Dialysate Potassium and Mortality in a Prospective Hemodialysis Cohort.

BackgroundStudies examining the association of dialysate potassium concentration and mortality in hemodialysis patients show conflicting findings. We hypothesized that low dialysate potassium concentrations are associated with higher mortality, particularly in patients with high pre-dialysis serum potassium concentrations.MethodsWe evaluated 624 hemodialysis patients from the prospective Malnutrition, Diet, and Racial Disparities in Kidney Disease study recruited from 16 outpatient dialysis facilities over 2011-2015 who underwent protocolized collection of dialysis treatment characteristics every 6 months. We examined the association of dialysate potassium concentration, categorized as 1, 2, and 3 mEq/L, with all-cause mortality risk in the -overall cohort, and stratified by pre-dialysis serum potassium (< 5 vs. ≥5 mEq/L) using case-mix adjusted Cox models.ResultsIn baseline analyses, dialysate potassium concentrations of 1 mEq/L were associated with higher mortality, whereas concentrations of 3 mEq/L were associated with similar mortality in the overall cohort (reference: 2 mEq/L): adjusted hazard ratios (aHRs; 95% CI) 1.70 (1.01-2.88) and 0.95 (0.64-1.39), respectively. In analyses stratified by serum potassium, baseline dialysate potassium concentrations of 1 mEq/L were associated with higher mortality in patients with serum potassium ≥5 mEq/L but not in those with serum potassium < 5 mEq/L: aHRs (95% CI) 2.87 (1.51-5.46) and 0.74 (0.27-2.07), respectively (p interaction = 0.04). These findings were robust with incremental adjustment for serum potassium, potassium-binding resins, and potassium-modifying medications.ConclusionLow (1 mEq/L) dialysate potassium -concentrations were associated with higher mortality, particularly in hemodialysis patients with high pre-dialysis serum potassium. Further studies are needed to identify therapeutic strategies that mitigate inter-dialytic serum potassium accumulation and subsequent high dialysate serum potassium gradients in this population

HDAC9 is implicated in atherosclerotic aortic calcification and affects vascular smooth muscle cell phenotype.

Aortic calcification is an important independent predictor of future cardiovascular events. We performed a genome-wide association meta-analysis to determine SNPs associated with the extent of abdominal aortic calcification (n = 9,417) or descending thoracic aortic calcification (n = 8,422). Two genetic loci, HDAC9 and RAP1GAP, were associated with abdominal aortic calcification at a genome-wide level (P < 5.0 × 10-8). No SNPs were associated with thoracic aortic calcification at the genome-wide threshold. Increased expression of HDAC9 in human aortic smooth muscle cells promoted calcification and reduced contractility, while inhibition of HDAC9 in human aortic smooth muscle cells inhibited calcification and enhanced cell contractility. In matrix Gla protein-deficient mice, a model of human vascular calcification, mice lacking HDAC9 had a 40% reduction in aortic calcification and improved survival. This translational genomic study identifies the first genetic risk locus associated with calcification of the abdominal aorta and describes a previously unknown role for HDAC9 in the development of vascular calcification

Plant Ontology (PO): a Controlled Vocabulary of Plant Structures and Growth Stages

The Plant Ontology Consortium (POC) (www.plantontology.org) is a collaborative effort among several plant databases and experts in plant systematics, botany and genomics. A primary goal of the POC is to develop simple yet robust and extensible controlled vocabularies that accurately reflect the biology of plant structures and developmental stages. These provide a network of vocabularies linked by relationships (ontology) to facilitate queries that cut across datasets within a database or between multiple databases. The current version of the ontology integrates diverse vocabularies used to describe Arabidopsis, maize and rice (Oryza sp.) anatomy, morphology and growth stages. Using the ontology browser, over 3500 gene annotations from three species-specific databases, The Arabidopsis Information Resource (TAIR) for Arabidopsis, Gramene for rice and MaizeGDB for maize, can now be queried and retrieved

The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations

The Plant Ontology Consortium (POC, http://www.plantontology.org) is a collaborative effort among model plant genome databases and plant researchers that aims to create, maintain and facilitate the use of a controlled vocabulary (ontology) for plants. The ontology allows users to ascribe attributes of plant structure (anatomy and morphology) and developmental stages to data types, such as genes and phenotypes, to provide a semantic framework to make meaningful cross-species and database comparisons. The POC builds upon groundbreaking work by the Gene Ontology Consortium (GOC) by adopting and extending the GOC's principles, existing software and database structure. Over the past year, POC has added hundreds of ontology terms to associate with thousands of genes and gene products from Arabidopsis, rice and maize, which are available through a newly updated web-based browser (http://www.plantontology.org/amigo/go.cgi) for viewing, searching and querying. The Consortium has also implemented new functionalities to facilitate the application of PO in genomic research and updated the website to keep the contents current. In this report, we present a brief description of resources available from the website, changes to the interfaces, data updates, community activities and future enhancement

The Plant Ontology Database: a community resource for plant structure and developmental stages controlled vocabulary and annotations

The Plant Ontology Consortium (POC, http://www.plantontology.org) is a collaborative effort among model plant genome databases and plant researchers that aims to create, maintain and facilitate the use of a controlled vocabulary (ontology) for plants. The ontology allows users to ascribe attributes of plant structure (anatomy and morphology) and developmental stages to data types, such as genes and phenotypes, to provide a semantic framework to make meaningful cross-species and database comparisons. The POC builds upon groundbreaking work by the Gene Ontology Consortium (GOC) by adopting and extending the GOC's principles, existing software and database structure. Over the past year, POC has added hundreds of ontology terms to associate with thousands of genes and gene products from Arabidopsis, rice and maize, which are available through a newly updated web-based browser (http://www.plantontology.org/amigo/go.cgi) for viewing, searching and querying. The Consortium has also implemented new functionalities to facilitate the application of PO in genomic research and updated the website to keep the contents current. In this report, we present a brief description of resources available from the website, changes to the interfaces, data updates, community activities and future enhancement

Metabolomics as a Hypothesis-Generating Functional Genomics Tool for the Annotation of Arabidopsis thaliana Genes of “Unknown Function”

Metabolomics is the methodology that identifies and measures global pools of small molecules (of less than about 1,000 Da) of a biological sample, which are collectively called the metabolome. Metabolomics can therefore reveal the metabolic outcome of a genetic or environmental perturbation of a metabolic regulatory network, and thus provide insights into the structure and regulation of that network. Because of the chemical complexity of the metabolome and limitations associated with individual analytical platforms for determining the metabolome, it is currently difficult to capture the complete metabolome of an organism or tissue, which is in contrast to genomics and transcriptomics. This paper describes the analysis of Arabidopsis metabolomics data sets acquired by a consortium that includes five analytical laboratories, bioinformaticists, and biostatisticians, which aims to develop and validate metabolomics as a hypothesis-generating functional genomics tool. The consortium is determining the metabolomes of Arabidopsis T-DNA mutant stocks, grown in standardized controlled environment optimized to minimize environmental impacts on the metabolomes. Metabolomics data were generated with seven analytical platforms, and the combined data is being provided to the research community to formulate initial hypotheses about genes of unknown function (GUFs). A public database (www.PlantMetabolomics.org) has been developed to provide the scientific community with access to the data along with tools to allow for its interactive analysis. Exemplary datasets are discussed to validate the approach, which illustrate how initial hypotheses can be generated from the consortium-produced metabolomics data, integrated with prior knowledge to provide a testable hypothesis concerning the functionality of GUFs

Review of methods for detecting glycemic disorders

Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity

Transcriptional Repressive H3K9 and H3K27 Methylations Contribute to DNMT1-Mediated DNA Methylation Recovery

DNA methylation and histone modifications are two major epigenetic events regulating gene expression and chromatin structure, and their alterations are linked to human carcinogenesis. DNA methylation plays an important role in tumor suppressor gene inactivation, and can be revised by DNA methylation inhibitors. The reversible nature of DNA methylation forms the basis of epigenetic cancer therapy. However, it has been reported that DNA re-methylation and gene re-silencing could occur after removal of demethylation treatment and this may significantly hamper the therapeutic value of DNA methylation inhibitors. In this study we have provided detailed evidence demonstrating that mammalian cells possess a bona fide DNA methylation recovery system. We have also shown that DNA methylation recovery was mediated by the major human DNA methyltransferase, DNMT1. In addition, we found that H3K9-tri-methylation and H3K27-tri-methylation were closely associated with this DNA methylation recovery. These persistent transcriptional repressive histone modifications may have a crucial role in regulating DNMT1-mediated DNA methylation recovery. Our findings may have important implications towards a better understanding of epigenetic regulation and future development of epigenetic therapeutic intervention