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Kobayashi T, Andoh A. Numerical analyses of intestinal microbiota by data mining. J Clin Biochem Nutr 2018; 62:124-131. [PMID: 29610551 PMCID: PMC5874238 DOI: 10.3164/jcbn.17-84] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 09/20/2017] [Indexed: 12/27/2022] Open
Abstract
The human intestinal microbiota has a close relationship with health control and causes of diseases, and a vast number of scientific papers on this topic have been published recently. Some progress has been made in identifying the causes or species of related microbiota, and successful results of data mining are reviewed here. Humans who are targets of a disease have their own individual characteristics, including various types of noise because of their individual life style and history. The quantitatively dominant bacterial species are not always deeply connected with a target disease. Instead of conventional simple comparisons of the statistical record, here the Gini-coefficient (i.e., evaluation of the uniformity of a group) was applied to minimize the effects of various types of noise in the data. A series of results were reviewed comparatively for normal daily life, disease and technical aspects of data mining. Some representative cases (i.e., heavy smokers, Crohn’s disease, coronary artery disease and prediction accuracy of diagnosis) are discussed in detail. In conclusion, data mining is useful for general diagnostic applications with reasonable cost and reproducibility.
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Affiliation(s)
- Toshio Kobayashi
- Miyagi University, 2-2-1 Hatatate, Taihaku-ku, Sendai-Shi, Miyagi 982-0215, Japan
| | - Akira Andoh
- Department of Medicine, Shiga University of Medical Science, Seta-Tsukinowa, Otsu, Shiga 520-2192, Japan
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Emoto T, Yamashita T, Kobayashi T, Sasaki N, Hirota Y, Hayashi T, So A, Kasahara K, Yodoi K, Matsumoto T, Mizoguchi T, Ogawa W, Hirata KI. Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease. Heart Vessels 2016; 32:39-46. [PMID: 27125213 DOI: 10.1007/s00380-016-0841-y] [Citation(s) in RCA: 96] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2016] [Accepted: 04/15/2016] [Indexed: 12/15/2022]
Abstract
The association between atherosclerosis and gut microbiota has been attracting increased attention. We previously demonstrated a possible link between gut microbiota and coronary artery disease. Our aim of this study was to clarify the gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism (T-RFLP). This study included 39 coronary artery disease (CAD) patients and 30 age- and sex- matched no-CAD controls (Ctrls) with coronary risk factors. Bacterial DNA was extracted from their fecal samples and analyzed by T-RFLP and data mining analysis using the classification and regression algorithm. Five additional CAD patients were newly recruited to confirm the reliability of this analysis. Data mining analysis could divide the composition of gut microbiota into 2 characteristic nodes. The CAD group was classified into 4 CAD pattern nodes (35/39 = 90 %), while the Ctrl group was classified into 3 Ctrl pattern nodes (28/30 = 93 %). Five additional CAD samples were applied to the same dividing model, which could validate the accuracy to predict the risk of CAD by data mining analysis. We could demonstrate that operational taxonomic unit 853 (OTU853), OTU657, and OTU990 were determined important both by the data mining method and by the usual statistical comparison. We classified the gut microbiota profiles in coronary artery disease patients using data mining analysis of T-RFLP data and demonstrated the possibility that gut microbiota is a diagnostic marker of suffering from CAD.
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Affiliation(s)
- Takuo Emoto
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
| | - Tomoya Yamashita
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan.
| | | | - Naoto Sasaki
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
| | - Yushi Hirota
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Tomohiro Hayashi
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
| | - Anna So
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Kazuyuki Kasahara
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
| | - Keiko Yodoi
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
| | - Takuya Matsumoto
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
| | - Taiji Mizoguchi
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
| | - Wataru Ogawa
- Division of Diabetes and Endocrinology, Department of Internal Medicine, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Ken-Ichi Hirata
- Division of Cardiovascular Medicine, Department of Internal Medicine, Kobe University Graduate School of Medicine, 7-5-1, Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
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Kobayashi T, Osaki T, Oikawa S. Use of T-RFLP and seven restriction enzymes to compare the faecal microbiota of obese and lean Japanese healthy men. Benef Microbes 2015; 6:735-45. [PMID: 26036145 DOI: 10.3920/bm2014.0147] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
The composition of the intestinal microbiota of 92 healthy Japanese men was measured following consumption of identical meals for 3 days; terminal restriction fragment length polymorphisms were then used to analyse the DNA content of their faeces. The obtained operational taxonomic units (OTUs) were further analysed using seven restriction enzymes: 516f-BslI and -HaeIII, 27f-MspI and -AluI, and 35f-HhaI, -MspI and -AluI. Subjects were classified by their body mass index (BMI) as lean (<18.5) or obese (>25.0). OTUs were then analysed using data mining software. Pearson correlation coefficients on data mining results indicated only a weak relationship between BMI and OTU diversity. Specific OTUs attributed to lean and obese subjects were further examined by data mining with six groups of enzymes and closely related accession numbers for lean and obese subjects were successfully narrowed down. 16S rRNA sequences showed Bacillus spp., Erysipelothrix spp. and Holdemania spp. to be present among 30 bacterial candidates related to the lean group. Fifteen candidates were classified Firmicutes, one was classified as Chloroflexi, and the others were not classified. 45 Microbacteriaceae, 11 uncultured Actinobacterium, and 3 other families were present among the 119 candidate OTUs related to obesity. We conclude that the presence of Firmicutes and Actinobacteria may be related to the BMI of the subject.
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Affiliation(s)
- T Kobayashi
- 1 Miyagi University, 2-2-1 Hatadate, Taihaku-ku, Sendai City, Miyagi 982-0215, Japan.,2 RIKEN, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - T Osaki
- 3 Kyorin University, School of Medicine, 6-20-2 Shinkawa Mitaka, Tokyo 181-8611, Japan
| | - S Oikawa
- 1 Miyagi University, 2-2-1 Hatadate, Taihaku-ku, Sendai City, Miyagi 982-0215, Japan
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Kobayashi T, Fujiwara K. Technical Aspects of Nominal Partitions on Accuracy of Data Mining Classification of Intestinal Microbiota - Comparison between 7 Restriction Enzymes. BIOSCIENCE OF MICROBIOTA FOOD AND HEALTH 2014; 33:129-38. [PMID: 25032086 PMCID: PMC4098652 DOI: 10.12938/bmfh.33.129] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/09/2013] [Accepted: 02/08/2014] [Indexed: 01/06/2023]
Abstract
The application of data mining analyses (DM) is effective for the quantitative
classification of human intestinal microbiota (HIM). However, there remain various
technical problems that must be overcome. This paper deals with the number of nominal
partitions (NP) of the target dataset, which is a major technical problem. We used here
terminal restriction fragment length polymorphism data, which was obtained from the feces
of 92 Japanese men. Data comprised operational taxonomic units (OTUs) and subject smoking
and drinking habits, which were effectively classified by two NP (2-NP; Yes or No). Using
the same OTU data, 3-NP and 5-NP were examined here and results were obtained, focusing on
the accuracies of prediction, and the reliability of the selected OTUs by DM were compared
to the former 2-NP. Restriction enzymes for PCR were further affected by the accuracy and
were compared with 7 enzymes. There were subjects who possess HIM at the border zones of
partitions, and the greater the number of partitions, the lower the obtained DM accuracy.
The application of balance nodes boosted and duplicated the data, and was able to improve
accuracy. More accurate and reliable DM operations are applicable to the classification of
unknown subjects for identifying various characteristics, including disease.
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Affiliation(s)
- Toshio Kobayashi
- 1 Miyagi University, 2-2-1 Hatadate, Taihaku-ku, Sendai City, Miyagi 982-0215, Japan ; 2 Riken, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Kenji Fujiwara
- 2 Riken, 2-1 Hirosawa, Wako, Saitama 351-0198, Japan ; 3 Yokohama Rosai Hospital, JLHWO, Kozukue-cho, Kohoku-ku, Yokohama 222-0036, Japan
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KOBAYASHI T, OSAKI T, OIKAWA S. Applying Data Mining to Classify Age by Intestinal Microbiota in 92 Healthy Men Using a Combination of Several Restriction Enzymes for T-RFLP Experiments. BIOSCIENCE OF MICROBIOTA, FOOD AND HEALTH 2014; 33:65-78. [PMID: 25003020 PMCID: PMC4081184 DOI: 10.12938/bmfh.33.65] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Accepted: 11/17/2013] [Indexed: 01/30/2023]
Abstract
The composition of the intestinal microbiota was measured following consumption of identical meals for 3 days in 92 Japanese men, and terminal restriction fragment length polymorphism (T-RFLP) was used to analyze their feces. The obtained operational taxonomic units (OTUs) and the subjects' ages were classified by using Data mining (DM) software that compared these data with continuous data and for 5 partitions for age divided at 5 years intervals between the ages of 30 and 50. The DM provided Decision trees in which the selected OTUs were closely related to the ages of the subjects. DM was also used to compare the OTUs from the T-RFLP data with seven restriction enzymes (two enzymes of 516f-BslI and 516f-HaeIII, two enzymes of 27f-MspI and 27f-AluI, three enzymes of 35f-HhaI, 35f-MspI and 35f-AluI) and their various combinations. The OTUs delivered from the five enzyme-digested partitions were analyzed to classify their age clusters. For use in future DM processing, we discussed the enzymes that were effective for accurate classification. We selected two OTUs (HA624 and HA995) that were useful for classifying the subject's ages. Depending on the 16S rRNA sequences of the OTUs, Ruminicoccus obeum clones 1-4 were present in 18 of 36 bacterial candidates in the older age group-related OTU (HA624). On the other hand, Ruminicoccus obeum clones 1-33 were present in 65 of 269 candidates in the younger age group-related OUT (HA995).
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Affiliation(s)
- Toshio KOBAYASHI
- Miyagi University, 2–2–1 Hatadate, Taihaku-ku, Sendai City, Miyagi 982-0215, Japan
- RIKEN, 2–1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Takako OSAKI
- Kyorin University, School of Medicine, 6–20–2 Shinkawa Mitaka, Tokyo 181-8611, Japan
| | - Shinya OIKAWA
- Miyagi University, 2–2–1 Hatadate, Taihaku-ku, Sendai City, Miyagi 982-0215, Japan
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