Biology subjects

Castellanos-Girouard, X.

Publications and source records attributed to Castellanos-Girouard, X..

5 recordsLinked to original sources

bioRxiv2026

Protein Stability, Turnover Kinetics, and Abundance Constrain the Scaling of Protein Interaction Networks

The propensity of proteins to form oligomers is ultimately dictated by their structural configuration(s). Proteins that persist in a discrete conformational state may form a limited number of specific interactions while those that sample a broader structural ensemble may instead associate with a wider array of partners. These intrinsic tendencies potentially constrain the way proteins navigate wider interaction networks. In this work, we aggregated and surveyed a wide variety of biophysical, biochemical, and cellular descriptors of the S. cerevisiae proteome to identify biases in the connectivity of its protein-protein interaction network. Using mass spectrometry-based interactome measurements and various protein stability estimates, we find that a disproportionate number of abundant, yet unstable binding proteins act as network hubs. Moreover, we show that these features alone can be used to discriminate between hubs and non-hub proteins with high accuracy (AUROC = 0.898). Interestingly, we find that half-lives of hub proteins depend on whether or not they reside within static complexes and/ or whether they interact with molecular chaperones. Finally, we note that the observed connectivity biases associated with abundant, unstable proteins only pertain to network hubs, but not to the bottlenecks that connect them. Together, our findings reveal how the conformational stability of a protein may constrain its context within protein-protein interaction networks.

systems biology↗

bioRxiv2026

ANYI: The ANnotated Yeast Interactome

Although several existing protein-protein interaction (PPI) databases provide yeast PPI data, none unify large-scale network topology information with detailed biophysical, proteostasis, and regulatory annotations in a single protein-centric framework. To address this gap, we developed the ANnotated Yeast Interactome (ANYI), an open, integrated resource that combines experimental yeast PPIs with sixteen feature annotation types, including protein abundance, half-life, disorder content, post-translational modifications, conformational stability, chaperone interactions, sequence, and structure. ANYI integrates 3,927 proteins with 155 annotation features, forming a unified matrix that enables systematic cross-layer analyses. Available via GitHub and Docker Hub with an interactive network browser for broad accessibility, ANYI provides both experienced and beginner computational scientists with tools to investigate the yeast interactome. For example, users can directly test whether highly connected hub proteins exhibit distinct stability, disorder, or proteostasis signatures relative to peripheral nodes. AVAILABILITY AND IMPLEMENTATIONThe code used to assemble ANYI is available on GitHub at https://github.com/NCEMS/energetic-origins-of-PPI-connectivity and the database itself and interactive browser tool are available on Docker Hub as dannissleypsu/anyi-browser:v1.0.2.

bioinformatics↗

bioRxiv2024

Protein-protein interaction is a major source of epistasis in genetic interaction networks

Genetic interaction (GI) and protein-protein interaction (PPI) networks have proved among the most important tools for inference of gene function and genotype-phenotype relationship1-6. It remains unclear, however, how these two networks are related to each other. Here we demonstrate that the strengths of epistatic genetic interactions between two genes strongly correlate with the binding free energies of interactions between their cognate proteins, for both yeast and human genomes. Consequently, we show that GI and PPI networks can reciprocally predict each other. Further, we observe that functional divergence and redundancy in duplicated genes (paralogs) are reflected in the binding affinity of their interactions with other proteins and in their genetic interaction strengths. Finally, we demonstrate that the overall topologies of GI and PPI networks significantly overlap in two topological features: Modules, in which genes/proteins are organized by common function, and connectors, which link modules to each other. This opens avenues for new integrated network approaches in understanding cellular processes and flow of genomic information.

systems biology↗

bioRxiv2023

Genetic landscape of an in vivo protein interactome

Protein-protein interaction (PPI) networks accurately map environmental perturbations to their molecular consequences in cells, but effects of genome-wide genetic variation on PPIs remain unknown. We hypothesized that PPI networks integrate genetic and environmental effects, potentially defining biochemical mechanisms underlying complex polygenic traits. Here, we measured 61 PPIs in inbred strains of Saccharomyces cerevisiae with [~]12,000 single-nucleotide polymorphisms (SNPs) across the genome. Unlike mRNA expression and protein abundance that are primarily affected by SNPs local (in "cis") to a gene, PPIs are predominantly affected by SNPs far (in "trans") to the genomic loci of the interacting proteins. However, consistent with the PPI networks small-world characteristic, these transacting SNPs are in neighboring genes in the network. We likewise discovered SNPs in non-coding RNAs and post-transcriptional regulators (3 UTRs) with, counterintuitively, larger PPI-modulating effects than SNPs within protein-coding regions. Finally, we inferred known and novel mechanisms of action for yeast and human drugs. HIGHLIGHTSO_LIProtein-interaction quantitative trait locus ("piQTL") mapping reveals sensitivity of in vivo PPIs to polymorphisms across the yeast genome C_LIO_LITrans-piQTLs significantly outnumber and are stronger than cis-piQTLs C_LIO_LISNPs in non-coding RNAs and 3 UTRs have comparable effects to PPI as SNPs in coding regions C_LIO_LIpiQTL mapping reveals known and novel mechanism of yeast and human drugs C_LI

genomics↗

bioRxiv2022

Peroxisome biogenesis initiated by protein phase separation

Peroxisomes are organelles that perform beta-oxidation of fatty acids and amino acids. Both rare and prevalent diseases are caused by their disfunction1. Among disease-causing mutant genes are those required for protein transport into the peroxisome. The peroxisomal protein import machinery, also shared with chloroplasts, is unique in transporting folded and large, up to 10 nm in diameter, protein complexes into peroxisomes2 and current models postulate a large pore formed by transmembrane proteins3. To date, however, no pore structure has been observed. In the budding yeast Saccharomyces cerevisiae, the minimum transport machinery includes membrane proteins Pex13 and Pex14 and cargo protein-binding transport receptor, Pex5. Here we show that Pex13 undergoes liquid-liquid phase separation (LLPS) with Pex5-cargo. Intrinsically disordered regions (IDR) in Pex13 and Pex5 resemble those found in nuclear pore complex (NPC) proteins. Cargo transport into peroxisomes depends on the number but not patterns of aromatic residues in these IDRs, consistent with their roles as stickers in associative polymer models of LLPS4,5. Finally, imaging Fluorescence Cross-Correlation Spectroscopy (iFCCS) shows that the transport of cargo correlates with transient focusing of GFP-Pex13/14 on the peroxisome membrane. Pex13 and Pex14 form foci in distinct time-frames, suggesting that they may form channels at different saturating concentrations of Pex5-cargo. Our results suggest a model in which LLPS of Pex5-cargo with Pex13/14 results in transient protein transport channels.

cell biology↗