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The emergence of resistance during multidrug chemotherapy impedes the treatment of many human diseases, including malaria, TB, HIV, and cancer. Although certain combination therapies have long been known to be more effective in curing patients than single drugs, the impact of such treatments on the evolution of drug resistance is unclear. In particular, very little is known about how the evolution of resistance is affected by the nature of the interactions—synergy or antagonism—between drugs. Here we directly measure the effect of various inhibitory and subinhibitory drug combinations on the rate of adaptation. We develop an automated assay for monitoring the parallel evolution of hundreds of Escherchia coli populations in a two-dimensional grid of drug gradients over many generations. We find a correlation between synergy and the rate of adaptation, whereby evolution in more synergistic drug combinations, typically preferred in clinical settings, is faster than evolution in antagonistic combinations. We also find that resistance to some synergistic combinations evolves faster than resistance to individual drugs. The accelerated evolution may be due to a larger selective advantage for resistance mutations in synergistic treatments. We describe a simple geometric model in which mutations conferring resistance to one drug of a synergistic pair prevent not only the inhibitory effect of that drug but also its enhancing effect on the other drug. Future study of the profound impact that synergy and other drug-pair properties can have on the rate of adaptation may suggest new treatment strategies for combating the spread of antibiotic resistance. [ABSTRACT FROM AUTHOR]

Can biodiversity evolve and persist in a uniform environment? This question is at the heart of the plankton paradox: in the natural world we observe many species sharing few resources, whereas the principle of competitive exclusion would lead us to expect that only a few species could coexist in such circumstances. To bridge the gap between theory and observation, previous studies have shown that the maximum number of species that can stably coexist is equal to the number of essential resources and that even more species can coexist out of equilibrium. These studies were viewed as a significant step toward a resolution of the paradox. Evolutionary dynamics, however, have been studied in this context only in limited cases, and it is largely unknown how mutations impact ecologically stable multispecies states, and whether large species consortia can spontaneously evolve. In the present study we introduce evolution to the standard ecological model of competition for essential resources. Combining numeric and analytic approaches. we find that ecologically stable species communities are severely destabilized by long-term evolutionary dynamics. Moreover, the number of species in spontaneously evolved consortia is much lower than the number of available resources. Contrary to expectations based on studies of two resources, these limits on biodiversity are not results of the occasional emergence of super-species, superior to all competitors; nor are they alleviated by the inclusion of tradeoffs in resource utilization. Rather, we show that it is an accelerated depletion of limiting resources, combined with the essentiality of resources to all species, that leads invariably to catastrophic extinctions. [ABSTRACT FROM AUTHOR]

The idea of evolutionary game theory is to relate the payoff of a game to reproductive success (=fitness). An underlying assumption in most models is that fitness is a linear function of the payoff. For stochastic evolutionary dynamics in finite populations, this leads to analytical results in the limit of weak selection, where the game has a small effect on overall fitness. But this linear function makes the analysis of strong selection difficult. Here, we show that analytical results can be obtained for any intensity of selection, if fitness is defined as an exponential function of payoff. This approach also works for group selection (=multi-level selection). We discuss the difference between our approach and that of inclusive fitness theory. [ABSTRACT FROM AUTHOR]

We describe how one can use chiral perturbation theory to obtain results for physical quantities, such as quark masses, using partially quenched simulations. [ABSTRACT FROM AUTHOR]

Rapid evolution of asexual populations, such as that of cancer cells or of microorganisms developing drug resistance, can include the simultaneous spread of distinct beneficial mutations. We demonstrate that evolution in such cases is driven by the fitness effects and appearance times of only a small minority of favorable mutations. The complexity of the mutation-selection process is thereby greatly reduced, and much of the evolutionary dynamics can be encapsulated in two parameters — an effective selection coefficient and effective rate of beneficial mutations. We confirm this theoretical finding and estimate the effective parameters for evolving populations of fluorescently labeled Escherichia coli. The effective parameters constitute a simple description and provide a natural standard for comparing adaptation between species and across environments. [ABSTRACT FROM AUTHOR]

Histone ChIP-seq is one of the primary methods for charting the cellular epigenomic landscape, the components of which play a critical regulatory role in gene expression. Analyzing the activity of regulatory elements across datasets and cell types can be challenging due to shifting peak positions and normalization artifacts resulting from, for example, differing read depths, ChIP efficiencies, and target sizes. Moreover, broad regions of enrichment seen in repressive histone marks often evade detection by commonly used peak callers. Here, we present a simple and versatile method for identifying enriched regions in ChIP-seq data that relies on estimating a gamma distribution fit to non-overlapping 5kB genomic bins to establish a global background. We use this distribution to assign a probability of being signal (PBS) between zero and one to each 5 kB bin. This approach, while lower in resolution than typical peak-calling methods, provides a straightforward way to identify enriched regions and compare enrichments among multiple datasets, by transforming the data to values that are universally normalized and can be readily visualized and integrated with downstream analysis methods. We demonstrate applications of PBS for both broad and narrow histone marks, and provide several illustrations of biological insights which can be gleaned by integrating PBS scores with downstream data types. Author summary: Histone modifications are key to epigenetic regulation of gene expression. Their genomic distributions are most commonly measured using ChIP-seq, a next-generation sequencing method which results in pileups of sequencing reads near regions with modified histones. Comparing the signal between histone modification profiles in different cellular contexts is often challenging due to the lack of a clear standard for interpreting signal strength, the inherent variability in the positions of histones, and the difficulty of detecting very broad regions that are only modestly enriched compared to the background. We present a method, which we call PBS, or probability of being signal, to simplify and streamline the process of identifying where true signal can be found in ChIP-seq datasets, regardless of whether the signal is broad or narrow. We demonstrate how our method can be used to visualize, quantify and compare signal among ChIP-seq datasets, and to easily integrate ChIP-seq data with additional data types. We show how PBS can both stand on its own, and also add power to other commonly used analysis methods. We anticipate that its versatility and straightforward interpretation will prove useful in many applications. [ABSTRACT FROM AUTHOR]

The author discusses data visualization techniques to support data exploration. He particularly talks on the use of graphical representation of data to improve pattern discovery. He mentions the two uses of data visualization which are to communicate research results and to guide the data exploration process as the scientific story unfolds. Furthermore, he explains patterns, data exploration, and Anscombe's quartet.

Background Unbiased assessment of the risks associated with acquisition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is critical to informing mitigation efforts during pandemics. The objective of our study was to understand the risk factors for acquiring coronavirus disease 2019 (COVID-19) in a large prospective cohort of adult residents in a large US metropolitan area. Methods We designed a fully remote longitudinal cohort study involving monthly at-home SARS-CoV-2 polymerase chain reaction (PCR) and serology self-testing and monthly surveys. Results Between October 2020 and January 2021, we enrolled 10 289 adults reflective of the Boston metropolitan area census data. At study entry, 567 (5.5%) participants had evidence of current or prior SARS-CoV-2 infection. This increased to 13.4% by June 15, 2021. Compared with Whites, Black non-Hispanic participants had a 2.2-fold greater risk of acquiring COVID-19 (hazard ratio [HR], 2.19; 95% CI, 1.91–2.50; P <.001), and Hispanics had a 1.5-fold greater risk (HR, 1.52; 95% CI, 1.32–1.71; P <.016). Individuals aged 18–29, those who worked outside the home, and those living with other adults and children were at an increased risk. Individuals in the second and third lowest disadvantaged neighborhood communities were associated with an increased risk of acquiring COVID-19. Individuals with medical risk factors for severe disease were at a decreased risk of SARS-CoV-2 acquisition. Conclusions These results demonstrate that race/ethnicity and socioeconomic status are the biggest determinants of acquisition of infection. This disparity is significantly underestimated if based on PCR data alone, as noted by the discrepancy in serology vs PCR detection for non-White participants, and points to persistent disparity in access to testing. Medical conditions and advanced age, which increase the risk for severity of SARS-CoV-2 disease, were associated with a lower risk of COVID-19 acquisition, suggesting the importance of behavior modifications. These findings highlight the need for mitigation programs that overcome challenges of structural racism in current and future pandemics. [ABSTRACT FROM AUTHOR]

Different combinations of histone modifications have been proposed to signal distinct gene regulatory functions, but this area is poorly addressed by existing technologies. We applied high-throughput single-molecule imaging to decode combinatorial modifications on millions of individual nucleosomes from pluripotent stem cells and lineage-committed cells.We identified definitively bivalent nucleosomes with concomitant repressive and activating marks, as well as other combinatorial modification states whose prevalence varies with developmental potency. We showed that genetic and chemical perturbations of chromatin enzymes preferentially affect nucleosomes harboring specific modification states. Last, we combined this proteomic platform with single-molecule DNA sequencing technology to simultaneously determine the modification states and genomic positions of individual nucleosomes. This single-molecule technology has the potential to address fundamental questions in chromatin biology and epigenetic regulation. [ABSTRACT FROM AUTHOR]

Chromatin profiling provides a versatile means to investigate functional genomic elements and their regulation. However, current methods yield ensemble profiles that are insensitive to cell-to-cell variation. Here we combine microfluidics, DNA barcoding and sequencing to collect chromatin data at single-cell resolution. We demonstrate the utility of the technology by assaying thousands of individual cells and using the data to deconvolute a mixture of ES cells, fibroblasts and hematopoietic progenitors into high-quality chromatin state maps for each cell type. The data from each single cell are sparse, comprising on the order of 1,000 unique reads. However, by assaying thousands of ES cells, we identify a spectrum of subpopulations defined by differences in chromatin signatures of pluripotency and differentiation priming. We corroborate these findings by comparison to orthogonal single-cell gene expression data. Our method for single-cell analysis reveals aspects of epigenetic heterogeneity not captured by transcriptional analysis alone. [ABSTRACT FROM AUTHOR]

The importance of single-cell level data is increasingly appreciated, and significant advances in this direction have been made in recent years. Common to these technologies is the need to physically segregate individual cells into containers, such as wells or chambers of a micro-fluidics chip. High-throughput Single-Cell Labeling (Hi-SCL) in drops is a novel method that uses drop-based libraries of oligonucleotide barcodes to index individual cells in a population. The use of drops as containers, and a microfluidics platform to manipulate them en-masse, yields a highly scalable methodological framework. Once tagged, labeled molecules from different cells may be mixed without losing the cell-of-origin information. Here we demonstrate an application of the method for generating RNA-sequencing data for multiple individual cells within a population. Barcoded oligonucleotides are used to prime cDNA synthesis within drops. Barcoded cDNAs are then combined and subjected to second generation sequencing. The data are deconvoluted based on the barcodes, yielding single-cell mRNA expression data. In a proof-of-concept set of experiments we show that this method yields data comparable to other existing methods, but with unique potential for assaying very large numbers of cells. [ABSTRACT FROM AUTHOR]

Duplicate genes from the whole-genome duplication (WGD) in yeast are often dispensable—removing one copy has little or no phenotypic consequence. It is unknown, however, whether such dispensability reflects insignificance of the ancestral function or compensation from paralogs. Here, using precise competition-based measurements of the fitness cost of single and double deletions, we estimate the exposed fitness contribution of WGD duplicate genes in metabolism and bound the importance of their ancestral pre-duplication function. We find that the functional overlap between paralogs sufficiently explains the apparent dispensability of individual WGD genes. Furthermore, the lower bound on the fitness value of the ancestral function, which is estimated by the degree of synergistic epistasis, is at least as large as the average fitness cost of deleting single non-WGD genes. These results suggest that most metabolic functions encoded by WGD genes are important today and were also important at the time of duplication. [ABSTRACT FROM AUTHOR]

Chromatin structure and transcription factor localization can be assayed genome-wide by sequencing genomic DNA fractionated by protein occupancy or other properties, but current technologies involve multiple steps that introduce bias and inefficiency. Here we apply a single-molecule approach to directly sequence chromatin immunoprecipitated DNA with minimal sample manipulation. This method is compatible with just 50 pg of DNA and should thus facilitate charting chromatin maps from limited cell populations. [ABSTRACT FROM AUTHOR]

Dual transcriptional profiling of host and bacteria during infection is challenging due to the low abundance of bacterial mRNA. We report Pathogen Hybrid Capture (PatH-Cap), a method to enrich for bacterial mRNA and deplete bacterial rRNA simultaneously from dual RNA-seq libraries using transcriptome-specific probes. By addressing both the differential RNA content of the host relative to the infecting bacterium and the overwhelming abundance of uninformative structural RNAs (rRNA, tRNA) of both species in a single step, this approach enables analysis of very low-input RNA samples. By sequencing libraries before (pre-PatH-Cap) and after (post-PatH-Cap) enrichment, we achieve dual transcriptional profiling of host and bacteria, respectively, from the same sample. Importantly, enrichment preserves relative transcript abundance and increases the number of unique bacterial transcripts per gene in post-PatH-Cap libraries compared to pre-PatH-Cap libraries at the same sequencing depth, thereby decreasing the sequencing depth required to fully capture the transcriptional profile of the infecting bacteria. We demonstrate that PatH-Cap enables the study of low-input samples including single eukaryotic cells infected by 1–3 Pseudomonas aeruginosa bacteria and paired host-pathogen temporal gene expression analysis of Mycobacterium tuberculosis infecting macrophages. PatH-Cap can be applied to the study of a range of pathogens and microbial species, and more generally, to lowly-abundant species in mixed populations. [ABSTRACT FROM AUTHOR]

Genomics offered the promise of transforming antibiotic discovery by revealing many new essential genes as good targets, but the results fell short of the promise. While numerous factors contributed to the disappointing yield, one factor was that essential genes for a bacterial species were often defined based on a single or limited number of strains grown under a single or limited number of in vitro laboratory conditions. In fact, the essentiality of a gene can depend on both the genetic background and growth condition. We thus developed a strategy for more rigorously defining the core essential genome of a bacterial species by studying many pathogen strains and growth conditions.We assessed how many strains must be examined to converge on a set of core essential genes for a species. We used transposon insertion sequencing (Tn-Seq) to define essential genes in nine strains of Pseudomonas aeruginosa on five different media and developed a statistical model, FiTnEss, to classify genes as essential versus nonessential across all strain-medium combinations. We defined a set of 321 core essential genes, representing 6.6% of the genome. We determined that analysis of four strains was typically sufficient in P. aeruginosa to converge on a set of core essential genes likely to be essential across the species across a wide range of conditions relevant to in vivo infection, and thus to represent attractive targets for novel drug discovery. [ABSTRACT FROM AUTHOR]

Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics. [Display omitted] • EN-TEx includes 1,635 datasets mapped to 4 personal genomes, ∼30 tissues × ∼15 assays • Comprehensive catalog of allele-specific activity, decorating regulatory elements • Model to transfer known eQTLs to difficult-to-profile tissues (e.g., skin→heart) • Transformer model for predicting allelic activity based on local sequence context Understanding the impact of genetic variants is important to functional genomics. EN-TEx provides epigenomes across tissues, coupled with long-read genome assemblies, to build generalizable models of variant impact. [ABSTRACT FROM AUTHOR]

Controlled experimental evolution during antibiotic treatment can help to explain the processes leading to antibiotic resistance in bacteria. Recently, intermittent antibiotic exposures have been shown to lead rapidly to the evolution of tolerance—that is, the ability to survive under treatment without developing resistance. However, whether tolerance delays or promotes the eventual emergence of resistance is unclear. Here we used in vitro evolution experiments to explore this question. We found that in all cases, tolerance preceded resistance. A mathematical population-genetics model showed how tolerance boosts the chances for resistance mutations to spread in the population. Thus, tolerance mutations pave the way for the rapid subsequent evolution of resistance. Preventing the evolution of tolerance may offer a new strategy for delaying the emergence of resistance. [ABSTRACT FROM AUTHOR]

Polyploidy is observed across the tree of life, yet its influence on evolution remains incompletely understood. Polyploidy, usually whole-genome duplication, is proposed to alter the rate of evolutionary adaptation. This could occur through complex effects on the frequency or fitness of beneficial mutations. For example, in diverse cell types and organisms, immediately after a whole-genome duplication, newly formed polyploids missegregate chromosomes and undergo genetic instability. The instability following whole-genome duplications is thought to provide adaptive mutations in microorganisms and can promote tumorigenesis in mammalian cells. Polyploidy may also affect adaptation independently of beneficial mutations through ploidy-specific changes in cell physiology. Here we perform in vitro evolution experiments to test directly whether polyploidy can accelerate evolutionary adaptation. Compared with haploids and diploids, tetraploids undergo significantly faster adaptation. Mathematical modelling suggests that rapid adaptation of tetraploids is driven by higher rates of beneficial mutations with stronger fitness effects, which is supported by whole-genome sequencing and phenotypic analyses of evolved clones. Chromosome aneuploidy, concerted chromosome loss, and point mutations all provide large fitness gains. We identify several mutations whose beneficial effects are manifest specifically in the tetraploid strains. Together, these results provide direct quantitative evidence that in some environments polyploidy can accelerate evolutionary adaptation. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis-regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4+ T-cell subsets, regulatory T cells, CD8+ T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10-20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models. [ABSTRACT FROM AUTHOR]

The great therapeutic achievements of antibiotics have been dramatically undercut by the evolution of bacterial strategies that overcome antibiotic stress. These strategies fall into two classes. 'Resistance' makes it possible for a microorganism to grow in the constant presence of the antibiotic, provided that the concentration of the antibiotic is not too high. 'Tolerance' allows a microorganism to survive antibiotic treatment, even at high antibiotic concentrations, as long as the duration of the treatment is limited. Although both resistance and tolerance are important reasons for the failure of antibiotic treatments, the evolution of resistance is much better understood than that of tolerance. Here we followed the evolution of bacterial populations under intermittent exposure to the high concentrations of antibiotics used in the clinic and characterized the evolved strains in terms of both resistance and tolerance. We found that all strains adapted by specific genetic mutations, which became fixed in the evolved populations. By monitoring the phenotypic changes at the population and single-cell levels, we found that the first adaptive change to antibiotic stress was the development of tolerance through a major adjustment in the single-cell lag-time distribution, without a change in resistance. Strikingly, we found that the lag time of bacteria before regrowth was optimized to match the duration of the antibiotic-exposure interval. Whole genome sequencing of the evolved strains and restoration of the wild-type alleles allowed us to identify target genes involved in this antibiotic-driven phenotype: 'tolerance by lag' (tbl). Better understanding of lag-time evolution as a key determinant of the survival of bacterial populations under high antibiotic concentrations could lead to new approaches to impeding the evolution of antibiotic resistance. [ABSTRACT FROM AUTHOR]

Summary: Differences in chromatin organization are key to the multiplicity of cell states that arise from a single genetic background, yet the landscapes of in vivo tissues remain largely uncharted. Here, we mapped chromatin genome-wide in a large and diverse collection of human tissues and stem cells. The maps yield unprecedented annotations of functional genomic elements and their regulation across developmental stages, lineages, and cellular environments. They also reveal global features of the epigenome, related to nuclear architecture, that also vary across cellular phenotypes. Specifically, developmental specification is accompanied by progressive chromatin restriction as the default state transitions from dynamic remodeling to generalized compaction. Exposure to serum in vitro triggers a distinct transition that involves de novo establishment of domains with features of constitutive heterochromatin. We describe how these global chromatin state transitions relate to chromosome and nuclear architecture, and discuss their implications for lineage fidelity, cellular senescence, and reprogramming. [Copyright &y& Elsevier]

The human genome encodes the blueprint of life, but the function of the vast majority of its nearly three billion bases is unknown. The Encyclopedia of DNA Elements (ENCODE) project has systematically mapped regions of transcription, transcription factor association, chromatin structure and histone modification. These data enabled us to assign biochemical functions for 80% of the genome, in particular outside of the well-studied protein-coding regions. Many discovered candidate regulatory elements are physically associated with one another and with expressed genes, providing new insights into the mechanisms of gene regulation. The newly identified elements also show a statistical correspondence to sequence variants linked to human disease, and can thereby guide interpretation of this variation. Overall, the project provides new insights into the organization and regulation of our genes and genome, and is an expansive resource of functional annotations for biomedical research. [ABSTRACT FROM AUTHOR]

Summary: Hundreds of chromatin regulators (CRs) control chromatin structure and function by catalyzing and binding histone modifications, yet the rules governing these key processes remain obscure. Here, we present a systematic approach to infer CR function. We developed ChIP-string, a meso-scale assay that combines chromatin immunoprecipitation with a signature readout of 487 representative loci. We applied ChIP-string to screen 145 antibodies, thereby identifying effective reagents, which we used to map the genome-wide binding of 29 CRs in two cell types. We found that specific combinations of CRs colocalize in characteristic patterns at distinct chromatin environments, at genes of coherent functions, and at distal regulatory elements. When comparing between cell types, CRs redistribute to different loci but maintain their modular and combinatorial associations. Our work provides a multiplex method that substantially enhances the ability to monitor CR binding, presents a large resource of CR maps, and reveals common principles for combinatorial CR function. [ABSTRACT FROM AUTHOR]

Chromatin profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. The approach is especially well suited to the characterization of non-coding portions of the genome, which critically contribute to cellular phenotypes yet remain largely uncharted. Here we map nine chromatin marks across nine cell types to systematically characterize regulatory elements, their cell-type specificities and their functional interactions. Focusing on cell-type-specific patterns of promoters and enhancers, we define multicell activity profiles for chromatin state, gene expression, regulatory motif enrichment and regulator expression. We use correlations between these profiles to link enhancers to putative target genes, and predict the cell-type-specific activators and repressors that modulate them. The resulting annotations and regulatory predictions have implications for the interpretation of genome-wide association studies. Top-scoring disease single nucleotide polymorphisms are frequently positioned within enhancer elements specifically active in relevant cell types, and in some cases affect a motif instance for a predicted regulator, thus suggesting a mechanism for the association. Our study presents a general framework for deciphering cis-regulatory connections and their roles in disease. [ABSTRACT FROM AUTHOR]

In the face of antibiotics, bacterial populations avoid extinction by harboring a subpopulation of dormant cells that are largely drug insensitive. This phenomenon, termed "persistence," is a major obstacle for the treatment of a number of infectious diseases. The mechanism that generates both actively growing as well as dormant cells within a genetically identical population is unknown. We present a detailed study of the toxin-antitoxin module implicated in antibiotic persistence of Escherichia coli. We find that bacterial cells become dormant if the toxin level is higher than a threshold, and that the amount by which the threshold is exceeded determines the duration of dormancy. Fluctuations in toxin levels above and below the threshold result in coexistence of dormant and growing cells. We conclude that toxin-antitoxin modules in general represent a mixed network motif that can serve to produce a subpopulation of dormant cells and to supply a mechanism for regulating the frequency and duration of growth arrest. Toxin-antitoxin modules thus provide a natural molecular design for implementing a bet-hedging strategy. [ABSTRACT FROM AUTHOR]

Widespread changes to DNA methylation and chromatin are well documented in cancer, but the fate of higher-order chromosomal structure remains obscure. Here we integrated topological maps for colon tumors and normal colons with epigenetic, transcriptional, and imaging data to characterize alterations to chromatin loops, topologically associated domains, and large-scale compartments. We found that spatial partitioning of the open and closed genome compartments is profoundly compromised in tumors. This reorganization is accompanied by compartment-specific hypomethylation and chromatin changes. Additionally, we identify a compartment at the interface between the canonical A and B compartments that is reorganized in tumors. Remarkably, similar shifts were evident in non-malignant cells that have accumulated excess divisions. Our analyses suggest that these topological changes repress stemness and invasion programs while inducing anti-tumor immunity genes and may therefore restrain malignant progression. Our findings call into question the conventional view that tumor-associated epigenomic alterations are primarily oncogenic. • Hierarchical layers of nuclear architecture are altered in colorectal tumors • An intermediate genome compartment is defined in primary tissues • Compartmental reorganization and hypomethylation occur in tumors and aging cells • Reorganization is associated with tumor-suppressive transcriptional programs Integrated analyses of genome topological, epigenetic, and transcriptional features of colorectal tumors highlight substantial genome compartmental reorganization associated with tumor-suppressive rather than oncogenic transcriptional outcomes. [ABSTRACT FROM AUTHOR]

A summary is presented of an article published online at http://dx.doi.org/10.1126/science.aaw0726 concerning the use of RNA analysis from bulk tissue to discover somatic mutations found in macroscopic somatic clones which may develop due to somatic events that drive positive selection and can possibly represent early tumorigenesis stages.

Rapid bacterial identification remains a critical challenge in infectious disease diagnostics. We developed a novel molecular approach to detect and identify a wide diversity of bacterial pathogens in a single, simple assay, exploiting the conservation, abundance, and rich phylogenetic content of ribosomal RNA in a rapid fluorescent hybridization assay that requires no amplification or enzymology. Of 117 isolates from 64 species across 4 phyla, this assay identified bacteria with >89% accuracy at the species level and 100% accuracy at the family level, enabling all critical clinical distinctions. In pilot studies on primary clinical specimens, including sputum, blood cultures, and pus, bacteria from 5 different phyla were identified. [ABSTRACT FROM AUTHOR]

Summary Genome-wide association studies (GWASs) implicate the PHACTR1 locus (6p24) in risk for five vascular diseases, including coronary artery disease, migraine headache, cervical artery dissection, fibromuscular dysplasia, and hypertension. Through genetic fine mapping, we prioritized rs9349379, a common SNP in the third intron of the PHACTR1 gene, as the putative causal variant. Epigenomic data from human tissue revealed an enhancer signature at rs9349379 exclusively in aorta, suggesting a regulatory function for this SNP in the vasculature. CRISPR-edited stem cell-derived endothelial cells demonstrate rs9349379 regulates expression of endothelin 1 ( EDN1 ), a gene located 600 kb upstream of PHACTR1 . The known physiologic effects of EDN1 on the vasculature may explain the pattern of risk for the five associated diseases. Overall, these data illustrate the integration of genetic, phenotypic, and epigenetic analysis to identify the biologic mechanism by which a common, non-coding variant can distally regulate a gene and contribute to the pathogenesis of multiple vascular diseases. [ABSTRACT FROM AUTHOR]