DNA polymerase ι compensates for Fanconi anemia pathway deficiency by countering DNA replication stress.

Fanconi anemia (FA) is caused by defects in cellular responses to DNA crosslinking damage and replication stress. Given the constant occurrence of endogenous DNA damage and replication fork stress, it is unclear why complete deletion of FA genes does not have a major impact on cell proliferation and germ-line FA patients are able to progress through development well into their adulthood. To identify potential cellular mechanisms that compensate for the FA deficiency, we performed dropout screens in FA mutant cells with a whole genome guide RNA library. This uncovered a comprehensive genome-wide profile of FA pathway synthetic lethality, including POLI and CDK4 As little is known of the cellular function of DNA polymerase iota (Pol ι), we focused on its role in the loss-of-function FA knockout mutants. Loss of both FA pathway function and Pol ι leads to synthetic defects in cell proliferation and cell survival, and an increase in DNA damage accumulation. Furthermore, FA-deficient cells depend on the function of Pol ι to resume replication upon replication fork stalling. Our results reveal a critical role for Pol ι in DNA repair and replication fork restart and suggest Pol ι as a target for therapeutic intervention in malignancies carrying an FA gene mutation.

A network of human functional gene interactions from knockout fitness screens in cancer cells.

Genetic interactions mediate the emergence of phenotype from genotype. The 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. Such assays 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 imply shared biological function. We constructed a network of genes with correlated fitness profiles across 276 high-quality 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 gene function and highlighting the modular organization of the cell.

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers.

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories.

PICKLES: the database of pooled in-vitro CRISPR knockout library essentiality screens.

The adaptation of CRISPR/Cas9 systems for pooled library genetic knockout screens in mammalian cells has substantially advanced the state of the art in human functional genomics. Screening panels of cell lines for genes whose knockout imposes a significant fitness defect has dramatically expanded our catalog of high-confidence essential genes, and has already proven useful in identifying tumor-specific essential genes for the development of targeted therapies. However, nonexperts currently lack an easy to use way to access this data and to identify whether their genes of interest are essential across different genetic backgrounds. The volume of screening data is expected to grow massively, making the problem more intractable. Here we describe PICKLES, the database of Pooled In vitro CRISPR Knockout Library Essentiality Screens, where end users can display and download raw or normalized essentiality profiles for more that 18 000 protein-coding genes across more than 50 cell lines. An additional data set with 15,000 genes targeted by pooled library shRNA in over 100 cell lines is also included. Researchers can see at a glance the relative fitness defect and tissue specificity of their genes of interest, generate and save figures locally, and download all raw data. The database is available at http://pickles.hart-lab.org.

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. Additionally, we provide measurements of doubling times and in-vivo metabolic fluxes through the central carbon metabolism. 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.