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Background HEARTBiT is a whole blood-based gene profiling assay using the nucleic acid counting NanoString technology for the exclusionary diagnosis of acute cellular rejection in heart transplant patients. The HEARTBiT score measures the risk of acute cellular rejection in the first year following heart transplant, distinguishing patients with stable grafts from those at risk for acute cellular rejection. Here, we provide the analytical performance characteristics of the HEARTBiT assay and the results on pilot clinical validation. Methods We used purified RNA collected from PAXgene blood samples to evaluate the characteristics of a 12-gene panel HEARTBiT assay, for its linearity range, quantitative bias, precision, and reproducibility. These parameters were estimated either from serial dilutions of individual samples or from repeated runs on pooled samples. Results We found that all 12 genes showed linear behavior within the recommended assay input range of 125 ng to 500 ng of purified RNA, with most genes showing 3% or lower quantitative bias and around 5% coefficient of variation. Total variation resulting from unique operators, reagent lots, and runs was less than 0.02 units standard deviation (SD). The performance of the analytically validated assay (AUC = 0.75) was equivalent to what we observed in the signature development dataset. Conclusion The analytical performance of the assay within the specification input range demonstrated reliable quantification of the HEARTBiT score within 0.02 SD units, measured on a 0 to 1 unit scale. This assay may therefore be of high utility in clinical validation of HEARTBiT in future biomarker observational trials.

Background Chronic obstructive pulmonary disease (COPD) is an age-related condition that has been associated with early telomere attrition; the clinical implications of telomere shortening in COPD are not well known. In this study we aimed to determine the relationship of the epigenetic regulation of telomeric length in peripheral blood with the risk of exacerbations and hospitalization in patients with COPD. Methods Blood DNA methylation profiles were obtained from 292 patients with COPD enrolled in the placebo arm of the Macrolide Azithromycin to Prevent Rapid Worsening of Symptoms Associated with Chronic Obstructive Pulmonary Disease (MACRO) Study and who were followed for 1-year. We calculated telomere length based on DNA methylation markers (DNAmTL) and related this biomarker to the risk of exacerbation and hospitalization and health status (St. George Respiratory Questionnaire [SGRQ]) score over time using a Cox proportional hazards model. We also used linear models to investigate the associations of DNAmTL with the rates of exacerbation and hospitalization (adjusted for chronological age, lung function, race, sex, smoking, body mass index and cell composition). Results Participants with short DNAmTL demonstrated increased risk of exacerbation (P = 0.02) and hospitalization (P = 0.03) compared to those with longer DNAmTL. DNAmTL age acceleration was associated with higher rates of exacerbation (P = 1.35 × 10–04) and hospitalization (P = 5.21 × 10–03) and poor health status (lower SGRQ scores) independent of chronological age (P = 0.03). Conclusion Telomeric age based on blood DNA methylation is associated with COPD exacerbation and hospitalization and thus a promising biomarker for poor outcomes in COPD.

BackgroundImmunoglobulin G (IgG) deficiency increases the risk of acute exacerbations and mortality in chronic obstructive pulmonary disease (COPD). However, the impact of IgG subclass deficiency on mortality in COPD is unknown. Here, we determined which IgG subclass, if any, is associated with increased risk of mortality in COPD.MethodsWe measured serum IgG subclass concentrations of 489 hospitalized patients with COPD who were enrolled in the Rapid Transition Program (clinicaltrials.gov identifier NCT02050022). To evaluate the impact of IgG subclass deficiency on 1-year mortality, Cox proportional hazards regression analyses were performed with adjustments for potential confounders.ResultsDeficiencies in IgG1, IgG2, IgG3, and IgG4 were present in 1.8%, 12.1%, 4.3%, and 11.2% of patients, respectively. One-year mortality was 56% in patients with IgG1 deficiency, 27% in IgG2 deficiency, 24% in IgG3 deficiency, and 31% in IgG4 deficiency. Cox proportional modeling showed that IgG1 and IgG4 deficiencies increased the 1-year mortality risk with an adjusted hazard ratio of 3.92 (95% confidence interval [CI] = 1.55–9.87) and 1.74 (95% CI = 1.02–2.98), respectively. Neither IgG2 nor IgG3 deficiency significantly increased 1-year mortality. Two or more IgG subclass deficiencies were observed in 5.3%. Patients with 2 or more IgG subclass deficiencies had a higher 1-year mortality than those without any deficiencies (46.2% vs. 19.7%, p ConclusionsIgG1 and IgG4 deficiency was observed in 1.8% and 11.2% of hospitalized patients with COPD, respectively, and these deficiencies were associated with a significantly increased risk of 1-year mortality.

Background Many risk models for predicting mortality, hospitalizations, or both in patients with heart failure have been developed but do not have sufficient discriminatory ability. The purpose of this study was to identify predictive biomarkers of hospitalizations in heart failure patients using omics-based technologies applied to blood and electrical monitoring of the heart. Methods Blood samples were collected from 58 heart failure patients during enrollment into this study. Each patient wore a 48-hour Holter monitor that recorded the electrical activity of their heart. The blood samples were profiled for gene expression using microarrays and protein levels using multiple reaction monitoring. Statistical deconvolution was used to estimate cellular frequencies of common blood cells. Classification models were developed using clinical variables, Holter variables, cell types, gene transcripts, and proteins to predict hospitalization status. Results Of the 58 patients recruited, 13 were hospitalized within 3 months after enrollment. These patients had lower diastolic and systolic blood pressures, higher brain natriuretic peptide levels, most had higher blood creatinine levels, and had been diagnosed with heart failure for a longer time period. The best-performing clinical model had an area under the receiver operating characteristic curve of 0.76. An ensemble biomarker panel consisting of Holter variables, cell types, gene transcripts, and proteins had an area under the receiver operating characteristic curve of 0.88. Conclusions Molecular-based analyses as well as sensory data might provide sensitive biomarkers for the prediction of hospitalizations in heart failure patients. These approaches may be combined with traditional clinical models for the development of improved risk prediction models for heart failure.

Obesity-associated visceral adipose tissue (AT) inflammation promotes insulin resistance and type 2 diabetes (T2D). In mice, lean visceral AT is populated with anti-inflammatory cells, notably regulatory T cells (Tregs) expressing the IL-33 receptor ST2. Conversely, obese AT contains fewer Tregs and more proinflammatory cells. In humans, however, there is limited evidence for a similar pattern of obesity-associated immunomodulation. We used flow cytometry and mRNA quantification to characterize human omental AT in 29 obese subjects, 18 of whom had T2D. Patients with T2D had increased proportions of inflammatory cells, including M1 macrophages, with positive correlations to body mass index. In contrast, Treg frequencies negatively correlated to body mass index but were comparable between T2D and non-T2D individuals. Compared to human thymic Tregs, omental AT Tregs expressed similar levels of FOXP3, CD25, IKZF2, and CTLA4, but higher levels of PPARG, CCR4, PRDM1, and CXCL2. ST2, however, was not detectable on omental AT Tregs from lean or obese subjects. This is the first comprehensive investigation into how omental AT immunity changes with obesity and T2D in humans, revealing important similarities and differences to paradigms in mice. These data increase our understanding of how pathways of immune regulation could be targeted to ameliorate AT inflammation in humans.

Background Etiologies of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are heterogeneous. We phenotyped severe AECOPD based on molecular pathogen detection of sputum samples collected at hospitalization of COPD patients and determined their outcomes. Methods We phenotyped 72 sputum samples of COPD patients who were hospitalized with a primary diagnosis of AECOPD using a molecular array that detected common bacterial and viral respiratory pathogens. Based on these results, the patients were classified into positive or negative pathogen groups. The pathogen-positive group was further divided into virus or bacteria subgroups. Admission day 1 blood samples were assayed for N-terminal prohormone brain natriuretic peptide, CRP, and complete blood counts. Results A total of 52 patients had a positive result on the array, while 20 patients had no pathogens detected. The most common bacterial pathogen detected was Haemophilus influenzae and the most common virus was rhinovirus. The pathogen-negative group had the worse outcomes with longer hospital stays (median 6.5 vs 5 days for bacteria-positive group, P=0.02) and a trend toward increased 1-year mortality (P=0.052). The bacteria-positive group had the best prognosis, whereas the virus-positive group had outcomes somewhere in between the bacteria-positive and pathogen-negative groups. Conclusion Molecular diagnostics on sputum can rapidly phenotype serious AECOPD into bacteria-, virus-, or pathogen-negative groups. The bacteria-positive group appears to have the best prognosis, while pathogen-negative group has the worst. These data suggest that AECOPD is a heterogeneous event and that accurate phenotyping of AECOPD may lead to novel management strategies that are personalized and more precise.

BackgroundThe global pandemic of COVID-19 cases caused by infection with SARS-CoV-2 is ongoing, with no approved antiviral intervention. We describe here the effects of treatment with interferon-α2b in a cohort of confirmed COVID-19 cases in Wuhan, China.MethodsIn this retrospective study, 77 adults hospitalized with confirmed COVID-19 were treated with either nebulized IFN-α2b (5mU b.i.d.), arbidol (200mg t.i.d.) or a combination of IFN-α2b plus arbidol. Serial SARS-CoV-2 testing along with hematological measurements, including cell counts and blood biochemistry, serum cytokine levels, temperature and blood oxygen saturation levels were recorded for each patient during their hospital stay.ResultsTreatment with IFN-α2b with or without arbidol significantly reduced the duration of detectable virus in the upper respiratory tract and in parallel reduced duration of elevated blood levels for the inflammatory markers IL-6 and CRP.ConclusionThese findings suggest that IFN-α2b should be further investigated as a therapy in COVID-19 cases.

Pulmonary exacerbations (PEx) are clinically impactful events for individuals with CF. Unfortunately, many CF individuals with PEx fail to regain their baseline lung function despite treatment. The objective of this study was to use unbiased proteomic technology to identify novel blood protein biomarkers that change following intravenous (IV) antibiotic treatment and to explore if changes correlate with clinical response by the end of treatment. Blood samples from 25 PEx events derived from 22 unique CF adults were collected within 24 hours of hospital admission, day 5, day 10, and IV antibiotic completion. Three-hundred and forty-six blood proteins were evaluated with label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) quantitative proteomics and immunoassays. Forty-seven plasma proteins changed significantly following 5 days of IV antibiotic treatment (q-value ≤ 0.10). Early change in IGF2R from hospital admission to day 5 correlated with overall change in symptom score (CFRSD-CRISS) by the end of treatment (r = −0.48, p-value = 0.04). Several plasma proteins identified and quantified by label-free LC-MS/MS changed early following treatment with IV antibiotics and many of these proteins are involved in complement activation and inflammatory/immune-related pathways. Early change in IGF2R correlated with symptom response following IV antibiotic treatment and requires further validation as a predictive biomarker of symptom response.

Nawaf M Alotaibi,1,2 Virginia Chen,1,3,4 Zsuzsanna Hollander,1,3,4 Cameron J Hague,5 Darra T Murphy,5 Jonathon A Leipsic,5 Mari L DeMarco,1,6 J Mark FitzGerald,7,8 Bruce M McManus,1,3,4,6 Raymond T Ng,4,9 Don D Sin1,3,7 1Centre for Heart Lung Innovation, James Hogg Research Centre, St Paul’s Hospital, Vancouver, BC, Canada; 2Division of Pulmonary Medicine, Department of Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 3Institute for Heart Lung Health, 4PROOF Centre of Excellence, 5Department of Radiology, St Paul’s Hospital, 6Department of Pathology and Laboratory Medicine, 7Division of Respiratory Medicine, Department of Medicine, University of British Columbia, 8The Lung Centre, Vancouver General Hospital, 9Department of Computer Sciences, University of British Columbia, Vancouver, BC, Canada Rationale: Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are caused by a variety of different etiologic agents. Our aim was to phenotype COPD exacerbations using imaging (chest X-ray [CXR] and computed tomography [CT]) and to determine the possible role of the blood tests (C-reactive protein [CRP], the N-terminal prohormone brain natriuretic peptide [NT-proBNP]) as diagnostic biomarkers. Materials and methods: Subjects who were hospitalized with a primary diagnosis of AECOPD and who had had CXRs, CT scans, and blood collection for CRP and NT-proBNP were assessed in this study. Radiologist blinded to the clinical and laboratory characteristics of the subjects interpreted their CXRs and CT images. ANOVA and Spearman’s correlation were performed to test for associations between these imaging parameters and the blood-based biomarkers NT-proBNP and CRP; logistic regression models were used to assess the performance of these biomarkers in predicting the radiological parameters. Results: A total of 309 subjects were examined for this study. Subjects had a mean age of 65.6±11.1 years, 66.7% of them were males, and 62.4% were current smokers, with a mean FEV1 54.4%±21.5% of predicted. Blood NT-proBNP concentrations were associated with cardiac enlargement (area under the curve [AUC] =0.72, P

Chronic Obstructive Pulmonary Disease (COPD) is an age-related condition that is linked to cellular senescence [1]. In COPD, contributors to cellular senescence include oxidative stress from environmental factors such as cigarette smoking and persistent lung inflammation [2]. These factors can also augment replicative senescence, which is characterised by progressive telomere attrition, ultimately leading to cell cycle arrest and death. Patients with COPD have shorter telomeres [3] and faster rates of telomere attrition [4] compared to controls; however, the clinical impact of cellular or replicative senescence in COPD remains uncertain. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: A.I. Hernandez Cordero has nothing to disclose. Conflict of interest: C.X. Yang has nothing to disclose. Conflict of interest: S. Milne has nothing to disclose. Conflict of interest: X. Li has nothing to disclose. Conflict of interest: Z. Hollander reports funding from Genome Canada, Genome British Columbia, Genome Quebec, the Canadian Institutes of Health Research, PROOF Centre of Excellence, St. Paul's Hospital Foundation and Providence Health Care for the Rapid Transition Program (RTP) cohort included in the manuscript. Conflict of interest: V. Chen reports funding from Genome Canada, Genome British Columbia, Genome Quebec, the Canadian Institutes of Health Research, PROOF Centre of Excellence, St. Paul's Hospital Foundation and Providence Health Care for the Rapid Transition Program (RTP) cohort included in the manuscript. Conflict of interest: R. Ng reports funding from Genome Canada, Genome British Columbia, Genome Quebec, the Canadian Institutes of Health Research, PROOF Centre of Excellence, St. Paul's Hospital Foundation and Providence Health Care for the Rapid Transition Program (RTP) cohort included in the manuscript. Conflict of interest: S.J. Tebbutt has nothing to disclose. Conflict of interest: J.M. Leung reports research research grant funding to their institution from Canadian Institutes of Health Research, Michael Smith Foundation for Health Research, BC Lung Association and Genome BC, outside the scope of the current manuscript. Conflict of interest: D.D. Sin declares grants from AstraZeneca for an investigator-initiated randomised controlled trial in COPD; consulting fees from Novaira for sitting on an advisory board for COPD; and honoraria for speaking engagements from AstraZeneca, Boehringer Ingelheim and Grifols, all in the 36 months prior to manuscript submission.

Aims Heart failure with preserved ejection fraction (HFpEF) accounts for 30–50% of patients with heart failure (HF). A major obstacle in HF management is the difficulty in differentiating between HFpEF and heart failure with reduced ejection fraction (HFrEF) using conventional clinical and laboratory investigations. The aim of this study is to develop robust transcriptomic and proteomic biomarker signatures that can differentiate HFpEF from HFrEF. Methods and results A total of 210 HF patients were recruited in participating institutions from the Alberta HEART study. An expert clinical adjudicating panel differentiated between patients with HFpEF and HFrEF. The discovery cohort consisted of 61 patients, and the replication cohort consisted of 70 patients. Transcriptomic and proteomic data were analysed to find panels of differentiating HFpEF from HFrEF. In the discovery cohort, a 22-transcript panel was found to differentiate HFpEF from HFrEF in male patients with a cross-validation AUC of 0.74, as compared with 0.70 for N-terminal pro-B-type natriuretic peptide (NT-proBNP) in those same patients. An ensemble of the transcript panel and NT-pro-BNP yielded a cross-validation AUC of 0.80. This performance improvement was also observed in the replication cohort. An ensemble of the transcriptomic panel with NT-proBNP produced a replication AUC of 0.90, as compared with 0.74 for NT-proBNP alone and 0.73 for the transcriptomic panel. Conclusions We have identified a male-specific transcriptomic biomarker panel that can differentiate between HFpEF and HFrEF. These biosignatures could be further replicated on other patients and potentially be developed into a blood test for better management of HF patients.

Background Nine mRNA transcripts associated with acute cellular rejection (ACR) in previous microarray studies were ported to the clinically amenable NanoString nCounter platform. Here we report the diagnostic performance of the resulting blood test to exclude ACR in heart allograft recipients: HEARTBiT. Methods Blood samples for transcriptomic profiling were collected during routine post-transplantation monitoring in 8 Canadian transplant centres participating in the Biomarkers in Transplantation initiative, a large (n = 1622) prospective observational study conducted between 2009 and 2014. All adult cardiac transplant patients were invited to participate (median age = 56 [17 to 71]). The reference standard for rejection status was histopathology grading of tissue from endomyocardial biopsy (EMB). All locally graded ISHLT ≥ 2R rejection samples were selected for analysis (n = 36). ISHLT 1R (n = 38) and 0R (n = 86) samples were randomly selected to create a cohort approximately matched for site, age, sex, and days post-transplantation, with a focus on early time points (median days post-transplant = 42 [7 to 506]). Results ISHLT ≥ 2R rejection was confirmed by EMB in 18 and excluded in 92 samples in the test set. HEARTBiT achieved 47% specificity (95% confidence interval [CI], 36%-57%) given ≥ 90% sensitivity, with a corresponding area under the receiver operating characteristic curve of 0.69 (95% CI, 0.56-0.81). Conclusions HEARTBiT’s diagnostic performance compares favourably to the only currently approved minimally invasive diagnostic test to rule out ACR, AlloMap (CareDx, Brisbane, CA) and may be used to inform care decisions in the first 2 months post-transplantation, when AlloMap is not approved, and most ACR episodes occur.

Background Effects of systemic corticosteroids on blood gene expression are largely unknown. This study determined gene expression signature associated with short-term oral prednisone therapy in patients with chronic obstructive pulmonary disease (COPD) and its relationship to 1-year mortality following an acute exacerbation of COPD (AECOPD). Methods Gene expression in whole blood was profiled using the Affymetrix Human Gene 1.1 ST microarray chips from two cohorts: 1) a prednisone cohort with 37 stable COPD patients randomly assigned to prednisone 30 mg/d + standard therapy for 4 days or standard therapy alone and 2) the Rapid Transition Program (RTP) cohort with 218 COPD patients who experienced AECOPD and were treated with systemic corticosteroids. All gene expression data were adjusted for the total number of white blood cells and their differential cell counts. Results In the prednisone cohort, 51 genes were differentially expressed between prednisone and standard therapy group at a false discovery rate of

In CF, pulmonary exacerbations (PEx) can lead to permanent loss in lung function and thus should be prevented. Previously, we identified a blood protein biosignature consisting of 6 proteins capable of predicting short-term PEx events in CF adults. In this study, we utilized blood samples from the placebo arm of a randomized controlled trial to assess whether this candidate protein biosignature was also capable of predicting short-term PEx events in CF children and adolescents. This pilot study provides preliminary evidence that blood inflammation can be monitored to predict short-term PEx risk in CF children and adolescents.

BACKGROUND. Multiple therapeutic strategies to restore immune regulation and slow type 1 diabetes (T1D) progression are in development and testing. A major challenge has been defining biomarkers to prospectively identify subjects likely to benefit from immunotherapy and/or measure intervention effects. We previously found that, compared with healthy controls, Tregs from children with new-onset T1D have an altered Treg gene signature (TGS), suggesting that this could be an immunoregulatory biomarker. METHODS. nanoString was used to assess the TGS in sorted Tregs (CD4((+))CD25((hi))CD127((lo))) or peripheral blood mononuclear cells (PBMCs) from individuals with T1D or type 2 diabetes, healthy controls, or T1D recipients of immunotherapy. Biomarker discovery pipelines were developed and applied to various sample group comparisons. RESULTS. Compared with controls, the TGS in isolated Tregs or PBMCs was altered in adult new-onset and cross-sectional T1D cohorts, with sensitivity or specificity of biomarkers increased by including T1D-associated SNPs in algorithms. The TGS was distinct in T1D versus type 2 diabetes, indicating disease-specific alterations. TGS measurement at the time of T1D onset revealed an algorithm that accurately predicted future rapid versus slow C-peptide decline, as determined by longitudinal analysis of placebo arms of START and T1DAL trials. The same algorithm stratified participants in a phase I/II clinical trial of ustekinumab (αIL-12/23p40) for future rapid versus slow C-peptide decline. CONCLUSION. These data suggest that biomarkers based on measuring TGSs could be a new approach to stratify patients and monitor autoimmune activity in T1D. FUNDING. JDRF (1-PNF-2015-113-Q-R, 2-PAR-2015-123-Q-R, 3-SRA-2016-209-Q-R, 3-PDF-2014-217-A-N), the JDRF Canadian Clinical Trials Network, the National Institute of Allergy and Infectious Diseases of the National Institutes of Health (UM1AI109565 and FY15ITN168), and BCCHRI.

Nawaf M Alotaibi,1,2 Virginia Chen,1,3,4 Zsuzsanna Hollander,1,3,4 Jonathon A Leipsic,5 Cameron J Hague,5 Darra T Murphy,5 Mari L DeMarco,1,6 JM FitzGerald,3,7,8 Bruce M McManus,1,3,4,6 Raymond T Ng,4,9 Don D Sin1,3,7 1Centre for Heart Lung Innovation, James Hogg Research Centre, St Paul’s Hospital, Vancouver, BC, Canada; 2Department of Medicine, Division of Pulmonary Medicine, College of Medicine, King Saud University, Riyadh, Saudi Arabia; 3Institute for Heart and Lung Health, Vancouver, BC, Canada; 4PROOF Centre of Excellence, Vancouver, BC, Canada; 5Department of Radiology, St Paul’s Hospital, Vancouver, BC, Canada; 6Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada; 7Division of Respiratory Medicine, Department of Medicine, University of British Columbia, Vancouver, BC, Canada; 8The Lung Centre, Vancouver General Hospital, Vancouver, BC, Canada; 9Department of Computer Sciences, University of British Columbia, Vancouver, BC, Canada Background: Etiologies of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are heterogeneous. We phenotyped severe AECOPD based on molecular pathogen detection of sputum samples collected at hospitalization of COPD patients and determined their outcomes.Methods: We phenotyped 72 sputum samples of COPD patients who were hospitalized with a primary diagnosis of AECOPD using a molecular array that detected common bacterial and viral respiratory pathogens. Based on these results, the patients were classified into positive or negative pathogen groups. The pathogen-positive group was further divided into virus or bacteria subgroups. Admission day 1 blood samples were assayed for N-terminal prohormone brain natriuretic peptide, CRP, and complete blood counts.Results: A total of 52 patients had a positive result on the array, while 20 patients had no pathogens detected. The most common bacterial pathogen detected was Haemophilus influenzae and the most common virus was rhinovirus. The pathogen-negative group had the worse outcomes with longer hospital stays (median 6.5 vs 5 days for bacteria-positive group, P=0.02) and a trend toward increased 1-year mortality (P=0.052). The bacteria-positive group had the best prognosis, whereas the virus-positive group had outcomes somewhere in between the bacteria-positive and pathogen-negative groups.Conclusion: Molecular diagnostics on sputum can rapidly phenotype serious AECOPD into bacteria-, virus-, or pathogen-negative groups. The bacteria-positive group appears to have the best prognosis, while pathogen-negative group has the worst. These data suggest that AECOPD is a heterogeneous event and that accurate phenotyping of AECOPD may lead to novel management strategies that are personalized and more precise. Keywords: COPD, molecular pathogen detection, exacerbation phenotypes

Rationale: Lung dysbiosis promotes airway inflammation and decreased local immunity, potentially playing a role in the pathogenesis of acute exacerbations of chronic obstructive pulmonary disease (...

Background Severe inotrope-dependent acute heart failure (AHF) is associated with poor clinical outcomes. There are currently no well-defined blood biomarkers of response to treatment that can guide management or identify recovery in this patient population. In the present study, we characterized the levels of novel and emerging circulating biomarkers of heart failure in patients with AHF over the first 30 days of medical management or mechanical circulatory support (MCS). We hypothesized a shared a plasma proteomic treatment response would be identifiable in both patient groups, representing reversal of the AHF phenotype. Methods and results Time course plasma samples of the first 30 days of therapy, obtained from patients managed medically (n = 8) or with implantable MCS (n = 5), underwent semi-targeted and candidate biomarker analyses, using multiple reaction monitoring (MRM) mass spectrometry, antibody arrays, and enzyme-linked immunosorbent assays. Differentially expressed proteins were identified using robust limma for MRM and antibody array data. Patients managed medically or with implantable MCS had a shared proteomic signature of six plasma proteins: circulating cardiotrophin 1, cardiac troponin T, clusterin, and dickopff 1 increased, while levels of C-reactive protein and growth differentiation factor 15 decreased in both groups over the 30 day time course. Conclusions We have characterized the temporal proteomic signature of clinical recovery in AHF patients managed medically or with MCS, over the first 30 days of treatment. Changes in biomarker expression over the time course of treatment may provide a basis for understanding the biological basis of AHF, potentially identifying novel markers and pathophysiologic mechanisms of recovery.

BACKGROUND Cholesterol efflux capacity (CEC) is a measure of HDL function that, in cell-based studies, has demonstrated an inverse association with cardiovascular disease. The cell-based measure of CEC is complex and low-throughput. We hypothesized that assessment of the lipoprotein proteome would allow for precise, high-throughput CEC prediction. METHODS After isolating lipoprotein particles from serum, we used LC-MS/MS to quantify 21 lipoprotein-associated proteins. A bioinformatic pipeline was used to identify proteins with univariate correlation to cell-based CEC measurements and generate a multivariate algorithm for CEC prediction (pCE). Using logistic regression, protein coefficients in the pCE model were reweighted to yield a new algorithm predicting coronary artery disease (pCAD). RESULTS Discovery using targeted LC-MS/MS analysis of 105 training and test samples yielded a pCE model comprising 5 proteins (Spearman r = 0.86). Evaluation of pCE in a case–control study of 231 specimens from healthy individuals and patients with coronary artery disease revealed lower pCE in cases (P = 0.03). Derived within this same study, the pCAD model significantly improved classification (P < 0.0001). Following analytical validation of the multiplexed proteomic method, we conducted a case–control study of myocardial infarction in 137 postmenopausal women that confirmed significant separation of specimen cohorts in both the pCE (P = 0.015) and pCAD (P = 0.001) models. CONCLUSIONS Development of a proteomic pCE provides a reproducible high-throughput alternative to traditional cell-based CEC assays. The pCAD model improves stratification of case and control cohorts and, with further studies to establish clinical validity, presents a new opportunity for the assessment of cardiovascular health.

Background: Chronic obstructive pulmonary disease (COPD) is currently the third leading cause of death and there is a huge unmet clinical need to identify disease biomarkers in peripheral blood. Compared to gene level differential expression approaches to identify gene signatures, network analyses provide a biologically intuitive approach which leverages the co-expression patterns in the transcriptome to identify modules of co-expressed genes. Methods: A weighted gene co-expression network analysis (WGCNA) was applied to peripheral blood transcriptome from 238 COPD subjects to discover co-expressed gene modules. We then determined the relationship between these modules and forced expiratory volume in 1 s (FEV1). In a second, independent cohort of 381 subjects, we determined the preservation of these modules and their relationship with FEV1. For those modules that were significantly related to FEV1, we determined the biological processes as well as the blood cell-specific gene expression that were over-represented using additional external datasets. Results: Using WGCNA, we identified 17 modules of co-expressed genes in the discovery cohort. Three of these modules were significantly correlated with FEV1 (FDR

Radio-frequency ablation (RFA) is used to locally disrupt electrical propagation in myocardium and treat arrhythmias, and direct visualization of ablation lesions by acoustic radiation force methods may benefit RFA procedures. This paper compares four imaging modalities, B-mode, acoustic radiation force impulse (ARFI), single-track-location shear wave elasticity imaging (STL-SWEI), and multiple-track-location shear wave elasticity imaging (MTL-SWEI), in their ability to resolve RFA lesions in four ex vivo experiments. Ablation lesions are shown to be marked by at least a local halving of ARFI displacements and doubling of shear wave speeds. In a controlled ablation of ex vivo porcine and canine cardiac tissue, STL-SWEI and ARFI are shown to have a similar CNR, better than MTL-SWEI and B-mode. The SWEI modalities are demonstrated to have improved imaging of distal lesion boundaries. Gaps smaller than 5 mm are visualized in ablation lines made of discretely spaced ablations, and complex structures are reconstructed through depth in an “x” ablation experiment. Scans of suspended atria show increased noise, but successfully visualize ablations in ARFI, MTL-SWEI, and STL-SWEI.

There are currently no accepted and validated blood tests available for diagnosing acute exacerbations of chronic obstructive pulmonary disease (AECOPD). In this study, we sought to determine the discriminatory power of blood C-reactive protein (CRP) and N-terminal prohormone brain natriuretic peptide (NT-proBNP) in the diagnosis of AECOPD requiring hospitalizations. The study cohort consisted of 468 patients recruited in the COPD Rapid Transition Program who were hospitalized with a primary diagnosis of AECOPD, and 110 stable COPD patients who served as controls. Logistic regression was used to build a classification model to separate AECOPD from convalescent or stable COPD patients. Performance was assessed using an independent validation set of patients who were not included in the discovery set. Serum CRP and whole blood NT-proBNP concentrations were highest at the time of hospitalization and progressively decreased over time. Of the 3 classification models, the one with both CRP and NT-proBNP had the highest AUC in discriminating AECOPD (cross-validated AUC of 0.80). These data were replicated in a validation cohort with an AUC of 0.88. A combination of CRP and NT-proBNP can reasonably discriminate AECOPD requiring hospitalization versus clinical stability and can be used to rapidly diagnose patients requiring hospitalization for AECOPD.

Background Gene network inference (GNI) algorithms can be used to identify sets of coordinately expressed genes, termed network modules from whole transcriptome gene expression data. The identification of such modules has become a popular approach to systems biology, with important applications in translational research. Although diverse computational and statistical approaches have been devised to identify such modules, their performance behavior is still not fully understood, particularly in complex human tissues. Given human heterogeneity, one important question is how the outputs of these computational methods are sensitive to the input sample set, or stability. A related question is how this sensitivity depends on the size of the sample set. We describe here the SABRE (Similarity Across Bootstrap RE-sampling) procedure for assessing the stability of gene network modules using a re-sampling strategy, introduce a novel criterion for identifying stable modules, and demonstrate the utility of this approach in a clinically-relevant cohort, using two different gene network module discovery algorithms. Results The stability of modules increased as sample size increased and stable modules were more likely to be replicated in larger sets of samples. Random modules derived from permutated gene expression data were consistently unstable, as assessed by SABRE, and provide a useful baseline value for our proposed stability criterion. Gene module sets identified by different algorithms varied with respect to their stability, as assessed by SABRE. Finally, stable modules were more readily annotated in various curated gene set databases. Conclusions The SABRE procedure and proposed stability criterion may provide guidance when designing systems biology studies in complex human disease and tissues. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1319-8) contains supplementary material, which is available to authorized users.

Aims Chronic heart failure is a costly epidemic that affects up to 2% of people in developed countries. The purpose of this study was to discover novel blood proteomic biomarker signatures of recovered heart function that could lead to more effective heart failure patient management by both primary care and specialty physicians. Methods and results The discovery cohort included 41 heart transplant patients and 20 healthy individuals. Plasma levels of 138 proteins were detected in at least 75% of these subjects by iTRAQ mass spectrometry. Eighteen proteins were identified that had (i) differential levels between pre-transplant patients with end-stage heart failure and healthy individuals; and (ii) levels that returned to normal by 1 month post-transplant in patients with stable heart function after transplantation. Seventeen of the 18 markers were validated by multiple reaction monitoring mass spectrometry in a cohort of 39 heart failure patients treated with drug therapy, of which 30 had recovered heart function and 9 had not. This 17-protein biomarker panel had 93% sensitivity and 89% specificity, while the RAMP® NT-proBNP assay had the same specificity but 80% sensitivity. Performance further improved when the panel was combined with NT-proBNP, yielding a net reclassification index relative to NT-proBNP of 0.28. Conclusions We have identified potential blood biomarkers of recovered heart function by harnessing data from transplant patients. These biomarkers can lead to the development of an inexpensive protein-based blood test that could be used by physicians to monitor response to therapy in heart failure, resulting in more personalized, front-line heart failure patient management.

Background Acute rejection in cardiac transplant patients remains a contributory factor to limited survival of implanted hearts. Currently, there are no biomarkers in clinical use that can predict, at the time of transplantation, the likelihood of post-transplant acute cellular rejection. Such a development would be of great value in personalizing immunosuppressive treatment. Methods Recipient age, donor age, cold ischemic time, warm ischemic time, panel-reactive antibody, gender mismatch, blood type mismatch and human leukocyte antigens (HLA-A, -B and -DR) mismatch between recipients and donors were tested in 53 heart transplant patients for their power to predict post-transplant acute cellular rejection. Donor transplant biopsy and recipient pre-transplant blood were also examined for the presence of genomic biomarkers in 7 rejection and 11 non-rejection patients, using non-targeted data mining techniques. Results The biomarker based on the 8 clinical variables had an area under the receiver operating characteristic curve (AUC) of 0.53. The pre-transplant recipient blood gene-based panel did not yield better performance, but the donor heart tissue gene-based panel had an AUC=0.78. A combination of 25 probe sets from the transplant donor biopsy and 18 probe sets from the pre-transplant recipient whole blood had an AUC=0.90. Biologic pathways implicated include VEGF- and EGFR-signaling, and MAPK. Conclusions Based on this study, the best predictive biomarker panel contains genes from recipient whole blood and donor myocardial tissue. This panel provides clinically relevant prediction power and, if validated, may personalize immunosuppressive treatment and rejection monitoring.

Background Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) result in considerable morbidity and mortality. However, there are no objective biomarkers to diagnose AECOPD. Methods We used multiple reaction monitoring mass spectrometry to quantify 129 distinct proteins in plasma samples from patients with COPD. This analytical approach was first performed in a biomarker cohort of patients hospitalized with AECOPD (Cohort A, n = 72). Proteins differentially expressed between AECOPD and convalescent states were chosen using a false discovery rate 1.2. Protein selection and classifier building were performed using an elastic net logistic regression model. The performance of the biomarker panel was then tested in two independent AECOPD cohorts (Cohort B, n = 37, and Cohort C, n = 109) using leave-pair-out cross-validation methods. Results Five proteins were identified distinguishing AECOPD and convalescent states in Cohort A. Biomarker scores derived from this model were significantly higher during AECOPD than in the convalescent state in the discovery cohort (p

Background COPD is currently the fourth leading cause of death worldwide. Statins are lipid lowering agents with documented cardiovascular benefits. Observational studies have shown that statins may have a beneficial role in COPD. The impact of statins on blood gene expression from COPD patients is largely unknown. Objective Identify blood gene signature associated with statin use in COPD patients, and the pathways underpinning this signature that could explain any potential benefits in COPD. Methods Whole blood gene expression was measured on 168 statin users and 451 non-users from the ECLIPSE study using the Affymetrix Human Gene 1.1 ST microarray chips. Factor Analysis for Robust Microarray Summarization (FARMS) was used to process the expression data. Differential gene expression analysis was undertaken using the Linear Models for Microarray data (Limma) package adjusting for propensity score and surrogate variables. Similarity of the expression signal with published gene expression profiles was performed in ProfileChaser. Results 25 genes were differentially expressed between statin users and non-users at an FDR of 10%, including LDLR, CXCR2, SC4MOL, FAM108A1, IFI35, FRYL, ABCG1, MYLIP, and DHCR24. The 25 genes were significantly enriched in cholesterol homeostasis and metabolism pathways. The resulting gene signature showed correlation with Huntington’s disease, Parkinson’s disease and acute myeloid leukemia gene signatures. Conclusion The blood gene signature of statins’ use in COPD patients was enriched in cholesterol homeostasis pathways. Further studies are needed to delineate the role of these pathways in lung biology.