1
|
Zhang YH, Hoopmann MR, Castaldi PJ, Simonsen KA, Midha MK, Cho MH, Criner GJ, Bueno R, Liu J, Moritz RL, Silverman EK. Lung proteomic biomarkers associated with chronic obstructive pulmonary disease. Am J Physiol Lung Cell Mol Physiol 2021; 321:L1119-L1130. [PMID: 34668408 PMCID: PMC8715017 DOI: 10.1152/ajplung.00198.2021] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 08/27/2021] [Accepted: 10/15/2021] [Indexed: 11/22/2022] Open
Abstract
Identifying protein biomarkers for chronic obstructive pulmonary disease (COPD) has been challenging. Most previous studies have used individual proteins or preselected protein panels measured in blood samples. Mass spectrometry proteomic studies of lung tissue have been based on small sample sizes. We used mass spectrometry proteomic approaches to discover protein biomarkers from 150 lung tissue samples representing COPD cases and controls. Top COPD-associated proteins were identified based on multiple linear regression analysis with false discovery rate (FDR) < 0.05. Correlations between pairs of COPD-associated proteins were examined. Machine learning models were also evaluated to identify potential combinations of protein biomarkers related to COPD. We identified 4,407 proteins passing quality controls. Twenty-five proteins were significantly associated with COPD at FDR < 0.05, including interleukin 33, ferritin (light chain and heavy chain), and two proteins related to caveolae (CAV1 and CAVIN1). Multiple previously reported plasma protein biomarkers for COPD were not significantly associated with proteomic analysis of COPD in lung tissue, although RAGE was borderline significant. Eleven pairs of top significant proteins were highly correlated (r > 0.8), including several strongly correlated with RAGE (EHD2 and CAVIN1). Machine learning models using Random Forests with the top 5% of protein biomarkers demonstrated reasonable accuracy (0.707) and area under the curve (0.714) for COPD prediction. Mass spectrometry-based proteomic analysis of lung tissue is a promising approach for the identification of biomarkers for COPD.
Collapse
Affiliation(s)
- Yu-Hang Zhang
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Peter J Castaldi
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | | | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Gerard J Criner
- Temple University School of Medicine, Philadelphia, Pennsylvania
| | - Raphael Bueno
- Division of Thoracic Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jiangyuan Liu
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | | | - Edwin K Silverman
- Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
2
|
Shuryak I, Turner HC, Perrier JR, Cunha L, Canadell MP, Durrani MH, Harken A, Bertucci A, Taveras M, Garty G, Brenner DJ. A High Throughput Approach to Reconstruct Partial-Body and Neutron Radiation Exposures on an Individual Basis. Sci Rep 2020; 10:2899. [PMID: 32076014 PMCID: PMC7031285 DOI: 10.1038/s41598-020-59695-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 01/27/2020] [Indexed: 11/28/2022] Open
Abstract
Biodosimetry-based individualized reconstruction of complex irradiation scenarios (partial-body shielding and/or neutron + photon mixtures) can improve treatment decisions after mass-casualty radiation-related incidents. We used a high-throughput micronucleus assay with automated scanning and imaging software on ex-vivo irradiated human lymphocytes to: a) reconstruct partial-body and/or neutron exposure, and b) estimate separately the photon and neutron doses in a mixed exposure. The mechanistic background is that, compared with total-body photon irradiations, neutrons produce more heavily-damaged lymphocytes with multiple micronuclei/binucleated cell, whereas partial-body exposures produce fewer such lymphocytes. To utilize these differences for biodosimetry, we developed metrics that describe micronuclei distributions in binucleated cells and serve as predictors in machine learning or parametric analyses of the following scenarios: (A) Homogeneous gamma-irradiation, mimicking total-body exposures, vs. mixtures of irradiated blood with unirradiated blood, mimicking partial-body exposures. (B) X rays vs. various neutron + photon mixtures. The results showed high accuracies of scenario and dose reconstructions. Specifically, receiver operating characteristic curve areas (AUC) for sample classification by exposure type reached 0.931 and 0.916 in scenarios A and B, respectively. R2 for actual vs. reconstructed doses in these scenarios reached 0.87 and 0.77, respectively. These encouraging findings demonstrate a proof-of-principle for the proposed approach of high-throughput reconstruction of clinically-relevant complex radiation exposure scenarios.
Collapse
Affiliation(s)
- Igor Shuryak
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA.
| | - Helen C Turner
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Jay R Perrier
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Lydia Cunha
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Monica Pujol Canadell
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Mohammad H Durrani
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Andrew Harken
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Antonella Bertucci
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Maria Taveras
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - Guy Garty
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| | - David J Brenner
- Center for Radiological Research, Columbia University Irving Medical Center, New York, NY, USA
| |
Collapse
|
3
|
Vecchiarello N, Timmick SM, Goodwine C, Crowell LE, Love KR, Love JC, Cramer SM. A combined screening and in silico strategy for the rapid design of integrated downstream processes for process and product‐related impurity removal. Biotechnol Bioeng 2019; 116:2178-2190. [DOI: 10.1002/bit.27018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 04/30/2019] [Accepted: 05/09/2019] [Indexed: 01/12/2023]
Affiliation(s)
- Nicholas Vecchiarello
- Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute Center for Biotechnology and Interdisciplinary Studies Troy New York
| | - Steven M. Timmick
- Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute Center for Biotechnology and Interdisciplinary Studies Troy New York
| | - Chaz Goodwine
- Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute Center for Biotechnology and Interdisciplinary Studies Troy New York
| | - Laura E. Crowell
- Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
| | - Kerry R. Love
- Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
| | - J. Christopher Love
- Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
| | - Steven M. Cramer
- Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute Center for Biotechnology and Interdisciplinary Studies Troy New York
| |
Collapse
|
4
|
Timmick SM, Vecchiarello N, Goodwine C, Crowell LE, Love KR, Love JC, Cramer SM. An impurity characterization based approach for the rapid development of integrated downstream purification processes. Biotechnol Bioeng 2018; 115:2048-2060. [DOI: 10.1002/bit.26718] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 03/09/2018] [Accepted: 04/17/2018] [Indexed: 01/13/2023]
Affiliation(s)
- Steven M. Timmick
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Troy New York
| | - Nicholas Vecchiarello
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Troy New York
| | - Chaz Goodwine
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Troy New York
| | - Laura E. Crowell
- Department of Chemical Engineering, Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
| | - Kerry R. Love
- Department of Chemical Engineering, Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
| | - J. Christopher Love
- Department of Chemical Engineering, Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts
| | - Steven M. Cramer
- Department of Chemical and Biological Engineering, Center for Biotechnology and Interdisciplinary Studies Rensselaer Polytechnic Institute Troy New York
| |
Collapse
|
5
|
Swanson RK, Xu R, Nettleton DS, Glatz CE. Accounting for host cell protein behavior in anion-exchange chromatography. Biotechnol Prog 2016; 32:1453-1463. [PMID: 27556579 DOI: 10.1002/btpr.2342] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Revised: 07/27/2016] [Indexed: 11/11/2022]
Abstract
Host cell proteins (HCP) are a problematic set of impurities in downstream processing (DSP) as they behave most similarly to the target protein during separation. Approaching DSP with the knowledge of HCP separation behavior would be beneficial for the production of high purity recombinant biologics. Therefore, this work was aimed at characterizing the separation behavior of complex mixtures of HCP during a commonly used method: anion-exchange chromatography (AEX). An additional goal was to evaluate the performance of a statistical methodology, based on the characterization data, as a tool for predicting protein separation behavior. Aqueous two-phase partitioning followed by two-dimensional electrophoresis provided data on the three physicochemical properties most commonly exploited during DSP for each HCP: pI (isoelectric point), molecular weight, and surface hydrophobicity. The protein separation behaviors of two alternative expression host extracts (corn germ and E. coli) were characterized. A multivariate random forest (MVRF) statistical methodology was then applied to the database of characterized proteins creating a tool for predicting the AEX behavior of a mixture of proteins. The accuracy of the MVRF method was determined by calculating a root mean squared error value for each database. This measure never exceeded a value of 0.045 (fraction of protein populating each of the multiple separation fractions) for AEX. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1453-1463, 2016.
Collapse
Affiliation(s)
- Ryan K Swanson
- Dept. of Chemical and Biological Engineering, Iowa State University, Ames, IA, 50011
| | - Ruo Xu
- Dept. of Statistics, Iowa State University, Ames, IA, 50011
| | | | - Charles E Glatz
- Dept. of Chemical and Biological Engineering, Iowa State University, Ames, IA, 50011
| |
Collapse
|
6
|
Hanke AT, Tsintavi E, Ramirez Vazquez MDP, van der Wielen LAM, Verhaert PDEM, Eppink MHM, van de Sandt EJAX, Ottens M. 3D-liquid chromatography as a complex mixture characterization tool for knowledge-based downstream process development. Biotechnol Prog 2016; 32:1283-1291. [DOI: 10.1002/btpr.2320] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Revised: 06/07/2016] [Indexed: 11/11/2022]
Affiliation(s)
- Alexander T. Hanke
- Dept. of Biotechnology; Delft University of Technology, Van der Maasweg 9, 2629 HZ; Delft The Netherlands
| | - Eleni Tsintavi
- Dept. of Biotechnology; Delft University of Technology, Van der Maasweg 9, 2629 HZ; Delft The Netherlands
| | | | - Luuk A. M. van der Wielen
- Dept. of Biotechnology; Delft University of Technology, Van der Maasweg 9, 2629 HZ; Delft The Netherlands
| | - Peter D. E. M. Verhaert
- Dept. of Biotechnology; Delft University of Technology, Van der Maasweg 9, 2629 HZ; Delft The Netherlands
| | - Michel H. M. Eppink
- Synthon Biopharmaceuticals B.V., Microweg 22, 6503 GN, Nijmegen; Nijmegen The Netherlands
| | | | - Marcel Ottens
- Dept. of Biotechnology; Delft University of Technology, Van der Maasweg 9, 2629 HZ; Delft The Netherlands
| |
Collapse
|
7
|
Hanke AT, Ottens M. Purifying biopharmaceuticals: knowledge-based chromatographic process development. Trends Biotechnol 2014; 32:210-20. [DOI: 10.1016/j.tibtech.2014.02.001] [Citation(s) in RCA: 123] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2013] [Revised: 01/24/2014] [Accepted: 02/04/2014] [Indexed: 01/04/2023]
|