DNA hypermethylation and DNA hypomethylation is present at different loci in chronic kidney disease

Genetic risk factors for chronic kidney disease (CKD) are being identified through international collaborations. By comparison, epigenetic risk factors for CKD have only recently been considered using population-based approaches. DNA methylation is a major epigenetic modification that is associated with complex diseases, so we investigated methylome-wide loci for association with CKD. A total of 485,577 unique features were evaluated in 255 individuals with CKD (cases) and 152 individuals without evidence of renal disease (controls). Following stringent quality control, raw data were quantile normalized and β values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls with resultant P values adjusted for multiple testing. Genes with significantly increased and decreased levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways in Partek Genomics Suite. Twenty-three genes, where more than one CpG per loci was identified with P_adjusted < 10⁻⁸, demonstrated significant methylation changes associated with CKD and additional support for these associated loci was sought from published literature. Strong biological candidates for CKD that showed statistically significant differential methylation include CUX1, ELMO1, FKBP5, INHBA-AS1, PTPRN2, and PRKAG2 genes; several genes are differentially methylated in kidney tissue and RNA-seq supports a functional role for differential methylation in ELMO1 and PRKAG2 genes. This study reports the largest, most comprehensive, genome-wide quantitative evaluation of DNA methylation for association with CKD. Evidence confirming methylation sites influence development of CKD would stimulate research to identify epigenetic therapies that might be clinically useful for CKD.     

DNA hypermethylation and DNA hypomethylation is present at different loci in chronic kidney disease

Genetic risk factors for chronic kidney disease (CKD) are being identified through international collaborations. By comparison, epigenetic risk factors for CKD have only recently been considered using population-based approaches. DNA methylation is a major epigenetic modification that is associated with complex diseases, so we investigated methylome-wide loci for association with CKD. A total of 485,577 unique features were evaluated in 255 individuals with CKD (cases) and 152 individuals without evidence of renal disease (controls). Following stringent quality control, raw data were quantile normalized and β values calculated to reflect the methylation status at each site. The difference in methylation status was evaluated between cases and controls with resultant P values adjusted for multiple testing. Genes with significantly increased and decreased levels of DNA methylation were considered for biological relevance by functional enrichment analysis using KEGG pathways in Partek Genomics Suite. Twenty-three genes, where more than one CpG per loci was identified with P_adjusted < 10^?8, demonstrated significant methylation changes associated with CKD and additional support for these associated loci was sought from published literature. Strong biological candidates for CKD that showed statistically significant differential methylation include CUX1, ELMO1, FKBP5, INHBA-AS1, PTPRN2, and PRKAG2 genes; several genes are differentially methylated in kidney tissue and RNA-seq supports a functional role for differential methylation in ELMO1 and PRKAG2 genes. This study reports the largest, most comprehensive, genome-wide quantitative evaluation of DNA methylation for association with CKD. Evidence confirming methylation sites influence development of CKD would stimulate research to identify epigenetic therapies that might be clinically useful for CKD.     

Role of non-coding variants in cardiovascular disease

Cardiovascular diseases (CVDs) constitute one of the significant causes of death worldwide. Different pathological states are linked to CVDs, which despite interventions and treatments, still have poor prognoses. The genetic component, as a beneficial tool in the risk stratification of CVD development, plays a role in the pathogenesis of this group of diseases. The emergence of genome-wide association studies (GWAS) have led to the identification of non-coding parts associated with cardiovascular traits and disorders. Variants located in functional non-coding regions, including promoters/enhancers, introns, miRNAs and 5′/3′ UTRs, account for 90% of all identified single-nucleotide polymorphisms associated with CVDs. Here, for the first time, we conducted a comprehensive review on the reported non-coding variants for different CVDs, including hypercholesterolemia, cardiomyopathies, congenital heart diseases, thoracic aortic aneurysms/dissections and coronary artery diseases. Additionally, we present the most commonly reported genes involved in each CVD. In total, 1469 non-coding variants constitute most reports on familial hypercholesterolemia, hypertrophic cardiomyopathy and dilated cardiomyopathy. The application and identification of non-coding variants are beneficial for the genetic diagnosis and better therapeutic management of CVDs.     

A Pharmaco-Metabonomic Study on Chronic Kidney Disease and Therapeutic Effect of Ergone by UPLC-QTOF/HDMS

Chronic kidney disease (CKD) is an important public health problem. Ergone has been proved to prevent the progression of CKD. UPLC-QTOF/HDMS was employed for metabolic profiling of adenine-induced CKD and to investigate the nephroprotective effects of ergone. Pharmacology parameters including blood biochemistry, histopathological evaluation and Western blot analysis were performed concurrently. The UPLC-MS data were analyzed by partial least squares-discriminate analysis, correlation analysis, heatmap analysis and mapped to KEGG pathways. Blood and serum biochemistry were observed to be significantly different in the CKD group than in the control group. In conjunction with biochemistry, histopathology and protein expression results, identified metabolites indicated perturbations in fatty acid metabolism, purine metabolism and amino acid metabolism as changes associated with adenine-induced CKD and the interventions of ergone. Upregulated expression of TGF-β1, ED-1, CTGF, bFGF and collagen I was observed in the CKD group. However, downregulated expression of these proteins was observed after oral administration of ergone. These results suggest that expression changes in these proteins had implications for fatty acid metabolism, purine metabolism and amino acid metabolism in the development of CKD and that ergone treatment could delay the development of CKD by normalizing or blocking abnormal changes in biomarker metabolites and protein expression in the CKD group.     

Identification of candidate disease genes by integrating Gene Ontologies and protein-interaction networks: case study of primary immunodeficiencies

Disease gene identification is still a challenge despite modern high-throughput methods. Many diseases are very rare or lethal and thus cannot be investigated with traditional methods. Several in silico methods have been developed but they have some limitations. We introduce a new method that combines information about protein-interaction network properties and Gene Ontology terms. Genes with high-calculated network scores and statistically significant gene ontology terms based on known diseases are prioritized as candidate genes. The method was applied to identify novel primary immunodeficiency-related genes, 26 of which were found. The investigation uses the protein-interaction network for all essential immunome human genes available in the Immunome Knowledge Base and an analysis of their enriched gene ontology annotations. The identified disease gene candidates are mainly involved in cellular signaling including receptors, protein kinases and adaptor and binding proteins as well as enzymes. The method can be generalized for any disease group with sufficient information.     

Evaluation of canine COL4A3 and COL4A4 as candidates for familial renal disease in the Norwegian elkhound

The collagen type IV alpha3 and alpha4 chains (COL4A3 and COL4A4) are part of the specialized glomerular basement membrane in the kidney. In human these genes are responsible for Alport syndrome (a type of hereditary nephritis). Histopathological similarities between kidneys of Norwegian elkhound dogs affected with familial renal disease and human Alport syndrome were the basis for a candidate gene approach in Norwegian elkhounds. Three microsatellites-tightly linked to canine COL4A3 and COL4A4--were developed. The microsatellites were used to analyze linkage between COL4A3 and COL4A4 and familial renal disease in a Norwegian elkhound pedigree segregating this disease. Presence of one recombinant between familial renal disease and COL4A3/COL4A4 suggests that these genes are not likely candidates for familial renal disease in this breed.     

Role of kidney biomarkers of chronic kidney disease: An update

Chronic kidney disease (CKD) is a progressive pathological condition marked by deteriorating renal function over time. Diagnostic of kidney disease depend on serum creatinine level and glomerular filtration rate which is detectable when kidney function become half. The detection of kidney damage in an early stage needs robust biomarkers. Biomarkers allow monitoring the disease progression at initial stages of disease. On the onset of impairment in cellular organization there is perturbation in signaling molecules which are either up-regulated or down-regulated and act as an indicator or biomarker of diseased stage. This review compiled the cell signaling of different kidney biomarkers associated with the onset of chronic kidney diseases. Delay in diagnosis of CKD will cause deterioration of nephron function which leads to End stage renal disease and at that point patients require dialysis or kidney transplant. Detailed information on the complex network in signaling pathway leading to a coordinated pattern of gene expression and regulation in CKD will undoubtedly provide important clues to develop novel prognostic and therapeutic strategies for CKD.     

Diagnosis, pathogenesis, and treatment prospects in cystic kidney disease

Cystic kidney diseases (CKDs) are a clinically and genetically heterogeneous group of disorders characterized by progressive fibrocystic renal and hepatobiliary changes. Recent findings have proven the cystogenic process to be compatible with cellular dedifferentiation, i. e. increased apoptosis and proliferation rates, altered protein sorting and secretory characteristics, as well as disorganization of the extracellular matrix. Compelling evidence suggests that cilia play a central pathogenic role and most cystic kidney disorders converge into a common pathogenic pathway. Recently, several promising trials have further extended our understanding of the pathophysiology of CKD and may have the potential for rational personalized therapies in future years. This review aims to summarize the current state of knowledge of the structure and function of proteins underlying polycystic kidney disease, to explore the clinical consequences of changes in respective genes, and to discuss potential therapeutic approaches.     

Protein interactions and disease: computational approaches to uncover the etiology of diseases

The genomic era has been characterised by vast amounts of data from diverse sources, creating a need for new tools to extract biologically meaningful information. Bioinformatics is, for the most part, responding to that need. The sparseness of the genomic data associated with diseases, however, creates a new challenge. Understanding the complex interplay between genes and proteins requires integration of data from a wide variety of sources, i.e. gene expression, genetic linkage, protein interaction, and protein structure among others. Thus, computational tools have become critical for the integration, representation and visualization of heterogeneous biomedical data. Furthermore, several bioinformatics methods have been developed to formulate predictions about the functional role of genes and proteins, including their role in diseases. After an introduction to the complex interplay between proteins and genetic diseases, this review explores recent approaches to the understanding of the mechanisms of disease at the molecular level. Finally, because most known mechanisms leading to disease involve some form of protein interaction, this review focuses on the recent methodologies for understanding diseases through their underlying protein interactions. Recent contributions from genetics, protein structure and protein interaction network analyses to the understanding of diseases are discussed here.     

Mutations in type I collagen genes resulting in osteogenesis imperfecta in humans

Osteogenesis imperfecta (OI), commonly known as "brittle bone disease", is a dominant autosomal disorder characterized by bone fragility and abnormalities of connective tissue. Biochemical and molecular genetic studies have shown that the vast majority of affected individuals have mutations in either the COL1A1 or COL1A2 genes that encode the chains of type I procollagen. OI is associated with a wide spectrum of phenotypes varying from mild to severe and lethal conditions. The mild forms are usually caused by mutations which inactivate one allele of COL1A1 gene and result in a reduced amount of normal type I collagen, while the severe and lethal forms result from dominant negative mutations in COL1A1 or COL1A2 which produce structural defects in the collagen molecule. The most common mutations are substitutions of glycine residues, which are crucial to formation and function of the collagen triple helix, by larger amino acids. Although type I collagen is the major structural protein of both bone and skin, the mutations in type I collagen genes cause a bone disease. Some reports showed that the mutant collagen can be expressed differently in bone and in skin. Since most mutations identified in OI are dominant negative, the gene therapy requires a fundamentally different approach from that used for genetic-recessive disorders. The antisense therapy, by reducing the expression of mutant genes, is able to change a structural mutation into a null mutation, and thus convert severe forms of the disease into mild OI type I.     

Circular RNA in cardiovascular disease: Expression,mechanisms and clinical prospects

Circular RNAs (circRNAs) are a group of covalently closed, endogenous, non-coding RNAs, which exist widely in human tissues including the heart. Increasing evidence has shown that cardiac circRNAs play crucial regulatory roles in cardiovascular diseases (CVDs). In this review, we aimed to provide a systemic understanding of circRNAs with a special emphasis on the cardiovascular system. We have summarized the current research on the classification, biogenesis and properties of circRNAs as well as their participation in the pathogenesis of CVDs. CircRNAs are conserved, stable and have specific spatiotemporal expression; thus, they have been accepted as a potential diagnostic marker or an incremental prognostic biomarker for CVDs.     

An overview on the correlation of neurological disorders with cardiovascular disease

Neurological disorders (NDs) are one of the leading causes of death especially in the developed countries. Among those NDs, Alzheimer’s disease (AD) and Parkinson disease (PD) are heading the table. There have been several reports in the scientific literatures which suggest the linkage between cardiovascular disorders (CVDs) and NDs. In the present communication, we have tried to compile NDs (AD and PD) association with CVDs reported in the literature. Based on the available scientific literature, we believe that further comprehensive study needs to be done to elucidate the molecular linking points associated with the above mentioned disorders.Abbreviations: AD, Alzheimer’s disease, Aβ, β amyloid, PD, Parkinson disease, l-DOPA, l-dihydroxyphenylalanine, LBs, Lewy bodies, DA, dopamine, APP, amyloid precursor protein, CVD, cardiovascular disease     

Characteristics of human oral microbiome and its non-invasive diagnostic value in chronic kidney disease

Background: Morbidity of chronic kidney disease (CKD) is increased, with many complications and high mortality rates. The characteristics of oral microbiome in CKD patients have not been reported. This study aims to analyze the oral microbiome, and to demonstrate the potential of microbiome as noninvasive biomarkers for CKD patients.Methods: The study collected 253 oral samples from different regions of China (Central China and East China) prospectively and finally 235 samples completed Miseq sequencing, including 103 samples from CKD patients and 132 healthy controls (HCs).Results: Compared with HCs (n=88), the oral microbial diversity in CKD patients (n=44) was increased. Fourteen genera including Streptococcus, Actinomyces and Leptotrichia were enriched, while six genera including Prevotella and Haemophilus were decreased in CKD patients. Moreover, 49 predicted microbial gene functions including arginine metabolism and tryptophan metabolism increased, while 55 functions including Ribosome and DNA repair recombination proteins decreased. Furthermore, correlation analysis demonstrated that 38 operational taxonomic units (OTUs) were closely related to 5 clinical indicators of CKD. Notably, 7 optimal biomarkers were identified using random forest model, and the classifier model respectively reached an area under the curve (AUC) of 0.9917 and 0.8026 in the discovery and validation phase, achieving a cross-region validation.Conclusions: We first illustrated the characteristics of the oral microbiome of patients with CKD, identified the potential of oral microbial makers as noninvasive tools for the diagnosis of CKD and achieved cross-region validation.     

Epistatic Role of the MYH9/APOL1 Region on Familial Hematuria Genes

Familial hematuria (FH) is explained by at least four different genes (see below). About 50% of patients develop late proteinuria and chronic kidney disease (CKD). We hypothesized that MYH9/APOL1, two closely linked genes associated with CKD, may be associated with adverse progression in FH. Our study included 102 thin basement membrane nephropathy (TBMN) patients with three known COL4A3/COL4A4 mutations (cohort A), 83 CFHR5/C3 glomerulopathy patients (cohort B) with a single CFHR5 mutation and 15 Alport syndrome patients (cohort C) with two known COL4A5 mild mutations, who were categorized as “Mild” (controls) or “Severe” (cases), based on renal manifestations. E1 and S1 MYH9 haplotypes and variant rs11089788 were analyzed for association with disease phenotype. Evidence for association with “Severe” progression in CFHR5 nephropathy was found with MYH9 variant rs11089788 and was confirmed in an independent FH cohort, D (cumulative p value = 0.001, odds ratio = 3.06, recessive model). No association was found with APOL1 gene. Quantitative Real time PCR did not reveal any functional significance for the rs11089788 risk allele. Our results derive additional evidence supporting previous reports according to which MYH9 is an important gene per se, predisposing to CKD, suggesting its usefulness as a prognostic marker for young hematuric patients.     

Ranking candidate disease genes from gene expression and protein interaction: a Katz-centrality based approach

Many diseases have complex genetic causes, where a set of alleles can affect the propensity of getting the disease. The identification of such disease genes is important to understand the mechanistic and evolutionary aspects of pathogenesis, improve diagnosis and treatment of the disease, and aid in drug discovery. Current genetic studies typically identify chromosomal regions associated specific diseases. But picking out an unknown disease gene from hundreds of candidates located on the same genomic interval is still challenging. In this study, we propose an approach to prioritize candidate genes by integrating data of gene expression level, protein-protein interaction strength and known disease genes. Our method is based only on two, simple, biologically motivated assumptions--that a gene is a good disease-gene candidate if it is differentially expressed in cases and controls, or that it is close to other disease-gene candidates in its protein interaction network. We tested our method on 40 diseases in 58 gene expression datasets of the NCBI Gene Expression Omnibus database. On these datasets our method is able to predict unknown disease genes as well as identifying pleiotropic genes involved in the physiological cellular processes of many diseases. Our study not only provides an effective algorithm for prioritizing candidate disease genes but is also a way to discover phenotypic interdependency, cooccurrence and shared pathophysiology between different disorders.     

Exome sequencing and metabolomic analysis of a chronic kidney disease and hearing loss patient family revealed RMND1 mutation induced sphingolipid metabolism defects

Mitochondrial disorders (MIDs) shows overlapping clinical presentations owing to the genetic and metabolic defects of mitochondria. However, specific relationship between inherited mutations in nuclear encoded mitochondrial proteins and their functional impacts in terms of metabolic defects in patients is not yet well explored. Therefore, using high throughput whole exome sequencing (WES), we screened a chronic kidney disease (CKD) and sensorineural hearing loss (SNHL) patient, and her family members to ascertain the mode of inheritance of the mutation, and healthy population controls to establish its rare frequency. The impact of mutation on biophysical characteristics of the protein was further studied by mapping it in 3D structure. Furthermore, LC-MS tandem mass spectrophotometry based untargeted metabolomic profiling was done to study the fluctuations in plasma metabolites relevant to disease causative mutations and kidney damage. We identified a very rare homozygous c.631G > A (p.Val211Met) pathogenic mutation in RMND1 gene in the proband, which is inherited in an autosomal recessive fashion. This gene is involved in the mitochondrial translational pathways and contribute in mitochondrial energy metabolism. The p.Val211Met mutation is found to disturb the structural orientation (RMSD is −2.95 Å) and stability (ΔΔG is −0.552 Kcal/mol) of the RMND1 protein. Plasma metabolomics analysis revealed the aberrant accumulation of metabolites connected to lipid and amino acid metabolism pathways. Of these metabolites, pathway networking has discovered ceramide, a metabolite of sphingolipids, which plays a role in different signaling cascades including mitochondrial membrane biosynthesis, is highly elevated in this patient. This study suggests that genetic defects in RMND1 gene alters the mitochondrial energy metabolism leading to the accumulation of ceramide, and subsequently promote dysregulated apoptosis and tissue necrosis in kidneys.