An integrated multi-omics approach to identify regulatory mechanisms in cancer metastatic processes.

BACKGROUND: Metastatic progress is the primary cause of death in most cancers, yet the regulatory dynamics driving the cellular changes necessary for metastasis remain poorly understood. Multi-omics approaches hold great promise for addressing this challenge; however, current analysis tools have limited capabilities to systematically integrate transcriptomic, epigenomic, and cistromic information to accurately define the regulatory networks critical for metastasis. RESULTS: To address this limitation, we use a purposefully generated cellular model of colon cancer invasiveness to generate multi-omics data, including expression, accessibility, and selected histone modification profiles, for increasing levels of invasiveness. We then adopt a rigorous probabilistic framework for joint inference from the resulting heterogeneous data, along with transcription factor binding profiles. Our approach uses probabilistic graphical models to leverage the functional information provided by specific epigenomic changes, models the influence of multiple transcription factors simultaneously, and automatically learns the activating or repressive roles of cis-regulatory events. Global analysis of these relationships reveals key transcription factors driving invasiveness, as well as their likely target genes. Disrupting the expression of one of the highly ranked transcription factors JunD, an AP-1 complex protein, confirms functional relevance to colon cancer cell migration and invasion. Transcriptomic profiling confirms key regulatory targets of JunD, and a gene signature derived from the model demonstrates strong prognostic potential in TCGA colorectal cancer data. CONCLUSIONS: Our work sheds new light into the complex molecular processes driving colon cancer metastasis and presents a statistically sound integrative approach to analyze multi-omics profiles of a dynamic biological process.

Mechanistic interpretation of non-coding variants for discovering transcriptional regulators of drug response.

BACKGROUND: Identification of functional non-coding variants and their mechanistic interpretation is a major challenge of modern genomics, especially for precision medicine. Transcription factor (TF) binding profiles and epigenomic landscapes in reference samples allow functional annotation of the genome, but do not provide ready answers regarding the effects of non-coding variants on phenotypes. A promising computational approach is to build models that predict TF-DNA binding from sequence, and use such models to score a variant's impact on TF binding strength. Here, we asked if this mechanistic approach to variant interpretation can be combined with information on genotype-phenotype associations to discover transcription factors regulating phenotypic variation among individuals. RESULTS: We developed a statistical approach that integrates phenotype, genotype, gene expression, TF ChIP-seq, and Hi-C chromatin interaction data to answer this question. Using drug sensitivity of lymphoblastoid cell lines as the phenotype of interest, we tested if non-coding variants statistically linked to the phenotype are enriched for strong predicted impact on DNA binding strength of a TF and thus identified TFs regulating individual differences in the phenotype. Our approach relies on a new method for predicting variant impact on TF-DNA binding that uses a combination of biophysical modeling and machine learning. We report statistical and literature-based support for many of the TFs discovered here as regulators of drug response variation. We show that the use of mechanistically driven variant impact predictors can identify TF-drug associations that would otherwise be missed. We examined in depth one reported association-that of the transcription factor ELF1 with the drug doxorubicin-and identified several genes that may mediate this regulatory relationship. CONCLUSION: Our work represents initial steps in utilizing predictions of variant impact on TF binding sites for discovery of regulatory mechanisms underlying phenotypic variation. Future advances on this topic will be greatly beneficial to the reconstruction of phenotype-associated gene regulatory networks.

Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation.

Recent studies have analyzed large-scale data sets of gene expression to identify genes associated with interindividual variation in phenotypes ranging from cancer subtypes to drug sensitivity, promising new avenues of research in personalized medicine. However, gene expression data alone is limited in its ability to reveal cis-regulatory mechanisms underlying phenotypic differences. In this study, we develop a new probabilistic model, called pGENMi, that integrates multi-omic data to investigate the transcriptional regulatory mechanisms underlying interindividual variation of a specific phenotype-that of cell line response to cytotoxic treatment. In particular, pGENMi simultaneously analyzes genotype, DNA methylation, gene expression, and transcription factor (TF)-DNA binding data, along with phenotypic measurements, to identify TFs regulating the phenotype. It does so by combining statistical information about expression quantitative trait loci (eQTLs) and expression-correlated methylation marks (eQTMs) located within TF binding sites, as well as observed correlations between gene expression and phenotype variation. Application of pGENMi to data from a panel of lymphoblastoid cell lines treated with 24 drugs, in conjunction with ENCODE TF ChIP data, yielded a number of known as well as novel (TF, Drug) associations. Experimental validations by TF knockdown confirmed 41% of the predicted and tested associations, compared to a 12% confirmation rate of tested nonassociations (controls). An extensive literature survey also corroborated 62% of the predicted associations above a stringent threshold. Moreover, associations predicted only when combining eQTL and eQTM data showed higher precision compared to an eQTL-only or eQTM-only analysis using pGENMi, further demonstrating the value of multi-omic integrative analysis.

Redeployment of a conserved gene regulatory network during Aedes aegypti development.

Changes in gene regulatory networks (GRNs) underlie the evolution of morphological novelty and developmental system drift. The fruitfly Drosophila melanogaster and the dengue and Zika vector mosquito Aedes aegypti have substantially similar nervous system morphology. Nevertheless, they show significant divergence in a set of genes co-expressed in the midline of the Drosophila central nervous system, including the master regulator single minded and downstream genes including short gastrulation, Star, and NetrinA. In contrast to Drosophila, we find that midline expression of these genes is either absent or severely diminished in A. aegypti. Instead, they are co-expressed in the lateral nervous system. This suggests that in A. aegypti this "midline GRN" has been redeployed to a new location while lost from its previous site of activity. In order to characterize the relevant GRNs, we employed the SCRMshaw method we previously developed to identify transcriptional cis-regulatory modules in both species. Analysis of these regulatory sequences in transgenic Drosophila suggests that the altered gene expression observed in A. aegypti is the result of trans-dependent redeployment of the GRN, potentially stemming from cis-mediated changes in the expression of sim and other as-yet unidentified regulators. Our results illustrate a novel "repeal, replace, and redeploy" mode of evolution in which a conserved GRN acquires a different function at a new site while its original function is co-opted by a different GRN. This represents a striking example of developmental system drift in which the dramatic shift in gene expression does not result in gross morphological changes, but in more subtle differences in development and function of the late embryonic nervous system.