Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of “-omics” technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i.e., the study of the metabolome), an “-omics” technology further down in the “-omics” cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software.

Keywords:
Commons arrangements, Free/libre open source software, Metabolomics, Public health, Sepsis
1.
Shankar-Hari M, Phillips GS, Levy ML, Seymour CW, Liu VX, Deutschman CS, Angus DC, Rubenfeld GD, Singer M; Sepsis Definitions Task Force: Developing a new definition and assessing new clinical criteria for septic shock: for the Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016; 315: 775–787.
2.
Angus DC, Seymour CW, Coopersmith CM, Deutschman CS, Klompas M, Levy MM, Martin GS, Osborn TM, Rhee C, Watson RS: A framework for the development and interpretation of different sepsis definitions and clinical criteria. Crit Care Med 2016; 44:e113–e121.
3.
Scott MC: Defining and diagnosing sepsis. Emerg Med Clin North Am 2017; 35: 1–9.
4.
Hotchkiss RS, Moldawer LL, Opal SM, Reinhart K, Turnbull IR, Vincent JL: Sepsis and septic shock. Nat Rev Dis Primers 2016; 2: 16045.
5.
Rivers EP, Coba V, Visbal A, Whitmill M, Amponsah D: Management of sepsis: early resuscitation. Clin Chest Med 2008; 29: 689–704.
6.
Arefian H, Heublein S, Scherag A, Brunkhorst FM, Younis MZ, Moerer O, Fischer D, Hartmann M: Hospital-related cost of sepsis: a systematic review. J Infect 2017; 74: 107–117.
7.
Prkno A, Wacker C, Brunkhorst FM, Schlattmann P: Procalcitonin-guided therapy in intensive care unit patients with severe sepsis and septic shock – a systematic review and meta-analysis. Crit Care 2013; 17:R291.
8.
Tiru B, DiNino EK, Orenstein A, Mailloux PT, Pesaturo A, Gupta A, McGee WT: The economic and humanistic burden of severe sepsis. Pharmacoeconomics 2015; 33: 925–937.
9.
Brand A, Brand H, Schulte in den Bäumen T: The impact of genetics and genomics on public health. Eur J Hum Genet 2008; 16: 5–13.
10.
Brand A, Evangelatos N, Satyamoorthy K: Public health genomics: the essential part for good governance in public health. Int J Public Health 2016; 61: 401–403.
11.
Christaki E, Giamarellos-Bourboulis EJ: The beginning of personalized medicine in sepsis: small steps to a bright future. Clin Genet 2014; 86: 56–61.
12.
Green ED, Watson JD, Collins FS: Human Genome Project: twenty-five years of big biology. Nature 2015; 526: 29–31.
13.
International HapMap Consortium: The International HapMap Project. Nature 2003; 426: 789–796.
14.
Franke A, McGovern DP, Barrett JC, Wang K, Radford-Smith GL, Ahmad T, Lees CW, Balschun T, Lee J, Roberts R, Anderson CA, Bis JC, Bumpstead S, Ellinghaus D, Festen EM, Georges M, Green T, Haritunians T, Jostins L, Latiano A, Mathew CG, Montgomery GW, Prescott NJ, Raychaudhuri S, Rotter JI, Schumm P, Sharma Y, Simms LA, Taylor KD, Whiteman D, Wijmenga C, Baldassano RN, Barclay M, Bayless TM, Brand S, Büning C, Cohen A, Colombel JF, Cottone M, Stronati L, Denson T, De Vos M, D’Inca R, Dubinsky M, Edwards C, Florin T, Franchimont D, Gearry R, Glas J, Van Gossum A, Guthery SL, Halfvarson J, Verspaget HW, Hugot JP, Karban A, Laukens D, Lawrance I, Lemann M, Levine A, Libioulle C, Louis E, Mowat C, Newman W, Panés J, Phillips A, Proctor DD, Regueiro M, Russell R, Rutgeerts P, Sanderson J, Sans M, Seibold F, Steinhart AH, Stokkers PC, Torkvist L, Kullak-Ublick G, Wilson D, Walters T, Targan SR, Brant SR, Rioux JD, D’Amato M, Weersma RK, Kugathasan S, Griffiths AM, Mansfield JC, Vermeire S, Duerr RH, Silverberg MS, Satsangi J, Schreiber S, Cho JH, Annese V, Hakonarson H, Daly MJ, Parkes M: Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci. Nat Genet 2010; 42: 1118–1125.
15.
Yamazaki K, Umeno J, Takahashi A, Hirano A, Johnson TA, Kumasaka N, Morizono T, Hosono N, Kawaguchi T, Takazoe M, Yamada T, Suzuki Y, Tanaka H, Motoya S, Hosokawa M, Arimura Y, Shinomura Y, Matsui T, Matsumoto T, Iida M, Tsunoda T, Nakamura Y, Kamatani N, Kubo M: A genome-wide association study identifies 2 susceptibility loci for Crohn’s disease in a Japanese population. Gastroenterology 2013; 144: 781–788.
16.
Ellinghaus D, Ellinghaus E, Nair RP, Stuart PE, Esko T, Metspalu A, Debrus S, Raelson JV, Tejasvi T, Belouchi M, West SL, Barker JN, Kõks S, Kingo K, Balschun T, Palmieri O, Annese V, Gieger C, Wichmann HE, Kabesch M, Trembath RC, Mathew CG, Abecasis GR, Weidinger S, Nikolaus S, Schreiber S, Elder JT, Weichenthal M, Nothnagel M, Franke A: Combined analysis of genome-wide association studies for Crohn disease and psoriasis identifies seven shared susceptibility loci. Am J Hum Genet 2012; 90: 636–647.
17.
Festen EA, Goyette P, Green T, Boucher G, Beauchamp C, Trynka G, Dubois PC, Lagacé C, Stokkers PC, Hommes DW, Barisani D, Palmieri O, Annese V, van Heel DA, Weersma RK, Daly MJ, Wijmenga C, Rioux JD: A meta-analysis of genome-wide association scans identifies IL18RAP, PTPN2, TAGAP, and PUS10 as shared risk loci for Crohn’s disease and celiac disease. PLoS Genet 2011; 7:e1001283.
18.
Zuo X, Sun L, Yin X, Gao J, Sheng Y, Xu J, Zhang J, He C, Qiu Y, Wen G, Tian H, Zheng X, Liu S, Wang W, Li W, Cheng Y, Liu L, Chang Y, Wang Z, Li Z, Li L, Wu J, Fang L, Shen C, Zhou F, Liang B, Chen G, Li H, Cui Y, Xu A, Yang X, Hao F, Xu L, Fan X, Li Y, Wu R, Wang X, Liu X, Zheng M, Song S, Ji B, Fang H, Yu J, Sun Y, Hui Y, Zhang F, Yang R, Yang S, Zhang X: Whole-exome SNP array identifies 15 new susceptibility loci for psoriasis. Nat Commun 2015; 6: 6793.
19.
Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M: A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 2007; 316: 1341–1345.
20.
Mega JL, Close SL, Wiviott SD, Shen L, Hockett RD, Brandt JT, Walker JR, Antman EM, Macias W, Braunwald E, Sabatine MS: Cytochrome p-450 polymorphisms and response to clopidogrel. N Engl J Med 2009; 360: 354–362.
21.
Sutherland AM, Walley KR, Manocha S, Russell JA: The association of interleukin 6 haplotype clades with mortality in critically ill adults. Arch Intern Med 2005; 165: 75–82.
22.
Thompson CM, Holden TD, Rona G, Laxmanan B, Black RA, O’Keefe GE, Wurfel MM: Toll-like receptor 1 polymorphisms and associated outcomes in sepsis after traumatic injury: a candidate gene association study. Ann Surg 2014; 259: 179–185.
23.
de Aguiar BB, Girardi I, Paskulin DD, de Franca E, Dornelles C, Dias FS, Bonorino C, Alho CS: CD14 expression in the first 24 h of sepsis: effect of –260C>T CD14 SNP. Immunol Invest 2008; 37: 752–769.
24.
Barber RC, Aragaki CC, Rivera-Chavez FA, Purdue GF, Hunt JL, Horton JW: TLR4 and TNF-α polymorphisms are associated with an increased risk for severe sepsis following burn injury. J Med Genet 2004; 41: 808–813.
25.
Zeng L, Gu W, Zhang AQ, Zhang M, Zhang LY, Du DY, Huang SN, Jiang JX: A functional variant of lipopolysaccharide binding protein predisposes to sepsis and organ dysfunction in patients with major trauma. Ann Surg 2012; 255: 147–157.
26.
Srinivasan L, Kirpalani H, Cotten CM: Elucidating the role of genomics in neonatal sepsis. Semin Perinatol 2015; 39: 611–616.
27.
Rhee C, Kadri SS, Danner RL, Suffredini AF, Massaro AF, Kitch BT, Lee G, Klompas M: Diagnosing sepsis is subjective and highly variable: a survey of intensivists using case vignettes. Crit Care 2016; 20: 89.
28.
Nelson CL, Pelak K, Podgoreanu MV, Ahn SH, Scott WK, Allen AS, Cowell LG, Rude TH, Zhang Y, Tong A, Ruffin F, Sharma-Kuinkel BK, Fowler VG Jr: A genome-wide association study of variants associated with acquisition of Staphylococcus aureus bacteremia in a healthcare setting. BMC Infect Dis 2014; 14: 83.
29.
Winkler TW, Day FR, Croteau-Chonka DC, Wood AR, Locke AE, Mägi R, Ferreira T, Fall T, Graff M, Justice AE, Luan J, Gustafsson S, Randall JC, Vedantam S, Workalemahu T, Kilpeläinen TO, Scherag A, Esko T, Kutalik Z, Heid IM, Loos RJ; Genetic Investigation of Anthropometric Traits (GIANT) Consortium: Quality control and conduct of genome-wide association meta-analyses. Nat Protoc 2014; 9: 1192–1212.
30.
Ebrahim S: Epigenetics: the next big thing. Int J Epidemiol 2012; 41: 1–3.
31.
Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, Chen RO, Brownstein BH, Cobb JP, Tschoeke SK, Miller-Graziano C, Moldawer LL, Mindrinos MN, Davis RW, Tompkins RG, Lowry SF; Inflamm. and Host Response to Injury Large Scale Collab. Res. Program: A network-based analysis of systemic inflammation in humans. Nature 2005; 437: 1032–1037.
32.
Manchanda H, Seidel N, Blaess MF, Claus RA, Linde J, Slevogt H, Sauerbrei A, Guthke R, Schmidtke M: Differential biphasic transcriptional host response associated with coevolution of hemagglutinin quasispecies of influenza A virus. Front Microbiol 2016; 7: 1167.
33.
Savijoki K, Iivanainen A, Siljamäki P, Laine PK, Paulin L, Karonen T, Pyörälä S, Kankainen M, Nyman TA, Salomäki T, Koskinen P, Holm L, Simojoki H, Taponen S, Sukura A, Kalkkinen N, Auvinen P, Varmanen P: Genomics and proteomics provide new insight into the commensal and pathogenic lifestyles of bovine- and human-associated Staphylococcus epidermidis strains. J Proteome Res 2014, DOI: 10.1021/pr500322d.
34.
Li Y, Alam HB: Modulation of acetylation: creating a pro-survival and anti-inflammatory phenotype in lethal hemorrhagic and septic shock. J Biomed Biotechnol 2011; 2011: 523481.
35.
Almansa R, Heredia-Rodríguez M, Gomez-Sanchez E, Andaluz-Ojeda D, Iglesias V, Rico L, Ortega A, Gomez-Pesquera E, Liu P, Aragón M, Eiros JM, Jiménez-Sousa MÁ, Resino S, Gómez-Herreras I, Bermejo-Martín JF, Tamayo E: Transcriptomic correlates of organ failure extent in sepsis. J Infect 2015; 70: 445–456.
36.
Wong HR: Clinical review: sepsis and septic shock – the potential of gene arrays. Crit Care 2012; 16: 204.
37.
Bush WS, Oetjens MT, Crawford DC: Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat Rev Genet 2016; 17: 129–145.
38.
Malmström E, Kilsgård O, Hauri S, Smeds E, Herwald H, Malmström L, Malmström J: Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics. Nat Commun 2016; 7: 10261.
39.
Cao Z, Robinson RA: The role of proteomics in understanding biological mechanisms of sepsis. Proteomics Clin Appl 2014; 8: 35–52.
40.
Ko ER, Yang WE, McClain MT, Woods CW, Ginsburg GS, Tsalik EL: What was old is new again: using the host response to diagnose infectious disease. Expert Rev Mol Diagn 2015; 15: 1143–1158.
41.
Holmes E, Wilson ID, Nicholson JK: Metabolic phenotyping in health and disease. Cell 2008; 134: 714–717.
42.
Ramsden JJ: Bioinformatics: An Introduction. London, Springer, 2009.
43.
Metabolomics Society. http://metabolomicssociety.org (accessed February 22, 2017).
44.
Metabolomics in pneumonia and sepsis. https://www.google.com/patents/WO2014004539A1?cl=en#backward-citations (accessed February 22, 2017).
45.
Serkova NJ, Standiford TJ, Stringer KA: The emerging field of quantitative blood metabolomics for biomarker discovery in critical illnesses. Am J Respir Crit Care Med 2011; 184: 647–655.
46.
Veenstra TD: Metabolomics: the final frontier? Genome Med 2012; 4: 40.
47.
Keating P, Cambrosio A: Biomedical Platforms: Realigning the Normal and the Pathological in Late-Twentieth-Century Medicine. Cambridge, MIT Press, 2003.
48.
Wishart DS: Advances in metabolite identification. Bioanalysis 2011; 3: 1769–1782.
49.
Hagen JB: The origins of bioinformatics. Nat Rev Genet 2000; 1: 231–236.
50.
Dunn WB, Broadhurst DI, Atherton HJ, Goodacre R, Griffin JL: Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 2011; 40: 387–426.
51.
Wishart DS: Computational approaches to metabolomics. Methods Mol Biol 2010; 593: 283–313.
52.
The Human Metabolome Database. http://www.hmdb.ca (accessed February 21, 2017).
53.
Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, Djoumbou Y, Mandal R, Aziat F, Dong E, Bouatra S, Sinelnikov I, Arndt D, Xia J, Liu P, Yallou F, Bjorndahl T, Perez-Pineiro R, Eisner R, Allen F, Neveu V, Greiner R, Scalbert A: HMDB 3.0: the Human Metabolome Database in 2013. Nucleic Acids Res 2013; 41:D801–D807.
54.
Liu X, Ren H, Peng D: Sepsis biomarkers: an omics perspective. Front Med 2014; 8: 58–67.
55.
Antcliffe D, Gordon AC: Metabonomics and intensive care. Crit Care 2016; 20: 68.
56.
Dumas ME, Maibaum EC, Teague C, Ueshima H, Zhou B, Lindon JC, Nicholson JK, Stamler J, Elliott P, Chan Q, Holmes E: Assessment of analytical reproducibility of 1H NMR spectroscopy based metabonomics for large-scale epidemiological research: the INTERMAP Study. Anal Chem 2006; 78: 2199–2208.
57.
Miller B, Malloy MA, Masek E, Wild C: Towards a framework for managing the information environment. Inf Knowledge Syst Manage 2001; 2: 359–384.
58.
Sartor MA, Ade A, Wright Z, States D, Omenn GS, Athey B, Karnovsky A: Metab2MeSH: annotating compounds with medical subject headings. Bioinformatics 2012; 28: 1408–1410.
59.
Grapov D, Wanichthanarak K, Fiehn O: MetaMapR: pathway independent metabo-lomic network analysis incorporating unknowns. Bioinformatics 2015; 31: 2757–2760.
60.
Krumsiek J, Suhre K, Evans AM, Mitchell MW, Mohney RP, Milburn MV, Wägele B, Römisch-Margl W, Illig T, Adamski J, Gieger C, Theis FJ, Kastenmüller G: Mining the unknown: a systems approach to metabo lite identification combining genetic and metabolic information. PLoS Genet 2012; 8:e1003005.
61.
Earl M: Open source software for mass spectrometry and metabolomics; in Harland L, Forster M (eds): Open Source Software in Life Science Research: Practical Solutions to Common Challenges in the Pharmaceutical Industry and Beyond. Amsterdam, Elsevier, 2012.
62.
Thampi SM: Bioinformatics. https://arxiv.org/pdf/0911.4230.pdf (accessed February 22, 2017).
63.
LeVeque RJ, Mitchell IM, Stodden V: Reproducible research for scientific computing: tools and strategies for changing the culture. https://pdfs.semanticscholar.org/c700/3227207685a3142531e85ec434367486b7c4.pdf (accessed February 22, 2017).
64.
Byrne L, Van Haren F: Fluid resuscitation in human sepsis: time to rewrite history? Ann Intensive Care 2017; 7: 4.
65.
Englert JA, Rogers AJ: Metabolism, metabolomics, and nutritional support of patients with sepsis. Clin Chest Med 2016; 37: 321–331.
66.
Langley RJ, Tsalik EL, Velkinburgh JC, Glickman SW, Rice BJ, Wang C, Chen B, Carin L, Suarez A, Mohney RP, Freeman DH, Wang M, You J, Wulff J, Thompson JW, Moseley MA, Reisinger S, Edmonds BT, Grinnell B, Nelson DR, Dinwiddie DL, Miller NA, Saunders CJ, Soden SS, Rogers AJ, Gazourian L, Fredenburgh LE, Massaro AF, Baron RM, Choi AM, Corey GR, Ginsburg GS, Cairns CB, Otero RM, Fowler VG Jr, Rivers EP, Woods CW, Kingsmore SF: An integrated clinico-metabolomic model improves prediction of death in sepsis. Sci Transl Med 2013; 5: 195ra195.
67.
Mickiewicz B, Vogel HJ, Wong HR, Winston BW: Metabolomics as a novel approach for early diagnosis of pediatric septic shock and its mortality. Am J Respir Crit Care Med 2013; 187: 967–976.
68.
Izquierdo-García JL, Nin N, Ruíz-Cabello J, Rojas Y, de Paula M, López-Cuenca S, Morales L, Martínez-Caro L, Fernández-Segoviano P, Esteban A, Lorente JA: A metabolomic approach for diagnosis of experimental sepsis. Intensive Care Med 2011; 37: 2023–2032.
69.
Kamisoglu K, Sleight KE, Calvano SE, Coyle SM, Corbett SA, Androulakis IP: Temporal metabolic profiling of plasma during endotoxemia in humans. Shock 2013; 40: 519–526.
70.
Garcia-Simon M, Morales JM, Modesto-Alapont V, Gonzalez-Marrachelli V, Vento-Rehues R, Jorda-Miñana A, Blanquer-Olivas J, Monleon D: Prognosis biomarkers of severe sepsis and septic shock by 1H NMR urine metabolomics in the intensive care unit. PLoS One 2015; 10:e0140993.
71.
Stringer KA, Younger JG, McHugh C, Yeomans L, Finkel MA, Puskarich MA, Jones AE, Trexel J, Karnovsky A: Whole blood reveals more metabolic detail of the human metabolome than serum as measured by 1H-NMR spectroscopy: implications for sepsis metabolomics. Shock 2015; 44: 200–208.
72.
Nickler M, Ottiger M, Steuer C, Huber A, Anderson JB, Müller B, Schuetz P: Systematic review regarding metabolic profiling for improved pathophysiological understanding of disease and outcome prediction in respiratory infections. Respir Res 2015; 16: 125.
73.
Tzika AA, Constantinou C, Bandyopadhaya A, Psychogios N, Lee S, Mindrinos M, Martyn JA, Tompkins RG, Rahme LG: A small volatile bacterial molecule triggers mitochondrial dysfunction in murine skeletal muscle. PLoS One 2013; 8:e74528.
74.
Rogers AJ, McGeachie M, Baron RM, Gazourian L, Haspel JA, Nakahira K, Fredenburgh LE, Hunninghake GM, Raby BA, Matthay MA, Otero RM, Fowler VG, Rivers EP, Woods CW, Kingsmore S, Langley RJ, Choi AM: Metabolomic derangements are associated with mortality in critically ill adult patients. PLoS One 2014; 9:e87538.
75.
Rogers AJ, Matthay MA: Applying metabolomics to uncover novel biology in ARDS. Am J Physiol Lung Cell Mol Physiol 2014; 306:L957–L961.
76.
Chaudhary T, Hohenstein C, Bayer O: The golden hour of sepsis: initial therapy should start in the prehospital setting. Med Klin Intensivmed Notfmed 2014; 109: 104–108.
77.
Ferrer R, Martin-Loeches I, Phillips G, Osborn TM, Townsend S, Dellinger RP, Artigas A, Schorr C, Levy MM: Empiric antibiotic treatment reduces mortality in severe sepsis and septic shock from the first hour: results from a guideline-based performance improvement program. Crit Care Med 2014; 42: 1749–1755.
78.
Neugebauer S, Giamarellos-Bourboulis EJ, Pelekanou A, Marioli A, Baziaka F, Tsangaris I, Bauer M, Kiehntopf M: Metabolite profiles in sepsis: developing prognostic tools based on the type of infection. Crit Care Med 2016; 44: 1649–1662.
79.
To KK, Lee KC, Wong SS, Sze KH, Ke YH, Lui YM, Tang BS, Li IW, Lau SK, Hung IF, Law CY, Lam CW, Yuen KY: Lipid metabolites as potential diagnostic and prognostic biomarkers for acute community acquired pneumonia. Diagn Microbiol Infect Dis 2016; 85: 249–254.
80.
Phua J, Dean NC, Guo Q, Kuan WS, Lim HF, Lim TK: Severe community-acquired pneumonia: timely management measures in the first 24 h. Crit Care 2016; 20: 237.
81.
Farkas J: PulmCrit: top ten problems with the new sepsis definition. https://emcrit.org/pulmcrit/problems-sepsis-3-definition/ (accessed February 26, 2017).
82.
Singer M, Deutschman CS, Seymour CW, Shankar-Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, Hotchkiss RS, Levy MM, Marshall JC, Martin GS, Opal SM, Rubenfeld GD, van der Poll T, Vincent JL, Angus DC: The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016; 315: 801–810.
83.
Churpek MM, Snyder A, Han X, Sokol S, Pettit N, Howell MD, Edelson DP: qSOFA, SIRS, and early warning scores for detecting clinical deterioration in infected patients outside the ICU. Am J Respir Crit Care Med 2017; 195: 906–911.
84.
Desautels T, Calvert J, Hoffman J, Jay M, Kerem Y, Shieh L, Shimabukuro D, Chettipally U, Feldman MD, Barton C, Wales DJ, Das R: Prediction of sepsis in the intensive care unit with minimal electronic health record data: a machine learning approach. JMIR Med Inform 2016; 4:e28.
85.
Sanz Herrero F, Blanquer Olivas J: Microbiology and risk factors for community-acquired pneumonia. Semin Respir Crit Care Med 2012; 33: 220–231.
86.
Koulenti D, Tsigou E, Rello J: Nosocomial pneumonia in 27 ICUs in Europe: perspectives from the EU-VAP/CAP study. Eur J Clin Microbiol Infect Dis 2017; 36: 1999–2006.
87.
Slupsky CM, Rankin KN, Fu H, Chang D, Rowe BH, Charles PG, McGeer A, Low D, Long R, Kunimoto D, Sawyer MB, Fedorak RN, Adamko DJ, Saude EJ, Shah SL, Marrie TJ: Pneumococcal pneumonia: potential for diagnosis through a urinary metabolic profile. J Proteome Res 2009; 8: 5550–5558.
88.
Slupsky CM, Cheypesh A, Chao DV, Fu H, Rankin KN, Marrie TJ, Lacy P: Streptococcus pneumoniae and Staphylococcus aureus pneumonia induce distinct metabolic responses. J Proteome Res 2009; 8: 3029–3036.
89.
van Oort PM, de Bruin S, Weda H, Knobel HH, Schultz MJ, Bos LD; On Behalf of the Mars Consortium: Exhaled breath metabolomics for the diagnosis of pneumonia in intubated and mechanically-ventilated intensive care unit (ICU)-patients. Int J Mol Sci 2017; 18:E449.
90.
Tørring T, Shames SR, Cho W, Roy CR, Crawford J: Acyl-histidines: new N-acyl amides from Legionella pneumophila. Chembiochem 2017; 18: 638–646.
91.
Hoerr V, Duggan GE, Zbytnuik L, Poon KK, Große C, Neugebauer U, Methling K, Löffler B, Vogel HJ: Characterization and prediction of the mechanism of action of antibiotics through NMR metabolomics. BMC Microbiol 2016; 16: 82.
92.
Leite HP, de Lima LF: Metabolic resuscitation in sepsis: a necessary step beyond the hemodynamic? J Thorac Dis 2016; 8:E552–E557.
93.
Shi X, Yang F, Zheng YN, Zhang H, Wang XX, Shao GJ, Lai XL: Metabolomic approach for the identification of therapeutic targets of erythropoietin against sepsis in rat models. Eur Rev Med Pharmacol Sci 2016; 20: 537–546.
94.
Lindahl A, Forshed J, Nordström A: Over lap in serum metabolic profiles between non-related diseases: implications for LC-MS metabolomics biomarker discovery. Biochem Biophys Res Commun 2016; 478: 1472–1477.
95.
Friedrich MJ: Scientists seek to sniff out diseases: electronic “noses” may someday be diagnostic tools. JAMA 2009; 301: 585–586.
96.
Palm D, Johansson K, Ozin A, Friedrich A, Grundmann H, Larsson J, Struelens M: Molecular epidemiology of human pathogens: how to translate breakthroughs into public health practice, Stockholm, November 2011. Euro Surveill 2012; 17: 20054.
97.
Wishart DS: Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 2016; 15: 473–484.
98.
Evangelatos N, Satyamoorthy K, Brand A: Personalized health in a public health perspective. Int J Public Health 2017, DOI: 10.1007/s00038-017-1055-5.
99.
Eckerle M, Ambroggio L, Puskarich MA, Winston B, Jones AE, Standiford TJ, Stringer KA: Metabolomics as a driver in advancing precision medicine in sepsis. Pharmacotherapy 2017; 37: 1023–1032.
100.
To KK, Lee KC, Wong SS, Lo KC, Lui YM, Jahan AS, Wu AL, Ke YH, Law CY, Sze KH, Lau SK, Woo PC, Lam CW, Yuen KY: Lipid mediators of inflammation as novel plasma biomarkers to identify patients with bacteremia. J Infect 2015; 70: 433–444.
101.
Ambroggio L, Florin TA, Shah SS, Ruddy R, Yeomans L, Trexel J, Stringer KA: Emerging biomarkers of illness severity: urinary metabolites associated with sepsis and necrotizing methicillin-resistant Staphylococcus aureus pneumonia. Pharmacotherapy 2017; 37: 1033–1042.
102.
Puskarich MA, Finkel MA, Karnovsky A, Jones AE, Trexel J, Harris BN, Stringer KA: Pharmacometabolomics of l-carnitine treatment response phenotypes in patients with septic shock. Ann Am Thorac Soc 2015; 12: 46–56.
103.
Ma YM, Yang MJ, Wang S, Li H, Peng XX: Liver functional metabolomics discloses an action of L-leucine against Streptococcus iniae infection in tilapias. Fish Shellfish Immunol 2015; 45: 414–421.
104.
De Buck J, Shaykhutdinov R, Barkema HW, Vogel HJ: Metabolomic profiling in cattle experimentally infected with Mycobacterium avium subsp. paratuberculosis. PLoS One 2014; 9:e111872.
105.
GNU Operating System: What is free software? The new software definition. https://www.gnu.org/philosophy/free-sw.html (accessed February 26, 2017).
106.
Evangelatos N, Reumann M, Lehrach H, Brand A: Clinical trial data as public goods: fair trade and the Virtual Knowledge Bank as a solution to the free rider problem – a framework for the promotion of innovation by facilitation of clinical trial data sharing among biopharmaceutical companies in the era of omics and big data. Public Health Genomics 2016; 19: 211–219.
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Drug Dosage: The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any changes in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug.
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