Identification of Heavy Smokers through Their Intestinal Microbiota by Data Mining Analysis
The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units and smoking habits of subjects were ana...
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| Published in | Bioscience of Microbiota, Food and Health Vol. 32; no. 2; pp. 77 - 80 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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Japan
BMFH Press
01.01.2013
Bioscience of Microbiota, Food and Health |
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| Abstract | The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units and smoking habits of subjects were analyzed by a data mining software. The constructed decision tree was able to identify explicitly the groups of smokers and nonsmokers. In particular, 4 smokers, who smoked 20 cigarettes/day, i.e., heavy smokers, were gathered in the same group of the decision tree and were clearly identified. Related operational taxonomic unit were traced to understand the species of bacteria, but all were found to be uncultured bacteria. |
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| AbstractList | The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units and smoking habits of subjects were analyzed by a data mining software. The constructed decision tree was able to identify explicitly the groups of smokers and nonsmokers. In particular, 4 smokers, who smoked 20 cigarettes/day, i.e., heavy smokers, were gathered in the same group of the decision tree and were clearly identified. Related operational taxonomic unit were traced to understand the species of bacteria, but all were found to be uncultured bacteria. The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units and smoking habits of subjects were analyzed by a data mining software. The constructed decision tree was able to identify explicitly the groups of smokers and nonsmokers. In particular, 4 smokers, who smoked 20 cigarettes/day, i.e., heavy smokers, were gathered in the same group of the decision tree and were clearly identified. Related operational taxonomic unit were traced to understand the species of bacteria, but all were found to be uncultured bacteria. The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units and smoking habits of subjects were analyzed by a data mining software. The constructed decision tree was able to identify explicitly the groups of smokers and nonsmokers. In particular, 4 smokers, who smoked 20 cigarettes/day, i.e., heavy smokers, were gathered in the same group of the decision tree and were clearly identified. Related operational taxonomic unit were traced to understand the species of bacteria, but all were found to be uncultured bacteria.The intestinal microbiota compositions of 92 Japanese men were identified following consumption of identical meals for 3 days, and collected feces were analyzed through terminal restriction fragment length polymorphism. The obtained operational taxonomic units and smoking habits of subjects were analyzed by a data mining software. The constructed decision tree was able to identify explicitly the groups of smokers and nonsmokers. In particular, 4 smokers, who smoked 20 cigarettes/day, i.e., heavy smokers, were gathered in the same group of the decision tree and were clearly identified. Related operational taxonomic unit were traced to understand the species of bacteria, but all were found to be uncultured bacteria. |
| Author | KOBAYASHI, Toshio FUJIWARA, Kenji |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24936365$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1111/j.1348-0421.2009.00140.x 10.1111/j.1399-302X.1998.tb00746.x 10.1128/AEM.70.1.167-173.2004 10.1128/AEM.69.2.1251-1262.2003 10.12938/bifidus.25.99 10.1152/physrev.00045.2009 |
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| Keywords | data mining analysis smoking habit decision tree operational taxonomic unit heavy smoker human intestinal microbiota restriction enzyme |
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| References | 2. Sato T, Sato M, Matsuyama J, Kalfas S, Sundqvist G, Hoshino E. 1998. Restriction fragment length polymorphism analysis of 16S rDNA from oral asaccharolytic Eubacterium species amplified by polymerase chain reaction. Oral Microbiol Immunol 13: 23–29. 4. Nagashima K, Hisada T, Sato M, Mochizuki J. 2003. Application of new primer-enzyme combinations to terminal restriction fragment length polymorphism profiling of bacterial populations in human feces. Appl Environ Microbiol 69: 1251–1262. 8. Sekirov I, Russell SL, Antunes LC, Finlay BB. 2010. Gut microbiota in health and disease. Physiol Rev 90: 859–904. 9. Santacruz A, Collado MC, GarciaValdes L, Segura MT, MartinLagos JA, Anjos T, MartiRomero M, Lopez RM, Florido J, Campoy C, San Y. 2010. Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br J Nutr 104: 83–92. 7. http://mica.ibest.uidaho.edu/pat.php. 3. Matsuki T, Watanabe K, Fujimoto J, Kado Y, Takada T, Matsumoto K, Tanaka R. 2004. Quantitative PCR with 16S rRNA-gene-targeted species-specific primers for analysis of human intestinal bifidobacteria. Appl Environ Microbiol 70: 167–173. 1. Jin J, Toyama M, Kibe R, Tanaka Y, Benno Y, Kobayashi T, Shimakawa M, Maruo T, Toda T, Matsuda I, Tagami H, Matsumoto M, Seo G, Sato N, Chounan O, Benno Y. Analysis of the human intestinal microbiota from 92 volunteers after ingestion of identical meals. Benef Microbes 5. Nagashima K, Mochizuki J, Hisada T, Suzuki S, Shimomura K. 2006. Phylogenetic analysis of 16S ribosomal RNA gene sequences from human fecal microbiota and improved utility of terminal restriction fragment length polymorphism profiling. Biosci Microflora 25: 99–107. 6. Matsumoto M, Sakamoto M, Benno Y. 2009. Dynamics of fecal microbiota in hospitalized elderly fed probiotic LKM512 yogurt. Microbiol Immunol 53: 421–432. 1 2 3 4 5 6 7 8 9 20205964 - Br J Nutr. 2010 Jul;104(1):83-92 20664075 - Physiol Rev. 2010 Jul;90(3):859-904 14711639 - Appl Environ Microbiol. 2004 Jan;70(1):167-73 9573818 - Oral Microbiol Immunol. 1998 Feb;13(1):23-9 23271065 - Benef Microbes. 2013 Jun 1;4(2):187-93 19659926 - Microbiol Immunol. 2009 Aug;53(8):421-32 12571054 - Appl Environ Microbiol. 2003 Feb;69(2):1251-62 |
| References_xml | – reference: 8. Sekirov I, Russell SL, Antunes LC, Finlay BB. 2010. Gut microbiota in health and disease. Physiol Rev 90: 859–904. – reference: 4. Nagashima K, Hisada T, Sato M, Mochizuki J. 2003. Application of new primer-enzyme combinations to terminal restriction fragment length polymorphism profiling of bacterial populations in human feces. Appl Environ Microbiol 69: 1251–1262. – reference: 1. Jin J, Toyama M, Kibe R, Tanaka Y, Benno Y, Kobayashi T, Shimakawa M, Maruo T, Toda T, Matsuda I, Tagami H, Matsumoto M, Seo G, Sato N, Chounan O, Benno Y. Analysis of the human intestinal microbiota from 92 volunteers after ingestion of identical meals. Benef Microbes – reference: 2. Sato T, Sato M, Matsuyama J, Kalfas S, Sundqvist G, Hoshino E. 1998. Restriction fragment length polymorphism analysis of 16S rDNA from oral asaccharolytic Eubacterium species amplified by polymerase chain reaction. Oral Microbiol Immunol 13: 23–29. – reference: 3. Matsuki T, Watanabe K, Fujimoto J, Kado Y, Takada T, Matsumoto K, Tanaka R. 2004. Quantitative PCR with 16S rRNA-gene-targeted species-specific primers for analysis of human intestinal bifidobacteria. Appl Environ Microbiol 70: 167–173. – reference: 6. Matsumoto M, Sakamoto M, Benno Y. 2009. Dynamics of fecal microbiota in hospitalized elderly fed probiotic LKM512 yogurt. Microbiol Immunol 53: 421–432. – reference: 5. Nagashima K, Mochizuki J, Hisada T, Suzuki S, Shimomura K. 2006. Phylogenetic analysis of 16S ribosomal RNA gene sequences from human fecal microbiota and improved utility of terminal restriction fragment length polymorphism profiling. Biosci Microflora 25: 99–107. – reference: 7. http://mica.ibest.uidaho.edu/pat.php. – reference: 9. Santacruz A, Collado MC, GarciaValdes L, Segura MT, MartinLagos JA, Anjos T, MartiRomero M, Lopez RM, Florido J, Campoy C, San Y. 2010. Gut microbiota composition is associated with body weight, weight gain and biochemical parameters in pregnant women. Br J Nutr 104: 83–92. – ident: 6 doi: 10.1111/j.1348-0421.2009.00140.x – ident: 2 doi: 10.1111/j.1399-302X.1998.tb00746.x – ident: 3 doi: 10.1128/AEM.70.1.167-173.2004 – ident: 1 – ident: 4 doi: 10.1128/AEM.69.2.1251-1262.2003 – ident: 5 doi: 10.12938/bifidus.25.99 – ident: 9 – ident: 7 – ident: 8 doi: 10.1152/physrev.00045.2009 – reference: 20664075 - Physiol Rev. 2010 Jul;90(3):859-904 – reference: 14711639 - Appl Environ Microbiol. 2004 Jan;70(1):167-73 – reference: 9573818 - Oral Microbiol Immunol. 1998 Feb;13(1):23-9 – reference: 23271065 - Benef Microbes. 2013 Jun 1;4(2):187-93 – reference: 20205964 - Br J Nutr. 2010 Jul;104(1):83-92 – reference: 19659926 - Microbiol Immunol. 2009 Aug;53(8):421-32 – reference: 12571054 - Appl Environ Microbiol. 2003 Feb;69(2):1251-62 |
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| SubjectTerms | data mining analysis decision tree heavy smoker human intestinal microbiota operational taxonomic unit restriction enzyme smoking habit |
| Title | Identification of Heavy Smokers through Their Intestinal Microbiota by Data Mining Analysis |
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