Abstract:
Microbial effectors like genotoxins have been shown to play a crucial role in the pathogenesis of diseases like colorectal cancer. These effectors are agents capable of causing DNA damage within cells and are pivotal in CRC development. Our study consists of two parts. The first part begins by aiming to discern genotoxins produced by microbes and elucidate their distribution patterns. Microbes, like bacteria, highly contribute significantly to genotoxin production due to their metabolic versatility and environmental interactions. By identifying and mapping microbial genotoxins, we can grasp their role in disease parthenogenesis and evaluate their similarities across diverse microbial species. In the second part, initiated to identify novel peptides or effectors with genotoxic potential, we extend our investigation to identify transmembrane proteins and signal peptides across all microbial species is paramount in understanding the intricate mechanisms underlying microbial physiology and pathogenesis. Utilizing reference proteomes from 169 species, we have created a proto-type of an atlas of the putative surfaceome and secretome of the human gut microbiome. This atlas is organized in clusters generated using similarities of various protein properties. To demonstrate the application of this atlas, we have leveraged this database to map to identify homologs of different genotoxin proteins and proteins showing similarity to different human surfacome and secretome proteins, focusing on a core set of 169 species. This investigation unearthed numerous novel proteins and elucidated their potential roles in microbial pathogenesis and host-microbe interactions. Our analysis has revealed significant overlaps between microbial proteins and disease-producing human proteins, putatively shedding light on potential mechanisms of microbial pathogenicity and disease progression. Keywords: Genotoxins, colorectal cancer, transmembrane proteins, signal peptides, microbial species, database creation, bioinformatics tools, protein prediction, clustering, microbial pathogenicity, disease progression, host-microbe interactions, infectious diseases, global health.
