A convex optimization approach for identification of human tissue-specific interactomes

Abstract

Analysis of organism-specific interactomes has yielded novel insights into cellular function and coordination, understanding of pathology, and identification of markers and drug targets. Genes, however, can exhibit varying levels of cell-type specificity in their expression, and their coordinated expression manifests in tissue-specific function and pathology. Tissue-specific/ selective interaction mechanisms have significant applications in drug discovery, as they are more likely to reveal drug targets. Furthermore, tissue-specific transcription factors (tsTFs) are significantly implicated in human disease, including cancers. Finally, disease genes and protein complexes have the tendency to be differentially expressed in tissues in which defects cause pathology. These observations motivate the construction of refined tissue-specific interactomes from organism-specific interactomes.

We present a novel technique for constructing human tissue-specific interactomes. Using a variety of validation tests (ESEA, GO Enrichment, Disease-Gene Subnetwork Compactness), we show that our proposed approach significantly outperforms state of the art techniques. Finally, using case studies of Alzheimer's and Parkinson's diseases, we show that tissue-specific interactomes derived from our study can be used to construct pathways implicated in pathology, and demonstrate the use of these pathways in identifying novel targets.

Keywords:

Tissue-specific interactome, Convex optimization, PPI, GTEx, UPC/SCAN


Supplementary Materials

  1. Matlab code for ActPro+test datasets from the paperPDF
  2. Processed GTEx dataset using UPC/SCAN PDF
  3. Global interactome PDF
  4. Tissue-specific transcriptomes PDF
  5. Tissue-specific interactomes PDF
  6. ESEA ranked edges PDF
  7. GWAS dataset PDF