%0 Journal Article %1 wolf2011proteomic %A Wolf, Thomas %A Oumeraci, Tonio %A Gottlieb, Jens %A Pich, Andreas %A Brors, Benedikt %A Eils, Roland %A Haverich, Axel %A Schlegelberger, Brigitte %A Welte, Tobias %A Zapatka, Marc %A von Neuhoff, Nils %D 2011 %J Transplantation %K A, Adult; Aged; Assay; Bronchiolitis Bronchoalveolar Calgranulin Desorption-Ionization; Enzyme-Linked Factors; Female; Fluid, Histatins, Humans; Immunosorbent Laser Lavage Lung Male; Mass, Matrix-Assisted Middle Obliterans, Predictive Proteome, Proteomics, Risk Spectrometry, Syndrome; Tests; Transplantation, Uteroglobin, Value adverse alpha-Defensins chemistry; diagnosis/etiology/metabolism; effects; metabolism; methods; of %N 4 %P 477--485 %R 10.1097/TP.0b013e318224c109 %T Proteomic bronchiolitis obliterans syndrome risk monitoring in lung transplant recipients. %U http://dx.doi.org/10.1097/TP.0b013e318224c109 %V 92 %X Obliterative bronchiolitis poses a primary obstacle for long-term survival of lung transplant recipients and manifests clinically as bronchiolitis obliterans syndrome (BOS). Establishing a molecular level screening method to detect BOS-related proteome changes before its diagnosis by forced expiratory volume surrogate marker criteria was the main objective of this study.Bronchoalveolar lavage was performed in 82 lung transplant recipients (48/34 with/without known BOS development) at different time points between 12 and 48 months after lung transplantation. A mass spectrometry-based method was devised to generate bronchoalveolar lavage fluid proteome profiles that were screened for BOS-specific alterations. Statistically significant marker peptides and proteins were identified and validated by in-gel digestion, tandem mass spectrometric sequencing, and quantitative immunoassays.Among the panel of statistically significant markers were Clara cell protein, calgranulin A, human neutrophil peptides, and the antimicrobial agent histatin. To assess their clinical relevance, a highly sensitive and specific classifier model was developed. Positive BOS classification by monitoring of seven polypeptides correlated strongly with a significant decrease in BOS-free time. Thus, it was possible to detect high-risk patients early on in the pathogenetic process.Monitoring the bronchoalveolar lavage fluid levels of seven polypeptides detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry allows a reliable prediction of early BOS using a Random Forest decision tree-based classifier model. The high accuracy of this robust model and its synergistic potential in combination with established forced expiratory volume-based diagnostics could make it an effective tool to supplement the current diagnostic regime after multicentric validation.

@article{wolf2011proteomic, __markedentry = {[bbrors:6]}, abstract = {Obliterative bronchiolitis poses a primary obstacle for long-term survival of lung transplant recipients and manifests clinically as bronchiolitis obliterans syndrome (BOS). Establishing a molecular level screening method to detect BOS-related proteome changes before its diagnosis by forced expiratory volume surrogate marker criteria was the main objective of this study.Bronchoalveolar lavage was performed in 82 lung transplant recipients (48/34 with/without known BOS development) at different time points between 12 and 48 months after lung transplantation. A mass spectrometry-based method was devised to generate bronchoalveolar lavage fluid proteome profiles that were screened for BOS-specific alterations. Statistically significant marker peptides and proteins were identified and validated by in-gel digestion, tandem mass spectrometric sequencing, and quantitative immunoassays.Among the panel of statistically significant markers were Clara cell protein, calgranulin A, human neutrophil peptides, and the antimicrobial agent histatin. To assess their clinical relevance, a highly sensitive and specific classifier model was developed. Positive BOS classification by monitoring of seven polypeptides correlated strongly with a significant decrease in BOS-free time. Thus, it was possible to detect high-risk patients early on in the pathogenetic process.Monitoring the bronchoalveolar lavage fluid levels of seven polypeptides detected by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry allows a reliable prediction of early BOS using a Random Forest decision tree-based classifier model. The high accuracy of this robust model and its synergistic potential in combination with established forced expiratory volume-based diagnostics could make it an effective tool to supplement the current diagnostic regime after multicentric validation.}, added-at = {2015-04-09T12:36:21.000+0200}, author = {Wolf, Thomas and Oumeraci, Tonio and Gottlieb, Jens and Pich, Andreas and Brors, Benedikt and Eils, Roland and Haverich, Axel and Schlegelberger, Brigitte and Welte, Tobias and Zapatka, Marc and {von Neuhoff}, Nils}, biburl = {https://www.bibsonomy.org/bibtex/29bc811758a1e6b9041104f73f3eff0dd/bbrors}, doi = {10.1097/TP.0b013e318224c109}, institution = {Institute of Cell and Molecular Pathology, Hannover Medical School (MHH), Hannover, Germany.}, interhash = {bb0591772871149aa59cf2e441748875}, intrahash = {9bc811758a1e6b9041104f73f3eff0dd}, journal = {Transplantation}, keywords = {A, Adult; Aged; Assay; Bronchiolitis Bronchoalveolar Calgranulin Desorption-Ionization; Enzyme-Linked Factors; Female; Fluid, Histatins, Humans; Immunosorbent Laser Lavage Lung Male; Mass, Matrix-Assisted Middle Obliterans, Predictive Proteome, Proteomics, Risk Spectrometry, Syndrome; Tests; Transplantation, Uteroglobin, Value adverse alpha-Defensins chemistry; diagnosis/etiology/metabolism; effects; metabolism; methods; of}, language = {eng}, medline-pst = {ppublish}, month = Aug, number = 4, owner = {bbrors}, pages = {477--485}, pmid = {21716170}, timestamp = {2015-04-09T12:36:21.000+0200}, title = {Proteomic bronchiolitis obliterans syndrome risk monitoring in lung transplant recipients.}, url = {http://dx.doi.org/10.1097/TP.0b013e318224c109}, volume = 92, year = 2011 }