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Escherichia coli LS5218 is an attractive host for producing polyhydroxybutyrate. The strain, however, strongly requires heterologous gene expressions like phaC for efficient production. For enhancing the production, the whole gene expressions relating to end product-producing flow should be optimized so that not only heterologous induced-genes but also other relating genes are comprehensively analyzed on the transcription levels, resulting in normally time-consuming mutant-creation. Additionally, the explanation for each transcriptional relationship is likely to follow the relationships on known metabolic pathway map to limit the consideration. This study aimed to infer gene regulatory networks within glycolysis, a central metabolic pathway in LS5218, using machine learning-based causal discovery methods. To construct a directed acyclic graph representing the gene regulatory network, we employed the NOTEARS algorithm (Non-combinatorial Optimization via Trace Exponential and Augmented lagRangian for Structure learning). Using transcription data of 264 time-resolved sampling points, we inferred the gene regulatory network and identified several distal regulatory relationships. Notably, gapA, a key enzyme controlling the transition between the preparatory and rewarding phases in glycolysis, was found to influence pgi, the enzyme at the pathway’s entry point. These findings suggest that inferring such nonlocal regulatory interactions can provide valuable insights for guiding genetic engineering strategies.

13 November 2025

Overview of hyperparameter optimization for generating candidate gene regulatory networks. Hyperparameters include L1 regularization weight λ1 and L2 regularization weight λ2 and threshold t for generating the adjacency matrix from the weighted adjacency matrix.

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by motor symptoms like tremor, rigidity, and bradykinesia. The WHO estimates that 10 million people currently have PD, with its prevalence expected to double to 20 million by 2050. Key risk factors include age, male sex, environmental contaminants, and family history. Emerging evidence links gut microbiota dysbiosis to PD, suggesting it contributes to neuroinflammation and disease progression, though the role of dietary interventions remains unclear. This study used computational simulations with genome-scale metabolic models (GEMs) to analyze how diet impacts the gut microbiota in PD patients. Fecal microbiota from PD patients and healthy controls were compared across three diets: high-fiber, Mediterranean, and vegan. Simulations revealed increased pro-inflammatory bacteria (e.g., Escherichia coli O157) in PD patients, likely due to reduced bacterial competition, alongside the decreased production of beneficial metabolites like butyrate, phenylalanine, and cysteine. The Mediterranean diet showed higher short-chain fatty acid production, potentially benefiting PD patients. These findings underscore the importance of dietary interventions in modulating the gut microbiome and suggest that targeted diets may complement PD therapies, improving patient outcomes.

3 November 2025

Species relationship heatmap of the bacterial communities modeled in the Bedarf et al. [15] and Keshavarzian et al., [8] studies for PD. (A): Competitiveness indexes. (B): Complementarity indexes. The list of abbreviations used in this graph is found in Table S8.
  • Brief Report
  • Open Access

RTGill-W1 Cells Response to Salmonella enterica Metabolites

  • Abdulhusein Jawdhari,
  • Robert Wolff and
  • Bianca-Maria Tihăuan
  • + 2 authors

This report investigates the interaction between the metabolites of the highly virulent bacteria Salmonella enterica and RTGill-W1 cells, a cell line derived from rainbow trout gills. As a facultative intracellular pathogen, Salmonella enterica infects both animals and humans through many routes. Upon entering an organism it can cause severe infection and pathology, which is also influenced by the bacterial metabolites. Although no intracellular presence of the pathogen in the exposed cell line could be detected, a dose-dependent effect of the metabolites on the cell line was observed, as exposure to 5%, 10%, and 20% concentrations led to enhanced metabolic activity and increased cytoplasmic neutral lipid droplets accumulation, whereas the lower dosage of 2.5% induced a lower metabolic rate compared to control and no significant intracellular lipid accumulation. The combination of all of the metabolites might be speculated to have increased the metabolic rate and lipid droplet production at higher concentrations due to possessing a growth factor or an endocrine effect, or as a response to a toxin. This paper may be the first report investigating the effect of a complete bacterial metabolite mixture in cultured cells.

2 November 2025

Metabolic rate of RTGill-W1 cells exposed to S. enterica metabolites. * = p < 0.05; ns = non-significant, resazurin test assay. RFU = relative fluorescence units.

Background: Past studies have documented the antimicrobial effects of dimethyl sulfoxide (D.M.SO). However, the side effects and toxicity profiles of DMSO in vivo have been a significant deterrent for its wide-ranging clinical use. Dimethyl sulfone (DMSO-2), a natural metabolite of DMSO, is currently used as a safe dietary supplement due to its antioxidant properties and multimodal mechanisms of action. While DMSO displays antimicrobial activity, little is known concerning DMSO-2’s antimicrobial effect. Thus, this investigation compares the antimicrobial effects of DMSO and DMSO-2 on the growth and viability of the pathogenic anaerobic bacteria, Porphyromonas gingivalis, and their cytotoxic effect on human oral epithelial (OKF6/TERT-2) cells. Methods: P. gingivalis was grown in TSBY media in the presence of DMSO or DMSO-2 (0–4%) for planktonic growth and viability determinations. OKF6/TERT-2 cells were expanded in vitro and similarly exposed to DMSO or DMSO-2 for viability studies. Results: After 24 h exposure to DMSO or DMSO-2, growth of P. gingivalis is inhibited by 57% and 77%, respectively, while viability is inhibited by 55% and 62%. In contrast, 24 h exposure to similar concentrations of DMSO or DMSO-2 induces 5% and 2% cytotoxicity in OKF6/TERT-2 cells, respectively. Conclusions: Both DMSO and DMSO-2 inhibit the growth and viability of P. gingivalis but show minimal toxic effect on OKF6/TERT-2 cells. Therefore, the utility of these two natural compounds as antimicrobial agents against anaerobic pathogens should be further investigated.

1 November 2025

Bioconversion of dimethylsulfide to dimethyl sulfone in the gut by microbial–mammalian co-metabolism.

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Bacteria - ISSN 2674-1334