Intermittent Hypoxia and Hypercapnia Reproducibly Change the Gut Microbiome and Metabolome across Rodent Model Systems

Author:

Tripathi Anupriya123,Xu Zhenjiang Zech4,Xue Jin2,Poulsen Orit2,Gonzalez Antonio2,Humphrey Gregory2,Meehan Michael J.3,Melnik Alexey V.3,Ackermann Gail2,Zhou Dan2,Malhotra Atul5,Haddad Gabriel G.267,Dorrestein Pieter C.289,Knight Rob2910

Affiliation:

1. Division of Biological Sciences, University of California—San Diego, San Diego, California, USA

2. Department of Pediatrics, University of California—San Diego, San Diego, California, USA

3. Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California—San Diego, San Diego, California, USA

4. State Key Laboratory of Food Science and Technology, Nanchang University, Nanchang, China

5. Division of Pulmonary, Critical Care and Sleep Medicine, University of California—San Diego, San Diego, California, USA

6. Department of Neuroscience, University of California—San Diego, San Diego, California, USA

7. Rady Children’s Hospital—San Diego, San Diego, California, USA

8. Collaborative Mass Spectrometry Innovation Center, University of California—San Diego, San Diego, California, USA

9. Center for Microbiome Innovation, University of California—San Diego, San Diego, California, USA

10. Department of Computer Science and Engineering, University of California—San Diego, San Diego, California, USA

Abstract

Reproducibility of microbiome research is a major topic of contemporary interest. Although it is often possible to distinguish individuals with specific diseases within a study, the differences are often inconsistent across cohorts, often due to systematic variation in analytical conditions. Here we study the same intervention in two different mouse models of cardiovascular disease (atherosclerosis) by profiling the microbiome and metabolome in stool specimens over time. We demonstrate that shared microbial and metabolic changes are involved in both models with the intervention. We then introduce a pipeline for finding similar results in other studies. This work will help find common features identified across different model systems that are most likely to apply in humans.

Publisher

American Society for Microbiology

Subject

Computer Science Applications,Genetics,Molecular Biology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology

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