Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis
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2017-03-08
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Abstract
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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Wang, L, ER Ko, JJ Gilchrist, KJ Pittman, A Rautanen, M Pirinen, JW Thompson, LG Dubois, et al. (2017). Human genetic and metabolite variation reveals that methylthioadenosine is a prognostic biomarker and an inflammatory regulator in sepsis. Science Advances, 3(3). 10.1126/sciadv.1602096 Retrieved from https://hdl.handle.net/10161/13814.
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Scholars@Duke

Liuyang Wang
Leveraging bioinformatics and big data to understand the intricacies of human diseases.
My overall research goals are centered on unraveling the molecular mechanism underpinning human disease susceptibility and harnessing these findings to innovative diagnostic and therapeutic strategies. I have adopted a multidisciplinary approach that integrates genomics, transcriptomics, and computational biology. Leveraging high-throughput cellular screening and genome-wide association study (GWAS), we have successfully identified hundreds of genomic loci associated with 8 different pathogens (Wang et al. 2018). Utilizing single-cell RNA-seq, we developed scHi-HOST to rapidly identify host genes associated with the influenza virus (Schott and Wang, et al. 2022). I also have developed several novel statistical tools, CPAG and iCPAGdb, that estimate genetic associations among human diseases and traits (Wang et al. 2015, 2021). Combining experimental and computational approaches, I expect to gain a deeper understanding of the genetic architecture of human susceptibility to infection and inflammatory disorders.

Emily Ray Ko
Clinical and translational research, COVID-19 therapeutics, clinical biomarkers for infectious disease.

Dennis Ko
Using Pathogens to Decipher Genetic Variation Connecting Cell Biology and Disease Susceptibility
Despite improvements in public health, advancements in vaccines, and the development of many classes of antibiotics, infectious disease is still responsible for over a quarter of all deaths worldwide. However, even for the most devastating of pandemics, individuals demonstrate a large variability in the severity of infection. The long-term goal of the lab is to understand the genetic basis for differences in susceptibility to infection and related inflammatory disorders. We approach this question through a combination of experimental and computational approaches that combine high-throughput cell biology with quantitative human genetics. The identified genetic differences serve as the starting point for exploring new cell biology and human disease susceptibility genes.
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