Diabetic nephropathy (DN) has become the most common cause of end-stage renal disease (ESRD) in most countries. Elucidating novel epigenetic contributors to DN can not only enhance our understanding of this complex disorder, but also lay the foundation for developing more effective monitoring tools and preventive interventions in the future, thus contributing to our ultimate goal of improving patient care.The 5hmC-Seal, a highly selective, chemical labeling technique, was used to profile genome-wide 5-hydroxymethylcytosines (5hmC), a stable cytosine modification type marking gene activation, in circulating cell-free DNA (cfDNA) samples from a cohort of patients recruited at Zhongnan Hospital, including T2D patients with nephropathy (DN, n = 12), T2D patients with non-DN vascular complications (non-DN, n = 29), and T2D patients without any complication (controls, n = 14). Differentially analysis was performed to find DN-associated 5hmC features, followed by the exploration of biomarker potential of 5hmC in cfDNA for DN using a machine learning approach.Genome-wide analyses of 5hmC in cfDNA detected 427 and 336 differential 5hmC modifications associated with DN, compared with non-DN individuals and controls, and suggested relevant pathways such as NOD-like receptor signaling pathway and tyrosine metabolism. Our exploration using a machine learning approach revealed an exploratory model comprised of ten 5hmC genes showing the possibility to distinguish DN from non-DN individuals or controls.Genome-wide analysis suggests the possibility of exploiting novel 5hmC in patient-derived cfDNA as a non-invasive tool for monitoring DN in high risk T2D patients in the future.
ABSTRACT Long-term complications of type 2 diabetes (T2D) are the major causes for T2D-related disability and mortality. Notably, diabetic nephropathy (DN) has become the most frequent cause of end-stage renal disease (ESRD) in most countries. Understanding epigenetic contributors to DN can provide novel insights into this complex disorder and lay the foundation for more effective monitoring tools and preventive interventions, critical for achieving the ultimate goal of improving patient care and reducing healthcare burden. We have used a selective chemical labeling technique (5hmC-Seal) to profile genome-wide distributions of 5-hydroxymethylcytosines (5hmC), a gene activation mark, in patient-derived circulating cell-free DNA (cfDNA). Differentially modified 5hmC genes were identified across T2D patients with DN (n = 12), T2D patients with non-DN vascular complications (non-DN) (n = 29), and T2D patients with no complications (controls) (n = 14). Specifically, differential 5hmC markers between DN and controls revealed relevant pathways such as NOD-like receptor signaling pathway and tyrosine metabolism. A ten-gene panel was shown to provide differential 5hmC patterns between controls and DN, as well as between controls and non-DN patients using a machine learning approach. The 5hmC profiles in cfDNA reflected novel DN-associated epigenetic modifications relevant to the disease pathogenesis of DN. Importantly, these findings in cfDNA, a convenient liquid biopsy, have the potential to be exploited as a clinically useful tool for predicting DN in high risk T2D patients.
Additional file 5. Table S2. Differentially modified 5hmC features and differentially expressed mRNAs between IDH1-WT (GBM) and IDH1-Mut astrocytoma tumors.
Epigenetic modifications play critical roles in gene regulation and disease pathobiology. Highly sensitive enabling technologies, including microarray- and sequencing-based approaches have allowed genome-wide profiling of cytosine modifications in DNAs in clinical samples to facilitate discovery of epigenetic biomarkers for disease diagnosis and prognosis. Historically, many previous studies, however, did not distinguish the most investigated 5-methylcytosines (5mC) from other modified cytosines, especially the biochemically stable 5-hydroxymethylcytosines (5hmC), which have been shown to have a distinct genomic distribution and regulatory role from 5mC. Notably, during the past several years, the 5hmC-Seal, a highly sensitive chemical labeling technique, has been demonstrated to be a powerful tool for genome-wide profiling of 5hmC in clinically feasible biospecimens (e.g. a few milliliter of plasma or serum). The 5hmC-Seal technique has been utilized by our team in biomarker discovery for human cancers and other complex diseases using circulating cell-free DNA (cfDNA), as well as the characterization of the first 5hmC Human Tissue Map. Convenient access to the accumulating 5hmC-Seal data will allow the research community to validate and re-use these results, potentially providing novel insights into epigenetic contribution to a range of human diseases. Here we introduce the PETCH-DB, an integrated database that was implemented to provide 5hmC-related results generated using the 5hmC-Seal technique. We aim the PETCH-DB to be a central portal, which will be available to the scientific community with regularly updated 5hmC data in clinical samples to reflect current advances in this field. Database URL http://petch-db.org/.
Abstract Background Grade 4 glioma is the most aggressive and currently incurable brain tumor with a median survival of one year in adult patients. Elucidating novel transcriptomic and epigenetic contributors to the molecular heterogeneity underlying its aggressiveness may lead to improved clinical outcomes. Methods To identify grade 4 glioma -associated 5-hydroxymethylcytosine (5hmC) and transcriptomic features as well as their cross-talks, genome-wide 5hmC and transcriptomic profiles of tissue samples from 61 patients with grade 4 gliomas and 9 normal controls were obtained for differential and co-regulation/co-modification analyses. Prognostic models on overall survival based on transcriptomic features and the 5hmC modifications summarized over genic regions (promoters, gene bodies) and brain-derived histone marks were developed using machine learning algorithms. Results Despite global reduction, the majority of differential 5hmC features showed higher modification levels in grade 4 gliomas as compared to normal controls. In addition, the bi-directional correlations between 5hmC modifications over promoter regions or gene bodies and gene expression were greatly disturbed in grade 4 gliomas regardless of IDH1 mutation status. Phenotype-associated co-regulated 5hmC–5hmC modules and 5hmC–mRNA modules not only are enriched with different molecular pathways that are indicative of the pathogenesis of grade 4 gliomas, but also are of prognostic significance comparable to IDH1 mutation status. Lastly, the best-performing 5hmC model can predict patient survival at a much higher accuracy (c-index = 74%) when compared to conventional prognostic factor IDH1 (c-index = 57%), capturing the molecular characteristics of tumors that are independent of IDH1 mutation status and gene expression-based molecular subtypes. Conclusions The 5hmC-based prognostic model could offer a robust tool to predict survival in patients with grade 4 gliomas, potentially outperforming existing prognostic factors such as IDH1 mutations. The crosstalk between 5hmC and gene expression revealed another layer of complexity underlying the molecular heterogeneity in grade 4 gliomas, offering opportunities for identifying novel therapeutic targets.
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