SWATH-MS co-expression profiles reveal paralogue interference in protein complex evolution

Abstract

Understanding the conservation and evolution of protein complexes is of critical value to decode their function in physiological and pathological processes. One prominent proposal posits gene duplication as a potential mechanism for protein complex evolution. In this study we take advantage of large-scale proteome expression datasets to systematically investigate the role of paralogues, and specifically self-interacting paralogues, in shaping the evolutionary trajectories of protein complexes. First, we show that protein co-expression derived from quantitative proteomic matrices is a good indicator for complex membership and is conserved across species. Second, we suggest that paralogues are commonly strongly co-expressed and that for the subset of paralogues that show diverging co-expression patterns, the divergent co-expression patterns reflect both sequence and functional divergence. Finally, on this basis, we show that homomeric paralogues known to be part of protein complexes display a unique co-expression pattern distribution, with a subset of them being highly diverging. These findings support the idea that homomeric paralogues can avoid cross-interference by diversifying their expression patterns, and corroborates the role of this mechanism as a force shaping protein complex evolution and specialization.

Discovery–Versus Hypothesis–Driven Detection of Protein–Protein Interactions and Complexes

“…The basis for full proteome co-variation studies are large MS datasets, which can be computationally leveraged to investigate co-variation of proteins. This strategy has been applied to identify evolutionarily conserved or highly variable protein complexes across species and individuals [ 34 , 35 ]. The sensitivity of such analyses largely depends on the number of samples at hand.…”

“…The same hypothesis set was applied in a study by Romanov et al, who analyzed protein complex co-variation across 11 full proteome datasets from humans and mice [ 34 ]. Stalder et al further applied complex-centric analysis based on ortholog mapping to provide evidence that complex covariance profiles are conserved across species [ 35 ].…”

“…Importantly, systematic co-variation studies across full-proteome datasets can enable the inference of protein complex level information for cohorts with sample amounts that are too limited for specific interactome data acquisition, for example, in clinical studies. While discovery analysis in such datasets is hampered by an exploding search space, complex-centric analysis approaches have been shown to provide valuable results [ 34 , 35 ].…”