Abstract: As new generations of targeted therapies emerge and tumor genome sequencing discovers increasingly comprehensive mutation repertoires, the functional relationships of mutations to tumor phenotypes remain largely unknown. Here, we measured ex vivo sensitivity of 246 blood cancers to 63 drugs alongside genome, transcriptome, and DNA methylome analysis to understand determinants of drug response. We assembled a primary blood cancer cell encyclopedia data set that revealed disease-specific sensitivities for each cancer. Within chronic lymphocytic leukemia (CLL), responses to 62% of drugs were associated with 2 or more mutations, and linked the B cell receptor (BCR) pathway to trisomy 12, an important driver of CLL. Based on drug responses, the disease could be organized into phenotypic subgroups characterized by exploitable dependencies on BCR, mTOR, or MEK signaling and associated with mutations, gene expression, and DNA methylation. Fourteen percent of CLLs were driven by mTOR signaling in a non–BCR-dependent manner. Multivariate modeling revealed immunoglobulin heavy chain variable gene (IGHV) mutation status and trisomy 12 as the most important modulators of response to kinase inhibitors in CLL. Ex vivo drug responses were associated with outcome. This study overcomes the perception that most mutations do not influence drug response of cancer, and points to an updated approach to understanding tumor biology, with implications for biomarker discovery and cancer care.
Abstract: Hypothesis weighting improves the power of large-scale multiple testing. We describe independent hypothesis weighting (IHW), a method that assigns weights using covariates independent of the P-values under the null hypothesis but informative of each test’s power or prior probability of the null hypothesis: www.bioconductor.org/packages/IHW. IHW increases power while controlling the false discovery rate and is a practical approach to discovering associations in genomics, high-throughput biology and other large data sets.
Abstract: We extended thermal proteome profiling to detect transmembrane protein–small molecule interactions in cultured human cells. When we assessed the effects of detergents on ATP-binding profiles, we observed shifts in denaturation temperature for ATP-binding transmembrane proteins. We also observed cellular thermal shifts in pervanadate-induced T cell–receptor signaling, delineating the membrane target CD45 and components of the downstream pathway, and with drugs affecting the transmembrane transporters ATP1A1 and MDR1.
Abstract: Studies on signalling dynamics in living embryos have been limited by a scarcity of in vivo reporters. Tandem fluorescent protein timers provide a generic method for detecting changes in protein population age and thus provide readouts for signalling events that lead to changes in protein stability or location. When imaged with quantitative dual-colour fluorescence microscopy, tandem timers offer detailed ‘snapshot’ readouts of signalling activity from subcellular to organismal scales, and therefore have the potential to revolutionize studies in developing embryos. Here we use computer modelling and embryo experiments to explore the behaviour of tandem timers in developing systems. We present a mathematical model of timer kinetics and provide software tools that will allow experimentalists to select the most appropriate timer designs for their biological question, and guide interpretation of the obtained readouts. Through the generation of a series of novel zebrafish reporter lines, we confirm experimentally that our quantitative model can accurately predict different timer responses in developing embryos and explain some less expected findings. For example, increasing the FRET efficiency of a tandem timer actually increases the ability of the timer to detect differences in protein half-life. Finally, while previous studies have used timers to monitor changes in protein turnover, our model shows that timers can also be used to facilitate the monitoring of gene expression kinetics in vivo.
Summary: Morphogenesis of multicellular organisms is driven by localized cell shape changes. How, and to what extent, changes in behavior in single cells or groups of cells influence neighboring cells and large-scale tissue remodeling remains an open question. Indeed, our understanding of multicellular dynamics is limited by the lack of methods allowing the modulation of cell behavior with high spatiotemporal precision. Here, we developed an optogenetic approach to achieve local modulation of cell contractility and used it to control morphogenetic movements during Drosophila embryogenesis. We show that local inhibition of apical constriction is sufficient to cause a global arrest of mesoderm invagination. By varying the spatial pattern of inhibition during invagination, we further demonstrate that coordinated contractile behavior responds to local tissue geometrical constraints. Together, these results show the efficacy of this optogenetic approach to dissect the interplay between cell-cell interaction, force transmission, and tissue geometry during complex morphogenetic processes.
Abstract: Expression of tissue-restricted self antigens (TRAs) in medullary thymic epithelial cells (mTECs) is essential for the induction of self-tolerance and prevents autoimmunity, with each TRA being expressed in only a few mTECs. How this process is regulated in single mTECs and is coordinated at the population level, such that the varied single-cell patterns add up to faithfully represent TRAs, is poorly understood. Here we used single-cell RNA sequencing and obtained evidence of numerous recurring TRA–co- expression patterns, each present in only a subset of mTECs. Co-expressed genes clustered in the genome and showed enhanced chromatin accessibility. Our findings characterize TRA expression in mTECs as a coordinated process that might involve local remodeling of chromatin and thus ensures a comprehensive representation of the immunological self.
Read more: Brennecke et al.ni2015
Abstract: Gene-gene interactions shape complex phenotypes and modify the effects of mutations during development and disease. The effects of statistical gene-gene interactions on phenotypes have been used to assign genes to functional modules. However, directional, epistatic interactions, which reflect regulatory relationships between genes, have been challenging to map at large-scale. Here, we used combinatorial RNA interference and automated single-cell phenotyping to generate a large genetic interaction map for 21 phenotypic features of Drosophila cells. We devised a method that combines genetic interactions on multiple phenotypes to reveal directional relationships. This network reconstructed the sequence of protein activities in mitosis. Moreover, it revealed that the Ras pathway interacts with the SWI/SNF chromatin-remodelling complex, an interaction that we show is conserved in human cancer cells. Our study presents a powerful approach for reconstructing directional regulatory networks and provides a resource for the interpretation of functional consequences of genetic alterations.
The Perspective paper Orchestrating high-throughput genomic analysis with Bioconductor is addressed at users and prospective developers. It gives an overview over the collaborative software development and delivery model of the Bioconductor project. At Readcube: http://rdcu.be/b2VE.
Abstract: Bioconductor is an open-source, open-development software project for the analysis and comprehension of high-throughput data in genomics and molecular biology. The project aims to enable interdisciplinary research, collaboration and rapid development of scientific software. Based on the statistical programming language R, Bioconductor comprises 934 interoperable packages contributed by a large, diverse community of scientists. Packages cover a range of bioinformatic and statistical applications. They undergo formal initial review and continuous automated testing. We present an overview for prospective users and contributors.
We are happy to announce our recent paper by Michael I Love, Wolfgang Huber and Simon Anders: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biology, 15:550 (2014).
Abstract: comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at Bioconductor.