Faculty Bibliography

For most patients with end-stage heart failure, heart transplantation is the treatment of choice. Allograft rejection is one of the major post-transplantation complications affecting graft outcome and survival. Recent advancements in science and technology offer an opportunity to integrate genomic and other omics-based biomarkers into clinical practice, facilitating noninvasive evaluation of allograft for diagnostic and prognostic purposes. Omics, including gene expression profiling (GEP) of blood immune cell components and donor-derived cell-free DNA (dd-cfDNA) are of special interest to researchers. Several studies have investigated levels of dd-cfDNA and miroRNAs in blood as potential markers for early detection of allograft rejection. One of the achievements in the field of transcriptomics is AlloMap, GEP of peripheral blood mononuclear cells (PBMC), which can identify 11 differentially expressed genes and help with detection of moderate and severe acute cellular rejection in stable heart transplant recipients. In recent years, the utilization of GEP of PBMC for identifying differentially expressed genes to diagnose acute antibody-mediated rejection and cardiac allograft vasculopathy has yielded promising results. Advancements in the field of metabolomics and proteomics as well as their potential implications have been further discussed in this paper.

WHAT IS KNOWN AND OBJECTIVE/OBJECTIVE:Pharmacogenomic biomarkers are now used in many clinical care settings and represent one of the successes of precision medicine. Genetic variants are associated with pharmacokinetic and pharmacodynamic changes leading to medication adverse effects and changes in clinical response. Actionable pharmacogenomic variants are common in transplant recipients and have implications for medications used in transplant, but yet are not broadly incorporated into practice. METHODS:From the Clinical Pharmacogenetics Implementation Consortium and Dutch Pharmacogenetics Working Group guidelines, and PharmGKB databases, 12 pharmacogenomic genes with 30 variants were selected and used to create diplotypes and actionable pharmacogenomic phenotypes. A total of 853 kidney allograft recipients who had genomic information available from a genome-wide association study were included. RESULTS:Each recipient had at least one actionable pharmacogenomic diplotype/phenotype, whereas the majority (58%) had three or four actionable diplotypes/phenotypes and 17.4% had five or more among the 12 genes. The participants carried actionable diplotypes/phenotypes for multiple medications, including tacrolimus, azathioprine, clopidogrel, warfarin, simvastatin, voriconazole, antidepressants and proton-pump inhibitors. WHAT IS NEW AND CONCLUSION/CONCLUSIONS:Pharmacogenomic variants are common in transplant recipients, and transplant recipients receive medications that have actionable variants. CLINICAL TRIAL/BACKGROUND:Genomics of Transplantation, clinicaltrials.gov (NCT01714440).

Genetic matching between transplant donor and recipient pairs has traditionally focused on the human leukocyte antigen (HLA) regions of the genome, but recent studies suggest that matching for non-HLA regions may be important as well. We assess four genetic matching scores for use in association analyses of transplant outcomes. These scores describe genetic ancestry distance using identity-by-state, or genetic incompatibility or mismatch of the two genomes and therefore may reflect different underlying biological mechanisms for donor and recipient genes to influence transplant outcomes. Our simulation studies show that jointly testing these scores with the recipient genotype is a powerful method for preliminary screening and discovery of transplant outcome related single nucleotide polymorphisms (SNPs) and gene regions. Following these joint tests with marginal testing of the recipient genotype and matching score separately can lead to further understanding of the biological mechanisms behind transplant outcomes. In addition, we present results of a liver transplant data analysis that shows joint testing can detect SNPs significantly associated with acute rejection in liver transplant.

BACKGROUND:Mitochondrial dysfunction is associated with poor allograft prognosis. Mitochondrial-related gene expression (GE) in endomyocardial biopsies (EMBs) could be useful as a nonimmune functional marker of rejection. We hypothesize that acute cardiac allograft rejection is associated with decreased mitochondrial-related GE in EMBs. METHODS:We collected 64 routines or clinically indicated EMB from 47 patients after heart transplant. The EMBs were subjected to mRNA sequencing. We conducted weighted gene coexpression network analysis to construct module-derived eigengenes. The modules were assessed by gene ontology enrichment and hub gene analysis. Modules were correlated with the EMBs following the International Society of Heart and Lung Transplantation histology-based criteria and a classification based on GE alone; we also correlated with clinical parameters. RESULTS:The modules enriched with mitochondria-related and immune-response genes showed the strongest correlation to the clinical traits. Compared with the no-rejection samples, rejection samples had a decreased activity of mitochondrial-related genes and an increased activity of immune-response genes. Biologic processes and hub genes in the mitochondria-related modules were primarily involved with energy generation, substrate metabolism, and regulation of oxidative stress. Compared with International Society of Heart and Lung Transplantation criteria, GE-based classification had stronger correlation to the weighted gene coexpression network analysis-derived functional modules. The brain natriuretic peptide level, ImmuKnow, and Allomap scores had negative relationships with the expression of mitochondria-related modules and positive relationships with immune-response modules. CONCLUSIONS:During acute cardiac allograft rejection, there was a decreased activity of mitochondrial-related genes, related to an increased activity of immune-response genes, and depressed allograft function manifested by brain natriuretic peptide elevation. This suggests a rejection-associated mitochondrial impairment.

Acute cellular rejection (ACR) is a common complication in liver transplantation recipients (LTRs), especially within the first 12 months, and it is associated with increased morbidity and mortality. Although abnormalities in standard liver biochemistries may raise the clinical suspicion for ACR, it lacks specificity, and invasive liver biopsies, which are associated with numerous risks, are required for definitive diagnoses. Biomarker discovery for minimally invasive tools for diagnosis and prognostication of ACR after liver transplantation (LT) has become a rapidly evolving field of research with a recent shift in focus to omics-based biomarker discovery. Although none are yet ready to replace the standard of care, there are several promising minimally invasive, blood-derived biomarkers that are under intensive research for the diagnosis of ACR in LTRs. These omics-based biomarkers, encompassing DNA, RNA, proteins, and metabolites, hold tremendous potential. Some are likely to become integrated into ACR diagnostic algorithms to assist clinical decision making with a high degree of accuracy that is cost-effective and reduces or even obviates the need for an invasive liver biopsy.

Donor-recipient (D-R) differences at human leukocyte antigen (HLA) loci are currently incorporated into organ sharing, allocation and immunosuppression decisions. However, while acute rejection episodes have substantially diminished, progressive histologic damage occurs in allografts and improved long-term survival remains an unrealized goal among kidney recipients. Here we tested the hypothesis that non-HLA dependent, genome-wide D-R genetic differences could contribute to unchecked alloimmunity with histologic and functional consequences, culminating in long-term allograft failure. Genome-wide single nucleotide polymorphism (SNP) array data, excluding the HLA region, was utilized from 385 transplants to study the role of D-R differences upon serial histology and allograft survival. ADMIXTURE analysis was performed to quantitatively estimate ancestry in each D-R pair and PLINK was used to estimate the proportion of genome-shared identity-by-descent (pIBD) between D-R pairs. Subsequently, quantitative measures of recipient ancestry based on non-HLA SNPs was associated with death-censored allograft survival in adjusted Cox models. In D-R pairs of similar ancestry, pIBD was significantly associated with allograft survival independent of HLA mismatches in 224 transplants. Surprisingly, pIBD and recipient ancestry were not associated with clinical or subclinical rejection at any time post-transplant. Significantly, in multivariable analysis, pIBD inversely correlated with vascular intimal fibrosis in 160 biopsies obtained less than one year which in turn was significantly associated with allograft survival. Thus, our novel data show that non-HLA D-R differences associate with early vascular intimal fibrosis and allograft survival.

Bipolar disorder is often comorbid with anxiety, which is itself associated with poorer clinical outcomes, including suicide. A better etiologic understanding of this comorbidity could inform diagnosis and treatment. The present study aims to test whether comorbid anxiety in bipolar disorder reflects shared genetic risk factors. We also sought to assess the contribution of genetic risk for anxiety to suicide attempts in bipolar disorder. Polygenic risk scores (PRS) were calculated from published genome-wide association studies of samples of controls and cases with anxiety (n = 83,566) or bipolar disorder (n = 51,710), then scored in independent target samples (total n = 3369) of individuals with bipolar disorder who reported or denied lifetime anxiety disorders or suicidal attempts in research interviews. Participants were recruited from clinical and nonclinical settings and genotyped for common genetic variants. The results show that polygenic risk for anxiety was associated with comorbid anxiety disorders and suicide attempts in bipolar disorder, while polygenic risk for bipolar disorder was not associated with any of these variables. Our findings point out that comorbid anxiety disorders in bipolar disorder reflect a dual burden of bipolar and anxiety-related genes; the latter may also contribute to suicide attempts. Clinical care that recognizes and addresses this dual burden may help improve outcomes in people living with comorbid bipolar and anxiety disorders.

Polygenic risk scores (PRSs) calculated from genome-wide association studies (GWASs) of non-melanoma skin cancer (NMSC) in a general, non-transplant setting have recently been shown to predict risk of and time to post-renal transplant skin cancer. In this study, we set out to test these findings in a cohort of heart, lung, and liver transplant patients to see whether these scores could be applied across different organ transplant types. Using the PRS from Stapleton et al (2018), PRS was calculated for each sample across a European ancestry heart, lung, and liver transplant cohorts (n = 523) and tested as predictor of time to NMSC post-transplant. The top PRS, squamous cell carcinoma (SCC) pT1 x 10^-5 , (n SNPs = 1953), SCC pT1 x 10^-6 , and SCC pT1 x 10^-6 (n SNPs = 1061) were significantly predictive in the time to NMSC, SCC, and basal cell carcinoma (BCC) analysis across organ (P = .006, .02, and .02, respectively). We observed here a similar direction of effect and effect size [NMSC HR = 1.31(1.08-1.59)] to that in the original discovery study with increased polygenic burden leading to a faster time to developing NMSC. In summary, we found that PRS of NMSC calculated from GWAS of NMSC in non-transplant populations independently replicated in this cohort of heart, lung, and liver transplant.

Copy number variants (CNVs) are suggested to have a widespread impact on the human genome and phenotypes. To understand the role of CNVs across human diseases, we examine the CNV genomic landscape of 100,028 unrelated individuals of European ancestry, using SNP and CGH array datasets. We observe an average CNV burden of ~650 kb, identifying a total of 11,314 deletion, 5625 duplication, and 2746 homozygous deletion CNV regions (CNVRs). In all, 13.7% are unreported, 58.6% overlap with at least one gene, and 32.8% interrupt coding exons. These CNVRs are significantly more likely to overlap OMIM genes (2.94-fold), GWAS loci (1.52-fold), and non-coding RNAs (1.44-fold), compared with random distribution (P < 1 × 10^-3). We uncover CNV associations with four major disease categories, including autoimmune, cardio-metabolic, oncologic, and neurological/psychiatric diseases, and identify several drug-repurposing opportunities. Our results demonstrate robust frequency definition for large-scale rare variant association studies, identify CNVs associated with major disease categories, and illustrate the pleiotropic impact of CNVs in human disease.