Mutations in disease

Mutations in disease

The loss or aberration of protein function through mutation occurs in many diseases, including cancers. However, it is difficult to determine which genes and mutations are responsible for disease-linked phenotypes when many mutations and variations are present, such as in cancers and in unsolved heritable diseases. Dr. Ashworth combines protein-based biophysical and functional predictions with genomic and system-wide molecular data in order to infer, identify and strengthen links between genetic mutations and their phenotypic effects in disease. This includes the investigation of mutations linked to the hallmarks of cancer in the The Cancer Genome Atlas, as well as the functional impacts of novel mutations in the genomes of families affected by heritable diseases.

Publications

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.

Human Disease

Human Disease

Mycobacterium tuberculosis causes ~9 million new cases of active disease and 1.4 million deaths each year, and our tools to combat tuberculosis (TB) disease are universally outdated and overmatched. This project combines separate advances in systems biology and network modeling to produce an experimentally grounded and verifiable systems-level model of the MTB regulatory networks that affect disease progression.

From initial infection to the onset of symptoms, tuberculosis (TB) is a remarkably complex disease. We are collaborating with groups at the Center for Infectious Disease Research to test the concept that behaviors of host and pathogen are coordinated by interwoven regulatory networks, and that the outcome of infection (bacterial containment or active disease) is the product of many network-network interactions that vary both spatially and temporally. If so, then perturbing specific networks will both illuminate the topology of the larger network and allow us to define the steps and components critical to infection outcome. Our consortium of two projects and four Cores will test this hypothesis and reveal key features of TB disease progression in an iterative cycle: perturb carefully chosen subnetworks within both MTB and host; collect matched omics data sets; model, predict, and validate with new experiments.

Publications

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.