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Microbiome2024; 12(1); 74; doi: 10.1186/s40168-024-01785-1

Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury.

Abstract: The equine gastrointestinal (GI) microbiome has been described in the context of various diseases. The observed changes, however, have not been linked to host function and therefore it remains unclear how specific changes in the microbiome alter cellular and molecular pathways within the GI tract. Further, non-invasive techniques to examine the host gene expression profile of the GI mucosa have been described in horses but not evaluated in response to interventions. Therefore, the objectives of our study were to (1) profile gene expression and metabolomic changes in an equine model of non-steroidal anti-inflammatory drug (NSAID)-induced intestinal inflammation and (2) apply computational data integration methods to examine host-microbiota interactions. Methods: Twenty horses were randomly assigned to 1 of 2 groups (n = 10): control (placebo paste) or NSAID (phenylbutazone 4.4 mg/kg orally once daily for 9 days). Fecal samples were collected on days 0 and 10 and analyzed with respect to microbiota (16S rDNA gene sequencing), metabolomic (untargeted metabolites), and host exfoliated cell transcriptomic (exfoliome) changes. Data were analyzed and integrated using a variety of computational techniques, and underlying regulatory mechanisms were inferred from features that were commonly identified by all computational approaches. Results: Phenylbutazone induced alterations in the microbiota, metabolome, and host transcriptome. Data integration identified correlation of specific bacterial genera with expression of several genes and metabolites that were linked to oxidative stress. Concomitant microbiota and metabolite changes resulted in the initiation of endoplasmic reticulum stress and unfolded protein response within the intestinal mucosa. Conclusions: Results of integrative analysis identified an important role for oxidative stress, and subsequent cell signaling responses, in a large animal model of GI inflammation. The computational approaches for combining non-invasive platforms for unbiased assessment of host GI responses (e.g., exfoliomics) with metabolomic and microbiota changes have broad application for the field of gastroenterology. Video Abstract.
© 2024. The Author(s).
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Publication Date: 2024-04-15 PubMed ID: 38622632PubMed Central: PMC11017594DOI: 10.1186/s40168-024-01785-1Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research investigates how changes in the gastrointestinal (GI) microbiome in horses affect the cellular and molecular activities within the GI tract, specifically in the context of intestinal inflammation induced by non-steroidal anti-inflammatory drugs (NSAIDs). The study found that NSAIDs cause alterations in the microbiome, metabolome, and host gene expression, and these changes are linked to oxidative stress and subsequent cell signalling responses.

Objective and Methods of the Study

  • The main goal of this research was to study how gene expression and metabolomic changes are affected in horses by consumption of NSAIDs, and how this impacts the host-microbiota interactions.
  • Twenty horses were randomly assigned to either a control group receiving a placebo or a second group receiving the NSAID named phenylbutazone. There were 10 horses in each group.
  • The NSAID was orally administered once daily at a dose of 4.4 mg/kg for 9 days.
  • Fecal samples were collected on the first and tenth days, and assessed for microbiota, metabolomic, and host exfoliated cell transcriptomic (exfoliome) changes.
  • The Big Data collected from these samples were analyzed and integrated using various computational techniques. The regulatory mechanisms behind the NSAID-induced changes were inferred from features commonly identified by all computational methods.

Results of the Study

  • The results showed that phenylbutazone, the NSAID used in the study, caused significant transformations in the microbiota, metabolome, and host transcriptome of the horses.
  • A thorough data integration revealed relationships between specific bacterial genera with the expression of genes and metabolites associated with oxidative stress.
  • This simultaneous change in microbiota and metabolites led to endoplasmic reticulum stress and unfolded protein response within the horse’s intestinal mucosa, which are typical cellular responses to dysfunction or damage.

Conclusions and Implications

  • Overall, through integrated analysis, the study revealed that oxidative stress and subsequent cellular signaling responses play a critical role in GI inflammation in a large animal model like horses.
  • The computational techniques used for data integration in this study, which combines exfoliomics, metabolomics, and microbiota changes, are proposed to have broad application in the field of gastroenterology.
  • This work will undoubtedly enhance our understanding of gastrointestinal diseases and help improve therapeutic strategies.

Cite This Article

APA
Whitfield-Cargile CM, Chung HC, Coleman MC, Cohen ND, Chamoun-Emanuelli AM, Ivanov I, Goldsby JS, Davidson LA, Gaynanova I, Ni Y, Chapkin RS. (2024). Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury. Microbiome, 12(1), 74. https://doi.org/10.1186/s40168-024-01785-1

Publication

Microbiome
ISSN: 2049-2618
NlmUniqueID: 101615147
Country: England
Language: English
Volume: 12
Issue: 1
Pages: 74

Researcher Affiliations

Whitfield-Cargile, C M
  • Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA. wcana@uga.edu.
Chung, H C
  • Department of Statistics, College of Arts & Sciences, Texas A&M University, College Station, TX, USA.
  • Mathematics & Statistics Department, College of Science, University of North Carolina Charlotte, Charlotte, NC, USA.
Coleman, M C
  • Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Cohen, N D
  • Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Chamoun-Emanuelli, A M
  • Department of Large Animal Clinical Sciences, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Ivanov, I
  • Department of Veterinary Physiology and Pharmacology, College of Veterinary Medicine & Biomedical Sciences, Texas A&M University, College Station, TX, USA.
Goldsby, J S
  • Program in Integrative Nutrition & Complex Diseases, College of Agriculture & Life Sciences, Texas A&M University, College Station, TX, USA.
Davidson, L A
  • Program in Integrative Nutrition & Complex Diseases, College of Agriculture & Life Sciences, Texas A&M University, College Station, TX, USA.
Gaynanova, I
  • Department of Statistics, College of Arts & Sciences, Texas A&M University, College Station, TX, USA.
Ni, Y
  • Department of Statistics, College of Arts & Sciences, Texas A&M University, College Station, TX, USA.
Chapkin, R S
  • Program in Integrative Nutrition & Complex Diseases, College of Agriculture & Life Sciences, Texas A&M University, College Station, TX, USA.

MeSH Terms

  • Animals
  • Horses / genetics
  • Microbiota
  • Intestinal Mucosa / metabolism
  • Metabolome
  • Feces / microbiology
  • Anti-Inflammatory Agents, Non-Steroidal / metabolism
  • Inflammation / metabolism
  • Phenylbutazone / metabolism
  • RNA, Ribosomal, 16S / genetics
  • RNA, Ribosomal, 16S / metabolism

Grant Funding

  • P30 ES029067 / NIEHS NIH HHS
  • R35 CA197707 / NCI NIH HHS
  • R35-CA197707 / NIH HHS
  • P30-ES029067 / NIH HHS

Conflict of Interest Statement

The authors declare no competing interests.

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