Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation.
Journal
Transplantation
ISSN: 1534-6080
Titre abrégé: Transplantation
Pays: United States
ID NLM: 0132144
Informations de publication
Date de publication:
01 10 2023
01 10 2023
Historique:
medline:
23
10
2023
pubmed:
23
2
2023
entrez:
22
2
2023
Statut:
ppublish
Résumé
Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.
Identifiants
pubmed: 36808112
doi: 10.1097/TP.0000000000004539
pii: 00007890-202310000-00013
doi:
Substances chimiques
Biomarkers
0
Types de publication
Review
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2126-2142Subventions
Organisme : CIHR
ID : 180351
Pays : Canada
Informations de copyright
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
The authors declare no conflicts of interest.
Références
Sellarés J, de Freitas DG, Mengel M, et al. Understanding the causes of kidney transplant failure: the dominant role of antibody-mediated rejection and nonadherence. Am J Transplant. 2012;12:388–399.
Stegall MD, Gaston RS, Cosio FG, et al. Through a glass darkly: seeking clarity in preventing late kidney transplant failure. J Am Soc Nephrol. 2015;26:20–29.
Gaston RS, Cecka JM, Kasiske BL, et al. Evidence for antibody-mediated injury as a major determinant of late kidney allograft failure. Transplantation. 2010;90:68–74.
Loupy A, Hill GS, Jordan SC. The impact of donor-specific anti-HLA antibodies on late kidney allograft failure. Nat Rev Nephrol. 2012;8:348–357.
Kienzl-Wagner K, Pratschke J, Brandacher G. Biomarker discovery in transplantation—proteomic adventure or mission impossible? Clin Biochem. 2013;46:497–505.
Vidhun J, Masciandro J, Varich L, et al. Safety and risk stratification of percutaneous biopsies of adult-sized renal allografts in infant and older pediatric recipients. Transplantation. 2003;76:552–557.
Colvin RB. The renal allograft biopsy. Kidney Int. 1996;50:1069–1082.
Londoño MC, Danger R, Giral M, et al. A need for biomarkers of operational tolerance in liver and kidney transplantation: biomarkers of operational tolerance. Am J Transplant. 2012;12:1370–1377.
Farid WRR, Verhoeven CJ, de Jonge J, et al. The ins and outs of microRNAs as biomarkers in liver disease and transplantation. Transpl Int. 2014;27:1222–1232.
Paladini SV, Pinto GH, Bueno RH, et al. Identification of candidate biomarkers for transplant rejection from transcriptome data: a systematic review. Mol Diagn Ther. 2019;23:439–458.
Findeisen P, Neumaier M. Mass spectrometry-based clinical proteomics profiling: current status and future directions. Expert Rev Proteomics. 2009;6:457–459.
Patterson SD, Aebersold RH. Proteomics: the first decade and beyond. Nat Genet. 2003;33:311–323.
Kienzl-Wagner K, Pratschke J, Brandacher G. Proteomics—a blessing or a curse? Application of proteomics technology to transplant medicine. Transplantation. 2011;92:499–509.
Tyers M, Mann M. From genomics to proteomics. Nature. 2003;422:193–197.
Pandey A, Mann M. Proteomics to study genes and genomes. Nature. 2000;405:837–846.
Graves PR, Haystead TAJ. Molecular biologist’s guide to proteomics. Microbiol Mol Biol Rev. 2002;66:39–63.
Nesvizhskii AI, Aebersold R. Interpretation of shotgun proteomic data. Mol Cell Proteomics. 2005;4:1419–1440.
Wong CCL, Cociorva D, Venable JD, et al. Comparison of different signal thresholds on data dependent sampling in orbitrap and LTQ mass spectrometry for the identification of peptides and proteins in complex mixtures. J Am Soc Mass Spectrom. 2009;20:1405–1414.
Geromanos SJ, Vissers JPC, Silva JC, et al. The detection, correlation, and comparison of peptide precursor and product ions from data independent LC-MS with data dependant LC-MS/MS. Proteomics. 2009;9:1683–1695.
Sigdel TK, Klassen RB, Sarwal MM. Interpreting the proteome and peptidome in transplantation. Adv Clin Chem. 2009;47:139–169.
Sigdel TK, Lau K, Schilling J, et al. Optimizing protein recovery for urinary proteomics, a tool to monitor renal transplantation: optimizing protocols for urinary proteomics. Clin Transplant. 2008;22:617–623.
Sigdel TK, Lee S, Sarwal MM. Profiling the proteome in renal transplantation. Prot Clin Appl. 2011;5:269–280.
Sigdel TK, Sarwal MM. Recent advances in biomarker discovery in solid organ transplant by proteomics. Expert Rev Proteomics. 2011;8:705–715.
Zaoui P. Predictive diagnostic of chronic allograft dysfunction using urinary proteomics analysis. Ann Transplant. 2012;17:52–60.
Christians U, Klawitter J, Klawitter J. Biomarkers in transplantation—Proteomics and metabolomics. Ther Drug Monit. 2016;38(Suppl 1):S70–S74.
Lo DJ, Kaplan B, Kirk AD. Biomarkers for kidney transplant rejection. Nat Rev Nephrol. 2014;10:215–225.
Roedder S, Vitalone M, Khatri P, et al. Biomarkers in solid organ transplantation: establishing personalized transplantation medicine. Genome Med. 2011;3:37.
Hanash S, Taguchi A. Application of proteomics to cancer early detection. Cancer J. 2011;17:423–428.
Jabbar KS, Arike L, Verbeke CS, et al. Highly accurate identification of cystic precursor lesions of pancreatic cancer through targeted mass spectrometry: a phase IIc diagnostic study. JCO. 2018;36:367–375.
Peng L, Cantor DI, Huang C, et al. Tissue and plasma proteomics for early stage cancer detection. Mol Omics. 2018;14:405–423.
Spasovski G, Rambabova-Bushljetik I, Trajceska L, et al. Urinary proteomics in kidney transplantation. PRILOZI. 2021;42:7–16.
Tirumalai RS, Chan KC, Prieto DA, et al. Characterization of the low molecular weight human serum proteome. Mol Cell Proteomics. 2003;2:1096–1103.
Hortin GL, Sviridov D, Anderson NL. High-abundance polypeptides of the human plasma proteome comprising the top 4 logs of polypeptide abundance. Clin Chem. 2008;54:1608–1616.
Anderson NL, Anderson NG. The human plasma proteome. Mol Cell Proteomics. 2002;1:845–867.
Solier C, Langen H. Antibody-based proteomics and biomarker research—current status and limitations. Proteomics. 2014;14:774–783.
Davalieva K, Kiprijanovska S, Maleva Kostovska I, et al. Comparative proteomics analysis of urine reveals down-regulation of acute phase response signaling and LXR/RXR activation pathways in prostate cancer. Proteomes. 2017;6:1E1.
Drabovich AP, Pavlou MP, Dimitromanolakis A, et al. Quantitative analysis of energy metabolic pathways in MCF-7 breast cancer cells by selected reaction monitoring assay. Mol Cell Proteomics. 2012;11:422–434.
Konvalinka A, Scholey JW, Diamandis EP. Searching for new biomarkers of renal diseases through proteomics. Clin Chem. 2012;58:353–365.
Drabovich AP, Dimitromanolakis A, Saraon P, et al. Differential diagnosis of azoospermia with proteomic biomarkers ECM1 and TEX101 quantified in seminal plasma. Sci Transl Med. 2013;5:212–ra160.
Martínez-Morillo E, Nielsen HM, Batruch I, et al. Assessment of peptide chemical modifications on the development of an accurate and precise multiplex selected reaction monitoring assay for apolipoprotein e isoforms. J Proteome Res. 2014;13:1077–1087.
Chen Y, Gruidl M, Remily-Wood E, et al. Quantification of beta-catenin signaling components in colon cancer cell lines, tissue sections, and microdissected tumor cells using reaction monitoring mass spectrometry. J Proteome Res. 2010;9:4215–4227.
Clotet-Freixas S, McEvoy CM, Batruch I, et al. Extracellular matrix injury of kidney allografts in antibody-mediated rejection: a proteomics study. JASN. 2020;31:2705–2724.
Kelly RT. Single-cell proteomics: progress and prospects. Mol Cell Proteomics. 2020;19:1739–1748.
Slavov N. Single-cell protein analysis by mass spectrometry. Curr Opin Chem Biol. 2021;60:1–9.
Picotti P, Aebersold R. Selected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions. Nat Methods. 2012;9:555–566.
Wu B, Wu H, Chen J, et al. Comparative proteomic analysis of human donor tissues during orthotopic liver transplantation: ischemia versus reperfusion. Hepatol Int. 2013;7:286–298.
Huang S, Ju W, Zhu Z, et al. Comprehensive and combined omics analysis reveals factors of ischemia-reperfusion injury in liver transplantation. Epigenomics. 2019;11:527–542.
Diamond DL, Krasnoselsky AL, Burnum KE, et al. Proteome and computational analyses reveal new insights into the mechanisms of hepatitis C virus-mediated liver disease posttransplantation. Hepatology. 2012;56:28–38.
Kim MS, Pinto SM, Getnet D, et al. A draft map of the human proteome. Nature. 2014;509:575–581.
Shahdordizadeh M, Taghdisi SM, Sankian M, et al. Design, isolation and evaluation of the binding efficiency of a DNA aptamer against interleukin 2 receptor alpha, in vitro. Int Immunopharmacol. 2017;53:96–104.
Khan MA, Alanazi F, Ahmed HA, et al. C5a blockade increases regulatory T cell numbers and protects against microvascular loss and epithelial damage in mouse airway allografts. Front Immunol. 2018;9:1010.
Kollar B, Shubin A, Borges TJ, et al. Increased levels of circulating MMP3 correlate with severe rejection in face transplantation. Sci Rep. 2018;8:14915.
Mansouri A, Abnous K, Nabavinia MS, et al. In vitro selection of tacrolimus binding aptamer by systematic evolution of ligands by exponential enrichment method for the development of a fluorescent aptasensor for sensitive detection of tacrolimus. J Pharm Biomed Anal. 2020;177:112853.
Almufleh A, Zhang L, Mielniczuk LM, et al. Biomarker discovery in cardiac allograft vasculopathy using targeted aptamer proteomics. Clin Transplant. 2020;34:e13765.
Egerstedt A, Berntsson J, Smith ML, et al. Profiling of the plasma proteome across different stages of human heart failure. Nat Commun. 2019;10:5830.
Kim DH, Ahn J, Kang HK, et al. Development of highly functional bioengineered human liver with perfusable vasculature. Biomaterials. 2021;265:120417.
Todd JL, Neely ML, Overton R, et al. Association of circulating proteins with death or lung transplant in patients with idiopathic pulmonary fibrosis in the IPF-PRO registry cohort. Lung. 2022;200:1911–1919.
Aebersold R, Burlingame AL, Bradshaw RA. Western blots versus selected reaction monitoring assays: time to turn the tables? Mol Cell Proteomics. 2013;12:2381–2382.
Gregorich ZR, Ge Y. Top-down proteomics in health and disease: challenges and opportunities. Proteomics. 2014;14:1195–1210.
Wang K, Huang C, Nice EC. Proteomics, genomics and transcriptomics: their emerging roles in the discovery and validation of colorectal cancer biomarkers. Expert Rev Proteomics. 2014;11:179–205.
Savaryn JP, Toby TK, Catherman AD, et al. Comparative top down proteomics of peripheral blood mononuclear cells from kidney transplant recipients with normal kidney biopsies or acute rejection. Proteomics. 2016;16:2048–2058.
Bensimon A, Heck AJR, Aebersold R. Mass spectrometry–based proteomics and network biology. Annu Rev Biochem. 2012;81:379–405.
Zhang Y, Fonslow BR, Shan B, et al. Protein analysis by shotgun/bottom-up proteomics. Chem Rev. 2013;113:2343–2394.
Cox J, Mann M. Quantitative, high-resolution proteomics for data-driven systems biology. Annu Rev Biochem. 2011;80:273–299.
Görg A, Weiss W, Dunn MJ. Current two-dimensional electrophoresis technology for proteomics. Proteomics. 2004;4:3665–3685.
O’Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem. 1975;250:4007–4021.
Kanzelmeyer NK, Zürbig P, Mischak H, et al. Urinary proteomics to diagnose chronic active antibody-mediated rejection in pediatric kidney transplantation—a pilot study. Transpl Int. 2019;32:28–37.
López-López V, Pérez-Sánz F, de Torre-Minguela C, et al. Proteomics in liver transplantation: a systematic review. Front Immunol. 2021;12:672829.
Aebersold R, Mann M. Mass spectrometry-based proteomics. Nature. 2003;422:198–207.
Zubarev RA, Makarov A. Orbitrap mass spectrometry. Anal Chem. 2013;85:5288–5296.
Geiger T, Cox J, Mann M. Proteomics on an orbitrap benchtop mass spectrometer using all-ion fragmentation. Mol Cell Proteomics 2010;9:2252–2261.
Venable JD, Dong MQ, Wohlschlegel J, et al. Automated approach for quantitative analysis of complex peptide mixtures from tandem mass spectra. Nat Methods. 2004;1:39–45.
Gillet LC, Navarro P, Tate S, 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. 2012;11:O111–016717.
Neilson KA, Ali NA, Muralidharan S, et al. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics. 2011;11:535–553.
Ong SE, Blagoev B, Kratchmarova I, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics. 2002;1:376–386.
Rauniyar N, Yates JR. Isobaric labeling-based relative quantification in shotgun proteomics. J Proteome Res. 2014;13:5293–5309.
Chen X, Sun Y, Zhang T, et al. Quantitative proteomics using isobaric labeling: a practical guide. Genomics Proteomics Bioinformatics. 2021;19:689–706.
Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol. 2006;24:971–983.
Paulovich AG, Whiteaker JR, Hoofnagle AN, et al. The interface between biomarker discovery and clinical validation: the tar pit of the protein biomarker pipeline. Prot Clin Appl. 2008;2:1386–1402.
Parker CE, Pearson TW, Anderson NL, et al. Mass-spectrometry-based clinical proteomics—a review and prospective. Analyst. 2010;135:1830.
Smith RD. Mass spectrometry in biomarker applications: from untargeted discovery to targeted verification, and implications for platform convergence and clinical application. Clin Chem. 2012;58:528–530.
Sobsey CA, Ibrahim S, Richard VR, et al. Targeted and untargeted proteomics approaches in biomarker development. Proteomics. 2020;20:1900029.
Marx V. Targeted proteomics. Nat Methods. 2013;10:19–22.
Rodríguez-Suárez E, Whetton AD. The application of quantification techniques in proteomics for biomedical research: quantification techniques in proteomics. Mass Spectrom Rev. 2013;32:1–26.
Baker M. Reproducibility crisis: blame it on the antibodies. Nature. 2015;521:274–276.
Prassas I, Brinc D, Farkona S, et al. False biomarker discovery due to reactivity of a commercial ELISA for CUZD1 with cancer antigen CA125. Clin Chem. 2014;60:381–388.
Espina V, Mehta AI, Winters ME, et al. Protein microarrays: molecular profiling technologies for clinical specimens. Proteomics. 2003;3:2091–2100.
Masuda M, Yamada T. Signaling pathway profiling by reverse-phase protein array for personalized cancer medicine. Biochim Biophys Acta. 2015;1854:651–657.
Malinowsky K, Wolff C, Ergin B, et al. Deciphering signaling pathways in clinical tissues for personalized medicine using protein microarrays. J Cell Physiol. 2010;225:364–370.
Wilson B, Liotta LA, Petricoin IE. Monitoring proteins and protein networks using reverse phase protein arrays. Dis Markers. 2010;28:225–232.
Voshol H, Ehrat M, Traenkle J, et al. Antibody-based proteomics: analysis of signaling networks using reverse protein arrays. FEBS J. 2009;276:6871–6879.
Huang S, Creighton C. Reverse Phase Protein Arrays in Signaling Pathways: A Data Integration Perspective. DDDT; 2015:3519.
Hultschig C, Kreutzberger J, Seitz H, et al. Recent advances of protein microarrays. Curr Opin Chem Biol. 2006;10:4–10.
Angenendt P. Progress in protein and antibody microarray technology. Drug Discov Today. 2005;10:503–511.
Srivastava M, Eidelman O, Torosyan Y, et al. Elevated expression levels of ANXA11, integrins β3 and α3, and TNF-α contribute to a candidate proteomic signature in urine for kidney allograft rejection. Prot Clin Appl. 2011;5:311–321.
Assarsson E, Lundberg M, Holmquist G, et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS One. 2014;9:e95192.
Gillette MA, Carr SA. Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry. Nat Methods. 2013;10:28–34.
Berna M, Ott L, Engle S, et al. Quantification of NTproBNP in rat serum using immunoprecipitation and LC/MS/MS: a biomarker of drug-induced cardiac hypertrophy. Anal Chem. 2008;80:561–566.
Anderson NL, Anderson NG, Haines LR, et al. Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA). J Proteome Res. 2004;3:235–244.
Whiteaker JR, Zhao L, Abbatiello SE, et al. Evaluation of large scale quantitative proteomic assay development using peptide affinity-based mass spectrometry. Mol Cell Proteomics. 2011;10:M110.005645.
Neubert H, Gale J, Muirhead D. Online high-flow peptide immunoaffinity enrichment and nanoflow LC-MS/MS: assay development for total salivary pepsin/pepsinogen. Clin Chem. 2010;56:1413–1423.
Gallien S, Duriez E, Domon B. Selected reaction monitoring applied to proteomics. J Mass Spectrom. 2011;46:298–312.
Lange V, Picotti P, Domon B, et al. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol. 2008;4:222.
Addona TA, Abbatiello SE, Schilling B, et al. Multi-site assessment of the precision and reproducibility of multiple reaction monitoring-based measurements of proteins in plasma. Nat Biotechnol. 2009;27:633–641.
Rauniyar N. Parallel reaction monitoring: a targeted experiment performed using high resolution and high mass accuracy mass spectrometry. IJMS. 2015;16:28566–28581.
Bourmaud A, Gallien S, Domon B. Parallel reaction monitoring using quadrupole-Orbitrap mass spectrometer: principle and applications. Proteomics. 2016;16:2146–2159.
Peterson AC, Russell JD, Bailey DJ, et al. Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics. Mol Cell Proteomics. 2012;11:1475–1488.
Gold L, Ayers D, Bertino J, et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One. 2010;5:e15004.
Rohloff JC, Gelinas AD, Jarvis TC, et al. Nucleic acid ligands with protein-like side chains: modified aptamers and their use as diagnostic and therapeutic agents. Mol Ther Nucleic Acids. 2014;3:e201.
Suhre K, McCarthy MI, Schwenk JM. Genetics meets proteomics: perspectives for large population-based studies. Nat Rev Genet. 2021;22:19–37.
Joshi A, Mayr M. In aptamers they trust: the caveats of the SOMAscan biomarker discovery platform from somalogic. Circulation. 2018;138:2482–2485.
Raffield LM, Dang H, Pratte KA, et al. Comparison of proteomic assessment methods in multiple cohort studies. Proteomics. 2020;20:1900278e1900278.
Günther OP, Chen V, Freue GC, et al. A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers. BMC Bioinf. 2012;13:326.
Mertens I, Willems H, Van Loon E, et al. Urinary protein biomarker panel for the diagnosis of antibody-mediated rejection in kidney transplant recipients. Kidney International Reports. 2020;5:1448–1458.
Sigdel TK, Gao Y, He J, et al. Mining the human urine proteome for monitoring renal transplant injury. Kidney Int. 2016;89:1244–1252.
Metzger J, Chatzikyrkou C, Broecker V, et al. Diagnosis of subclinical and clinical acute T-cell-mediated rejection in renal transplant patients by urinary proteome analysis. Prot Clin Appl. 2011;5:322–333.
Gwinner W, Karch A, Braesen JH, et al. Noninvasive diagnosis of acute rejection in renal transplant patients using mass spectrometric analysis of urine samples: a multicenter diagnostic phase III trial. Transplantation Direct. 2022;8:e1316.
Lin D, Cohen Freue G, Hollander Z, et al. Plasma protein biosignatures for detection of cardiac allograft vasculopathy. J Heart Lung Transplant. 2013;32:723–733.
Wei D, Trenson S, Van Keer JM, et al. The novel proteomic signature for cardiac allograft vasculopathy. ESC Heart Failure. 2022;9:1216–1227.
Jiang Q, Ru Y, Yu Y, et al. iTRAQ-based quantitative proteomic analysis reveals potential early diagnostic markers in serum of acute cellular rejection after liver transplantation. Transpl Immunol. 2019;53:7–12.
Massoud O, Heimbach J, Viker K, et al. Noninvasive diagnosis of acute cellular rejection in liver transplant recipients: a proteomic signature validated by enzyme-linked immunosorbent assay: noninvasive diagnosis of acute cellular rejection. Liver Transpl. 2011;17:723–732.
Hricik DE, Nickerson P, Formica RN, et al. Multicenter validation of urinary CXCL9 as a risk-stratifying biomarker for kidney transplant injury. Am J Transplant. 2013;13:2634–2644.
van Leeuwen LL, Spraakman NA, Brat A, et al. Proteomic analysis of machine perfusion solution from brain dead donor kidneys reveals that elevated complement, cytoskeleton and lipid metabolism proteins are associated with 1-year outcome. Transpl Int. 2021;34:1618–1629.
Shah RJ, Bellamy SL, Localio AR, et al. A panel of lung injury biomarkers enhances the definition of primary graft dysfunction (PGD) after lung transplantation. J Heart Lung Transplant. 2012;31:942–949.
Machuca TN, Cypel M, Yeung JC, et al. Protein expression profiling predicts graft performance in clinical ex vivo lung perfusion. Ann Surg. 2015;261:591–597.
Machuca TN, Cypel M, Zhao Y, et al. The role of the endothelin-1 pathway as a biomarker for donor lung assessment in clinical ex vivo lung perfusion. J Heart Lung Transplant. 2015;34:849–857.
Allen JG, Lee MT, Weiss ES, et al. Preoperative recipient cytokine levels are associated with early lung allograft dysfunction. Ann Thorac Surg. 2012;93:1843–1849.
Jia J, Nie Y, Geng L, et al. Identification of HO-1 as a novel biomarker for graft acute cellular rejection and prognosis prediction after liver transplantation. Ann Transl Med. 2020;8:221–221.
Chauveau B, Raymond AA, Di Tommaso S, et al. The proteome of antibody-mediated rejection: from glomerulitis to transplant glomerulopathy. Biomedicines. 2022;10:569.
Mohammed-Ali Z, Tokar T, Batruch I, et al. Urine Angiotensin II signature proteins as markers of fibrosis in kidney transplant recipients. Transplantation. 2019;103:e146–e158.
Pisitkun T, Gandolfo MT, Das S, et al. Application of systems biology principles to protein biomarker discovery: urinary exosomal proteome in renal transplantation. Proteomics Clin Appl. 2012;6:268–278.
Clotet-Freixas S, Kotlyar M, McEvoy CM, et al. Increased autoantibodies against Ro/SS-A, CENP-B, and La/SS-B in patients with kidney allograft antibody-mediated rejection. Transplantation Direct. 2021;7:e768.
Verleden SE, Ruttens D, Vos R, et al. Differential cytokine, chemokine and growth factor expression in phenotypes of chronic lung allograft dysfunction. Transplantation. 2015;99:86–93.
Berra G, Farkona S, Mohammed-Ali Z, et al. Association between the renin–angiotensin system and chronic lung allograft dysfunction. Eur Respir J. 2021;58:2002975.
Gates KV, Panicker AJ, Biendarra-Tiegs SM, et al. Shotgun immunoproteomics for identification of nonhuman leukocyte antigens associated with cellular dysfunction in heart transplant rejection. Transplantation. 2022;106:1376–1389.
Thorgersen EB, Barratt-Due A, Haugaa H, et al. The role of complement in liver injury, regeneration, and transplantation. Hepatology. 2019;70:725–736.
Marshall KM, He S, Zhong Z, et al. Dissecting the complement pathway in hepatic injury and regeneration with a novel protective strategy. J Exp Med. 2014;211:1793–1805.
Fondevila C, Shen XD, Tsuchihashi S, et al. The membrane attack complex (C5b-9) in liver cold ischemia and reperfusion injury. Liver Transpl. 2008;14:1133–1141.
Fisher AJ, Donnelly SC, Hirani N, et al. Elevated levels of interleukin-8 in donor lungs is associated with early graft failure after lung transplantation. Am J Respir Crit Care Med. 2001;163:259–265.
Hamilton BCS, Kukreja J, Ware LB, et al. Protein biomarkers associated with primary graft dysfunction following lung transplantation. Am J Physiol Lung Cell Mol Physiol. 2017;312:L531–L541.
Mathur A, Baz M, Staples ED, et al. Cytokine profile after lung transplantation: correlation with allograft injury. Ann Thorac Surg. 2006;81:1844–1850.
Schaub S. Proteomic-based detection of urine proteins associated with acute renal allograft rejection. J Am Soc Nephrol. 2004;15:219–227.
Schaub S, Wilkins JA, Antonovici M, et al. Proteomic-based identification of cleaved urinary beta2-microglobulin as a potential marker for acute tubular injury in renal allografts. Am J Transplant. 2005;5(4 Pt 1):729–738.
Carroll R, Ho J, Jevnikar A, et al. Urine CXCL10 Monitoring Trial in Kidney Transplant. Available at https://clinicaltrials.gov/ct2/show/NCT03206801 . Accessed July 2, 2017.
Konvalinka A, Zhou J, Dimitromanolakis A, et al. Determination of an angiotensin II-regulated proteome in primary human kidney cells by Stable Isotope Labeling of Amino Acids in Cell Culture (SILAC). J Biol Chem. 2013;288:24834–24847.
Bae EH, Konvalinka A, Fang F, et al. Characterization of the intrarenal renin-angiotensin system in experimental alport syndrome. Am J Pathol. 2015;185:1423–1435.
Bing X, Lovelace T, Bunea F, et al. Essential regression: a generalizable framework for inferring causal latent factors from multi-omic datasets. Patterns. 2022;3:100473.
Peters LA, Perrigoue J, Mortha A, et al. A functional genomics predictive network model identifies regulators of inflammatory bowel disease. Nat Genet. 2017;49:1437–1449.
Goyette P, Boucher G, Mallon D, et al. High-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis. Nat Genet. 2015;47:172–179.
Wu J, Moheimani H, Li S, et al. High dimensional multi-omics reveals unique characteristics of early plasma administration in polytrauma patients with TBI. Ann Surg. 2022;276:673–683.
Hermjakob H, Apweiler R. The Proteomics Identifications Database (PRIDE) and the ProteomExchange Consortium: making proteomics data accessible. Expert Rev Proteomics. 2006;3:1–3.
Ternent T, Csordas A, Qi D, et al. How to submit MS proteomics data to ProteomeXchange via the PRIDE database. Proteomics. 2014;14:2233–2241.
Vizcaíno JA, Deutsch EW, Wang R, et al. ProteomeXchange provides globally coordinated proteomics data submission and dissemination. Nat Biotechnol. 2014;32:223–226.
Kotlyar M, Pastrello C, Ahmed Z, et al. IID 2021: towards context-specific protein interaction analyses by increased coverage, enhanced annotation and enrichment analysis. Nucleic Acids Res. 2022;50:D640–D647.
Brown KR, Otasek D, Ali M, et al. NAViGaTOR: network analysis, visualization and graphing Toronto. Bioinformatics. 2009;25:3327–3329.
Gödel M, Temerinac D, Grahammer F, et al. Microtubule associated protein 1b (MAP1B) is a marker of the microtubular cytoskeleton in podocytes but is not essential for the function of the kidney filtration barrier in mice. PLoS One. 2015;10:e0140116.
Kobayashi N. Mechanism of the process formation; podocytes vs. neurons. Microsc Res Tech. 2002;57:217–223.
Verma A, Ebanks K, Fok CY, et al. In silico comparative analysis of LRRK2 interactomes from brain, kidney and lung. Brain Res. 2021;1765:147503.
Cassidy H, Slyne J, Frain H, et al. High-throughput proteomic approaches to the elucidation of potential biomarkers of chronic allograft injury (CAI). Proteomes. 2013;1:159–179.
Cappelletti V, Hauser T, Piazza I, et al. Dynamic 3D proteomes reveal protein functional alterations at high resolution in situ. Cell. 2021;184:545–559.e22.
Bäuerlein FJB, Baumeister W. Towards visual proteomics at high resolution. J Mol Biol. 2021;433:167187.
Mund A, Coscia F, Kriston A, et al. Deep visual proteomics defines single-cell identity and heterogeneity. Nat Biotechnol. 2022;40:1231–1240.