16SrRNA解析を用いた細菌同定

金沢大学附属病院1. 研究目的多くの微生物検査室では、コロニー性状、栄養要求性、生化学的性状により細菌を同定する。しかし、日常検査では変異株や稀な菌などの同定困難な菌に遭遇することが珍しくない。本研究では、①16S rRNA解析を行い、同定困難であった細菌を同定すること、②解析株の表現型および菌株出現の臨床背景を検討することを目的とする。2. 研究方法日常検査において同定困難であった株を対象とした。16S rRNA解析は大楠らの方法に従い、菌株のDNA抽出(煮沸法)⇒16S rRNA領域の増幅(PCR法)⇒増幅産物の精製⇒シーケンス反応⇒DNA配列の解読⇒相同性検索(BLAST, BlBl, EzTaxon-e)により行なった。表現型はコロニー性状、生化学的性状を中心に観察した。菌株の出現背景は診療録を参照した。3. 主な研究成果(1) 16S rRNA解析結果 : Sphingomonas paucimobilisオキシダーゼ弱陽性、黄色色素産生、DNase陽性のグラム陰性桿菌。特に黄色色素産生が特徴的であった。NF18(ニッスイ)で同定可能であった。Walk away (ベックマン・コールター)では同定確率50%で本菌種と同定された。S. paucimobillusの病原性は低く日和見感染症の起因菌とされている。本症例は免疫抑制の患者で、分離材料より起因菌と推測され貴重な症例と思われた。(2) 16SrRNA解析結果 : Staphylococcus aureus本菌は免疫抑制状態である患者の口腔由来検体より検出された。カタラーゼ陰性のグラム陽性球菌で、コロニーはS. aureus様で、PSラテックス栄研、LV反応陽性であった。Walk away (ベックマン・コールター)では99.9%の確率でS. aureusと同定された。以上より、カタラーゼ陰性のS. aureusと同定された。Staphylococcus属は通常カタラーゼ陽性であり、検査ではカタラーゼ陰性株の存在を考慮すべきと思われた。研究課題/領域番号:15H00624, 研究期間(年度):201

Classification of Imbalanced Data Represented as Binary Features

Typically, classification is conducted on a dataset that consists of numerical features and target classes. For instance, a grayscale image, which is usually represented as a matrix of integers varying from 0 to 255, enables one to apply various classification algorithms to image classification tasks. However, datasets represented as binary features cannot use many standard machine learning algorithms optimally, yet their amount is not negligible. On the other hand, oversampling algorithms such as synthetic minority oversampling technique (SMOTE) and its variants are often used if the dataset for classification is imbalanced. However, since SMOTE and its variants synthesize new minority samples based on the original samples, the diversity of the samples synthesized from binary features is highly limited due to the poor representation of original features. To solve this problem, a preprocessing approach is studied. By converting binary features into numerical ones using feature extraction methods, succeeding oversampling methods can fully display their potential in improving the classifiers’ performances. Through comprehensive experiments using benchmark datasets and real medical datasets, it was observed that a converted dataset consisting of numerical features is better for oversampling methods (maximum improvements of accuracy and F1-score were 35.11% and 42.17%, respectively). In addition, it is confirmed that feature extraction and oversampling synergistically contribute to the improvement of classification performance

Collagen adhesion gene is associated with blood stream infections caused by methicillin-resistant Staphylococcus aureus

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. (c) 2019 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases