Carlisle, S. M. | bioRxiv Science

bioRxiv2022

Mitochondrial Redox Adaptations Enable Aspartate Synthesis in SDH-deficient Cells

The oxidative tricarboxylic acid (TCA) cycle is a central mitochondrial pathway integrating catabolic conversions of NAD+ to NADH and anabolic production of aspartate, a key amino acid for cell proliferation. Several TCA cycle components are implicated in tumorigenesis, including loss of function mutations in subunits of succinate dehydrogenase (SDH), also known as complex II of the electron transport chain (ETC). Mechanistic understanding of how proliferating cells tolerate the metabolic defects of SDH loss is still lacking. Here, we identify that SDH supports cell proliferation through aspartate synthesis but, unlike other ETC impairments, is not restored by electron acceptor supplementation. Interestingly, we find aspartate production and cell proliferation are restored to SDH impaired cells by concomitant inhibition of ETC complex I (CI). We determine that the benefits of CI inhibition in this context are dependent on decreasing mitochondrial NAD+/NADH, which drives SDH-independent aspartate production. We also find that genetic loss or restoration of SDH selects for cells with concordant CI activity, establishing distinct modalities of mitochondrial metabolism for maintaining aspartate synthesis. Collectively, these data identify a metabolically beneficial mechanism for CI loss in proliferating cells and reveal that compartmentalized redox changes can impact cellular fitness.

biochemistry↗

bioRxiv2018

Inferring metabolite interactomes via molecular structure informed Bayesian graphical model selection with an application to coronary artery disease

IntroductionWhile the generation of reference genomes facilitates the elucidation of gene-phenome associations, reference models of the metabolome that are specific to organism, sample type (e.g. plasma, serum, urine, cell-culture), and state (including disease), remain uncommon. In studying heart disease in humans, a reference model describing the relationships between metabolites in plasma has not been determined but would have great utility as a reference for comparing acute disease states such as myocardial infarction.\n\nMaterials and MethodsWe present a methodology for deriving probabilistic models that describe the partial correlation structure of metabolite distributions (\"interactomes\") from metabolomics data. As determining partial correlation structures requires estimating p*(p-1)/2 parameters for p metabolites, the dimension of the search space for parameter values is immense. Consequently, we have developed a Bayesian methodology for the penalized estimation of model parameters in which the magnitude of penalization is drawn from probability distributions with hyperparameters linked to molecular structure similarity. In our work, structural similarity was determined as the Tanimoto coefficient of algorithmically-generated \"atom colors\" that capture the local structure around each atom within each structure. A Gibbs sampler (a Markov chain Monte Carlo technique) was implemented for simulating the posterior distribution of model parameters. We have made software for implementing this methodology publicly available via the R package BayesianGLasso.\n\nResults / ConclusionsFirst, we demonstrate robust performance of our methodology (sensitivity, specificity, and measures of accuracy) for recovering the true underlying partial correlation structure over simulated datasets (with simulated metabolite abundances and simulated known structural similarity). We then present an interactome model for stable heart disease inferred from non-targeted mass spectrometry data via this methodology. Inspection of the local graph topology about cholate reveals probabilistic interactions with other primary bile acids, secondary bile acids, and many steroid hormones sharing the same precursors.

systems biology↗