A genome-scale genetic interaction map was constructed by examining 5.4 million gene-gene pairs for synthetic genetic interactions, generating quantitative genetic interaction profiles for ~75% of all genes in the budding yeast, Saccharomyces cerevisiae. A network based on genetic interaction profiles reveals a functional map of the cell in which genes of similar biological processes cluster together in coherent subsets, and highly correlated profiles delineate specific pathways to define gene function. The global network identifies functional cross-connections between all bioprocesses, mapping a cellular wiring diagram of pleiotropy. Genetic interaction degree correlated with a number of different gene attributes, which may be informative about genetic network hubs in other organisms. We also demonstrate that extensive and unbiased mapping of the genetic landscape provides a key for interpretation of chemical-genetic interactions and drug target identification.

A genetic interaction network containing approximately 1000 genes and approximately 4000 interactions was mapped by crossing mutations in 132 different query genes into a set of approximately 4700 viable gene yeast deletion mutants and scoring the double mutant progeny for fitness defects. Network connectivity was predictive of function because interactions often occurred among functionally related genes, and similar patterns of interactions tended to identify components of the same pathway. The genetic network exhibited dense local neighborhoods; therefore, the position of a gene on a partially mapped network is predictive of other genetic interactions. Because digenic interactions are common in yeast, similar networks may underlie the complex genetics associated with inherited phenotypes in other organisms.

Defining the functional relationships between proteins is critical for understanding virtually all aspects of cell biology. Large-scale identification of protein complexes has provided one important step towards this goal; however, even knowledge of the stoichiometry, affinity and lifetime of every protein-protein interaction would not reveal the functional relationships between and within such complexes. Genetic interactions can provide functional information that is largely invisible to protein-protein interaction data sets. Here we present an epistatic miniarray profile (E-MAP) consisting of quantitative pairwise measurements of the genetic interactions between 743 Saccharomyces cerevisiae genes involved in various aspects of chromosome biology (including DNA replication/repair, chromatid segregation and transcriptional regulation). This E-MAP reveals that physical interactions fall into two well-represented classes distinguished by whether or not the individual proteins act coherently to carry out a common function. Thus, genetic interaction data make it possible to dissect functionally multi-protein complexes, including Mediator, and to organize distinct protein complexes into pathways. In one pathway defined here, we show that Rtt109 is the founding member of a novel class of histone acetyltransferases responsible for Asf1-dependent acetylation of histone H3 on lysine 56. This modification, in turn, enables a ubiquitin ligase complex containing the cullin Rtt101 to ensure genomic integrity during DNA replication.

Whole-exome sequencing of cell-free DNA (cfDNA) could enable comprehensive profiling of tumors from blood but the genome-wide concordance between cfDNA and tumor biopsies is uncertain. Here we report ichorCNA, software that quantifies tumor content in cfDNA from 0.1× coverage whole-genome sequencing data without prior knowledge of tumor mutations. We apply ichorCNA to 1439 blood samples from 520 patients with metastatic prostate or breast cancers. In the earliest tested sample for each patient, 34% of patients have ≥10% tumor-derived cfDNA, sufficient for standard coverage whole-exome sequencing. Using whole-exome sequencing, we validate the concordance of clonal somatic mutations (88%), copy number alterations (80%), mutational signatures, and neoantigens between cfDNA and matched tumor biopsies from 41 patients with ≥10% cfDNA tumor content. In summary, we provide methods to identify patients eligible for comprehensive cfDNA profiling, revealing its applicability to many patients, and demonstrate high concordance of cfDNA and metastatic tumor whole-exome sequencing.

The extracellular matrix (ECM) is a fundamental component of multicellular organisms that provides mechanical and chemical cues that orchestrate cellular and tissue organization and functions. Degradation, hyperproduction or alteration of the composition of the ECM cause or accompany numerous pathologies. Thus, a better characterization of ECM composition, metabolism, and biology can lead to the identification of novel prognostic and diagnostic markers and therapeutic opportunities. The development over the last few years of high-throughput (“omics”) approaches has considerably accelerated the pace of discovery in life sciences. In this review, we describe new bioinformatic tools and experimental strategies for ECM research, and illustrate how these tools and approaches can be exploited to provide novel insights in our understanding of ECM biology. We also introduce a web platform “the matrisome project” and the database MatrisomeDB that compiles in silico and in vivo data on the matrisome, defined as the ensemble of genes encoding ECM and ECM-associated proteins. Finally, we present a first draft of an ECM atlas built by compiling proteomics data on the ECM composition of 14 different tissues and tumor types.

SUMMARY Heart development is exquisitely sensitive to the precise temporal regulation of thousands of genes that govern developmental decisions during differentiation. However, we currently lack a detailed understanding of how chromatin and gene expression patterns are coordinated during developmental transitions in the cardiac lineage. Here, we interrogated the transcriptome and several histone modifications across the genome during defined stages of cardiac differentiation. We find distinct chromatin patterns that are coordinated with stage-specific expression of functionally related genes, including many human disease-associated genes. Moreover, we discover a novel pre-activation chromatin pattern at the promoters of genes associated with heart development and cardiac function. We further identify stage-specific distal enhancer elements and find enriched DNA binding motifs within these regions that predict sets of transcription factors that orchestrate cardiac differentiation. Together, these findings form a basis for understanding developmentally regulated chromatin transitions during lineage commitment and the molecular etiology of congenital heart disease.

Summary Long noncoding RNAs (lncRNAs) are often expressed in a development-specific manner, yet little is known about their roles in lineage commitment. Here, we identified Braveheart (Bvht), a heart-associated lncRNA in mouse. Using multiple embryonic stem cell (ESC) differentiation strategies, we show that Bvht is required for progression of nascent mesoderm towards a cardiac fate. We find that Bvht is necessary for activation of a core cardiovascular gene network and functions upstream of MesP1 (mesoderm posterior 1), a master regulator of a common multipotent cardiovascular progenitor. We also show that Bvht interacts with SUZ12, a component of Polycomb Repressive Complex 2 (PRC2), during cardiomyocyte differentiation suggesting that Bvht mediates epigenetic regulation of cardiac commitment. Finally, we demonstrate a role for Bvht in maintaining cardiac fate in neonatal cardiomyocytes. Together, our work provides evidence for a long noncoding RNA with critical roles in the establishment of the cardiovascular lineage during mammalian development.

Bioactive compounds can be valuable research tools and drug leads, but it is often difficult to identify their mechanism of action or cellular target. Here we investigate the potential for integration of chemical-genetic and genetic interaction data to reveal information about the pathways and targets of inhibitory compounds. Taking advantage of the existing complete set of yeast haploid deletion mutants, we generated drug-hypersensitivity (chemical-genetic) profiles for 12 compounds. In addition to a set of compound-specific interactions, the chemical-genetic profiles identified a large group of genes required for multidrug resistance. In particular, yeast mutants lacking a functional vacuolar H(+)-ATPase show multidrug sensitivity, a phenomenon that may be conserved in mammalian cells. By filtering chemical-genetic profiles for the multidrug-resistant genes and then clustering the compound-specific profiles with a compendium of large-scale genetic interaction profiles, we were able to identify target pathways or proteins. This method thus provides a powerful means for inferring mechanism of action.