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Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis.

Publication ,  Journal Article
Wang, L; Ko, ER; Gilchrist, JJ; Pittman, KJ; Rautanen, A; Pirinen, M; Thompson, JW; Dubois, LG; Langley, RJ; Jaslow, SL; Salinas, RE; Mturi, N ...
Published in: Sci Adv
March 2017

Sepsis is a deleterious inflammatory response to infection with high mortality. Reliable sepsis biomarkers could improve diagnosis, prognosis, and treatment. Integration of human genetics, patient metabolite and cytokine measurements, and testing in a mouse model demonstrate that the methionine salvage pathway is a regulator of sepsis that can accurately predict prognosis in patients. Pathway-based genome-wide association analysis of nontyphoidal Salmonella bacteremia showed a strong enrichment for single-nucleotide polymorphisms near the components of the methionine salvage pathway. Measurement of the pathway's substrate, methylthioadenosine (MTA), in two cohorts of sepsis patients demonstrated increased plasma MTA in nonsurvivors. Plasma MTA was correlated with levels of inflammatory cytokines, indicating that elevated MTA marks a subset of patients with excessive inflammation. A machine-learning model combining MTA and other variables yielded approximately 80% accuracy (area under the curve) in predicting death. Furthermore, mice infected with Salmonella had prolonged survival when MTA was administered before infection, suggesting that manipulating MTA levels could regulate the severity of the inflammatory response. Our results demonstrate how combining genetic data, biomolecule measurements, and animal models can shape our understanding of disease and lead to new biomarkers for patient stratification and potential therapeutic targeting.

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Published In

Sci Adv

DOI

EISSN

2375-2548

Publication Date

March 2017

Volume

3

Issue

3

Start / End Page

e1602096

Location

United States

Related Subject Headings

  • Sepsis
  • Salmonella Infections
  • Salmonella
  • Polymorphism, Single Nucleotide
  • Models, Biological
  • Male
  • Machine Learning
  • Humans
  • Human Genetics
  • Genome-Wide Association Study
 

Citation

APA
Chicago
ICMJE
MLA
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Wang, L., Ko, E. R., Gilchrist, J. J., Pittman, K. J., Rautanen, A., Pirinen, M., … Ko, D. C. (2017). Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis. Sci Adv, 3(3), e1602096. https://doi.org/10.1126/sciadv.1602096
Wang, Liuyang, Emily R. Ko, James J. Gilchrist, Kelly J. Pittman, Anna Rautanen, Matti Pirinen, J Will Thompson, et al. “Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis.Sci Adv 3, no. 3 (March 2017): e1602096. https://doi.org/10.1126/sciadv.1602096.
Wang L, Ko ER, Gilchrist JJ, Pittman KJ, Rautanen A, Pirinen M, et al. Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis. Sci Adv. 2017 Mar;3(3):e1602096.
Wang, Liuyang, et al. “Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis.Sci Adv, vol. 3, no. 3, Mar. 2017, p. e1602096. Pubmed, doi:10.1126/sciadv.1602096.
Wang L, Ko ER, Gilchrist JJ, Pittman KJ, Rautanen A, Pirinen M, Thompson JW, Dubois LG, Langley RJ, Jaslow SL, Salinas RE, Rouse DC, Moseley MA, Mwarumba S, Njuguna P, Mturi N, Wellcome Trust Case Control Consortium 2, Kenyan Bacteraemia Study Group, Williams TN, Scott JAG, Hill AVS, Woods CW, Ginsburg GS, Tsalik EL, Ko DC. Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis. Sci Adv. 2017 Mar;3(3):e1602096.

Published In

Sci Adv

DOI

EISSN

2375-2548

Publication Date

March 2017

Volume

3

Issue

3

Start / End Page

e1602096

Location

United States

Related Subject Headings

  • Sepsis
  • Salmonella Infections
  • Salmonella
  • Polymorphism, Single Nucleotide
  • Models, Biological
  • Male
  • Machine Learning
  • Humans
  • Human Genetics
  • Genome-Wide Association Study