RNA expression profiling of renal allografts in a nonhuman primate identifies variation in NK and endothelial gene expression

Abstract

RNA transcript expression estimates are a promising method to study the mechanisms and classification of renal allograft rejections. Here we use the Nanostring platform to profile RNA expression in renal allografts in a nonhuman primate (NHP), the Cynomolgus monkey. We analyzed protocol and indication 278 archival renal allograft samples, both protocol and indication from 76 animals with diagnoses of chronic antibody-mediated rejection (CAMR), acute cellular rejection (TCMR), and MIXED (both CAMR and TCMR), plus normals and samples with no pathological rejection using a Cynomolgus-specific probe set of 67 genes. Analysis identified RNA expression heterogeneity of endothelial and NK genes within CAMR and TCMR, including the stages of CAMR. Three factors were partitioned into additional groups. One group with the longest allograft survival time is pure CAMR without NK or CD3. Three mixed groups show variation in NK and CD3. TCMR was split into 2 groups with variation in NK genes. Additional validation of the complete gene-set correlated many of the genes with diagnoses of CAMR, MIXED, and TCMR rejections and with Banff histologic criteria defined in human subjects. These NHP data demonstrate the utility of RNA expression profiling to identify additional heterogeneity of endothelial and NK RNA gene expressions.

Erythropoietin inhibits SGK1-dependent Th17 cell induction and Th17 cell–dependent kidney disease

“…Our recently identified immune-modulating effects of EPO may be also responsible for the prolonged graft survival in renal transplant patients treated with high doses of rEPO (54) and are consistent with the high expression of EPO mRNA in renal allografts of immunologically tolerant cynomolgus monkeys (55) and with the reported association between EPO-R transcripts in peripheral blood mononuclear cells from kidney transplant recipients and acute rejection (56). Immune modulating effects of EPO may also be, at least in part, responsible for the its beneficial effects in patients on dialysis (57), whose chronic inflammatory state has been linked to increased cardiovascular risk (58).…”

Banff 2019 Meeting Report: Molecular diagnostics in solid organ transplantation–Consensus for the Banff Human Organ Transplant (B-HOT) gene panel and open source multicenter validation

“…Recently, more practical technologies based on FFPE biopsy analysis are now available, in particular the NanoString nCounter system (NanoString Technologies, Seattle, WA). Several NanoString publications using FFPE transplant specimens identify similar transcript associations with the molecular and histologic phenotypes as those reported in microarray studies 3,4,13‐18,27‐29,29‐33 . Among the advantages of NanoString are (1) a separate core processed at the time of biopsy is not required; (2) transcripts are assessed in the same sample analyzed by light microscopy; and (3) large retrospective and longitudinal analyses of archived samples can be readily performed in the setting of multicenter studies, which will enable retrospective randomization with long‐term survival end points available (Table 1).…”

“…After redundant and duplicate genes were removed, the list contained 1749 genes. Then the MDWG members identified overlap between these genes and genes described in the peer‐reviewed literature 2,8,12,29,32,33,38‐50,9,51,52,10,53‐56,11,57‐64,65 as being strongly associated with relevant clinical phenotypes and identified 1050 genes to be considered for inclusion. In the next step, a list including all genes with consensus expert opinion were selected and for which all Hugo duplicates were then combined, leaving 670 unique genes.…”

Validation of the Nanostring of the Banff Human Organ Transplant Gene Panel in Archival Data from Human Kidney Transplants and its Modelling Complexity

“…Using principal components of cell types and pathways is conceptually easier to understand immunologically. Creating unsupervised principal components is the easiest for feature selection and has an advantage that a latent variable or pathway may be present, which is not readily identi ed by the rst two gene selection methods [18,19]. These three methods, including just nding the highest genes by t-tests, will likely vary between independently derived data sets because results are very dependent on the sample size of the data set, the balance of the classes employed, and the purity of the annotated class diagnosis.…”

“…Allograft gene expression better de nes endpoints and risk in renal allografts [10,11], subtypes of ABMR [12], dysfunction [4,13,14], de ne a new diagnosis of C4d-negative ABMR [15,16], and identify NK cells in ABMR [17]. In addition, in non-human primates, unsupervised principal components from Nanostring gene expression identi ed gene expression patterns that predicted outcomes including tolerance [18,19].…”