Inferring a cancer-miRNA regulatory network

Inferring a cancer-miRNA regulatory network

Genes regulated by the same miRNA can be discovered by virtue of their coexpression at the transcriptional level and the presence of a conserved miRNA-binding site in their 3′ UTRs. Using this principle we have integrated the three best performing and complementary algorithms into a framework for inference of regulation by miRNAs (FIRM) from sets of coexpressed genes. We demonstrate the utility of FIRM by inferring a cancer-miRNA regulatory network through the analysis of 2240 gene coexpression signatures from 46 cancers. By analyzing this network for functional enrichment of known hallmarks of cancer we have discovered a subset of 13 miRNAs that regulate oncogenic processes across diverse cancers. We have performed experiments to test predictions from this miRNA-regulatory network to demonstrate that miRNAs of the miR-29 family (miR-29a, miR-29b, and miR-29c) regulate specific genes associated with tissue invasion and metastasis in lung adenocarcinoma. Further, we highlight the specificity of using FIRM inferences to identify miRNA-regulated genes by experimentally validating that miR-767-5p, which partially shares the miR-29 seed sequence, regulates only a subset of miR-29 targets. By providing mechanistic linkage between miRNA dysregulation in cancer, their binding sites in the 3’UTRs of specific sets of coexpressed genes, and their associations with known hallmarks of cancer, FIRM, and the inferred cancer miRNA-regulatory network will serve as a powerful public resource for discovery of potential cancer biomarkers.

Fluctuating environments and extinction

Fluctuating environments and extinction

Mechanisms driving and rescuing microbial extinctions in fluctuating resource environments

There are many theories regarding why certain network topologies are necessary for generating specific environmental response dynamics, the rapidity with which regulatory networks evolve, and the impact of gene regulation on fitness. Testing the underlying hypotheses will require an integrated and coordinated systems biology approach to simultaneously infer the architecture of regulatory networks associated with relevant environmental responses of interacting organisms, analyze consequences of perturbing architecture of these networks, and track evolution of these networks during co-evolution or adaptation to a new environment

We are investigating the structure and evolution of gene regulatory networks that govern physiological responses of individual and interacting organisms in a synthetically established syntrophic pairing between a sulfate reducing bacterium, Desulfovibrio vulgaris Hildenborough (DvH) and a methanogenic archaeon Methanococcus maripaludis (Mmp).

Related Publications

Algorithms

Algorithms

Adaptive Prediction Emergence in Microbial Populations

Adaptive Prediction Emergence in Microbial Populations

The structure and predictability of coupled environmental factor changes has shaped the evolution of all organisms. Indeed, microorganisms have taken advantage of sequentially coupled changes in two or more environmental factors to evolve adaptive prediction as a strategy to improve fitness. Continue reading

How Gene Regulation Drives Cellular Physiology

How Gene Regulation Drives Cellular Physiology

how regulatory genetic diversity drives systems-level cellular physiology

A large portion of cellular physiology and adaptation depends upon the finely tuned molecular interactions that constitute gene regulatory networks. Genetic variability of the components that participate in these interactions is highly apparent, and is likely responsible for a significant portion of the differences in biology between closely related organisms. A complete understanding of (and ability to predict) the consequences of genetically encoded regulatory variation requires a molecular model of functional change, and a means of extrapolating its effect to changes in systems-level cellular behavior. This can be accomplished by analyzing the molecular variability of regulatory elements within the context of known large-scale regulatory networks.

Regulatory Networks of Tuberculosis

Regulatory Networks of Tuberculosis

Mycobacterium tuberculosis (MTB) is the bacterium that causes tuberculosis, a disease which kills one person every 30 seconds. Its success as a human pathogen is largely due to its ability to alter gene expression and adapt to the complex host environment within which it resides. Scientists at ISB and Seattle Biomed to track gene expression in MTB by constructing a gene regulatory network model.