Biology subjects

Flaherty, P.

Publications and source records attributed to Flaherty, P..

6 recordsLinked to original sources

bioRxiv2025

Stress testing reveals selective vulnerabilities in protein homeostasis

Protein quality control (PQC) systems are essential for cellular resilience to proteotoxic stress. Despite intensive study for decades, functional redundancies in the system obscure the contributions of the collectively important individual genes. Here, we leverage transposon sequencing across bacteria strains lacking key chaperones and proteases to reveal hidden determinants of stress response in protein homeostasis. By profiling fitness under multiple proteotoxic stresses, we uncover stress-specific vulnerabilities and reveal how major players of PQC mask correlations between transcriptomic responses and gene fitness. As an illustration of unexpected connections, we identify a heat-specific synthetic lethality between the disaggregase ClpB and DNA Polymerase I (PolA) mediated by persistent aggregation of the RecA recombinase and toxic persistence of the heat shock regulon. Our findings reveal that stress-induced aggregation is not broadly toxic. Rather, it becomes lethal in specific genetic or environmental contexts due to the depletion of components only needed in those specific circumstances. This work presents a framework to reveal normally hidden fragility in stress responses using gene fitness scores adaptable to a variety of systems.

molecular biology↗

bioRxiv2024

Discovering Genetic Modulators of the Protein Homeostasis System through Multilevel Analysis

Every protein progresses through a natural lifecycle from birth to maturation to death; this process is coordinated by the protein homeostasis system. Environmental or physiological conditions trigger pathways that maintain the homeostasis of the proteome. An open question is how these pathways are modulated to respond to the many stresses that an organism encounters during its lifetime. To address this question, we tested how the fitness landscape changes in response to environmental and genetic perturbations using directed and massively parallel transposon mutagenesis in Caulobacter crescentus. We developed a general computational pipeline for the analysis of gene-by-environment interactions in transposon mutagenesis experiments. This pipeline uses a combination of general linear models (GLMs), statistical knockoffs, and a nonparametric Bayesian statistical model to identify essential genetic network components that are shared across environmental perturbations. This analysis allows us to quantify the similarity of proteotoxic environmental perturbations from the perspective of the fitness landscape. We find that essential genes vary more by genetic background than by environmental conditions, with limited overlap among mutant strains targeting different facets of the protein homeostasis system. We also identified 146 unique fitness determinants across different strains, with 19 genes common to at least two strains, showing varying resilience to proteotoxic stresses. Experiments exposing cells to a combination of genetic perturbations and dual environmental stressors show that perturbations that are quantitatively dissimilar from the perspective of the fitness landscape are likely to have a synergistic effect on the growth defect. Significance StatementThis study provides critical insights into how cells adapt to environmental and genetic challenges affecting protein homeostasis. Using multilevel statistical analysis and transposon mutagenesis, we find that a model organism, Caulobacter crescentus, lacks a universal redundancy mechanism for coping with stress, as evidenced by the limited overlap in essential genes across different environmental and genetic perturbations. Our methods also pinpoint key fitness determinants and enable the prediction of perturbation combinations that synergistically affect cell growth.

systems biology↗

bioRxiv2021

Identification of Significant Gene Expression Changes in Multiple Perturbation Experiments using Knockoffs

MotivationLarge-scale multiple perturbation experiments have the potential to reveal a more detailed understanding of the molecular pathways that respond to genetic and environmental changes. A key question in these studies is which gene expression changes are important for the response to the perturbation. ResultsWe present here a method based on the model-X knockoffs framework to identify significant gene expression changes in multiple perturbation experiments. This approach makes no assumptions on the functional form of the dependence between the responses and the perturbations and provides finite sample false discovery rate control for the set of important gene expression responses. In a large-scale multiple perturbation gene expression data set from the Library of Integrated Network-Based Cellular Signature (LINCS) NIH program, we identified important genes whose expression is modulated in response to perturbation with anthracycline, vorinostat, trichostatin-a, geldanamycin, and sirolimus. Furthermore, we compared the set of important genes that respond to these small molecules to identify co-responsive pathways. Availability and Implementationhttps://github.com/flahertylab/deepYknockoff Contactpflaherty@umass.edu and zhaott0416@gmail.com Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

Model-based identification of conditionally-essential genes from transposon-insertion sequencing data

The understanding of bacterial gene function has been greatly enhanced by recent advancements in the deep sequencing of microbial genomes. Transposon insertion sequencing methods combines next-generation sequencing techniques with transposon mutagenesis for the exploration of the essentiality of genes under different environmental conditions. We propose a model-based method that uses regularized negative binomial regression to estimate the change in transposon insertions attributable to gene-environment changes without transformations or uniform normalization. An empirical Bayes model for estimating the local false discovery rate combines unique and total count information to test for genes that show a statistically significant change in transposon counts. When applied to RB-TnSeq (randomized barcode transposon sequencing) and Tn-seq (transposon sequencing) libraries made in strains of Caulobacter crescentus using both total and unique count data the model was able to identify a set of conditionally essential genes for each target condition that shed light on their functions and roles during various stress conditions. Author summaryTransposon insertion sequencing allows the study of bacterial gene function by combining next-generation sequencing techniques with transposon mutagenesis under different genetic and environmental perturbations. Our proposed regularized negative binomial regression method improves the quality of analysis of this data.

bioinformatics↗

bioRxiv2020

A Bayesian Nonparametric Model for Inferring Subclonal Populations from Structured DNA Sequencing Data

There are distinguishing features or "hallmarks" of cancer that are found across tumors, individuals, and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet, within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment. We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma-Poisson hierarchy and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals co-mutated loci across samples.

bioinformatics↗

bioRxiv2020

SCSIM: Jointly simulating correlated single-cell and bulk next-generation DNA sequencing data

BackgroundRecently, it has become possible to collect next-generation DNA sequencing data sets that are composed of multiple samples from multiple biological units where each of these samples may be from a single cell or bulk tissue. Yet, there does not yet exist a tool for simulating DNA sequencing data from such a nested sampling arrangement with single-cell and bulk samples so that developers of analysis methods can assess accuracy and precision. ResultsWe have developed a tool that simulates DNA sequencing data from hierarchically grouped (correlated) samples where each sample is designated bulk or single-cell. Our tool uses a simple configuration file to define the experimental arrangement and can be integrated into software pipelines for testing of variant callers or other genomic tools. ConclusionsThe DNA sequencing data generated by our simulator is representative of real data and integrates seamlessly with standard downstream analysis tools.

bioinformatics↗