Biology subjects

Shoresh, N.

Publications and source records attributed to Shoresh, N..

11 recordsLinked to original sources

bioRxiv2025

A multiplexed, target-based phenotypic screening platform using CRISPR interference in Mycobacterium abscessus

The rise of difficult-to-treat Mycobacterium abscessus infections presents a growing clinical challenge due to the immense arsenal of intrinsic, inducible and acquired antibiotic resistance mechanisms that render many existing antibiotics ineffective against this pathogen. Moreover, the limited success in discovery of novel compounds that inhibit novel pathways underscores the need for innovative drug discovery strategies. Here, we report a strategic advancement in PROSPECT (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), which is an antimicrobial discovery strategy that measures chemical-genetic interactions between small molecules and a pool of bacterial mutants, each depleted of a different essential protein target, to identify whole-cell active compounds with high sensitivity. Applying this modified strategy to M. abscessus, in contrast to previously described versions of PROSPECT which utilized protein degradation or promoter replacement strategies for generating engineered hypomorphic strains, here we leveraged CRISPR interference (CRISPRi) to more efficiently generate mutants each depleted of a different essential gene involved in cell wall synthesis or located at the bacterial surface. We applied this platform to perform a pooled PROSPECT pilot screen of a library of 809 compounds using CRISPRi guides as mutant barcodes. We identified a range of active hits, including compounds targeting InhA, a well-known mycobacterial target but under-explored in the M. abscessus space. The unexpected susceptibility to isoniazid, traditionally considered to be ineffective in M. abscessus, suggested a complex interplay of several intrinsic resistance mechanisms. While further complementary efforts will be needed to change the landscape of therapeutic options for M. abscessus, we propose that PROSPECT with CRISPRi engineering provides an increasingly accessible, high-throughput target-based phenotypic screening platform and thus represents an important step towards accelerating early-stage drug discovery.

microbiology↗

bioRxiv2025

Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis

In an era of increasing resistance, new and effective strategies are needed for antibiotic discovery. Whole-cell active screens yield candidate compounds lacking mechanism-of-action (MOA) information and thus do not provide biological insight for prioritization. We previously reported PROSPECT (PRimary screening Of Strains to Prioritize Expanded Chemistry and Targets), an antimicrobial discovery strategy that measures chemical-genetic interactions between small molecules and a pool of Mycobacterium tuberculosis mutants, each depleted of a different essential protein target. PROSPECT facilitates efficient hit prioritization by simultaneously identifying whole-cell active compounds with high sensitivity and providing early insights into their MOA. Here, we report a reference-based approach to infer MOA from often complex PROSPECT data. For this aim, we curated a reference set of 437 compounds with published, annotated MOA and known or suspected antitubercular activity, and applied PROSPECT to it. We then developed Perturbagen CLass (PCL) analysis, a computational method that predicts MOA by comparing chemical-genetic interaction profiles of unknown compounds to those of this reference set. In leave-one-out cross-validation, PCL analysis correctly predicted MOA with 70% sensitivity and 75% precision. When applied to 75 antitubercular leads with known MOA previously reported by GlaxoSmithKline (GSK), PCL analysis similarly achieved 69% sensitivity and 87% precision. We also analyzed 98 GSK compounds lacking MOA information, predicting 60 of them to act via a reference MOA, and followed up with functional validation of 29 compounds predicted to target respiration-related MOAs. Finally, we applied PROSPECT and PCL analysis to [~]5,000 compounds from larger unbiased libraries that had not been preselected for antitubercular activity. PCL analysis identified a novel scaffold lacking wild-type activity but predicted to inhibit respiration via QcrB, and we confirmed this prediction while chemically optimizing this scaffold to achieve wild-type activity. PCL analysis of PROSPECT data thus enables rapid MOA assignment and hit prioritization, advancing the discovery of new, potent antitubercular compounds.

microbiology↗

bioRxiv2024

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 fulfill its promise of transforming target elucidation due to challenges in identifying the most relevant, direct responses to target inhibition. We developed an unbiased strategy for MOA prediction, called Perturbation-Specific Transcriptional Mapping (PerSpecTM), in which large-throughput expression profiling of wildtype or hypomorphic mutants, depleted for essential targets, enables a computational strategy to address this challenge. We applied PerSpecTM to perform reference-based MOA prediction based on the principle that similar perturbations, whether chemical or genetic, will elicit similar transcriptional responses. Using this approach, we elucidated the MOAs of three new molecules with activity against Pseudomonas aeruginosa by comparing their expression profiles to those of a reference set of antimicrobial compounds with known MOAs. We also show that transcriptional responses to small molecule inhibition resemble those resulting from genetic depletion of essential targets by CRISPRi by PerSpecTM, demonstrating proof-of-concept that correlations between expression profiles of small molecule and genetic perturbations can facilitate MOA prediction when no chemical entities exist to serve as a reference. Empowered by PerSpecTM, this work lays the foundation for an unbiased, readily scalable, systematic reference-based strategy for MOA elucidation that could transform antibiotic discovery efforts. Significance StatementNew antibiotics are critically needed in the face of increasing antibiotic resistance. However, mechanism-of-action (MOA) elucidation remains challenging and imposes a major bottleneck in antibiotic discovery and development. Building on the principle that molecules with similar MOAs elicit similar transcriptional responses, we have 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 MOAs 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.

genomics↗

bioRxiv2024

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

SUMMARYThe 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 PROSPECT1, 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 {beta}-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.

microbiology↗

bioRxiv2023

Multi-locus CRISPRi targeting with a single truncated guide RNA

A critical goal in functional genomics is evaluating which non-coding elements contribute to gene expression, cellular function, and disease. Functional characterization remains a challenge due to the abundance and complexity of candidate elements. Here, we develop a CRISPRi- based approach for multi-locus screening of putative transcription factor binding sites with a single truncated guide. A truncated guide with hundreds of sequence match sites can reliably disrupt enhancer activity, which expands the targeting scope of CRISPRi while maintaining repressive efficacy. We screen over 13,000 possible CTCF binding sites with 24 guides at 10 nucleotides in spacer length. These truncated guides direct CRISPRi-mediated deposition of repressive H3K9me3 marks and disrupt transcription factor binding at most sequence match target sites. This approach is valuable for elucidating functional transcription factor binding motifs or other repeated genomic sequences and is easily implementable with existing tools.

molecular biology↗

bioRxiv2023

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.

genomics↗

bioRxiv2021

Multi-tissue integrative analysis of personal epigenomes

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 personal epigenomes, for [~]25 tissues and >10 assays in four donors (>1500 open-access functional genomic and proteomic datasets, in total). Each dataset is mapped to a matched, diploid personal genome, which has long-read phasing and structural variants. The mappings enable us to identify >1 million loci with allele-specific behavior. These loci exhibit coordinated epigenetic activity along haplotypes and less conservation than matched, non-allele-specific loci, in a fashion broadly paralleling tissue-specificity. Surprisingly, they can be accurately modelled just based on local nucleotide-sequence context. Combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci and enables models for transferring known eQTLs to difficult-to-profile tissues. Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.

genomics↗

bioRxiv2020

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.

bioinformatics↗

bioRxiv2019

A Competitive-Aging Method for Quantitative Genetic Analysis of 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.

genetics↗

bioRxiv2018

A comprehensive analysis of RNA sequences reveals macroscopic somatic clonal expansion across normal tissues

Cancer genome studies have significantly advanced our knowledge of somatic mutations. However, how these mutations accumulate in normal cells and whether they promote pre-cancerous lesions remains poorly understood. Here we perform a comprehensive analysis of normal tissues by utilizing RNA sequencing data from [~]6,700 samples across 29 normal tissues collected as part of the Genotype-Tissue Expression (GTEx) project. We identify somatic mutations using a newly developed pipeline, RNA-MuTect, for calling somatic mutations directly from RNA-seq samples and their matched-normal DNA. When applied to the GTEx dataset, we detect multiple variants across different tissues and find that mutation burden is associated with both the age of the individual and tissue proliferation rate. We also detect hotspot cancer mutations that share tissue specificity with their matched cancer type. This study is the first to analyze a large number of samples across multiple normal tissues, identifying clones with genomic aberrations observed in cancer.

genomics↗

bioRxiv2018

Defining the core essential genome of Pseudomonas aeruginosa

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. It is becoming clear that a major limitation 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 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 novel statistical model, FiTnEss, to classify genes as essential versus non-essential across all strain-media combinations. We defined a set of 321 core essential genes, representing 6.6% of the genome. We determined that analysis of 4 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.

microbiology↗