Biology subjects

Flister, M. J.

Publications and source records attributed to Flister, M. J..

7 recordsLinked to original sources

bioRxiv2024

Mapping Genetic Interactions of All Paralogs in the Human Genome

Paralogs are abundant in the human genome and thought to be a primary source of synthetic lethality, yet the vast paralogome remains largely uncharacterized. A digenic screen of all paralogous gene pairs in the human genome revealed that synthetic lethalities were infrequent and varied in penetrance in different tumor backgrounds. We hypothesized that the variable penetrance of synthetic lethalities resulted from complex polygenic interactions with different cellular contexts. A machine learning classifier of a subset of paralog pairs tested across 49 cancer models revealed that endogenous perturbations in related pathways predicted paralog synthetic lethality. Further, predictive modeling of paralog synthetic lethality revealed that the strength of synthetic lethal interactions were largely due to the overlap and essentiality of the protein-protein interaction networks shared by the paralogs pairs. Collectively, this study tested all digenic paralog interactions and delineated the key feature classes that underlie the heterogeneity of paralog synthetic lethalities. HighlightsO_LISynthetic lethal paralogs have long been known, yet most paralog interactions are untested. C_LIO_LIWe report the first combinatorial screen of all 36,648 known human paralog pairs. C_LIO_LIA meta-analysis of 461 paralog pairs in 49 cell models revealed context-dependent interactions. C_LIO_LIThe essentiality of paralog interaction networks dictate the strength of synthetic lethality. C_LI eTOCSynthetic lethal paralogs have long been known, yet most paralog interactions are untested. Flister et al report a screen of all 36,648 known human paralog pairs. These data combined with a meta-analysis of 461 pairs across 49 cell models revealed insights to the molecular underpinnings of context-dependent synthetic lethality. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=195 SRC="FIGDIR/small/603642v3_ufig1.gif" ALT="Figure 1"> View larger version (61K): org.highwire.dtl.DTLVardef@1356ac3org.highwire.dtl.DTLVardef@1f43108org.highwire.dtl.DTLVardef@bf2a26org.highwire.dtl.DTLVardef@6844f8_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

A genome-wide CRISPR screen supported by human genetics identifies the TNRC18 gene locus as a novel regulator of inflammatory signaling

Interleukin-1{beta} (IL-1{beta}) is dysregulated in many chronic inflammatory diseases, yet the genetic factors influencing IL-1{beta} production and signaling remain largely unknown. Myeloid-derived cells are the primary producers of IL-1{beta}, prompting a genome-wide CRISPR knockout screen in the human myeloid-derived U937 cell model, treated with lipopolysaccharide (LPS) to mimic inflammatory conditions, and sorted for high and low intracellular IL-1{beta} levels. A total of 295 genes were identified as regulators of IL-1{beta} production, including known mediators, such as TLR4, JAK-STAT, IL-10 receptor, and the Cullin ring finger ligase complex. Notably, 57 out of the 295 genes overlapped with loci associated with human inflammatory diseases, including the TNRC18 gene on chromosome 7p22.1 associated with multiple diseases in the Finnish population. U937 cells engineered with the homozygous rs748670681 risk allele associated with inflammatory bowel disease, demonstrated decreased levels of mRNA for TNRC18 and an adjacent gene WIPI2, reduction in LPS-dependent gene activation and cytokine production, but elevation of interferon-responsive gene programs. Transcriptomic profiles for individual knockouts of TNRC18 and WIPI2 attributed the loss of LPS-dependent signaling primarily to TNRC18 while the exacerbation of interferon signaling is a hallmark of loss of WIPI2. Collectively, these findings delineate the global regulatory mechanisms of IL-1{beta} production and provide molecular insights to the role of the rs748670681 variant as a pleiotropic risk factor for inflammatory diseases.

genomics↗

bioRxiv2022

Machine Learning Reveals Genetic Modifiers of the Immune Microenvironment of Cancer

Heritability in the immune tumor microenvironment (iTME) has been widely observed, yet remains largely uncharacterized and systematic approaches to discover germline genetic modifiers of the iTME still being established. Here, we developed the first machine learning approach to map iTME modifiers within loci from genome-wide association studies (GWAS) for breast cancer (BrCa) incidence and outcome. A random forest model was trained on a positive set of immune-oncology (I-O) targets using BrCa and immune phenotypes from genetic perturbation studies, comparative genomics, Mendelian genetics, and colocalization with autoimmunity and inflammatory disease risk loci. Compared with random negative sets, an I-O target probability score was assigned to the 1,362 candidate genes in linkage disequilibrium with 155 BrCa GWAS loci. Pathway analysis of the most probable I-O targets revealed significant enrichment in drivers of BrCa and immune biology, including the LSP1 locus associated with BrCa incidence and outcome. Quantitative cell type-specific immunofluorescent imaging of 1,109 BrCa patient biopsies revealed that LSP1 expression is restricted to tumor infiltrating leukocytes and correlated with BrCa patient outcome (HR = 1.73, p < 0.001). The human BrCa patient-based genomic and proteomic evidence, combined with phenotypic evidence that LSP1 is a negative regulator of leukocyte trafficking, prioritized LSP1 as a novel I-O target. Finally, a novel comparative mapping strategy using mouse genetic linkage revealed TLR1 as a plausible therapeutic candidate with strong genomic and phenotypic evidence. Collectively, these data demonstrate a robust and flexible analytical framework for functionally fine-mapping GWAS risk loci to identify the most translatable therapeutic targets for the associated disease.

cancer biology↗

bioRxiv2022

Optimization of genomewide CRISPR screens using AsCas12a and multi-guide arrays

Genomewide loss-of-function (LOF) screening using CRISPR (clustered regularly interspaced short palindromic repeats) has facilitated the discovery of novel gene functions across diverse physiological and pathophysiological systems. A challenge with conventional genomewide CRISPR/Cas9 libraries is the unwieldy size (60,000 to 120,000 constructs), which is resource intensive and prohibitive in some experimental contexts. One solution to streamlining CRISPR screening is by multiplexing two or more guides per gene on a single construct, which enables functional redundancy to compensate for suboptimal gene knockout by individual guides. In this regard, AsCas12a (Cpf1) and its derivatives, e.g., enhanced AsCas12a (enAsCas12a), have enabled multiplexed guide arrays to be specifically and efficiently processed for genome editing. Prior studies have established that multiplexed CRISPR/Cas12a libraries perform comparably to the larger equivalent CRISPR/Cas9 libraries, yet the most efficient CRISPR/Cas12a library design remains unresolved. Here, we demonstrate that CRISPR/Cas12a genomewide LOF screening performed optimally with three guides arrayed per gene construct and could be adapted to robotic cell culture without noticeable differences in screen performance. Thus, the conclusions from this study provide novel insight to streamlining genomewide LOF screening using CRISPR/Cas12a and robotic cell culture.

genetics↗

bioRxiv2022

TCGADEPMAP -- Mapping Translational Dependencies and Synthetic Lethalities within The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA) has yielded unprecedented genetic and molecular characterization of the cancer genome, yet the functional consequences and patient-relevance of many putative cancer drivers remain undefined. TCGADEPMAP is the first hybrid map of translational tumor dependencies that was built from machine learning of gene essentiality in the Cancer Dependency Map (DEPMAP) and then translated to TCGA patients. TCGADEPMAP captured well-known and novel cancer lineage dependencies, oncogenes, and synthetic lethalities, demonstrating the robustness of TCGADEPMAP as a translational dependency map. Exploratory analyses of TCGADEPMAP also unveiled novel synthetic lethalities, including the dependency of PAPSS1 driven by loss of PAPSS2 which is collaterally deleted with the tumor suppressor gene PTEN. Synthetic lethality of PAPSS1/2 was validated in vitro and in vivo, including the underlying mechanism of synthetic lethality caused by the loss of protein sulfonation that requires PAPSS1 or PAPSS2. Moreover, TCGADEPMAP demonstrated that patients with predicted PAPSS1/2 synthetic lethality have worse overall survival, suggesting that these patients are in greater need of drug discovery efforts to target PAPSS1. Other map "extensions" were built to capture unique aspects of patient-relevant tumor dependencies using the flexible analytical framework of TCGADEPMAP, including translating gene essentiality to drug response in patient-derived xenograft (PDX) models (i.e., PDXEDEPMAP) and predicting gene tolerability within normal tissues (GTEXDEPMAP). Collectively, this study demonstrates how translational dependency maps can be used to leverage the rapidly expanding catalog of patient genomic datasets to identify and prioritize novel therapeutic targets with the best therapeutic indices.

cancer biology↗

bioRxiv2019

Regulation of CNS regeneration-associated genes is driven by a temporally changing cast of transcription factors

In contrast to mammals, adult fish display a remarkable ability to fully regenerate central nervous system (CNS) axons, enabling functional recovery from CNS injury. Both fish and mammals normally undergo a developmental downregulation of axon growth activity as neurons mature. Fish are able to undergo damage-induced \"reprogramming\" through re-expression of genes necessary for axon growth and guidance, however, the gene regulatory mechanisms remain unknown. Here we present the first comprehensive analysis of gene regulatory reprogramming in zebrafish retinal ganglion cells at specific time points along the axon regeneration continuum from early growth to target reinnervation. Our analyses reveal a regeneration program characterized by sequential activation of stage-specific pathways, regulated by a temporally changing cast of transcription factors that bind to stably accessible DNA regulatory regions. Strikingly, we also find a discrete set of regulatory regions that change in accessibility, consistent with higher-order changes in chromatin organization that mark (1) the beginning of regenerative axon growth in the optic nerve, and (2) the re-establishment of synaptic connections in the brain. Together, these data provide valuable insight into the regulatory logic driving successful vertebrate CNS axon regeneration, revealing key gene regulatory candidates for therapeutic development.

neuroscience↗

bioRxiv2017

Exploring Drivers of Gene Expression in The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA) has greatly advanced cancer research by generating, curating, and publicly releasing deeply measured molecular data from thousands of tumor samples. In particular, gene expression measures, both within and across cancer types, have been used to determine the genes and proteins that are active in tumor cells. To more thoroughly investigate the behavior of gene expression in TCGA tumor samples, we introduce a statistical framework for partitioning the variation in gene expression due to a variety of molecular variables including somatic mutations, transcription factors (TFs), microRNAs, copy number alternations, methylation, and germ-line genetic variation. As proof-of-principle, we identify and validate specific TFs that influence the expression of PTPN14 in breast cancer cells. We provide a freely available, user-friendly, browseable interactive web-based application for exploring the results of our transcriptome-wide analyses across 17 different cancers in TCGA at http://ls-shiny-prod.uwm.edu/edge_in_tcga.

cancer biology↗