Genome analysis of Mycobacterium avium subsp. paratuberculosis and pipeline construction for microbiome analysis

Abstract

Johne’s disease is a chronic granulomatous enteropathy in ruminants and Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease. It is characterized by chronic diarrhea, progressive wasting, and eventual death. It is also an economically important disease because the infected individuals show weight loss and reduced milk production worldwide. In addition, the association of MAP with Crohn's disease has constantly being raised in many studies which is a type of chronic inflammatory bowel disease in humans. In this study, comparative genomic analysis and functional genomic analysis was performed to obtain insight of MAP genome. Also, a new program was constructed to analysis microbiome for understanding the environment of MAP infected host. MAP genomes were compared to find the commonalities and differences. Thirteen complete genomes of MAP were used to find genomic differences. It was found that there was genome flip in some MAP genomes and there were many gaps were found. Three large gaps which contain transposase was observed and five novel tandem repeats (TRs) was found which could be used for genotyping strains. Both features were genome feature of MAP and could be used as marker to distinguish strains more detailly. Pan-genome analysis was performed with 40 published genomes to find how many genes were shared in each genome. It was found that MAP genome is highly conserved, and they have closed pan-genome. Functional analysis of pan-genome was conducted using three virulence factor databases (PATRIC, VFDB, and Victors) to predict phenotypic diversity of genomes in terms of pathogenicity. Based on the results of the pan-genome analysis, a real-time PCR experiment was developed to distinguish the type of MAP (S-, B-, and C-type). Johne’s disease primarily affects the small intestine of ruminants and, nowadays, host-pathogen interactions are intended to be used to understand new phenomena or to find new therapies. An analysis pipeline named TaxaAssignpy was constructed to analysis microbiome. The pipeline was written Python language and utilize two free external programs, VSEARCH and RDP-Classifier. TaxaAssignPy accept 16S rRNA sequence data as inputs and give users the compositions of input samples and the number of compositions as outputs. The pipeline summarizes the result in separate files depend on taxonomic rank from domain to genus.