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Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates.

ABSTRACT: Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (kcats) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epistatic interactions between metabolic genes that shape evolutionary trajectories and influence the magnitude of evolved kcats. Diminishing returns epistasis prevents enzymes from developing higher kcats in all reactions and keeps the organism far from the potential fitness optimum. Multifunctional enzymes cause synergistic epistasis that slows down adaptation. The resulting fitness landscape allows kcat evolution to be convergent. Predicted kcat parameters show a significant correlation with experimental data, validating our modeling approach. Our analysis reveals how evolutionary forces shape modern kcats and the whole of metabolism.

SUBMITTER: Heckmann D

PROVIDER: S-EPMC6288127 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates.

Heckmann David D Zielinski Daniel C DC Palsson Bernhard O BO

Nature communications 20181210 1

Systems biology describes cellular phenotypes as properties that emerge from the complex interactions of individual system components. Little is known about how these interactions have affected the evolution of metabolic enzymes. Here, we combine genome-scale metabolic modeling with population genetics models to simulate the evolution of enzyme turnover numbers (k<sub>cat</sub>s) from a theoretical ancestor with inefficient enzymes. This systems view of biochemical evolution reveals strong epist ...[more]

PMID: 30532008

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Project description:The interplay between genes harboring single nucleotide polymorphisms (SNPs) is vital to better understand underlying contributions to the etiology of breast cancer. Much attention has been paid to epistasis between nuclear genes or mutations in the mitochondrial genome. However, there is limited understanding about the epistatic effects of genetic variants in the nuclear and mitochondrial genomes jointly on breast cancer. We tested the interaction of germline SNPs in the mitochondrial (mtSNPs) and nuclear (nuSNPs) genomes of female breast cancer patients in The Cancer Genome Atlas (TCGA) for association with morphological features extracted from hematoxylin and eosin (H&E)-stained pathology images. We identified 115 significant (q-value < 0.05) mito-nuclear interactions that increased nuclei size by as much as 12%. One interaction between nuSNP rs17320521 in an intron of the WSC Domain Containing 2 (WSCD2) gene and mtSNP rs869096886, a synonymous variant mapped to the mitochondrially-encoded NADH dehydrogenase 4 (MT-ND4) gene, was confirmed in an independent breast cancer data set from the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). None of the 10 mito-nuclear interactions identified from non-diseased female breast tissues from the Genotype-Expression (GTEx) project resulted in an increase in nuclei size. Comparisons of gene expression data from the TCGA breast cancer patients with the genotype homozygous for the minor alleles of the SNPs in WSCD2 and MT-ND4 versus the other genotypes revealed core transcriptional regulator interactions and an association with insulin. Finally, a Cox proportional hazards ratio = 1.7 (C.I. 0.98-2.9, p-value = 0.042) and Kaplan-Meier plot suggest that the TCGA female breast cancer patients with low gene expression of WSCD2 coupled with large nuclei have an increased risk of mortality. The intergenomic dependency between the two variants may constitute an inherent susceptibility of a more severe form of breast cancer and points to genetic targets for further investigation of additional determinants of the disease.

Dataset Information

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates.

Publications

Modeling genome-wide enzyme evolution predicts strong epistasis underlying catalytic turnover rates.

Similar Datasets

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