An integrated transcriptome and expressed variant analysis of sepsis survival and death

Ephraim L Tsalik, Raymond J Langley, Darrell L Dinwiddie, Neil A Miller, Byunggil Yoo, Jennifer C van Velkinburgh, Laurie D Smith, Isabella Thiffault, Anja K Jaehne, Ashlee M Valente, Ricardo Henao, Xin Yuan, Seth W Glickman, Brandon J Rice, Micah T McClain, Lawrence Carin, G Ralph Corey, Geoffrey S Ginsburg, Charles B Cairns, Ronny M Otero, Vance G Fowler Jr, Emanuel P Rivers, Christopher W Woods, Stephen F Kingsmore, Ephraim L Tsalik, Raymond J Langley, Darrell L Dinwiddie, Neil A Miller, Byunggil Yoo, Jennifer C van Velkinburgh, Laurie D Smith, Isabella Thiffault, Anja K Jaehne, Ashlee M Valente, Ricardo Henao, Xin Yuan, Seth W Glickman, Brandon J Rice, Micah T McClain, Lawrence Carin, G Ralph Corey, Geoffrey S Ginsburg, Charles B Cairns, Ronny M Otero, Vance G Fowler Jr, Emanuel P Rivers, Christopher W Woods, Stephen F Kingsmore

Abstract

Background: Sepsis, a leading cause of morbidity and mortality, is not a homogeneous disease but rather a syndrome encompassing many heterogeneous pathophysiologies. Patient factors including genetics predispose to poor outcomes, though current clinical characterizations fail to identify those at greatest risk of progression and mortality.

Methods: The Community Acquired Pneumonia and Sepsis Outcome Diagnostic study enrolled 1,152 subjects with suspected sepsis. We sequenced peripheral blood RNA of 129 representative subjects with systemic inflammatory response syndrome (SIRS) or sepsis (SIRS due to infection), including 78 sepsis survivors and 28 sepsis non-survivors who had previously undergone plasma proteomic and metabolomic profiling. Gene expression differences were identified between sepsis survivors, sepsis non-survivors, and SIRS followed by gene enrichment pathway analysis. Expressed sequence variants were identified followed by testing for association with sepsis outcomes.

Results: The expression of 338 genes differed between subjects with SIRS and those with sepsis, primarily reflecting immune activation in sepsis. Expression of 1,238 genes differed with sepsis outcome: non-survivors had lower expression of many immune function-related genes. Functional genetic variants associated with sepsis mortality were sought based on a common disease-rare variant hypothesis. VPS9D1, whose expression was increased in sepsis survivors, had a higher burden of missense variants in sepsis survivors. The presence of variants was associated with altered expression of 3,799 genes, primarily reflecting Golgi and endosome biology.

Conclusions: The activation of immune response-related genes seen in sepsis survivors was muted in sepsis non-survivors. The association of sepsis survival with a robust immune response and the presence of missense variants in VPS9D1 warrants replication and further functional studies.

Trial registration: ClinicalTrials.gov NCT00258869. Registered on 23 November 2005.

Figures

Figure 1
Figure 1
A systems survey of sepsis survival. (A) Schematic representing the different trajectories enrolled subjects might take. X-axis represents time (not to scale), emphasizing the illness progresses from local to systemic infection prior to clinical presentation (t0). The green line is flat only to distinguish subjects without infection, although these individuals could also have the full spectrum of clinical illness severity. Blue lines represent subjects with sepsis of different severities, all of whom survive at 28 days. This is in contrast to subjects with sepsis who die within 28 days, independent of initial sepsis severity. (B) Analytical plan for the CAPSOD cohort including previously published metabolome and proteome [11]. Metabolomic and proteomic analyses were performed on samples obtained at t0 and 24 h later. Transcriptomic analysis was performed on samples obtained at t0.
Figure 2
Figure 2
CONSORT flow chart of patient enrollment and selection. The planned study design was to analyze 30 subjects each with uncomplicated sepsis, severe sepsis (sepsis with organ dysfunction), septic shock, sepsis deaths, and SIRS (no infection present). However, limited sample quality or quantity in some cases decreased the number available per group. The analysis population includes 78 sepsis survivors, 28 sepsis non-survivors, and 23 SIRS survivors. Three SIRS non-survivors represented too few subjects to define their own analysis subgroup and were therefore removed prior to analysis.
Figure 3
Figure 3
Differentially expressed genes and pathways. (A) Number and overlap among the differentially expressed, annotated genes in each pairwise comparison. (B) Hierarchical clustering of 2,140 differentially expressed gene (including 314 unannotated loci) using Pearson’s moment correlations applied to subjects with SIRS, Sepsis Non-survivors, and Sepsis Survivors. ANOVA with 7.5% FDR correction; −log10 P value = 2.21. (C) Highly represented ToppGene pathways and processes among the annotated genes differentially expressed between SIRS and Sepsis Survivors as well as Sepsis Survivors and Sepsis Non-survivors.
Figure 4
Figure 4
Protein structure of VPS9D1 showing approximate location of variants associated with sepsis survival.
Figure 5
Figure 5
Expression ofVPS9D1.VPS9D1 is represented by two different genetic loci: XLOC_011354 (Cufflinks Transcript ID TCONS_00032132; RefSeq ID NM_004913) and XLOC_010886 (Cufflinks Transcript ID TCONS_00030416; RefSeq ID NM_004913). The former demonstrated greater sequencing coverage and is presented here. Results for XLOC_010886 were similar (data not shown). (A) Level of VPS9D1 expression in sepsis survivors (n = 74) and sepsis non-survivors (n = 26). (B) Level of VPS9D1 expression as a function of the VPS9D1 reference (n = 64) or variant sequence (n = 36) among subjects with adequate coverage. (C) Volcano plot depicting differentially expressed genes as a function of the VPS9D1 reference or variant allele.

References

    1. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: analysis of incidence, outcome, and associated costs of care. Crit Care Med. 2001;29:1303–1310. doi: 10.1097/00003246-200107000-00002.
    1. Adhikari NK, Fowler RA, Bhagwanjee S, Rubenfeld GD. Critical care and the global burden of critical illness in adults. Lancet. 2010;376:1339–1346. doi: 10.1016/S0140-6736(10)60446-1.
    1. Kumar G, Kumar N, Taneja A, Kaleekal T, Tarima S, McGinley E, Jimenez E, Mohan A, Khan RA, Whittle J, Jacobs E, Nanchal R, Milwaukee Initiative in Critical Care Outcomes Research Group of Investigators Nationwide trends of severe sepsis in the 21st century (2000–2007) Chest. 2011;140:1223–1231. doi: 10.1378/chest.11-0352.
    1. Liu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC, Iwashyna TJ. Hospital deaths in patients with sepsis from 2 independent cohorts. JAMA. 2014;312:90–92. doi: 10.1001/jama.2014.5804.
    1. Winters BD, Eberlein M, Leung J, Needham DM, Pronovost PJ, Sevransky JE. Long-term mortality and quality of life in sepsis: a systematic review. Crit Care Med. 2010;38:1276–1283.
    1. Bone RC, Balk RA, Cerra FB, Dellinger RP, Fein AM, Knaus WA, Schein RM, Sibbald WJ. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest. 1992;101:1644–1655.
    1. Goswami ND, Pfeiffer CD, Horton JR, Chiswell K, Tasneem A, Tsalik EL. The state of infectious diseases clinical trials: a systematic review of . PLoS One. 2013;8:e77086. doi: 10.1371/journal.pone.0077086.
    1. Singer M. Biomarkers in sepsis. Curr Opin Pulm Med. 2013;19:305–309. doi: 10.1097/MCP.0b013e32835f1b49.
    1. Ahn SH, Tsalik EL, Cyr DD, Zhang Y, van Velkinburgh JC, Langley RJ, Glickman SW, Cairns CB, Zaas AK, Rivers EP, Otero RM, Veldman T, Kingsmore SF, Lucas J, Woods CW, Ginsburg GS, Fowler VG., Jr Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans. PLoS One. 2013;8:e48979. doi: 10.1371/journal.pone.0048979.
    1. Glickman SW, Cairns CB, Otero RM, Woods CW, Tsalik EL, Langley RJ, van Velkinburgh JC, Park LP, Glickman LT, Fowler VG, Jr, Kingsmore SF, Rivers EP. Disease progression in hemodynamically stable patients presenting to the emergency department with sepsis. Acad Emerg Med. 2010;17:383–390. doi: 10.1111/j.1553-2712.2010.00664.x.
    1. 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,et al.: An integrated clinico-metabolomic model improves prediction of death in sepsis.Sci Transl Med 2013, 5:195ra195.
    1. Tsalik EL, Jaggers LB, Glickman SW, Langley RJ, van Velkinburgh JC, Park LP, Fowler VG, Cairns CB, Kingsmore SF, Woods CW. Discriminative value of inflammatory biomarkers for suspected sepsis. J Emerg Med. 2012;43:97–106. doi: 10.1016/j.jemermed.2011.05.072.
    1. Tsalik EL, Jones D, Nicholson B, Waring L, Liesenfeld O, Park LP, Glickman SW, Caram LB, Langley RJ, van Velkinburgh JC, Cairns CB, Rivers EP, Otero RM, Kingsmore SF, Lalani T, Fowler VG, Woods CW. Multiplex PCR to diagnose bloodstream infections in patients admitted from the emergency department with sepsis. J Clin Microbiol. 2010;48:26–33. doi: 10.1128/JCM.01447-09.
    1. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13:818–829. doi: 10.1097/00003246-198510000-00009.
    1. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonca A, Bruining H, Reinhart CK, Suter PM, Thijs LG. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med. 1996;22:707–710. doi: 10.1007/BF01709751.
    1. Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33:159–174. doi: 10.2307/2529310.
    1. Mudge J, Miller NA, Khrebtukova I, Lindquist IE, May GD, Huntley JJ, Luo S, Zhang L, van Velkinburgh JC, Farmer AD, Lewis S, Beavis WD, Schilkey FD, Virk SM, Black CF, Myers MK, Mader LC, Langley RJ, Utsey JP, Kim RW, Roberts RC, Khalsa SK, Garcia M, Ambriz-Griffith V, Harlan R, Czika W, Martin S, Wolfinger RD, Perrone-Bizzozero NI, Schroth GP, et al. Genomic convergence analysis of schizophrenia: mRNA sequencing reveals altered synaptic vesicular transport in post-mortem cerebellum. PLoS ONE. 2008;3:e3625. doi: 10.1371/journal.pone.0003625.
    1. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21. doi: 10.1093/bioinformatics/bts635.
    1. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, Garimella K, Altshuler D, Gabriel S, Daly M, DePristo MA. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–1303. doi: 10.1101/gr.107524.110.
    1. DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, McKenna A, Fennell TJ, Kernytsky AM, Sivachenko AY, Cibulskis K, Gabriel SB, Altshuler D, Daly MJ. A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet. 2011;43:491–498. doi: 10.1038/ng.806.
    1. Ewing B, Hillier L, Wendl MC, Green P. Base-calling of automated sequencer traces using phred. Genome Res. 1998;8:175–185. doi: 10.1101/gr.8.3.175.
    1. Ewing B, Green P. Base-calling of automated sequencer traces using phred. Genome Res. 1998;8:186–194. doi: 10.1101/gr.8.3.175.
    1. Saunders CJ, Miller NA, Soden SE, Dinwiddie DL, Noll A, Alnadi NA, Andraws N, Patterson ML, Krivohlavek LA, Fellis J, Humphray S, Saffrey P, Kingsbury Z, Weir JC, Betley J, Grocock RJ, Margulies EH, Farrow EG, Artman M, Safina NP, Petrikin JE, Hall KP, Kingsmore SF: Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units.Sci Transl Med 2012, 4:154ra135.
    1. McLaren W, Pritchard B, Rios D, Chen Y, Flicek P, Cunningham F. Deriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor. Bioinformatics. 2010;26:2069–2070. doi: 10.1093/bioinformatics/btq330.
    1. Stenson PD, Ball EV, Howells K, Phillips AD, Mort M, Cooper DN. The Human Gene Mutation Database: providing a comprehensive central mutation database for molecular diagnostics and personalized genomics. Hum Genomics. 2009;4:69–72. doi: 10.1186/1479-7364-4-2-69.
    1. Storey JD. A direct approach to false discovery rates. Roy Stat Soc: Series B (Statistical Methodology) 2002;64:479–498. doi: 10.1111/1467-9868.00346.
    1. Storey JD, Taylor JE, Siegmund D: Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach.J Teh Roy Stat Soc: Series B (Statistical Methodology) 2004, 66:187–205.
    1. Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing.J Roy Statist Soc Ser 1995, B 57:289–300.
    1. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;37:W305–W311. doi: 10.1093/nar/gkp427.
    1. Li B, Leal SM. Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data. Am J Hum Genet. 2008;83:311–321. doi: 10.1016/j.ajhg.2008.06.024.
    1. Consortium GP An integrated map of genetic variation from 1,092 human genomes. Nature. 2012;491:56–65. doi: 10.1038/nature11632.
    1. Exome Variant Server, NHLBI GO: Exome Sequencing Project (ESP), Seattle, WA. 2012. []
    1. Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10:57–63. doi: 10.1038/nrg2484.
    1. Baranzini SE, Mudge J, van Velkinburgh JC, Khankhanian P, Khrebtukova I, Miller NA, Zhang L, Farmer AD, Bell CJ, Kim RW, May GD, Woodward JE, Caillier SJ, McElroy JP, Gomez R, Pando MJ, Clendenen LE, Ganusova EE, Schilkey FD, Ramaraj T, Khan OA, Huntley JJ, Luo S, Kwok PY, Wu TD, Schroth GP, Oksenberg JR, Hauser SL, Kingsmore SF. Genome, epigenome and RNA sequences of monozygotic twins discordant for multiple sclerosis. Nature. 2010;464:1351–1356. doi: 10.1038/nature08990.
    1. Sugarbaker DJ, Richards WG, Gordon GJ, Dong L, De Rienzo A, Maulik G, Glickman JN, Chirieac LR, Hartman ML, Taillon BE, Du L, Bouffard P, Kingsmore SF, Miller NA, Farmer AD, Jensen RV, Gullans SR, Bueno R. Transcriptome sequencing of malignant pleural mesothelioma tumors. Proc Natl Acad Sci U S A. 2008;105:3521–3526. doi: 10.1073/pnas.0712399105.
    1. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schroth GP, Burge CB. Alternative isoform regulation in human tissue transcriptomes. Nature. 2008;456:470–476. doi: 10.1038/nature07509.
    1. Bernard GR, Margolis BD, Shanies HM, Ely EW, Wheeler AP, Levy H, Wong K, Wright TJ. Extended evaluation of recombinant human activated protein C United States Trial (ENHANCE US): a single-arm, phase 3B, multicenter study of drotrecogin alfa (activated) in severe sepsis. Chest. 2004;125:2206–2216. doi: 10.1378/chest.125.6.2206.
    1. Bernard GR, Vincent JL, Laterre PF, LaRosa SP, Dhainaut JF, Lopez-Rodriguez A, Steingrub JS, Garber GE, Helterbrand JD, Ely EW, Fisher CJ., Jr Efficacy and safety of recombinant human activated protein C for severe sepsis. N Engl J Med. 2001;344:699–709. doi: 10.1056/NEJM200103083441001.
    1. Martí-Carvajal AJ, Solà I, Lathyris D, Cardona AF. Human recombinant activated protein C for severe sepsis. Cochrane Database of Systematic Reviews. 2011;4
    1. Carney DS, Davies BA, Horazdovsky BF. Vps9 domain-containing proteins: activators of Rab5 GTPases from yeast to neurons. Trends Cell Biol. 2006;16:27–35. doi: 10.1016/j.tcb.2005.11.001.
    1. Carre JE, Orban JC, Re L, Felsmann K, Iffert W, Bauer M, Suliman HB, Piantadosi CA, Mayhew TM, Breen P, Stotz M, Singer M. Survival in critical illness is associated with early activation of mitochondrial biogenesis. Am J Respir Crit Care Med. 2010;182:745–751. doi: 10.1164/rccm.201003-0326OC.
    1. Langley RJ, Tipper JL, Bruse S, Baron RM, Tsalik EL, Huntley J, Rogers AJ, Jaramillo RJ, O’Donnell D, Mega WM, Keaton M, Kensicki E, Gazourian L, Fredenburgh LE, Massaro AF, Otero RM, Fowler VG, Rivers EP, Woods CW, Kingsmore SF, Sopori ML, Perrella MA, Choi AMK, Harrod KS. Integrative “omic” analysis of experimental bacteremia identifies a metabolic signature that distinguishes human sepsis from systemic inflammatory response syndromes. Am J Respir Crit Care Med. 2014;190:445–455. doi: 10.1164/rccm.201404-0624OC.
    1. 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. doi: 10.1371/journal.pone.0087538.
    1. Baudouin SV, Saunders D, Tiangyou W, Elson JL, Poynter J, Pyle A, Keers S, Turnbull DM, Howell N, Chinnery PF. Mitochondrial DNA and survival after sepsis: a prospective study. Lancet. 2005;366:2118–2121. doi: 10.1016/S0140-6736(05)67890-7.
    1. Gomez R, O’Keeffe T, Chang LY, Huebinger RM, Minei JP, Barber RC. Association of mitochondrial allele 4216C with increased risk for complicated sepsis and death after traumatic injury. J Trauma. 2009;66:850–857. doi: 10.1097/TA.0b013e3181991ac8.
    1. Kloss-Brandstatter A, Pacher D, Schonherr S, Weissensteiner H, Binna R, Specht G, Kronenberg F. HaploGrep: a fast and reliable algorithm for automatic classification of mitochondrial DNA haplogroups. Hum Mutat. 2011;32:25–32. doi: 10.1002/humu.21382.
    1. Wallace DC, Chalkia D. Mitochondrial DNA genetics and the heteroplasmy conundrum in evolution and disease. Cold Spring Harb Perspect Biol. 2013;5:a021220. doi: 10.1101/cshperspect.a021220.
    1. Ramos A, Santos C, Mateiu L, Gonzalez Mdel M, Alvarez L, Azevedo L, Amorim A, Aluja MP. Frequency and pattern of heteroplasmy in the complete human mitochondrial genome. PLoS One. 2013;8:e74636. doi: 10.1371/journal.pone.0074636.
    1. Li M, Schonberg A, Schaefer M, Schroeder R, Nasidze I, Stoneking M. Detecting heteroplasmy from high-throughput sequencing of complete human mitochondrial DNA genomes. Am J Hum Genet. 2010;87:237–249. doi: 10.1016/j.ajhg.2010.07.014.
    1. Severino P, Silva E, Baggio-Zappia GL, Brunialti MK, Nucci LA, Rigato O, Jr, da Silva ID, Machado FR, Salomao R. Patterns of gene expression in peripheral blood mononuclear cells and outcomes from patients with sepsis secondary to community acquired pneumonia. PLoS One. 2014;9:e91886. doi: 10.1371/journal.pone.0091886.
    1. Wong HR, Cvijanovich N, Allen GL, Lin R, Anas N, Meyer K, Freishtat RJ, Monaco M, Odoms K, Sakthivel B, Shanley TP, Genomics of Pediatric SIRS/Septic Shock Investigators Genomic expression profiling across the pediatric systemic inflammatory response syndrome, sepsis, and septic shock spectrum. Crit Care Med. 2009;37:1558–1566. doi: 10.1097/CCM.0b013e31819fcc08.
    1. Tang BM, McLean AS, Dawes IW, Huang SJ, Lin RC. Gene-expression profiling of peripheral blood mononuclear cells in sepsis. Crit Care Med. 2009;37:882–888. doi: 10.1097/CCM.0b013e31819b52fd.
    1. Lambeck S, Weber M, Gonnert FA, Mrowka R, Bauer M. Comparison of sepsis-induced transcriptomic changes in a murine model to clinical blood samples identifies common response patterns. Front Microbiol. 2012;3:284. doi: 10.3389/fmicb.2012.00284.
    1. 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. doi: 10.1097/SHK.0000000000000063.
    1. Mickiewicz B, Duggan GE, Winston BW, Doig C, Kubes P, Vogel HJ, Alberta Sepsis N. Metabolic profiling of serum samples by 1H nuclear magnetic resonance spectroscopy as a potential diagnostic approach for septic shock. Crit Care Med. 2014;42:1140–1149. doi: 10.1097/CCM.0000000000000142.
    1. Cao Z, Yende S, Kellum JA, Angus DC, Robinson RA. Proteomics reveals age-related differences in the host immune response to sepsis. J Proteome Res. 2014;13:422–432. doi: 10.1021/pr400814s.
    1. Kalenka A, Feldmann RE, Jr, Otero K, Maurer MH, Waschke KF, Fiedler F. Changes in the serum proteome of patients with sepsis and septic shock. Anesth Analg. 2006;103:1522–1526. doi: 10.1213/01.ane.0000242533.59457.70.
    1. Shen Z, Want EJ, Chen W, Keating W, Nussbaumer W, Moore R, Gentle TM, Siuzdak G. Sepsis plasma protein profiling with immunodepletion, three-dimensional liquid chromatography tandem mass spectrometry, and spectrum counting. J Proteome Res. 2006;5:3154–3160. doi: 10.1021/pr060327k.
    1. Wang H, Zhang P, Chen W, Feng D, Jia Y, Xie L. Serum microRNA signatures identified by Solexa sequencing predict sepsis patients’ mortality: a prospective observational study. PLoS One. 2012;7:e38885. doi: 10.1371/journal.pone.0038885.
    1. Ma Y, Vilanova D, Atalar K, Delfour O, Edgeworth J, Ostermann M, Hernandez-Fuentes M, Razafimahatratra S, Michot B, Persing DH, Ziegler I, Toros B, Molling P, Olcen P, Beale R, Lord GM. Genome-wide sequencing of cellular microRNAs identifies a combinatorial expression signature diagnostic of sepsis. PLoS One. 2013;8:e75918. doi: 10.1371/journal.pone.0075918.
    1. Wang HJ, Zhang PJ, Chen WJ, Jie D, Dan F, Jia YH, Xie LX. Characterization and Identification of novel serum microRNAs in sepsis patients with different outcomes. Shock. 2013;39:480–487. doi: 10.1097/SHK.0b013e3182940cb8.
    1. Otto GP, Sossdorf M, Claus RA, Rodel J, Menge K, Reinhart K, Bauer M, Riedemann NC. The late phase of sepsis is characterized by an increased microbiological burden and death rate. Crit Care. 2011;15:R183. doi: 10.1186/cc10332.
    1. Mizumura K, Cloonan SM, Haspel JA, Choi AM. The emerging importance of autophagy in pulmonary diseases. Chest. 2012;142:1289–1299. doi: 10.1378/chest.12-0809.
    1. Simard JC, Cesaro A, Chapeton-Montes J, Tardif M, Antoine F, Girard D, Tessier PA. S100A8 and S100A9 induce cytokine expression and regulate the NLRP3 inflammasome via ROS-dependent activation of NF-kappaB(1.) PLoS One. 2013;8:e72138. doi: 10.1371/journal.pone.0072138.
    1. Sugimoto J, Hatakeyama T, Isobe M. Isolation and mapping of a putative b subunit of human ATP synthase (ATP-BL) from human leukocytes. DNA Res. 1999;6:29–35. doi: 10.1093/dnares/6.1.29.
    1. Bandyopadhyay S, Chiang CY, Srivastava J, Gersten M, White S, Bell R, Kurschner C, Martin C, Smoot M, Sahasrabudhe S, Barber DL, Chanda SK, Ideker T. A human MAP kinase interactome. Nat Methods. 2010;7:801–805. doi: 10.1038/nmeth.1506.
    1. Buday L, Egan SE, Rodriguez Viciana P, Cantrell DA, Downward J. A complex of Grb2 adaptor protein, Sos exchange factor, and a 36-kDa membrane-bound tyrosine phosphoprotein is implicated in ras activation in T cells. J Biol Chem. 1994;269:9019–9023.
    1. Stork B, Engelke M, Frey J, Horejsi V, Hamm-Baarke A, Schraven B, Kurosaki T, Wienands J. Grb2 and the non-T cell activation linker NTAL constitute a Ca(2+)-regulating signal circuit in B lymphocytes. Immunity. 2004;21:681–691. doi: 10.1016/j.immuni.2004.09.007.
    1. Hart CP, Martin JE, Reed MA, Keval AA, Pustelnik MJ, Northrop JP, Patel DV, Grove JR. Potent inhibitory ligands of the GRB2 SH2 domain from recombinant peptide libraries. Cell Signal. 1999;11:453–464. doi: 10.1016/S0898-6568(99)00017-0.
    1. Romero F, Ramos-Morales F, Dominguez A, Rios RM, Schweighoffer F, Tocque B, Pintor-Toro JA, Fischer S, Tortolero M. Grb2 and its apoptotic isoform Grb3-3 associate with heterogeneous nuclear ribonucleoprotein C, and these interactions are modulated by poly(U) RNA. J Biol Chem. 1998;273:7776–7781. doi: 10.1074/jbc.273.13.7776.
    1. Iwashyna TJ, Netzer G, Langa KM, Cigolle C. Spurious inferences about long-term outcomes: the case of severe sepsis and geriatric conditions. Am J Respir Crit Care Med. 2012;185:835–841. doi: 10.1164/rccm.201109-1660OC.

Source: PubMed

Sponsorer og samarbejdspartnere

Medicinske tilstande

Narkotikainterventioner

3
Abonner