UTILIZATION OF AN EMR-BIOREPOSITORY TO IDENTIFY THE GENETIC PREDICTORS OF CALCINEURIN-INHIBITOR TOXICITY IN HEART TRANSPLANT RECIPIENTS

Calcineurin-inhibitors CI are immunosuppressive agents prescribed to patients after solid organ transplant to prevent rejection. Although these drugs have been transformative for allograft survival, long-term use is complicated by side effects including nephrotoxicity. Given the narrow therapeutic index of CI, therapeutic drug monitoring is used to prevent acute rejection from underdosing and acute toxicity from overdosing, but drug monitoring does not alleviate long-term side effects. Patients on calcineurin-inhibitors for long periods almost universally experience declines in renal function, and a subpopulation of transplant recipients ultimately develop chronic kidney disease that may progress to end stage renal disease attributable to calcineurin inhibitor toxicity (CNIT). Pharmacogenomics has the potential to identify patients who are at high risk for developing advanced chronic kidney disease caused by CNIT and providing them with existing alternate immunosuppressive therapy. In this study we utilized BioVU, Vanderbilt University Medical Center's DNA biorepository linked to de-identified electronic medical records to identify a cohort of 115 heart transplant recipients prescribed calcineurin-inhibitors to identify genetic risk factors for CNIT We identified 37 cases of nephrotoxicity in our cohort, defining nephrotoxicity as a monthly median estimated glomerular filtration rate (eGFR) <30 mL/min/1.73m2 at least six months post-transplant for at least three consecutive months. All heart transplant patients were genotyped on the Illumina ADME Core Panel, a pharmacogenomic genotyping platform that assays 184 variants across 34 genes. In Cox regression analysis adjusting for age at transplant, pre-transplant chronic kidney disease, pre-transplant diabetes, and the three most significant principal components (PCAs), we did not identify any markers that met our multiple-testing threshold. As a secondary analysis we also modeled post-transplant eGFR directly with linear mixed models adjusted for age at transplant, cyclosporine use, median BMI, and the three most significant principal components. While no SNPs met our threshold for significance, a SNP previously identified in genetic studies of the dosing of tacrolimus CYP3A5 rs776746, replicated in an adjusted analysis at an uncorrected p-value of 0.02 (coeff(S.E.) = 14.60(6.41)). While larger independent studies will be required to further validate this finding, this study underscores the EMRs usefulness as a resource for longitudinal pharmacogenetic study designs

A Juxtamembrane Mutation in the N Terminus of the Dopamine Transporter Induces Preference for an Inward-Facing Conformation

The human dopamine transporter (hDAT) regulates synaptic dopamine (DA) levels and is the site of action of abused and therapeutic drugs. Here we study the effect of a threonine residue (Thr62 in hDAT) that is highly conserved within a canonical phosphorylation site (RETW) in the juxtamembrane N-terminal region of monoamine transporters. In stably transfected human embryonic kidney 293T cells, expression of T62D-hDAT was reduced compared with hDAT or T62A-hDAT. T62D-hDAT displayed dramatically reduced [3H]dopamine up-take but exhibited a higher basal dopamine efflux compared with hDAT or T62A-hDAT, as determined by measurements of [3H]dopamine efflux and amperometry. The high constitutive efflux in T62D-hDAT precluded the measurement of amphetamine-stimulated [3H]dopamine efflux, but when dopamine was added internally into voltage-clamped T62D-hDAT cells, amphetamine-induced efflux comparable with hDAT was detected by amperometry. In accordance with findings that Zn2+ can rescue reduced DA uptake in mutant transporters that are predominantly inward-facing, micromolar concentrations of Zn2+ markedly potentiated [3H]dopamine uptake in T62D-hDAT and permitted the measurement of amphetamine-stimulated dopamine efflux. These results suggest that T62D-hDAT prefers an inward-facing conformation in the transition between inward- and outward-facing conformations. For T62A-hDAT, however, the measured 50% reduction in both [3H]dopamine uptake and [3H]dopamine efflux was consistent with a slowed transition between inward- and outward-facing conformations. The mechanism underlying the important functional role of Thr62 in hDAT activity suggested by these findings is examined in a structural context using dynamic simulations of a three-dimensional molecular model of DAT

Deciphering Signaling Pathway Networks to Understand the Molecular Mechanisms of Metformin Action

A drug exerts its effects typically through a signal transduction cascade, which is non-linear and involves intertwined networks of multiple signaling pathways. Construction of such a signaling pathway network (SPNetwork) can enable identification of novel drug targets and deep understanding of drug action. However, it is challenging to synopsize critical components of these interwoven pathways into one network. To tackle this issue, we developed a novel computational framework, the Drug-specific Signaling Pathway Network (DSPathNet). The DSPathNet amalgamates the prior drug knowledge and drug-induced gene expression via random walk algorithms. Using the drug metformin, we illustrated this framework and obtained one metformin-specific SPNetwork containing 477 nodes and 1,366 edges. To evaluate this network, we performed the gene set enrichment analysis using the disease genes of type 2 diabetes (T2D) and cancer, one T2D genome-wide association study (GWAS) dataset, three cancer GWAS datasets, and one GWAS dataset of cancer patients with T2D on metformin. The results showed that the metformin network was significantly enriched with disease genes for both T2D and cancer, and that the network also included genes that may be associated with metformin-associated cancer survival. Furthermore, from the metformin SPNetwork and common genes to T2D and cancer, we generated a subnetwork to highlight the molecule crosstalk between T2D and cancer. The follow-up network analyses and literature mining revealed that seven genes (CDKN1A, ESR1, MAX, MYC, PPARGC1A, SP1, and STK11) and one novel MYC-centered pathway with CDKN1A, SP1, and STK11 might play important roles in metformin's antidiabetic and anticancer effects. Some results are supported by previous studies. In summary, our study 1) develops a novel framework to construct drug-specific signal transduction networks; 2) provides insights into the molecular mode of metformin; 3) serves a model for exploring signaling pathways to facilitate understanding of drug action, disease pathogenesis, and identification of drug targets

Syntaxin 1A interaction with the dopamine transporter promotes amphetamine-induced dopamine efflux

The SNARE protein syntaxin1A (SYN1A) interacts with and regulates the function of transmembrane proteins including ion channels and neurotransmitter transporters. Here we define the first 33 amino acids of the N-terminus of the dopamine (DA) transporter (DAT) as the site of direct interaction with SYN1A. Amphetamine (AMPH) increases the association of SYN1A with human DAT (hDAT) in a heterologous expression system (hDAT cells) and with native DAT in murine striatal synaptosomes. Immunoprecipitation of DAT from the biotinylated fraction shows that the AMPH-induced increase in DAT/SYN1A association occurs at the plasma membrane. In a superfusion assay of DA efflux, cells overexpressing SYN1A exhibited significantly greater AMPH-induced DA release with respect to control cells. By combining the patch clamp technique with amperometry we measured DA release under voltage clamp. At −60 mV, a physiological resting potential, AMPH did not induce DA efflux in hDAT cells and DA neurons. In contrast, perfusion of exogenous SYN1A (3 µM) into the cell with the whole-cell pipette enabled AMPH-induced DA efflux at −60 mV both in hDAT cells and DA neurons. Recently, it has been shown that Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) is activated by AMPH and regulates AMPH-induced DA efflux. Here we show that AMPH-induced association between DAT and SYN1A requires CaMKII activity and that inhibition of CaMKII blocks the ability of exogenous SYN1A to promote DA efflux. These data suggest that AMPH activation of CaMKII supports DAT/SYN1A association, resulting in a mode of DAT capable of DA efflux

Calmodulin kinase II interacts with the dopamine transporter C terminus to regulate amphetamine-induced reverse transport

SummaryEfflux of dopamine through the dopamine transporter (DAT) is critical for the psychostimulatory properties of amphetamines, but the underlying mechanism is unclear. Here we show that Ca2+/calmodulin-dependent protein kinase II (CaMKII) plays a key role in this efflux. CaMKIIα bound to the distal C terminus of DAT and colocalized with DAT in dopaminergic neurons. CaMKIIα stimulated dopamine efflux via DAT in response to amphetamine in heterologous cells and in dopaminergic neurons. CaMKIIα phosphorylated serines in the distal N terminus of DAT in vitro, and mutation of these serines eliminated the stimulatory effects of CaMKIIα. A mutation of the DAT C terminus impairing CaMKIIα binding also impaired amphetamine-induced dopamine efflux. An in vivo role for CaMKII was supported by chronoamperometry measurements showing reduced amphetamine-induced dopamine efflux in response to the CaMKII inhibitor KN93. Our data suggest that CaMKIIα binding to the DAT C terminus facilitates phosphorylation of the DAT N terminus and mediates amphetamine-induced dopamine efflux

Functional comparison of the common genes, upstream network genes, and downstream network genes.

<p>The common genes were those found in both metformin upstream network and downstream network. The upstream network genes were those only belonging to the metformin upstream network. The downstream network genes were those only belonging to the metformin downstream network. A) Proportion of genes of interest in Gene Ontology (GO) molecular function domain. B) Comparison of proportion of enriched pathway in the three gene sets at the first-level category of KEGG annotation. C) The clustering of enriched pathways for the three gene sets at second-level category of KEGG annotation.</p