Peripheral blood mononuclear cell transcriptome profiles suggest T-cell immunosuppression after uncomplicated mechanical circulatory support device surgery

Current indications for transplantation: stratification of severe heart failure and shared decision-making

Heart failure (HF) is a complex clinical syndrome that results from structural or functional cardiovascular disorders causing a mismatch between demand and supply of oxygenated blood and consecutive failure of the body's organs. For those patients with stage D HF, advanced therapies, such as mechanical circulatory support (MCS) or heart transplantation (HTx), are potentially life-saving options. The role of risk stratification of patients with stage D HF in a value-based healthcare framework is to predict which subset might benefit from advanced HF (AdHF) therapies, to improve outcomes related to the individual patient including mortality, morbidity and patient experience as well as to optimize health care delivery system outcomes such as cost-effectiveness. Risk stratification and subsequent outcome prediction as well as therapeutic recommendation-making need to be based on the comparative survival benefit rationale. A robust model needs to (I) have the power to discriminate (i.e., to correctly risk stratify patients); (II) calibrate (i.e., to show agreement between the predicted and observed risk); (III) to be applicable to the general population; and (IV) provide good external validation. The Seattle Heart Failure Model (SHFM) and the Heart Failure Survival Score (HFSS) are two of the most widely utilized scores. However, outcomes for patients with HF are highly variable which make clinical predictions challenging. Despite our clinical expertise and current prediction tools, the best short- and long-term survival for the individual patient, particularly the sickest patient, is not easy to identify because among the most severely ill, elderly and frail patients, most preoperative prediction tools have the tendency to be imprecise in estimating risk. They should be used as a guide in a clinical encounter grounded in a culture of shared decision-making, with the expert healthcare professional team as consultants and the patient as an empowered decision-maker in a trustful safe therapeutic relationship.

Association between preoperative peripheral blood mononuclear cell gene expression profiles, early postoperative organ function recovery potential and long-term survival in advanced heart failure patients undergoing mechanical circulatory support

BACKGROUND

Multiorgan dysfunction syndrome contributes to adverse outcomes in advanced heart failure (AdHF) patients after mechanical circulatory support (MCS) implantation and is associated with aberrant leukocyte activity. We tested the hypothesis that preoperative peripheral blood mononuclear cell (PBMC) gene expression profiles (GEP) can predict early postoperative improvement or non-improvement in patients undergoing MCS implantation. We believe this information may be useful in developing prognostic biomarkers.

METHODS & DESIGN

We conducted a study with 29 patients undergoing MCS-surgery in a tertiary academic medical center from 2012 to 2014. PBMC samples were collected one day before surgery (day -1). Clinical data was collected on day -1 and day 8 postoperatively. Patients were classified by Sequential Organ Failure Assessment score and Model of End-stage Liver Disease Except INR score (measured eight days after surgery): Group I = improving (both scores improved from day -1 to day 8, n = 17) and Group II = not improving (either one or both scores did not improve from day -1 to day 8, n = 12). RNA-sequencing was performed on purified mRNA and analyzed using Next Generation Sequencing Strand. Differentially expressed genes (DEGs) were identified by Mann-Whitney test with Benjamini-Hochberg correction. Preoperative DEGs were used to construct a support vector machine algorithm to predict Group I vs. Group II membership.

RESULTS

Out of 28 MCS-surgery patients alive 8 days postoperatively, one-year survival was 88% in Group I and 27% in Group II. We identified 28 preoperative DEGs between Group I and II, with an average 93% prediction accuracy. Out of 105 DEGs identified preoperatively between year 1 survivors and non-survivors, 12 genes overlapped with the 28 predictive genes.

CONCLUSIONS

In AdHF patients following MCS implantation, preoperative PBMC-GEP predicts early changes in organ function scores and correlates with long-term outcomes. Therefore, gene expression lends itself to outcome prediction and warrants further studies in larger longitudinal cohorts.

Use of Durable Continuous-Flow Ventricular Assist Devices in Patients on Immunosuppression

Durable ventricular assist device (VAD) therapy remains an important treatment for end-stage heart failure. Despite advancements in device design, postimplant infectious complications continue to plague this population. In this study, we aim to evaluate the use of durable VAD therapy in patients on active immunosuppression. All patients undergoing durable, continuous-flow VAD placement on active immunosuppression at our center from 2004 to 2012 were included in the analysis (group immunosuppressed [IS]; n = 13). Demographic data, comorbidities, device details, immunosuppression details including indication, postimplant infections, and outcomes were collected and compared with patients without immunosuppression (group non-IS; n = 259). Mean age in the IS group was 56.3 ± 12.4 years, and 12 patients (92.3%) were male. Twelve patients (92.3%) were implanted as a bridge to transplant. Incidence of any postimplant infection and device-related infection was 1.15 infections/patient × year and 0.38 infections/patient × year, respectively. Survival to discharge was 84.6% and 93.1% (p = 0.25), and 1 year survival was 75.0% and 83.1% (p = 0.47) in the IS and non-IS group, respectively. Mean duration of support was 384 ± 466 days, and mean follow-up was 2.1 ± 1.5 years. Active immunosuppression may lead to a modest increase in postimplant infection rate in durable VAD patients than in non-IS patients undergoing the same treatment; however, late on-device survival is not affected. Immunosuppression should not be considered an absolute contraindication to device implant.

Integrative model of leukocyte genomics and organ dysfunction in heart failure patients requiring mechanical circulatory support: a prospective observational study

BACKGROUND

The implantation of mechanical circulatory support devices in heart failure patients is associated with a systemic inflammatory response, potentially leading to death from multiple organ dysfunction syndrome. Previous studies point to the involvement of many mechanisms, but an integrative hypothesis does not yet exist. Using time-dependent whole-genome mRNA expression in circulating leukocytes, we constructed a systems-model to improve mechanistic understanding and prediction of adverse outcomes.

METHODS

We sampled peripheral blood mononuclear cells from 22 consecutive patients undergoing mechanical circulatory support device (MCS) surgery, at 5 timepoints: day -1 preoperative, and postoperative days 1, 3, 5, and 8. Clinical phenotyping was performed using 12 clinical parameters, 2 organ dysfunction scoring systems, and survival outcomes. We constructed a strictly phenotype-driven time-dependent non-supervised systems-representation using weighted gene co-expression network analysis, and annotated eigengenes using gene ontology, pathway, and transcription factor binding site enrichment analyses. Genes and eigengenes were mapped to the clinical phenotype using a linear mixed-effect model, with Cox models also fit at each timepoint to survival outcomes.

RESULTS

We inferred a 19-module network, in which most module eigengenes correlated with at least one aspect of the clinical phenotype. We observed a response of advanced heart failure patients to surgery orchestrated into stages: first, activation of the innate immune response, followed by anti-inflammation, and finally reparative processes such as mitosis, coagulation, and apoptosis. Eigengenes related to red blood cell production and extracellular matrix degradation became predictors of survival late in the timecourse corresponding to multiorgan dysfunction and disseminated intravascular coagulation.

CONCLUSIONS

Our model provides an integrative representation of leukocyte biology during the systemic inflammatory response following MCS device implantation. It demonstrates consistency with previous hypotheses, identifying a number of known mechanisms. At the same time, it suggests novel hypotheses about time-specific targets.

Comparison of whole blood and peripheral blood mononuclear cell gene expression for evaluation of the perioperative inflammatory response in patients with advanced heart failure

BACKGROUND

Heart failure (HF) prevalence is increasing in the United States. Mechanical Circulatory Support (MCS) therapy is an option for Advanced HF (AdHF) patients. Perioperatively, multiorgan dysfunction (MOD) is linked to the effects of device implantation, augmented by preexisting HF. Early recognition of MOD allows for better diagnosis, treatment, and risk prediction. Gene expression profiling (GEP) was used to evaluate clinical phenotypes of peripheral blood mononuclear cells (PBMC) transcriptomes obtained from patients' blood samples. Whole blood (WB) samples are clinically more feasible, but their performance in comparison to PBMC samples has not been determined.

METHODS

We collected blood samples from 31 HF patients (57±15 years old) undergoing cardiothoracic surgery and 7 healthy age-matched controls, between 2010 and 2011, at a single institution. WB and PBMC samples were collected at a single timepoint postoperatively (median day 8 postoperatively) (25-75% IQR 7-14 days) and subjected to Illumina single color Human BeadChip HT12 v4 whole genome expression array analysis. The Sequential Organ Failure Assessment (SOFA) score was used to characterize the severity of MOD into low (≤ 4 points), intermediate (5-11), and high (≥ 12) risk categories correlating with GEP.

RESULTS

Results indicate that the direction of change in GEP of individuals with MOD as compared to controls is similar when determined from PBMC versus WB. The main enriched terms by Gene Ontology (GO) analysis included those involved in the inflammatory response, apoptosis, and other stress response related pathways. The data revealed 35 significant GO categories and 26 pathways overlapping between PBMC and WB. Additionally, class prediction using machine learning tools demonstrated that the subset of significant genes shared by PBMC and WB are sufficient to train as a predictor separating the SOFA groups.

CONCLUSION

GEP analysis of WB has the potential to become a clinical tool for immune-monitoring in patients with MOD.

PBMCs reflect the immune component of the WAT transcriptome--implications as biomarkers of metabolic health in the postprandial state

SCOPE

Food and nutrition studies often require accessing metabolically active tissues, including adipose tissue. This can involve invasive biopsy procedures that can be a limiting factor in study design. In contrast, peripheral blood mononuclear cells (PBMCs) are a population of circulating immune cells that are easily accessible through venipuncture. As transcriptomics is of growing importance in food and metabolism research, understanding the transcriptomic relationship between these tissue types can provide insight into the utility of PBMCs in this field.

METHODS AND RESULTS

We examine this relationship within eight subjects, in two postprandial states (following oral lipid tolerance test and oral glucose tolerance test). Multivariate analysis techniques were used to examine variation between tissues, samples, and subjects in order to define which genes havecommon/disparate expression profiles associated with highly defined metabolic phenotypes. We demonstrate global similarities in gene expression between PBMCs and white adipose tissue, irrespective of the metabolic challenge type. Closer examination of individual genes revealed this similarity to be strongest in pathways related to immune response/inflammation. Notably, the expression of metabolism-related nuclear receptors, including PPARs, LXR, etc. was discordant between tissues

CONCLUSION

The PBMC transcriptome may therefore provide a unique insight into the inflammatory component of metabolic health, as opposed to directly reflecting the metabolic component of the adipose tissue transcriptome.

Identification of differentially expressed transcripts and pathways in blood one week and six months following implant of left ventricular assist devices

Introduction

Continuous-flow left ventricular assist devices (LVADs) are an established therapy for patients with end-stage heart failure. The short- and long-term impact of these devices on peripheral blood gene expression has not been characterized, and may provide insight into the molecular pathways mediated in response to left ventricular remodeling and an improvement in overall systemic circulation. We performed RNA sequencing to identify genes and pathways influenced by these devices.

Methods

RNA was extracted from blood of 9 heart failure patients (8 male) prior to LVAD implantation, and at 7 and 180 days postoperatively. Libraries were sequenced on an Illumina HiSeq2000 and sequences mapped to the human Ensembl GRCh37.67 genome assembly.

Results

A specific set of genes involved in regulating cellular immune response, antigen presentation, and T cell activation and survival were down-regulated 7 days after LVAD placement. 6 months following LVAD placement, the expression levels of these genes were significantly increased; yet importantly, remained significantly lower than age and sex-matched samples from healthy controls.

Conclusions

In summary, this genomic analysis identified a significant decrease in the expression of genes that promote a healthy immune response in patients with heart failure that was partially restored 6 months following LVAD implant.

Challenges and solutions in the development of genomic biomarker panels: a systematic phased approach

In the post-genome era, high throughput gene expression profiling has been successfully used to develop genomic biomarker panels (GBP) that can be integrated into clinical decision making. The development of GBPs in the context of personalized medicine is a scientifically challenging and resource-intense process. It needs to be accomplished in a systematic phased approach to address biological variation related to a clinical phenotype (e.g. disease etiology, gender, etc.) and minimize technical variation (noise). Here we present the methodological aspects of GBP development based on the experience of the Cardiac Allograft Rejection Gene Expression Observation (CARGO) study, a study that lead to the development of a molecular classifier for rejection screening in heart transplant patients.

Gene Expression Signatures of Peripheral Blood Mononuclear Cells during the Early Post-Transplant Period in Patients Developing Cardiac Allograft Vasculopathy

Background. Cardiac allograft vasculopathy (CAV) is a major cause of graft loss and death after heart transplantation. Currently, no diagnostic methods are available during the early post-transplant period to accurately identify patients at risk of CAV. We hypothesized that PBMC gene expression profiles (GEP) can identify patients at risk of CAV. Methods. We retrospectively analyzed a limited set of whole-genome PBMC microarrays from 10 post-transplant patients who did (n = 3) or did not (n = 7) develop advanced grade CAV during their long-term follow-up. We used significance analysis of microarrays to identify differentially expressed genes and High-Throughput GoMiner to assess gene ontology (GO) categories. We corroborated our findings by retrospective analysis of PBMC real-time PCR data from 33 patients. Results. Over 300 genes were differentially expressed (FDR < 5%), and 18 GO-categories including “macrophage activation”, “Interleukin-6 pathway”, “NF-KappaB cascade”, and “response to virus” were enriched by these genes (FDR < 5%). Out of 8 transcripts available for RT-PCR analysis, we confirmed 6 transcripts (75.0%) including FPRL1, S100A9, CXCL10, PRO1073, and MMP9 (P < .05). Conclusion. Our pilot data suggest that GEP of PBMC may become a valuable tool in the evaluation of patients at risk of CAV. Larger prospectively designed studies are needed to corroborate our hypothesis.