Recent application of metabolomics in the diagnosis, pathogenesis, treatment, and prognosis of sepsis

Sepsis is a life-threatening organ dysfunction caused by dysregulated host response to infections. It is a leading cause of morbidity and mortality in hospitalized patients. Patients with sepsis often require care in the intensive care unit (ICU) which is costly to the patients and their families. Sepsis has no specific clinical manifestations, and its pathophysiological mechanism is complex. The disease progresses rapidly which makes early diagnosis difficult. Severe forms of the disease, such as septic shock, may lead to organ dysfunction, organ failure, and death. As an emerging “-omics” technology, metabolomics has revolutionized the clinical and research landscape of sepsis. Metabolomics has been applied in the prognosis, diagnosis, and risk stratification in patients with sepsis. This technology provides details on the metabolites and biochemical pathways commonly associated with the pathophysiology of sepsis. At present, it is mostly used to identify metabolites in various diseases. Using this technology, metabolites in body fluids such as blood and urine are detected and analyzed in relation to disease progresssion. The technology therefore helps to understand the pathogenesis of diseases and promote early diagnosis and treatment of the disease. So far, the applicaition of metabolomics in patients with sepsis has not been well defined. This article briefly reviews the application of metabolomics technology in patients with sepsis in recent years, to generate ideas for improving rapid diagnosis and prognosis evaluation of patients with sepsis.

Sepsis; Metabolomics; NMR; GC-MS; LC-MS

[1] Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, et al. The third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). The Journal of the American Medical Association. 2016; 315: 801.

[2] Rudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: analysis for the Global Burden of Disease Study. The Lancet. 2020; 395: 200–211.

[3] Xie J, Wang H, Kang Y, Zhou L, Liu Z, Qin B, et al. The Epidemiology of Sepsis in Chinese ICUs. Critical Care Medicine. 2020; 48: e209–e218.

[4] Rinschen MM, Ivanisevic J, Giera M, Siuzdak G. Identification of bioactive metabolites using activity metabolomics. Nature Reviews Molecular Cell Biology. 2019; 20: 353–367.

[5] Oliver SG, Winson MK, Kell DB, Baganz F. Systematic functional analysis of the yeast genome. Trends in Biotechnology. 1998; 16: 373–378.

[6] Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999; 29: 1181–1189.

[7] Siddiqui MA, Pandey S, Azim A, Sinha N, Siddiqui MH. Metabolomics: an emerging potential approach to decipher critical illnesses. Biophysical Chemistry. 2020; 267: 106462.

[8] Wishart DS. Metabolomics for Investigating Physiological and Patho-physiological Processes. Physiological Reviews. 2019; 99: 1819–1875.

[9] Guijas C, Montenegro-Burke JR, Warth B, Spilker ME, Siuzdak G. Metabolomics activity screening for identifying metabolites that modulate phenotype. Nature Biotechnology. 2018; 36: 316–320.

[10] Wei R, Li G, Seymour AB. High-throughput and multiplexed LC/MS/MRM method for targeted metabolomics. Analytical Chemistry. 2010; 82: 5527–5533.

[11] Zhang H, Wang L, Hou Z, Ma H, Mamtimin B, Hasim A, et al. Metabolomic profiling reveals potential biomarkers in esophageal cancer progression using liquid chromatography-mass spectrometry platform. Biochemical and Biophysical Research Communications. 2017; 491: 119–125.

[12] Azad RK, Shulaev V. Metabolomics technology and bioinformatics for precision medicine. Briefings in Bioinformatics. 2019; 20: 1957–1971.

[13] Pinu FR, Beale DJ, Paten AM, Kouremenos K, Swarup S, Schirra HJ, et al. Systems Biology and Multi-Omics Integration: Viewpoints from the Metabolomics Research Community. Metabolites. 2019; 9: 76.

[14] Fiehn O. Metabolomics–the link between genotypes and phenotypes. Plant Molecular Biology. 2002; 48: 155–171.

[15] Ghazani AA, McDermott S, Pectasides M, Sebas M, Mino-Kenudson M, Lee H, et al. Comparison of select cancer biomarkers in human circulating and bulk tumor cells using magnetic nanoparticles and a miniaturized micro-NMR system. Nanomedicine. 2013; 9: 1009–1017.

[16] Wilson DM, Burlingame AL. Deuterium and carbon-13 tracer studies of ethanol metabolism in the rat by 2H, 1H-decoupled 13C nuclear magnetic resonance. Biochemical and Biophysical Research Communications. 1974; 56: 828–835.

[17] Euceda LR, Andersen MK, Tessem M, Moestue SA, Grinde MT, Bathen TF. NMR-Based Prostate Cancer Metabolomics. Methods in Molecular Biology. 2016; 65: 237–257.

[18] Amberg A, Riefke B, Schlotterbeck G, Ross A, Senn H, Dieterle F, et al. NMR and MS Methods for Metabolomics. Methods in Molecular Biology. 2017; 1641: 229–258.

[19] Zhang H, Li K, Zhao Y, Zhang Y, Sun J, Li S, et al. Long-term use of fluoxetine accelerates bone loss through the disruption of sphingolipids metabolism in bone marrow adipose tissue. Translational Psychiatry. 2020; 10: 138.

[20] Urpi-Sarda M, Almanza-Aguilera E, Llorach R, Vázquez-Fresno R, Estruch R, Corella D, et al. Non-targeted metabolomic biomarkers and metabotypes of type 2 diabetes: a cross-sectional study of PREDIMED trial participants. Diabetes & Metabolism. 2019; 45: 167–174.

[21] Shen D, Zhao H, Gao S, Li Y, Cheng Q, Bi C, et al. Clinical serum metabolomics study on fluoxetine hydrochloride for depression. Neuroscience Letters. 2021; 746: 135585.

[22] Li S, Chai XN, Zuo CY, Lv P, Tang Y, Tan HJ, et al. Metabolic profiling of dialysate at sensitized acupoints in knee osteoarthritis patients. Medicine. 2019; 98: e17843.

[23] Li K, Pidatala VR, Shaik R, Datta R, Ramakrishna W. Integrated Metabolomic and Proteomic Approaches Dissect the Effect of Metal-Resistant Bacteria on Maize Biomass and Copper Uptake. Environmental Science & Technology. 2014; 48: 1184–1193.

[24] Hoving LR, Heijink M, van Harmelen V, van Dijk KW, Giera M. GC-MS Analysis of Short-Chain Fatty Acids in Feces, Cecum Content, and Blood Samples. Methods in Molecular Biology. 2018; 1730: 247–256.

[25] Beale DJ, Pinu FR, Kouremenos KA, Poojary MM, Narayana VK, Boughton BA, et al. Review of recent developments in GC–MS approaches to metabolomics-based research. Metabolomics. 2018; 14: 152.

[26] Brockbals L, Kraemer T, Steuer AE. Analytical considerations for postmortem metabolomics using GC-high-resolution MS. Analytical and Bioanalytical Chemistry. 2020; 412: 6241–6255.

[27] Lavergne FD, Broeckling CD, Cockrell DM, Haley SD, Peairs FB, Jahn CE, et al. GC-MS Metabolomics to Evaluate the Composition of Plant Cuticular Waxes for Four Triticum aestivum Cultivars. International Journal of Molecular Sciences. 2018; 19: 249.

[28] Tsiropoulou S, McBride M, Padmanabhan S. Urine Metabolomics in Hypertension Research. Methods in Molecular Biology. 2017; 1527: 61–68.

[29] Fiehn O. Metabolomics by Gas Chromatography-Mass Spectrometry: Combined Targeted and Untargeted Profiling. Current Protocols in Molecular Biology. 2016; 114: 30.34.31–30.34.32.

[30] Everett JR, Loo RL, Pullen FS. Pharmacometabonomics and personalized medicine. Annals of Clinical Biochemistry. 2013; 50: 523–545.

[31] Papadimitropoulos MP, Vasilopoulou CG, Maga-Nteve C, Klapa MI. Untargeted GC-MS Metabolomics. Methods in Molecular Biology. 2018; 48: 133–147.

[32] Wang Q, Yan K, Li K, Gao L, Wang X, Liu H, et al. Targeting hip-pocampal phospholipid and tryptophan metabolism for antidepressant-like effects of albiflorin. Phytomedicine. 2021; 92: 153735.

[33] Ji J, Song L, Wang J, Yang Z, Yan H, Li T, et al. Association between urinary per- and poly-fluoroalkyl substances and COVID-19 susceptibility. Environment International. 2021; 153: 106524.

[34] Zhou B, Xiao JF, Tuli L, Ressom HW. LC-MS-based metabolomics. Molecular BioSystems. 2012; 8: 470–481.

[35] Cui S, Li L, Zhang Y, Lu J, Wang X, Song X, et al. Machine Learning Identifies Metabolic Signatures that Predict the Risk of Recurrent Angina in Remitted Patients after Percutaneous Coronary Intervention: a Multicenter Prospective Cohort Study. Advanced Science. 2021; 8: 2003893.

[36] Lippi G. Sepsis biomarkers: past, present and future. Clinical Chemistry and Laboratory Medicine. 2019; 57: 1281–1283.

[37] Li J, Li G, Jing X, Li Y, Ye Q, Jia H, et al. Assessment of clinical sepsis-associated biomarkers in a septic mouse model. The Journal of International Medical Research. 2018; 46: 2410–2422.

[38] Fabri-Faja N, Calvo-Lozano O, Dey P, Terborg RA, Estevez M, Belushkin A, et al. Early sepsis diagnosis via protein and miRNA biomarkers using a novel point-of-care photonic biosensor. Analytica Chimica Acta. 2019; 1077: 232–242.

[39] Kauppi AM, Edin A, Ziegler I, Mölling P, Sjöstedt A, Gylfe Å, et al. Metabolites in Blood for Prediction of Bacteremic Sepsis in the Emergency Room. PLoS ONE. 2016; 11: e0147670.

[40] Grauslys A, Phelan MM, Broughton C, Baines PB, Jennings R, Siner S, et al. NMR-based metabolic profiling provides diagnostic and prognostic information in critically ill children with suspected infection. Scientific Reports. 2020; 10: 20198.

[41] Anderson JR, Phelan MM, Clegg PD, Peffers MJ, Rubio-Martinez LM. Synovial Fluid Metabolites Differentiate between Septic and Nonseptic Joint Pathologies. Journal of Proteome Research. 2018; 17: 2735–2743.

[42] Lin S, Fan J, Zhu J, Zhao Y, Wang C, Zhang M, et al. Exploring plasma metabolomic changes in sepsis: a clinical matching study based on gas chromatography–mass spectrometry. Annals of Translational Medicine. 2020; 8: 1568–1568.

[43] Liu R, Li Y, Yang DX, Xu M, Yan YX, Zhou FG, et al. Serum metabolic markers and metabolic pathways in rats with metabolomic cecal ligation and puncture-induced sepsis. Journal of Biological Regulators and Homeostatic Agents. 2020; 34: 2069–2077.

[44] Hao Y, Zheng H, Wang R, Li H, Yang L, Bhandari S, et al. Maresin1 Alleviates Metabolic Dysfunction in Septic Mice: a 1H NMR-Based Metabolomics Analysis. Mediators of Inflammation. 2019; 2019: 1–11.

[45] Wang S, Xiao C, Liu C, Li J, Fang F, Lu X, et al. Identification of Biomarkers of Sepsis-Associated Acute Kidney Injury in Pediatric Patients Based on UPLC-QTOF/MS. Inflammation. 2020; 43: 629–640.

[46] Elmassry MM, Mudaliar NS, Colmer-Hamood JA, San Francisco MJ, Griswold JA, Dissanaike S, et al. New markers for sepsis caused by Pseudomonas aeruginosa during burn infection. Metabolomics. 2020; 16: 40.

[47] Lin G, McGinley JP, Drysdale SB, Pollard AJ. Epidemiology and Immune Pathogenesis of Viral Sepsis. Frontiers in Immunology. 2018; 9: 2147.

[48] Rello J, Valenzuela-Sánchez F, Ruiz-Rodriguez M, Moyano S. Sepsis: a Review of Advances in Management. Advances in Therapy. 2017; 34: 2393–2411.

[49] Pool R, Gomez H, Kellum JA. Mechanisms of Organ Dysfunction in Sepsis. Critical Care Clinics. 2018; 34: 63–80.

[50] Chousterman BG, Swirski FK, Weber GF. Cytokine storm and sepsis disease pathogenesis. Seminars in Immunopathology. 2017; 39: 517–528.

[51] van der Poll T, van de Veerdonk FL, Scicluna BP, Netea MG. The immunopathology of sepsis and potential therapeutic targets. Nature Reviews. Immunology. 2017; 17: 407–420.

[52] Iba T, Umemura Y, Watanabe E, Wada T, Hayashida K, Kushimoto S. Diagnosis of sepsis-induced disseminated intravascular coagulation and coagulopathy. Acute Medicine & Surgery. 2019; 6: 223–232.

[53] Mantzarlis K, Tsolaki V, Zakynthinos E. Role of Oxidative Stress and Mitochondrial Dysfunction in Sepsis and Potential Therapies. Oxidative Medicine and Cellular Longevity. 2017; 2017: 5985209.

[54] Haak BW, Wiersinga WJ. The role of the gut microbiota in sepsis. The Lancet. Gastroenterology & Hepatology. 2017; 2: 135–143.

[55] Zuo L, Zhou L, Xu T, Li Z, Liu L, Shi Y, et al. Antiseptic Activity of Ethnomedicinal Xuebijing Revealed by the Metabolomics Analysis Using UHPLC-Q-Orbitrap HRMS. Frontiers in Pharmacology. 2018; 9: 300.

[56] McGarrity S, Anuforo Ó, Halldórsson H, Bergmann A, Halldórsson S, Palsson S, et al. Metabolic systems analysis of LPS induced endothelial dysfunction applied to sepsis patient stratification. Scientific Reports. 2018; 8: 6811.

[57] Mogensen KM, Lasky-Su J, Rogers AJ, Baron RM, Fredenburgh LE, Rawn J, et al. Metabolites Associated with Malnutrition in the Intensive Care Unit are also Associated with 28-Day Mortality. Journal of Parenteral and Enteral Nutrition. 2017; 41: 188–197.

[58] Stewart CJ, Embleton ND, Marrs ECL, Smith DP, Fofanova T, Nelson A, et al. Longitudinal development of the gut microbiome and metabolome in preterm neonates with late onset sepsis and healthy controls. Microbiome. 2017; 5: 75.

[59] Jaurila H, Koivukangas V, Koskela M, Gäddnäs F, Myllymaa S, Kullaa A, et al. 1H NMR Based Metabolomics in Human Sepsis and Healthy Serum. Metabolites. 2020; 10: 70.

[60] Khaliq W, Großmann P, Neugebauer S, Kleyman A, Domizi R, Calcinaro S, et al. Lipid metabolic signatures deviate in sepsis survivors compared to non-survivors. Computational and Structural Biotechnology Journal. 2020; 18: 3678–3691.

[61] Zhu J, Zhang M, Han T, Wu H, Xiao Z, Lin S, et al. Exploring the Biomarkers of Sepsis-Associated Encephalopathy (SAE): Metabolomics Evidence from Gas Chromatography-Mass Spectrometry. BioMed Re-search International. 2019; 2019: 1–10.

[62] Evans CR, Karnovsky A, Puskarich MA, Michailidis G, Jones AE, Stringer KA. Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors. Journal of Proteome Research. 2019; 18: 2004–2011.

[63] Wu T, Xu F, Su C, Li H, Lv N, Liu Y, et al. Alterations in the Gut Microbiome and Cecal Metabolome During Klebsiella pneumoniae-Induced Pneumosepsis. Frontiers in Immunology. 2020; 11: 1331.

[64] Chen L, Li H, Chen Y, Yang Y. Probiotic Lactobacillus rhamnosus GG reduces mortality of septic mice by modulating gut microbiota composition and metabolic profiles. Nutrition. 2020; 78: 110863.

[65] Infantino V, Iacobazzi V, Palmieri F, Menga A. ATP-citrate lyase is essential for macrophage inflammatory response. Biochemical and Biophysical Research Communications. 2013; 440: 105–111.

[66] Nguyen HB, Rivers EP, Knoblich BP, Jacobsen G, Muzzin A, Ressler JA, et al. Early lactate clearance is associated with improved outcome in severe sepsis and septic shock. Critical Care Medicine. 2004; 32: 1637–1642.

[67] Liu LW, Shi YY, Li ZL, Zuo LH, Tang M, Jing ZW, et al. Metabolomic Insights Into the Synergistic Effect of Biapenem in Combination With Xuebijing Injection Against Sepsis. Frontiers in Pharmacology. 2020; 11: 502.

[68] Wang J, Sun Y, Teng S, Li K. Prediction of sepsis mortality using metabolite biomarkers in the blood: a meta-analysis of death-related pathways and prospective validation. BMC Medicine. 2020; 18: 83.