Summary
Background
Chronic injury in kidney transplants remains a major cause of allograft loss. The aim of this study was to identify a gene set capable of predicting renal allografts at risk of progressive injury due to fibrosis.
Methods
This Genomics of Chronic Allograft Rejection (GoCAR) study is a prospective, multicentre study. We prospectively collected biopsies from renal allograft recipients (n=204) with stable renal function 3 months after transplantation. We used microarray analysis to investigate gene expression in 159 of these tissue samples. We aimed to identify genes that correlated with the Chronic Allograft Damage Index (CADI) score at 12 months, but not fibrosis at the time of the biopsy. We applied a penalised regression model in combination with permutation-based approach to derive an optimal gene set to predict allograft fibrosis. The GoCAR study is registered with ClinicalTrials.gov, number NCT00611702.
Findings
We identified a set of 13 genes that was independently predictive for the development of fibrosis at 1 year (ie, CADI-12 ≥2). The gene set had high predictive capacity (area under the curve [AUC] 0·967), which was superior to that of baseline clinical variables (AUC 0·706) and clinical and pathological variables (AUC 0·806). Furthermore routine pathological variables were unable to identify which histologically normal allografts would progress to fibrosis (AUC 0·754), whereas the predictive gene set accurately discriminated between transplants at high and low risk of progression (AUC 0·916). The 13 genes also accurately predicted early allograft loss (AUC 0·842 at 2 years and 0·844 at 3 years). We validated the predictive value of this gene set in an independent cohort from the GoCAR study (n=45, AUC 0·866) and two independent, publically available expression datasets (n=282, AUC 0·831 and n=24, AUC 0·972).
Interpretation
Our results suggest that this set of 13 genes could be used to identify kidney transplant recipients at risk of allograft loss before the development of irreversible damage, thus allowing therapy to be modified to prevent progression to fibrosis.
Funding
National Institutes of Health.
Type 1 diabetes (T1D) shows ~ 40% concordance rate in monozygotic twins (MZ) suggesting a role for environmental factors and/or epigenetic modifications in the etiology of the disease. The aim of our study was to dissect the contribution of epigenetic factors, particularly, DNA methylation (DNAm), to the incomplete penetrance of T1D. We performed DNAm profiling in lymphocyte cell lines from 3 monozygotic (MZ) twin pairs discordant for T1D and 6 MZ twin pairs concordant for the disease using HumanMethylation27 BeadChip. This assay assesses the methylation state of 27,578 CpG sites, mostly located within proximal promoter regions. We identified 88 CpG sites displaying significant methylation changes in all T1D-discordant MZ twin pairs. Functional annotation of the genes with distinct CpG methylation profiles in T1D samples showed differential DNAm of immune response and defense response pathways between affected and unaffected twins. Integration of DNAm data with GWAS data mapped several known T1D associated genes, HLA, INS, IL-2RB, CD226, which showed significant differences in DNAm between affected and unaffected of twins. Our findings suggest that abnormalities of DNA methylation patterns, known to regulate gene transcription, may be involved in the pathogenesis of T1D.
To identify the factors mediating the progression of diabetic nephropathy (DN), we performed RNA sequencing of kidney biopsy samples from patients with early DN, advanced DN, and normal kidney tissue from nephrectomy samples. A set of genes that were upregulated at early but downregulated in late DN were shown to be largely renoprotective, which included genes in the retinoic acid pathway and glucagon-like peptide 1 receptor. Another group of genes that were downregulated at early but highly upregulated in advanced DN consisted mostly of genes associated with kidney disease pathogenesis, such as those related to immune response and fibrosis. Correlation with estimated glomerular filtration rate (eGFR) identified genes in the pathways of iron transport and cell differentiation to be positively associated with eGFR, while those in the immune response and fibrosis pathways were negatively associated. Correlation with various histopathological features also identified the association with the distinct gene ontological pathways. Deconvolution analysis of the RNA sequencing data set indicated a significant increase in monocytes, fibroblasts, and myofibroblasts in advanced DN kidneys. Our study thus provides potential molecular mechanisms for DN progression and association of differential gene expression with the functional and structural changes observed in patients with early and advanced DN.
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