Dataset Information

The Genetic Landscape of a Metabolic Interaction.

ABSTRACT: 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.

SUBMITTER: Nguyen TN

PROVIDER: S-EPMC10461916 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

The Genetic Landscape of a Metabolic Interaction.

Nguyen Thuy N TN Ingle Christine C Thompson Samuel S Reynolds Kimberly A KA

bioRxiv : the preprint server for biology 20230817

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 const ...[more]

PMID: 37645784

Similar Datasets

Project description:BackgroundCommon metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body's metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors.MethodsWe analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group.ResultsIn contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing β-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension.ConclusionsOur results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

Project description:Dynamic acetylation of metabolic proteins has emerged as a ubiquitous post-translational modification of human metabolic proteins. However, the corresponding modifying enzymes and the functions of the modification await exploration. Using a genome-wide synthetic lethality screen, we constructed a genetic interaction network of human histone deacetylases (HDACs) and discovered many metabolic substrates of these enzymes. We further confirmed that the adenosine monophosphate-activated protein kinase (AMPK) catalytic subunit is acetylated and deacetylated by EP300 and HDAC1, respectively. Deacetylation of AMPK catalytic subunit enhances physical interaction with the upstream kinase LKB1, and leads to AMPK phosphorylation and activation. These findings highlight the importance of genetic interaction profiling to identify specific substrates of individual HDACs and elucidate how cells use protein (de)acetylation to coordinate nutrient availability and cellular energy status. To study the functional specificity of individual HDAC, we developed a genome-wide genetic interaction profiling technology in cultured human cells by RNAi using pooled TRC (The RNAi Consortium) 75k human shRNA library and complexity deconvolution by high-density microarray with a half-hairpin barcode design. The HCT116 colon cancer cell line was chosen as the screen platform because of its quasinormal diploid karyotype. The performance of the customized microarray we designed was first evaluated by the receiver operating characteristic (ROC) curve showing high sensitivity (89%), specificity (94%) and area under curve (0.95), as well as a control hybridization without DNA sample showing low background signal. Both results suggest that the fluorescent signals were generated by specific hybridization reactions. The high correlation of signal between Cy5 and Cy3 channel as well as dye-swap experiments indicated that biases between the two labeling fluorophores during competitive hybridization were negligible. Moreover, correlation between technical and biological replicates confirmed the high reproducibility of the methodology. In the screen, we used stable polyclonal cells expressing shRNA constructs targeting firefly luciferase (shLuciferase) as control. We checked the knockdown efficiency in protein or RNA level of individual shRNA constructs for 12 human HDAC genes (HDAC1~4, HDAC6~9, SIRT1~3 and SIRT5) by immunoblotting or quantitative PCR, and generated stable polyclonal query cell lines expressing two shRNA constructs with the highest knockdown efficiency for each HDAC gene. After transduction by TRC shRNA lentiviral pools, benchmark samples were harvested prior to puromycin selection, and the remaining cells were propagated under selection and harvested again after 18 population doublings as the end samples. Half-hairpin barcode library of the benchmark and end samples was recovered from genomic DNA respectively by PCR with Cy5 and Cy3-labeled primers, gel purified, and hybridized to the microarray. For each shRNA construct, the Z-score of the log2(Cy5/Cy3) was computed, and Z-score difference was calculated by subtracting the Z-score of the control sample from that of the query sample. Z-score differences larger than 1.5 and less than -1.5 were used as arbitrary thresholds to define candidate negative and positive genetic interactions, respectively.

Dataset Information

The Genetic Landscape of a Metabolic Interaction.

Publications

The Genetic Landscape of a Metabolic Interaction.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets