Decoding the cellular effects of genetic variation through interaction proteomics

Recurrent innovation of protein-protein interactions in the Drosophila piRNA pathway

Despite being essential for fertility, genome-defense-pathway genes often evolve rapidly. However, little is known about the molecular basis of this adaptation. Here, we characterized the evolution of a protein interaction network within the PIWI-interacting small RNA (piRNA) genome-defense pathway in Drosophila at unprecedented scale and evolutionary resolution. We uncovered the pervasive rapid evolution of a protein interaction network anchored at the heterochromatin protein 1 (HP1) paralog Rhino. Through cross-species high-throughput yeast-two-hybrid screening, we identified three distinct evolutionary protein interaction trajectories across ~40 million years of Drosophila evolution. While several protein interactions are fully conserved, indicating functional conservation despite rapid amino acid-sequence change, other interactions are preserved through coevolution and were detected only between proteins within or from closely related species. We also identified species-restricted protein interactions, revealing insight into the mechanistic diversity and ongoing molecular innovation in Drosophila piRNA production. In sum, our analyses reveal principles of interaction evolution in an adaptively evolving protein-protein interaction network, and support intermolecular interaction innovation as a central molecular mechanism of evolutionary adaptation in protein-coding genes.

Mutational scanning pinpoints distinct binding sites of key ATGL regulators in lipolysis

ATGL is a key enzyme in intracellular lipolysis and plays an important role in metabolic and cardiovascular diseases. ATGL is tightly regulated by a known set of protein-protein interaction partners with activating or inhibiting functions in the control of lipolysis. Here, we use deep mutational protein interaction perturbation scanning and generate comprehensive profiles of single amino acid variants that affect the interactions of ATGL with its regulatory partners: CGI-58, G0S2, PLIN1, PLIN5 and CIDEC. Twenty-three ATGL amino acid variants yield a specific interaction perturbation pattern when validated in co-immunoprecipitation experiments in mammalian cells. We identify and characterize eleven highly selective ATGL switch mutations which affect the interaction of one of the five partners without affecting the others. Switch mutations thus provide distinct interaction determinants for ATGL's key regulatory proteins at an amino acid resolution. When we test triglyceride hydrolase activity in vitro and lipolysis in cells, the activity patterns of the ATGL switch variants trace to their protein interaction profile. In the context of structural data, the integration of variant binding and activity profiles provides insights into the regulation of lipolysis and the impact of mutations in human disease.

Missense variant interaction scanning reveals a critical role of the FERM domain for tumor suppressor protein NF2 conformation and function

NF2 (moesin-ezrin-radixin-like [MERLIN] tumor suppressor) is frequently inactivated in cancer, where its NF2 tumor suppressor functionality is tightly coupled to protein conformation. How NF2 conformation is regulated and how NF2 conformation influences tumor suppressor activity is a largely open question. Here, we systematically characterized three NF2 conformation-dependent protein interactions utilizing deep mutational scanning interaction perturbation analyses. We identified two regions in NF2 with clustered mutations which affected conformation-dependent protein interactions. NF2 variants in the F2-F3 subdomain and the α3H helix region substantially modulated NF2 conformation and homomerization. Mutations in the F2-F3 subdomain altered proliferation in three cell lines and matched patterns of disease mutations in NF2 related-schwannomatosis. This study highlights the power of systematic mutational interaction perturbation analysis to identify missense variants impacting NF2 conformation and provides insight into NF2 tumor suppressor function.

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life's Mechanism

In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5-10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.