Category Archives: Transhuman News

The story so far: A consortium of scientists, which includes several of them from India, has mapped the genome of the Indian cobra considered to be among the most poisonous. Few snakes have had their genomes sequenced in detail and less so for a species of India. The Indian cobra genome sequencing is reportedly the most detailed blueprint of a snakes genes.

The Indian cobra, common cobra, spectacled cobra are names for the same species, Naja naja, and is part of the so called Big 4: the Indian cobra, the common krait (Bungarus caeruleus), Russells viper, and the saw-scaled viper. The quartet has long been considered responsible for most snake bites on the Indian subcontinent. The king cobra (Ophiophagus hannah), another poisonous snake, had its genome sequenced in 2013 by a research team in the U.K. (along with an international team). The foray into the Indian cobra genome involved making a map of its 38 chromosomes. Chromosomes are where DNA is tightly packed and the reptiles gene map unveiled in the latest issue of the scientific journal, Nature Genetics revealed over 12,000 genes. The human genome, in comparison, has 23 chromosomes and the estimated number of protein coding genes in the 20,000-25,000 range.

Knowing the sequence of genes could aid in understanding the chemical constituents of the venom and contribute to development of new antivenom therapies, which have remained practically unchanged for over a century.

According to the World Health Organisation, though the exact number of snake bites is unknown, an estimated 5.4 million people are bitten each year with up to 2.7 million envenomings. Around 81,000 to 1,38000 people die each year because of snake bites, and around three times as many amputations and other permanent disabilities are caused by snakebites annually. Somasekar Seshagiri of the SciGenome Research Foundation, Bengaluru (and the Molecular Biology Department, Genentech, Inc., South San Francisco, CA, U.S.), who is one of the authors of the study, used the genome and gene expression data from 14 different cobra tissues. Among the genes mapped are 139 toxin genes, or those that produce biological products specific to toxins. Nineteen of them are venom-specific, and expressed only in the venom gland. These are the constituents of venom that cause paralysis, internal bleeding and death associated with snakebite. Knowing these genes, the scientists argue in their paper, can help scientists design new antivenom using recombinant protein technologies. If genomes of more snakes are sequenced, there is a bigger possibility of genes commonly associated with venom production (across snake species) are identified and more broad-spectrum antivenoms are made.

Sequencing a genome is an important step to making antivenom but will not on its own solve the problem of making and supplying enough of the product to address the huge volume and variety of snakebites in India. A study in December by Kartik Sunagar, Assistant Professor at the Centre for Ecological Sciences, Indian Institute of Science, Bengaluru, said that though bites from 60 of 270 species of Indian snakes are known to kill or maim, the antivenom now available is effective only against the Big 4. The study is titled Beyond the big four: Venom profiling of the medically important yet neglected Indian snakes reveals disturbing antivenom deficiencies, published in the journal, PLOS Neglected Tropical Disease. For instance, the monocled cobra (Naja kaouthia), found in east and north-east India, is not among the Big 4, but its venom, tested in mice, is more potent than that of the Indian cobra. But the commercial antivenom is not effective against the monocled cobra. Sunagars research also shows that the antivenom is effective against the common krait in south India but is not against the same species in Punjab. His study also notes that while various medically important species of cobras (N. sagittifera, N. oxiana, N. kaouthia), kraits (B. andamanensis, B. fasciatus, B. niger, B. sindanus), vipers (Hypnale hypnale, Ovophis monticola, E.c. sochureki, Macrovipera labetina), coral snakes (Calliophis nigrescens, Sinomicrurus macclellandi), sea snakes (Pelamis platurus, Enhydrina schistose, Hydrophis cyanocinctus) and sea kraits (Laticauda colubrina), etc., are capable of delivering clinically significant and, even, fatal bites, specific antivenoms do not exist. This is because snake venom is surprisingly varied and comes in a staggering variety that has not been completely understood by scientists.

Antivenom is made by extracting venom from the snake and injecting small amounts into rabbits or horses. In the case of the polyvalent antivenom available in India, it is made by injecting it into horses. The antibodies that form are then collected from the domestic animals blood, purified and isolated. The first antivenom for snakes was actually made for the Indian cobra by Lon Charles Albert Calmette, a French scientist of the Pasteur Institute, in 1894. The process is considered laborious, expensive and time consuming. Using recombinant technology, the genetic sequence for each toxin can be pasted into a yeast or E. coli bacterium, and have them multiply. Then they can be compared to libraries of human antibodies to check which ones stick best and make stable antivenom.

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Will the sequencing of the Indian cobra genome help in discovering broad-based antivenoms? - The Hindu

NEW YORK--(BUSINESS WIRE)--Emendo Biotherapeutics, a leader in next-generation gene editing using synthetic biology to address untreatable diseases, today announced a Series B investment totaling $61 million led by AnGes, Inc., a Japan-based biopharma, reflecting its strategic interest in partnering with Emendo on the development of specific indications.

This financing provides a strong foundation from which we can accelerate our proprietary OMNI gene editing platform towards a broad clinical pipeline for addressing devastating untreatable diseases, said Dr. David Baram, President & CEO, Emendo Biotherapeutics. We are grateful for such strong support from so many high-quality investors and strategic partners including AnGes, OrbiMed Advisors, OrbiMed Israel Partners and Takeda Ventures who share our vision to translate this powerful science into transformative medicines.

Emendo Biotherapeutics is pioneering OMNI, a next-generation allele-specific gene editing platform that uses synthetic biology to expand what is possible in genome-editing. In 2019, Emendo granted an option to Takeda to use the OMNI nuclease gene editing program for two research and development targets. Emendo received an undisclosed investment from Takeda Ventures that was converted in the Series B.

Emendos OMNI technology enables precision gene editing while maintaining high efficiencies, uniquely addressing dominant indications such as Severe Congenital Neutropenia (SCN), caused by mutations in the neutrophil elastase gene ELANE. Dominant indications represent the vast majority of genetic diseases which until now have been untreatable.

About AnGes

AnGes is a Tokyo and Osaka, Japan-based biopharmaceutical company focused on the development and commercialization of gene-based medicines including gene therapy and oligonucleotide molecules. AnGes lead product HGF plasmid, which received conditional approval in Japan in 2019, is a DNA plasmid which encodes the human Hepatocyte Growth Factor (HGF) gene, an angiogenic (new blood vessel growth) factor for critical limb ischemia (CLI). The company is also developing NF-kB Decoy oligonucleotide for the treatment of inflammatory diseases. For more information please visit http://www.anges.co.jp.

About Emendo

Emendo Biotherapeutics is transforming the landscape of genome-editing based medicine through its use of novel CRISPR nucleases, advanced cutting-edge protein engineering platforms, diverse pipeline of clinical programs and extensive intellectual property portfolio. For more information please visit us at http://www.emendobio.com.

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Emendo Biotherapeutics Raises $61 Million to Advance Next Generation Genome Editing Therapeutics - Business Wire

Abstract

One of the hallmark behaviors of social groups is division of labor, where different group members become specialized to carry out complementary tasks. By dividing labor, cooperative groups increase efficiency, thereby raising group fitness even if these behaviors reduce individual fitness. We find that antibiotic production in colonies of Streptomyces coelicolor is coordinated by a division of labor. We show that S. coelicolor colonies are genetically heterogeneous because of amplifications and deletions to the chromosome. Cells with chromosomal changes produce diversified secondary metabolites and secrete more antibiotics; however, these changes reduced individual fitness, providing evidence for a trade-off between antibiotic production and fitness. Last, we show that colonies containing mixtures of mutants and their parents produce significantly more antibiotics, while colony-wide spore production remains unchanged. By generating specialized mutants that hyper-produce antibiotics, streptomycetes reduce the fitness costs of secreted secondary metabolites while maximizing the yield and diversity of these products.

Social insects provide some of the most compelling examples of divisions of labor, with extremes in morphological differentiation associated with highly specialized functions and reproductive sterility in all colony members, except the queen (1). However, conditions that select for division of labor are not limited to animals, and it has become increasingly clear that microbes offer unique opportunities to identify and study the mechanistic underpinnings of divisions of labor (28). First, microbes are typically clonal, which helps ensure that a division of labor is favored by kin selection (4). Second, microbial populations are highly social, often cooperating to carry out coordinated behaviors such as migration or biofilm formation that require the secretion of metabolically expensive public goods that can be shared among clonemates (9, 10). If these conditions are met, and investment in public good secretion trades off with fitness, divisions of labor are predicted to evolve (4, 11).

Here, we describe the cause and evolutionary benefits of a unique division of labor that has evolved in colonies of the filamentous actinomycete Streptomyces coelicolor. After germinating from unichromosomal spores, these bacteria establish multicellular networks of vegetative hyphae, reminiscent of fungal colonies (1214). Vegetative hyphae secrete a broad variety of public goods, such as chitinases and cellulases that are used to acquire resources, as well as a chemically diverse suite of antibiotics that are used to kill or inhibit competing organisms (1517). Streptomycetes are prolific producers of antibiotics and are responsible for producing more than 50% of our clinically relevant antibiotics (18). Although the terminal differentiation of Streptomyces colonies into vegetative hyphae (soma) and viable spores (germ) is well understood (1921), no other divisions of labor in these multicellular bacteria are known. However, opportunities for phenotypic differentiation are possible, because although colonies begin clonally, they can become genetically heterogeneous because of unexplained high-frequency rearrangements and deletions in their large, ~9-Mb linear chromosome (2225). The work we describe shows that these two phenomena are intertwined. Briefly, we find that genomic instability causes irreversible genetic differentiation within a subpopulation of growing cells. This differentiation, in turn, gives rise to a division of labor that increases the productivity and diversity of secreted antibiotics and increases colony-wide fitness.

Genomic instability and phenotypic heterogeneity have been observed in several Streptomyces species (2632), but there are no explanations for the evolution or functional consequences of this extreme mutability. To begin addressing this question, we quantified the phenotypic heterogeneity arising within 81 random single colonies of S. coelicolor M145 by harvesting the spores of each of these colonies and then replating the collected spores onto a new agar surface. Although most progeny are morphologically homogeneous and similar to the wild type (WT), notably aberrant colonies (Fig. 1A) arise at high frequencies (0.79 0.06%, mean SEM, ranging from 0 to 2.15%, n = 81) (Fig. 1A). Similarly high rates were obtained on two minimal media (MM: 2.13 0.14%; MM + casamino acids: 5.13 0.37%; mean SEM; n = 30 and n = 40, respectively) (fig. S1), thereby ruling out the possibility that these mutations are an artifact of rapid growth on rich resources. The differences we observe on these two media types also suggest that the mutant frequencies we estimate based on spore counts may underestimate their values within growing colonies, given that mutants may be compromised in growth or sporulation (as we confirm below). This is supported by the nearly twofold difference in mutant frequencies on MM + casamino acids compared to unsupplemented MM, where auxotrophs arising by mutation would be unable to persist. To determine the heritability of these aberrant phenotypes, we restreaked 15 random colonies from different plates onto a new agar plate, which revealed remarkable variability in colony morphology (Fig. 1B). Rather than reverting to the WT morphology, as would be anticipated if the initial heterogeneity was due to phenotypic plasticity or another form of bistability, the colonies derived from mutant colonies are themselves hypervariable, giving rise to up to nine diverse phenotypes from any single colony. Thus, in the course of two cycles of colony outgrowth, an array of colony types that differ in size, shape, and color emerged (Fig. 1B). Because our ability to discern colony heterogeneity is limited to only a few visually distinct phenotypic characters, we assume that these estimates of diversity are lower than their true level of occurrence.

(A) WT (top) and mutant (bottom) colonies and the frequency that mutants emerge from WT colonies on SFM agar (right). (B) Phenotypically diverse progeny (top) emerges after restreaking mutant colonies that vary in size, shape, and pigmentation. Representative colonies are shown. The bottom graph depicts the range of distinct morphologies that emerge after restreaking 15 random colonies. Each color represents a distinct colony phenotype.

Using whole-genome sequencing of eight random mutants, we confirmed that these isolates contained profound chromosomal changes. As shown in Fig. 2A, large genome deletions were observed at the chromosome ends in all eight strains. In three cases, we found an ~297-kb amplification on the left chromosomal arm flanked by the Insertion Sequence IS1649, encoded by SCO0091 and SCO0368. Average sequence coverage of the amplified region suggests that it contains between 2 and 15 copies of this amplification (Fig. 2A and fig. S2). Sequencing results were expanded using pulsed-field gel electrophoresis (PFGE) analysis of 30 mutant isolates (Fig. 2B and fig. S3). Consistent with our sequencing results, this analysis revealed that mutants contained variably sized deletions of up to ~240 or ~872 kb on the left chromosome arm and up to 1.6 Mb on the right chromosome arm, deleting more than 1000 genes. In addition, 8 of 30 strains contained the same large amplification between copies of IS1649 as noted above. These strains are conspicuously yellow, which might be caused by the overproduction of carotenoids due to the amplification of the crt gene cluster (SCO0185-0191) (3335). In addition to this and other phenotypic effects associated with these changes that are discussed below, deletions to the right chromosome arm cause the loss of two loci, argG (SCO7036) and cmlR1 (SCO7526)/cmlR2 (SCO7662), that result in two easily scorable phenotypes: arginine auxotrophy and chloramphenicol susceptibility, respectively. Scoring these phenotypes allows rapid determination of the minimal size of the deletion on the right chromosome arm in the absence of molecular characterization. Chloramphenicol susceptibility indicates a deletion of at least 322 kb, while the addition of arginine auxotrophy indicates a deletion of at least 843 kb (Fig. 2B).

Values in (A) correspond to the size (in kilobases) of genome deletions, while the hexagons represent an ~297-kb genome amplification. Each line in (B) depicts the range of deletion sizes (gray) in each mutant class, together with their respective frequencies from 30 sampled mutant strains.

Mutant strains were conspicuously pigmented when compared to their parental WT strains (Fig. 1). Because several antibiotics produced by S. coelicolor are pigmented, namely, actinorhodin, prodigines, and coelimycin P1, which are blue, red, and yellow, respectively, we tested whether mutant strains had altered secondary metabolite and inhibitory profiles. Secreted metabolites from mutant and WT strains grown on agar surfaces were analyzed using quantitative 1H nuclear magnetic resonance (NMR) profiling (36, 37). Principal components analysis (Fig. 3A) supports the partition of strains into three well-separated groups: WT and WT-like strains and then two clusters of mutant isolates. In each case, groupings corresponded to the size of genomic lesions mentioned above. More specifically, strains grouping in the WT and WT-like cluster were chloramphenicol resistant (CamR) and arginine prototrophic (Arg+), while those clustering within the blue ellipse were chloramphenicol susceptible (CamS) and arginine protrophic (Arg+), and those in the red ellipsed cluster were chloramphenicol susceptible (CamS) and arginine auxotrophic (Arg). To assess whether genomic deletions affected antibiotic biosynthesis, we used mass spectrometry (MS)based quantitative proteomics on five representative strains from the two mutant clusters. This analysis revealed that the biosynthetic pathways for actinorhodin, coelimycin P1, and calcium-dependent antibiotic were significantly up-regulated in all mutants (Fig. 3, B and C; fig. S4; and table S1). Because the expression level of biosynthetic enzymes directly correlates with antibiotic production (38), these MS results are consistent with increased antibiotic production in these strains (Fig. 3, B to D; fig. S4; and table S1). In addition to antibiotic biosynthesis clusters, pathways regulating arginine and pyrimidine biosynthesis were also increased in both arginine auxotrophic strains (fig. S4B and table S1) (39). No antibiotic-related proteins were down-regulated in this analysis.

(A) Principal components (PC) analysis plot of 1H NMR data. Each cluster enclosed in a colored ellipse (with 95% confidence interval) corresponds to a mutant class with a different phenotype and degree of genomic instability: WT-like strains (gray), CamSArg+ strains (blue), and CamSArg strains (red). (B and C) Volcano plots of MS-based quantitative proteomics of two representative strains 9H1A (CamSArg) (B) and 9H1B (CamSArg+) (C). Protein level is indicated by the size of the dot, and genes with 2-fold change and/or P 0.05 are grayed out. (D) Zones of inhibition of each strain when grown with a B. subtilis soft agar overlay. Colors represent the same mutant classes as in (A). The large dot represents the mean of four replicates, while error bars represent the SE. (E) Partial least-squares (PLS) plot of 1H NMR data partitioned by the same clusters as in (A). The heat map indicates the size of the zone of inhibition on B. subtilis. (F) Zones of inhibition of four representative mutant strains with an overlay of 40 different natural streptomycetes, each represented by a different line. Statistics are given in the main text.

We next asked whether these different metabolic and proteomic profiles translated to differences in biological activity, specifically the ability to inhibit the growth of other bacteria. Thirty mutant strains were grown on agar plates and then covered with a soft agar overlay containing Bacillus subtilis. Inhibition was visualized as an absence of growth surrounding the mutant colony, and the extent of inhibition was determined from the size of the inhibition zone. As shown in Fig. 3D, all but three WT-like mutant strains produced significantly larger zones of inhibition than the WT strain (one-tailed t tests, all P < 0.05). In addition, we observed significant heterogeneity among mutant strains in halo size [one-way analysis of variance (ANOVA), F29,90 = 5.45, P < 0.001].

To test whether the increased inhibition we observed against B. subtilis was correlated with the 1H NMR profiles, we used a partial least-squares regression (Fig. 3E) (37). This showed that the separation into different groups significantly correlates with halo size (Q2 = 0.879), which was further validated by both permutation tests and ANOVA of cross-validated residuals (CV-ANOVA, F8,116 = 104.443, P < 0.001). To identify possible compounds that are overproduced in mutants compared to WT, we identified several 1H NMR signals that varied across strains and strongly correlated with the size of the zone of inhibition against B. subtilis (table S2); notable among these are several aromatic signals, which correspond to actinorhodin, consistent with our proteomic analyses (Fig. 3, B and C; fig. S4; and table S1).

Phenotypic results indicate that mutant strains produce more antibiotics than their WT parent when assayed against a single bacterial target, as anticipated given our NMR and proteomic results. However, they do not distinguish whether strains can be further partitioned on the basis of which other species they inhibit. Score plots of principal components analysis based on 1H NMR signals reveal clear separation between mutant clones within and between clusters (Fig. 3A), suggesting that their inhibitory spectra may vary. In addition, quantitative proteomic data show that different strains vary in their production of known antimicrobials. To test this, we measured the ability of four mutant clones to inhibit 48 recently isolated Streptomyces strains (40). Streptomyces targets were chosen because these are likely to represent important competitors for other streptomycetes in soil environments. At least one of the four mutant strains produced a significantly larger halo than the WT strain against 40 of 48 targets, indicating increased inhibition (Fig. 3F). For these 40 targets, we observed significant differences between the mutant strains themselves. We found differences in the size of the zone of inhibition on different target species (two-way ANOVA, F39,117 = 21.21, P < 0.001) as well as a significant interaction between mutant strain and the target species (two-way ANOVA, F117,320 = 5.83, P < 0.001), indicating that the inhibitory profile of each mutant strain is distinct from the others. Together, these results reveal that mutants arising within colonies not only are more effective at inhibiting other strains but also are diversified in who they can inhibit because their inhibition spectra do not overlap. They also suggest that the beneficiary of diversified antibiotic secretion is the parent strain, because competing bacteria are unlikely to be resistant to this broadened combination of secreted antimicrobials.

Having shown that Streptomyces colonies differentiate into distinct subpopulations that vary in their antibiotic production, we next asked how this differentiation affects colony fitness. To answer this, we measured the fitness of each mutant strain by quantifying the number of spores they produce when grown in isolation. As shown in Fig. 4A, mutants produce significantly fewer spores than the WT strain (28 of 30; P < 0.01, two-sample t tests) and, in extreme cases, as much as 10,000-fold less, with significant heterogeneity among strains (one-way ANOVA, F29,59 = 132.57, P < 0.001). The reduction in spore production is significantly negatively correlated with antibiotic production (F1,29 = 26.58, r2 = 0.478, P < 0.001) (fig. S5A). This provides evidence that antibiotic production is costly to S. coelicolor and that there is a direct trade-off between antibiotic production and reproductive capacity, possibly because energy is redirected from development to antibiotic production (41). In addition, we observed a significant negative correlation between the size of the genome deletion and colony-forming unit (CFU) (F1,7 = 12.32, r2 = 0.638, P = 0.0099) and a positive correlation between deletion size and bioactivity against B. subtilis (F1,7 = 37.97, r2 = 0.844, P < 0.001), suggesting that these phenotypes scale with the magnitude of genomic changes (Fig. 4B).

(A) Fitness [colony-forming unit (CFU)] of mutant strains. (B) Decreases in genome size negatively correlate with fitness (top) and positively correlate with antibiotic production (bottom). (C) Division of labor during coculture of the WT and strain 9H1A at different starting frequencies. Increasing frequencies of 9H1A cause increased antibiotic production (F2,7 = 107.7, r2 = 0.969, P < 0.001) (red) but only negatively affect colony fitness at frequencies >~50% (F2,7 = 37.95, r2 = 0.916, P < 0.001) (black). Quadratic regression lines include the 95% confidence interval.

To examine the effects of mutant strains on the colony as a whole, we mixed mutant strains with their WT parent at increasing frequencies and quantified colony-wide spore production and the ability of these mixtures to kill B. subtilis. We measured responses across a broad range of initial mutant frequencies to reflect the large variation in these values across media types and colonies and to also address uncertainties in their frequencies and spatial distribution during colony growth. Results of these experiments, shown in Fig. 4C, support two important conclusions: (i) Increasing fractions of mutants lead to increased antibiotic production, and (ii) although mutant strains have individually reduced fitness (fig. S5B), their presence within colonies has no effect on colony-wide spore production, until the mutant frequency exceeds >50% of the total. We carried out the same assay with three additional mutant clones, but at fewer frequencies, to estimate spore production and observed concordant results (fig. S6): Up to a frequency of ~50%, mutant strains have no effect on colony-wide spore production, while each incremental increase in the frequency of these strains enhances colony-wide antibiotic output. These results indicate that the benefits of producing cells with chromosomal lesions are evident across a broad range of frequencies, but that even with extremely high mutation rates, the costs to colony-wide fitness are minimal or entirely absent.

Streptomycetes are prolific producers of antibiotics, with genomes typically containing more than 20 secondary metabolite gene clusters that comprise more than 5% of their entire genome (34, 42, 43). They invest heavily in these products, and their biosynthesis and secretion are costly. Our results suggest that, by limiting antibiotic production to a fraction of the colony through division of labor, S. coelicolor can eliminate the overall costs of biosynthesis while maximizing both the magnitude and diversity of their secreted antibiotics. Although this comes at a large individual cost, it increases group fitness by improving the ability for S. coelicolor to inhibit their competitors. Moreover, our results reveal that the range of conditions that select for a division of labor are quite broad, because colony-wide fitness is unaffected, even if mutant strains are as frequent as ~50%.

Division of labor is predicted to be favored in this system for several reasons. First, Streptomyces colonies emerge from a single spore and are clonal (19). This fact, together with their filamentous mode of growth, ensures that costly individual traits can be maintained because of their indirect fitness benefits (4, 5, 13). In addition, because resistance to the diversified antibiotic profile of mutant strains is unlikely to be present in competing strains, only the parent strain stands to benefit from their sacrifice. Second, the costs of antibiotic production via large and dedicated multistep biosynthetic pathways, e.g., nonribosomal peptide or polyketide synthases, are likely to be highest at the initiation of antibiotic production but diminish thereafter, meaning that producing cells become more efficient at making antibiotics through time (11); furthermore, we show that antibiotic production trades off with reproduction. Last, many antibiotics are secreted, so the entire colony, but not susceptible competitors, can benefit from the protection they provide (44).

Even if conditions predispose to a division of labor, there must still be a process that generates phenotypic heterogeneity. Our results show that, in Streptomyces, this is caused by genomic instability that creates a subpopulation of cells within colonies that contain large deletions or amplifications at the termini (23, 25, 31). These mutations are severe and irreversible. Because strains, or portions of colonies, containing these deletions have significantly reduced fitness, they effectively behave like a sterile caste that provide direct benefits to the rest of the colony and receive little in return (1). Even when the initial frequency of mutants in mixed colonies approaches 80%, their final frequency declines to less than 1% after one cycle of colony growth (fig. S7). This suggests that the division of labor in S. coelicolor is reestablished independently and differently in each colony, a mechanism that may help to maximize the diversity of secreted antibiotics.

It remains unclear whether there are mechanisms regulating the size and frequency of chromosomal deletions and amplifications. One possibility is that these events are induced by external environmental conditions and that their rate is context dependent. Instability can be elevated by exposure to certain toxicants, e.g., mitomycin C or nitrous acid (45), although no explicit stress was added in the experiments we report, and it also varies with media type. It may also be increased during competition with other strains, a process that is known to alter the secretion of secondary metabolites (16, 46). Another possibility is that deletions and the benefits they bring for antibiotic production are a fortuitous by-product of the cell death that accompanies development (19). By this argument, chromosome degradation would be regulated but would not always be lethal. Although we have not confirmed this experimentally, it is likely that conserved amplifications result from the flanking copies of IS1649, which can facilitate intragenomic rearrangements (47). In either case, the expectation is that increased antibiotic production results from the deregulation of biosynthetic clusters following the deletion of hundreds of genes, many known to coordinate antibiotic biosynthesis (34). In addition, because deletions are stochastic, especially following the removal of protective telomeres at the ends of linear chromosomes, this would also cause antibiotic production to vary in different sections of the colony.

Our preliminary surveys have found similar levels of genomic instability in streptomycete strains that we have freshly isolated from soil, suggesting that the division of labor we describe here is general. We are limited, however, in our ability to detect polymorphisms; color changes are conspicuous and are invariably associated with changes to pigmented secondary metabolites, but other secreted public goods may also become modified in these multicellular bacteria. Understanding which, if any other, public goods vary in the ways shown here is crucial because it will help to identify conditions that lead to a genetically encoded division of labor as compared to other forms of regulation that allow complex multicellular microbial systems to coordinate their behaviors and maximize their fitness.

S. coelicolor A3(2) M145 was obtained from the John Innes Centre strain collection. The strain was cultivated at 30C on soy flour mannitol medium agar plates (SFM) for strain isolation and to quantify CFU (48). SFM contains mannitol (20 g liter1), agar (20 g liter1), and soya flour (20 g liter1). To examine antibiotic production and to extract secondary metabolites, we used MM supplied with 0.5% mannitol and casamino acids (740 g ml1). MM contains asparagine (0.5 g liter1), K2HPO4 (0.5 g liter1), MgSO47H2O (0.2 g liter1), FeSO47H20 (0.01 g liter1), and agar (10 g liter1). For DNA extraction, strains were grown in liquid flasks shaken at 200 rpm at 30C in TSBS:YEME (1:1, v:v) supplemented with 0.5% glycine and 5 mM MgCl2. TSBS contains tryptic soya broth powder (30 g liter1) and sucrose (100 g liter1), and YEME contains 3 g of yeast extract, 5 g of peptone, 3 g of malt extract, 10 g of glucose, and 340 g of sucrose. Escherichia coli and B. subtilis were cultivated at 37C in LB media with constant shaking or on LB agar plates.

All strains were derived from a single isolate of S. coelicolor A3(2) M145 (designed as WT). Briefly, samples from a frozen spore stock were diluted and plated onto SFM agar to obtain single colonies. After 5 days of growth, single colonies with WT morphology were diluted and plated onto another SFM plate. From each plate, single colonies with conspicuously mutant phenotypes were picked into sterile water and plated at appropriate dilutions onto SFM agar (n = 3 per colony), from which we estimated the frequency of different mutant phenotype classes. Each derived type was plated to confluence on SFM agar, and after 7 days of growth, spores were harvested to generate spore stocks, which were stored at 80C in 20% glycerol. To quantify mutation frequency, single colonies were grown for 5 days on three different media, and then we picked the colonies with WT morphology, diluted, and plated them onto the corresponding media. Mutation frequency was scored on the basis of the phenotypes after 3 to 5 days. Table S3 provides strain designations and indicates which strains were examined in each set of assays.

Two phenotypes that are related to the loss of loci in the right arm were scored (n = 3 per strain). The arginine auxotrophs were identified by replicating 103 CFUs of each strain on MM supplied with 0.5% mannitol with and without arginine (37 g ml1) (45). After 5 days of growth, auxotrophs were identified as those strains that only grow on the media supplied with arginine. Chloramphenicol resistance was estimated by using the disk diffusion method. In detail, 2 105 spores were spread onto MM supplemented with casamino acids (740 g ml1) (45) in 12-mm square petri dishes, followed by placing a paper disk containing 25 g of chloramphenicol on it. After 4 days, the radius of the inhibition zone around the disk was measured using ImageJ (49). Inhibition zones that were smaller than 5 mm were scored as resistant, while those that are larger than 5 mm were scored as susceptible.

Spores of each strain were diluted to 105 CFU ml1 in Milli-Q water, and 1 l was spotted onto MM + casamino acid agar plates (n = 4 per strain). After growth for 5 days at 30C, plates were covered with 15 ml of LB soft agar (0.7%) containing 300 l of a freshly grown indicator strain [optical density at 600 nm (OD600) = 0.4 to 0.6]. After overnight incubation at 30C, zones of inhibition around producing colonies were measured using ImageJ.

The bioactivity against Streptomyces isolates was tested for four strains: 2H1A, 8H1B, 9H1B, and 9H1A. Three milliliters of SFM agar was poured onto each well of a 100-mm square petri dish (Thermo Fisher Scientific), after which we spotted 1 l of each test strain containing ~106 total spores in the corner of each well. After 5-day growth, 500 l of MM supplemented with 0.5% mannitol and casamino acids (740 g ml1) containing ~105 spores of the target strain was overlaid on top. Zones of inhibition were measured 2 days later using ImageJ.

Spores (2 105) were spread onto MM + casamino acids in 12-mm square petri dishes (n = 3 per strain, except n = 2 for one WT clone). After 5 days of incubation at 30C, agar was chopped into small pieces using a sterile metal spatula and secreted compounds were extracted in 50 ml of ethyl acetate for 72 hours at room temperature. Next, the supernatant was poured off and evaporated at 37C using a rotating evaporator. Pellets were obtained by drying at room temperature to remove extra solvents and then freeze-dried to remove remaining water. After adding 500 l of methanol-d4 to the dried pellets, the mixtures were vortexed for 30 s followed by a 10-min centrifugation at 16,000 rpm. The supernatants were then loaded into a 3-mm NMR tube and analyzed using 60-MHz 1H NMR (Bruker, Karlsruhe, Germany) (36, 37).

Data bucketing of NMR profiles was performed using AMIX software (version 3.9.12, Bruker BioSpin GmbH) set to include the region from 10.02 to 0.2 with a bin of 0.04 parts per million scaled to total intensity, while the signal regions of residual H2O in methanol ( 4.9 to 4.7) and methanol ( 3.34 to 3.28) were excluded. Multivariate data analysis was performed with the SIMCA software (version 15, Umetrics, Sweden) (36).

Spores (104) were spotted on SFM agar covered with cellophane and incubated for 5 days at 30C. Colonies were scraped off and snap-frozen in liquid N2 in tubes and then lysed three times in a precooled TissueLyser (Qiagen, The Netherlands). Proteins were dissolved in lysis buffer [4% SDS, 100 mM tris-HCl (pH 7.6), 50 mM EDTA] and then precipitated using chloroform-methanol (50). The dried proteins were dissolved in 0.1% RapiGest SF surfactant (Waters, USA) at 95C. Protein digestion steps were done according to van Rooden et al. (51). After digestion, trifluoroacetic acid was added for complete degradation and removal of RapiGest SF. Peptide solution containing 8 g of peptide was then cleaned and desalted using the STAGE-Tipping technique (52). Final peptide concentration was adjusted to 40 ng l1 with 3% acetonitrile solution containing 0.5% formic acid. Two hundred nanograms of digested peptide was injected and analyzed by reversed-phase liquid chromatography on a nanoACQUITY UPLC system (Waters) equipped with HSS-T3 C18 1.8 m, 75 m 250 mm column (Waters). A gradient from 1 to 40% acetonitrile in 110 min was applied, and [Glu1]-fibrinopeptide B was used as the lock mass compound and sampled every 30 s. Online MS/MS analysis was done using a Synapt G2-Si HDMS mass spectrometer (Waters) with an UDMSE method setup as described (51).

Mass spectrum data were generated using ProteinLynx Global SERVER (PLGS, version 3.0.3), with MSE processing parameters with charge 2 lock mass 785.8426. Reference protein database was downloaded from GenBank with the accession number NC_003888.3. The resulting data were imported to ISOQuant (53) for label-free quantification. TOP3 quantification result from ISOQuant was used in later data processing steps.

In total, of the 7767 proteins from the database, 2261 proteins were identified across all samples. For each sample, on average, 1435 proteins were identified. TOP3 quantification was filtered to remove identifications meeting both criteria: (i) identified in less than 70% of samples of each strain and (ii) the sum of TOP3 value less than 1 105. This led to the removal of 297 protein quantification results. Proteins were considered significantly altered in expression when log2 fold change 1 and P 0.05. Volcano plots were made from filtered data, with the four biosynthetic gene clusters color-coded.

To quantify CFU, 104 spores of each strain were spread onto SFM agar (n = 3 per strain, except n = 2 for 9H1B) and left to grow for 7 days to confluence. After 7 days, spores were harvested by first adding 10 ml of Milli-Q water onto the plate and then using a cotton swab to remove spores and mycelial fragments from the plate surface. Next, the water suspension was filtered through an 18-gauge syringe plugged with cotton wool to remove mycelial fragments. After centrifuging the filtered suspension at 4000 rpm for 10 min, the supernatant was poured off and the spore pellet was dissolved in a total volume of 1 ml of 20% glycerol. CFU per plate was determined via serial dilution onto SFM agar.

Nine strains, including one WT and eight mutants, were selected for sequencing with the Sequel Systems from Pacific Biosciences (PacBio, USA). Roughly 108 spores were inoculated in 25 ml of TSBS:YEME (1:1, v:v) supplemented with 0.5% glycine and 5 mM MgCl2 and cultivated at 30C with 200 rpm shaking speed overnight. The pellet was then collected after centrifugation and washed twice with 10.3% sucrose. Samples were then resuspended in DNA/RNA Shield (Zymo Research, USA) with 10 volume at room temperature and sent to be commercially sequenced at BaseClear (Leiden, The Netherlands).

Subreads of the sequenced results shorter than 50 base pairs (bp) were filtered and stored in BAM format. The reference alignments were performed against S. coelicolor A3(2) genome (NC_003888.3) using BLASR (v5.3.2) (54). Resulting BAM files were then sorted and indexed using SAMtools (v1.9) (55). For the calculation of genome rearrangements, the depths were called and exported through the depth function in SAMtools. The edges of genome were identified by manually checking the break point where the coverage drops to zero. The size of the amplified region was defined by the markedly higher coverage compared to the adjacent sequences. All results were further confirmed by visualizing them in IGV (v2.4.15) (56, 57).

Approximately 108 spores were inoculated into 25 ml of TSBS:YEME (1:1, v:v) supplemented with 0.5% glycine and 5 mM MgCl2 and cultivated overnight at 30C at 200 rpm. After centrifuging the culture at 4000 rpm for 10 min, the pellet was resuspended in 400 l of cell suspension buffer containing 100 mM tris:100 mM EDTA (pH 8.0) and lysozyme (1 mg ml1) and mixed with the same volume of 1% SeaKem Gold Agarose (Lonza, USA) in TE buffer containing 10 mM tris:1 mM EDTA (pH 8.0) with 1% SDS. This mixture was immediately loaded into the PFGE plug mold (Bio-Rad, USA). Next, plugs were lysed in 5 ml of cell lysis buffer containing 50 mM tris:50 mM EDTA (pH 8.0), 1% N-lauroylsarcosine sodium salt, and lysozyme (4 mg ml1) incubated for 4 hours at 37C with gentle agitation. This was then followed by a 5-hour incubation in 5-ml cell lysis buffer containing proteinase K (0.1 mg ml1; Qiagen, The Netherlands) at 56C and 50 rpm. Last, the plug was washed twice in preheated Milli-Q water and four times in preheated TE buffer and incubated at 56C for at least 15 min with gentle mixing. Plugs were sliced into 2-mm width pieces and presoaked in 200 l of 1 NEBuffer 3.1 for at least 30 min. After replacing the buffer with 200 l of 1 NEBuffer 3.1, 2 l of the rare-cutter Ase I (New England Biolabs, UK) was added and incubated at 30C overnight. Agarose (1%) was used for running fragments in 0.5 freshly prepared tris-borate-EDTA. S. cerevisiae chromosomal DNA (0.225 to 2.2 Mb; Bio-Rad, USA) and WT S. coelicolor DNA were used as size markers to estimate fragment sizes. Two electrophoresis conditions were applied to separate and visualize the smaller (<1016 kb) and larger (>1016 kb) fragments: (switch time: 2.2 to 75 s; voltage: 200 V; running time: 19 hours) and (switch time: 60 to 125 s; voltage: 200 V; running time: 20 hours), respectively.

PFGE results were compared to the Ase I restriction maps of the WT strain, which contains 17 fragments ranging from 26 bp to 1601 kb (fig. S3). Two fragments, 240 and 632 kb, can be easily resolved if they are deleted from the left arm, while one large 1601-kb fragment can be affected when deletions occur in the right arm.

The relative fitness of four fully sequenced mutant isolates2H1A, 8H1B, 9H1B, and 9H1Awas estimated during pairwise competition with the WT parent. To distinguish strains, we first transformed mutant and WT strains with the integrating plasmids PIJ82 and pSET152, which confer hygromycin B and apramycin resistance, respectively. Potential fitness effects of the markers were determined by generating two WT variants that were transformed with either single marker. No effects were observed in these control experiments (one-tailed t test, t = 2.029, P = 0.082). Fitness assays were initiated by normalizing each strain to a density of 106 spores ml1 and then mixing at different starting ratios of mutant:WT. One hundred microliters of this mixture, containing 105 spores, was plated as a lawn onto SFM agar and incubated at 30C for 5 days, while another fraction of the sample was plated after serial dilution onto SFM containing either apramycin (50 g ml1) or hygromycin B (50 g ml1) to precisely quantify the densities of each strain. After 5 days of growth, bacteria were harvested as above and plated by serial dilution onto SFM containing either apramycin (50 g ml1) or hygromycin B (50 g ml1). Fitness was quantified, following Lenski et al. (58), by calculating the ratio of the Malthusian parameters of both strains. Values below 1 indicate that mutant strains have lower fitness than the WT strain. More detailed assays were carried out with strain 9H1A, where we simultaneously estimated the fitness of this strain at a broader range of frequencies from 10 to 99% and determined how the frequency of the mutant strain influenced antibiotic production, as measured by the size of the zone of inhibition against a B. subtilis indicator in a soft agar overlay.

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/3/eaay5781/DC1

Fig. S1. Mutant frequencies in different media.

Fig. S2. PacBio sequencing results of nine selected strains.

Fig. S3. PFGE results of all sampled strains.

Fig. S4. Volcano plots of proteomics from four mutant strain.

Fig. S5. Trade-off between fitness and antibiotic production.

Fig. S6. Extended evidence for division of labor during coculture of the WT and three mutant strains at different starting frequencies.

Fig. S7. Competition assays between WT and mutant strain 9H1A at different starting frequencies.

Table S1. Filtered proteomics data.

Table S2. 1H NMR signals ranked by X and Y weights (w* and c) for PLS component 1.

Table S3. Mutant strains used in this study.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

D. A. Hopwood, Streptomyces in Nature and Medicine: The Antibiotic Makers (Oxford Univ. Press, 2007).

P. Leblond, P. Demuyter, L. Moutier, M. Laakel, B. Decaris, J. Simonet, Hypervariability, a new phenomenon of genetic instability, related to DNA amplification in Streptomyces ambofaciens. 171, 419423 (1989).

T. Kieser, M. J. Bibb, M. J. Buttner, K. F. Chater, D. A. Hopwood, Practical Streptomyces genetics (John Innes Foundation, Norwich, 2000).

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Antibiotic production in Streptomyces is organized by a division of labor through terminal genomic differentiation - Science Advances

SAN FRANCISCO Investor confidence in Exact Sciences Corp. has started to stumble in recent months. The Madison, Wis.-based companys valuation peaked at about $15.5 billion just a few short weeks after it announced that it would acquire Genomic Health Inc. in late July. Its market cap trajectory has been uneven since and took another hit when the company preannounced 2019 earnings at the J.P. Morgan (JPM) Healthcare Conference, falling to around $13.5 billion.

That comes even as its revenues were consistent with Wall Street expectations. Investors seem to be waiting for a clear signal that Exact Sciences will be able to maintain its rapid revenue growth rate even as the colorectal screening market starts to mature and as it digests the sizeable Genomic Health deal that was worth $2.8 billion in cash and stock.

Cologuard backbone

Cologuard revenue and volume were at the high end of management guidance[,] and Genomic Health volume exceeded our expectations for the full quarter, commented Cowen analyst Doug Schenkel. While some may have been looking for a bit more, we dont think investor expectations were too much higher. This was a good close to a solid 2019 that positions Exact Sciences to generate 2020 revenue at least in line with consensus.

The total revenue for the fourth quarter is expected to be between $294 million and $296 million. This is an increase of 61% over the same period in 2018. That range includes $229 million to $230 million from the legacy screening business and $65 million to $66 million for the precision oncology business from Genomic Health. The acquisition closed Nov. 8.

Total 2019 revenue was between $874.5 million and $876.5 million. Genomic Health revenues grew much more slowly than those of Exact Sciences; the overall 2019 revenue growth rate was 16% for the former and 78% for the latter. Exact Sciences will provide 2020 guidance on its February earnings call.

Upbeat analyst assessments of the company figure entirely around continued momentum for the core Cologuard colorectal cancer screening test, rather than any expectations for the Genomic Health acquisition.

Since Cologuard was approved in 2014, Exact has secured broad reimbursement coverage, developed a large and experienced sales force, increased its lab capacity to 7 million tests per year, and made significant investments in IT infrastructure, opined BTIG analyst Amanda Murphy. We believe Cologuard is currently at an inflection point and expect the company to hit its long-term goal of 40% market penetration by the end of 2030.

Better together?

In his JPM presentation, however, Exact Sciences chairman and CEO Kevin Conroy tried to emphasize the strength of Cologuard, as well of Oncotype Dx, the lead product from Genomic Health. Our key priorities for 2020 are, number one, delivering more answers from our current core products, our current tests; secondly, to enhance our customer experience. We know that we can grow the number of people ordering Cologuard tests and the frequency that they order Cologuard if we can make that ordering process easier, he said. So, it's really important for us to make screening inevitable and to make the use of Oncotype Dx inevitable in prostate cancer patients and breast cancer patients.

The current U.S. screening rate using Cologuard is 5.4%, well short of the 40% its aiming for in the long term. Exact Sciences is working on a next-gen version, Cologuard 2.0, which is expected to improve the specificity, thereby decreasing the false positive rate and requiring fewer unnecessary follow-up colonoscopies. The company recently started a 10,000-patient colorectal cancer study with the next-gen version, which is slated to wrap up in 2021.

Exact Sciences picked up Genomic Health in a bid to become the leading cancer diagnostics company to enable both early and precision treatment, while the field is still relatively wide open. It is playing a long game, but its not clear how well that will pay off in the near term as the deal tamps down the companys existing high growth rate and may distract it from its own burgeoning internal pipeline.

Cologuard is a mail-in stool test that uses DNA biomarkers to identify patients with colorectal cancer. It was developed as part of a deal with Mayo Clinic.

Mayo and Exact have continued their joint R&D efforts, expanding into 15 different cancers. Up next, Exact Sciences plans to roll out a liquid biopsy liver cancer test later this year. That is expected to be followed by screening tests for cancers of the pancreas, esophagus and bladder.

Genomic Health is focused primarily on cancer treatment guidance testing, while Exact Sciences arena has been early screening tests. It remains unclear whether the acquisition was just for breadth, or if the technologies can reinforce one another internally.

Conroy sought to make the case that the pairing will enable unique benefits. Will we see cross-pollination between the two companies Exact Sciences and Genomic Health? he said, repeating an audience question in the breakout. One proof point of us coming together, validating the reasons why is we're bringing up our liver cancer test in California with incredible laboratory facilities and capabilities and team in the Palmetto district. So, that's one of the things that we were looking at for several years is how could we bring up a new lab in a MolDX state. Our oncology sales force should be able to carry the Cologuard test and bring that to the physician or to the patients who need it.

Genomic Health was based in Redwood City, Calif. The Molecular Diagnostic Services (MolDX) Program was developed by Palmetto GBA in 2011 to identify and establish coverage and reimbursement for molecular diagnostic tests. Palmetto GBA is one of the largest Medicare administrative contractors.

He added another example, Early-stage breast cancer patients, per the TAILORx data, are more likely to recur with another cancer that is not breast cancer than they are breast cancer. Those people need to be screened. They've had a lot of medical procedures. Why not introduce Cologuard as a screening opportunity if they're out of date or out of compliance with colon cancer screening?

Over the long term, the pipeline team, the research and development capabilities in Redwood City will certainly help us bring some of the capabilities, Conroy summed up. For example, we're looking at a bladder cancer test. We think our work with the Mayo Clinic can help us there and help deliver a new test to the urology team that has about 50 people in the field. That's part of the synergy that we see is a positive revenue synergy and impacting patients over time.

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Wall Street wonders where Exact Sciences is headed in wake of Genomic Health acquisition - BioWorld Online

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Global Personalized Cancer Genome Sequencing market A brief by Transparency Market Research (TMR)

The business report on the global Personalized Cancer Genome Sequencing market serves a compilation of market behavior and the manner in which the market has been performing and responding to various situations. With the help of DROT analysis and Porters Five Forces analysis, the authors of the report have presented the factors positive and negative that are influencing the market progress.

As per the report, the global market of Personalized Cancer Genome Sequencing is US$ xx Mn/Bn in 2018 with xx% CAGR from 2014 to 2018 and it is spectated to peg US$ xx Mn/Bn by the end of 2029 with a CAGR of xx% from 2019 to 2029.

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Regional Outlook

From a geographical perspective, the personalized cancer genome sequencing market has been examined in the report for regional markets such as Asia-Pacific, Europe, North America, and Middle East and Africa. Of these, the North America market presently dominates, followed by the Europe market. Factors such as the highly developed healthcare infrastructures, high prevalence of cancer, presence of some of the worlds leading biotechnology companies, and high expenditure on healthcare present vast development opportunities for the market in these regions.

Asia-Pacific is also expected to emerge as a promising destination for development of the personalized cancer genome sequencing market in the next few years. This regional market is expected to exhibit growth at a healthy pace in the next few years. Factors such as the rising disposable incomes, increased expenditure on healthcare and wellness, and an improving healthcare infrastructure across developing economies such as India and China will drive the market. The thriving medical tourism industry in these countries is also expected to be a key driving factor. However, high costs of personalized cancer genome testing could limit the rate of adoption of this technique across the region to a certain extent.

Global Personalized Cancer Genome Sequencing Market: Competitive Dynamics

Although the scope of growth of the global personalized cancer genome sequencing market is huge, the market has witnessed the entry of very few companies owing to the stringent regulatory scenario governing the development prospects of new solutions and their marketing across regional markets with differing sets of approval processes. In the next few years as well, this factor is expected to permit few new players to foray into the global personalized cancer genome sequencing market.

Some of the leading players operating in the market are Ambry Genetics, Beckman Coulter Genomics, Illumina, Inc., Cofactor Genomics, and BGI Americas Corporation.

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Personalized Cancer Genome Sequencing Market to Surge at a Robust Pace in Terms of Revenue Over 2016 2024 Dagoretti News - Dagoretti News

publication date: Jan. 17, 2020

Researchers at Johns Hopkins University have developed an integrated genomic approach that potentially could help physicians predict which patients with non-small cell lung cancer will respond to therapy with immune checkpoint inhibitors.

Tumor mutational burden is considered an emerging biomarker of response, but TMB values are confounded by the tumor puritythe amount of tumor versus normal cellsof the sample analyzed.

The research team, led by Valsamo Anagnostou, assistant professor of oncology, developed a novel computational approach that more accurately computes TMB. The researchers also developed an integrated model of response that combined corrected TMB with nuanced genomic features and each patients antigen presentation ability. A description of the patent pending work is published in the inaugural issue of Nature Cancer.

The method also could be used to accurately estimate TMB and optimize prediction of response to immunotherapy among patients with lung cancer, colon cancer, melanoma and other solid tumors, Anagnostou, the lead study author, said in a statement.

Immunotherapy is an exciting treatment modality for many tumors, but what we dont truly know is who will respond to immunotherapy and why, and if there are specific molecular features that can help predict response, Anagnostou said.

Current biomarkers used to assess a patients response to immunotherapy include a test to measure the amount of the protein PD-L1 on cancer cells and TMB.

There are more and more studies coming out that show TMB is actually not as predictive as we thought it would be, Victor Velculescu, professor of oncology and the studys senior author, said in a statement. Some tumors with a high TMB do not Continue reading Multimodal genomic analyses predict response to immunotherapy in NSCLCTo access this members-only content, please log in.Institutional subscribers, please log in with your IP.If you're not a subscriber why not join today?To gain access to the members only content click here to subscribe.You will be given immediate access to premium content on the site.Click here to join.

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Multimodal genomic analyses predict response to immunotherapy in NSCLC - The Cancer Letter

True Fit, the data-driven personalization platform for footwear and apparel, has struck a strategic partnership with Google Cloud to make it easy for retailers to leverage the Fashion Genome, the largest connected data set for fashion.

Retailers can use the Fashion Genome to insert style, fit, and size recommendations into every phase of the customer journey.

Together, True Fit and Google Cloud are helping retailers go beyond the hype cycle of artificial intelligence and machine learning, to drive value straight to retailers P&L.

Retailers that use True Fits platform on Google Clouds infrastructure drive incremental revenue, reduce returns and increase customer lifetime value and loyalty.

With True Fit on Google Cloud, the improved processing power, speed, and reliability results in faster machine learning for improved shopper experiences, relevance, and insights.

Data can either be a retailers biggest challenge or biggest gift. Were thrilled to partner with True Fit and further help retailers harness the power of data to build better, more personalized shopping experiences that ultimately drive sales and build a more loyal customer base.

Carrie Tharp, Vice President of Retail, Google Cloud

Please use the comments section below or head over to our Facebook Group for Small Business Sellers and interact with other small business owners.

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True Fit and Google Cloud Partner to Help Retailers Leverage the Fashion Genome - eSellerCafe

Cell-to-cell contacts are necessary for the survival of human cells under protein-damaging conditions and stress. This was one of the conclusions made by a research team working under the leadership of Lea Sistonen, Professor in Cell and Molecular Biology at bo Akademi University.

The researchers were surprised by the findings because the molecules they studied are usually linked with other cellular functions.

Our results show, for the first time, that the contacts between cells, known as cell adhesion, are essential for cells to survive stress. The findings also suggest that impaired cell adhesion may sensitize cancer cells to drugs that damage cell proteins and cause stress, Sistonen explains.

The research project focused on heat shock factor 2 (HSF2), a specialized gene regulating protein, and its impact on cells capacity to survive protein-damaging stress. Protein-damaging stress is caused by, for example, high temperatures, virus infections and certain anti-cancer medications.

The results showed that HSF2 contributes to protecting cells against stress by regulating those genes that mediate cell adhesion contacts.

The results were obtained by studying, among other things, how cancer cells respond to certain commonly used anti-cancer drugs. Cancer cells with impaired cell adhesion contacts were significantly less successful in surviving the drug treatment than the cells showing intact cell adhesion.

Cell-to-cell contacts are essential for normal tissue functioning and mechanisms. Cancer cells are known to utilize these contacts to form aggressive tumors and metastases. Our results show, indeed, that cancer cells become more vulnerable to drug treatment, when their cell contacts are weakened, says Sistonen.

Cell adhesion contacts are mediated by proteins known as cadherins, which serve as the source of message chains regulating cell death, but understanding of the molecular basis for these processes calls for further research. Individual differences in these particular cell processes may partly explain why certain drugs work effectively for some patients but not for others.

Reference

Joutsen et al. (2020) Heat Shock Factor 2 Protects against Proteotoxicity by Maintaining Cell-Cell Adhesion. Cell Reports. DOI: https://doi.org/10.1016/j.celrep.2019.12.037

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

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Cells Protect Themselves Against Stress by Keeping Together - Technology Networks

Wheat helps feed the world. It grows well in temperate climates and its grains are an excellent source of starch, protein, fiber, B vitamins and more. Among cereal grain production, wheat is second only to corn: In 2019, farmers were expected to grow about 767 million metric tons of the stuff, according to the Food and Agricultural Organization of the United Nations.

Yet over the past two decades, a growing threat has attacked wheat production in Africa, and could in time decimate wheat harvests around the world. The scourge known by the innocuous name Ug99 is a strain of the stem rust Puccinia graminis f. sp. tritici. Stem rusts are fungal pathogens that parasitize wheat, but Ug99 is particularly effective at decimating whole fields. It is also spreading rapidly after appearing to be contained.

Crops affected by stem rust are often entirely destroyed, and until the 1950s, the fungus was able to wreak havoc on agriculture across the globe including in the United States. Researchers eventually managed to identify strong resistance genes against the fungus, and successfully bred those genes into new plant varieties beginning in the 1960s, leaving the fungus contained and all but forgotten.

A generation later, however a new strain of wheat stem rust appeared this time in Uganda, in 1998. This new strain, called Ug99 (Ug for the country where it was first discovered, 99 for the year when it was officially named), was immune to most of the known resistance genes. By 2010, it had emerged as a global threat and is now found in wheat fields across Africa and the Middle East, and shows signs of spreading farther. Ug99s global spread could devastate wheat production and trigger famine.

Since this fungus is airborne it is very difficult to limit its spread, said Dr. Benjamin Schwessinger of Australian National University. Rust isolates from South Africa have migrated to Australia, likely through wind patterns.

About 80 to 90 percent of todays wheat strains are susceptible to Ug99, said Dr. Schwessinger. Research has identified some of the genetic underpinnings of Ug99s deadly talents such as its ability to infect wheat strains containing Sr31, a resistance locus affective against other stem rust isolates. But Ug99 is spreading at a fast pace, and scientists need a quicker pipeline to study the strains genetic and molecular properties.

Dr. Schwessinger is part of a team of researchers from the United States, South Africa and Australia that recently employed a genome-wide approach to investigate Ug99s provenance and virulence. The group, led by Dr. Melania Figueroa and Dr. Peter Dodds at Australias Commonwealth Scientific and Industrial Research Organisation (CSIRO), set out to assemble chromosome-length reference genomes for Ug99 and Pgt21-0, an older stem rust strain from Africa.

Their efforts uncovered genomic evidence for Ug99s surprising origins, as well as potential genetic vulnerabilities that researchers could exploit to breed resistant wheat strains achievements all the more remarkable given a major genomic complication: Stem rusts, like many fungi, have two haploid nuclei per cell. Traditional next-generation sequencing methods could not easily discern which scaffolds belonged to which nucleus commented joint first author of the publication, CSIRO scientist Dr. Narayana Upadhyaya.

The team began by generating reads on Illumina and PacBio platforms. After assembling and collapsing reads into scaffolds for both Ug99 and Pgt21-0, the team discovered something unexpected: half the scaffolds in Ug99 and Pgt21-0 were more than 99 percent identical. The two strains appeared to share half their genome.

The roughly half-genomes worth of sequence that was common between Ug99 and Pgt21-0 could be a sign of shared ancestry. But unlike people, who have just one route to pass genetic information to the next generation, stem rusts and other fungi with multiple nuclei have several reproductive options. Growing on wheat, stem rusts can reproduce via asexual spores for generations. They can also undergo sexual reproduction, though that requires an intermediate host, the common barberry. Scientists have also uncovered evidence hinting that strains could exchange nuclei.

To divine among these possibilities, the team turned to Phase Genomics to assign scaffolds to their nucleus of origin within each strain. They generated Hi-C libraries for Pgt21-0, which they used not only to create a chromosome-length assembly, but also assign chromosomes to each of the two nuclei.

This complete, nuclear-sorted assembly revealed that Ug99 and Pgt21-0s shared sequences arent a mixture of the two nuclei as expected for sexual reproduction. Instead, the common sequences were confined to a single haploid nucleus. That key conclusion indicates that Ug99 likely arose through somatic hybridization, in which hyphae from different strains fuse and exchange nuclei. Since one nucleus in Ug99 so closely resembles Pgt21-0, it likely came from a Pgt-like strain.

We were not expecting this at all, said Dr. Figueroa. It was one of those amazing moments in science when you stop and think how much there is still to learn about nature.

By pairing two haploid nuclei from different genetic lineages, a somatic hybridization event in stem rusts can instantly create new combinations of alleles without sexual reproduction and meiosis. This may explain Ug99s sudden emergence in the 1990s, as well as why most wheat strains are vulnerable to infection: Ug99s unique genetic makeup is too divergent from the more established stem rust strains that have been around for decades or longer.

There are ramifications of such a big discovery, such as what this means for disease management and pathogen surveillance, added Dr.Figueroa.

The team has already started to mine the Ug99 and Pgt21-0 genomes for information that could help scientists decipher the details of Ug99s virulence and breed countermeasures into vulnerable wheat strains. They confirmed past research indicating that Ug99 is a heterozygous carrier of AvrSr35 and AvrSr50, two dominant factors that activate anti-rust immune responses in wheat. These and other loci may be immunological routes in wheat that researchers could exploit. In addition, the completeness of the Ug99 and Pgt21-0 genomes will help researchers find more loci like these explains Dr. Dodds. These resources will also make it easier to survey the genetic diversity and the movement of nuclei among stem rust populations, and may help scientists identify the strain that gave Ug99 its second nucleus.

Together these tools could ensure that Ug99s departure is as abrupt as its arrival and give wheat, one of our most important staple crops, some much-needed relief.

This research was supported by the 2Blade Foundation, USDA-Agriculture and Food Research Initiative (AFRI) Competitive Grant (Proposal No. 2017-08221), an USDA-NIFA Postdoctoral Fellowship award (2017-67012-26117), an ARC Future Fellowship (FT180100024) and the University of Minnesota Lieberman-Okinow and Stakman Endowment.

Kaylee Mueller, a product marketing manager at Phase Genomics, a biotechology company that addresses the impact of genetics on society, often writes about genomics and metagenomics. Follow her on Twitter @Kayleezyme

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Famine in Africa and the Middle East? Devastating stem rust fungus threatens global staple crop, but genomic solution may finally be on the horizon -...