Whole blood genomic biomarkers of acute cardiac allograft rejection

Bioinformatics analysis characterizes immune infiltration landscape and identifies potential blood biomarkers for heart transplantation

BACKGROUND

Cardiac allograft rejection (AR) remains a significant complication following heart transplantation. The primary objective of our study is to gain a comprehensive understanding of the fundamental mechanisms involved in AR and identify possible therapeutic targets.

METHODS

We acquired the GSE87301 dataset from the Gene Expression Omnibus database. In GSE87301, a comparison was conducted on blood samples from patients with and without cardiac allograft rejection (AR and NAR) to detect differentially expressed genes (DEGs). Enrichment analysis was conducted to identify the pathways that show significant enrichment during AR. Machine learning techniques, including the least absolute shrinkage and selection operator regression (LASSO) and random forest (RF) algorithms, were employed to identify potential genes for the diagnosis of AR. The diagnostic value was evaluated using a nomogram and receiver operating characteristic (ROC) curve. Additionally, immune cell infiltration was analyzed to explore any dysregulation of immune cells in AR.

RESULTS

A total of 114 DEGs were identified from the GSE87301 dataset. These DEGs were mainly found to be enriched in pathways related to the immune system. To identify the signature genes, the LASSO and RF algorithms were used, and four genes, namely ALAS2, HBD, EPB42, and FECH, were identified. The performance of these signature genes was evaluated using the receiver operating characteristic curve (ROC) analysis, which showed that the area under the curve (AUC) values for ALAS2, HBD, EPB42, and FECH were 0.906, 0.881, 0.900, and 0.856, respectively. These findings were further confirmed in the independent datasets and clinical samples. The selection of these specific genes was made to construct a nomogram, which demonstrated excellent diagnostic ability. Additionally, the results of the single-sample gene set enrichment analysis (ssGSEA) revealed that these genes may be involved in immune cell infiltration.

CONCLUSION

We identified four signature genes (ALAS2, HBD, EPB42, and FECH) as potential peripheral blood diagnostic candidates for AR diagnosis. Additionally, a nomogram was constructed to aid in the diagnosis of heart transplantation. This study offers valuable insights into the identification of candidate genes for heart transplantation using peripheral blood samples.

Imaging alloreactive T cells provides early warning of organ transplant rejection

Diagnosis of organ transplant rejection relies upon biopsy approaches to confirm alloreactive T cell infiltration in the graft. Immune molecular monitoring is under investigation to screen for rejection, though these techniques have suffered from low specificity and lack of spatial information. ImmunoPET utilizing antibodies conjugated to radioisotopes has the potential to improve early and accurate detection of graft rejection. ImmunoPET is capable of noninvasively visualizing the dynamic distribution of cells expressing specific immune markers in the entire body over time. In this work, we identify and characterize OX40 as a surrogate biomarker for alloreactive T cells in organ transplant rejection and monitor its expression by utilizing immunoPET. In a dual murine heart transplant model that has both syngeneic and allogeneic hearts engrafted in bilateral ear pinna on the recipients, OX40 immunoPET clearly depicted alloreactive T cells in the allograft and draining lymph node that were not observed in their respective isograft counterparts. OX40 immunoPET signals also reflected the subject’s immunosuppression level with tacrolimus in this study. OX40 immunoPET is a promising approach that may bridge molecular monitoring and morphological assessment for improved transplant rejection diagnosis.

A Changing Paradigm in Heart Transplantation: An Integrative Approach for Invasive and Non-Invasive Allograft Rejection Monitoring

Cardiac allograft rejection following heart transplantation is challenging to diagnose. Tissue biopsies are the gold standard in monitoring the different types of rejection. The last decade has seen an increased emphasis on identifying non-invasive methods to improve rejection diagnosis and overcome tissue biopsy invasiveness. Liquid biopsy, as an efficient non-invasive diagnostic and prognostic oncological monitoring tool, seems to be applicable in heart transplant follow-ups. Moreover, molecular techniques applied on blood can be translated to tissue samples to provide novel perspectives on tissue and reveal new diagnostic and prognostic biomarkers. This review aims to provide a comprehensive overview of the state-of-the-art of the new methodologies in cardiac allograft rejection monitoring and investigate the future perspectives on invasive and non-invasive rejection biomarkers identification. We reviewed literature from the most used scientific databases, such as PubMed, Google Scholar, and Scopus. We extracted 192 papers and, after a selection and exclusion process, we included in the review 81 papers. The described limitations notwithstanding, this review show how molecular biology techniques and omics science could be deployed complementarily to the histopathological rejection diagnosis on tissue biopsies, thus representing an integrated approach for heart transplant patients monitoring.

Identification of Candidate Biomarkers for Transplant Rejection from Transcriptome Data: A Systematic Review

BACKGROUND

Traditional methods for rejection control in transplanted patients are considered invasive, risky, and prone to sampling errors. Using molecular biomarkers as an alternative protocol to biopsies, for monitoring rejection may help to mitigate some of these problems, increasing the survival rates and well-being of patients. Recent advances in omics technologies provide an opportunity for screening new molecular biomarkers to identify those with clinical utility.

OBJECTIVE

This systematic literature review (SLR) aimed to summarize existing evidence derived from large-scale expression profiling regarding differentially expressed mRNA and miRNA in graft rejection, highlighting potential molecular biomarkers in transplantation.

METHODS

The study was conducted following PRISMA methodology and the BiSLR guide for performing SLR in bioinformatics. PubMed, ScienceDirect, and EMBASE were searched for publications from January 2001 to January 2018, and studies (i) aiming at the identification of transplant rejection biomarkers, (ii) including human subjects, and (iii) applying methodologies for differential expression analysis from large-scale expression profiling were considered eligible. Differential expression patterns reported for genes and miRNAs in rejection were summarized from both cross-organ and organ-specific perspectives, and pathways enrichment analysis was performed for candidate biomarkers to interrogate their functional role in transplant rejection.

RESULTS

A total of 821 references were collected, resulting in 604 studies after removal of duplicates. After application of inclusion and exclusion criteria, 33 studies were included in our analysis. Among the 1517 genes and 174 miRNAs identifed, CXCL9, CXCL10, STAT1, hsa-miR-142-3p, and hsa-miR-155 appeared to be particularly promising as biomarkers in transplantation, with an increased expression associated with transplant rejection in multiple organs. In addition, hsa-miR-28-5p was consistently decreased in samples taken from rejected organs.

CONCLUSION

Despite the need for further research to fill existing knowledge gaps, transcriptomic technologies have a relevant role in the discovery of accurate biomarkers for transplant rejection diagnostics. Studies have reported consistent evidence of differential expression associated with transplant rejection, although issues such as experimental heterogeneity hinder a more systematic characterization of observed molecular changes. Special attention has been giving to large-scale mRNA expression profiling in rejection, whereas there is still room for improvements in the characterization of miRnome in this condition.

PROSPERO REGISTRATION NUMBER

CRD42018083321.

HEARTBiT: A Transcriptomic Signature for Excluding Acute Cellular Rejection in Adult Heart Allograft 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.

Microarray and proteome array in an atherosclerosis mouse model for identification of biomarkers in whole blood

Cardiovascular disease (CVD) is highly fatal, and 80 percent of the mortality is attributed to heart attack and stroke. Atherosclerosis is a disease that increases a patient's risk to CVD and is characterized by atheroma formed by immune cells, lipids, and smooth muscle cells. When an atherosclerotic lesion grows and blocks blood vessels or when an atheroma ruptures and blocks blood vessels by embolism, sudden angina, or stroke can occur. It is therefore important to diagnose atherosclerosis early and prevent its progression to more severe disease. Although myeloperoxidase, plasma fibrinogen, cardiac troponin-I, and C-reactive protein have been considered as diagnostic markers for multiple cardiac risks, specific biomarkers for atherosclerosis have not been clearly determined yet. Particularly, reliable biomarkers for the diagnosis of atherosclerosis using whole blood are not yet available. In this study, we screened potential biomarker genes and proteins from whole blood of apolipoprotein E knockout (ApoE-/- ) mice maintained on a Western diet, by comparing them to ApoE+/+ mice. We used whole blood for microarray and proteome array. Candidate genes and proteins identified from each method were confirmed with quantitative real-time PCR and ELISA. Based on our data, we speculate that Lilrb4a, n-R5s136, and IL-5 are potential targets that can be developed into novel biomarkers of atherosclerosis. Our study contributes to the diagnosis of atherosclerosis using whole blood in clinical settings.

The Expressed Genome in Cardiovascular Diseases and Stroke: Refinement, Diagnosis, and Prediction: A Scientific Statement From the American Heart Association

There have been major advances in our knowledge of the contribution of DNA sequence variations to cardiovascular disease and stroke. However, the inner workings of the body reflect the complex interplay of factors beyond the DNA sequence, including epigenetic modifications, RNA transcripts, proteins, and metabolites, which together can be considered the "expressed genome." The emergence of high-throughput technologies, including epigenomics, transcriptomics, proteomics, and metabolomics, is now making it possible to address the contributions of the expressed genome to cardiovascular disorders. This statement describes how the expressed genome can currently and, in the future, potentially be used to diagnose diseases and to predict who will develop diseases such as coronary artery disease, stroke, heart failure, and arrhythmias.

Identifying low-grade cellular rejection after heart transplantation in children by using gene expression profiling

Endomyocardial biopsy (EMB) remains the gold standard for detecting rejection after heart transplantation but is costly and invasive. This study aims to distinguish no rejection (0R) from low-grade rejection (1R/2R) after heart transplantation in children by using global gene expression profiling in blood. A total of 106 blood samples with corresponding EMB from 18 children who underwent heart transplantation from 2011 to 2014 were analyzed (18 baseline/pretransplantation samples, 88 EMB samples). Corresponding rejection grades for each blood sample were 0R in 39% (34/88), 1R in 51% (45/88), and 2R in 10% (9/88). mRNA from each sample was sequenced. Differential expression analysis was performed at the gene level. A k-nearest neighbor (kNN) analysis was applied to the most differentially expressed (DE) genes to identify rejection after transplantation. Mean age at transplantation was 10.0 ± 5.4 yr. Expression of B cell and T cell receptor sequences was used to measure the effect of posttransplantation immunosuppression. Follow-up samples had lower levels of immunoglobulin gene families compared with pretransplantation ( P < 3E-5) (lower numbers of activated B cells). T cell receptor alpha and beta gene families had decreased expression in 0R samples compared with pretransplantation ( P < 4E-5) but recovered to near baseline levels in 1R/2R samples. kNN using the most DE gene (MKS1) and k = 9 nearest neighbors correctly identified 83% (73/88) of 1R/2R compared with 0R by leave-one-out cross validation. Using a genomic approach we can distinguish low-grade cellular allograft rejection (1R/2R) from no rejection (0R) after heart transplantation in children despite a wide age range.

Heart failure therapies: new strategies for old treatments and new treatments for old strategies

Heart failure, whether acute or chronic, remains a major health care crisis affecting almost 6 million Americans and over 23 million people worldwide. Roughly half of those affected will die within 5 years, and the annual cost exceeds $30 billion in the US alone. Although medical therapy has made some modest inroads in partially stemming the heart failure tsunami, there remains a significant population for whom medication is unsuccessful or has ceased being effective; such patients can benefit from heart transplantation or mechanical circulatory support. Indeed, in the past quarter century (and as covered in Cardiovascular Pathology over those years), significant improvements in pathologic understanding and in engineering design have materially enhanced the toolkit of options for such refractory patients. Mechanical devices, whether total artificial hearts or ventricular assist devices, have been reengineered to reduce complications and basic wear and tear. Transplant survival has also been extended through a better comprehension of and improved therapies for transplant vasculopathy and antibody-mediated rejection. Here we review the ideas and treatments from the last 25 years and highlight some of the new directions in nonpharmacologic heart failure therapy.

Detecting rejection in cardiac transplantation: applications of genomic medicine

The primary focus of care in heart transplant recipients is to prevent rejection. Checking serum levels of immunosuppressant drugs as well as performing biopsies to obtain tissue samples for histological analysis have been the mainstays of rejection surveillance. Novel applications of genomics have proven to be useful in adding a noninvasive alternative to the standard of care. In particular, the use of gene-expression profiling to detect upregulation of rejection-related genes, detection of donor-derived cell-free DNA as a marker of graft apoptosis and rejection, as well as mRNA and miRNA as intragraft and peripheral markers of rejection have come to the forefront of genomics as they relate to transplantation. This review focuses on the use of genomics in heart transplant recipients.

Transcriptomic Profile of Whole Blood Cells from Elderly Subjects Fed Probiotic Bacteria Lactobacillus rhamnosus GG ATCC 53103 (LGG) in a Phase I Open Label Study

We examined gene expression of whole blood cells (WBC) from 11 healthy elderly volunteers participating on a Phase I open label study before and after oral treatment with Lactobacillus rhamnosus GG-ATCC 53103 (LGG)) using RNA-sequencing (RNA-Seq). Elderly patients (65–80 yrs) completed a clinical assessment for health status and had blood drawn for cellular RNA extraction at study admission (Baseline), after 28 days of daily LGG treatment (Day 28) and at the end of the study (Day 56) after LGG treatment had been suspended for 28 days. Treatment compliance was verified by measuring LGG-DNA copy levels detected in host fecal samples. Normalized gene expression levels in WBC RNA were analyzed using a paired design built within three analysis platforms (edgeR, DESeq2 and TSPM) commonly used for gene count data analysis. From the 25,990 transcripts detected, 95 differentially expressed genes (DEGs) were detected in common by all analysis platforms with a nominal significant difference in gene expression at Day 28 following LGG treatment (FDR<0.1; 77 decreased and 18 increased). With a more stringent significance threshold (FDR<0.05), only two genes (FCER2 and LY86), were down-regulated more than 1.5 fold and met the criteria for differential expression across two analysis platforms. The remaining 93 genes were only detected at this threshold level with DESeq2 platform. Data analysis for biological interpretation of DEGs with an absolute fold change of 1.5 revealed down-regulation of overlapping genes involved with Cellular movement, Cell to cell signaling interactions, Immune cell trafficking and Inflammatory response. These data provide evidence for LGG-induced transcriptional modulation in healthy elderly volunteers because pre-treatment transcription levels were restored at 28 days after LGG treatment was stopped. To gain insight into the signaling pathways affected in response to LGG treatment, DEG were mapped using biological pathways and genomic data mining packages to indicate significant biological relevance.

Trial Registration: ClinicalTrials.gov NCT01274598

Multicenter Analysis of Immune Biomarkers and Heart Transplant Outcomes: Results of the Clinical Trials in Organ Transplantation-05 Study

Identification of biomarkers that assess posttransplant risk is needed to improve long-term outcomes following heart transplantation. The Clinical Trials in Organ Transplantation (CTOT)-05 protocol was an observational, multicenter, cohort study of 200 heart transplant recipients followed for the first posttransplant year. The primary endpoint was a composite of death, graft loss/retransplantation, biopsy-proven acute rejection (BPAR), and cardiac allograft vasculopathy (CAV) as defined by intravascular ultrasound (IVUS). We serially measured anti-HLA- and auto-antibodies, angiogenic proteins, peripheral blood allo-reactivity, and peripheral blood gene expression patterns. We correlated assay results and clinical characteristics with the composite endpoint and its components. The composite endpoint was associated with older donor allografts (p < 0.03) and with recipient anti-HLA antibody (p < 0.04). Recipient CMV-negativity (regardless of donor status) was associated with BPAR (p < 0.001), and increases in plasma vascular endothelial growth factor-C (OR 20; 95%CI:1.9-218) combined with decreases in endothelin-1 (OR 0.14; 95%CI:0.02-0.97) associated with CAV. The remaining biomarkers showed no relationships with the study endpoints. While suboptimal endpoint definitions and lower than anticipated event rates were identified as potential study limitations, the results of this multicenter study do not yet support routine use of the selected assays as noninvasive approaches to detect BPAR and/or CAV following heart transplantation.

Circulating biomarker responses to medical management vs. mechanical circulatory support in severe inotrope-dependent acute heart failure

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.

Computational Models for Transplant Biomarker Discovery

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems.

Assessment of sub-clinical acute cellular rejection after heart transplantation: comparison of cardiac magnetic resonance imaging and endomyocardial biopsy

OBJECTIVE

Comparing the diagnostic value of multi-sequential cardiac magnetic resonance imaging (CMR) with endomyocardial biopsy (EMB) for sub-clinical cardiac allograft rejection.

METHODS

One hundred and forty-six examinations in 73 patients (mean age 53 ± 12 years, 58 men) were performed using a 1.5 Tesla system and compared to EMB. Examinations included a STIR (short tau inversion recovery) sequence for calculation of edema ratio (ER), a T1-weighted spin-echo sequence for assessment of global relative enhancement (gRE), and inversion-recovery sequences to visualize late gadolinium enhancement (LGE). Histological grade ≥1B was considered relevant rejection.

RESULTS

One hundred and twenty-seven (127/146 = 87 %) EMBs demonstrated no or mild signs of rejection (grades ≤1A) and 19/146 (13 %) a relevant rejection (grade ≥1B). Sensitivity, specificity, positive predictive, and negative predictive values were as follows: ER: 63 %, 78 %, 30 %, and 93 %; gRE: 63 %, 70 %, 24 %, and 93 %; LGE: 68 %, 36 %, 13 %, and 87 %; with the combination of ER and gRE with at least one out of two positive: 84 %, 57 %, 23 %, and 96 %. ROC analysis revealed an area under the curve of 0.724 for ER and 0.659 for gRE.

CONCLUSION

CMR parameters for myocarditis are useful to detect sub-clinical acute cellular rejection after heart transplantation. Comparable results to myocarditis can be achieved with a combination of parameters.

KEY POINTS

• Magnetic resonance imaging is useful for the assessment of cardiac allograft rejection. • CMR has a high negative predictive value for exclusion of allograft rejection. • Diagnostic performance is not yet good enough to replace endomyocardial biopsy.

Evaluation of gene expression classification studies: factors associated with classification performance

Classification methods used in microarray studies for gene expression are diverse in the way they deal with the underlying complexity of the data, as well as in the technique used to build the classification model. The MAQC II study on cancer classification problems has found that performance was affected by factors such as the classification algorithm, cross validation method, number of genes, and gene selection method. In this paper, we study the hypothesis that the disease under study significantly determines which method is optimal, and that additionally sample size, class imbalance, type of medical question (diagnostic, prognostic or treatment response), and microarray platform are potentially influential. A systematic literature review was used to extract the information from 48 published articles on non-cancer microarray classification studies. The impact of the various factors on the reported classification accuracy was analyzed through random-intercept logistic regression. The type of medical question and method of cross validation dominated the explained variation in accuracy among studies, followed by disease category and microarray platform. In total, 42% of the between study variation was explained by all the study specific and problem specific factors that we studied together.

Variation in RNA-Seq transcriptome profiles of peripheral whole blood from healthy individuals with and without globin depletion

Background

The molecular profile of circulating blood can reflect physiological and pathological events occurring in other tissues and organs of the body and delivers a comprehensive view of the status of the immune system. Blood has been useful in studying the pathobiology of many diseases. It is accessible and easily collected making it ideally suited to the development of diagnostic biomarker tests. The blood transcriptome has a high complement of globin RNA that could potentially saturate next-generation sequencing platforms, masking lower abundance transcripts. Methods to deplete globin mRNA are available, but their effect has not been comprehensively studied in peripheral whole blood RNA-Seq data. In this study we aimed to assess technical variability associated with globin depletion in addition to assessing general technical variability in RNA-Seq from whole blood derived samples.

Results

We compared technical and biological replicates having undergone globin depletion or not and found that the experimental globin depletion protocol employed removed approximately 80% of globin transcripts, improved the correlation of technical replicates, allowed for reliable detection of thousands of additional transcripts and generally increased transcript abundance measures. Differential expression analysis revealed thousands of genes significantly up-regulated as a result of globin depletion. In addition, globin depletion resulted in the down-regulation of genes involved in both iron and zinc metal ion bonding.

Conclusions

Globin depletion appears to meaningfully improve the quality of peripheral whole blood RNA-Seq data, and may improve our ability to detect true biological variation. Some concerns remain, however. Key amongst them the significant reduction in RNA yields following globin depletion. More generally, our investigation of technical and biological variation with and without globin depletion finds that high-throughput sequencing by RNA-Seq is highly reproducible within a large dynamic range of detection and provides an accurate estimation of RNA concentration in peripheral whole blood. High-throughput sequencing is thus a promising technology for whole blood transcriptomics and biomarker discovery.

T lymphocyte abnormalities in juvenile systemic sclerosis patients

Multi-center evaluations of pediatric patients with juvenile systemic sclerosis (jSSc) have suggested that the pathogenesis of jSSc may differ from that of systemic sclerosis (SSc) in adult patients. Therefore, we undertook to identify abnormalities in the T lymphocytes of jSSc patients and to determine if they differed from the abnormalities reported in the T lymphocytes of adult SSc patients. We identified decreases in the frequency of resting regulatory T lymphocytes and an increased frequency of CD45RA expressing effector memory (EMRA) CD4 T lymphocytes, which were characterized by an increased frequency of CCR7 protein expressing cells. Neither the increases in the EMRA subpopulation nor the increased CCR7 protein expression have been reported in adult SSc patients. The decrease in resting regulatory T lymphocytes in jSSc patients may permit the expansion of the disease initiating CD4 T lymphocytes present in the CCR7 expressing EMRA CD4 T lymphocyte subpopulation.

Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.

A computational pipeline for the development of multi-marker bio-signature panels and ensemble classifiers

Background

Biomarker panels derived separately from genomic and proteomic data and with a variety of computational methods have demonstrated promising classification performance in various diseases. An open question is how to create effective proteo-genomic panels. The framework of ensemble classifiers has been applied successfully in various analytical domains to combine classifiers so that the performance of the ensemble exceeds the performance of individual classifiers. Using blood-based diagnosis of acute renal allograft rejection as a case study, we address the following question in this paper: Can acute rejection classification performance be improved by combining individual genomic and proteomic classifiers in an ensemble?

Results

The first part of the paper presents a computational biomarker development pipeline for genomic and proteomic data. The pipeline begins with data acquisition (e.g., from bio-samples to microarray data), quality control, statistical analysis and mining of the data, and finally various forms of validation. The pipeline ensures that the various classifiers to be combined later in an ensemble are diverse and adequate for clinical use. Five mRNA genomic and five proteomic classifiers were developed independently using single time-point blood samples from 11 acute-rejection and 22 non-rejection renal transplant patients. The second part of the paper examines five ensembles ranging in size from two to 10 individual classifiers. Performance of ensembles is characterized by area under the curve (AUC), sensitivity, and specificity, as derived from the probability of acute rejection for individual classifiers in the ensemble in combination with one of two aggregation methods: (1) Average Probability or (2) Vote Threshold. One ensemble demonstrated superior performance and was able to improve sensitivity and AUC beyond the best values observed for any of the individual classifiers in the ensemble, while staying within the range of observed specificity. The Vote Threshold aggregation method achieved improved sensitivity for all 5 ensembles, but typically at the cost of decreased specificity.

Conclusion

Proteo-genomic biomarker ensemble classifiers show promise in the diagnosis of acute renal allograft rejection and can improve classification performance beyond that of individual genomic or proteomic classifiers alone. Validation of our results in an international multicenter study is currently underway.

Genetic and genomic approaches to the detection of heart transplant rejection

Since Christiaan Barnard performed the first heart transplant in 1967, over 100,000 heart transplants have been performed worldwide. As was true then, rejection remains the major threat to the function and survival of the allograft. The development of the endomyocardial biopsy as a means to monitor for rejection has allowed heart transplantation to thrive as a therapy for patients with end-stage heart disease. The need for a noninvasive method of rejection surveillance led to the development of the first genetic test for allograft rejection, the AlloMap^®. In this article, after presenting the pathological and clinical features of cardiac allograft rejection, the authors discuss the development and application of gene-expression testing for the detection of cardiac allograft rejection. We then explore emerging 'omic' approaches that will be the rejection detection methods of the future.

White blood cell transcriptome correlates with renal function in acute heart failure

It is notoriously difficult to classify patients with acute heart failure (AHF) because of variations in clinical presentation, different etiologies, the impact of comorbidities, and variable prognoses. In this study, we used DNA whole-genome microarrays to classify 24 patients with AHF based on the transcriptome of their peripheral blood nuclear cells. The main purpose was to verify whether any transcriptomic sub-clusters had clinical correlations. We identified two distinct groups of transcriptomic profiles that correlated with normal (1.125 mg/dL) and increased (1.783 mg/dL) mean blood creatinine concentrations. These two subgroups of patients (n = 12) differed in the expression of more than 6000 genes and 108 signaling pathways. The most significant regulated signaling pathway was the aldosterone-regulated sodium reabsorption pathway and the most significant regulated genes included the angiotensin-converting enzyme gene. This suggests that kidney impairment in patients with AHF is related to dysregulation of the renin-angiotensin-aldosterone system. The interesting findings of our study were the significant differences in expression of genes belonging to the aldosterone-regulated signaling pathway: Na⁺/K⁺ transporting ATPase and NEDD4L (neuronal precursor cell expressed developmentally down-regulated 4-like) between patients with and without renal dysfunction. Future studies of blood-cell transcriptomic profiles in patients with AHF will provide further insights into the molecular pathogenesis of this cardiorenal disorder.

White blood cell differentials enrich whole blood expression data in the context of acute cardiac allograft rejection

Acute cardiac allograft rejection is a serious complication of heart transplantation. Investigating molecular processes in whole blood via microarrays is a promising avenue of research in transplantation, particularly due to the non-invasive nature of blood sampling. However, whole blood is a complex tissue and the consequent heterogeneity in composition amongst samples is ignored in traditional microarray analysis. This complicates the biological interpretation of microarray data. Here we have applied a statistical deconvolution approach, cell-specific significance analysis of microarrays (csSAM), to whole blood samples from subjects either undergoing acute heart allograft rejection (AR) or not (NR). We identified eight differentially expressed probe-sets significantly correlated to monocytes (mapping to 6 genes, all down-regulated in ARs versus NRs) at a false discovery rate (FDR) ≤ 15%. None of the genes identified are present in a biomarker panel of acute heart rejection previously published by our group and discovered in the same data***.

Gene expression profiling of human whole blood samples with the Illumina WG-DASL assay

Background

Microarray-based gene expression analysis of peripheral whole blood is a common strategy in the development of clinically relevant biomarker panels for a variety of human diseases. However, the results of such an analysis are often plagued by decreased sensitivity and reliability due to the effects of relatively high levels of globin mRNA in whole blood. Globin reduction assays have been shown to overcome such effects, but they require large amounts of total RNA and may induce distinct gene expression profiles. The Illumina whole genome DASL assay can detect gene expression levels using partially degraded RNA samples and has the potential to detect rare transcripts present in highly heterogeneous whole blood samples without the need for globin reduction. We assessed the utility of the whole genome DASL assay in an analysis of peripheral whole blood gene expression profiles.

Results

We find that gene expression detection is significantly increased with the use of whole genome DASL compared to the standard IVT-based direct hybridization. Additionally, globin-probe negative whole genome DASL did not exhibit significant improvements over globin-probe positive whole genome DASL. Globin reduction further increases the detection sensitivity and reliability of both whole genome DASL and IVT-based direct hybridization with little effect on raw intensity correlations. Raw intensity correlations between total RNA and globin reduced RNA were 0.955 for IVT-based direct hybridization and 0.979 for whole genome DASL.

Conclusions

Overall, the detection sensitivity of the whole genome DASL assay is higher than the IVT-based direct hybridization assay, with or without globin reduction, and should be considered in conjunction with globin reduction methods for future blood-based gene expression studies.

Universal noninvasive detection of solid organ transplant rejection

It is challenging to monitor the health of transplanted organs, particularly with respect to rejection by the host immune system. Because transplanted organs have genomes that are distinct from the recipient's genome, we used high throughput shotgun sequencing to develop a universal noninvasive approach to monitoring organ health. We analyzed cell-free DNA circulating in the blood of heart transplant recipients and observed significantly increased levels of cell-free DNA from the donor genome at times when an endomyocardial biopsy independently established the presence of acute cellular rejection in these heart transplant recipients. Our results demonstrate that cell-free DNA can be used to detect an organ-specific signature that correlates with rejection, and this measurement can be made on any combination of donor and recipient. This noninvasive test holds promise for replacing the endomyocardial biopsy in heart transplant recipients and may be applicable to other solid organ transplants.

Whole blood biomarkers of acute cardiac allograft rejection: double-crossing the biopsy

BACKGROUND

Acute rejection is still a significant barrier to long-term survival of the allograft. Current acute rejection diagnostic methods are not specific enough or are invasive. There have been a number of studies that have explored the blood or the biopsy to discover genomic biomarkers of acute rejection; however, none of the studies to date have used both.

METHODS

We analyzed endomyocardial biopsy tissue and whole blood-derived messenger RNA from 11 acute rejection and 20 nonrejection patients using Affymetrix Human Genome U133 Plus 2.0 chips. We used a novel approach and gained insight into the biology of rejection based on gene expression in the biopsy, and applied this knowledge to the blood analysis to identify novel blood biomarkers.

RESULTS

We identified probesets that are differentially expressed between acute rejection and nonrejection patients in the biopsy and blood, and developed three biomarker panels: (1) based on biopsy-only (area under the curve=0.85), (2) based on biopsy-targeted whole blood (area under the curve=0.83), and (3) based on whole blood-only (area under the curve=0.60) analyses.

CONCLUSIONS

Most of the probesets replicated between biopsy and blood are regulated in opposite direction between the two sources of information. We also observed that the biopsy-targeted blood biomarker discovery approach can improve performance of the biomarker panel. The biomarker panel developed using this targeted approach is able to diagnose acute cardiac allograft rejection almost as well as the biopsy-only based biomarker panel.

Molecular diagnostics in transplantation

The past few decades are characterized by an explosive evolution of genetics and molecular cell biology. Advances in chemistry and engineering have enabled increased data throughput, permitting the study of complete sets of molecules with increasing speed and accuracy using techniques such as genomics, transcriptomics, proteomics, and metabolomics. Prediction of long-term outcomes in transplantation is hampered by the absence of sufficiently robust biomarkers and a lack of adequate insight into the mechanisms of acute and chronic alloimmune injury and the adaptive mechanisms of immunological quiescence that may support transplantation tolerance. Here, we discuss some of the great opportunities that molecular diagnostic tools have to offer both basic scientists and translational researchers for bench-to-bedside clinical application in transplantation medicine, with special focus on genomics and genome-wide association studies, epigenetics (DNA methylation and histone modifications), gene expression studies and transcriptomics (including microRNA and small interfering RNA studies), proteomics and peptidomics, antibodyomics, metabolomics, chemical genomics and functional imaging with nanoparticles. We address the challenges and opportunities associated with the newer high-throughput sequencing technologies, especially in the field of bioinformatics and biostatistics, and demonstrate the importance of integrative approaches. Although this Review focuses on transplantation research and clinical transplantation, the concepts addressed are valid for all translational research.

Assessing the human immune system through blood transcriptomics

Blood is the pipeline of the immune system. Assessing changes in transcript abundance in blood on a genome-wide scale affords a comprehensive view of the status of the immune system in health and disease. This review summarizes the work that has used this approach to identify therapeutic targets and biomarker signatures in the field of autoimmunity and infectious disease. Recent technological and methodological advances that will carry the blood transcriptome research field forward are also discussed.