Crohn’s disease, an Inflammatory Bowel Disease, is a chronic inflammation of the gastrointestinal tract, attributed to many factors such as genetics, environmental factors, microbial infections, and the immune system. When mucosal irritants, such as luminal antigens and microbes disrupt the epithelial barrier, chronic inflammation occurs. Of the various ecological niches on the human body, the gut microbiome is the most complex. The interactions between the gut microbiome, its metabolome, and the host are in a dynamic state. Hence, a snapshot of the metabolome, and microbiome of the gut will elucidate some of the shifts that occur when the system is perturbed and these can be analyzed using Correlation Networks. We used an Interleukin-10 gene knock-out mouse model that spontaneously develops colitis, to study Crohn’s disease. A probiotic preparation, VSL#3, attenuates inflammation, reduces mucosal levels of pro-inflammatory cytokines and restores gut barrier function. Correlations Networks analysis of the microbiome, metabolome, and immunome is a systems biology approach that is an effective way to gain insight into the metabolome-microbiome-host, relationships and functions. Therefore, we modeled the metabolome, microbiome, and immunome to understand the effect of probiotics on IL10-gene deficient mice.
Aim: The goal of the study is to determine the effect of probiotics on (i).Metabolite-metabolite, (ii). Microbiome-microbiome, and (iii). Microbiome-cytokine correlations in IL10 gene deficient mice.
Methods: In this study, the wild-type and IL10 gene deficient mice (IL10−/−) were treated with probiotic formulation VSL#3 or control diet for 14 days and sacrificed. Two experiments were performed. In the first experiment, the liver and caecal contents metabolites were analyzed by NMR spectroscopy. In the second experiment, in addition to analyzing cytokines in the colon and ileum using electro-chemiluminescent multiplex kit, microbiome abundances for Cecal Contents and Cecal Mucosa were determined using Multi-Tag Pyrosequencing method.
Results: There is a striking reversal of liver metabolite correlations from negative in control-ILIL10−/− mice (C_IL-10) to positive in probiotic- ILIL10−/− mice (P_IL10), compared to the subtle differences in cecum metabolite correlations. Pyruvate metabolism, Glycolysis/Gluconeogenesis pathway and Krebs-Cycle in the liver, and Butanoate, Methane, Tryptophan, Phenylalanine and Tyrosine, Glycine, Serine and Threonine metabolism, and Krebs cycle, are significantly different between C_IL10 and P_IL10 correlation networks.
Microbiome-microbiome correlations network analysis shows that Bacteroidetes and Firmicutes increase in Cecal Contents, whereas, Proteobacteria, Bacteroidetes, and Firmicutes increases in Cecal Mucosa of P_IL10 mice. The interactions involving Bacteroides, Paraprevotella, Roseburia, and Robinsoniella are significant different in the Cecal Contents and Cecal Mucosa, correlations networks between C_IL10 and P_IL10.
Probiotics establish Actinobacteria, and Proteobacteria in Cecal Contents and Cecal Mucosa networks in C_IL10. Tissue differences between Cecal Contents and Cecal Mucosa microbiomes correlations networks of C_IL10 and P_IL10 indicates that Proteobacteria are absent in C_IL10 Cecal Contents, present in C_IL10 Cecal Mucosa, present in P_IL10 Cecal Contents, and absent in P_IL10 Cecal Mucosa. Significant differential correlations analysis between C_IL10 and P_IL10 indicates that Barnesiella, Butyricimonas , Parabacteroides, Paludibacter, Syntrophococcus, and Roseburia have significantly differential correlations in both Cecal Contents and Cecal Mucosa networks. Proteobacteria (Parasutterella and Ralstonia) are absent in Cecal Contents and present in Cecal Mucosa significant correlation differential networks.
Conclusion: Our results show that probiotics affect the metabolome, and microbiome correlation networks in IL10-knock-out mice. Probiotics also affect tissue specific differential microbiomes in the gut.
|Advisor:||Gillevet, Patrick M.|
|Commitee:||Kinser, Jason, Madsen, Karen, Rangwala, Huzefa|
|School:||George Mason University|
|Department:||Bioinformatics and Computational Biology|
|School Location:||United States -- Virginia|
|Source:||DAI-B 75/10(E), Dissertation Abstracts International|
|Subjects:||Microbiology, Bioinformatics, Physiology|
|Keywords:||Correlation networks, Crohn's disease, Gut microbiome, IL_10 knock-out mice, Inflammatory bowel disease, Metabolome, Probiotics|
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