Main Article Content
Abstract
The microbiome is the genetic material of all microbes, viz. bacteria, fungi, parasites, and viruses that live within the human digestive tract; the human gut microbiome co-evolves with its host for thousands of years. Thus, these microorganisms can evaluate any host's health status, including metabolic diseases such as DM2. In this study, thirty-six stool specimens were collected from participant patients (20) and control (16) who attended Alemara laboratory in Misan governorate. The investigation period was from September 2021 to February 2022. The results showed that many types of bacteria in the human intestine belong to the phyla of Firmicutes, Bacteroidetes, Verrucomicrobia, Proteobacter, Lentisphaerae, Elusimicrobia, and Tenericutes species. Our findings also showed no significant differences in the microbiome between diabetes mellitus type 2 and controls (P=0.099) using different bioinformatics approaches. The Verrucomicrobia (2.9%), Proteobacteria (12.70%) and Fusobacteria (0.47%) display the highest percentages in diabetes mellitus type 2 compared to the control group (0.5, 9.06 and 0%), respectively. The Firmicutes (36.78%), Bacteroidetes (44.89%), Tenericutes (0.195%), and Actinobacteria (0.34%) revealed the lowest percentages in diabetes mellitus type 2 compared with the control group (39.9, 47.6, 1.7 and 0.48%), respectively.
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The authors transfer the copyrights of their papers to the Iranian Society of Ichthyology. However, the information could be used in accordance with the Creative Commons licence (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
References
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- Moffatt, M.F. & Cookson, W.O. 2017. The lung microbiome in health and disease. Clinical Medicine 17(6): 525.
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- Willis, A.D. 2019. Rarefaction, alpha diversity, and statistics. Frontiers in Microbiology 10: 2407.
- Woese, C.R. & Fox, G.E. 1977. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proceedings of the National Academy of Sciences 74(11): 5088-5090
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References
Andermann, T.; Antonelli, A.; Barrett, R.L. & Silvestro, D. 2022. Estimating alpha, beta, and gamma diversity through deep learning. Frontiers in Plant Science 13.
Barlow, G.M.; Yu, A.; Mathur R. 2015. Role of the gut microbiome in obesity and diabetes mellitus. Nutrition in Clinical Practice 30(6): 787-797.
Casey, G.; Conti, D.; Haile, R. & Duggan, D. 2013. Next generation sequencing and a new era of medicine. Gut 62(6): 920-932.
Chakraborty, S.; Helb, D.; Burday, M.; Connell, N. & Alland, D. 2007. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. Journal of Microbiological Methods 69(2): 330-339.
De Filippis, F.; La Storia, A.; Villani, F. & Ercolini, D. 2013. Exploring the sources of bacterial spoilers in beefsteaks by culture-independent high-throughput sequencing. PloS One 8(7): e70222.
Fox, G.E.; Magrum, L.J.; Balch, W.E.; Wolfe, R.S. & Woese, C.R. (1977). Classification of methanogenic bacteria by 16S ribosomal RNA characterization. Proceedings of the National Academy of Sciences 74(10): 4537-4541
Fu, L., Niu, B.; Zhu, Z.; Wu, S. & Li, W. 2012. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28(23): 3150-3152.
Fujio-Vejar, S.; Vasquez, Y.; Morales, P.; Magne, F.; Vera-Wolf, P. Ugalde, J.A. & Gotteland, M. 2017. The gut microbiota of healthy llumin subjects reveals a high abundance of the phylum verrucomicrobia. Frontiers in Microbiology 8: 1221.
Goswami, K. & Sanan-Mishra, N. 2022. RNA-Seq for revealing the function of the transcriptome. Bioinformatics: Methods and Application pp: 105-129.
Gurung, M.; Li, Z.; You, H.; Rodrigues, R.; Jump, D.B.; Morgun, A. & Shulzhenko, N. 2020. Role of gut microbiota in type 2 diabetes pathophysiology. EBioMedicine 51: 102590.
Han, J.L. & Lin, H.L. 2014. Intestinal microbiota and type 2 diabetes: from mechanism insights to therapeutic perspective. World Journal of Gastroenterology 20(47): 17737.
Harris, K.; Kassis, A.; Major, G. & Chou, C.J. 2012. Is the gut microbiota a new factor contributing to obesity and its metabolic disorders?. Journal of Obesity 2012
Johnson, J.S.; Spakowicz, D.J.; Hong, B.Y.; Petersen, L.M.; Demkowicz, P.; Chen, L. & Weinstock, G. M. 2019. Evaluation of 16S rRNA gene sequencing for species and strain-level microbiome analysis. Nature Communications 10(1): 1-11.
Jones, E.W.; Carlson, J.M.; Sivak, D.A. & Ludington, W.B. 2022. Stochastic microbiome assembly depends on context. Proceedings of the National Academy of Sciences 119(7): e2115877119.
Karlsson, F.H.; Tremaroli, V.; Nookaew, I.; Bergström, G.; Behre, C.J.; Fagerberg, B.; Nielsen, J. & Bäckhed, F. 2013. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498(7452): 99-103
Knight, R.; Vrbanac, A.; Taylor, B.C.; Aksenov, A.; Callewaert, C.; Debelius, J. & Dorrestein, P.C. 2018. Best practices for analysing microbiomes. Nature Reviews Microbiology 16(7): 410-422.
Lapidus, A.L.2009.Genome sequence Databases Sequenceing and Assembly,Joint Genome Institute,Walnut Creek,C A,U S A. published by Elsevier Inc. pp:196-210.
Larsen, N.; Vogensen, F.K.; Van Den Berg, F.W.; Nielsen, D.S.; Andreasen, A.S.; Pedersen, B.K. & Jakobsen, M. 2010. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PloS One 5(2): e9085.
Lederberg, J. & McCray, A.T. 2001. Ome SweetOmics-A genealogical treasury of words. The Scientist 15(7): 8.
Ley, R.E.; Bäckhed, F.; Turnbaugh, P.; Lozupone, C.A.; Knight, R.D. & Gordon, J.I. 2005. Obesity alters gut microbial ecology. Proceedings of the National Academy of Sciences 102(31): 11070-11075.
Lin, H. & Peddada, S.D. 2020. Analysis of microbial compositions: a review of normalization and differential abundance analysis. NPJ Biofilms and Microbiomes 6(1): 1-13.
Lv, Y.; Zhao, X.; Guo, W.; Gao, Y.; Yang, S.; Li, Z. & Wang, G. 2018. The relationship between frequently used glucose-lowering agents and gut microbiota in type 2 diabetes mellitus. Journal of Diabetes Research 2018.
Masella, A.P.; Bartram, A.K.; Truszkowski, J.M.; Brown, D.G. & Neufeld, J.D. 2012. PANDAseq: paired-end assembler for llumine sequences. BMC bioinformatics, 13(1), 1-7.
Metzker, M.L. 2010. Sequencing technologies-the next generation. Nature Reviews Genetics 11(1): 31-46
Miyoshi, T.; Iwatsuki, T. & Naganuma, T. 2005. Phylogenetic characterization of 16S rRNA gene clones from deep-groundwater microorganisms that pass through 0.2 micrometer-pore-size filters. Applied and Environmental Microbiology 71(2): 1084-1088.
Moffatt, M.F. & Cookson, W.O. 2017. The lung microbiome in health and disease. Clinical Medicine 17(6): 525.
Nookaew, K.F.T.V. 2013. I Bergström G Behre CJ Fagerberg B Nielsen J Bäckhed F. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498: 99-103. .
Proal, A.D.; Albert, P.J. & Marshall, T.G. 2014. Inflammatory disease and the human microbiome. Discovery Medicine 17: 257-265
Qin, J.; Li, Y.; Cai, Z.; Li, S.; Zhu, J.; Zhang, F. & Wang, J. 2012. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature, 490(7418): 55-60.
Rojo, C. 2021. Community assembly: perspectives from phytoplankton’s studies. Hydrobiologia 848(1): 31-52.
Schwiertz, A.; Taras, D.; Schäfer, K.; Beijer, S.; Bos, N.A.; Donus, C. & Hardt, P.D. 2010. Microbiota and SCFA in lean and overweight healthy subjects. Obesity, 18(1), 190-195.
Turnbaugh, P.J.; Ley, R.E.; Mahowald, M.A.; Magrini, V.; Mardis, E.R. & Gordon, J.I. 2006. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444(7122): 1027-1031.
Van Voorhis, M.; Knopp, S.; Julliard, W.; Fechner, J.H.; Zhang, X.; Schauer, J.J. & Mezrich, J.D. 2013. Exposure to atmospheric particulate matter enhances Th17 polarization through the aryl hydrocarbon receptor. PloS One 8(12): e82545
Willis, A.D. 2019. Rarefaction, alpha diversity, and statistics. Frontiers in Microbiology 10: 2407.
Woese, C.R. & Fox, G.E. 1977. Phylogenetic structure of the prokaryotic domain: the primary kingdoms. Proceedings of the National Academy of Sciences 74(11): 5088-5090
Xia, Y.; Sun, J. & Chen, D.G. 2018. Introductory overview of statistical analysis of microbiome data. Statistical Analysis of Microbiome Data with R. pp: 43-75.
Zhang, Y.; Xu, J.; Wang, X.; Ren, X. & Liu, Y. 2019. Changes of Intestinal Bacterial Microbiota in Coronary Heart Disease Complicated With Nonalcoholic Fatty Liver Disease. BMC Genomics 20(1): 862.