Defining hierarchical protein interaction networks from spectral analysis of bacterial proteomes

Abstract

Cellular behaviors emerge from layers of molecular interactions: proteins interact to form complexes, pathways, and phenotypes. We show that hierarchical networks of protein interactions can be defined from the statistical pattern of proteome variation measured across thousands of diverse bacteria and that these networks reflect the emergence of complex bacterial phenotypes. Our results are validated through gene-set enrichment analysis and comparison to existing experimentally-derived databases. We demonstrate the biological utility of our approach by creating a model of motility in Pseudomonas aeruginosa and using it to identify a protein that affects pilus-mediated motility. Our method, SCALES (Spectral Correlation Analysis of Layered Evolutionary Signals), may be useful for interrogating genotype-phenotype relationships in bacteria.

An evolution-based framework for describing human gut bacteria

“…To test our hypothesis that evolutionary relationships across a wealth of sequenced bacteria could aid in revealing strain-level genomic differences within our strain bank, we turned to a large database of sequenced non-redundant bacterial strains procured across a diversity of environments. Our previous work described a phylogenomic analysis of the kingdom Bacteria using all reference proteomes in the UniProt database (>7,000 strains in total) ( 31 , 32 ). Analysis of this database illustrated that co-evolutionary patterns defined a hierarchy of phylogeny.…”

“…Unlike engineered systems, existing or 'extant' biological systems arise from ancestors through the evolutionary process (44)(45)(46)(47)(48). Therefore, understanding how patterns of genetic interactions encode behaviors is inextricably intertwined with defining commonalities and divergences in molecular structure (32,(49)(50)(51)(52)(53)(54). Current statistical strategies for parsing differences amongst genomes involve so-called 'factorization' approaches that discover low-dimensional representations of high-dimensional patterns of variation.…”