Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations

Functional network motifs defined through integration of protein-protein and genetic interactions

Cells are enticingly complex systems. The identification of feedback regulation is critically important for understanding this complexity. Network motifs defined as small graphlets that occur more frequently than expected by chance have revolutionized our understanding of feedback circuits in cellular networks. However, with their definition solely based on statistical over-representation, network motifs often lack biological context, which limits their usefulness. Here, we define functional network motifs (FNMs) through the systematic integration of genetic interaction data that directly inform on functional relationships between genes and encoded proteins. Occurring two orders of magnitude less frequently than conventional network motifs, we found FNMs significantly enriched in genes known to be functionally related. Moreover, our comprehensive analyses of FNMs in yeast showed that they are powerful at capturing both known and putative novel regulatory interactions, thus suggesting a promising strategy towards the systematic identification of feedback regulation in biological networks. Many FNMs appeared as excellent candidates for the prioritization of follow-up biochemical characterization, which is a recurring bottleneck in the targeting of complex diseases. More generally, our work highlights a fruitful avenue for integrating and harnessing genomic network data.

Splicing and Chromatin Factors Jointly Regulate Epidermal Differentiation

Epidermal homeostasis requires balanced progenitor cell proliferation and loss of differentiated cells from the epidermal surface. During this process, cells undergo major changes in their transcriptional programs to accommodate new cellular functions. We found that transcriptional and post-transcriptional mechanisms underlying these changes jointly control genes involved in cell adhesion, a key process in epidermal maintenance. Using siRNA-based perturbation screens, we identified DNA and/or RNA binding regulators of epidermal differentiation. Computational modeling and experimental validation identified functional interactions between the matrin-type 2 zinc-finger protein ZMAT2 and the epigenetic modifiers ING5, SMARCA5, BRD1, UHRF1, BPTF, and SMARCC2. ZMAT2 is an interactor of the pre-spliceosome that is required to keep cells in an undifferentiated, proliferative state. RNA immunoprecipitation and transcriptome-wide RNA splicing analysis showed that ZMAT2 associates with and regulates transcripts involved in cell adhesion in conjunction with ING5. Thus, joint control by splicing regulation, histone, and DNA modification is important to maintain epidermal cells in an undifferentiated state.

WDR5, BRCA1, and BARD1 Co-regulate the DNA Damage Response and Modulate the Mesenchymal-to-Epithelial Transition during Early Reprogramming

Differentiated cells are epigenetically stable, but can be reprogrammed to pluripotency by expression of the OSKM transcription factors. Despite significant effort, relatively little is known about the cellular requirements for reprogramming and how they affect the properties of induced pluripotent stem cells. We have performed high-content screening with small interfering RNAs targeting 300 chromatin-associated factors and extracted colony-level quantitative features. This revealed five morphological phenotypes in early reprogramming, including one displaying large round colonies exhibiting an early block of reprogramming. Using RNA sequencing, we identified transcriptional changes associated with these phenotypes. Furthermore, double knockdown epistasis experiments revealed that BRCA1, BARD1, and WDR5 functionally interact and are required for the DNA damage response. In addition, the mesenchymal-to-epithelial transition is affected in Brca1, Bard1, and Wdr5 knockdowns. Our data provide a resource of chromatin-associated factors in early reprogramming and underline colony morphology as an important high-dimensional readout for reprogramming quality.

REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES

Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

Inferring modulators of genetic interactions with epistatic nested effects models

Maps of genetic interactions can dissect functional redundancies in cellular networks. Gene expression profiles as high-dimensional molecular readouts of combinatorial perturbations provide a detailed view of genetic interactions, but can be hard to interpret if different gene sets respond in different ways (called mixed epistasis). Here we test the hypothesis that mixed epistasis between a gene pair can be explained by the action of a third gene that modulates the interaction. We have extended the framework of Nested Effects Models (NEMs), a type of graphical model specifically tailored to analyze high-dimensional gene perturbation data, to incorporate logical functions that describe interactions between regulators on downstream genes and proteins. We benchmark our approach in the controlled setting of a simulation study and show high accuracy in inferring the correct model. In an application to data from deletion mutants of kinases and phosphatases in S. cerevisiae we show that epistatic NEMs can point to modulators of genetic interactions. Our approach is implemented in the R-package 'epiNEM' available from https://github.com/cbg-ethz/epiNEM and https://bioconductor.org/packages/epiNEM/.

How can functional annotations be derived from profiles of phenotypic annotations?

Background

Loss-of-function phenotypes are widely used to infer gene function using the principle that similar phenotypes are indicative of similar functions. However, converting phenotypic to functional annotations requires careful interpretation of phenotypic descriptions and assessment of phenotypic similarity. Understanding how functions and phenotypes are linked will be crucial for the development of methods for the automatic conversion of gene loss-of-function phenotypes to gene functional annotations.

Results

We explored the relation between cellular phenotypes from RNAi-based screens in human cells and gene annotations of cellular functions as provided by the Gene Ontology (GO). Comparing different similarity measures, we found that information content-based measures of phenotypic similarity were the best at capturing gene functional similarity. However, phenotypic similarities did not map to the Gene Ontology organization of gene function but to functions defined as groups of GO terms with shared gene annotations.

Conclusions

Our observations have implications for the use and interpretation of phenotypic similarities as a proxy for gene functions both in RNAi screen data analysis and curation and in the prediction of disease genes.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1503-5) contains supplementary material, which is available to authorized users.

Constructing Networks of Organelle Functional Modules in Arabidopsis

With the rapid accumulation of gene expression data, gene functional module identification has become a widely used approach in functional analysis. However, tools to identify organelle functional modules and analyze their relationships are still missing. We present a soft thresholding approach to construct networks of functional modules using gene expression datasets, in which nodes are strongly co-expressed genes that encode proteins residing in the same subcellular localization, and links represent strong inter-module connections. Our algorithm has three steps. First, we identify functional modules by analyzing gene expression data. Next, we use a self-adaptive approach to construct a mixed network of functional modules and genes. Finally, we link functional modules that are tightly connected in the mixed network. Analysis of experimental data from Arabidopsis demonstrates that our approach is effective in improving the interpretability of high-throughput transcriptomic data and inferring function of unknown genes.

Quantitative assessment of gene expression network module-validation methods

Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.

ChIP-ping away at EWS/ETS transcription networks

In this issue of Cancer Cell, Riggi and colleagues use a genomic approach to define two distinct molecular mechanisms through which the chimeric EWS/FLI1 oncoprotein regulates target genes in Ewing sarcoma, expanding a framework upon which to model the target gene network and test strategies for antagonizing growth of this tumor.

Population distribution analyses reveal a hierarchy of molecular players underlying parallel endocytic pathways

Single-cell-resolved measurements reveal heterogeneous distributions of clathrin-dependent (CD) and -independent (CLIC/GEEC: CG) endocytic activity in Drosophila cell populations. dsRNA-mediated knockdown of core versus peripheral endocytic machinery induces strong changes in the mean, or subtle changes in the shapes of these distributions, respectively. By quantifying these subtle shape changes for 27 single-cell features which report on endocytic activity and cell morphology, we organize 1072 Drosophila genes into a tree-like hierarchy. We find that tree nodes contain gene sets enriched in functional classes and protein complexes, providing a portrait of core and peripheral control of CD and CG endocytosis. For 470 genes we obtain additional features from separate assays and classify them into early- or late-acting genes of the endocytic pathways. Detailed analyses of specific genes at intermediate levels of the tree suggest that Vacuolar ATPase and lysosomal genes involved in vacuolar biogenesis play an evolutionarily conserved role in CG endocytosis.

Predicting functional gene interactions with the hierarchical interaction score

Systems biology aims to unravel the vast network of functional interactions that govern biological systems. To date, the inference of gene interactions from large-scale 'omics data is typically achieved using correlations. We present the hierarchical interaction score (HIS) and show that the HIS outperforms commonly used methods in the inference of functional interactions between genes measured in large-scale experiments, making it a valuable statistic for systems biology.

Diverse epigenetic strategies interact to control epidermal differentiation

It is becoming clear that interconnected functional gene networks, rather than individual genes, govern stem cell self-renewal and differentiation. To identify epigenetic factors that impact on human epidermal stem cells we performed siRNA-based genetic screens for 332 chromatin modifiers. We developed a Bayesian mixture model to predict putative functional interactions between epigenetic modifiers that regulate differentiation. We discovered a network of genetic interactions involving EZH2, UHRF1 (both known to regulate epidermal self-renewal), ING5 (a MORF complex component), BPTF and SMARCA5 (NURF complex components). Genome-wide localization and global mRNA expression analysis revealed that these factors impact two distinct but functionally related gene sets, including integrin extracellular matrix receptors that mediate anchorage of epidermal stem cells to their niche. Using a competitive epidermal reconstitution assay we confirmed that ING5, BPTF, SMARCA5, EZH2 and UHRF1 control differentiation under physiological conditions. Thus, regulation of distinct gene expression programs through the interplay between diverse epigenetic strategies protects epidermal stem cells from differentiation.