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Multiple biomarkers of sepsis identified by novel time-lapse proteomics of patient serum


Autoři: Nobuhiro Hayashi aff001;  Syunta Yamaguchi aff001;  Frans Rodenburg aff001;  Sing Ying Wong aff001;  Kei Ujimoto aff002;  Takahiro Miki aff003;  Toshiaki Iba aff004
Působiště autorů: School of Life Science and Technology, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan aff001;  Graduate School of Bioscience and Biotechnology, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan aff002;  Nihon University Surugadai Hospital, Kanda-Surugadai, Chiyoda-ku, Tokyo, Japan aff003;  Department of Emergency and Disaster Medicine, Juntendo University, Hongo, Bunkyo-ku, Tokyo, Japan aff004
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0222403

Souhrn

Serum components of sepsis patients vary with the severity of infection, the resulting inflammatory response, per individual, and even over time. Tracking these changes is crucial in properly treating sepsis. Hence, several blood-derived biomarkers have been studied for their potential in assessing sepsis severity. However, the classical approach of selecting individual biomarkers is problematic in terms of accuracy and efficiency. We therefore present a novel approach for detecting biomarkers using longitudinal proteomics data. This does not require a predetermined set of proteins and can therefore reveal previously unknown related proteins. Our approach involves examining changes over time of both protein abundance and post-translational modifications in serum, using two-dimensional gel electrophoresis (2D-PAGE). 2D-PAGE was conducted using serum from n = 20 patients, collected at five time points, starting from the onset of sepsis. Changes in protein spots were examined using 49 spots for which the signal intensity changed by at least two-fold over time. These were then screened for significant spikes or dips in intensity that occurred exclusively in patients with adverse outcome. Individual level variation was handled by a mixed effects model. Finally, for each time transition, partial correlations between spots were estimated through a Gaussian graphical model (GGM) based on the ridge penalty. Identifications of spots of interest by tandem mass spectrometry revealed that many were either known biomarkers for inflammation (complement components), or had previously been suggested as biomarkers for kidney failure (haptoglobin) or liver failure (ceruloplasmin). The latter two are common complications in severe sepsis. In the GGM, many of the tightly connected spots shared known biological functions or even belonged to the same protein; including hemoglobin chains and acute phase proteins. Altogether, these results suggest that our screening method can successfully identify biomarkers for disease states and cluster biologically related proteins using longitudinal proteomics data derived from 2D-PAGE.

Klíčová slova:

Biomarkers – C-reactive proteins – Hemoglobin – Inflammation – Proteomic databases – Sepsis – Serine proteases – Haptoglobins


Zdroje

1. Dellinger RP, Levy MM, Rhodes A, Annane D, Gerlach H, Opal SM, et al. Surviving Sepsis Campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 2013;39(2):165–228. Epub 2013/01/31. doi: 10.1007/s00134-012-2769-8 23361625.

2. Skibsted S, Bhasin MK, Aird WC, Shapiro NI. Bench-to-bedside review: future novel diagnostics for sepsis—a systems biology approach. Crit Care. 2013;17(5):231. Epub 2013/10/08. doi: 10.1186/cc12693 24093155; PubMed Central PMCID: PMC4057467.

3. Stevenson EK, Rubenstein AR, Radin GT, Wiener RS, Walkey AJ. Two decades of mortality trends among patients with severe sepsis: a comparative meta-analysis*. Crit Care Med. 2014;42(3):625–31. Epub 2013/11/10. doi: 10.1097/CCM.0000000000000026 24201173; PubMed Central PMCID: PMC4313930.

4. 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(7):1303–10. Epub 2001/07/11. doi: 10.1097/00003246-200107000-00002 11445675.

5. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonca A, Bruining H, et al. 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(7):707–10. Epub 1996/07/01. doi: 10.1007/bf01709751 8844239.

6. Povoa P. C-reactive protein: a valuable marker of sepsis. Intensive Care Med. 2002;28(3):235–43. Epub 2002/03/21. doi: 10.1007/s00134-002-1209-6 11904651.

7. Meisner M, Tschaikowsky K, Palmaers T, Schmidt J. Comparison of procalcitonin (PCT) and C-reactive protein (CRP) plasma concentrations at different SOFA scores during the course of sepsis and MODS. Crit Care. 1999;3(1):45–50. Epub 2000/11/01. doi: 10.1186/cc306 11056723; PubMed Central PMCID: PMC29013.

8. Herzum I, Renz H. Inflammatory markers in SIRS, sepsis and septic shock. Curr Med Chem. 2008;15(6):581–7. Epub 2008/03/14. doi: 10.2174/092986708783769704 18336272.

9. Pierrakos C, Vincent JL. Sepsis biomarkers: a review. Crit Care. 2010;14(1):R15. Epub 2010/02/11. doi: 10.1186/cc8872 20144219; PubMed Central PMCID: PMC2875530.

10. Van den Bruel A, Thompson MJ, Haj-Hassan T, Stevens R, Moll H, Lakhanpaul M, et al. Diagnostic value of laboratory tests in identifying serious infections in febrile children: systematic review. BMJ. 2011;342:d3082. Epub 2011/06/10. doi: 10.1136/bmj.d3082 21653621.

11. Oda S, Hirasawa H, Shiga H, Nakanishi K, Matsuda K, Nakamua M. Sequential measurement of IL-6 blood levels in patients with systemic inflammatory response syndrome (SIRS)/sepsis. Cytokine. 2005;29(4):169–75. Epub 2005/01/18. doi: 10.1016/j.cyto.2004.10.010 15652449.

12. Abe R, Oda S, Sadahiro T, Nakamura M, Hirayama Y, Tateishi Y, et al. Gram-negative bacteremia induces greater magnitude of inflammatory response than Gram-positive bacteremia. Crit Care. 2010;14(2):R27. Epub 2010/03/06. doi: 10.1186/cc8898 20202204; PubMed Central PMCID: PMC2887127.

13. Schroeder S, Hochreiter M, Koehler T, Schweiger AM, Bein B, Keck FS, et al. Procalcitonin (PCT)-guided algorithm reduces length of antibiotic treatment in surgical intensive care patients with severe sepsis: results of a prospective randomized study. Langenbecks Arch Surg. 2009;394(2):221–6. Epub 2008/11/27. doi: 10.1007/s00423-008-0432-1 19034493.

14. Maier M, Wutzler S, Lehnert M, Szermutzky M, Wyen H, Bingold T, et al. Serum procalcitonin levels in patients with multiple injuries including visceral trauma. J Trauma. 2009;66(1):243–9. Epub 2009/01/10. doi: 10.1097/TA.0b013e31817c966f 19131834.

15. Morath C, Sis J, Haensch GM, Zeier M, Andrassy K, Schwenger V. Procalcitonin as marker of infection in patients with Goodpasture's syndrome is misleading. Nephrol Dial Transplant. 2007;22(9):2701–4. Epub 2007/06/09. doi: 10.1093/ndt/gfm353 17556410.

16. Hausfater P, Hurtado M, Pease S, Juillien G, Lvovschi VE, Salehabadi S, et al. Is procalcitonin a marker of critical illness in heatstroke? Intensive Care Med. 2008;34(8):1377–83. Epub 2008/03/29. doi: 10.1007/s00134-008-1083-y 18369592.

17. Hayashi N. High-accuracy and high-throughput profiling of organisms; High performance proteomics using an improved two-dimensional electrophoresis as the fundamental technology. Expected Materials for the Future. 2011;11(1):42–9.

18. Laemmli UK. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature. 1970;227(5259):680–5. Epub 1970/08/15. doi: 10.1038/227680a0 5432063.

19. Kurosawa G, Sumitomo M, Akahori Y, Matsuda K, Muramatsu C, Takasaki A, et al. Methods for comprehensive identification of membrane proteins recognized by a large number of monoclonal antibodies. J Immunol Methods. 2009;351(1–2):1–12. Epub 2009/09/22. doi: 10.1016/j.jim.2009.09.003 19766650.

20. Li GZ, Vissers JP, Silva JC, Golick D, Gorenstein MV, Geromanos SJ. Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Proteomics. 2009;9(6):1696–719. Epub 2009/03/19. doi: 10.1002/pmic.200800564 19294629.

21. Silva JC, Denny R, Dorschel CA, Gorenstein M, Kass IJ, Li GZ, et al. Quantitative proteomic analysis by accurate mass retention time pairs. Anal Chem. 2005;77(7):2187–200. Epub 2005/04/02. doi: 10.1021/ac048455k 15801753.

22. Silva JC, Gorenstein MV, Li GZ, Vissers JP, Geromanos SJ. Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition. Mol Cell Proteomics. 2006;5(1):144–56. Epub 2005/10/13. doi: 10.1074/mcp.M500230-MCP200 16219938.

23. R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

24. Bates Douglas, Maechler Martin, Bolker Ben, Walker Steve (2015). Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software, 67(1), 1–48. doi: 10.18637/jss.v067.i01

25. Peeters C.F.W., Bilgrau A.E., and van Wieringen W.N. (2019) rags2ridges: Ridge Estimation of Precision Matrices from High-Dimensional Data. R package version 2.2.1. https://CRAN.R-project.org/package=rags2ridges

26. van Wieringen W.N. and Peeters C.F.W. (2016). Ridge Estimation of Inverse Covariance Matrices from High-Dimensional Data. Computational Statistics & Data Analysis, vol. 103: 284–303.

27. Schäfer J, Strimmer K. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Stat Appl Genet Mol Biol. 2005; 4: Article32.

28. van Wieringen W.N. and Peeters C.F.W. (2015). Application of a New Ridge Estimator of the Inverse Covariance Matrix to the Reconstruction of Gene-Gene Interaction Networks. In: di Serio C., Lio P., Nonis A., and Tagliaferri R. (Eds.) Computational Intelligence Methods for Bioinformatics and Biostatistics. Lecture Notes in Computer Science, vol. 8623. Springer International Publishing, pp. 170–179.

29. Kiehntopf M, Schmerler D, Brunkhorst FM, Winkler R, Ludewig K, Osterloh D, et al. Mass spectometry-based protein patterns in the diagnosis of sepsis/systemic inflammatory response syndrome. Shock. 2011;36(6):560–9. Epub 2011/10/14. doi: 10.1097/SHK.0b013e318237ea7c 21993444.

30. Ono T, Mimuro J, Madoiwa S, Soejima K, Kashiwakura Y, Ishiwata A, et al. Severe secondary deficiency of von Willebrand factor-cleaving protease (ADAMTS13) in patients with sepsis-induced disseminated intravascular coagulation: its correlation with development of renal failure. Blood. 2006;107(2):528–34. Epub 2005/09/29. doi: 10.1182/blood-2005-03-1087 16189276.

31. Kremer Hovinga JA, Zeerleder S, Kessler P, Romani de Wit T, van Mourik JA, Hack CE, et al. ADAMTS-13, von Willebrand factor and related parameters in severe sepsis and septic shock. J Thromb Haemost. 2007;5(11):2284–90. Epub 2007/09/04. doi: 10.1111/j.1538-7836.2007.02743.x 17764538.

32. Fukushima H, Nishio K, Asai H, Watanabe T, Seki T, Matsui H, et al. Ratio of von Willebrand factor propeptide to ADAMTS13 is associated with severity of sepsis. Shock. 2013;39(5):409–14. Epub 2013/03/14. doi: 10.1097/SHK.0b013e3182908ea7 23481506.

33. Habe K, Wada H, Ito-Habe N, Hatada T, Matsumoto T, Ohishi K, et al. Plasma ADAMTS13, von Willebrand factor (VWF) and VWF propeptide profiles in patients with DIC and related diseases. Thromb Res. 2012;129(5):598–602. Epub 2011/11/11. doi: 10.1016/j.thromres.2011.10.011 22070827.

34. Reiter CD, Wang X, Tanus-Santos JE, Hogg N, Cannon RO 3rd, Schechter AN, et al. Cell-free hemoglobin limits nitric oxide bioavailability in sickle-cell disease. Nat Med. 2002;8(12):1383–9. Epub 2002/11/12. doi: 10.1038/nm1202-799 12426562.

35. Janz DR, Bastarache JA, Sills G, Wickersham N, May AK, Bernard GR, et al. Association between haptoglobin, hemopexin and mortality in adults with sepsis. Crit Care. 2013;17(6):R272. Epub 2013/11/15. doi: 10.1186/cc13108 24225252; PubMed Central PMCID: PMC4056258.

36. Adamzik M, Hamburger T, Petrat F, Peters J, de Groot H, Hartmann M. Free hemoglobin concentration in severe sepsis: methods of measurement and prediction of outcome. Crit Care. 2012;16(4):R125. Epub 2012/07/18. doi: 10.1186/cc11425 22800762; PubMed Central PMCID: PMC3580706.

37. Pandey NR, Bian YY, Shou ST. Significance of blood pressure variability in patients with sepsis. World J Emerg Med. 2014;5(1):42–7. doi: 10.5847/wjem.j.1920-8642.2014.01.007 25215146; PubMed Central PMCID: PMC4129862.

38. Yang H, Wang H, Wang Y, Addorisio M, Li J, Postiglione MJ, Chavan SS, Al-Abed Y, Antoine DJ, Andersson U, Tracey KJ. The haptoglobin beta subunit sequesters HMGB1 toxicity in sterile and infectious inflammation. J Intern Med. 2017 Jul;282(1):76–93. doi: 10.1111/joim.12619 Epub 2017 May 31. 28464519; PubMed Central PMCID: PMC5477782.

39. Dépret F, Dunyach C, De Tymowski C, Chaussard M, Bataille A, Ferry A, Moreno N, Cupaciu A, Soussi S, Benyamina M, Mebazaa A, Serror K, Chaouat M, Garnier JP, Pirracchio R, Legrand M; PRONOBURN group. Undetectable haptoglobin is associated with major adverse kidney events in critically ill burn patients. Crit Care. 2017 Sep 26;21(1):245. doi: 10.1186/s13054-017-1837-4 28946897; PubMed Central PMCID: PMC5613314.

40. Lv M, Ye HG, Zhao XS, Zhao XY, Chang YJ, Liu DH, Xu LP, Huang XJ. Ceruloplasmin is a potential biomarker for aGvHD following allogeneic hematopoietic stem cell transplantation. PLoS One. 8(3):e58735. doi: 10.1371/journal.pone.0058735 Epub 2013 Mar 7. 23505556; PubMed Central PMCID: PMC3591372.


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