Whole-genome Mining of a Lead-resistant Bacterial Strain ( Bacillus cereus )
Vivek Raj, Brajendra Kumar, Arun Kumar, Santosh Kumar KarnBackground
Heavy metals cause neurotoxicity/neuroinflammation in animals, including humans, and heavy metal contamination of soil/water is one of the major factors in neurodegenerative diseases. Soil bacteria that persist in metal-impacted environments often carry genomic determinants for metal detoxification alongside biosynthetic pathways for the production of structurally diverse secondary metabolites. We combined phenotypic identification, whole-genome sequencing and annotation-based genome mining to characterise a soil
Purpose
Annotation-based mining to identify heavy-metal resistance genes involved in metal transformation, which is essential for understanding metal detoxification mechanisms and their potential implications for neurodegenerative diseases.
Methods
Isolates were identified by matrix-assisted laser desorption/ionisation–time-of-flight mass spectrometry (Bruker Biotyper). The VLR6 genome (5,411,809 bp, single contig) was annotated with Bakta v1.12.0 (database v6.0) and screened for acquired antimicrobial resistance determinants with the Comprehensive Antibiotic Resistance Database Resistance Gene Identifier (CARD-RGI). Biosynthetic gene clusters (BGCs) were predicted using antiSMASH v8.0.4 (default rules; Minimum Information about a Biosynthetic Gene Cluster 3.0 KnownClusterBlast comparisons) and identified through systematic keyword screening of the Bakta functional annotation.
Results
VLR4–VLR6 were identified as
Conclusion
The VLR6 genome carries a genomically confirmed set of BGCs, most confidently a petrobactin siderophore cluster corroborated by two independent methods alongside an annotation-supported heavy-metal-resistance gene set. Phenotypic lead-MIC determination and metabolomic or transcriptomic validation are still required before any bioactivity, neuroactive or otherwise, can be attributed to this strain or its predicted metabolites.