Perturbation-Specific Transcriptional Mapping facilitates unbiased target elucidation of antibiotics

The rising prevalence of antibiotic resistance threatens human health. While more sophisticated strategies for antibiotic discovery are being developed, target elucidation of new chemical entities remains challenging. In the post-genomic era, expression profiling can play an important role in mechanism-of-action (MOA) prediction by reporting on the cellular response to perturbation. However, the broad application of transcriptomics has yet to fully fulfill its promise of transforming target elucidation. We developed an unbiased strategy for MOA prediction, called Perturbation-Specific Transcriptional Mapping (PerSpecTM), in which large-throughput expression profiling of wild-type or hypomorphic mutants depleted for essential targets, enables a reference-based strategy to associate similar perturbations, whether small molecule or genetic, based on the principle that similar perturbations will elicit similar transcriptional responses. We applied this approach to rapidly elucidate the MOAs of three new molecules with activity against the important bacterial pathogen Pseudomonas aeruginosa by mapping their expression profiles to those generated for a reference set of antimicrobial compounds with known MOA. Meanwhile, we also show that small molecule inhibition can be mapped to genetic depletion of essential targets by CRISPRi by PerSpecTM, thus demonstrating proof-of-concept that correlations between expression profiles of small molecule and genetic perturbations can predict MOA when no chemical entities exist to serve as a reference. Empowered by PerSpecTM and its leveraging of reference transcriptional profiles for known small molecule or genetic perturbations, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts.

Significance Statement

In an era of increasing antibiotic resistance, new antibiotics are critically needed. However, MOA elucidation remains a challenging step in antibiotic discovery and thus a major bottleneck. Building on the principle that molecules with similar MOAs elicit similar transcriptional responses, we have now developed a highly scalable strategy for MOA prediction in the important bacterial pathogen Pseudomonas aeruginosa based on correlations between the expression profiles of new molecules and known perturbations, either small molecule inhibition by known antibiotics or transcriptional repression of essential targets by CRISPRi. By rapidly assigning MOA to three new molecules with anti-pseudomonal activity, we provide proof-of-concept for a rapid, comprehensive, systematic, reference-based approach to MOA prediction, with the potential to transform antibiotic discovery efforts.

"Multiplexed screen identifies a Pseudomonas aeruginosa -specific small molecule targeting the outer membrane protein OprH and its interaction with LPS"

The surge of antimicrobial resistance threatens efficacy of current antibiotics, particularly against Pseudomonas aeruginosa , a highly resistant gram-negative pathogen. The asymmetric outer membrane (OM) of P. aeruginosa combined with its array of efflux pumps provide a barrier to xenobiotic accumulation, thus making antibiotic discovery challenging. We adapted PROSPECT 1 , a target-based, whole-cell screening strategy, to discover small molecule probes that kill P. aeruginosa mutants depleted for essential proteins localized at the OM. We identified BRD1401, a small molecule that has specific activity against a P. aeruginosa mutant depleted for the essential lipoprotein, OprL. Genetic and chemical biological studies identified that BRD1401 acts by targeting the OM β-barrel protein OprH to disrupt its interaction with LPS and increase membrane fluidity. Studies with BRD1401 also revealed an interaction between OprL and OprH, directly linking the OM with peptidoglycan. Thus, a whole-cell, multiplexed screen can identify species-specific chemical probes to reveal novel pathogen biology.

Analyzing histone ChIP-seq data with a bin-based probability of being signal

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.

The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models

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.

Single-cell multi-scale footprinting reveals the modular organization of DNA regulatory elements

Cis-regulatory elements control gene expression and are dynamic in their structure, reflecting changes to the composition of diverse effector proteins over time1-3. Here we sought to connect the structural changes at cis-regulatory elements to alterations in cellular fate and function. To do this we developed PRINT, a computational method that uses deep learning to correct sequence bias in chromatin accessibility data and identifies multi-scale footprints of DNA-protein interactions. We find that multi-scale footprints enable more accurate inference of TF and nucleosome binding. Using PRINT with single-cell multi-omics, we discover wide-spread changes to the structure and function of candidate cis-regulatory elements (cCREs) across hematopoiesis, wherein nucleosomes slide, expose DNA for TF binding, and promote gene expression. Activity segmentation using the co-variance across cell states identifies "sub-cCREs" as modular cCRE subunits of regulatory DNA. We apply this single-cell and PRINT approach to characterize the age-associated alterations to cCREs within hematopoietic stem cells (HSCs). Remarkably, we find a spectrum of aging alterations among HSCs corresponding to a global gain of sub-cCRE activity while preserving cCRE accessibility. Collectively, we reveal the functional importance of cCRE structure across cell states, highlighting changes to gene regulation at single-cell and single-base-pair resolution.

At-home Testing and Risk Factors for Acquisition of SARS-CoV-2 Infection in a Major US Metropolitan Area

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.

Long-range phasing of dynamic, tissue-specific and allele-specific regulatory elements

Epigenomic maps identify gene regulatory elements by their chromatin state. However, prevailing short-read sequencing methods cannot effectively distinguish alleles, evaluate the interdependence of elements in a locus or capture single-molecule dynamics. Here, we apply targeted nanopore sequencing to profile chromatin accessibility and DNA methylation on contiguous ~100-kb DNA molecules that span loci relevant to development, immunity and imprinting. We detect promoters, enhancers, insulators and transcription factor footprints on single molecules based on exogenous GpC methylation. We infer relationships among dynamic elements within immune loci, and order successive remodeling events during T cell stimulation. Finally, we phase primary sequence and regulatory elements across the H19/IGF2 locus, uncovering primate-specific features. These include a segmental duplication that stabilizes the imprinting control region and a noncanonical enhancer that drives biallelic IGF2 expression in specific contexts. Our study advances emerging strategies for phasing gene regulatory landscapes and reveals a mechanism that overrides IGF2 imprinting in human cells.

At-home Testing and Risk Factors for Acquisition of SARS-CoV-2 Infection in a Major US Metropolitan Area

IMPORTANCE

Unbiased assessment of risks associated with acquisition of SARS-CoV-2 is critical to informing mitigation efforts during pandemics.

OBJECTIVE

Understand risk factors for acquiring COVID-19 in a large, prospective cohort of adult residents recruited to be representative of a large US metropolitan area.

DESIGN

Fully remote longitudinal cohort study launched in October 2020 and ongoing; Study data reported through June 15, 2021.

SETTING

Brigham and Women’s Hospital, Boston MA.

PARTICIPANTS

Adults within 45 miles of Boston, MA.

INTERVENTION

Monthly at-home SARS-CoV-2 viral and antibody testing.

MAIN OUTCOMES

Between October 2020 and January 2021, we enrolled 10,289 adults reflective of Massachusetts 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 to whites, Black non-Hispanic participants had a 2.2 fold greater risk of acquiring COVID-19 (HR 2.19, 95% CI 1.91-2.50; p=<0.001) and Hispanics had a 1.5 fold greater risk (HR 1.52, 95% CI 1.32-1.71; p=<0.016). 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, as measured by the area deprivation index as a marker for socioeconomic status by census block group, were associated with an increased risk in developing COVID-19. Individuals with medical risk factors for severe COVID-19 disease were at a decreased risk of SARS-CoV-2 acquisition.

CONCLUSIONS

These results demonstrate that race/ethnicity and socioeconomic status are not only risk factors for severity of disease but are also the biggest determinants of acquisition of infection. Importantly, 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. Meanwhile, medical conditions and advanced age that increase the risk for severity of SARS-CoV-2 disease were associated with a lower risk of acquisition of COVID-19 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.

TRIAL REGISTRATION

N/A.

KEY POINTS

QUESTION

What population and occupational groups in the United States are at increased risk for acquiring COVID-19?

FINDINGS

In this remote, longitudinal cohort study involving monthly PCR and serology self-testing of 10,289 adult residents of the Boston metropolitan area, 9257 (90.0%) of TestBoston participants acquired evidence of immunity to SARS-CoV-2 through vaccination, infection, or both as of June 15, 2021. Residents identifying as Black, Hispanic/Latinx had an increased risk of acquisition of COVID-19. Healthcare workers were not at increased risk of SARS-CoV-2 acquisition. Individuals with medical risk factors for severe COVID-19 disease were at a decreased risk of SARS-CoV-2 acquisition.

MEANING

These results demonstrate that race/ethnicity and socioeconomic status are not only risk factors for severity of disease but also are the biggest determinants of acquisition of infection. These findings highlight the need to address the consequences of structural racism during the development of mitigation programs for current and future pandemics.

Large-Scale Topological Changes Restrain Malignant Progression in Colorectal Cancer

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.

Detecting sample swaps in diverse NGS data types using linkage disequilibrium

As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets.

Parallelized analysis in clinical genomics can lead to sample or data mislabelling, and could have serious downstream consequences. Here the authors present a tool to quantify sample genetic relatedness and detect such mistakes, and apply it to thousands of datasets from the ENCODE consortium.

Expanded encyclopaedias of DNA elements in the human and mouse genomes

The human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE1 and Roadmap Epigenomics2 data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes.

An Optimized Competitive-Aging Method Reveals Gene-Drug Interactions Underlying the Chronological Lifespan of Saccharomyces cerevisiae

The chronological lifespan of budding yeast is a model of aging and age-related diseases. This paradigm has recently allowed genome-wide screening of genetic factors underlying post-mitotic viability in a simple unicellular system, which underscores its potential to provide a comprehensive view of the aging process. However, results from different large-scale studies show little overlap and typically lack quantitative resolution to derive interactions among different aging factors. We previously introduced a sensitive, parallelizable approach to measure the chronological-lifespan effects of gene deletions based on the competitive aging of fluorescence-labeled strains. Here, we present a thorough description of the method, including an improved multiple-regression model to estimate the association between death rates and fluorescent signals, which accounts for possible differences in growth rate and experimental batch effects. We illustrate the experimental procedure-from data acquisition to calculation of relative survivorship-for ten deletion strains with known lifespan phenotypes, which is achieved with high technical replicability. We apply our method to screen for gene-drug interactions in an array of yeast deletion strains, which reveals a functional link between protein glycosylation and lifespan extension by metformin. Competitive-aging screening coupled to multiple-regression modeling provides a powerful, straight-forward way to identify aging factors in yeast and their interactions with pharmacological interventions.

Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination

Multidrug resistant organisms (MDROs) are a serious threat to human health^1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying MDROs increase mortality^3,4 and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution⁵, require several days before informing key clinical decisions. Rapid AST would transform the care of infected patients while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here, we describe a rapid assay for combined Genotypic and Phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94–99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology, and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24–36 hours faster than standard workflows, with <4 hour assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.

RNA sequence analysis reveals macroscopic somatic clonal expansion across normal tissues

How somatic mutations accumulate in normal cells is poorly understood. A comprehensive analysis of RNA sequencing data from ~6700 samples across 29 normal tissues revealed multiple somatic variants, demonstrating that macroscopic clones can be found in many normal tissues. We found that sun-exposed skin, esophagus, and lung have a higher mutation burden than other tested tissues, which suggests that environmental factors can promote somatic mosaicism. Mutation burden was associated with both age and tissue-specific cell proliferation rate, highlighting that mutations accumulate over both time and number of cell divisions. Finally, normal tissues were found to harbor mutations in known cancer genes and hotspots. This study provides a broad view of macroscopic clonal expansion in human tissues, thus serving as a foundation for associating clonal expansion with environmental factors, aging, and risk of disease.

Enhancer signatures stratify and predict outcomes of non-functional pancreatic neuroendocrine tumors

Most pancreatic neuroendocrine tumors (PNETs) do not produce excess hormones and are therefore considered 'non-functional'^1-3. As clinical behaviors vary widely and distant metastases are eventually lethal^2,4, biological classifications might guide treatment. Using enhancer maps to infer gene regulatory programs, we find that non-functional PNETs fall into two major subtypes, with epigenomes and transcriptomes that partially resemble islet α- and β-cells. Transcription factors ARX and PDX1 specify these normal cells, respectively^5,6, and 84% of 142 non-functional PNETs expressed one or the other factor, occasionally both. Among 103 cases, distant relapses occurred almost exclusively in patients with ARX⁺PDX1^- tumors and, within this subtype, in cases with alternative lengthening of telomeres. These markedly different outcomes belied similar clinical presentations and histology and, in one cohort, occurred irrespective of MEN1 mutation. This robust molecular stratification provides insight into cell lineage correlates of non-functional PNETs, accurately predicts disease course and can inform postoperative clinical decisions.

A Genetic Variant Associated with Five Vascular Diseases Is a Distal Regulator of Endothelin-1 Gene Expression

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 LB-231: Identifying cancer-related processes in normal tissues via RNA-seq

Cancer genome studies have significantly contributed to the discovery of somatic mutations and processes that drive cancer. However, how these mutations accumulate in normal cells and contribute to early cancer development remains poorly understood. Recent studies have addressed this question by studying normal blood and a small number of skin samples, discovering both driver mutations as well as mutational processes observed in cancer. Here we extend these studies by analyzing RNA from ~7,000 samples across 30 normal tissues from ~600 individuals compared to their germline DNA, collected as part of the GTEx project. To accomplish this goal we first developed a new pipeline termed RNA-MuTect for calling somatic mutations directly from RNA-seq samples and their matched-normal DNA. RNA-MuTect includes multiple filtering steps designed specifically for analyzing RNA-seq. We first validated RNA-MuTect by analyzing TCGA samples where both DNA and RNA data are available. Comparing the set of mutations detected by RNA-MuTect to those identified in the DNA, we show that whenever there is sufficient coverage to detect the mutations in RNA, we have a high sensitivity. Most importantly, RNA-MuTect has a very low false-positive rate with specificity &gt;90%. We further demonstrate that we can discover most of the known driver genes in this cohort using the mutations detected based on the RNA data. Moreover, using the RNA data, we can detect the same mutational processes as identified in the DNA, including UV, aging, smoking and others. To study clonal expansion in normal tissues and investigate whether known cancer-related genes and processes can be identified, we applied RNA-MuTect to the GTEx dataset. As expected, multiple variants were detected across tissues, with skin, lung and esophagus having the highest number of somatic mutations. Overall, different cancer-related events were detected. Specifically: (1) we found 15 hotspot mutations in 6 different genes including TP53, KRAS and PIK3CA; (2) Mutated genes in 8 different tissues were found to be enriched with Cancer Gene Census genes; (3) Known cancer genes were found to be under positive selection in different tissues; (4) Known cancer-related mutational signatures were captured in normal tissues; (5) Cases of allelic imbalance were detected in various tissues. This study is the first to analyze a large number of samples across many tissues to explore the fundamental question of cancer initiation. Many cancer-related processes and events are discovered across different tissues, laying the foundation for studying the earliest stages of cancer development.

Note: This abstract was not presented at the meeting.

Citation Format: Keren Yizhak, Jaegil Kim, Francois Aguet, Julian Hess, Hailei Zhang, Eila Arich-Landkof, Noam Shoresh, Ayelet Segre, Chip Stewart, Dainel Rosebrock, Dimitri Livitz, Nicholas Haradhvala, Paz Polak, Tim Sullivan, Xiao Li, Kristin Ardlie, Gad Getz. Identifying cancer-related processes in normal tissues via RNA-seq [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-231. doi:10.1158/1538-7445.AM2017-LB-231

Antibiotic tolerance facilitates the evolution of resistance

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.

Single-molecule decoding of combinatorially modified nucleosomes

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.

Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state

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 is sparse, comprising on the order of 1000 unique reads. However, by assaying thousands of ES cells, we identify a spectrum of sub-populations 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.

High-Throughput Single-Cell Labeling (Hi-SCL) for RNA-Seq Using Drop-Based Microfluidics

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.

Integrative analysis of 111 reference human epigenomes

The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.

In silico abstraction of zinc finger nuclease cleavage profiles reveals an expanded landscape of off-target sites

Gene-editing nucleases enable targeted modification of DNA sequences in living cells, thereby facilitating efficient knockout and precise editing of endogenous loci. Engineered nucleases also have the potential to introduce mutations at off-target sites of action. Such unintended alterations can confound interpretation of experiments and can have implications for development of therapeutic applications. Recently, two improved methods for identifying the off-target effects of zinc finger nucleases (ZFNs) were described–one using an in vitro cleavage site selection method and the other exploiting the insertion of integration-defective lentiviruses into nuclease-induced double-stranded DNA breaks. However, application of these two methods to a ZFN pair targeted to the human CCR5 gene led to identification of largely non-overlapping off-target sites, raising the possibility that additional off-target sites might exist. Here, we show that in silico abstraction of ZFN cleavage profiles obtained from in vitro cleavage site selections can greatly enhance the ability to identify potential off-target sites in human cells. Our improved method should enable more comprehensive profiling of ZFN specificities.

ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia

Chromatin immunoprecipitation (ChIP) followed by high-throughput DNA sequencing (ChIP-seq) has become a valuable and widely used approach for mapping the genomic location of transcription-factor binding and histone modifications in living cells. Despite its widespread use, there are considerable differences in how these experiments are conducted, how the results are scored and evaluated for quality, and how the data and metadata are archived for public use. These practices affect the quality and utility of any global ChIP experiment. Through our experience in performing ChIP-seq experiments, the ENCODE and modENCODE consortia have developed a set of working standards and guidelines for ChIP experiments that are updated routinely. The current guidelines address antibody validation, experimental replication, sequencing depth, data and metadata reporting, and data quality assessment. We discuss how ChIP quality, assessed in these ways, affects different uses of ChIP-seq data. All data sets used in the analysis have been deposited for public viewing and downloading at the ENCODE (http://encodeproject.org/ENCODE/) and modENCODE (http://www.modencode.org/) portals.

Genome-wide chromatin state transitions associated with developmental and environmental cues

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.

Combinatorial patterning of chromatin regulators uncovered by genome-wide location analysis in human cells

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.

Chromatin profiling by directly sequencing small quantities of immunoprecipitated DNA

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.

Evolution exacerbates the paradox of the plankton

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 superspecies, 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.

Exposing the fitness contribution of duplicated genes

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.

Analytical results for individual and group selection of any intensity

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.

An equivalence principle for the incorporation of favorable mutations in asexual populations

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.