Redox controls metabolic robustness in the gas-fermenting acetogen Clostridium autoethanogenum.

Living biological systems display a fascinating ability to self-organize their metabolism. This ability ultimately determines the metabolic robustness that is fundamental to controlling cellular behavior. However, fluctuations in metabolism can affect cellular homeostasis through transient oscillations. For example, yeast cultures exhibit rhythmic oscillatory behavior in high cell-density continuous cultures. Oscillatory behavior provides a unique opportunity for quantitating the robustness of metabolism, as cells respond to changes by inherently compromising metabolic efficiency. Here, we quantify the limits of metabolic robustness in self-oscillating autotrophic continuous cultures of the gas-fermenting acetogen Clostridium autoethanogenum Online gas analysis and high-resolution temporal metabolomics showed oscillations in gas uptake rates and extracellular byproducts synchronized with biomass levels. The data show initial growth on CO, followed by growth on CO and H2 Growth on CO and H2 results in an accelerated growth phase, after which a downcycle is observed in synchrony with a loss in H2 uptake. Intriguingly, oscillations are not linked to translational control, as no differences were observed in protein expression during oscillations. Intracellular metabolomics analysis revealed decreasing levels of redox ratios in synchrony with the cycles. We then developed a thermodynamic metabolic flux analysis model to investigate whether regulation in acetogens is controlled at the thermodynamic level. We used endo- and exo-metabolomics data to show that the thermodynamic driving force of critical reactions collapsed as H2 uptake is lost. The oscillations are coordinated with redox. The data indicate that metabolic oscillations in acetogen gas fermentation are controlled at the thermodynamic level.

multiTFA: a Python package for multi-variate thermodynamics-based flux analysis.

MOTIVATION: We achieve a significant improvement in thermodynamic-based flux analysis (TFA) by introducing multivariate treatment of thermodynamic variables and leveraging component contribution, the state-of-the-art implementation of the group contribution methodology. Overall, the method greatly reduces the uncertainty of thermodynamic variables. RESULTS: We present multiTFA, a Python implementation of our framework. We evaluated our application using the core Escherichia coli model and achieved a median reduction of 6.8 kJ/mol in reaction Gibbs free energy ranges, while three out of 12 reactions in glycolysis changed from reversible to irreversible. AVAILABILITY AND IMPLEMENTATION: Our framework along with documentation is available on https://github.com/biosustain/multitfa. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Understanding the role of interactions between host and Mycobacterium tuberculosis under hypoxic condition: an in silico approach.

BACKGROUND: Mycobacterium tuberculosis infection in humans is often associated with extended period of latency. To adapt to the hostile hypoxic environment inside a macrophage, M. tuberculosis cells undergo several physiological and metabolic changes. Previous studies have mostly focused on inspecting individual facets of this complex process. In order to gain deeper insights into the infection process and to understand the coordination among different regulatory/ metabolic pathways in the pathogen, the current in silico study investigates three aspects, namely, (i) host-pathogen interactions (HPIs) between human and M. tuberculosis proteins, (ii) gene regulatory network pertaining to adaptation of M. tuberculosis to hypoxia and (iii) alterations in M. tuberculosis metabolism under hypoxic condition. Subsequently, cross-talks between these components have been probed to evaluate possible gene-regulatory events as well as HPIs which are likely to drive metabolic changes during pathogen's adaptation to the intra-host hypoxic environment. RESULTS: The newly identified HPIs suggest the pathogen's ability to subvert host mediated reactive oxygen intermediates/ reactive nitrogen intermediates (ROI/ RNI) stress as well as their potential role in modulating host cell cycle and cytoskeleton structure. The results also indicate a significantly pronounced effect of HPIs on hypoxic metabolism of M. tuberculosis. Findings from the current study underscore the necessity of investigating the infection process from a systems-level perspective incorporating different facets of intra-cellular survival of the pathogen. CONCLUSIONS: The comprehensive host-pathogen interaction network, a Boolean model of M. tuberculosis H37Rv (Mtb) hypoxic gene-regulation, as well as a genome scale metabolic model of Mtb, built for this study are expected to be useful resources for future studies on tuberculosis infection.

Absolute Proteome Quantification in the Gas-Fermenting Acetogen Clostridium autoethanogenum.

Microbes that can recycle one-carbon (C1) greenhouse gases into fuels and chemicals are vital for the biosustainability of future industries. Acetogens are the most efficient known microbes for fixing carbon oxides CO2 and CO. Understanding proteome allocation is important for metabolic engineering as it dictates metabolic fitness. Here, we use absolute proteomics to quantify intracellular concentrations for >1,000 proteins in the model acetogen Clostridium autoethanogenum grown autotrophically on three gas mixtures (CO, CO+H2, or CO+CO2+H2). We detect the prioritization of proteome allocation for C1 fixation and the significant expression of proteins involved in the production of acetate and ethanol as well as proteins with unclear functions. The data also revealed which isoenzymes are likely relevant in vivo for CO oxidation, H2 metabolism, and ethanol production. The integration of proteomic and metabolic flux data demonstrated that enzymes catalyze high fluxes with high concentrations and high in vivo catalytic rates. We show that flux adjustments were dominantly accompanied by changing enzyme catalytic rates rather than concentrations. IMPORTANCE Acetogen bacteria are important for maintaining biosustainability as they can recycle gaseous C1 waste feedstocks (e.g., industrial waste gases and syngas from gasified biomass or municipal solid waste) into fuels and chemicals. Notably, the acetogen Clostridium autoethanogenum is being used as a cell factory in industrial-scale gas fermentation. Here, we perform reliable absolute proteome quantification for the first time in an acetogen. This is important as our work advances both rational metabolic engineering of acetogen cell factories and accurate in silico reconstruction of their phenotypes. Furthermore, this absolute proteomics data set serves as a reference toward a better systems-level understanding of the ancient metabolism of acetogens.

Chaperonin Abundance Enhances Bacterial Fitness.

The ability of chaperonins to buffer mutations that affect protein folding pathways suggests that their abundance should be evolutionarily advantageous. Here, we investigate the effect of chaperonin overproduction on cellular fitness in Escherichia coli. We demonstrate that chaperonin abundance confers 1) an ability to tolerate higher temperatures, 2) improved cellular fitness, and 3) enhanced folding of metabolic enzymes, which is expected to lead to enhanced energy harvesting potential.