Yasunori Iwata1, Kenji Satou2, Kengo Furuichi3, Ikuko Yoneda4, Takuhiro Matsumura5, Masahiro Yutani5, Yukako Fujinaga5, Atsushi Hase2, Hidetoshi Morita6, Toshiko Ohta7, Yasuko Senda8, Yukiko Sakai-Takemori8, Taizo Wada8, Shinichi Fujita8, Taito Miyake9, Haruka Yasuda10, Norihiko Sakai11, Shinji Kitajima9, Tadashi Toyama9, Yasuyuki Shinozaki9, Akihiro Sagara9, Taro Miyagawa9, Akinori Hara9, Miho Shimizu9, Yasutaka Kamikawa9, Kazuho Ikeo12, Shigeyuki Shichino13, Satoshi Ueha13, Takuya Nakajima13, Kouji Matsushima13, Shuichi Kaneko14, Takashi Wada15. 1. Division of Infection Control, Kanazawa University, Kanazawa, Japan; Division of Nephrology, Kanazawa University, Kanazawa, Japan. Electronic address: iwatay@staff.kanazawa-u.ac.jp. 2. Faculty of Electrical and Computer Engineering, Kanazawa University, Kanazawa, Japan. 3. Division of Nephrology, Kanazawa Medical University School of Medicine, Ishikawa, Japan. 4. Division of Nephrology, Kanazawa University, Kanazawa, Japan. 5. Department of Bacteriology, Kanazawa University, Kanazawa, Japan. 6. Graduate School of Environmental and Life Science, Okayama University, Okayama, Japan. 7. University of Tsukuba, Tsukuba, Japan. 8. Division of Infection Control, Kanazawa University, Kanazawa, Japan. 9. Division of Nephrology, Kanazawa University, Kanazawa, Japan; Department of Disease Control and Homeostasis, Kanazawa University, Kanazawa, Japan. 10. Department of Nephrology and Laboratory Medicine, Kanazawa University, Kanazawa, Japan. 11. Division of Nephrology, Kanazawa University, Kanazawa, Japan; Division of Blood Purification, Kanazawa University, Kanazawa, Japan. 12. Laboratory of DNA Data Analysis, National Institute of Genetics, Shizuoka, Japan. 13. Department of Molecular Preventive Medicine, University of Tokyo, Tokyo, Japan; Division of Molecular Regulation of Inflammatory and Immune Diseases, Research Institute of Biomedical Sciences, Tokyo University of Science, Noda, Japan. 14. Department of Disease Control and Homeostasis, Kanazawa University, Kanazawa, Japan. 15. Division of Nephrology, Kanazawa University, Kanazawa, Japan; Department of Nephrology and Laboratory Medicine, Kanazawa University, Kanazawa, Japan.
Abstract
OBJECTIVES: Methicillin-resistant Staphylococcus aureus (MRSA) causes hospital- and community-acquired infections. It is not clear whether genetic characteristics of the bacteria contribute to disease pathogenesis in MRSA infection. We hypothesized that whole genome analysis of MRSA strains could reveal the key gene loci and/or the gene mutations that affect clinical manifestations of MRSA infection. METHODS: Whole genome sequences (WGS) of MRSA of 154 strains were analyzed with respect to clinical manifestations and data. Further, we evaluated the association between clinical manifestations in MRSA infection and genomic information. RESULTS: WGS revealed gene mutations that correlated with clinical manifestations of MRSA infection. Moreover, 12 mutations were selected as important mutations by Random Forest analysis. Cluster analysis revealed strains associated with a high frequency of bloodstream infection (BSI). Twenty seven out of 34 strains in this cluster caused BSI. These strains were all positive for collagen adhesion gene (cna) and have mutations in the locus, those were selected by Random Forest analysis. Univariate and multivariate analysis revealed that these gene mutations were the predictor for the incidence of BSI. Interestingly, mutant CNA protein showed lower attachment ability to collagen, suggesting that the mutant protein might contribute to the dissemination of bacteria. CONCLUSIONS: These findings suggest that the bacterial genotype affects the clinical characteristics of MRSA infection.
OBJECTIVES:Methicillin-resistant Staphylococcus aureus (MRSA) causes hospital- and community-acquired infections. It is not clear whether genetic characteristics of the bacteria contribute to disease pathogenesis in MRSA infection. We hypothesized that whole genome analysis of MRSA strains could reveal the key gene loci and/or the gene mutations that affect clinical manifestations of MRSA infection. METHODS: Whole genome sequences (WGS) of MRSA of 154 strains were analyzed with respect to clinical manifestations and data. Further, we evaluated the association between clinical manifestations in MRSA infection and genomic information. RESULTS: WGS revealed gene mutations that correlated with clinical manifestations of MRSA infection. Moreover, 12 mutations were selected as important mutations by Random Forest analysis. Cluster analysis revealed strains associated with a high frequency of bloodstream infection (BSI). Twenty seven out of 34 strains in this cluster caused BSI. These strains were all positive for collagen adhesion gene (cna) and have mutations in the locus, those were selected by Random Forest analysis. Univariate and multivariate analysis revealed that these gene mutations were the predictor for the incidence of BSI. Interestingly, mutant CNA protein showed lower attachment ability to collagen, suggesting that the mutant protein might contribute to the dissemination of bacteria. CONCLUSIONS: These findings suggest that the bacterial genotype affects the clinical characteristics of MRSA infection.