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Protein interaction networks define the genetic architecture of preterm birth.

ABSTRACT: The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We analyzed the data using a multi-sample, protein-protein interaction (PPI) tool to identify significant clusters of patients associated with preterm birth. We identified shared protein interaction networks among preterm cases in two statistically significant clusters, p < 0.001. We also found two small control-dominated clusters. We replicated these data on an independent, large birth cohort. Separation testing showed significant similarity scores between the clusters from the two independent cohorts of patients. Canonical pathway analysis of the unique genes defining these clusters demonstrated enrichment in inflammatory signaling pathways, the glucocorticoid receptor, the insulin receptor, EGF and B-cell signaling, These results support a genetic architecture defined by subgroups of patients that share variants in genes in specific networks and pathways which are sufficient to give rise to the disease phenotype.

SUBMITTER: Uzun A

PROVIDER: S-EPMC8748950 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Protein interaction networks define the genetic architecture of preterm birth.

Uzun Alper A Schuster Jessica S JS Stabila Joan J Zarate Valeria V Tollefson George A GA Agudelo Anthony A Kothiyal Prachi P Wong Wendy S W WSW Padbury James J

Scientific reports 20220110 1

The likely genetic architecture of complex diseases is that subgroups of patients share variants in genes in specific networks sufficient to express a shared phenotype. We combined high throughput sequencing with advanced bioinformatic approaches to identify such subgroups of patients with variants in shared networks. We performed targeted sequencing of patients with 2 or 3 generations of preterm birth on genes, gene sets and haplotype blocks that were highly associated with preterm birth. We an ...[more]

PMID: 35013336

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Project description:Proteins function in crowded cellular environments in which they must bind to specific target proteins but also avoid binding to many other off-target proteins. In large protein families this task is particularly challenging because many off-target proteins have very similar structures. How this specificity of physical protein-protein interactions in cellular networks is encoded and evolves is not very well understood. Here we address the question of specificity-encoding by comprehensively quantifying the effects of all mutations in one protein, JUN, on its binding to all other members of a protein family, the 54 human basic leucine zipper transcription factors. Fitting a global thermodynamic model to the data reveals that most affinity changing mutations equally affect JUN’s propensity to bind to all its interaction partners. Mutations that alter the specificity of binding are much rarer. These specificity-altering mutations are, however, distributed throughout the JUN interaction interface. JUN’s interaction specificity is encoded by both positive determinants that promote on-target interactions and negative determinants that prevent off-target interactions. Indeed, about half of the specificity-defining residues in JUN have dual functions and both promote on-target binding and prevent off-target binding. Whereas nearly all mutations that alter specificity are pleiotropic and also alter the affinity of binding to all interaction partners, the converse is not true with mutations outside of the interface able to tune affinity without affecting specificity. Our results provide the first global view of how mutations in a protein affect binding to all its potential interaction partners and reveal the distributed encoding of specificity and affinity in an interaction interface. They also show how the modular architecture of coiled-coils provides an elegant solution to the challenge of optimising specificity and affinity in a large protein family.

Project description:IntroductionA short cervix (cervical length <25 mm) in the midtrimester (18-24 weeks) of pregnancy is a powerful predictor of spontaneous preterm delivery. Although the biological mechanisms of cervical change during pregnancy have been the subject of extensive investigation, little is known about whether genes influence the length of the cervix, or the extent to which genetic factors contribute to premature cervical shortening. Defining the genetic architecture of cervical length is foundational to understanding the aetiology of a short cervix and its contribution to an increased risk of spontaneous preterm delivery.Methods/analysisThe proposed study is designed to characterise the genetic architecture of cervical length and its genetic relationship to gestational age at delivery in a large cohort of Black/African American women, who are at an increased risk of developing a short cervix and delivering preterm. Repeated measurements of cervical length will be modelled as a longitudinal growth curve, with parameters estimating the initial length of the cervix at the beginning of pregnancy, and its rate of change over time. Genome-wide complex trait analysis methods will be used to estimate the heritability of cervical length growth parameters and their bivariate genetic correlation with gestational age at delivery. Polygenic risk profiling will assess maternal genetic risk for developing a short cervix and subsequently delivering preterm and evaluate the role of cervical length in mediating the relationship between maternal genetic variation and gestational age at delivery.Ethics/disseminationThe proposed analyses will be conducted using deidentified data from participants in an IRB-approved study of longitudinal cervical length who provided blood samples and written informed consent for their use in future genetic research. These analyses are preregistered with the Center for Open Science using the AsPredicted format and the results and genomic summary statistics will be published in a peer-reviewed journal.

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Protein interaction networks define the genetic architecture of preterm birth.

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Protein interaction networks define the genetic architecture of preterm birth.

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