Molecular profiling improves diagnoses of rejection and infection in transplanted organs

Exploring personalized treatment for cardiac graft rejection based on a four-archetype analysis model and bioinformatics analysis

Heart transplantation is the gold standard for treating patients with advanced heart failure. Although improvements in immunosuppressive therapies have significantly reduced the frequency of cardiac graft rejection, the incidences of T cell-mediated rejection (TCMR) and antibody-mediated rejection remain almost unchanged. A four-archetype analysis (4AA) model, developed by Philip F. Halloran, illustrated this problem well. It provided a new dimension to improve the accuracy of diagnoses and an independent system for recalibrating the histology guidelines. However, this model was based on the invasive method of endocardial biopsy, which undoubtedly increased the postoperative risk of heart transplant patients. Currently, little is known regarding the associated genes and specific functions of the different phenotypes. We performed bioinformatics analysis (using machine-learning methods and the WGCNA algorithm) to screen for hub-specific genes related to different phenotypes, based Gene Expression Omnibus accession number GSE124897. More immune cell infiltration was observed with the ABMR, TCMR, and injury phenotypes than with the stable phenotype. Hub-specific genes for each of the four archetypes were verified successfully using an external test set (accession number GSE2596). Logistic-regression models based on TCMR-specific hub genes and common hub genes were constructed with accurate diagnostic utility (area under the curve > 0.95). RELA, NFKB1, and SOX14 were identified as transcription factors important for TCMR/injury phenotypes and common genes, respectively. Additionally, 11 Food and Drug Administration-approved drugs were chosen from the DrugBank Database for each four-archetype model. Tyrosine kinase inhibitors may be a promising new option for transplant rejection treatment. KRAS signaling in cardiac transplant rejection is worth further investigation. Our results showed that heart transplant rejection subtypes can be accurately diagnosed by detecting expression of the corresponding specific genes, thereby enabling precise treatment or medication.

High-dimensional profiling of pediatric immune responses to solid organ transplantation

Solid organ transplant remains a life-saving therapy for children with end-stage heart, lung, liver, or kidney disease; however, ∼33% of allograft recipients experience acute rejection within the first year after transplant. Our ability to detect early rejection is hampered by an incomplete understanding of the immune changes associated with allograft health, particularly in the pediatric population. We performed detailed, multilineage, single-cell analysis of the peripheral blood immune composition in pediatric solid organ transplant recipients, with high-dimensional mass cytometry. Supervised and unsupervised analysis methods to study cell-type proportions indicate that the allograft type strongly influences the post-transplant immune profile. Further, when organ-specific differences are considered, graft health is associated with changes in the proportion of distinct T cell subpopulations. Together, these data form the basis for mechanistic studies into the pathobiology of rejection and allow for the development of new immunosuppressive agents with greater specificity.

Multiplexed Plasma Immune Mediator Signatures Can Differentiate Sepsis From NonInfective SIRS: American Surgical Association 2020 Annual Meeting Paper

OBJECTIVES

Sepsis and sterile both release "danger signals' that induce the systemic inflammatory response syndrome (SIRS). So differentiating infection from SIRS can be challenging. Precision diagnostic assays could limit unnecessary antibiotic use, improving outcomes.

METHODS

After surveying human leukocyte cytokine production responses to sterile damage-associated molecular patterns (DAMPs), bacterial pathogen-associated molecular patterns, and bacteria we created a multiplex assay for 31 cytokines. We then studied plasma from patients with bacteremia, septic shock, "severe sepsis," or trauma (ISS ≥15 with circulating DAMPs) as well as controls. Infections were adjudicated based on post-hospitalization review. Plasma was studied in infection and injury using univariate and multivariate means to determine how such multiplex assays could best distinguish infective from noninfective SIRS.

RESULTS

Infected patients had high plasma interleukin (IL)-6, IL-1α, and triggering receptor expressed on myeloid cells-1 (TREM-1) compared to controls [false discovery rates (FDR) <0.01, <0.01, <0.0001]. Conversely, injury suppressed many mediators including MDC (FDR <0.0001), TREM-1 (FDR <0.001), IP-10 (FDR <0.01), MCP-3 (FDR <0.05), FLT3L (FDR <0.05), Tweak, (FDR <0.05), GRO-α (FDR <0.05), and ENA-78 (FDR <0.05). In univariate studies, analyte overlap between clinical groups prevented clinical relevance. Multivariate models discriminated injury and infection much better, with the 2-group random-forest model classifying 11/11 injury and 28/29 infection patients correctly in out-of-bag validation.

CONCLUSIONS

Circulating cytokines in traumatic SIRS differ markedly from those in health or sepsis. Variability limits the accuracy of single-mediator assays but machine learning based on multiplexed plasma assays revealed distinct patterns in sepsis- and injury-related SIRS. Defining biomarker release patterns that distinguish specific SIRS populations might allow decreased antibiotic use in those clinical situations. Large prospective studies are needed to validate and operationalize this approach.

Key driver genes as potential therapeutic targets in renal allograft rejection

Acute rejection (AR) in renal transplantation is an established risk factor for reduced allograft survival. Molecules with regulatory control among immune pathways of AR that are inadequately suppressed, despite standard-of-care immunosuppression, could serve as important targets for therapeutic manipulation to prevent rejection. Here, an integrative, network-based computational strategy incorporating gene expression and genotype data of human renal allograft biopsy tissue was applied, to identify the master regulators - the key driver genes (KDGs) - within dysregulated AR pathways. A 982-meta-gene signature with differential expression in AR versus non-AR was identified from a meta-analysis of microarray data from 735 human kidney allograft biopsy samples across 7 data sets. Fourteen KDGs were derived from this signature. Interrogation of 2 publicly available databases identified compounds with predicted efficacy against individual KDGs or a key driver-based gene set, respectively, which could be repurposed for AR prevention. Minocycline, a tetracycline antibiotic, was chosen for experimental validation in a murine cardiac allograft model of AR. Minocycline attenuated the inflammatory profile of AR compared with controls and when coadministered with immunosuppression prolonged graft survival. This study demonstrates that a network-based strategy, using expression and genotype data to predict KDGs, assists target prioritization for therapeutics in renal allograft rejection.

Set-based differential covariance testing for genomics

The problem of detecting the changes in covariance for a single pair of genomic features has been studied in some detail but may be limited in importance or general applicability. For testing equality of covariance matrices of a set of features, many methods have been limited to the two-sample problem and involve varying assumptions on the number of features p versus the sample size n. More general covariance regression approaches are appealing but have been insufficiently structured to provide interpretable testing. To address these deficiencies, we propose a simple uniform framework to test association of covariance matrices with an experimental variable, whether discrete or continuous. We describe four different summary statistics, to ensure power and flexibility under various alternatives, including a new "connectivity" statistic that is sensitive to the changes in overall covariance magnitude. For continuous experimental variables, a natural individual "risk score" is associated with several of the statistics. We establish asymptotic results applicable to both continuous and discrete responses, with relatively mild conditions and allowing for situations where p>n. We also show that the proposed statistics are permutationally equivalent to some existing methods in the two-sample special case. We demonstrate the power and utility of our approaches via simulation and analysis of real data. The R package CorDiff is published on R CRAN.

Inflammatory macrophage-associated 3-gene signature predicts subclinical allograft injury and graft survival

Late allograft failure is characterized by cumulative subclinical insults manifesting over many years. Although immunomodulatory therapies targeting host T cells have improved short-term survival rates, rates of chronic allograft loss remain high. We hypothesized that other immune cell types may drive subclinical injury, ultimately leading to graft failure. We collected whole-genome transcriptome profiles from 15 independent cohorts composed of 1,697 biopsy samples to assess the association of an inflammatory macrophage polarization-specific gene signature with subclinical injury. We applied penalized regression to a subset of the data sets and identified a 3-gene inflammatory macrophage-derived signature. We validated discriminatory power of the 3-gene signature in 3 independent renal transplant data sets with mean AUC of 0.91. In a longitudinal cohort, the 3-gene signature strongly correlated with extent of injury and accurately predicted progression of subclinical injury 18 months before clinical manifestation. The 3-gene signature also stratified patients at high risk of graft failure as soon as 15 days after biopsy. We found that the 3-gene signature also distinguished acute rejection (AR) accurately in 3 heart transplant data sets but not in lung transplant. Overall, we identified a parsimonious signature capable of diagnosing AR, recognizing subclinical injury, and risk-stratifying renal transplant patients. Our results strongly suggest that inflammatory macrophages may be a viable therapeutic target to improve long-term outcomes for organ transplantation patients.

Transkingdom Networks: A Systems Biology Approach to Identify Causal Members of Host-Microbiota Interactions

Improvements in sequencing technologies and reduced experimental costs have resulted in a vast number of studies generating high-throughput data. Although the number of methods to analyze these "omics" data has also increased, computational complexity and lack of documentation hinder researchers from analyzing their high-throughput data to its true potential. In this chapter we detail our data-driven, transkingdom network (TransNet) analysis protocol to integrate and interrogate multi-omics data. This systems biology approach has allowed us to successfully identify important causal relationships between different taxonomic kingdoms (e.g., mammals and microbes) using diverse types of data.

Transplant genetics and genomics

Ever since the discovery of the major histocompatibility complex, scientific and clinical understanding in the field of transplantation has been advanced through genetic and genomic studies. Candidate-gene approaches and recent genome-wide association studies (GWAS) have enabled a deeper understanding of the complex interplay of the donor-recipient interactions that lead to transplant tolerance or rejection. Genetic analysis in transplantation, when linked to demographic and clinical outcomes, has the potential to drive personalized medicine by enabling individualized risk stratification and immunosuppression through the identification of variants associated with immune-mediated complications, post-transplant disease or alterations in drug-metabolizing genes.

Gene Expression in Biopsies of Acute Rejection and Interstitial Fibrosis/Tubular Atrophy Reveals Highly Shared Mechanisms That Correlate With Worse Long-Term Outcomes

Interstitial fibrosis and tubular atrophy (IFTA) is found in approximately 25% of 1-year biopsies posttransplant. It is known that IFTA correlates with decreased graft survival when histological evidence of inflammation is present. Identifying the mechanistic etiology of IFTA is important to understanding why long-term graft survival has not changed as expected despite improved immunosuppression and dramatically reduced rates of clinical acute rejection (AR) (Services UDoHaH. http://www.ustransplant.org/annual_reports/current/509a_ki.htm). Gene expression profiles of 234 graft biopsy samples were obtained with matching clinical and outcome data. Eighty-one IFTA biopsies were divided into subphenotypes by degree of histological inflammation: IFTA with AR, IFTA with inflammation, and IFTA without inflammation. Samples with AR (n = 54) and normally functioning transplants (TX; n = 99) were used in comparisons. A novel analysis using gene coexpression networks revealed that all IFTA phenotypes were strongly enriched for dysregulated gene pathways and these were shared with the biopsy profiles of AR, including IFTA samples without histological evidence of inflammation. Thus, by molecular profiling we demonstrate that most IFTA samples have ongoing immune-mediated injury or chronic rejection that is more sensitively detected by gene expression profiling. These molecular biopsy profiles correlated with future graft loss in IFTA samples without inflammation.

Genetic polymorphism of interferon regulatory factor 5 (IRF5) correlates with allograft acute rejection of liver transplantation

BACKGROUND

Although liver transplantation is one of the most efficient curative therapies of end stage liver diseases, recipients may suffer liver graft loss opst-operation. IRF-5, a member of Interferon Regulatory Factors, functions as a key regulator in TLR4 cascade, and is capable of inducing inflammatory cytokines. Although TLR4 has been proved to contribute to acute allograft rejection, including after liver transplantation, the correlation between IRF5 gene and acute rejection has not been elucidated yet.

METHODS

The study enrolled a total of 289 recipients, including 39 females and 250 males, and 39 recipients developed acute allograft rejection within 6 months post-transplantation. The allograft rejections were diagnosed by liver biopsies. Genome DNA of recipients was extracted from pre-operative peripheral blood. Genotyping of IRF-5, including rs3757385, rs752637 and rs11761199, was performed, followed by SNP frequency and Hardy-Weinberg equilibrium analysis.

RESULTS

The genetic polymorphism of rs3757385 was found associated with acute rejection. G/G homozygous individuals were at higher risk of acute rejection, with a P value of 0.042 (OR = 2.34 (1.07-5.10)).

CONCLUSIONS

IRF5, which transcriptionally activates inflammatory cytokines, is genetically associated with acute rejection and might function as a risk factor for acute rejection of liver transplantations.

Human cytokines activate JAK-STAT signaling pathway in porcine ocular tissue

Background

The JAK/STAT (Janus Tyrosine Kinase, Signal Transducers and Activators of Transcription) pathway is associated with cytokine or growth factor receptors and it is critical for growth control, developmental regulation and homeostasis. The use of porcine ocular cells as putative xenotransplants appears theoretically possible. The aim of this study was to investigate the response of various porcine ocular cells in vitro to human cytokines in regard to the activation of JAK-STAT signaling pathways.

Methods

Porcine lens epithelial cells, pigmented iris epithelial cells and pigmented ciliary body cells were used in this study. These cells were isolated from freshly enucleated porcine eyes by enzymatic digestion. Cultured cells between passages 3–8 were used in all experiments. Electromobility shift assay (EMSA), proliferation assay, immunofluorescence staining and flow cytometry were used to evaluate the JAK-STAT signaling pathway in these cells.

Results

JAK/STAT signaling pathways could be activated in porcine pigmented epithelial ciliary body cells, in pigmented iris epithelial cells and in lens epithelial cells in response to porcine and human interferons and cytokines. All cells showed very strong STAT1 activation upon stimulation with porcine interferon-gamma. Porcine ocular cells also respond to human cytokines; IFN-alpha induced strong activation of STAT1 in EMSA, flow cytometry and immunofluorescence experiments whereas activation of STAT3 was less strong in EMSA, but strong in flow cytometry and immunofluorescence. Human recombinant IL-6 activated STAT3 and human IL-4 activated STAT6. With the help of immunofluorescence assay and flow cytometry we observed nuclear localization of STAT proteins after activation of porcine ocular cells with cytokines and interferons. Human IFN-α had an inhibitory effect on porcine ocular cells in proliferation assays.

Conclusion

Our study demonstrated that some types of human cytokines and interferon activate intracellular JAK-STAT signaling pathways in porcine ocular cells. We hypothesize that direct stimulation of the JAK-STAT pathway in porcine cells in response to human cytokines will lead to complications or failure, if pig-to-human ocular tissue xenotransplantation were to be carried out. For successful xenotransplantation among other obstacles there must be new approaches developed to regulate signaling pathways.

A common rejection module (CRM) for acute rejection across multiple organs identifies novel therapeutics for organ transplantation

A set of 11 genes, termed the common rejection module, predicts acute graft rejection in solid organ transplant patients and may help to identify novel drug targets in transplantation.

Using meta-analysis of eight independent transplant datasets (236 graft biopsy samples) from four organs, we identified a common rejection module (CRM) consisting of 11 genes that were significantly overexpressed in acute rejection (AR) across all transplanted organs. The CRM genes could diagnose AR with high specificity and sensitivity in three additional independent cohorts (794 samples). In another two independent cohorts (151 renal transplant biopsies), the CRM genes correlated with the extent of graft injury and predicted future injury to a graft using protocol biopsies. Inferred drug mechanisms from the literature suggested that two FDA-approved drugs (atorvastatin and dasatinib), approved for nontransplant indications, could regulate specific CRM genes and reduce the number of graft-infiltrating cells during AR. We treated mice with HLA-mismatched mouse cardiac transplant with atorvastatin and dasatinib and showed reduction of the CRM genes, significant reduction of graft-infiltrating cells, and extended graft survival. We further validated the beneficial effect of atorvastatin on graft survival by retrospective analysis of electronic medical records of a single-center cohort of 2,515 renal transplant patients followed for up to 22 yr. In conclusion, we identified a CRM in transplantation that provides new opportunities for diagnosis, drug repositioning, and rational drug design.

The effect of chemokine receptor gene polymorphisms (CCR2V64I, CCR5-59029G>A and CCR5Δ32) on renal allograft survival in Pakistani transplant patients

BACKGROUND

Gene polymorphisms of the chemokine receptors CCR2 and CCR5 (CCR2V64I, CCR5-59029G>A and CCR5Δ32) have been shown to be associated with renal allograft rejection. The aim of this study was to investigate the association of these polymorphisms with allograft rejection among Pakistani transplant patients.

METHOD

A total of 606 renal transplant patients and an equal number of their donors were included in this study. DNA samples were used to amplify polymorphic regions of CCR2V64I, CCR5-59029G>A and CCR5Δ32 by polymerase chain reaction using sequence specific primers. The amplified products of CCRV64I and CCR5-59029G>A were digested with restriction enzymes (BsaB1 and Bsp12861) respectively. The CCR5Δ32 genotypes were determined by sizing the PCR amplicons. The association of these polymorphisms with the biopsy proven rejection and other clinical parameters was evaluated using the statistical software SPSS v.17.

RESULTS

In this study, the G/G genotype of CCR2V64I was associated with a high frequency of allograft rejection (p=0.009; OR=2.14; 95% CI=1.2-3.7). Rejection episode(s) in the GA+AA genotypes were found to be significantly lower as compared to the GG genotype (p=0.009; OR=0.4; 95% CI=0.2-0.8). The Kaplan-Meier curve also indicated a reduced overall allograft survival for patients with the G/G genotype of CCR2V64I (59.2 ± 1.4 weeks, log p=0.008). There was a significant association with rejection by female donors possessing the CCR2 GG genotype (p=0.02; OR=2.6; CI=1.1-6.3) and male donors with the CCR5-59029 GG genotype (p=0.004; OR=1.7; CI=1.03-3.01).

CONCLUSION

This study shows an association of the CCR2V64I (G/G) genotype with renal allograft rejection. However, no such association was found for the CCR5 gene polymorphisms. Therapeutic interventions such as blocking the CCR2 receptor (especially G polymorphism) may yield better survival of renal allograft in this patient group. Further, chemokine receptors may be added to the spectrum of the immunogenetic factors that are known to be associated with renal allograft rejection.

Recent advances in biomarker discovery in solid organ transplant by proteomics

The identification and clinical use of more sensitive and specific biomarkers in the field of solid organ transplantation is an urgent need in medicine. Solid organ transplantation has seen improvements in the short-term survival of transplanted organs due to recent advancements in immunosuppressive therapy. However, the currently available methods of allograft monitoring are not optimal. Recent advancements in assaying methods for biomolecules such as genes, mRNA and proteins have helped to identify surrogate biomarkers that can be used to monitor the transplanted organ. These high-throughput 'omic' methods can help researchers to significantly speed up the identification and the validation steps, which are crucial factors for biomarker discovery efforts. Still, the progress towards identifying more sensitive and specific biomarkers remains a great deal slower than expected. In this article, we have evaluated the current status of biomarker discovery using proteomics tools in different solid organ transplants in recent years. This article summarizes recent reports and current status, along with the hurdles in efficient biomarker discovery of protein biomarkers using proteomics approaches. Finally, we will touch upon personalized medicine as a future direction for better management of transplanted organs, and provide what we think could be a recipe for success in this field.

Gene expression profiling in acute allograft rejection: challenging the immunologic constant of rejection hypothesis

In humans, the role and relationship between molecular pathways that lead to tissue destruction during acute allograft rejection are not fully understood. Based on studies conducted in humans, we recently hypothesized that different immune-mediated tissue destruction processes (i.e. cancer, infection, autoimmunity) share common convergent final mechanisms. We called this phenomenon the "Immunologic Constant of Rejection (ICR)." The elements of the ICR include molecular pathways that are consistently described through different immune-mediated tissue destruction processes and demonstrate the activation of interferon-stimulated genes (ISGs), the recruitment of cytotoxic immune cells (primarily through CXCR3/CCR5 ligand pathways), and the activation of immune effector function genes (IEF genes; granzymes A/B, perforin, etc.). Here, we challenge the ICR hypothesis by using a meta-analytical approach and systematically reviewing microarray studies evaluating gene expression on tissue biopsies during acute allograft rejection. We found the pillars of the ICR consistently present among the studies reviewed, despite implicit heterogeneity. Additionally, we provide a descriptive mechanistic overview of acute allograft rejection by describing those molecular pathways most frequently encountered and thereby thought to be most significant. The biological role of the following molecular pathways is described: IFN-γ, CXCR3/CCR5 ligand, IEF genes, TNF-α, IL-10, IRF-1/STAT-1, and complement pathways. The role of NK cell, B cell and T-regulatory cell signatures are also addressed.

Disruption of K(2P)6.1 produces vascular dysfunction and hypertension in mice

K(2P)6.1, a member of the 2-pore domain K channel family, is highly expressed in the vascular system; however, its function is unknown. We tested the following hypotheses. K(2P)6.1 regulates the following: (1) systemic blood pressure; (2) the contractile state of arteries; (3) vascular smooth muscle cell migration; (4) proliferation; and/or (5) volume regulation. Mice lacking K(2P)6.1 (KO) were generated by deleting exon 1 of Kcnk6. Mean arterial blood pressure in both anesthetized and awake KO mice was increased by 17±2 and 26±3 mm Hg, respectively (P<0.05). The resting membrane potential in freshly dispersed vascular smooth muscle cells was depolarized by 17±2 mV in the KO compared with wild-type littermates (P<0.05). The contractile responses to KCl (P<0.05) and BAY K 8644 (P<0.01), an activator of L-type calcium channels, were enhanced in isolated segments of aorta from KO mice. However, there was no difference in the current density of L-type calcium channels. Responses to U46619, an agent that activates rho kinase, showed an enhanced contraction in aorta from KO mice (P<0.001). The BAY K 8644-mediated increase in contraction was decreased to wild-type levels when treated with Y27632, a rho kinase inhibitor, (P<0.05). K(2P)6.1 does not appear to be involved with migration, proliferation, or volume regulation in cultured vascular smooth muscle cells. We conclude that K(2P)6.1 deficiency induces vascular dysfunction and hypertension through a mechanism that may involve smooth muscle cell depolarization and enhanced rho kinase activity.

Identification and classification of acute cardiac rejection by intragraft transcriptional profiling

BACKGROUND

Treatment of acute rejection (AR) in heart transplantation relies on histopathological grading of endomyocardial biopsies according to International Society for Heart and Lung Transplantation guidelines. Intragraft gene expression profiling may be a way to complement histological evaluation.

METHODS AND RESULTS

Transcriptional profiling was performed on 26 endomyocardial biopsies, and expression patterns were compared with the 1990 International Society for Heart and Lung Transplantation AR grades. Importantly, transcriptional profiles from settings with an equivalent AR grade appeared the same. In addition, grade 0 profiles could not be distinguished from 1A profiles, and grade 3A profiles could not be distinguished from 3B profiles. Comparing the AR groupings (0+1A, 1B, and 3A+3B), 0+1A showed more striking differences from 1B than from 3A+3B. When these findings were extrapolated to the 2005 revised guidelines, the combination of 1A and 1B into a single category (1R) appears to have brought together endomyocardial biopsies with different underlying processes that are not evident from histological evaluation. Grade 1B was associated with upregulated immune response genes, as 1 categorical distinction from grade 1A. Although grade 1B was distinct from the clinically relevant AR grades 3A and 3B, all of these grades shared a small number of overlapping pathways consistent with common physiological underpinnings.

CONCLUSION

The gene expression similarities and differences identified here in different AR settings have the potential to revise the clinical perspective on acute graft rejection, pending the results of larger studies.

Transcriptomic biomarkers for the accurate diagnosis of myocarditis

BACKGROUND

Lymphocytic myocarditis is a clinically important condition that is difficult to diagnose and distinguish. We hypothesized that the transcriptome obtained from an endomyocardial biopsy would yield clinically relevant and accurate molecular signatures.

METHODS AND RESULTS

Microarray analysis was performed on samples from patients with histologically proven lymphocytic myocarditis (n=16) and idiopathic dilated cardiomyopathy (n=32) to develop accurate diagnostic transcriptome-based biomarkers using multiple classification algorithms. We identified 9878 differentially expressed genes in lymphocytic myocarditis versus idiopathic dilated cardiomyopathy (fold change >1.2; false discovery rate <5%) from which a transcriptome-based biomarker containing 62 genes was identified that distinguished myocarditis with 100% sensitivity (95% confidence interval, 46 to 100) and 100% specificity (95% confidence interval, 66 to 100) and was generalizable to a broad range of secondary cardiomyopathies associated with inflammation (n=27), ischemic cardiomyopathy (n=8), and the normal heart (n=11). Multiple classification algorithms and quantitative real-time reverse-transcription polymerase chain reaction analysis further reduced this subset to a highly robust molecular signature of 13 genes, which still performed with 100% accuracy.

CONCLUSIONS

Together, these findings demonstrate that transcriptomic biomarkers from a single endomyocardial biopsy can improve the clinical detection of patients with inflammatory diseases of the heart. This approach advances the clinical management and treatment of cardiac disorders with highly variable outcome.

Feasibility of diagnosing renal allograft dysfunction by oligonucleotide array: Gene expression profile correlates with histopathology

BACKGROUND

Effective non-invasive monitoring method to tell histopathology is a big challenge in renal transplantation.

METHODS

We used 70-mer long oligonucleotide array with 449 immune related genes to determine gene expression profiles of peripheral blood mononuclear cells (PBMCs) under different immune status including stable renal function (TX), acute tubular necrosis (ATN), biopsy conformed acute rejection (AR), clinical rejection with pathology of borderline changes (BL), clinical rejection without biopsy proven/presumed rejection (PR) and renal dysfunction without rejection (NR).

RESULTS

Distinct molecular expression signatures in each group were found to correlate with histopathology. And we concluded that B cell chemokine CXCL13 and mast cell may play a role in renal allograft rejection through significant difference analysis and functional pathway analysis.

CONCLUSIONS

It provides a potential non-invasive method for monitoring renal allograft function and immune status of renal transplant recipients.

Differentially expressed RNA from public microarray data identifies serum protein biomarkers for cross-organ transplant rejection and other conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.

Protein biomarkers in the blood are urgently needed for the diagnosis of a wide variety of diseases to improve health care. We aim to find a fast and cost-effective strategy to discover diagnostic protein biomarkers. Hundreds of diseases have already been investigated using microarray technology, measuring the mRNA expression of all genes in the disease-damaged tissues. We analyzed biopsy-based microarray data for 41 diseases in the public repository, identified genes with dysregulated mRNA expressions and detectable-protein abundance in the blood, and predicted them as candidate diagnostic protein biomarkers. We found that clinically and preclinically validated diagnostic protein biomarkers were significantly enriched in our predicted protein candidates for 22 diseases. We then measured the concentrations of ten predicted protein biomarkers in the serum samples from 39 renal transplant patients. Three of them were confirmed to be diagnostic of acute rejection after renal transplantation. All three proteins were further confirmed to be diagnostic of acute rejection in 63 cardiac transplant recipients. Our results show that publically available genome-wide gene expression data on disease-damaged tissues can be effectively translated into diagnostic protein biomarkers.

Genetics and genomics in human lung transplantation

Lung transplantation is the only effective treatment for many advanced lung diseases. However, long-term survival after transplantation remains relatively poor, thus limiting the application of lung transplantation to patients with end-stage disease only. Acute and chronic rejection is the main reason for allograft failure. Attempts to treat or prevent rejection have been stymied by our incomplete understanding of the mechanisms leading to this devastating complication and the lack of representative animal models. A systems-biology approach to lung transplantation with the use of genomics and gene expression profiling has led to new insights into the pathogenesis of rejection, by elucidating the mechanisms of T-cell activation and uncovering the role of B cells and innate immunity. Systems-biology approaches, such as genetics and genomics, may allow minimally invasive diagnosis of rejection and permit individually tailored immunosuppressive regimens. Herein we review the emerging application of genomics and genetics to human lung transplantation and highlight the tremendous potential for these approaches to enhance clinical practice and augment our understanding of basic transplant biology.

Interpreting the proteome and peptidome in transplantation

Publication of the human proteome has prompted efforts to develop high-throughput techniques that can catalogue and quantify proteins and peptides present in different tissue types. The field of proteomics aims to identify, quantify, analyze, and functionally define a large number of proteins in cellular processes in different disease states on a global scale. Peptidomics, a newer name in the -omics world, measures and identifies naturally occurring low molecular weight peptides, also providing an insight into enzymatic processes and molecular events occurring in the system of interest. One area of major interest is the use of proteomics to identify diagnostic and prognostic biomarkers for different diseases as well as for various clinical phenotypes in organ transplantation that can advance targeted therapy for various forms of graft injury. Outcomes in organ transplantation can be potentially improved by identifying noninvasive biomarkers that will serve as triggers that predate graft injury, and can offer a means to customize patient treatment by differentiating among causes of acute and chronic graft injury. Proteomic and peptidomic strategies can be harnessed for frequent noninvasive measurements in tissue fluids, allowing for serial monitoring of organ disease. In this review, we describe the basic techniques used in proteomic and peptidomic approaches, point out special considerations in using these methods, and discuss their applications in recently published studies in organ transplantation.

Microarrays: monitoring for transplant tolerance and mechanistic insights

With recent advances in immunology and a growing understanding of transplantation biology, the development of reliable assays that may be used for identification and prediction of the current state of an immune response (rejection and tolerance) are urgently needed to allow us to predict the development of immunologic graft injury, individualize immunosuppression, rationally minimize immunosuppressive drug toxicity, promote a better understanding of the mechanisms underlying stable graft acceptance, and aid in the design of tolerance-inducing clinical transplantation trials. Microarrays can provide nonbiased, simultaneous global expression patterns for more than 40,000 human genes across different experiments. High throughput microarray technology offers a means to study disease-specific transcriptional changes in tissue biopsy, peripheral blood, and biofluids.

In praise of arrays

Microarray technologies have both fascinated and frustrated the transplant community since their introduction roughly a decade ago. Fascination arose from the possibility offered by the technology to gain a profound insight into the cellular response to immunogenic injury and the potential that this genomic signature would be indicative of the biological mechanism by which that stress was induced. Frustrations have arisen primarily from technical factors such as data variance, the requirement for the application of advanced statistical and mathematical analyses, and difficulties associated with actually recognizing signature gene-expression patterns and discerning mechanisms. To aid the understanding of this powerful tool, its versatility, and how it is dramatically changing the molecular approach to biomedical and clinical research, this teaching review describes the technology and its applications, as well as the limitations and evolution of microarrays, in the field of organ transplantation. Finally, it calls upon the attention of the transplant community to integrate into multidisciplinary teams, to take advantage of this technology and its expanding applications in unraveling the complex injury circuits that currently limit transplant survival.

Analysis of independent microarray datasets of renal biopsies identifies a robust transcript signature of acute allograft rejection

Transcriptomics could contribute significantly to the early and specific diagnosis of rejection episodes by defining 'molecular Banff' signatures. Recently, the description of pathogenesis-based transcript sets offered a new opportunity for objective and quantitative diagnosis. Generating high-quality transcript panels is thus critical to define high-performance diagnostic classifier. In this study, a comparative analysis was performed across four different microarray datasets of heterogeneous sample collections from two published clinical datasets and two own datasets including biopsies for clinical indication, and samples from nonhuman primates. We characterized a common transcriptional profile of 70 genes, defined as acute rejection transcript set (ARTS). ARTS expression is significantly up-regulated in all AR samples as compared with stable allografts or healthy kidneys, and strongly correlates with the severity of Banff AR types. Similarly, ARTS were tested as a classifier in a large collection of 143 independent biopsies recently published by the University of Alberta. Results demonstrate that the 'in silico' approach applied in this study is able to identify a robust and reliable molecular signature for AR, supporting a specific and sensitive molecular diagnostic approach for renal transplant monitoring.

Transcriptomic biomarkers for individual risk assessment in new-onset heart failure

BACKGROUND

Prediction of prognosis remains a major unmet need in new-onset heart failure (HF). Although several clinical tests are in use, none accurately distinguish between patients with poor versus excellent survival. We hypothesized that a transcriptomic signature, generated from a single endomyocardial biopsy, could serve as a novel prognostic biomarker in HF.

METHODS AND RESULTS

Endomyocardial biopsy samples and clinical data were collected from all patients presenting with new-onset HF from 1997 to 2006. Among a total of 350 endomyocardial biopsy samples, 180 were identified as idiopathic dilated cardiomyopathy. Patients with phenotypic extremes in survival were selected: good prognosis (event-free survival for at least 5 years; n=25) and poor prognosis (events [death, requirement for left ventricular assist device, or cardiac transplant] within the first 2 years of presentation with HF symptoms; n=18). We used human U133 Plus 2.0 microarrays (Affymetrix) and analyzed the data with significance analysis of microarrays and prediction analysis of microarrays. We identified 46 overexpressed genes in patients with good versus poor prognosis, of which 45 genes were selected by prediction analysis of microarrays for prediction of prognosis in a train set (n=29) with subsequent validation in test sets (n=14 each). The biomarker performed with 74% sensitivity (95% CI 69% to 79%) and 90% specificity (95% CI 87% to 93%) after 50 random partitions.

CONCLUSIONS

These findings suggest the potential of transcriptomic biomarkers to predict prognosis in patients with new-onset HF from a single endomyocardial biopsy sample. In addition, our findings offer potential novel therapeutic targets for HF and cardiomyopathy.

The use of transcriptomic biomarkers for personalized medicine

Microarrays are a high throughput technology that allows the quantification of tens of thousands of RNA transcripts in a single reaction. This new technology offers the promise of comprehensive study of disease at a genomic level, potentially identifying novel molecular abnormalities, developing novel clinical biomarkers, and investigating drug efficacy. The ability to develop a molecular profile corresponding to a therapeutic effect is the basis for the concept of drug repositioning. With regard to prediction of clinical events, microarray technology has the potential to contribute to the development of sophisticated new biomarkers useful as predictors of disease etiology, outcome, and responsiveness to therapy-so-called personalized medicine. Currently progress in the field is hampered by a degree of skepticism about the reliability of microarray data and its relevance for clinical applications. Here we discuss possible pitfalls of transcriptomic analysis, review current developments in the cardiovascular area and address the use of transcriptomics for clinical applications.

Reviews of Translational Medicine and Genomics in Cardiovascular Disease: New Disease Taxonomy and Therapeutic Implications

The enduring subdivision of cardiomyopathies into hypertrophic (HCM), dilated (DCM), and restrictive (RCM) categories reflects the emphasis of traditional classifications on morphology. Rapid advances in the genetic interrogation of these disorders have redefined their taxonomy and revealed potential conflicts between the old and new classifications. Hypertrophic cardiomyopathy has been redefined as a disease of perturbed sarcomere function. Dilated cardiomyopathy is a disease that results from more varied perturbations, including, but not limited to, defects of the cytoskeleton. Positional cloning and candidate gene approaches have been successful in identifying >40 disease loci, many of which have led to disease genes in HCM, DCM, RCM, and arrhythmogenic right ventricular cardiomyopathy. These findings provide mechanistic insights, permit genetic screening, and to a limited extent, facilitate prognostication. Although single gene analyses rapidly focus down to the underlying mechanistic pathways, they do not take account of all relevant variation in the human genome. Correspondingly, advances in genomics, through microarrays, have facilitated characterization of these broader downstream elements. As well as refining the taxonomic reclassification of cardiomyopathies, these genomic approaches, coupled with functional studies, have identified novel potential therapeutic targets, such as cardiac energetics, calcium handling, and apoptosis. We review the successes and pitfalls of genetic and genomic approaches to cardiomyopathy and their impact on current and future clinical care.