RESUMO
The gut microbiota influences human health and the development of chronic diseases. However, our understanding of potentially protective or harmful microbe-host interactions at the molecular level is still in its infancy. To gain further insights into the hidden gut metabolome and its impact, we identified a cryptic non-ribosomal peptide BGC in the genome of Bacillus cereus DSM 28590 from the mouse intestine ( www.dsmz.de/miBC ), which was predicted to encode a thiazol(in)e substructure. Cloning and heterologous expression of this BGC revealed that it produces bacillamide D. In-depth functional evaluation showed potent cytotoxicity and inhibition of cell migration using the human cell lines HCT116 and HEK293, which was validated using primary mouse organoids. This work establishes the bacillamides as selective cytotoxins from a bacterial gut isolate that affect mammalian cells. Our targeted structure-function-predictive approach is demonstrated to be a streamlined method to discover deleterious gut microbial metabolites with potential effects on human health.
Assuntos
Bacillus cereus , Microbioma Gastrointestinal , Bacillus cereus/metabolismo , Bacillus cereus/genética , Animais , Camundongos , Humanos , Células HEK293 , Citotoxinas/metabolismo , Citotoxinas/genética , Células HCT116 , Intestinos/microbiologia , Movimento Celular , Organoides/metabolismoRESUMO
The Bacillus cereus sensu stricto (s.s.) species comprises strains of biovar Thuringiensis (Bt) known for their bioinsecticidal activity, as well as strains with foodborne pathogenic potential. Bt strains are identified (i) based on the production of insecticidal crystal proteins, also known as Bt toxins, or (ii) based on the presence of cry, cyt, and vip genes, which encode Bt toxins. Multiple bioinformatics tools have been developed for the detection of crystal protein-encoding genes based on whole-genome sequencing (WGS) data. However, the performance of these tools is yet to be evaluated using phenotypic data. Thus, the goal of this study was to assess the performance of four bioinformatics tools for the detection of crystal protein-encoding genes. The accuracy of sequence-based identification of Bt was determined in reference to phenotypic microscope-based screening for the production of crystal proteins. A total of 58 diverse B. cereus sensu lato strains isolated from clinical, food, environmental, and commercial biopesticide products underwent WGS. Isolates were examined for crystal protein production using phase contrast microscopy. Crystal protein-encoding genes were detected using BtToxin_Digger, BTyper3, IDOPS (identification of pesticidal sequences), and Cry_processor. Out of 58 isolates, the phenotypic production of crystal proteins was confirmed for 18 isolates. Specificity and sensitivity of Bt identification based on sequences were 0.85 and 0.94 for BtToxin_Digger, 0.97 and 0.89 for BTyper3, 0.95 and 0.94 for IDOPS, and 0.88 and 1.00 for Cry_processor, respectively. Cry_processor predicted crystal protein production with the highest specificity, and BtToxin_Digger and IDOPS predicted crystal protein production with the highest sensitivity. Three out of four tested bioinformatics tools performed well overall, with IDOPS achieving high sensitivity and specificity (>0.90).IMPORTANCEStrains of Bacillus cereus sensu stricto (s.s.) biovar Thuringiensis (Bt) are used as organic biopesticides. Bt is differentiated from the foodborne pathogen Bacillus cereus s.s. by the production of insecticidal crystal proteins. Thus, reliable genomic identification of biovar Thuringiensis is necessary to ensure food safety and facilitate risk assessment. This study assessed the accuracy of whole-genome sequencing (WGS)-based identification of Bt compared to phenotypic microscopy-based screening for crystal protein production. Multiple bioinformatics tools were compared to assess their performance in predicting crystal protein production. Among them, identification of pesticidal sequences performed best overall at WGS-based Bt identification.