform extraction and precipitated with isopropyl alcohol and 5 l of glycogen (20 mg/ml; Roche, 10901393001). The extracted RNA was spiked-in with 4sU-labeled S2 RNA and was further fragmented by base hydrolysis in 0.2 M NaOH (15 min, on ice), neutralized by adding 1 volume of 1 M tris-HCl (pH 6.8) and precipitated with isopropyl alcohol. Biotinylation reaction of 4sU-labeled RNA was carried out in a total volume of 250 l, containing 100 g of total RNA, 10 mM Hepes (pH 7.5), 1 mM EDTA, and 5 g of biotin-XX-MTSEA (Biotium, 90066) dissolved in DMF (final concentration of DMF, 20%) at room temperature for 30 min.

After biotinylation, excess biotin reagents were removed by extraction with chloroform and phase lock gel. RNA supernatant was precipitated with a 1:10 volume of 5 M NaCl and an equal volume of isopropyl alcohol. The RNA pellet was resuspended in 200 l of RNase-free water. After denaturation of RNA samples at 65C for 5 min followed by rapid cooling on ice for 5 min, biotinylated RNA was purified using 50 l of Dynabeads MyOne streptavidin C1 (Thermo Fisher Scientific, 65001). MyOne streptavidin beads were incubated with RNA samples for 15 min with rotation at room temperature. Beads were then washed three times with wash buffer [10 mM tris-HCl (pH 7.4), 1 mM EDTA, 1 M NaCl, and 0.1% Tween 20], followed by one step wash at 65C. 4sU-RNA was eluted with 100 l of freshly made 100 mM DTT, followed by a second elution with an additional 100 l of 100 mM DTT.

The eluted RNA was purified with the Sera-Mag carboxylatemodified magnetic beads and was subjected to strand-specific RNA-seq library preparation. Libraries were made with the NEBNext Ultra RNA Library Prep Kit for Illumina and subjected to next-generation sequencing. TT-seq reads were aligned to the human genome (UCSC hg38) with Bowtie 1.1.2 (43), allowing only uniquely mapping reads with up to three mismatches within the 50-bp read. The resulting reads were normalized to total reads aligned (reads per million) for each strand with deepTools 3.3.0 (44). The reads coverage at indicated regions were calculated by bedtools multicov (version 2.25.0) or HTSeq-count and were then normalized to total reads aligned.

Chromatin-associated RNA-seq was performed as described before (49). Briefly, 10 million HEK293T cells were harvested via trypsin digestion and were washed twice with 10 ml of ice-cold phosphate-buffered saline. Cells were resuspended in 5 ml of ice-cold NUN buffer [20 mM Hepes (pH 7.9), 7.5 mM MgCl2, 0.2 mM EDTA, 300 mM NaCl, 1 M urea, 1% NP-40, 1 mM DTT, RNase inhibitor (20 U/ml), and 1 cOmplete EDTA-free protease inhibitor cocktails (Roche, 04693132001)] with gentle pipetting. The suspension was placed on ice for 10 min and centrifuged at 3000g for 5 min at 4C. The chromatin pellet was further washed with 10 ml of NUN buffer four times and lysed with 1 ml of TRIzol reagent. The lysates were heated to 65C to dissolve the chromatin pellets, and RNA extraction was performed according to the manufacturers instruction. The RNA was polyadenylation depleted with Oligo(dT)25 magnetic beads (NEB, #S1419S) and treated with RNase-free deoxyribonuclease I (DNase I) to remove potential mRNA and genomic DNA contamination. The RNA was then purified with phenol/chloroform extraction and ethanol precipitation. Five hundred nanograms of RNA was further ribodepleted with the Ribo-off rRNA depletion kit (Vazyme, #N406), and library preparation was performed with the NEBNext ultra RNA library prep kit for Illumina. Chromatin-associated RNA reads were aligned to the human genome (UCSC hg38) using Bowtie 1.1.2. For track files, reads were normalized by reads per million. Reads at all of the protein-coding genes were counted by HTSeq-count with the union model.

PRO-seq was performed as described previously (47, 50). Briefly, all four biotinylated nucleotides (PerkinElmer) were used at 25 mM for the nuclear run-on reaction. RNA 5 pyrophosphohydrolase (NEB, M0356S) was used to remove the RNA cap to facilitate the downstream library preparation. PRO-seq reads were mapped to the human genome (UCSC hg38) using Bowtie version 1.1.2, allowing only uniquely mapping reads with up to three mismatches (43). Aligned reads were then converted to strand-specific bigwig files with the bamCoverage command from deepTools 3.3.0 (44). PRO-seq genome browser track examples show coverage of the entire length of the read for visualization. The read counts at indicated regions were calculated by bedtools multicov (version 2.25.0). The heatmaps were generated with the indicated regions with ngs.plot or the computeMatrix command from deepTools 3.3.0 (44).

DRIPc-seq experiments were performed according to a previously published protocol (18) with modifications. A total of 8 106 293 T cells were harvested and resuspended in 1.6 ml of TE buffer including 50 l of 20% SDS and 5 l of proteinase K (20 mg/ml). After digestion at 37C for 12 hours, phenol/chloroform extraction and ethanol precipitation were performed to purify the genomic DNA. The DNA was further digested with NEB restriction enzymes (EcoR I, Ssp I, BsrG I, Xba I, and Hind III) or NEBNext dsDNA fragmentase (NEB, M0348). Eight micrograms of digested genomic DNA was incubated with 100 ng of GST-His6-2HBD protein or 20 g of S9.6 mAb in DRIP-binding buffer [10 mM NaPO4 (pH 7.0), 140 mM NaCl, and 0.05% Triton X-100] at 4C overnight. The 2HBD:DNA-RNA hybrid complexes and S9.6:hybrid complexes were purified with 20 l of glutathione magnetic agarose beads (Thermo Fisher Scientific, 78601) or protein A/G agarose at 4C for 4 hours with rotation. After extensive washes four times, the nucleic acids were eluted and purified as previously described (18).

For DRIPc-seq library preparation, the DNA molecules were digested with NEB DNase I for 1 hour at 37C, and the RNA molecules were recovered with ethanol precipitation. The libraries were prepared with the NEBNext Ultra Directional RNA Library Prep Kit and subjected to LabChip analysis and next-generation sequencing. DRIPc-seq reads were aligned to the human genome (UCSC hg38) with Bowtie 1.1.2, allowing only uniquely mapping reads with up to two mismatches within the first 50 nucleotides (43). The resulting reads were normalized with total reads aligned for visualization. The heatmaps were generated with the indicated regions with the computeMatrix and plotHeatmap commands from deepTools 3.3.0 (44). The read numbers at indicated regions were counted by the multicov command from bedtools v2.25.0 or HTSeq-count.

Quantitative real-time polymerase chain reaction (PCR) was performed in triplicate using a Jena qTOWER G real-time PCR thermal cycler. Primer sequences for all quantitative PCR reactions are RPL13A forward (5-AGG TGC CTT GCT CAC AGA GT-3), RPL13A reverse (5-GGT TGC ATT GCC CTC ATT AC-3), TFPT forward (5-TCT GGG AGT CCA AGC AGA CT-3), TFPT reverse (5-AAG GAG CCA CTG AAG GGT TT-3), EGR1 forward (5-GAA CGT TCA GCC TCG TTC TC-3), and EGR1 reverse (5-GGA AGG TGG AAG GAA ACA CA-3).

All quantitative results were analyzed with the test indicated in the figure legends, after confirming that the data met appropriate assumptions (normality, homogeneous variance, and independent sampling). All the P values are two tailed, and the data are presented as means SD. The peak or gene size (n) in the heatmaps indicates the number of peaks or genes included. In the violin plots, the white dot indicates the median, and the solid box indicates the interquartile range. The statistical tests were performed with R (version 3.6.1).

Acknowledgments: We thank the staffs at the Research Center for Medicine and Structural Biology of Wuhan University for technical assistance. We are grateful to D. Gao at the core facility of Wuhan Institute of Virology for biolayer interferometry advice and E. Smith at the Northwestern University Feinberg School of Medicine for helpful discussion. We thank S. Henikoff at the Division of Basic Sciences of Fred Hutchinson Cancer Research Center for the 3Flag-pA-Tn5-Fl plasmid. Funding: This study was supported by the National Key Research and Development Program of China Stem Cell and Translational Research (no. 2019YFA0111100) and the Thousand Youth Talents Plan awarded to K.L. and the Youth Program of National Natural Science Foundation of China (nos. 32001049 and 82000114) awarded to P.F. and Y.X. Author contributions: K.L., K.W., and P.F. conceived and designed the experiments. K.L., K.W., H.W., C.L., Y.X., and Q.L. conducted the experiments. Z.Y., W.W., and K.L. performed the bioinformatics and statistical analysis of the genomic data. K.W., P.F., and K.L. drafted the manuscript with the input from all authors. All authors contributed to editing the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: The MapR, R-ChIP, H3K36me ChIP-seq, and DRIPc-seq raw data were downloaded from NCBI GEO: GSE120637, GSE97072, GSE145160, and GSE102474, respectively. The datasets generated during this study are available at NCBI GEO: GSE156400. Further information and requests for reagents may be directed to and will be fulfilled by the corresponding author K.L. upon request. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials.

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Genomic profiling of native R loops with a DNA-RNA hybrid recognition sensor - Science Advances

WASHINGTON (NewsNation Now) The White House COVID-19 response team announced Wednesday that the Biden administration will invest $1.6 billion to support testing in schools, increase genomic sequencing and manufacture testing supplies.

We need to test broadly and rapidly to turn the tide of this pandemic, said Carole Johnson, White House COVID-19 testing coordinator. But we still dont have enough testing and we dont have enough testing in all the places it needs to be.

$815 million will be used to increase domestic manufacturing of testing supplies, particularly those that are facing a shortage including molded plastics and filter pipette tips, the Biden administration said.

Nearly $200 million will be invested by the Centers for Disease Control and Prevention to increase virus genome sequencing to identify and track emerging variant virus strains throughout the country. This comes as CDC Director Rochelle Walensky confirmed that the variant strain first found in Bristol, England was reported in the United States.

$650 million will be used to expand testing in schools and underserved populations. The Department of Health and Human Services in partnership with the Department of Defense will use the money to expand testing opportunities for K-8 schools and underserved congregate settings, such as homeless shelters, according to the Biden administration.

Even though we dont feel that every teacher needs to be vaccinated before you can open a school, that doesnt take away from the fact that we strongly support the vaccination of teachers, said Dr. Anthony Fauci.

The CDC Friday provided a long-awaited road map for reopening schools emphasizing mask-wearing and social distancing and saying vaccination of teachers is important but not a prerequisite for reopening.

Agency officials were careful to say they are not calling for a mandate that all U.S. schools be reopened. But they said there is strong evidence now that in-person schooling can be done safely, especially at lower grade levels, and the guidance is targeted at schools that teach kindergarten up to 12th grade.

The guidance was issued as President Biden faces increasing pressure to deliver on his promise to get the majority of schools back to in-person learning by the end of his first 100 days in office. The White House said last week that a national strategy would be guided by science. Biden reiterated his stance at a Wisconsin town hall Tuesday night.

Asked when the nation would see kindergarten through eighth grades back to in-person learning five days a week, Biden said, Well be close to that at the end of the first 100 days. He said he expected many schools would push to stay open through the summer, but suggested reopening would take longer for high schools due to a higher risk of contagion among older students.

Biden alsoannounced a vaccination goal of 100 million coronavirus shots in his first 100 days in office.

New figures from the White House show the steady increase in the pace of vaccinations over President Joe Bidens first month in office.

Much of the increase, according to data from the Centers for Disease Control and Prevention, comes from people receiving their second dose of the approved vaccines from Moderna and Pfizer.

Biden is on track to beat his goal of 100 million injections in his first 100 days in office though the pace must pick up even further to meet his plans to vaccinate nearly all adults by the end of the summer.

More than 71 million vaccine doses have been distributed across the United States, with more than 55.2 million doses administered, according to the CDC.

As the average daily new virus cases dipped below 100,000 for the first time in months as the United States seeks to picture a return to life as it was pre-pandemic. There have been more than 27.7 million confirmed cases in the United States and nearly 490,000 Americans have died from the virus, according to data complied by Johns Hopkins University.

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Biden administration to invest $1.6 billion to expand testing in schools, genomic sequencing - WGN TV Chicago

Ancient DNA has revolutionized how we understand human evolution, revealing how populations moved and interacted and introducing us to relatives like the Denisovans, a "ghost lineage" that we wouldn't realize existed if it weren't for discovering their DNA. But humans aren't the only ones who have left DNA behind in their bones, and the same analyses that worked for humans can work for any other group of species.

Today, the mammoths take their turn in the spotlight, helped by what appears to be the oldest DNA ever sequenced. DNA from three ancient molars, one likely to be over a million years old, has revealed that there is a ghost lineage of mammoths that interbred with distant relatives to produce the North American mammoth population.

Mammoths share something with humans: Like us, they started as an African population but spread across much of the planet. Having spread out much earlier, mammoth populations spent enough time separated from each other to form different species. After branching off from elephants, the mammoths first split into what are called southern and steppe species. Later still, adaptations to ice age climates produced the woolly mammoth and its close relative, the North American mammoth, called the Columbian mammoth. All of those species, however, are extinct, and the only living relatives are the elephants.

We have obtained DNA from two of these species, the woolly and Columbian mammoths. These revealed both a number of adaptations to cold climates and a small degree of interbreeding, as woolly mammoths made their way into North America and contributed a small amount (about 10 percent) to the genome of the Columbia population.

The new work focused on mammoth teeth found in Siberia, where conditions have favored both the preservation of remains and the preservation of the DNA they contain. The teeth come from layers of material that appear to have been deposited at the start of the most recent glacial period, which is when the ancestors of the woolly mammoth population should have been present in the area.

We don't have precise dates for any of the teeth, as they appear to be too old for carbon dating. Instead, dates have been inferred using a combination of the species present in the deposits and the known timing of flips in the orientation of Earth's magnetic field. In addition, the shape of the teeth provides some hints about what species they group with and some further indication of when they were deposited. In all, one tooth is likely to be at least 500,000 years old, another about a million years old, and a third somewhat older still.

Previously, the oldest DNA obtained from animal remains is roughly the age of the youngest of these samples. But the researchers were able to recover some elephant-like DNA from each of the molars, although it was badly fragmented, and many individual bases were damaged. Researchers were able to isolate the full mitochondrial genome for each of the three teeth, as each cell contains many copies of this genome in each of its mitochondria. Only fragments of the nuclear genome could be obtained, howeverat most, about 10 percent of one genome, and at worst under 2 percent. (Less than 2 percent is still tens of millions of individual bases.)

Using the differences between the mammoth and elephant DNA and assuming a constant rate of mutation, the research team was able to derive independent dates for when each of the animals that left a tooth must have lived. Based on the mitochondria genome, the dates were 1.6 million, 1.3 million, and 900,000 years ago. For the two that had enough nuclear genome to analyze, the dates were 1.3 million and 600,000 years ago. The DNA-based dates for these two lined up nicely with each other and the date of the material they were found in. The oldest sample might be older than the deposit it's in, and thus it might have been moved after death.

While these dates are fairly uncertain, they pretty clearly place two of the samples as the oldest DNA ever obtained from animals. And it would mean that these mammoths were living in Siberia shortly after ice-age conditions prevailed, although before there was a clear woolly mammoth lineage. They'd also predate the known appearance of mammoths in North America.

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Million-Year-Old DNA Rewrites Mammoths' Evolutionary Tree - WIRED

If the double helix is an icon of the modern age, then the genome is one of the last grand narratives of modernity, writes Lara Choksey in her new book, Narrative in the Age of the Genome. Hybridizing literary criticism with a genre-spanning consideration of a dozen distinct literary works, and imbued throughout with deep concern for the peripheral, the possible, and the political, the book seeks to challenge the whole imaginative apparatus for constructing the self into a coherent narrative, via the lexicon and syntax of the molecular.

To a reading of Richard Dawkins's The Selfish Gene (1976) as a repudiation of class struggle and E. O. Wilson's Sociobiology (1975) as a defense of warfare, Choksey juxtaposes another kind of ambiguous heterotopia in which genetic engineering is a tool of neoliberal self-fashioning. In Samuel R. Delany's Trouble on Triton (1976), Bron, a transgender ex-gigolo turned informatics expert, is caught between sociobiology and the selfish gene, between the liberal developmentalism of progressive evolution, and the neoliberal extraction and rearrangement of biological information. Even the undulating interruptions and parentheticals of Bron's thoughts [mimic] the description of the activation and silencing of genes, she suggests, tying together gene and genre in a way that encapsulates neoliberal alienation.

Choksey next explores the ways in which collectivist fantasies of biological reinvention under Soviet Lysenkoism fused code and cultivation through a close reading of Arkady and Boris Strugatsky's Roadside Picnic (1972) in which cultivated utopian dreamworlds become contaminated by alien forces, resulting in fundamental ecological transformations beyond the promised reach of human control. The novel brings to light not forgotten Soviet utopias but literal zombies and mutations. In a world where planned cultivation fails entirely in the face of the unfamiliar, even as new biological weapons are being developed, Earth itself viscerally reflects a fractured reality of lost promisesa world in crisis with all meaning gone, and survival itself a chancy proposition.

Framed as a family history, The Immortal Life of Henrietta Lacks is actually a horror story, argues Choksey.

As the promise of precision medicine emerged, so too did new forms of memoir. In Kazuo Ishiguro's Never Let Me Go (2005) and the film Gattaca (1997), for example, the traditional aspirational narrative of a pilgrim's progress is subverted: As the unitary subject disappears into data, algorithms, and commodities, a new grammar of existence emerges, albeit one in which the inherited problems of the pastracism, ableism, and the fiction of heteronormativityremain ever-present.

In Saidiya Hartman's Lose Your Mother (2006) and Yaa Gyasi's Homegoing (2016), Choksey sees a reorientation of genomics away from the reduction of self to code and toward new forms of kinship and belonging that offer a reckoning with the histories of brutalization and displacement upon which liberal humanism is founded. Even as genomics seeks to locate the trauma of enslavement at the level of the molecular, communities seeking reunion and reparation know that technology alone cannot do the cultural work of caring for history that narrative can offer.

Reading Rebecca Skloot's The Immortal Life of Henrietta Lacks (2010) as a biography of Black horror which tries, time and again, to resolve itself as family romance, Choksey identifies the perils of narratives unable to recognize their own genre. She argues that by blurring the lines not between fact and fiction but between horror and family history, the dehumanization of Black lives as experimental biomatter echoes inescapably with larger histories of the extraction of Black flesh for the expansion of colonial-capitalist production.

What emerges as most compelling out of this entire tapestry of readings is the author's interpretation of the limits and failures of the extraordinary cultural power of the genome. Concluding that genomics has privileged a particular conception of the human that is in the process of being reconfigured, Choksey ventures that the uncomplicated subject, the Vitruvian Man of the Human Genome Project, has reached its end. What is left is neither dust, stardust, nor a face erased in the sand (as Foucault would have it) but rather whatever might emerge next from the unwieldy kaleidoscope of possible meanings.

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Genomics and genre - Science Magazine

From the start of the coronavirus outbreak, technology from Illumina has been at work helping defeat the pandemic. As a result of two Chinese university teams using the US biotechs sequencing equipment, the genome (or genetical material) of the coronavirus was published on January 10, 2020 and vaccines from Oxford University/AstraZeneca, Moderna and Pfizer-BioNtech vaccines were designed within days of this blueprint being revealed.

The latter two are the worlds first genome-based vaccines and have been developed without the companies ever needing to have the virus on site. Sequencing is instrumental to understanding not only the make-up of the virus, but its epidemiology, how it mutates, how it evolves, and also to develop a vaccine for it, says Illumina CFO Sam Samad.

One year on and sequencing the virus is as important as ever in the pandemic fight. Illuminas technology is powering genomic surveillance in countries around world, such as at the Sanger Centre in Cambridge where the COG-UK team identified the new B1.1.7 variant; and in the US where Illumina is working with the US CDC and the company, Helix, to plot the path of new variants across US States.

Our ability to use sequencing to do surveillance, is critical. We need to understand how the virus is mutating and how its also transmitting across communities. Surveillance will also tell us whether the virus is evolving to escape the vaccines that are now being delivered in most countries.

That really underscores the importance of our technology in this fight, says Samad, who joined the San Diego, California-based firm at the start of 2017. Other pandemics will happen in the future, so the question also becomes how do we prevent them? How do we, catch them before what happened with Covid repeats? asks the Canadian.

Illumina was founded in 1998 based on BeadArray technology discovered at Tufts University. Arrays require a prior knowledge of the genome of the sample being investigated. In 2007, Illumina acquired the UK-based company, Solexa, for its next generation sequencing (NGS) technology which Illumina has gone on to develop. NGS doesnt require any understanding of the sample to be analysed and can work out the full genome of any organism.

Illumina has gone on to develop a range of products servicing the sequencing, genotyping, gene expression and proteomics markets has resulted in the rapid growth of its share price- resulting in the firm having a market value of $54bn by the start of the year.

Major sites have been developed in Foster City, near San Francisco, Cambridge in the UK focusing on R&D, and an Asian hub in Singapore combining shared services and manufacturing functions, as well as a major plant in China.

Infectious diseases is just one area of focus for Illumina. Another is oncology, where the company is working with a number of pharma and biotech companies to develop cancer testing to determine which medicines are best for which cancer patients. NGS is also fundamental to identifying the cause of rare genetic diseases in families; and to understanding the chromosomal health of an unborn baby through non-invasive prenatal testing (NIPT).

Oncology is now our biggest area, but we also work in genetic diseases, and reproductive health and non-invasive prenatal testing, taking sequencing and evolving it into a standard of care in health systems around the globe. Just shy of 50 percent of our revenues are in the clinical setting, says Samad.

A big change in Illuminas offering came five years ago when it pivoted from a mainly research approach, supplying instruments, reagents and consumables to academic labs and large genome centres, to focusing on clinical applications of genomics. It acquired Verinata Health, a leading provider of NIPT, and with it, NGS and Illumina started to become as familiar to clinicians as they had been to scientists.

The speed of development reflects the need to innovate in a fast-changing area of science. I think we have an obligation through our technology to move fast.

What you thought was possible 10 years ago, is completely different than what we think is possible today. Who would have thought that through sequencing, we could offer early screening to potentially predict, find it and cure cancer before it becomes deadly, he says.

The pace of development was challenged by the coronavirus pandemic where the majority of staff had to move to remote working but having lab staff designated essential workers meant operations could continue unabated. It meant R&D and manufacturing staff could come to the labs to continue work, says Samad.

A colour coding of sites, from green meaning nothings wrong to red, requiring all staff working remotely, except for essential functions was devised. At some point, most of our sites became really red and orange, resulting in 7,500 out of 8,000 staff working from home, but everybody handled it really well, he says.

Samad came into Illumina with a skill set in finance developed across the healthcare sector. After completing a finance degree and MBA at McMaster University in Ontario, he joined US pharma giant Eli Lilly where over the course of 12 years he rose from being a financial analyst to finance director of the groups Swiss operation before finishing as CFO of Eli Lilly Canada.

It was an opportunity to develop the rigour and discipline demanded by working in finance in a global player. I cant emphasise enough how important it is to get some experience in a large, well-run disciplined institution like Eli Lilly. It was really, really important for me just to get those building blocks and foundations in my career, he says.

But it was at Cardinal Healthcare, another major US player, that he went on to group leadership roles, as CFO of its pharma segment and then treasurer of the whole firm, positions demanding strong decision-making. If you get it wrong, you can send the company into a tailspin that might mean it goes bankrupt, because youre talking about debt issuances and capital availability, he says.

There was also the challenge of addressing the expectations of debt investors, where conservatism pays off in terms of how you manage your cash position, adds Samad.

The opportunity to become group CFO at Illumina offered the chance to join a fast growth company with an offer based on cutting edge science. I felt there was so much runway ahead as the space was so under-penetrated, he says.

What he could bring was the discipline needed for a business that had listed 20 years ago but he says was still operating in an accelerated growth, start-up mode, with some processes not having been fully built out.

What Samad sought to develop was a stronger engagement between the finance team and the rest of the business in an organisational structure for optimally supporting the business through single point accountability.

We needed, for example, a research and development (R&D) person in finance that supports the R&D organisation and another in finance supporting the commercial organisation, and another specifically supporting the product side of the business.

The first six months of Samads time finessing the finance function was a process of evolution. You dont get a structure right the first time, you do it incrementally, and you do it over time, he says.

A major innovation was devising exhaustive and comprehensive dashboards on data visualisation software Tableau covering everything from revenues to balance sheets and R&D. Its reviewed by the executive team twice a month, but I want people to have access 24/7.

That helps with speed of decision making, but it also helps in terms of managing bandwidth and understanding resource constraints on the organisation. because youre not having to ask people all the time to run reports for you, adds Samad.

Despite the challenges presented by the coronavirus pandemic, in September 2020, Illumina announced the proposed $8bn acquisition of cancer screening specialist Grail. The deal raised some eyebrows in the market given that Illumina had created and spun out Grail just four years previously, but the move reflects the willingness to consider any action that can bring together the right ingredients for value creation, even if it appears unwieldy.

Samad says the decision reflects a strategic assessment that Grails proposition today: using blood-based tests known as liquid biopsies to catch cancer early, would fit well in the group, after we had stepped out of the space, to allow it to thrive and evolve through $2bn of R&D funding.

Following fantastic, really promising results for the Grailtests, the decision was taken in late 2019 to acquire the business to grow Illuminas footprint in cancer. But what was crucial to explain to the market was that its a $60bn target market thats totally incremental to us, says Samad.

You need to set the stage in terms of how you explain it to your shareholders, to make sure they get why we can accelerate this market with our commercial capability, with global our operations, he adds.

Samad says he balances him time between priorities such as explaining Grail the story to investors, to focusing on areas for funding over the next five-year cycle.

These are often very difficult conversations, that are always emotional. So you need your people to help and you need to have a good rapport with your executive team, your peers and partners to do that as well, he says.

He says that without his key staff in accounting, treasury, FP&A, and leaders across global regions, I wouldnt be able to have the capacity and bandwidth that would allow me to focus on the key things, he explains.

Samad says closing the Grail deal created a unique set of challenges with the restrictions that we have in terms of travel. But a big focus of ours is that even in times of global crisis, we are really focused on making sure that we continue to find opportunities to move our strategy forward.

We believe these times present a unique opportunity for companies that really focus on executing on their strategy. If they are bold and make big steps, they will come out on the other side of this ahead, he adds.

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Illumina CFO on using genome tech to beat pandemic - Financial Director

ILLUSTRATION: MICHELLE KONDRICH

The availability of a fully sequenced human genome and genome-wide analyses of genetic variation have made DNA-based ancestry tests possible. These consumer DNA tests are now widely marketed as a way to discover or confirm family history. But what do they really tell us about our past, and what do they leave out? We asked young scientists to tell us about their family traditions, stories, and culture, and how they understood their DNA test results in the context of their lived experiences. Their stories are below. To read more reflections by young scientists, find past NextGen Voices pieces at https://science.sciencemag.org/collection/nextgen-voices. Follow NextGen Voices on Twitter with hashtag #NextGenSci. Jennifer Sills

My family comes from Jamaica and the Virgin Islands. There is no meal I would rather have than my mom's home-cooked traditional Jamaican food. Now living in Florida, my mom grows many fruits and vegetables native to Jamaica in a garden that occupies her entire yard. When I visit, we spend most of our time together outside picking fresh mangoes, ackee (a tropical fruit grown in Jamaica), or whatever else happens to be in season. On Christmas, she makes oxtail (a kind of beef stew, my personal favorite), fried dumplings, and ackee with saltfish (its traditional complement of salted cod). These foods are well-spicedalthough not always spicyand flavorful.

Where my family originated is mostly hearsay, and the full history beyond a few generations is hard to trace. My DNA test results confirmed that we have some background in Europe and likely moved to the Caribbean through the slave trade. The details echoed a story on my mom's side of the family that one of our ancestors was the child of an Irish slave master and a woman he enslaved.

I have mixed feelings about the business model of consumer DNA test companies, which make their profit based on the use of others' genetic informationin my mind, the most personal information one can share. However, my mom really wanted me or my dad to do the test to see how that side of our ancestry looked. I chose a company that gives users more control over who can access the results. Of course, these tests are not as accurate for those of us from non-European backgrounds, but the results were roughly what I expected, and it is humbling to think about where our family began compared with where it is now.

Gregg Duncan Fischell Department of Bioengineering, University of Maryland, College Park, MD 20742, USA. E-mail: gaduncan{at}umd.edu

My family is Han, the largest nationality of China. Like most families in China, we celebrate the Spring Festival (Chinese New Year) by gathering together to make and eat jiaozi (dumplings filled with vegetables and meat), which are shaped like ancient Chinese gold ingots to symbolize wealth. We hang festival couplets (two lines of poetry with the same number of words) that are painted along with intricate designs on red paper, and we put red lanterns and red candles on display throughout the house; the decorations symbolize happiness and protect us from the mythical monster named Nian, who is said to be afraid of the color red. While we wait for the New Year to arrive, we listen to Hebei Bangzi, the local opera, which sounds similar to the Beijing opera but is more difficult for people outside Hebei province to understand because the singers use pronunciations unique to the region. In my hometown (Shijiazhuang, Hebei), people of the same surname gather together to extend best wishes to their elders before the first sunrise of the new year.

Such traditions are a reminder that my surname (Ji) is not common in China. I hoped that finding out more about my family's origins would help to explain my unusual name. My DNA test results told me that 46.34% of my genome came from North China (Han), 20.13% from South China (Han), and 12.21% from Northeast Asia (Japan). I was disappointed that the results contained no detailed information that I found useful. I do not know how many Chinese people have a genetic pattern similar to mine, andunlike scientific researchthe company did not give me the raw data of my genome. Without more information about how the company analyzed my genomic data, I don't know what conclusions I can draw or even whether I should believe the test results.

Yongsheng Ji Division of Life Science and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, China. Email: jiys2020{at}ustc.edu.cn

Fifteen years ago, I probably would have said that my family didn't have a French cultural identity, despite being raised in France. Today, after having been expatriated 10 years in New Zealand, I can confirm that we have a strong French cultural identity, especially when it comes to food. Yet, after we returned to France 3 years ago, our attachment to our home country and its culture and traditions did not feel quite the same. I believe that we unintentionally took bits of New Zealand back to France with us.

Our ever-evolving celebration of Mardi Gras encapsulates our cultural journey. Before our move, we had always celebrated the French holiday in its traditional (if less religious) form. Around the end of February, we would make and eat loads of French crpes, and kids would dress up in festive costumes and attend the carnival. After our move, we discovered that New Zealanders do not observe Mardi Gras, so we adopted a different yet similar tradition, which was brought to the country from overseas and stuck: Halloween. Every year on the 31st of October, my eldest boy dressed up in a scary costume. But because good food is so deeply rooted in our culture, Halloween candy didn't feel sufficient. To supplement the prepackaged treats, we created our own tradition of the Halloween scary lunch. Each year, I would prepare a lunch box filled with funny and scary little monsters, skeletons, and ghosts made of pancakes, carved fruits, and (for the mummies) baked sausages in pastry strings.

Now back in France, we have resumed our celebration of Mardi Gras in February. The kids dress up for school and for carnivals, just like Halloween, but with an emphasis on festive instead of scary, and we make crpes, as we've done in the past. We've also kept our own multicultural family traditions. To adapt our New Zealand Halloween lunches, we now have a Halloween-themed French dinner in October. We've also updated the tradition of hiding a fve (trinket) in our galette des rois (king cake) by using a koru necklace (a traditional kiwi artifact) instead.

Our unique and changing traditions showed me that we could be open to incorporating new values and ideas when we learned the results of our DNA tests. My husband and I are both researchers in ecology and environmental genetics, manipulating DNA data daily and studying insect population genetics. It seemed only natural that we would want to see our own DNA test results. We originally thought that the genetic admixture might be quite high within our family home given that we were born 12,000 km apartI grew up in northern France, and he was raised on the French island of La Runion in the Indian Ocean. We were quite surprised by the results. For instance, I learned that I had ancestors from Italy and Scandinavia but very little French or Western European lineage, whereas my husband, despite being born in the Southern Hemisphere, has more Western European lineage than I do. (His results could perhaps be explained by the fact that half of the first settlers in La Runion were from Brittany.) Although my husband has ancestors in many parts of the world where I do not (such as India, Africa, and Indonesia), we share an unexpectedly high rate of ancestry from the Iberian Peninsula (Spain and Portugal). The results have not changed our lives, but it is interesting to know that, genetically, we are more an Iberian family than a French one! We now want to travel to and discover more about the culture of these southwestern parts of Europe and pass on this heritage to our children. As ecologists, we are curious about the natural and geological histories of the Iberian region, but we would make food an important part of the trip as well. They may not have French crpes in Portugal, but I have heard that the delicious bolo lvedo (Portuguese muffins) are not to be missed.

Marie-Caroline Lefort Cellule de Valorisation Pdagogique, Universit de Tours, Tours, France. Email: marie-caroline.lefort{at}univ-tours.fr

As a Jewish woman born in Iran and living in Israel, I feel connected to the ancient history of my people. Because it is rare to find an Iranian woman in science who keeps Jewish traditions, I feel a responsibility to manifest all the good that is in each part of my background.

My family celebrates the traditional holiday of Rosh Hashanah (the Jewish New Year). Wearing white clothing to symbolize purity, we light candles and look into the flames as we give thanks and ask for blessings in the coming year. We celebrate this contemplative holiday with a festive meal steeped in symbolism and tradition. We eat apples dipped in honey and pomegranates to symbolize our hopes for a sweet, peaceful, happy new year that is full of good deeds. The honey represents sweetness, and the apple tree is the only tree that has more fruit than leaves, reminding us that we should maximize our purpose in this world. The numerous seeds in pomegranates, a native fruit of ancient Persia, symbolize the many good deeds we should carry out during the coming year. We also make a traditional Iranian-Jewish stew out of quince, a native fruit of west Asia (including Iran and Israel) that looks like an apple. The sweet smell fills the entire house with a magical floral and fresh perfume. During Rosh Hashanah, the shofar (an ancient musical instrument typically made of a ram's horn) is blown 100 times. The sound marks the time to make our wishes for the new year, which we read in Hebrew.

My DNA test results show that I am mostly Persian, with a very small percentage (0.8%) of Egyptian in my ancestry. The data echo the Biblical and rabbinical stories that I consider my roots. Our cultural history tells us that our ancestors were in ancient Egypt for hundreds of years before moving to Israel with Moses. In 722 BCE, the Jews were exiled from Israel to other regions, including Iran. My father was born in a city that was first settled by the exiled Jewish people from Israel, and my mom is from a city that is well known in Iran as the site of the story of Esther and Mordechai, traditionally told during the holiday of Purim. My family moved to Israel after the revolution in Iran in 1979. My DNA results mirror both these ancient tales and my own family's story.

Ruty Mehrian-Shai Pediatric Hemato-Oncology, Brain Cancer Molecular Medicine, Sheba Medical Center, Ramat Gan, 52621, Israel. Email: ruty.shai{at}sheba.health.gov.il

I've always struggled with being identified as simply Indian. My name reflects my Indian heritage better than I do, as a Montreal-born, New York City native living in Louisiana. No DNA test could reflect the mix of American and Indian cultural practices that my family has created. Take, for example, American Thanksgiving, which my family co-opted when I was young and combined with a traditional West Bengali feast. At our table, we served the turkey alongside traditional Indian luchi (oil-fried puffed dough) and fusion dishes such as vegetarian shepherd's pie with Indian spices. Because my birthday falls near Thanksgiving, the meal was often followed by a turkey-shaped ice cream cake, Indian sweets like jalebi (a bright orange pretzel of fried sweet dough), gulab jamun (fried syrupy-sweet milk balls), and a spiced tea. We did adhere to the American tradition of overstuffing ourselves with food.

During the holiday, we listened to Bollywood pop, with high-pitched Indian women singing in Hindi or Bengali. Later in the season, my father would mix in some Nat King Cole or Frank Sinatra, or we would play an album from jazz pianist Vince Guaraldi. Being in Queens, I would always play Christmas in Hollis by the Queens-native hip-hop group Run DMC. My parents enjoyed it about as much as I did their Bollywood music, which is to say, not much.

In December, the large extended family of cousins, uncles, and aunts (all with a different honorific based on their birth position relative to my parents) would come over, each removing their shoes at the door out of respect. The men, in sweaters and ties, played bridge cross-legged in a corner on the floor. The women, in saris and their finest gold necklaces and earrings (gaudier than any of the jewelry worn by the hip-hip artists I worshiped), congregated in the dining area, where they teased each other, told stories in Bengali, and prepared meals. Food was served constantly from the moment the first guests arrived until they left. The smell of food cooking, mostly oil and spices, radiated and permeated through every fabric of the house. Chatter, the sounds of food frying, and playful arguing filled every room with noise. Our home was festively decorated; Santa Claus had equal billing with Durga, Kali, and Ganesh.

The kids watched American football or challenged each other to an Indian game called carrom, which is similar to billiards but played on a flat smooth table on the floor. Players use their fingers to flick flat wooden discs into different corner pockets. We would play different tournament styles and use a mix of Bengali and English to taunt and tease each other over missed shots or lucky wins.

Before our current chapter as Americans, my family's Indian past stretches back to time immemorial, but India has a complicated history of invasions and rule. I hoped a DNA test would help clarify some ancestry questions. I wanted the results to say 25% Genghis Khan, 25% Gandhi, 25% Alexander the Great, and 25% unknown. What I got was 64% Central Asian, 30% South Asian, 3% Eastern European, 2% Southeast Asian, and 1% Siberian. So, I could claim Genghis, Gandhi, and Alexander! But of course, not really. I wondered when and where the mingling of my different geographic ancestors took place and if the results were more a reflection of the current genetic reference populations in those areas. The DNA results didn't make me feel differently about my identity, and they were not as interesting as the results I received from a genetic profile that revealed an inversion in one of my chromosomes. That genetic result made me realize how hardy our genomes are and how similar we are as humans; even the 1% or so that makes each of us unique is almost meaningless when considering the bigger picture.

Prosanta Chakrabarty Louisiana State University Museum of Natural Science, Baton Rouge, LA 708033216, USA. Email: prosanta{at}lsu.edu

ILLUSTRATION: MICHELLE KONDRICH

Born in South America, I identify as Latina and have always been aware of my mixed ethnicity. My family's celebration of Christmas and Novena (the previous 9 days, an important observance in Colombia) exemplifies our love of food, music, and dance. During the first 8 days, family and friends meet at different houses to share deep-fried cheesy dough and sweets. On Christmas day and the morning after, we eat homemade Colombian tamales wrapped in plantain leaves and boiled for hours, and we drink hot chocolatefirst adding salty cheese to the mugs and eating it with a spoon once it has melted (a delicacy unique to Bogot, Colombia's capital). Sometimes we also eat cheese arepas (flat corn bread) and almojabnas (cheese bread of Spanish-Arab origin). Meanwhile, my mum prepares about 20 liters of her famous ajiaco, a traditional soup from the Bogota plateau. She uses three kinds of potatoes (one of them endemic to the Northern Andes), guascas (Galinsoga parviflora), corn, chicken, capers, and cream. Toward the end of the day, the whole family gathers for a bowl of ajiaco. We admire our araucaria tree, decorated with lights and ornaments, and the creatively assembled nativity scene (often including llamas, lions, jaguars, and the occasional dinosaur) while waiting for midnight to come.

My family seems to carry music in our blood. There is always a moment when my uncle plays the guitar and everyone else joins in with percussion and voices, singing the melodies of cumbia, vallenato, and bambucomusical styles incorporating strings and accordions from Europe, wind instruments from Indigenous communities, and African drums. The upbeat tunes belie the bittersweet themes in the Spanish lyrics. Soon, everyone is dancing to the energetic, fast-moving rhythms of cumbia, salsa, and merengue. Salsa originated with the Latin and Afro-Latin son cubano and jazz musicians from the Bronx in the United States. The music later made its way to Colombia, where it developed into something new, incorporating cumbia and vallenato elements and a faster dancing style.

I took a DNA test because I work in the fields of population genomics and phylogenomics and thought it would be fun to see my own genome sequences. Half of the sites sequenced on my genome were assigned to populations in Spain, Morocco, and West Africa; the other half to Native American populations. The results were not a surprise, but they encouraged me to dig deeper into my family's history. I wish I could learn about and celebrate the Native American traditions of my ancestors, but most were never documented and are now lost. Important traditions are kept in the Amazon regions, such as chontaduro dancing, where communities share the chontaduro fruit (from the Bactris gasipaes palm) and drinks to celebrate abundance and usher in a good fishing season. Traditions around the cassava, plant growing seasons, and hunting also still take place, but because I grew up in the city, I don't feel personally connected to them. I do take pride in using the words from Quechua, Muisca, and even Arabic languages that have been assimilated into Colombian Spanish.

We knew my grandfather was Indigenous from the south (as the government labeled him back in the day), but the DNA test results suggest that our Indigenous ancestry could have been more recent and likely than we thought. I found the test interesting; I received a set of raw data that I can analyze myself, and the results brought my father and me together in a quest for the documents and stories surrounding my family.

Maria Fernanda Torres Jimenez Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden. Email: mftorres27{at}gmail.com

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Beyond DNA: The rest of the story - Science Magazine

Our machines could handle thousands or hundreds of thousands, said Dr. Neville Sanjana, a scientist with a lab at the New York Genome Center in Lower Manhattan. So the capacity is just not the issue.

The issue for research laboratories strangely enough, amid a pandemic that has probably infected more than a quarter of New Yorkers is access to samples. In New York, there is no high-volume pipeline of positive virus samples from hospitals or testing sites to research laboratories to conduct genetic surveillance.

Its really just organizing that sample collection that, I think, is whats missing, said Dr. Sanjana, whose research has involved searching for which medicines might block infection by inhibiting the human genes that the coronavirus hijacks.

What is needed, scientists said in interviews, is for the city or another entity to essentially bifurcate the current coronavirus testing process. Each day, tens of thousands of New Yorkers provide swabbed samples, which are generally sent to a few large laboratories for testing. If those labs could set aside a portion of the samples, those portions could later be used for genome sequencing if they turned out to be positive.

Its solvable, but it needs resources and it needs people to coordinate, Professor Heguy said, as she listed the necessary steps: A portion of the original sample would need to be set aside; RNA would need to be isolated from it; and someone would need to transport the RNA samples to a laboratory that does genome sequencing.

The citys goal of expanding sequencing at least tenfold will require enlisting a range of outside laboratories and research projects, big and small. The city anticipates that the largest share of the genomic sequencing will happen at a laboratory in Long Island City, Queens, that is run by a small robotics company.

The company, Opentrons, also runs a facility in Manhattan called the Pandemic Response Laboratory. That laboratory was built last year to help the city solve the testing crisis that emerged during the summer, when big commercial laboratories were struggling to handle the soaring caseload. People were having to wait several days, and sometimes a week or two, for coronavirus test results. The laboratory now tests 20,000 samples a day.

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New York City Barely Tests for Virus Variants. Can That Change? - The New York Times

Twenty years ago this month, the first draft of the human genome was publicly released. One of the major surprises that came from that project was the revelation that only 1.5 percent of the human genome consists of protein-coding genes.

Over the past two decades, it has become apparent that those noncoding stretches of DNA, originally thought to be junk DNA, play critical roles in development and gene regulation. In a new study published today, a team of researchers from MIT has published the most comprehensive map yet of this noncoding DNA.

This map provides in-depth annotation of epigenomic marks modifications indicating which genes are turned on or off in different types of cells across 833 tissues and cell types, a significant increase over what has been covered before. The researchers also identified groups of regulatory elements that control specific biological programs, and they uncovered candidate mechanisms of action for about 30,000 genetic variants linked to 540 specific traits.

What were delivering is really the circuitry of the human genome. Twenty years later, we not only have the genes, we not only have the noncoding annotations, but we have the modules, the upstream regulators, the downstream targets, the disease variants, and the interpretation of these disease variants, says Manolis Kellis, a professor of computer science, a member of MITs Computer Science and Artificial Intelligence Laboratory and of the Broad Institute of MIT and Harvard, and the senior author of the new study.

MIT graduate student Carles Boix is the lead author of the paper, which appears today in Nature. Other authors of the paper are MIT graduate students Benjamin James and former MIT postdocs Yongjin Park and Wouter Meuleman, who are now principal investigators at the University of British Columbia and the Altius Institute for Biomedical Sciences, respectively. The researchers have made all of their data publicly available for the broader scientific community to use.

Epigenomic control

Layered atop the human genome the sequence of nucleotides that makes up the genetic code is the epigenome. The epigenome consists of chemical marks that help determine which genes are expressed at different times, and in different cells. These marks include histone modifications, DNA methylation, and how accessible a given stretch of DNA is.

Epigenomics directly reads the marks used by our cells to remember what to turn on and what to turn off in every cell type, and in every tissue of our body. They act as post-it notes, highlighters, and underlining, Kellis says. Epigenomics allows us to peek at what each cell marked as important in every cell type, and thus understand how the genome actually functions.

Mapping these epigenomic annotations can reveal genetic control elements, and the cell types in which different elements are active. These control elements can be grouped into clusters or modules that function together to control specific biological functions. Some of these elements are enhancers, which are bound by proteins that activate gene expression, while others are repressors that turn genes off.

The new map, EpiMap (Epigenome Integration across Multiple Annotation Projects), builds on and combines data from several large-scale mapping consortia, including ENCODE, Roadmap Epigenomics, and Genomics of Gene Regulation.

The researchers assembled a total of 833 biosamples, representing diverse tissues and cell types, each of which was mapped with a slightly different subset of epigenomic marks, making it difficult to fully integrate data across the multiple consortia. They then filled in the missing datasets, by combining available data for similar marks and biosamples, and used the resulting compendium of 10,000 marks across 833 biosamples to study gene regulation and human disease.

The researchers annotated more than 2 million enhancer sites, covering only 0.8 percent of each biosample, and collectively 13 percent of the genome. They grouped them into 300 modules based on their activity patterns, and linked them to the biological processes they control, the regulators that control them, and the short sequence motifs that mediate this control. The researchers also predicted 3.3 million links between control elements and the genes that they target based on their coordinated activity patterns, representing the most complete circuitry of the human genome to date.

Disease links

Since the final draft of the human genome was completed in 2003, researchers have performed thousands of genome-wide association studies (GWAS), revealing common genetic variants that predispose their carriers to a particular trait or disease.

These studies have yielded about 120,000 variants, but only 7 percent of these are located within protein-coding genes, leaving 93 percent that lie in regions of noncoding DNA.

How noncoding variants act is extremely difficult to resolve, however, for many reasons. First, genetic variants are inherited in blocks, making it difficult to pinpoint causal variants among dozens of variants in each disease-associated region. Moreover, noncoding variants can act at large distances, sometimes millions of nucleotides away, making it difficult to find their target gene of action. They are also extremely dynamic, making it difficult to know which tissue they act in. Lastly, understanding their upstream regulators remains an unsolved problem.

In this study, the researchers were able to address these questions and provide candidate mechanistic insights for more than 30,000 of these noncoding GWAS variants. The researchers found that variants associated with the same trait tended to be enriched in specific tissues that are biologically relevant to the trait. For example, genetic variants linked to intelligence were found to be in noncoding regions active in the brain, while variants associated with cholesterol level are in regions active in the liver.

The researchers also showed that some traits or diseases are affected by enhancers active in many different tissue types. For example, they found that genetic variants associated with coronary heart disease (CAD) were active in adipose tissue, coronary arteries, and the liver, among many other tissues.

Kellis lab is now working with diverse collaborators to pursue their leads in specific diseases, guided by these genome-wide predictions. They are profiling heart tissue from patients with coronary artery disease, microglia from Alzheimers patients, and muscle, adipose, and blood from obesity patients, which are predicted mediators of these disease based on the current paper, and his labs previous work.

Many other labs are already using the EpiMap data to pursue studies of diverse diseases. We hope that our predictions will be used broadly in industry and in academia to help elucidate genetic variants and their mechanisms of action, help target therapies to the most promising targets, and help accelerate drug development for many disorders, Kellis says.

The research was funded by the National Institutes of Health.

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Epigenomic map reveals circuitry of 30000 human disease regions - MIT News