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The genetic landscape of a physical interaction.

ABSTRACT: A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay - deepPCA - we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes - interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions.

SUBMITTER: Diss G

PROVIDER: S-EPMC5896888 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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The genetic landscape of a physical interaction.

Diss Guillaume G Lehner Ben B

eLife 20180411

A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay - <i>deepPCA</i> - we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 t ...[more]

PMID: 29638215

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The genetic landscape of a physical interaction

Project description:A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here we provide the first comprehensive view of how point mutations in two genes combine to alter a molecular phenotype. Using a new sequencing-based protein interaction assay – deepPCA – we quantified the effects of >100,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. We then compared how mutations interact in cis (within one protein) and in trans (between the two molecules). Genetic interactions are abundant and consist of two classes –interactions driven by thermodynamics and captured by a global model that explains ~90% of the double mutant data in both cis and trans, and structural interactions that are enriched between proximally located residues. There are more structural interactions between mutations within one gene than between the two genes. These results reveal how physical interactions generate abundant and quantitatively predictable genetic interactions, even in the absence of complex regulatory dynamics.

2018-04-06 | GSE102901 | GEO

The Genetic Landscape of a Metabolic Interaction.

Project description:Enzyme abundance, catalytic activity, and ultimately sequence are all shaped by the need of growing cells to maintain metabolic flux while minimizing accumulation of deleterious intermediates. While much prior work has explored the constraints on protein sequence and evolution induced by physical protein-protein interactions, the sequence-level constraints emerging from non-binding functional interactions in metabolism remain unclear. To quantify how variation in the activity of one enzyme constrains the biochemical parameters and sequence of another, we focused on dihydrofolate reductase (DHFR) and thymidylate synthase (TYMS), a pair of enzymes catalyzing consecutive reactions in folate metabolism. We used deep mutational scanning to quantify the growth rate effect of 2,696 DHFR single mutations in 3 TYMS backgrounds under conditions selected to emphasize biochemical epistasis. Our data are well-described by a relatively simple enzyme velocity to growth rate model that quantifies how metabolic context tunes enzyme mutational tolerance. Together our results reveal the structural distribution of epistasis in a metabolic enzyme and establish a foundation for the design of multi-enzyme systems.

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Genetic and physical interaction between the NPHP5 and NPHP6 gene products.

Project description:Nephronophthisis (NPHP) is an autosomal recessive cystic kidney disease, caused by mutations of at least nine different genes. Several extrarenal manifestations characterize this disorder, including cerebellar defects, situs inversus and retinitis pigmentosa. While the clinical manifestations vary significantly in NPHP, mutations of NPHP5 and NPHP6 are always associated with progressive blindness. This clinical finding suggests that the gene products, nephrocystin-5 and nephrocystin-6, participate in overlapping signaling pathways to maintain photoreceptor homeostasis. To analyze the genetic interaction between these two proteins in more detail, we studied zebrafish embryos after depletion of NPHP5 and NPHP6. Knockdown of zebrafish zNPHP5 and zNPHP6 produced similar phenotypes, and synergistic effects were observed after the combined knockdown of zNPHP5 and zNPHP6. The N-terminal domain of nephrocystin-6-bound nephrocystin-5, and mapping studies delineated the interacting site from amino acid 696 to 896 of NPHP6. In Xenopus laevis, knockdown of NPHP5 caused substantial neural tube closure defects. This phenotype was copied by expression of the nephrocystin-5-binding fragment of nephrocystin-6, and rescued by co-expression of nephrocystin-5, supporting a physical interaction between both gene products in vivo. Since the N- and C-terminal fragments of nephrocystin-6 engage in the formation of homo- and heteromeric protein complexes, conformational changes seem to regulate the interaction of nephrocystin-6 with its binding partners.

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Project description: Not available

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Genetic and physical interaction of Meis2, Pax3 and Pax7 during dorsal midbrain development.

Project description:BACKGROUND: During early stages of brain development, secreted molecules, components of intracellular signaling pathways and transcriptional regulators act in positive and negative feed-back or feed-forward loops at the mid-hindbrain boundary. These genetic interactions are of central importance for the specification and subsequent development of the adjacent mid- and hindbrain. Much less, however, is known about the regulatory relationship and functional interaction of molecules that are expressed in the tectal anlage after tectal fate specification has taken place and tectal development has commenced. RESULTS: Here, we provide experimental evidence for reciprocal regulation and subsequent cooperation of the paired-type transcription factors Pax3, Pax7 and the TALE-homeodomain protein Meis2 in the tectal anlage. Using in ovo electroporation of the mesencephalic vesicle of chick embryos we show that (i) Pax3 and Pax7 mutually regulate each other's expression in the mesencephalic vesicle, (ii) Meis2 acts downstream of Pax3/7 and requires balanced expression levels of both proteins, and (iii) Meis2 physically interacts with Pax3 and Pax7. These results extend our previous observation that Meis2 cooperates with Otx2 in tectal development to include Pax3 and Pax7 as Meis2 interacting proteins in the tectal anlage. CONCLUSION: The results described here suggest a model in which interdependent regulatory loops involving Pax3 and Pax7 in the dorsal mesencephalic vesicle modulate Meis2 expression. Physical interaction with Meis2 may then confer tectal specificity to a wide range of otherwise broadly expressed transcriptional regulators, including Otx2, Pax3 and Pax7.

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Project description:Members of the SH2 domain family modulate signal transduction by binding to short peptides containing phosphorylated tyrosines. Each domain displays a distinct preference for the sequence context of the phosphorylated residue. We have developed a high-density peptide chip technology that allows for probing of the affinity of most SH2 domains for a large fraction of the entire complement of tyrosine phosphopeptides in the human proteome. Using this technique, we have experimentally identified thousands of putative SH2-peptide interactions for more than 70 different SH2 domains. By integrating this rich data set with orthogonal context-specific information, we have assembled an SH2-mediated probabilistic interaction network, which we make available as a community resource in the PepspotDB database. A predicted dynamic interaction between the SH2 domains of the tyrosine phosphatase SHP2 and the phosphorylated tyrosine in the extracellular signal-regulated kinase activation loop was validated by experiments in living cells.

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Interaction of a genetic risk score with physical activity, physical inactivity, and body mass index in relation to venous thromboembolism risk.

Project description:INTRODUCTION:Venous thromboembolism (VTE) is highly heritable. Physical activity, physical inactivity and body mass index (BMI) are also risk factors, but evidence of interaction between genetic and environmental risk factors is limited. METHODS:Data on 2,134 VTE cases and 3,890 matched controls were obtained from the Nurses' Health Study (NHS), Nurses' Health Study II (NHS II), and Health Professionals Follow-up Study (HPFS). We calculated a weighted genetic risk score (wGRS) using 16 single nucleotide polymorphisms associated with VTE risk in published genome-wide association studies (GWAS). Data on three risk factors, physical activity (metabolic equivalent [MET] hours per week), physical inactivity (sitting hours per week) and BMI, were obtained from biennial questionnaires. VTE cases were incident since cohort inception; controls were matched to cases on age, cohort, and genotype array. Using conditional logistic regression, we assessed joint effects and interaction effects on both additive and multiplicative scales. We also ran models using continuous wGRS stratified by risk-factor categories. RESULTS:We observed a supra-additive interaction between wGRS and BMI. Having both high wGRS and high BMI was associated with a 3.4-fold greater risk of VTE (relative excess risk due to interaction = 0.69, p = 0.046). However, we did not find evidence for a multiplicative interaction with BMI. No interactions were observed for physical activity or inactivity. CONCLUSION:We found a synergetic effect between a genetic risk score and high BMI on the risk of VTE. Intervention efforts lowering BMI to decrease VTE risk may have particularly large beneficial effects among individuals with high genetic risk.

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Dosage Suppression Genetic Interaction Networks

Project description:A functionally diverse subset of yeast temperatures sensitive deletion mutants were transformed with a pool of barcoded yeast ORFs to identify dosage suppressors. Objective: To assess the relative strain proportion after growing DNA barcoded yeast deletion strains in a mixed pools. The barcodes are amplified from the mixed population, and the intensity of each labelled barcodes on the Tag4 array indicate the relative contribution of that strain to the mixed pool. Note: File MoBY_strains.txt available in E-TABM-1147.additional.zip

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