Toxinome-the bacterial protein toxin database.

IMPORTANCE: Microbes use protein toxins as important tools to attack neighboring cells, microbial or eukaryotic, and for self-killing when attacked by viruses. These toxins work through different mechanisms to inhibit cell growth or kill cells. Microbes also use antitoxin proteins to neutralize the toxin activities. Here, we developed a comprehensive database called Toxinome of nearly two million toxins and antitoxins that are encoded in 59,475 bacterial genomes. We described the distribution of bacterial toxins and identified that they are depleted by bacteria that live in hot and cold temperatures. We found 5,161 cases in which toxins and antitoxins are densely clustered in bacterial genomes and termed these areas "Toxin Islands." The Toxinome database is a useful resource for anyone interested in toxin biology and evolution, and it can guide the discovery of new toxins.

Comparative genomic analysis reveals differential genomic characteristics and featured genes between rapid- and slow-growing non-tuberculous mycobacteria.

Introduction: Non-tuberculous mycobacteria (NTM) is a major category of environmental bacteria in nature that can be divided into rapidly growing mycobacteria (RGM) and slowly growing mycobacteria (SGM) based on their distinct growth rates. To explore differential molecular mechanisms between RGM and SGM is crucial to understand their survival state, environmental/host adaptation and pathogenicity. Comparative genomic analysis provides a powerful tool for deeply investigating differential molecular mechanisms between them. However, large-scale comparative genomic analysis between RGM and SGM is still uncovered. Methods: In this study, we screened 335 high-quality, non-redundant NTM genome sequences covering 187 species from 3,478 online NTM genomes, and then performed a comprehensive comparative genomic analysis to identify differential genomic characteristics and featured genes/protein domains between RGM and SGM. Results: Our findings reveal that RGM has a larger genome size, more genes, lower GC content, and more featured genes/protein domains in metabolism of some main substances (e.g. carbohydrates, amino acids, nucleotides, ions, and coenzymes), energy metabolism, signal transduction, replication, transcription, and translation processes, which are essential for its rapid growth requirements. On the other hand, SGM has a smaller genome size, fewer genes, higher GC content, and more featured genes/protein domains in lipid and secondary metabolite metabolisms and cellular defense mechanisms, which help enhance its genome stability and environmental adaptability. Additionally, orthogroup analysis revealed the important roles of bacterial division and bacteriophage associated genes in RGM and secretion system related genes for better environmental adaptation in SGM. Notably, PCoA analysis of the top 20 genes/protein domains showed precision classification between RGM and SGM, indicating the credibility of our screening/classification strategies. Discussion: Overall, our findings shed light on differential underlying molecular mechanisms in survival state, adaptation and pathogenicity between RGM and SGM, show the potential for our comparative genomic pipeline to investigate differential genes/protein domains at whole genomic level across different bacterial species on a large scale, and provide an important reference and improved understanding of NTM.