Differential methylation as a diagnostic biomarker of rare renal diseases: a systematic review

Site-Selective Modification of Secondary Amine Moieties on Native Peptides, Proteins, and Natural Products with Ynones

Site-selective modification of biologically relevant secondary amines in peptides, proteins, and natural products has been challenging due to the similar reactivity between primary and secondary amines. Even for the secondary amines, their reactivities are significantly influenced by their structures and environment. Herein, we report a ynone Michael bioconjugation method for selective modification of secondary amines in unprotected peptides and proteins and complex natural products. We show that fine tuning the electronic effect of the ynones enables controlling the Michael acceptor reactivity for the selective reaction with the structurally different secondary amines in densely functionalized complex structures and complicated biological environment.

Omics and Artificial Intelligence in Kidney Diseases

Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.

Molecular Mechanisms of Epigenetic Regulation, Inflammation, and Cell Death in ADPKD

Autosomal dominant polycystic kidney disease (ADPKD) is a genetic disorder, which is caused by mutations in the PKD1 and PKD2 genes, characterizing by progressive growth of multiple cysts in the kidneys, eventually leading to end-stage kidney disease (ESKD) and requiring renal replacement therapy. In addition, studies indicate that disease progression is as a result of a combination of factors. Understanding the molecular mechanisms, therefore, should facilitate the development of precise therapeutic strategies for ADPKD treatment. The roles of epigenetic modulation, interstitial inflammation, and regulated cell death have recently become the focuses in ADPKD. Different epigenetic regulators, and the presence of inflammatory markers detectable even before cyst growth, have been linked to cyst progression. Moreover, the infiltration of inflammatory cells, such as macrophages and T cells, have been associated with cyst growth and deteriorating renal function in humans and PKD animal models. There is evidence supporting a direct role of the PKD gene mutations to the regulation of epigenetic mechanisms and inflammatory response in ADPKD. In addition, the role of regulated cell death, including apoptosis, autophagy and ferroptosis, have been investigated in ADPKD. However, there is no consensus whether cell death promotes or delays cyst growth in ADPKD. It is therefore necessary to develop an interactive picture between PKD gene mutations, the epigenome, inflammation, and cell death to understand why inherited PKD gene mutations in patients may result in the dysregulation of these processes that increase the progression of renal cyst formation.

Harnessing the Full Potential of Multi-Omic Analyses to Advance the Study and Treatment of Chronic Kidney Disease

Chronic kidney disease (CKD) was the 12th leading cause of death globally in 2017 with the prevalence of CKD estimated at ~9%. Early detection and intervention for CKD may improve patient outcomes, but standard testing approaches even in developed countries do not facilitate identification of patients at high risk of developing CKD, nor those progressing to end-stage kidney disease (ESKD). Recent advances in CKD research are moving towards a more personalised approach for CKD. Heritability for CKD ranges from 30% to 75%, yet identified genetic risk factors account for only a small proportion of the inherited contribution to CKD. More in depth analysis of genomic sequencing data in large cohorts is revealing new genetic risk factors for common diagnoses of CKD and providing novel diagnoses for rare forms of CKD. Multi-omic approaches are now being harnessed to improve our understanding of CKD and explain some of the so-called 'missing heritability'. The most common omic analyses employed for CKD are genomics, epigenomics, transcriptomics, metabolomics, proteomics and phenomics. While each of these omics have been reviewed individually, considering integrated multi-omic analysis offers considerable scope to improve our understanding and treatment of CKD. This narrative review summarises current understanding of multi-omic research alongside recent experimental and analytical approaches, discusses current challenges and future perspectives, and offers new insights for CKD.

Recent Discoveries in Epigenetic Modifications of Polycystic Kidney Disease

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a heritable renal disease that results in end-stage kidney disease, due to the uncontrolled bilateral growth of cysts throughout the kidneys. While it is known that a mutation within a PKD-causing gene is required for the development of ADPKD, the underlying mechanism(s) causing cystogenesis and progression of the disease are not well understood. Limited therapeutic options are currently available to slow the rate of cystic growth. Epigenetic modifications, including DNA methylation, are known to be altered in neoplasia, and several FDA-approved therapeutics target these disease-specific changes. As there are many similarities between ADPKD and neoplasia, we (and others) have postulated that ADPKD kidneys contain alterations to their epigenetic landscape that could be exploited for future therapeutic discovery. Here we summarise the current understanding of epigenetic changes that are associated with ADPKD, with a particular focus on the burgeoning field of ADPKD-specific alterations in DNA methylation.

Estimands in epigenome-wide association studies

Background

In DNA methylation analyses like epigenome-wide association studies, effects in differentially methylated CpG sites are assessed. Two kinds of outcomes can be used for statistical analysis: Beta-values and M-values. M-values follow a normal distribution and help to detect differentially methylated CpG sites. As biological effect measures, differences of M-values are more or less meaningless. Beta-values are of more interest since they can be interpreted directly as differences in percentage of DNA methylation at a given CpG site, but they have poor statistical properties. Different frameworks are proposed for reporting estimands in DNA methylation analysis, relying on Beta-values, M-values, or both.

Results

We present and discuss four possible approaches of achieving estimands in DNA methylation analysis. In addition, we present the usage of M-values or Beta-values in the context of bioinformatical pipelines, which often demand a predefined outcome. We show the dependencies between the differences in M-values to differences in Beta-values in two data simulations: a analysis with and without confounder effect. Without present confounder effects, M-values can be used for the statistical analysis and Beta-values statistics for the reporting. If confounder effects exist, we demonstrate the deviations and correct the effects by the intercept method. Finally, we demonstrate the theoretical problem on two large human genome-wide DNA methylation datasets to verify the results.

Conclusions

The usage of M-values in the analysis of DNA methylation data will produce effect estimates, which cannot be biologically interpreted. The parallel usage of Beta-value statistics ignores possible confounder effects and can therefore not be recommended. Hence, if the differences in Beta-values are the focus of the study, the intercept method is recommendable. Hyper- or hypomethylated CpG sites must then be carefully evaluated. If an exploratory analysis of possible CpG sites is the aim of the study, M-values can be used for inference.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01083-9.

Selective Triazenation Reaction (STaR) of Secondary Amines for Tagging Monomethyl Lysine Post-Translational Modifications

Lysine monomethylation (Kme) is an impactful post-translational modification (PTM) responsible for regulating biological processes and implicated in diseases, thus there is great interest in identifying these methylation marks globally. However, the progress in this area has been challenging because the addition of a small methyl group on lysine leads to negligible change in the bulk, charge, and hydrophobicity. Herein, we report an empowering chemical technology selective triazenation reaction, which we term "STaR", of secondary amines using arene diazonium salts to achieve highly selective, rapid, and robust tagging of Kme peptides from a complex mixture under biocompatible conditions. Although the resulting triazene-linkage with Kme is stable, we highlight the efficient decoupling of the triazene-conjugate to afford unmodified starting components under mild conditions when desired. Our work establishes a unique chemoselective, traceless bioconjugation strategy for the selective enrichment of Kme PTMs.

DNA Methylation Associated With Diabetic Kidney Disease in Blood-Derived DNA

A subset of individuals with type 1 diabetes will develop diabetic kidney disease (DKD). DKD is heritable and large-scale genome-wide association studies have begun to identify genetic factors that influence DKD. Complementary to genetic factors, we know that a person’s epigenetic profile is also altered with DKD. This study reports analysis of DNA methylation, a major epigenetic feature, evaluating methylome-wide loci for association with DKD. Unique features (n = 485,577; 482,421 CpG probes) were evaluated in blood-derived DNA from carefully phenotyped White European individuals diagnosed with type 1 diabetes with (cases) or without (controls) DKD (n = 677 samples). Explicitly, 150 cases were compared to 100 controls using the 450K array, with subsequent analysis using data previously generated for a further 96 cases and 96 controls on the 27K array, and de novo methylation data generated for replication in 139 cases and 96 controls. Following stringent quality control, raw data were quantile normalized and beta values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls; resultant P-values for array-based data were adjusted for multiple testing. Genes with significantly increased (hypermethylated) and/or decreased (hypomethylated) levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways. Twenty-two loci demonstrated statistically significant fold changes associated with DKD and additional support for these associated loci was sought using independent samples derived from patients recruited with similar inclusion criteria. Markers associated with CCNL1 and ZNF187 genes are supported as differentially regulated loci (P < 10^–8), with evidence also presented for AFF3, which has been identified from a meta-analysis and subsequent replication of genome-wide association studies. Further supporting evidence for differential gene expression in CCNL1 and ZNF187 is presented from kidney biopsy and blood-derived RNA in people with and without kidney disease from NephroSeq. Evidence confirming that methylation sites influence the development of DKD may aid risk prediction tools and stimulate research to identify epigenomic therapies which might be clinically useful for this disease.