50 research outputs found

    Implementing community-engaged pharmacogenomics in Indigenous communities

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    Innovative pharmacogenomic approaches (genetic variation related to medication response) are needed to reduce disease and disparities in Indigenous communities. We support community-based pharmacogenomics research, inclusive of Indigenous values and priorities, to improve the health and well-being of Indigenous peoples

    Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch.

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    BackgroundAcute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant donor-recipient mismatching focuses on blood type and human leukocyte antigens (HLA), while it remains unclear whether non-HLA genetic mismatching is related to post-transplant complications.MethodsWe carried out a genome-wide scan (HLA and non-HLA regions) on AR with a large kidney transplant cohort of 784 living donor-recipient pairs of European ancestry. An AR polygenic risk score (PRS) was constructed with the non-HLA single nucleotide polymorphisms (SNPs) filtered by independence (r2 ResultsBy the genome-wide scan, we identified one significant SNP rs6749137 with HR = 2.49 and P-value = 2.15×10-8. 1,307 non-HLA PRS SNPs passed the clumping plus thresholding and the PRS exhibited significant association with the AR in the validation cohort (HR = 1.54, 95% CI = (1.07, 2.22), p = 0.019). Further pathway analysis attributed the PRS genes into 13 categories, and the over-representation test identified 42 significant biological processes, the most significant of which is the cell morphogenesis (GO:0000902), with 4.08 fold of the percentage from homo species reference and FDR-adjusted P-value = 8.6×10-4.ConclusionsOur results show the importance of donor-recipient mismatching in non-HLA regions. Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. Trial registry: Deterioration of Kidney Allograft Function Genomics (NCT00270712) and Genomics of Kidney Transplantation (NCT01714440) are registered on ClinicalTrials.gov

    Differentially expressed gene transcripts using RNA sequencing from the blood of immunosuppressed kidney allograft recipients.

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    We performed RNA sequencing (RNAseq) on peripheral blood mononuclear cells (PBMCs) to identify differentially expressed gene transcripts (DEGs) after kidney transplantation and after the start of immunosuppressive drugs. RNAseq is superior to microarray to determine DEGs because its not limited to available probes, has increased sensitivity, and detects alternative and previously unknown transcripts. DEGs were determined in 32 adult kidney recipients, without clinical acute rejection (AR), treated with antibody induction, calcineurin inhibitor, mycophenolate, with and without steroids. Blood was obtained pre-transplant (baseline), week 1, months 3 and 6 post-transplant. PBMCs were isolated, RNA extracted and gene expression measured using RNAseq. Principal components (PCs) were computed using a surrogate variable approach. DEGs post-transplant were identified by controlling false discovery rate (FDR) at < 0.01 with at least a 2 fold change in expression from pre-transplant. The top 5 DEGs with higher levels of transcripts in blood at week 1 were TOMM40L, TMEM205, OLFM4, MMP8, and OSBPL9 compared to baseline. The top 5 DEGs with lower levels at week 1 post-transplant were IL7R, KLRC3, CD3E, CD3D, and KLRC2 (Striking Image) compared to baseline. The top pathways from genes with lower levels at 1 week post-transplant compared to baseline, were T cell receptor signaling and iCOS-iCOSL signaling while the top pathways from genes with higher levels than baseline were axonal guidance signaling and LXR/RXR activation. Gene expression signatures at month 3 were similar to week 1. DEGs at 6 months post-transplant create a different gene signature than week 1 or month 3 post-transplant. RNAseq analysis identified more DEGs with lower than higher levels in blood compared to baseline at week 1 and month 3. The number of DEGs decreased with time post-transplant. Further investigations to determine the specific lymphocyte(s) responsible for differential gene expression may be important in selecting and personalizing immune suppressant drugs and may lead to targeted therapies

    487 Digital Spatial Profiling of Allograft Loss in Kidney Biopsies with Chronic Allograft Dysfunction

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    OBJECTIVES/GOALS: Assess molecular and cellular mechanisms of allograft loss in kidney biopsies using digital spatial profiling and clinical outcomes data. METHODS/STUDY POPULATION: Patients with chronic allograft dysfunction (CGD), enrolled in the Deterioration of Kidney Allograft Function (DeKAF) study, with or without eventual allograft loss, were included. CGD was defined as a >25% increase in creatinine over 3 months relative to a baseline. Kidney biopsy tissue was assessed by Nanostring GeoMX digital spatial profiling (DSP) after staining with anti-pan-cytokeratin, anti-CD45, anti-CD68, Syto-13, to identify specific cell populations, and Nanostring’s Whole Transcriptome Atlas (WTA), to quantify the distribution of transcripts across the biopsy. Up to 14 regions of interest (ROIs) were selected, with or without glomerulus. CIBERSORT was used to perform cell deconvolution. Clinical and outcomes data were from the DeKAF study and United States Renal Data System. RESULTS/ANTICIPATED RESULTS: Macrophage (M1) cell population abundance was significantly different in ROIs with glomerulus between graft loss and no graft loss. Principle component analysis of differentially expressed genes resulted in transcriptomes in ROIs that cluster together by clinical outcome of graft loss or no graft loss. There were 203 DEGs in ROIs with glomerulus that were different by graft loss or no graft loss. By pathway analysis, these 203 DEGS were enriched in the T-cell activation, integrin signaling and inflammation pathways. DISCUSSION/SIGNIFICANCE: DSP of kidney allograft biopsies allows for the identification and quantification of specific cell types, such as macrophages and molecular transcripts as potential drug targets. This data can be used to understand mechanisms of kidney allograft loss and may lead to improved immune suppression in kidney transplant recipients
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