Coenzyme A restriction as a factor underlying pre-eclampsia and other antenatal conditions.

Pre-eclampsia is the most common pregnancy complication affecting 1 in 20 pregnancies, characterized by high blood pressure and signs of organ damage, most often to the liver and kidneys. Metabolic network analysis of published lipidomic data points to a shortage of Coenzyme A (CoA). Gene-expression profile data reveal alterations to many areas of metabolism and, crucially, to conflicting cellular regulatory mechanisms arising from the overproduction of signalling lipids driven by CoA limitation. Adverse feedback loops appear, forming sphingosine-1-phosphate (a cause of hypertension, hypoxia and inflammation), cytotoxic isoketovaleric acid (inducing acidosis and organ damage) and a thrombogenic lysophosphatidyl serine. These also induce mitochondrial and oxidative stress, leading to untimely apoptosis, which is possibly the cause of CoA restriction. This work provides a molecular basis for the signs of pre-eclampsia, why other conditions are risk factors and what might be done to treat and reduce the risk of this and related diseases.

Novel Drivers of Virulence in Clostridioides difficile Identified via Context-Specific Metabolic Network Analysis

Clostridioides difficile has become the leading single cause of hospital-acquired infections. Numerous studies have demonstrated the importance of specific metabolic pathways in aspects of C. difficile pathophysiology, from initial colonization to regulation of virulence factors.

Transcriptomic and Metabolic Network Analysis of Metabolic Reprogramming and IGF-1 Modulation in SCA3 Transgenic Mice

Spinocerebellar ataxia type 3 (SCA3) is a genetic neurodegenerative disease for which a cure is still needed. Growth hormone (GH) therapy has shown positive effects on the exercise behavior of mice with cerebellar atrophy, retains more Purkinje cells, and exhibits less DNA damage after GH intervention. Insulin-like growth factor 1 (IGF-1) is the downstream mediator of GH that participates in signaling and metabolic regulation for cell growth and modulation pathways, including SCA3-affected pathways. However, the underlying therapeutic mechanisms of GH or IGF-1 in SCA3 are not fully understood. In the present study, tissue-specific genome-scale metabolic network models for SCA3 transgenic mice were proposed based on RNA-seq. An integrative transcriptomic and metabolic network analysis of a SCA3 transgenic mouse model revealed that metabolic signaling pathways were activated to compensate for the metabolic remodeling caused by SCA3 genetic modifications. The effect of IGF-1 intervention on the pathology and balance of SCA3 disease was also explored. IGF-1 has been shown to invoke signaling pathways and improve mitochondrial function and glycolysis pathways to restore cellular functions. As one of the downregulated factors in SCA3 transgenic mice, IGF-1 could be a potential biomarker and therapeutic target.

A systems-guided approach to discover the intracellular target of a novel evolution-drug lead

Understanding intracellular antibiotic targeting and the associated mechanisms leading to bacterial growth inhibition has been a difficult problem. Here, we discovered the additional intracellular targets of the novelevolution-drug lead CD15-3 designed to delay the emergence of antibiotic resistance by inhibiting bacterial DHFR and its Trimethoprim resistant variants. Overexpression of DHFR only partially rescued inhibition of E. coli growth by CD15.3 suggesting that CD15.3 also inhibits a non-DHFR target in the cell. We utilized untargeted global metabolomics and the metabolic network analysis along with structural similarity search of the putative targets to identify the additional target of CD15-3. We validated in vivo and in vitro that besides DHFR CD15-3 inhibits HPPK (folK), an essential protein upstream of DHFR in bacterial folate metabolism. This bivalent cellular targeting makes CD15-3 a promising lead to develop a monotherapy analogue of combination drugs.

Conserved virulence-linked metabolic reprogramming in Clostridioides difficile identified through genome-scale metabolic network analysis

The bacterial pathogen Clostridioides difficile causes a toxin-mediated diarrheal illness and is now the leading cause of hospital-acquired infection in the US. Due to growing threats of antibiotic resistance and recurrent infection, targeting components of metabolism presents a novel approach to combat this infection. Analyses of bacterial genome-scale metabolic network reconstructions (GENREs) have identified new therapeutic targets and helped uncover properties that drive cellular behaviors. We sought to leverage this approach and thus constructed highly-curated C. difficile GENREs for a hyper-virulent isolate (R20291) as well as a historic strain (630). Growth simulations of carbon source usage revealed significant correlations between in silico and experimentally measured values (p-values ≤ 0.002, PPV ≈ 95%), and single-gene deletion analysis showed accuracies of >89% compared with transposon mutant libraries. Contextualizing these models with in situ omics datasets revealed conserved patterns of elevated proline, leucine, and valine fermentation that corresponded with significant increases in expression of multiple virulence factors during infection. Collectively, our results support that C. difficile utilizes distinct metabolic programs as infection progresses and highlights that GENREs can reveal the underpinnings of bacterial pathogenesis.

Thermodynamics and Kinetics of Glycolytic Reactions. Part I: Kinetic Modeling Based on Irreversible Thermodynamics and Validation by Calorimetry

In systems biology, material balances, kinetic models, and thermodynamic boundary conditions are increasingly used for metabolic network analysis. It is remarkable that the reversibility of enzyme-catalyzed reactions and the influence of cytosolic conditions are often neglected in kinetic models. In fact, enzyme-catalyzed reactions in numerous metabolic pathways such as in glycolysis are often reversible, i.e., they only proceed until an equilibrium state is reached and not until the substrate is completely consumed. Here, we propose the use of irreversible thermodynamics to describe the kinetic approximation to the equilibrium state in a consistent way with very few adjustable parameters. Using a flux-force approach allowed describing the influence of cytosolic conditions on the kinetics by only one single parameter. The approach was applied to reaction steps 2 and 9 of glycolysis (i.e., the phosphoglucose isomerase reaction from glucose 6-phosphate to fructose 6-phosphate and the enolase-catalyzed reaction from 2-phosphoglycerate to phosphoenolpyruvate and water). The temperature dependence of the kinetic parameter fulfills the Arrhenius relation and the derived activation energies are plausible. All the data obtained in this work were measured efficiently and accurately by means of isothermal titration calorimetry (ITC). The combination of calorimetric monitoring with simple flux-force relations has the potential for adequate consideration of cytosolic conditions in a simple manner.

Efficient reaction deletion algorithms for redesign of constraint-based metabolic networks for metabolite production with weak coupling

BackgroundMetabolic engineering strategies enabling the production of specific target metabolites by host strains can be identified in silico through the use of metabolic network analysis such as flux balance analysis. This type of metabolic redesign is based on the computation of reactions that should be deleted from the original network representing the metabolism of the host strain to enable the production of the target metabolites while still ensuring its growth (the concept of growth coupling). In this context, it is important to use algorithms that enable this growth-coupled reaction deletions identification for any metabolic network topologies and any potential target metabolites. A recent method using a strong growth coupling assumption has been shown to be able to identify such computational redesign for nearly all metabolites included in the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae when cultivated under aerobic conditions. However, this approach enables the computational redesign of S. cerevisiae for only 3.9% of all metabolites if under anaerobic conditions. Therefore, it is necessary to develop algorithms able to perform for various culture conditions.ResultsThe author developed an algorithm that could calculate the reaction deletions that achieve the coupling of growth and production for 91.3% metabolites in genome-scale models of S. cerevisiae under anaerobic conditions. Computational experiments showed that the proposed algorithm is efficient also for aerobic conditions and Escherichia coli. In these analyses, the least target production rates were evaluated using flux variability analysis when multiple fluxes yield the highest growth rate. To demonstrate the feasibility of the coupling, the author derived appropriate reaction deletions using the new algorithm for target production in which the search space was divided into small cubes (CubeProd).ConclusionsThe author developed a novel algorithm, CubeProd, to demonstrate that growth coupling is possible for most metabolites in S.cerevisiae under anaerobic conditions. This may imply that growth coupling is possible by reaction deletions for most target metabolites in any genome-scale constraint-based metabolic networks. The developed software, CubeProd, implemented in MATLAB, and the obtained reaction deletion strategies are freely available.