Browse > Article
http://dx.doi.org/10.3839/jabc.2021.054

Differences in fecal and cecal microbiota in C57BL/6J mice fed normal and high fat diet  

Lee, Sunwoo (Faculty of Biotechnology, College of Applied Life Sciences, SARI, Jeju National University)
Vineet, Singh (Department of Applied Biosciences, Kyungpook National University)
Unno, Tatsuya (Faculty of Biotechnology, College of Applied Life Sciences, SARI, Jeju National University)
Publication Information
Journal of Applied Biological Chemistry / v.64, no.4, 2021 , pp. 399-405 More about this Journal
Abstract
A number of studies have been conducted to prevent obesity due to the worldwide increasing rate of obesity and its adverse effects on our health. Recently, a relationship between obesity and gut microbiome has been reported. Fecal and cecal microbiota are generally targeted for examining the gut microbiome during dietary interventions. There is, however, no common understanding on which microbiota and how results elucidated from the data would differ. In this study, we conducted dietary induced obesity study and compared fecal and cecal microbiota affected by dietary interventions. Normal Diet and high fat diet were fed to 6 weeks old mice for 12 weeks, and 16 S rRNA genes amplified from fecal and cecal DNA were sequenced using MiSeq. Our results show that the 𝛼-diversity showed significant differences between the dietary interventions as well as cecal and fecal microbiota. The difference in the taxonomic compositions between cecal and fecal microbiota had become clearer at the family and genus level. At the genus level, Faecalibaculum and Lactobacillus were more abundant in the cecal and fecal microbiota, respectively. In general dietary intervention studies, dietary effects are more significant than type difference. However, the microbiota analysis results should be interpreted carefully, considering both diet and samples (feces/caecum).
Keywords
Cecal microbiota; Fecal microbiota; Gut microbiota; Obesity;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Neyrinck AM, Possemiers S, Verstraete W, De Backer F, Cani PD, Delzenne NM (2012) Dietary modulation of clostridial cluster XIVa gut bacteria (Roseburia spp.) by chitin-glucan fiber improves host metabolic alterations induced by high-fat diet in mice. J Nutr Biochem 23: 51-59. doi: 10.1016/j.jnutbio.2010.10.008   DOI
2 Visconti A, Le Roy CI, Rosa F, Rossi N, Martin TC, Mohney RP, Li W, de Rinaldis E, Bell JT, Venter JC (2019) Interplay between the human gut microbiome and host metabolism. Nat Commun 10: 1-10. doi: 10.1038/s41467-019-12476-z   DOI
3 Rahim MBHA, Chilloux J, Martinez-Gili L, Neves AL, Myridakis A, Gooderham N, Dumas M-E (2019) Diet-induced metabolic changes of the human gut microbiome: importance of short-chain fatty acids, methylamines and indoles. Acta Diabetol 56: 493-500. doi: 10.1007/s00592-019-01312-x   DOI
4 Kong C, Gao R, Yan X, Huang L, Qin H (2019) Probiotics improve gut microbiota dysbiosis in obese mice fed a high-fat or high-sucrose diet. Nutrition 60: 175-184. doi: 10.1016/j.nut.2018.10.002   DOI
5 Larsen PE, Dai Y (2015) Metabolome of human gut microbiome is predictive of host dysbiosis. GigaScience 4: doi: 10.1186/s13742-015-0084-3   DOI
6 Levy M, Kolodziejczyk AA, Thaiss CA, Elinav E (2017) Dysbiosis and the immune system. Nat Rev Immunol 17: 219-232. doi: 10.1038/nri.2017.7   DOI
7 Derakhshani H, Tun HM, Khafipour E (2016) An extended single-index multiplexed 16S rRNA sequencing for microbial community analysis on MiSeq illumina platforms. J Basic Microbiol 56: 321-326. doi: 10.1002/jobm.201500420   DOI
8 Hughes HK, Rose D, Ashwood P (2018) The gut microbiota and dysbiosis in autism spectrum disorders. Curr Neurol Neurosci Rep 18: 81. doi: 10.1007/s11910-018-0887-6   DOI
9 Panasevich MR, Wankhade UD, Chintapalli SV, Shankar K, Rector RS (2018) Cecal versus fecal microbiota in Ossabaw swine and implications for obesity. Physiol. Genomics 50: 355-368. doi: 10.1152/physiolgenomics.00110.2017   DOI
10 Tremaroli V, Backhed F (2012) Functional interactions between the gut microbiota and host metabolism. Nature 489: 242-249. doi: 10.1038/nature11552   DOI
11 Wang G, Yu Y, Wang YZ, Wang JJ, Guan R, Sun Y, Shi F, Gao J, Fu XL (2019) Role of SCFAs in gut microbiome and glycolysis for colorectal cancer therapy. J Cell Physiol 234: 17023-17049. doi: 10.1002/jcp.28436   DOI
12 Tilg H, Kaser A (2011) Gut microbiome, obesity, and metabolic dysfunction. The J Clin Investig 121: 2126-2132. doi: 10.1172/JCI58109   DOI
13 Nguyen TLA, Vieira-Silva S, Liston A, Raes J (2015) How informative is the mouse for human gut microbiota research? Dis Model Mech 8: 1-16. doi: 10.1242/dmm.017400   DOI
14 Uzbay T (2019) Germ-free animal experiments in the gut microbiota studies. Curr Opin Pharmacol 49: 6-10. doi: 10.1016/j.coph.2019.03.016   DOI
15 Willis C, Desai D, LaRoche J (2019) Influence of 16S rRNA variable region on perceived diversity of marine microbial communities of the Northern North Atlantic. FEMS Microbiol Lett 366: fnz152. doi: 10.1093/femsle/fnz152   DOI
16 Stanley D, Geier MS, Chen H, Hughes RJ, Moore RJ (2015) Comparison of fecal and cecal microbiotas reveals qualitative similarities but quantitative differences. BMC Microbiol 15. doi: 10.1186/s12866-015-0388-6   DOI
17 Jeon D, Singh V, Unno T (2021) In vitro investigation of food effects on human gut microbiota. J Appl Biol Chem 64: 75-81. doi: 10.3839/jabc.2021.012   DOI
18 Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75: 7537-7541. doi: 10.1128/AEM.01541-09   DOI
19 Halfvarson J, Brislawn CJ, Lamendella R, Vazquez-Baeza Y, Walters WA, Bramer LM, D'amato M, Bonfiglio F, McDonald D, Gonzalez A, McClure E, Dunklebarger M, Knight R, Jansson J (2017) Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol 2: 17004. doi: 10.1038/nmicrobiol.2017.4.   DOI
20 David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA (2014) Diet rapidly and reproducibly alters the human gut microbiome. Nature 505: 559-563. doi: 10.1038/nature12820   DOI
21 Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2012) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41: D590-D596. doi: 10.1093/nar/gks1219   DOI
22 Peeters T, Vantrappen G (1975) The Paneth cell: a source of intestinal lysozyme. Gut 16: 553-558. doi: 10.1136/gut.16.7.553   DOI
23 Shin J, Lee S, Go M-J, Lee SY, Kim SC, Lee C-H, Cho B-K (2016) Analysis of the mouse gut microbiome using full-length 16S rRNA amplicon sequencing. Sci Rep 6: 1-10. doi: 10.1038/srep29681   DOI
24 Kozik AJ, Nakatsu CH, Chun H, Jones-Hall YL (2019) Comparison of the fecal, cecal, and mucus microbiome in male and female mice after TNBS-induced colitis. PLoS One 14: e0225079. doi: 10.1371/journal.pone.0225079   DOI
25 Tanca A, Manghina V, Fraumene C, Palomba A, Abbondio M, Deligios M, Silverman M, Uzzau S (2017) Metaproteogenomics reveals taxonomic and functional changes between cecal and fecal microbiota in mouse. Front Microbiol 8: 391. doi: 10.3389/fmicb.2017.00391   DOI
26 Arora T, Singh S, Sharma RK (2013) Probiotics: interaction with gut microbiome and antiobesity potential. Nutrition 29: 591-596. doi: 10.1016/j.nut.2012.07.017   DOI
27 Singh V, Muthuramalingam K, Kim YM, Park S, Kim SH, Lee J, Hyun C, Unno T, Cho M (2021) Synbiotic supplementation with prebiotic Schizophyllum commune derived β-(1,3/1,6)-glucan and probiotic concoction benefits gut microbiota and its associated metabolic activities. Appl Biol Chem 64: 1-10. doi: 10.1186/s13765-020-00572-4   DOI
28 Kim K-A, Gu W, Lee I-A, Joh E-H, Kim D-H (2012) High fat diet-induced gut microbiota exacerbates inflammation and obesity in mice via the TLR4 signaling pathway PLoS One 7(10): e47713. doi: 10.1371/journal.pone.0047713   DOI
29 Wang Q, Garrity GM, Tiedje JM, Cole JR (2007) Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl Environ Microbiol 73: 5261-5267. doi: 10.1128/AEM.00062-07   DOI
30 McKenzie C, Tan J, Macia L, Mackay CR (2017) The nutrition-gut microbiome-physiology axis and allergic diseases. Immunol Rev 278: 277-295. doi: 10.1111/imr.12556   DOI