Divay Chandra

Chronic obstructive pulmonary disease (COPD) was the third leading cause of global mortality in 2011 but receives limited attention and research funding. This Review describes the current knowledge on COPD risk factors, including genetic and epigenetic determinants and their interactions with the microbiome and environmental exposures. Preclinical models are being refined and single-cell transcriptomic, metabolomic, and proteomic technologies are being implemented to investigate the molecular mechanisms of disease progression. Patient cohorts to define biomarkers of early disease and the latest approaches to diagnose pre-COPD are essential to accelerate the development of novel and effective therapeutic interventions and translate new findings into clinical trials. This Review is a summary of topics covered by a symposium organized by the COPD-iNET consortium, an international network of researchers who have established a platform that facilitates collaboration of this multidisciplinary group of preclinical, translational, and clinical researchers.

Our integrative genomic and functional analysis identified transforming acidic coiled-coil–containing protein 2 (TACC2) as a chronic obstructive pulmonary disease (COPD) candidate gene. Here, we found that smokers with COPD exhibit a marked decrease in lung TACC2 protein levels relative to smokers without COPD. Single cell RNA sequencing reveals that TACC2 is expressed primarily in lung epithelial cells in normal human lungs. Furthermore, suppression of TACC2 expression impairs the efficiency of homologous recombination repair and augments spontaneous and cigarette smoke extract–induced (CSE-induced) DNA damage and cytotoxicity in immortalized human bronchial epithelial cells. By contrast, enforced expression of TACC2 attenuates the CSE effects. We also found that CSE enhances TACC2 degradation via the ubiquitin-proteasome system mediated by the ubiquitin E3 ligase subunit, F box L7. Furthermore, cellularly expressed TACC2 proteins harboring naturally occurring mutations exhibited altered protein lifespan coupled with modified DNA damage repair and cytotoxic responses. CS triggers emphysematous changes accompanied by accumulated DNA damage, apoptosis of alveolar epithelia, and lung inflammation in Tacc2 ^–/– compared with Tacc2 ^+/+ mice. Our results suggest that CS destabilizes TACC2 protein in lung epithelia by the ubiquitin proteasome system, leading to subsequent DNA damage, cytotoxicity, and emphysema.

Individuals with HIV are at increased risk for coronary artery disease (CAD). Early detection of subclinical CAD by assessment of coronary artery calcium (CAC) may help risk stratify and prevent CAD events in these individuals. However, the current standard to quantify CAC i.e. Agatston scoring requires EKG-gated cardiac CT imaging. To determine if the assessment of CAC using non-EKG-gated chest CT and the Weston scoring system is a useful surrogate for Agatston scores in HIV-infected and HIV-uninfected individuals. CAC was assessed by both the Weston and Agatston score in 108 men enrolled in the Multicenter AIDS Cohort Study. Participants were 55.2 (IQR 50.4; 59.9) years old and 62 (57.4%) were seropositive for HIV. Inter-observer agreement (r_s = 0.94, κ = 90.0%, pn = 21) and intra-observer agreement (r_s = 0.95, κ = 95.2%, pn = 97) for category of Weston score were excellent. Weston scores were associated with similar CAD risk factors as Agatston scores (age, race, HDL cholesterol level, all pr_s = 0.92, pw = 0.77, p<0.001) between Weston and Agatston scores. This study is the first to examine calcium scoring using chest CT in HIV-infected individuals and to independently validate the Weston score as a surrogate for the Agatston score. In clinical or research settings where EKG-gated cardiac CT is not feasible for the assessment of coronary calcium, Weston scoring by using chest CT should be considered.

In modern biomedical research of complex diseases, a large number of demographic and clinical variables, herein called phenomic data, are often collected and missing values (MVs) are inevitable in the data collection process. Since many downstream statistical and bioinformatics methods require complete data matrix, imputation is a common and practical solution. In high-throughput experiments such as microarray experiments, continuous intensities are measured and many mature missing value imputation methods have been developed and widely applied. Numerous methods for missing data imputation of microarray data have been developed. Large phenomic data, however, contain continuous, nominal, binary and ordinal data types, which void application of most methods. Though several methods have been developed in the past few years, not a single complete guideline is proposed with respect to phenomic missing data imputation. In this paper, we investigated existing imputation methods for phenomic data, proposed a self-training selection (STS) scheme to select the best imputation method and provide a practical guideline for general applications. We introduced a novel concept of "imputability measure" (IM) to identify missing values that are fundamentally inadequate to impute. In addition, we also developed four variations of K-nearest-neighbor (KNN) methods and compared with two existing methods, multivariate imputation by chained equations (MICE) and missForest. The four variations are imputation by variables (KNN-V), by subjects (KNN-S), their weighted hybrid (KNN-H) and an adaptively weighted hybrid (KNN-A). We performed simulations and applied different imputation methods and the STS scheme to three lung disease phenomic datasets to evaluate the methods. An R package "phenomeImpute" is made publicly available. Simulations and applications to real datasets showed that MICE often did not perform well; KNN-A, KNN-H and random forest were among the top performers although no method universally performed the best. Imputation of missing values with low imputability measures increased imputation errors greatly and could potentially deteriorate downstream analyses. The STS scheme was accurate in selecting the optimal method by evaluating methods in a second layer of missingness simulation. All source files for the simulation and the real data analyses are available on the author's publication website.

Background: The increased use of computed tomography pulmonary angiography (CTPA) is often justified by finding alternative diagnoses explaining patients’ symptoms. However, this has not been rigorously examined. Methods: We retrospectively reviewed CTPA done at our center over an eleven year period (2000 – 2010) in patients with suspected pulmonary embolus (PE). We then reviewed in detail the medical records of a representative sample of patients in three index years – 2000, 2005 and 2008. We determined whether CTPA revealed pulmonary pathology other than PE that was not readily identifiable from the patient’s history, physical examination and prior chest X-ray. We also assessed whether the use of pre-test probability guided diagnostic strategy for PE. Results: A total of 12,640 CTPA were performed at our center from year 2000 to 2010. The number of CTPA performed increased from 84 in 2000 to 2287 in 2010, a 27 fold increase. Only 7.6 percent of all CTPA and 3.2 percent of avoidable CTPAs (low or intermediate pre-test probability and negative D-dimer) revealed previously unknown findings of any clinical significance. When we compared 2008 to 2000 and 2005, more CTPAs were performed in younger patients (mean age (years) for 2000: 67, 2005: 63, and 2008: 60, (p=0.004, one–way ANOVA)). Patients were less acutely ill with fewer risk factors for PE. Assessment of pre-test probability of PE and D-dimer measurement were rarely used to select appropriate patients for CTPA (pre-test probability of PE documented in chart (% total) in year 2000: 4.1%, 2005: 1.6%, 2008: 3.1%). Conclusions: Our data do not support the argument that increased CTPA use is justified by finding an alternative pulmonary pathology that could explain patients’ symptoms. CTPA is being increasingly used as the first and only test for suspected PE.

Rationale: It is unclear if lung perfusion can predict response to lung volume reduction surgery (LVRS). Objectives: To study the role of perfusion scintigraphy in patient selection for LVRS. Methods: We performed an intention-to-treat analysis of 1,045 of 1,218 patients enrolled in the National Emphysema Treatment Trial who were non–high risk for LVRS and had complete perfusion scintigraphy results at baseline. The median follow-up was 6.0 years. Patients were classified as having upper or non–upper lobe–predominant emphysema on visual examination of the chest computed tomography and high or low exercise capacity on cardiopulmonary exercise testing at baseline. Low upper zone perfusion was defined as less than 20% of total lung perfusion distributed to the upper third of both lungs as measured on perfusion scintigraphy. Measurements and Main Results: Among 284 of 1,045 patients with upper lobe–predominant emphysema and low exercise capacity at baseline, the 202 with low upper zone perfusion had lower mortality with LVRS versus medical management (risk ratio [RR], 0.56; P = 0.008) unlike the remaining 82 with high perfusion where mortality was unchanged (RR, 0.97; P = 0.62). Similarly, among 404 of 1,045 patients with upper lobe–predominant emphysema and high exercise capacity, the 278 with low upper zone perfusion had lower mortality with LVRS (RR, 0.70; P = 0.02) unlike the remaining 126 with high perfusion (RR, 1.05; P = 1.00). Among the 357 patients with non–upper lobe–predominant emphysema (75 with low and 282 with high exercise capacity) there was no improvement in survival with LVRS and measurement of upper zone perfusion did not contribute new prognostic information. Conclusions: Compared with optimal medical management, LVRS reduces mortality in patients with upper lobe–predominant emphysema when there is low rather than high perfusion to the upper lung.