Large-scale inference of protein tissue origin in gram-positive sepsis plasma using quantitative targeted proteomics

Nature Communications

Volumn 7, Issue , 2016, Pages

  Malmström, Erik ^a   Kilsgård, Ola ^a   Hauri, Simon ^a   Smeds, Emanuel ^a   Herwald, Heiko ^a   Malmström, Lars ^b   Malmström, Johan ^a  

^a LUND UNIVERSITY   (Sweden)

^b UNIVERSITY OF ZURICH   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

PROTEOME; BIOLOGICAL MARKER; PROTEIN;

BLOOD; CELLS AND CELL COMPONENTS; MASS SPECTROMETRY; PROTEIN; PROTEOMICS; QUANTITATIVE ANALYSIS; RODENT;

ANIMAL EXPERIMENT; ANIMAL MODEL; ARTICLE; CONTROLLED STUDY; DISEASE ASSOCIATION; DISEASE SEVERITY; GRAM POSITIVE INFECTION; MASS SPECTROMETRY; MOUSE; NONHUMAN; PEPTIDE LIBRARY; PROTEOMICS; QUANTITATIVE ANALYSIS; SEPSIS; ANIMAL; BLOOD; CHEMISTRY; CLASSIFICATION; FEMALE; GENE EXPRESSION REGULATION; HUMAN; METABOLISM; MICROBIOLOGY; PHYSIOLOGY; PLASMA; PROCEDURES; REPRODUCIBILITY; STREPTOCOCCUS INFECTION; STREPTOCOCCUS PYOGENES;

ANIMALIA; MURINAE; POSIBACTERIA;

ANIMALS; BIOMARKERS; FEMALE; GENE EXPRESSION REGULATION; HUMANS; MASS SPECTROMETRY; MICE; PLASMA; PROTEINS; PROTEOMICS; REPRODUCIBILITY OF RESULTS; SEPSIS; STREPTOCOCCAL INFECTIONS; STREPTOCOCCUS PYOGENES;

EID: 84954468136     PISSN: None     EISSN: 20411723     Source Type: Journal    
DOI: 10.1038/ncomms10261     Document Type: Article

References (37)

1. Farrah, T. et al. A high-confidence human plasma proteome reference set with estimated concentrations in peptide atlas. Mol. Cell. Proteomics 10, M110.006353-M110.006353 (2011).
2. Guo, T. et al. Rapid mass spectrometric conversion of tissue biopsy samples into permanent quantitative digital proteome maps. Nat. Med. 21, 407-413 (2015).
3. Röst, H. L. et al. OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data. Nat. Biotechnol. 32, 219-223 (2014).
4. Gillet, L. C. et al. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol. Cell. Proteomics 11, O111.016717-O111.016717 (2012).
5. Reinhart, K. et al. The burden of sepsis-a call to action in support of World Sepsis Day 2013. J. Crit. Care 28, 526-528 (2013).
6. van der Poll, T., Opal, S. M. Host-pathogen interactions in sepsis. Lancet Infect. Dis. 8, 32-43 (2008).
7. Russell, J. A. Management of sepsis. N. Engl. J. Med. 355, 1699-1713 (2006).
8. Mi, H., Muruganujan, A., Thomas, P. D. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41, D377-D386 (2012).
9. Ishihama, Y. et al. Exponentially modified protein abundance index (emPAI) for estimation of absolute protein amount in proteomics by the number of sequenced peptides per protein. Mol. Cell. Proteomics 4, 1265-1272 (2005).
10. Malmström, J. et al. Proteome-wide cellular protein concentrations of the human pathogen Leptospira interrogans. Nature 460, 762-765 (2009).
11. Geiger, T. et al. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol. Cell. Proteomics 12, 1709-1722 (2013).
12. Huttlin, E. L. et al. A tissue-specific atlas of mouse protein phosphorylation and expression. Cell 143, 1174-1189 (2010).
13. Kislinger, T. et al. Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling. Cell 125, 173-186 (2006).
14. Addona, T. A. et al. A pipeline that integrates the discovery and verification of plasma protein biomarkers reveals candidate markers for cardiovascular disease. Nat. Biotechnol. 29, 635-643 (2011).
15. Mulatero, P., Verhovez, A., Morello, F., Veglio, F. Diagnosis and treatment of low-renin hypertension. Clin. Endocrinol. 67, 324-334 (2007).
16. Uhlen, M. et al. Tissue-based map of the human proteome. Science 347, 1260419-1260419 (2015).
17. Kettunen, J. et al. Genome-wide association study identifies multiple loci influencing human serum metabolite levels. Nat. Genet. 44, 269-276 (2012).
18. Teleman, J. et al. DIANA-algorithmic improvements for analysis of data-independent acquisition MS data. Bioinformatics 31, 555-562 (2015).
19. Rosenberger, G. et al. A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci. Data 1, 140031 (2014).
20. Van Der Maaten, L., Barnes-Hut-SNE, L. arXiv:1301.3342v1 [cs.LG] (2013).
21. Reynolds, A. P., Richards, G., la Iglesia, de, B., Rayward-Smith, V. J. Clustering rules: a comparison of partitioning and hierarchical clustering algorithms. J. Math. Model. Algor. 5, 475-504 (2006).
22. Basu, S., Binder, R. J., Suto, R., Anderson, K. M., Srivastava, P. K. Necrotic but not apoptotic cell death releases heat shock proteins, which deliver a partial maturation signal to dendritic cells and activate the NF-kappa B pathway. Int. Immunol. 12, 1539-1546 (2000).
23. Pierrakos, C., Vincent, J.-L. Sepsis biomarkers: a review. Crit. Care 14, R15 (2010).
24. Liu, Y. et al. Quantitative variability of 342 plasma proteins in a human twin population. Mol. Syst. Biol. 11, 786-786 (2015).
25. Shevchenko, A., Wilm, M., Vorm, O., Mann, M. Mass spectrometric sequencing of proteins from silver stained polyacrylamide gels. Anal. Chem. 68, 850-858 (1996).
26. Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918-920 (2012).
27. Bauch, A. et al. openBIS: a flexible framework for managing and analyzing complex data in biology research. BMC Bioinformatics 12, 468 (2011).
28. Kunszt, P. et al. iPortal: the swiss grid proteomics portal: Requirements and new features based on experience and usability considerations. Concurrency and Computation: Practice and Experience 27, 433-445 (2015).
29. Malmström, L., Marko-Varga, G., Westergren-Thorsson, G., Laurell, T., Malmström, J. 2DDB-a bioinformatics solution for analysis of quantitative proteomics data. BMC Bioinformatics 7, 158 (2006).
30. Malmström, L., Nordenfelt, P., Malmström, J. Business intelligence strategies enables rapid analysis of quantitative proteomics data. J. Proteome Sci. Comput. Biol. 1, 5 (2012).
31. Malmström, L., Malmström, J., Marko-Varga, G. Proteomic 2DE database for spot selection, automated annotation, and data analysis. J. Proteome Res. 1, 135-138 (2002).
32. Quandt, A. et al. ScienceDirect. EUPROT 5, 21-31 (2014).
33. Craig, R., Beavis, R. C. TANDEM: matching proteins with tandem mass spectra. Bioinformatics 20, 1466-1467 (2004).
34. Geer, L. Y. et al. Open mass spectrometry search algorithm. J. Proteome Res. 3, 958-964 (2004).
35. Keller, A., Nesvizhskii, A. I., Kolker, E., Aebersold, R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, 5383-5392 (2002).
36. Shteynberg, D. et al. iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates. Mol. Cell. Proteomics 10, M111.007690-M111.007690 (2011).
37. Nesvizhskii, A. I., Keller, A., Kolker, E., Aebersold, R. A statistical model for identifying proteins by tandem mass spectrometry. Anal. Chem. 75, 4646-4658 (2003).