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

2020 ◽  
Author(s):  
Matthew L Jenior ◽  
Jhansi L Leslie ◽  
Deborah A Powers ◽  
Elizabeth M Garrett ◽  
Kimberly A Walker ◽  
...  
Keyword(s):  
Metabolic Network ◽  
Single Gene ◽  
Bacterial Genome ◽  
Bacterial Pathogen ◽  
Recurrent Infection ◽  
Metabolic Reprogramming ◽  
Metabolic Network Analysis ◽  
Clostridioides Difficile ◽  
Novel Approach ◽  
Genome Scale

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.

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

2021 ◽  
Vol 22 (15) ◽  
pp. 7974
Author(s):  
Yu-Te Lin ◽  
Yong-Shiou Lin ◽  
Wen-Ling Cheng ◽  
Jui-Chih Chang ◽  
Yi-Chun Chao ◽  
...  
Keyword(s):  
Network Analysis ◽  
Transgenic Mice ◽  
Signaling Pathways ◽  
Metabolic Network ◽  
Metabolic Reprogramming ◽  
Spinocerebellar Ataxia Type ◽  
Positive Effects ◽  
Metabolic Network Analysis ◽  
Potential Biomarker ◽  
Metabolic Remodeling

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.

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

mSystems ◽  
2021 ◽  
Author(s):  
Matthew L. Jenior ◽  
Jhansi L. Leslie ◽  
Deborah A. Powers ◽  
Elizabeth M. Garrett ◽  
Kimberly A. Walker ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Virulence Factors ◽  
Metabolic Pathways ◽  
Content Type ◽  
Hospital Acquired Infections ◽  
Metabolic Network Analysis ◽  
Clostridioides Difficile ◽  
Context Specific ◽  
Hospital Acquired

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.

scholarly journals Functional Anabolic Network Analysis of Human-associated Lactobacillus Strains

2019 ◽  
Author(s):  
Thomas J. Moutinho ◽  
Benjamin C. Neubert ◽  
Matthew L. Jenior ◽  
Maureen A. Carey ◽  
Gregory L. Medlock ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metabolic Network ◽  
Health Benefits ◽  
Network Reconstruction ◽  
Metabolic Network Reconstruction ◽  
Metabolic Network Analysis ◽  
A Genome ◽  
Direct Interpretation ◽  
The Difference ◽  
Genome Scale

AbstractMembers of the Lactobacillus genus are frequently utilized in the probiotic industry with many species conferring demonstrated health benefits; however, these effects are largely strain-dependent. We designed a method called PROTEAN (Probabilistic Reconstruction Of constituent Anabolic Networks) to computationally analyze the genomic annotations and predicted metabolic production capabilities of 144 strains across 16 species of Lactobacillus isolated from human intestinal, oral, and vaginal body sites. Using PROTEAN we conducted a genome-scale metabolic network comparison between strains, revealing that metabolic capabilities differ by isolation site. Notably, PROTEAN does not require a well-curated genome-scale metabolic network reconstruction to provide biological insights. We found that predicted metabolic capabilities of lactobacilli isolated from the vaginal microbiota cluster separately from intestinal and oral isolates, and we also uncovered an overlap in the predicted metabolic production capabilities of intestinal and oral isolates. Using machine learning, we determined the most informative metabolic products driving the difference between predicted metabolic capabilities of intestinal, oral, and vaginal isolates. Notably, intestinal and oral isolates were predicted to have a higher likelihood of producing D-alanine, D/L-serine, and L-proline, while the vaginal isolates were distinguished by a higher predicted likelihood of producing L-arginine, citrulline, and D/L-lactate. We found the distinguishing products to be consistent with published experimental literature. This study showcases a systematic technique, PROTEAN, for comparing the predicted functional metabolic output of microbes using genome-scale metabolic network analysis and computational modeling and provides unique insight into human-associated Lactobacillus biology.ImportanceThe Lactobacillus genus has been shown to be important for human health. Lactobacilli have been isolated from human intestinal, oral, and vaginal sites. Members of the genus contribute significantly to the maintenance of vaginal health by providing colonization resistance to invading pathogens. A wide variety of clinical studies have indicated that Lactobacillus-based probiotics confer health benefits for several gut- and immune-associated diseases. Microbes interact with the human body in several ways, including the production of metabolites that influence physiology or other surrounding microbes. We have conducted a strain-level genome-scale metabolic network reconstruction analysis of human-associated Lactobacillus strains, revealing that predicted metabolic capabilities differ when comparing intestinal/oral isolate to vaginal isolates. The technique we present here allows for direct interpretation of discriminating features between the experimental groups.

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

2020 ◽  
Author(s):  
Takeyuki Tamura
Keyword(s):  
Escherichia Coli ◽  
Metabolic Network ◽  
Metabolic Networks ◽  
Search Space ◽  
Anaerobic Conditions ◽  
Aerobic Conditions ◽  
Metabolic Network Analysis ◽  
Balance Analysis ◽  
Target Production ◽  
Genome Scale

Abstract 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.

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