Lactiplantibacillus plantarum J-15 reduced calcium oxalate kidney stones by regulating intestinal microbiota, metabolism, and inflammation in rats.

The prevention role of Lactiplantibacillus plantarum against the formation of kidney stones has been increasingly recognized; its mechanism, however, has mainly been focused on inhibiting the inflammation in the colon in the gastrointestinal (GI) system, and the intestinal metabolites from microflora have not been revealed fully with regarding to the stone formation. In this study, we investigated the effect of L. plantarum J-15 on kidney stone formation in renal calcium oxalate (CaOx) rats induced by ethylene glycol and monitored the changes of intestinal microflora and their metabolites detected by 16S rRNA sequencing and widely targeted analysis, followed by the evaluation of the intestinal barrier function and inflammation levels in the colon, blood and kidney. The results showed that L. plantarum J-15 effectively reduced renal crystallization and urinary oxalic acid. Ten microbial genera, including anti-inflammatory and SCFAs-related Faecalibaculum, were enriched in the J-15 treatment group. There are 136 metabolites from 11 categories significantly different in the J-15 supplementation group compared with CaOx model rats, most of which were enriched in the amino acid metabolic and secondary bile acid pathways. The expression of intestinal tight junction protein Occludin and the concentration of pro-inflammatory cytokines and prostaglandin were decreased in the intestine, which further reduced the translocated lipopolysaccharide and inflammation levels in the blood upon J-15 treatment. Thus, the inflammation and injury in the kidney might be alleviated by downregulating TLR4/NF-κB/COX-2 signaling pathway. It suggested that L. plantarum J-15 might reduce kidney stone formation by restoring intestinal microflora and metabolic disorder, protecting intestinal barrier function, and alleviating inflammation. This finding provides new insights into the therapies for renal stones.

Novel extracellular lipase gene Lip1728 influences nutrient-dependent performance bacterial quorum sensing of Burkholderia pyrrocinia WZ10-3.

Quorum sensing (QS) is a cellular communication mechanism in which bacteria secrete and recognize signaling molecules to regulate group behavior. Lipases provide energy for bacterial cell growth but it is unknown whether they influence nutrient-dependent QS by hydrolyzing substrate. A high-yield lipase-producing strain, Burkholderia pyrrocinia WZ10-3, was previously identified in our laboratory, but the composition of its crude enzymes was not elucidated. Here, we identified a key extracellular lipase, Lip1728, in WZ10-3, which accounts for 99 % of the extracellular lipase activity. Lip1728 prefers to hydrolyze triglycerides at sn-1,3 positions, with pNP-C16 being its optimal substrate. Lip1728 exhibited activity at pH 5.0-10.0 and regardless of the presence of metal ions. It had strong resistance to sodium dodecyl sulfate and short-chain alcohols and was activated by phenylmethanesulfonylfluoride (PMSF). Lip1728 knockout significantly affected lipid metabolism and biofilm formation in the presence of olive oil. Finally, oleic acid, a hydrolysate of Lip1728, influenced the production of the signal molecule N-acyl homoserine lactone (AHL) and biofilm formation by downregulating the AHL synthetase gene pyrI. In conclusion, Lip1728, as a key extracellular lipase in B. pyrrocinia WZ10-3, exhibits superior properties that make it suitable for biodiesel production and plays a crucial role in QS.

deepAMPNet: a novel antimicrobial peptide predictor employing AlphaFold2 predicted structures and a bi-directional long short-term memory protein language model.

Background: Global public health is seriously threatened by the escalating issue of antimicrobial resistance (AMR). Antimicrobial peptides (AMPs), pivotal components of the innate immune system, have emerged as a potent solution to AMR due to their therapeutic potential. Employing computational methodologies for the prompt recognition of these antimicrobial peptides indeed unlocks fresh perspectives, thereby potentially revolutionizing antimicrobial drug development. Methods: In this study, we have developed a model named as deepAMPNet. This model, which leverages graph neural networks, excels at the swift identification of AMPs. It employs structures of antimicrobial peptides predicted by AlphaFold2, encodes residue-level features through a bi-directional long short-term memory (Bi-LSTM) protein language model, and constructs adjacency matrices anchored on amino acids' contact maps. Results: In a comparative study with other state-of-the-art AMP predictors on two external independent test datasets, deepAMPNet outperformed in accuracy. Furthermore, in terms of commonly accepted evaluation matrices such as AUC, Mcc, sensitivity, and specificity, deepAMPNet achieved the highest or highly comparable performances against other predictors. Conclusion: deepAMPNet interweaves both structural and sequence information of AMPs, stands as a high-performance identification model that propels the evolution and design in antimicrobial peptide pharmaceuticals. The data and code utilized in this study can be accessed at https://github.com/Iseeu233/deepAMPNet.