Biology subjects

Lenoir, W. F.

Publications and source records attributed to Lenoir, W. F..

4 recordsLinked to original sources

bioRxiv2020

Discovery of novel tumor suppressors from CRISPR screens reveals lipid-sensitive subtype of AML

CRISPR knockout screens in hundreds of cancer cell lines have revealed a substantial number of context-specific essential genes that, when associated with a biomarker such as lineage or oncogenic mutation, offer candidate tumor-specific vulnerabilities for targeted therapies or novel drug development. Data-driven analysis of knockout fitness screens also yields many other functionally coherent modules that show emergent essentiality or, in rarer cases, the opposite phenotype of faster proliferation. We develop a systematic approach to classify these suppressors of proliferation, which are highly enriched for tumor suppressor genes, and define a network of 145 genes in 22 discrete modules. One surprising module contains several elements of the glycerolipid biosynthesis pathway and operates exclusively in a subset of AML lines, which we call Fatty Acid Synthesis/Tumor Suppressor (FASTS) cells. The proliferation suppressor activity of genes involved in the synthesis of saturated fatty acids, coupled with a more severe fitness phenotype for the desaturation pathway, suggests that these cells operate at the limit of their carrying capacity for saturated fatty acids, which we confirmed biochemically. Overexpression of genes in this module is associated with a survival advantage in an age-matched cohort of AML patients, suggesting the gene cluster driving an in vitro phenotype may be associated with a novel, clinically relevant subtype.

genetics↗

bioRxiv2018

Hierarchical organization of the human cell from a cancer coessentiality network

Genetic interactions mediate the emergence of phenotype from genotype. Systematic survey of genetic interactions in yeast showed that genes operating in the same biological process have highly correlated genetic interaction profiles, and this observation has been exploited to infer gene function in model organisms. Systematic surveys of digenic perturbations in human cells are also highly informative, but are not scalable, even with CRISPR-mediated methods. As an alternative, we developed an indirect method of deriving functional interactions. We show that genes having correlated knockout fitness profiles across diverse, non-isogenic cell lines are analogous to genes having correlated genetic interaction profiles across isogenic query strains, and similarly implies shared biological function. We constructed a network of genes with correlated fitness profiles across 400 CRISPR knockout screens in cancer cell lines into a \"coessentiality network,\" with up to 500-fold enrichment for co-functional gene pairs, enabling strong inference of human gene function. Modules in the network are connected in a layered web that gives insight into the hierarchical organization of the cell.

systems biology↗

bioRxiv2017

Identifying drug-gene interactions from CRISPR knockout screens with drugZ

Chemogenetic profiling enables the identification of gene mutations that enhance or suppress the activity of small molecules. This knowledge provides insights into drug mechanism-of-action, genetic vulnerabilities, and resistance mechanisms, all of which may help stratify patient populations. We present drugZ, an algorithm for identifying both synergistic and suppressor chemogenetic interactions from highly sensitive CRISPR screens, available at github.com/hart-lab/drugz. In screens for interactions with a poly(ADP-ribose) polymerase (PARP) inhibitor, DrugZ identifies a greater fraction of the homologous recombination repair pathway than contemporary methods, and confirms KEAP1 loss as a resistance factor for ERK inhibitors.

bioinformatics↗

bioRxiv2016

The E. coli molecular phenotype under different growth conditions

Modern systems biology requires extensive, carefully curated measurements of cellular components in response to different environmental conditions. While high-throughput methods have made transcriptomics and proteomics datasets widely accessible and relatively economical to generate, systematic measurements of both mRNA and protein abundances under a wide range of different conditions are still relatively rare. Here we present a detailed, genome-wide transcriptomics and proteomics dataset of E. coli grown under 34 different conditions. We manipulate concentrations of sodium and magnesium in the growth media, and we consider four different carbon sources glucose, gluconate, lactate, and glycerol. Moreover, samples are taken both in exponential and stationary phase, and we include two extensive time-courses, with multiple samples taken between 3 hours and 2 weeks. We find that exponential-phase samples systematically differ from stationary-phase samples, in particular at the level of mRNA. Regulatory responses to different carbon sources or salt stresses are more moderate, but we find numerous differentially expressed genes for growth on gluconate and under salt and magnesium stress. Our data set provides a rich resource for future computational modeling of E. coli gene regulation, transcription, and translation.

bioinformatics↗