John Platig

Exploiting Synthetic Lethality for the Therapy of ABC Diffuse Large B Cell Lymphoma

Knowledge of oncogenic mutations can inspire therapeutic strategies that are synthetically lethal, affecting cancer cells while sparing normal cells. Lenalidomide is an active agent in the activated B cell-like (ABC) subtype of diffuse large B cell lymphoma (DLBCL), but its mechanism of action is unknown. Lenalidomide kills ABC DLBCL cells by augmenting interferon β (IFNβ) production, owing to the oncogenic MYD88 mutations in these lymphomas. In a cereblon-dependent fashion, lenalidomide downregulates IRF4 and SPIB, transcription factors that together prevent IFNβ production by repressing IRF7 and amplify prosurvival NF-κB signaling by transactivating CARD11. Blockade of B cell receptor signaling using the BTK inhibitor ibrutinib also downregulates IRF4 and consequently synergizes with lenalidomide in killing ABC DLBCLs, suggesting attractive therapeutic strategies.

External periodic driving of large systems of globally coupled phase oscillators

Large systems of coupled oscillators subjected to a periodic external drive occur in many situations in physics and biology. Here the simple paradigmatic case of equal strength, all-to-all sine coupling of phase oscillators subject to a sinusoidal external drive, is considered. The stationary states and their stability are determined. Using the stability information and numerical experiments, parameter space phase diagrams showing when different types of system behavior apply are constructed, and the bifurcations marking transitions between different types of behavior are delineated. The analysis is supported by results of direct numerical simulation of an ensemble of oscillators.

Robustness of network measures to link errors.

In various applications involving complex networks, network measures are employed to assess the relative importance of network nodes. However, the robustness of such measures in the presence of link inaccuracies has not been well characterized. Here we present two simple stochastic models of false and missing links and study the effect of link errors on three commonly used node centrality measures: degree centrality, betweenness centrality, and dynamical importance. We perform numerical simulations to assess robustness of these three centrality measures. We also develop an analytical theory, which we compare with our simulations, obtaining very good agreement.

Understanding Tissue-Specific Gene Regulation

Summary Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

Bipartite Community Structure of eQTLs

Genome Wide Association Studies (GWAS) and expression quantitative trait locus (eQTL) analyses have identified genetic associations with a wide range of human phenotypes. However, many of these variants have weak effects and understanding their combined effect remains a challenge. One hypothesis is that multiple SNPs interact in complex networks to influence functional processes that ultimately lead to complex phenotypes, including disease states. Here we present CONDOR, a method that represents both cis- and trans-acting SNPs and the genes with which they are associated as a bipartite graph and then uses the modular structure of that graph to place SNPs into a functional context. In applying CONDOR to eQTLs in chronic obstructive pulmonary disease (COPD), we found the global network “hub” SNPs were devoid of disease associations through GWAS. However, the network was organized into 52 communities of SNPs and genes, many of which were enriched for genes in specific functional classes. We identified local hubs within each community (“core SNPs”) and these were enriched for GWAS SNPs for COPD and many other diseases. These results speak to our intuition: rather than single SNPs influencing single genes, we see groups of SNPs associated with the expression of families of functionally related genes and that disease SNPs are associated with the perturbation of those functions. These methods are not limited in their application to COPD and can be used in the analysis of a wide variety of disease processes and other phenotypic traits.

Sexual dimorphism in gene expression and regulatory networks across human tissues

Sexual dimorphism manifests in many diseases and may drive sex-specific therapeutic responses. To understand the molecular basis of sexual dimorphism, we conducted a comprehensive assessment of gene expression and regulatory network modeling in 31 tissues using 8716 human transcriptomes from GTEx. We observed sexually dimorphic patterns of gene expression involving as many as 60% of autosomal genes, depending on the tissue. Interestingly, sex hormone receptors do not exhibit sexually dimorphic expression in most tissues; however, differential network targeting by hormone receptors and other transcription factors (TFs) captures their downstream sexually dimorphic gene expression. Furthermore, differential network wiring was found extensively in several tissues, particularly in brain, in which not all regions exhibit strong differential expression. This systems-based analysis provides a new perspective on the drivers of sexual dimorphism, one in which a repertoire of TFs plays important roles in sex-specific rewiring of gene regulatory networks. Highlights Sexual dimorphism manifests in both gene expression and gene regulatory networks Substantial sexual dimorphism in regulatory networks was found in several tissues Many differentially regulated genes are not differentially expressed Sex hormone receptors do not exhibit sexually dimorphic expression in most tissues

Thermodynamic measures of cancer: Gibbs free energy and entropy of protein-protein interactions.

Thermodynamics is an important driving factor for chemical processes and for life. Earlier work has shown that each cancer has its own molecular signaling network that supports its life cycle and that different cancers have different thermodynamic entropies characterizing their signaling networks. The respective thermodynamic entropies correlate with 5-year survival for each cancer. We now show that by overlaying mRNA transcription data from a specific tumor type onto a human protein–protein interaction network, we can derive the Gibbs free energy for the specific cancer. The Gibbs free energy correlates with 5-year survival (Pearson correlation of –0.7181, p value of 0.0294). Using an expression relating entropy and Gibbs free energy to enthalpy, we derive an empirical relation for cancer network enthalpy. Combining this with previously published results, we now show a complete set of extensive thermodynamic properties and cancer type with 5-year survival.

Exploring regulation in tissues with eQTL networks

Significance A core tenet in genetics is that genotype influences phenotype. In an individual, the same genome can be expressed in substantially different ways, depending on the tissue. Expression quantitative trait locus (eQTL) analysis, which associates genetic variants at millions of locations across the genome with the expression levels of each gene, can provide insight into genetic regulation of phenotype. In each of 13 tissues we performed an eQTL analysis, represented significant associations as edges in a network, and explored the structure of those networks. We found clusters of eQTL linked to shared functions across tissues and tissue-specific clusters linked to tissue-specific functions, driven by genetic variants with tissue-specific regulatory potential. Our findings provide unique insight into the genotype–phenotype relationship. Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated.

Regulatory network changes between cell lines and their tissues of origin

BackgroundCell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin.ResultsWe compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE.ConclusionsOur results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data

BackgroundAlthough ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis.ResultsWe find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project.ConclusionsAn R package instantiating YARN is available at http://bioconductor.org/packages/yarn.