Wu et al. Critical Care (2014) 18:508 DOI 10.1186/s13054-014-0508-y RESEARCH Open Access Dynamic gene expressions of peripheral blood mononuclear cells in patients with acute exacerbation of chronic obstructive pulmonary disease: a preliminary study Xiaodan Wu1†, Xiaoru Sun2†, Chengshui Chen2*, Chunxue Bai3 and Xiangdong Wang2,3* Abstract Introduction: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a serious event that is responsible for the progress of the disease, increases in medical costs and high mortality. Methods: Theaimofthepresentstudywastoidentify AECOPD-specific biomarkers by evaluating the dynamic gene ex- pression profiling of peripheral blood mononuclear cells (PBMCs) from patients with AECOPD on days 1, 3 and 10 after hospital admission and to compare the derived data with data from healthy controls or patients with stable COPD. Results: We found that 14 genes were co–differentially upregulated and 2 downregulated greater than 10-fold in patients with COPD or AECOPD compared with the healthy individuals. Eight co–differentially upregulated genes and six down- regulated genes were identified as a panel of AECOPD-specific genes. Downregulation of TCF7 in PBMCs was found to be associated with the severity of COPD. Dynamic changes of Aminolevulinate-delta-synthase 2 and carbonic anhydrase I had similar patterns of Digital Evaluation Score System scores and may serve as potential genes of interest during the course of AECOPD. Conclusion: Thus, our findings indicate a panel of altered gene expression patterns in PBMCs that can be used as AECOPD-specific dynamic biomarkers to monitor the course of AECOPD. Introduction along with a progressive decline in lung function; Chronic obstructive pulmonary disease (COPD) is an AECOPD becomes more frequent and severe when the inflammation-based syndrome characterized by progres- severity of disease increases [4,5]. There is a great need for sive deterioration of pulmonary function and increasing early and sensitive diagnosis and novel therapeutic targets airway obstruction [1]. COPD is a major and growing for the disease, especially for patients with AECOPD in public health burden, ranking as the fourth leading cause whom COPD is diagnosed in the late phase of disease, of death in the world [2]. In China, it is the fourth leading when they have significant or irreversible impairment [6]. cause of mortality in urban areas and the third leading The progress of COPD is accelerated by the occurrence cause in rural areas [3]. Patients with COPD often expe- of the exacerbation induced by multiple factors, including rience a sudden deterioration, termed acute exacerbations infection. AECOPD is a serious event that is related of chronic obstructive pulmonary disease (AECOPD), to decreased health status, increased medical and social costs and increased mortality [7]. Inflammatory cells * Correspondence: [email protected]; xiangdong.wang@ (for example, lymphocytes, monocytes or macrophages, clintransmed.org † and their products) could interact with each other or with Equal contributors 2Department of Respiratory Medicine, Wenzhou Medical University and The structural cells in the airways and the lung parenchymal First Hospital, Nanbaixiang, 325000, Wenzhou, China and pulmonary vasculature, leading to the worsening of 3 Shanghai Institute of Clinical Bioinformatics, Fudan University Center for COPD [8]. Increased numbers of CD8+ lymphocytes were Clinical Bioinformatics, Shanghai Respiratory Research Medicine, Fenglin Rd. ’ No 180, 200032, Shanghai, China suggested as one of COPDs characteristics, being present Full list of author information is available at the end of the article only in smokers who develop the disease [9]. Increased © 2014 Wu et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Wu et al. Critical Care (2014) 18:508 Page 2 of 27 pulmonary inflammatory mediators in patients with COPD Material and methods could attract inflammatory cells from the circulation, Patient population amplify the inflammatory process and induce structural The present study was approved by the Ethical Evaluation changes [9]. Committee of Zhongshan Hospital and designed using a Peripheral blood mononuclear cells (PBMCs) act as a case–control approach. From among 220 candidates critical component in the immune system to fight infection comprising blood donors (60 healthy controls), inpatients and adapt to intruders and play an important role in the (80 patients with AECOPD) and outpatients (80 patients development of AECOPD. Gene expression profiles of with stable COPD) in Zhongshan Hospital, patients with PBMCs were found to be disease-specific and associated AECOPD, patients with stable COPD and healthy controls with severity [10]. PBMC samples were suggested as easy matched for age and sex were recruited into the study to gather and important to the discovery of biomarkers for between October 2011 and March 2012. The inclusion diagnosis and therapeutic management of COPD [11,12], criteria for patients with COPD were as follows: (1) forced although gene expression changes in lung tissues were expiratory volume in 1 second (FEV1)<80%ofpredicted noted to be associated with COPD [13-15]. The aim of value adjusted for age, weight and height, and (2) an the present study was to determine AECOPD-specific improvement in FEV1 following bronchodilator inhal- biomarkers of PBMCs using the concept of clinical ation <12% of baseline FEV1. Patients with asthma who bioinformatics and integrating genomics, bioinformatics, had a persistent airflow obstruction were excluded. Stable clinical informatics and systems biology [16-18]. We trans- COPD was defined according to American Thoracic lated all clinical measures, including patient complaints, Society/European Respiratory Society consensus criteria history, therapies, clinical symptoms and signs, physician’s as no requirement for increased treatment above mainten- examinations, biochemical analyses, imaging profiles, ance therapy, other than bronchodilators, for 30 days [1]. pathologies and other measurements, into digital format AECOPD was the reason for hospital admission and was using a digital evaluation scoring system. PBMCs were characterized as a worsening of the patient’srespiratory isolated from healthy volunteers and patients with symptoms that was beyond normal day-to-day variations stable COPD or AECOPD, and we investigated the disease and led to a change in medication [4,19]. Healthy specificity that we inferred from clinical informatics controls enrolled were blood donors at Zhongshan analysis to search for COPD- or AECOPD-specific genes Hospital. Subjects with respiratory diseases, or any and dynamic biomarkers for AECOPD. family history of lung disease, were excluded. PBMCs Volunteers Recruitment Health Criteria Outpatients Humans sCOPD AECOPD D1 D3 D10 Disease severity Clinical Digital Evaluation Clinical Isolated Informatics Score System Information monocytes Disease-specific Functional Data & analyses Gene microarray Biomarkers networks Figure 1 Details of the study design. Healthy volunteers and patients with stable chronic obstructive pulmonary disease (sCOPD) or acute exacerbation of COPD (AECOPD) at day 1 (D1), day 3 (D3) or day 10 (D10) of hospital admission of hospital were recruited into the present study according to the criteria stated in the text. All clinical information was collected and transferred into the clinical informatics database using the Digital Evaluation Score System. mRNAs of peripheral blood monocytes were harvested, and gene expression profiles were measured by human gene expression array and subjected to bioinformatics analysis. AECOPD-specific biomarkers were selected by integrating gene functional networks and profiles with clinical informatics data. Wu et al. Critical Care (2014) 18:508 Page 3 of 27 Table 1 Clinical phenotypes of healthy controls, patients with stable chronic obstructive pulmonary disease and patients with acute exacerbation of chronic obstructive pulmonary diseasea Groups Subject no. Age (yr) Smoking status FEV1/FVC% FEV1/pred% Goddard emphysema score Control 1 56 Nonsmoker 75 85 0 2 53 Nonsmoker 80 87 0 3 62 Nonsmoker 77 91 0 4 68 Nonsmoker 81 83 0 5 58 Nonsmoker 79 81 0 6 67 Nonsmoker 76 90 0 Mean ± SE 60.7 ± 2.5 78.0 ± 1.0 86.2 ± 1.6 0.0 ± 0.0 Stable COPD 1 71 Ex-smoker 57 47 10 2 75 Ex-smoker 46 66 6 3 61 Ex-smoker 46 47 8 4 57 Ex-smoker 38 29 12 5 59 Ex-smoker 67 66 7 6 53 Ex-smoker 29 36 11 Mean ± SE 62.7 ± 3.5 47.2 ± 5.5 48.5 ± 6.2 9.0 ± 1.0 AECOPD 1 77 Ex-smoker 40 42 10 2 72 Ex-smoker 36 27 11 3 65 Ex-smoker 28 33 16 4 56 Ex-smoker 48 61 6 5 61 Ex-smoker 69 55 4 6 67 Ex-smoker 56 60 8 Mean ± SE 66.3 ± 3.1 46.2 ± 6.0 46.3 ± 5.9 9.2 ± 1.7 a AECOPD, Acute exacerbation of chronic obstructive pulmonary disease; COPD, Chronic obstructive pulmonary disease; FEV1, Forced expiratory volume in 1 second; FVC, Forced vital capacity; pred, Prediction. Data represent information gathered on days 1, 3 and 10 of hospital admission. were harvested once from healthy controls and patients with stable COPD or AECOPD. The details of the study with stable COPD, as well as from patients with AECOPD, design are explained in Figure 1. on the admission day and 3 and 10 days after the admission. Informed consent was given by the subjects Digital evaluation score system themselves before they underwent lung function tests, The Digital Evaluation Score System (DESS) is a score high-resolution computed tomography and blood collec- index used to translate clinical descriptions and informa- tion. The time points used in the present study were tion into clinical informatics, as described previously selected on the basis of our previous study for collecting [20].