The thermophilic strain of the genus , is a promising bacterial chassis for a wide range of biotechnological applications. In this study, we explored the metabolic potential of for the production of 2,3-butanediol (2,3-BTD), one of the cost-effective commodity chemicals. Here we present a genome-scale metabolic model for constructed using an auto-generating pipeline with consequent thorough manual curation. The model contains 1321 genes and includes 1676 reactions and 1589 metabolites, representing the most-complete and publicly available model of the genus . The developed model provides new insights into thermophilic bacterial metabolism and highlights new strategies for biotechnological applications of the strain. Our analysis suggests that has a potential for 2,3-butanediol production from a variety of utilized carbon sources, including glycerine, a common byproduct of biofuel production. We identified a set of solutions for enhancing 2,3-BTD production, including cultivation under anaerobic or microaerophilic conditions and decreasing the TCA flux to succinate via reducing citrate synthase activity. Both in silico predicted metabolic alternatives have been previously experimentally verified for closely related strains including the genus
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http://dx.doi.org/10.3390/microorganisms8071002 | DOI Listing |
Nat Commun
December 2024
School of Biological, Earth and Environmental Sciences, University of New South Wales, Sydney, Australia.
Sponges harbour complex microbiomes and as ancient metazoans and important ecosystem players are emerging as powerful models to understand the evolution and ecology of symbiotic interactions. Metagenomic studies have previously described the functional features of sponge symbionts, however, little is known about the metabolic interactions and processes that occur under different environmental conditions. To address this issue, we construct here constraint-based, genome-scale metabolic networks for the microbiome of the sponge Stylissa sp.
View Article and Find Full Text PDFGenome Biol
December 2024
Department of Statistics, University of British Columbia, Vancouver, Canada.
Single-cell DNA methylation measurements reveal genome-scale inter-cellular epigenetic heterogeneity, but extreme sparsity and noise challenges rigorous analysis. Previous methods to detect variably methylated regions (VMRs) have relied on predefined regions or sliding windows and report regions insensitive to heterogeneity level present in input. We present vmrseq, a statistical method that overcomes these challenges to detect VMRs with increased accuracy in synthetic benchmarks and improved feature selection in case studies.
View Article and Find Full Text PDFBiotechnol Bioeng
December 2024
Centre for Biotechnology and Bioengineering (CeBiB), Department of Chemical Engineering, Biotechnology and Materials, University of Chile, Santiago, Chile.
Production of specialized metabolites are restricted to the metabolic capabilities of the organisms. Genome-scale models (GEM)s are useful to study the whole metabolism and to find metabolic engineering targets to increase the yield of a target compound. In this work we use a modified model of Streptomyces coelicolor M145 to simulate the production of lagmysin A (LP4) and the novel lagmysin B (LP2) lasso peptide, in the heterologous host Streptomyces coelicolor M1152.
View Article and Find Full Text PDFCommun Biol
December 2024
Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
Epithelial-to-mesenchymal transition (EMT) is a conserved cellular process critical for embryogenesis, wound healing, and cancer metastasis. During EMT, cells undergo large-scale metabolic reprogramming that supports multiple functional phenotypes including migration, invasion, survival, chemo-resistance and stemness. However, the extent of metabolic network rewiring during EMT is unclear.
View Article and Find Full Text PDFFront Immunol
December 2024
Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
Background: The heterogeneity of cancer makes it challenging to predict its response to immunotherapy, highlighting the need to find reliable biomarkers for assessment. The sophisticated role of cancer stemness in mediating resistance to immune checkpoint inhibitors (ICIs) is still inadequately comprehended.
Methods: Genome-scale CRISPR screening of RNA sequencing data from Project Achilles was utilized to pinpoint crucial genes unique to Ovarian Cancer (OV).
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