MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication

Tarun Kumar

Abstract:

With the evolution of multicellularity, communication among cells in different organs/tissues
became pivotal to life. Molecular basis of such communication has long been studied, but genome-wide
screens for biomolecules/genes mediating tissue-tissue signaling are lacking. To systematically identify
inter-tissue mediators, we present a novel computational approach MultiCens (Multilayer/Multi-tissue
network Centrality measures). Unlike single-layer network methods, MultiCens can distinguish within- vs.
across-layer connectivity to quantify the “influence” of any gene in a tissue on a query set of genes of
interest in another tissue. MultiCens enjoys theoretical guarantees on convergence and decomposability,
and excels on synthetic benchmarks. On human multi-tissue datasets, MultiCens predicts known and novel
genes linked to hormones. MultiCens further reveals shifts in gene network architecture among four brain
regions in Alzheimer’s disease. MultiCens-prioritized hypotheses from these two diverse applications, and
potential future ones like “Multi-tissue-expanded Gene Ontology” analysis, can enable whole-body yet
molecular-level investigations in humans.