Genomic characteristics of Mycobacterium tuberculosis isolates of cutaneous tuberculosis

Objectives Cutaneous tuberculosis with various manifestations can be divided into several clinical types according to the host's immune status and infective route. However, the etiological factors of this disease remain unclear. The objective of this study is to investigate the pathogens associated with the occurrence and different types of cutaneous tuberculosis. Methods 58 Mycobacterium tuberculosis strains isolated from cutaneous tuberculosis over the last 20 years were sequenced and analyzed for genomic characteristics including lineage distribution, drug-resistance mutations, and mutations potentially associated with different sites of infection. Results The M. tuberculosis strains from four major types of cutaneous tuberculosis and pulmonary tuberculosis shared similar genotypes and genomic composition. The strains isolated from cutaneous tuberculosis had a lower rate of drug resistance. Phylogenic analysis showed cutaneous tuberculosis and pulmonary tuberculosis isolates scattered on the three. Several SNPs in metabolism related genes exhibited a strong correlation with different infection sites. Conclusions The different infection sites of TB may barely be affected by large genomic changes in M. tuberculosis isolates, but the significant difference in SNPs of drug resistance gene and metabolism-related genes still deserves more attention.

[1]  Anna Allué-Guardia,et al.  Evolution of Drug-Resistant Mycobacterium tuberculosis Strains and Their Adaptation to the Human Lung Environment , 2021, Frontiers in Microbiology.

[2]  T. Clark,et al.  Robust barcoding and identification of Mycobacterium tuberculosis lineages for epidemiological and clinical studies , 2020, Genome medicine.

[3]  Ling Li,et al.  Determining Mycobacterium tuberculosis Drug Resistance and Risk Factors for Multidrug-Resistant Tuberculosis in Sputum Smear-Positive Tuberculosis Outpatients in Anhui Province, China, 2015–2016 , 2020, Infection and drug resistance.

[4]  V. Chongsuvivatwong,et al.  The geno-spatio analysis of Mycobacterium tuberculosis complex in hot and cold spots of Guangxi, China , 2020, BMC Infectious Diseases.

[5]  Jim F Huggett,et al.  Integrating informatics tools and portable sequencing technology for rapid detection of resistance to anti-tuberculous drugs , 2019, Genome Medicine.

[6]  Matthew W. Snyder,et al.  GWAS for quantitative resistance phenotypes in Mycobacterium tuberculosis reveals resistance genes and regulatory regions , 2019, Nature Communications.

[7]  Q. Xia,et al.  OrthoVenn2: a web server for whole-genome comparison and annotation of orthologous clusters across multiple species , 2019, Nucleic Acids Res..

[8]  A. Rodríguez-Morales,et al.  Cutaneous Mycobacterial Infections , 2018, Clinical Microbiology Reviews.

[9]  Jingfa Xiao,et al.  Pan-Genomic Study of Mycobacterium tuberculosis Reflecting the Primary/Secondary Genes, Generality/Individuality, and the Interconversion Through Copy Number Variations , 2018, Front. Microbiol..

[10]  T. Cohen,et al.  Internal migration and transmission dynamics of tuberculosis in Shanghai, China: an epidemiological, spatial, genomic analysis. , 2018, The Lancet. Infectious diseases.

[11]  Daniel Gautheret,et al.  CRISPRCasFinder, an update of CRISRFinder, includes a portable version, enhanced performance and integrates search for Cas proteins , 2018, Nucleic Acids Res..

[12]  G. Wang,et al.  Cutaneous tuberculosis in China – A multicentre retrospective study of cases diagnosed between 1957 and 2013 , 2018, Journal of the European Academy of Dermatology and Venereology : JEADV.

[13]  Jia Gu,et al.  fastp: an ultra-fast all-in-one FASTQ preprocessor , 2018, bioRxiv.

[14]  Jukka Corander,et al.  pyseer: a comprehensive tool for microbial pangenome-wide association studies , 2018, bioRxiv.

[15]  I. Saleh,et al.  The Existence of Mycobacterium tuberculosis in Microenvironment of Bone , 2017, Mycobacterium - Research and Development.

[16]  Robert D. Finn,et al.  Rfam 13.0: shifting to a genome-centric resource for non-coding RNA families , 2017, Nucleic Acids Res..

[17]  W. Eisenreich,et al.  Lactate oxidation facilitates growth of Mycobacterium tuberculosis in human macrophages , 2017, Scientific Reports.

[18]  Richard A Neher,et al.  TreeTime: Maximum-likelihood phylodynamic analysis , 2017, bioRxiv.

[19]  D. Sarkar,et al.  Mycobacterium tuberculosis virulence‐regulator PhoP interacts with alternative sigma factor SigE during acid‐stress response , 2017, Molecular microbiology.

[20]  Lonneke Scheffer,et al.  Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary , 2016, Genome Biology.

[21]  Måns Magnusson,et al.  MultiQC: summarize analysis results for multiple tools and samples in a single report , 2016, Bioinform..

[22]  Thomas Abeel,et al.  Genomic and functional analyses of Mycobacterium tuberculosis strains implicate ald in D-cycloserine resistance , 2016, Nature Genetics.

[23]  Simon R. Harris,et al.  SNP-sites: rapid efficient extraction of SNPs from multi-FASTA alignments , 2016, bioRxiv.

[24]  Andrew J. Page,et al.  Roary: rapid large-scale prokaryote pan genome analysis , 2015, bioRxiv.

[25]  Edwin Cuppen,et al.  Sambamba: fast processing of NGS alignment formats , 2015, Bioinform..

[26]  E. Hammer,et al.  Using a Label Free Quantitative Proteomics Approach to Identify Changes in Protein Abundance in Multidrug-Resistant Mycobacterium tuberculosis , 2015, Indian Journal of Microbiology.

[27]  A. von Haeseler,et al.  IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies , 2014, Molecular biology and evolution.

[28]  P. Singh,et al.  Phosphorylation of pyruvate kinase A by protein kinase J leads to the altered growth and differential rate of intracellular survival of mycobacteria , 2014, Applied Microbiology and Biotechnology.

[29]  John L. Johnson,et al.  Epidemiology of extrapulmonary tuberculosis in Brazil: a hierarchical model , 2014, BMC Infectious Diseases.

[30]  I. Comas,et al.  Mapping of Genotype–Phenotype Diversity among Clinical Isolates of Mycobacterium tuberculosis by Sequence-Based Transcriptional Profiling , 2013, Genome biology and evolution.

[31]  J. Pedrosa,et al.  Evidence for diversifying selection in a set of Mycobacterium tuberculosis genes in response to antibiotic- and nonantibiotic-related pressure. , 2013, Molecular biology and evolution.

[32]  M. V. D. Werf,et al.  Extrapulmonary tuberculosis in the European Union and European Economic Area, 2002 to 2011. , 2013, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[33]  Jian Wang,et al.  SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler , 2012, GigaScience.

[34]  Robert A. Edwards,et al.  PhiSpy: a novel algorithm for finding prophages in bacterial genomes that combines similarity- and composition-based strategies , 2012, Nucleic acids research.

[35]  Pablo Cingolani,et al.  © 2012 Landes Bioscience. Do not distribute. , 2022 .

[36]  Qiao Liu,et al.  Molecular typing of mycobacterium tuberculosis isolates circulating in Jiangsu Province, China , 2011, BMC infectious diseases.

[37]  Peer Bork,et al.  Interactive Tree Of Life v2: online annotation and display of phylogenetic trees made easy , 2011, Nucleic Acids Res..

[38]  Richard Durbin,et al.  Sequence analysis Fast and accurate short read alignment with Burrows – Wheeler transform , 2009 .

[39]  Rick L. Stevens,et al.  The RAST Server: Rapid Annotations using Subsystems Technology , 2008, BMC Genomics.

[40]  Peter F. Hallin,et al.  RNAmmer: consistent and rapid annotation of ribosomal RNA genes , 2007, Nucleic acids research.

[41]  M. James,et al.  The molecular structure of Rv1873, a conserved hypothetical protein from Mycobacterium tuberculosis, at 1.38 A resolution. , 2006, Acta crystallographica. Section F, Structural biology and crystallization communications.

[42]  Piero Fariselli,et al.  I-Mutant2.0: predicting stability changes upon mutation from the protein sequence or structure , 2005, Nucleic Acids Res..

[43]  Peter Schattner,et al.  The tRNAscan-SE, snoscan and snoGPS web servers for the detection of tRNAs and snoRNAs , 2005, Nucleic Acids Res..

[44]  C. Sanders,et al.  Cutaneous Tuberculosis , 2017 .

[45]  L. Gabbasova,et al.  Global tuberculosis report (2014) , 2014 .