Module-based functional pathway enrichment analysis of a protein‑protein interaction network to study the effects of intestinal microbiota depletion in mice

Jia,Zhen‑Yi; Xia,Yang; Tong,Danian; Yao,Jing; Chen,Hong‑Qi; Yang,Jun

doi:10.3892/mmr.2014.2137

Module-based functional pathway enrichment analysis of a protein‑protein interaction network to study the effects of intestinal microbiota depletion in mice

Authors:
- Zhen‑Yi Jia
- Yang Xia
- Danian Tong
- Jing Yao
- Hong‑Qi Chen
- Jun Yang
View Affiliations

Affiliations: Department of General Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, P.R. China
Published online on: April 9, 2014 https://doi.org/10.3892/mmr.2014.2137
Pages: 2205-2212
Copyright: © Jia et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Complex communities of microorganisms play important roles in human health, and alterations in the intestinal microbiota may induce intestinal inflammation and numerous diseases. The purpose of this study was to identify the key genes and processes affected by depletion of the intestinal microbiota in a murine model. The Affymetrix microarray dataset GSE22648 was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified using the limma package in R. A protein‑protein interaction (PPI) network was constructed for the DEGs using the Cytoscape software, and the network was divided into several modules using the MCODE plugin. Furthermore, the modules were functionally annotated using the PiNGO plugin, and DEG‑related pathways were retrieved and analyzed using the GenMAPP software. A total of 53 DEGs were identified, of which 26 were upregulated and 27 were downregulated. The PPI network of these DEGs comprised 3 modules. The most significant module‑related DEGs were the cytochrome P450 (CYP) 4B1 isozyme gene (CYP4B1) in module 1, CYP4F14 in module 2 and the tachykinin precursor 1 gene (TAC1) in module 3. The majority of enriched pathways of module 1 and 2 were oxidation reduction pathways (metabolism of xenobiotics by CYPs) and lipid metabolism‑related pathways, including linoleic acid and arachidonic acid metabolism. The neuropeptide signaling pathway was the most significantly enriched functional pathway of module 3. In conclusion, our findings strongly suggest that intestinal microbiota depletion affects cellular metabolism and oxidation reduction pathways. In addition, this is the first time, to the best of our knowledge, that the neuropeptide signaling pathway is reported to be affected by intestinal microbiota depletion in mice. The present study provides a list of candidate genes and processes related to the interaction of microbiota with the intestinal tract.

View Figures

View References

1	Qin J, Li R, Raes J, et al: A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 464:59–65. 2010. View Article : Google Scholar : PubMed/NCBI
2	Tremaroli V and Backhed F: Functional interactions between the gut microbiota and host metabolism. Nature. 489:242–249. 2012. View Article : Google Scholar : PubMed/NCBI
3	Gerritsen J, Smidt H, Rijkers GT and de Vos WM: Intestinal microbiota in human health and disease: the impact of probiotics. Genes Nutr. 6:209–240. 2011. View Article : Google Scholar : PubMed/NCBI
4	Jernberg C, Lofmark S, Edlund C and Jansson JK: Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology. 156:3216–3223. 2010. View Article : Google Scholar : PubMed/NCBI
5	Arroyo G, Penas E, Pedrazuela A and Prestamo G: Intestinal microbiota in rats fed with tofu (soy curd) treated under high pressure. Eur Food Res Technol. 220:395–400. 2005. View Article : Google Scholar
6	Bjorksten B, Sepp E, Julge K, Voor T and Mikelsaar M: Allergy development and the intestinal microflora during the first year of life. J Allergy Clin Immunol. 108:516–520. 2001.PubMed/NCBI
7	Ohigashi S, Sudo K, Kobayashi D, et al: Changes of the intestinal microbiota, short chain fatty acids, and fecal pH in patients with colorectal cancer. Dig Dis Sci. 58:1717–1726. 2013. View Article : Google Scholar : PubMed/NCBI
8	Rawls JF: Enteric infection and inflammation alter gut microbial ecology. Cell Host Microbe. 16:73–74. 2007. View Article : Google Scholar : PubMed/NCBI
9	Human Microbiome Jumpstart Reference Strains Consortium. Nelson KE, Weinstock GM, Highlander SK, et al: A catalog of reference genomes from the human microbiome. Science. 328:994–999. 2010. View Article : Google Scholar : PubMed/NCBI
10	Reikvam DH, Erofeev A, Sandvik A, et al: Depletion of murine intestinal microbiota: effects on gut mucosa and epithelial gene expression. PLoS One. 6:e179962011. View Article : Google Scholar : PubMed/NCBI
11	Irizarry R, Gautier L and Cope L: An R package for analyses of Affymetrix oligonucleotide arrays. The Analysis of Gene Expression Data: Methods and Software. Parmigiani G, Garrett ES, Irizarry RA and Zeger SL: Springer-Verlag; Berlin: pp. 102–119. 2003, View Article : Google Scholar
12	Smyth GK: limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Gentleman R, Carey VJ, Huber W, Irizarry RA and Dudoit S: Springer; New York, NY: pp. 397–420. 2005, View Article : Google Scholar
13	Shao X, Huang B, Lee JM, Xu F and Espejo A: Bayesian method for multirate data synthesis and model calibration. AIChE J. 57:1514–1525. 2011. View Article : Google Scholar
14	Hur AB, Elisseeff A and Guyon I: Identification of co-regulation patterns by unsupervised cluster analysis of gene expression data. US Patent 8,489,531. Filed February 2, 2011; issued July 16, 2013.
15	Szklarczyk D, Franceschini A, Kuhn M, et al: The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39:D561–D568. 2011. View Article : Google Scholar : PubMed/NCBI
16	Shannon P, Markiel A, Ozier O, et al: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI
17	Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI
18	Smoot M, Ono K, Ideker T and Maere S: PiNGO: a Cytoscape plugin to find candidate genes in biological networks. Bioinformatics. 27:1030–1031. 2011. View Article : Google Scholar : PubMed/NCBI
19	Kanehisa M and Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
20	Xue J, Zhao C and Liang A: Pathway analysis of the differential expression genes of oligonucleotide microarray of airway allergic diseases using GenMAPP. Lin Chung Er Bi Yan Hou Tou Jing Wai Ke Za Zhi. 25:371–373. 2011.(In Chinese).
21	Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC and Conklin BR: GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat Genet. 31:19–20. 2002. View Article : Google Scholar : PubMed/NCBI
22	Velagapudi VR, Hezaveh R, Reigstad CS, et al: The gut microbiota modulates host energy and lipid metabolism in mice. J Lipid Res. 51:1101–1112. 2010. View Article : Google Scholar : PubMed/NCBI
23	Bäckhed F: Programming of host metabolism by the gut microbiota. Ann Nutr Metab. 58:44–52. 2011.
24	Espey MG: Role of oxygen gradients in shaping redox relationships between the human intestine and its microbiota. Free Radic Biol Med. 55:130–140. 2013. View Article : Google Scholar : PubMed/NCBI
25	Tamaki Y, Arai T, Sugimura H, et al: Association between cancer risk and drug-metabolizing enzyme gene (CYP2A6, CYP2A13, CYP4B1, SULT1A1, GSTM1, and GSTT1) polymorphisms in cases of lung cancer in Japan. Drug Metab Pharmacokinet. 26:516–522. 2011. View Article : Google Scholar : PubMed/NCBI
26	Roos PH, Belik R, Follmann W, et al: Expression of cytochrome P450 enzymes CYP1A1, CYP1B1, CYP2E1 and CYP4B1 in cultured transitional cells from specimens of the human urinary tract and from urinary sediments. Arch Toxicol. 80:45–52. 2006. View Article : Google Scholar : PubMed/NCBI
27	Jang SJ, Kang JH, Lee TS, et al: Prodrug-activating gene therapy with rabbit cytochrome P450 4B1/4-ipomeanol or 2-aminoanthracene system in glioma cells. Nucl Med Mol Imaging. 44:193–198. 2010. View Article : Google Scholar : PubMed/NCBI
28	Antonovic L: A physiological role of cytochromes P450 4Fs: mouse model (unpublished dissertation). The University of Texas. AAI3218710. 2006
29	Li N, Liu JY, Qiu H, et al: Use of metabolomic profiling in the study of arachidonic acid metabolism in cardiovascular disease. Congest Heart Fail. 17:42–46. 2011. View Article : Google Scholar : PubMed/NCBI
30	Gaullier JM, Halse J, Hoye K, et al: Supplementation with conjugated linoleic acid for 24 months is well tolerated by and reduces body fat mass in healthy, overweight humans. J Nutr. 135:778–784. 2005.PubMed/NCBI
31	Gaullier JM, Halse J, Hoye K, et al: Conjugated linoleic acid supplementation for 1 y reduces body fat mass in healthy overweight humans. Am J Clin Nutr. 79:1118–1125. 2004.PubMed/NCBI
32	Kreider RB, Ferreira MP, Greenwood M, Wilson M and Almada AL: Effects of conjugated linoleic acid supplementation during resistance training on body composition, bone density, strength, and selected hematological markers. J Strength Cond Res. 16:325–334. 2002.PubMed/NCBI
33	Graham GJ, Stevens JM, Page NM, et al: Tachykinins regulate the function of platelets. Blood. 104:1058–1065. 2004. View Article : Google Scholar : PubMed/NCBI
34	Brain SD: Sensory neuropeptides: their role in inflammation and wound healing. Immunopharmacology. 37:133–152. 1997. View Article : Google Scholar : PubMed/NCBI
35	Mayer EA: Gut feelings: the emerging biology of gut-brain communication. Nat Rev Neurosci. 12:453–466. 2011. View Article : Google Scholar : PubMed/NCBI
36	Vijay-Kumar M, Aitken JD, Carvalho FA, et al: Metabolic syndrome and altered gut microbiota in mice lacking Toll-like receptor 5. Science. 328:228–231. 2010. View Article : Google Scholar : PubMed/NCBI