Gut microbiota, inflammation, and molecular signatures of host response to infection

Wanglong Gou; Yuanqing Fu; Liang Yue; Geng-Dong Chen; Xue Cai; Menglei Shuai; Fengzhe Xu; Xiao Yi; Hao Chen; Yi Zhu; Mian-Li Xiao; Zengliang Jiang; Zelei Miao; Congmei Xiao; Bo Shen; Xiaomai Wu; Haihong Zhao; Wenhua Ling; Jun Wang; Yu-Ming Chen; Tiannan Guo; Ju-Sheng Zheng

doi:10.1016/j.jgg.2021.04.002

Volume 48 Issue 9

Sep. 2021

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Article Navigation > Journal of Genetics and Genomics > 2021 > 48(9): 792-802

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Gut microbiota, inflammation, and molecular signatures of host response to infection

doi: 10.1016/j.jgg.2021.04.002

a. College of Life Sciences, Zhejiang University, Hangzhou 310058, China;
b. Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China;
c. Center for Infectious Disease Research, Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China;
d. Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, China;
e. Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, China;
f. Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510275, China;
g. Taizhou Hospital, Wenzhou Medical University, Linhai 325035, China;
h. CAS Key Laboratory for Pathogenic Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China

Funds:

and Westlake University Supercomputer Center for assistance in data storage and computation

and all the medical staff members who are on the front line fighting against COVID-19.

This study was supported by the National Natural Science Foundation of China (82073529, 81903316, 81773416, 81972492, 21904107, 81672086), Zhejiang Ten-thousand Talents Program (2019R52039), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), the 5010 Program for Clinical Researches (2007032) of the Sun Yat-sen University, Hangzhou Agriculture and Society Advancement Program (20190101A04), and Tencent foundation (2020). We thank all the participants for their collaboration as well as Dr. C.R. Palmer for his invaluable comments to this study

Received Date: 2021-01-20
Accepted Date: 2021-04-15
Rev Recd Date: 2021-04-05
Publish Date: 2021-05-03

Abstract

Abstract

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.
- Gut microbiota,
- Proinflammatory cytokines,
- Proteomic biomarkers,
- COVID-19,
- Host infection response

These authors contributed equally to this word.

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References

Artzi, N.S., Shilo, S., Hadar, E., Rossman, H., Barbash-Hazan, S., Ben-Haroush, A., Balicer, R.D., Feldman, B., Wiznitzer, A., Segal, E., 2020. Prediction of gestational diabetes based on nationwide electronic health records. Nat. Med. 26, 71-76.

Belkaid, Y., Hand, T.W., 2014. Role of the microbiota in immunity and inflammation. Cell 157, 121-141.

Brown, A., Fernández, I.S., Gordiyenko, Y., Ramakrishnan, V., 2016. Ribosome-dependent activation of stringent control. Nature 534, 277-280.

Brown, M.V., Reader, J.S., Tzima, E., 2010. Mammalian aminoacyl-tRNA synthetases: cell signaling functions of the protein translation machinery. Vasc. Pharmacol. 52, 21-26.

Cabreiro, F., Au, C., Leung, K.-Y., Vergara-Irigaray, N., Cochemé, H.M., Noori, T., Weinkove, D., Schuster, E., Greene, N.D.E., Gems, D., 2013. Metformin retards aging in C. elegans by altering microbial folate and methionine metabolism. Cell 153, 228-239.

Cani, P.D., Jordan, B.F., 2018. Gut microbiota-mediated inflammation in obesity: a link with gastrointestinal cancer. Nat. Rev. Gastroenterol. Hepatol. 15, 671-682.

Chen, N., Zhou, M., Dong, X., Qu, J., Gong, F., Han, Y., Qiu, Y., Wang, J., Liu, Y., Wei, Y., 2020. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet (London, England) 395, 507-513.

Chong, J., Soufan, O., Li, C., Caraus, I., Li, S., Bourque, G., Wishart, D.S., Xia, J., 2018. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 46, W486-W494.

Duffy, D., 2020. Understanding immune variation for improved translational medicine. Curr. Opin. Immunol. 65, 83-88.

Gilbert, J.A., Blaser, M.J., Caporaso, J.G., Jansson, J.K., Lynch, S.V., Knight, R., 2018. Current understanding of the human microbiome. Nat. Med. 24, 392-400.

Gu, S., Chen, Yanfei, Wu, Z., Chen, Yunbo, Gao, H., Lv, L., Guo, F., Zhang, X., 2020. Alterations of the gut microbiota in patients with coronavirus disease 2019 or H1N1 influenza. Clin. Infect. Dis. 71, 2669-2678.

Harding, H.P., Zhang, Y., Zeng, H., Novoa, I., Lu, P.D., Calfon, M., Sadri, N., Yun, C., Popko, B., Paules, R., 2003. An integrated stress response regulates amino acid metabolism and resistance to oxidative stress. Mol. Cell 11, 619-633.

Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., 2020. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet (London, England) 395, 497-506.

Jiang, Y., Lü, X., Man, C., Han, L., Shan, Y., Qu, X., Liu, Y., Yang, S., Xue, Y., Zhang, Y., 2012. Lactobacillus acidophilus induces cytokine and chemokine production via NF-κB and p38 mitogen-activated protein kinase signaling pathways in intestinal epithelial cells. Clin. Vaccine Immunol. 19, 603-608.

Ke, G., Qi, M., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., Liu, T.-Y., 2017. LightGBM: a highly efficient gradient boosting decision tree. NIPS (News Physiol. Sci.) 30, 3149-3157.

Kim, Y., Sundrud, M.S., Zhou, C., Edenius, M., Zocco, D., Powers, K., Zhang, M., Mazitschek, R., Rao, A., Yeo, C.-Y., 2020. Aminoacyl-tRNA synthetase inhibition activates a pathway that branches from the canonical amino acid response in mammalian cells. Proc. Natl. Acad. Sci. U. S. A. 117, 8900-8911.

Lee, S.M.L.S.-I., 2017. A Unified Apporach to Interpreting Model Predictions, 07874 arXiv: 1705.

Lundberg, S.M., Erion, G., Chen, H., DeGrave, A., Prutkin, J.M., Nair, B., Katz, R., Himmelfarb, J., Bansal, N., Lee, S.-I., 2020. From local explanations to global understanding with explainable ai for trees. Nat. Mach. Intell. 2, 56-67.

Lundberg, Scott M., Erion, G.G., Lee, S.-I., Consistent individualized feature attribution for tree ensembles. arXiv 1802.03888.

Lundberg, Scott M., Nair, B., Vavilala, M.S., Horibe, M., Eisses, M.J., Adams, T., Liston, D.E., Low, D.K.W., Newman, S.F., 2018. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat. Biomed. Eng. 2, 749-760.

Murray, P.J., 2016. Amino acid auxotrophy as a system of immunological control nodes. Nat. Immunol. 17, 132-139.

Pedregosa, F., Varoquaux, G., Weiss, R., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., 2011. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825-2830.

Pohjavuori, E., Viljanen, M., Korpela, R., Kuitunen, M., Tiittanen, M., Vaarala, O., Savilahti, E., 2004. Lactobacillus GG effect in increasing IFN-gamma production in infants with cow's milk allergy. J. Allergy Clin. Immunol. 114, 131-136.

Ruff, W.E., Greiling, T.M., Kriegel, M.A., 2020. Host-microbiota interactions in immune-mediated diseases. Nat. Rev. Microbiol. 18, 521-538.

Shen, B., Yi, X., Sun, Y., Bi, X., Du, J., Zhang, C., Quan, S., Zhang, F., Sun, R., Qian, L., 2020. Proteomic and metabolomic characterization of COVID-19 patient sera. Cell 182, 1-14.

Vich Vila, A., Collij, V., Sanna, S., Sinha, T., Imhann, F., Bourgonje, A.R., Mujagic, Z., Jonkers, D.M.A.E., Masclee, A.A.M., Fu, J., 2020. Impact of commonly used drugs on the composition and metabolic function of the gut microbiota. Nat. Commun. 11, 362.

World Health Organization, , 2020. Coronavirus disease (COVID-19) pandemic. https://www.who.int/emergencies/diseases/novel-coronavirus-2019. (Accessed December 2020).

Yang, Y., Shen, C., Li, J., Yuan, J., Wei, J., Huang, F., Wang, F., Li, G., Li, Y., Xing, L., 2020. Plasma IP-10 and MCP-3 levels are highly associated with disease severity and predict the progression of COVID-19. J. Allergy Clin. Immunol. 146, 119-127.

Yeoh, Y.K., Zuo, T., Lui, G.C.-Y., Zhang, F., Liu, Q., Li, A.Y., Chung, A.C., Cheung, C.P., Tso, E.Y., Fung, K.S., 2021. Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. Gut 70, 698-706.

Yoshida, K., Matsumoto, T., Tateda, K., Uchida, K., Tsujimoto, S., Yamaguchi, K., 2001. Induction of interleukin-10 and down-regulation of cytokine production by Klebsiella pneumoniae capsule in mice with pulmonary infection. J. Med. Microbiol. 50, 456-461.

Zhang, S., Zeng, X., Ren, M., Mao, X., Qiao, S., 2017. Novel metabolic and physiological functions of branched chain amino acids: a review. J. Anim. Sci. Biotechnol. 23, 8-10.

Zhang, Z.-Q., He, L.-P., Liu, Y.-H., Liu, J., Su, Y.-X., Chen, Y.-M., 2014. Association between dietary intake of flavonoid and bone mineral density in middle aged and elderly Chinese women and men. Osteoporos. Int. 25, 2417-2425.

Zheng, D., Liwinski, T., Elinav, E., 2020. Interaction between microbiota and immunity in health and disease. Cell Res. 30, 492-506.

Zhou, F., Yu, T., Du, R., Fan, G., Liu, Y., Liu, Z., Xiang, J., Wang, Y., Song, B., Gu, X., 2020. Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study. Lancet (London, England) 395, 1054-1062.

Zuo, T., Zhang, F., Lui, G.C.Y., Yeoh, Y.K., Li, A.Y.L., Zhan, H., Wan, Y., Chung, A., Cheung, C.P., Chen, N., 2020. Alterations in gut microbiota of patients with COVID-19 during time of hospitalization. Gastroenterology 159, 944-955.

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