Defiant: (DMRs: easy, fast, identification and ANnoTation) identifies differentially Methylated regions from iron-deficient rat hippocampus.

BACKGROUND: Identification of differentially methylated regions (DMRs) is the initial step towards the study of DNA methylation-mediated gene regulation. Previous approaches to call DMRs suffer from false prediction, use extreme resources, and/or require library installation and input conversion. RESULTS: We developed a new approach called Defiant to identify DMRs. Employing Weighted Welch Expansion (WWE), Defiant showed superior performance to other predictors in the series of benchmarking tests on artificial and real data. Defiant was subsequently used to investigate DNA methylation changes in iron-deficient rat hippocampus. Defiant identified DMRs close to genes associated with neuronal development and plasticity, which were not identified by its competitor. Importantly, Defiant runs between 5 to 479 times faster than currently available software packages. Also, Defiant accepts 10 different input formats widely used for DNA methylation data. CONCLUSIONS: Defiant effectively identifies DMRs for whole-genome bisulfite sequencing (WGBS), reduced-representation bisulfite sequencing (RRBS), Tet-assisted bisulfite sequencing (TAB-seq), and HpaII tiny fragment enrichment by ligation-mediated PCR-tag (HELP) assays.

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People With Type 2 Diabetes.

OBJECTIVE: To identify genetic risk factors for incident cardiovascular disease (CVD) among people with type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS: We conducted a multiancestry time-to-event genome-wide association study for incident CVD among people with T2D. We also tested 204 known coronary artery disease (CAD) variants for association with incident CVD. RESULTS: Among 49,230 participants with T2D, 8,956 had incident CVD events (event rate 18.2%). We identified three novel genetic loci for incident CVD: rs147138607 (near CACNA1E/ZNF648, hazard ratio [HR] 1.23, P = 3.6 × 10-9), rs77142250 (near HS3ST1, HR 1.89, P = 9.9 × 10-9), and rs335407 (near TFB1M/NOX3, HR 1.25, P = 1.5 × 10-8). Among 204 known CAD loci, 5 were associated with incident CVD in T2D (multiple comparison-adjusted P < 0.00024, 0.05/204). A standardized polygenic score of these 204 variants was associated with incident CVD with HR 1.14 (P = 1.0 × 10-16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

The nuclear magnetic resonance of CCCC RNA reveals a right-handed helix, and revised parameters for AMBER force field torsions improve structural predictions from molecular dynamics.

The sequence dependence of RNA energetics is important for predicting RNA structure. Hairpins with C(n) loops are consistently less stable than hairpins with other loops, which suggests the structure of C(n) regions could be unusual in the "unfolded" state. For example, previous nuclear magnetic resonance (NMR) evidence suggested that polycytidylic acid forms a left-handed helix. In this study, UV melting experiments show that the hairpin formed by r(5'GGACCCCCGUCC) is less stable than r(5'GGACUUUUGUCC). NMR spectra for single-stranded C(4) oligonucleotide, mimicking the unfolded hairpin loop, are consistent with a right-handed A-form-like helix. Comparisons between NMR spectra and molecular dynamics (MD) simulations suggest that recent reparametrizations, parm99χ_YIL and parm99TOR, of the AMBER parm99 force field improve the agreement between structural features for C(4) determined by NMR and predicted by MD. Evidently, the force field revisions to parm99 improve the modeling of RNA energetics and therefore structure.

Time-to-Event Genome-Wide Association Study for Incident Cardiovascular Disease in People with Type 2 Diabetes Mellitus.

BACKGROUND: Type 2 diabetes mellitus (T2D) confers a two- to three-fold increased risk of cardiovascular disease (CVD). However, the mechanisms underlying increased CVD risk among people with T2D are only partially understood. We hypothesized that a genetic association study among people with T2D at risk for developing incident cardiovascular complications could provide insights into molecular genetic aspects underlying CVD. METHODS: From 16 studies of the Cohorts for Heart & Aging Research in Genomic Epidemiology (CHARGE) Consortium, we conducted a multi-ancestry time-to-event genome-wide association study (GWAS) for incident CVD among people with T2D using Cox proportional hazards models. Incident CVD was defined based on a composite of coronary artery disease (CAD), stroke, and cardiovascular death that occurred at least one year after the diagnosis of T2D. Cohort-level estimated effect sizes were combined using inverse variance weighted fixed effects meta-analysis. We also tested 204 known CAD variants for association with incident CVD among patients with T2D. RESULTS: A total of 49,230 participants with T2D were included in the analyses (31,118 European ancestries and 18,112 non-European ancestries) which consisted of 8,956 incident CVD cases over a range of mean follow-up duration between 3.2 and 33.7 years (event rate 18.2%). We identified three novel, distinct genetic loci for incident CVD among individuals with T2D that reached the threshold for genome-wide significance (P<5.0×10-8): rs147138607 (intergenic variant between CACNA1E and ZNF648) with a hazard ratio (HR) 1.23, 95% confidence interval (CI) 1.15 - 1.32, P=3.6×10-9, rs11444867 (intergenic variant near HS3ST1) with HR 1.89, 95% CI 1.52 - 2.35, P=9.9×10-9, and rs335407 (intergenic variant between TFB1M and NOX3) HR 1.25, 95% CI 1.16 - 1.35, P=1.5×10-8. Among 204 known CAD loci, 32 were associated with incident CVD in people with T2D with P<0.05, and 5 were significant after Bonferroni correction (P<0.00024, 0.05/204). A polygenic score of these 204 variants was significantly associated with incident CVD with HR 1.14 (95% CI 1.12 - 1.16) per 1 standard deviation increase (P=1.0×10-16). CONCLUSIONS: The data point to novel and known genomic regions associated with incident CVD among individuals with T2D.

Establishing analytical validity of BeadChip array genotype data by comparison to whole-genome sequence and standard benchmark datasets.

BACKGROUND: Clinical use of genotype data requires high positive predictive value (PPV) and thorough understanding of the genotyping platform characteristics. BeadChip arrays, such as the Global Screening Array (GSA), potentially offer a high-throughput, low-cost clinical screen for known variants. We hypothesize that quality assessment and comparison to whole-genome sequence and benchmark data establish the analytical validity of GSA genotyping. METHODS: To test this hypothesis, we selected 263 samples from Coriell, generated GSA genotypes in triplicate, generated whole genome sequence (rWGS) genotypes, assessed the quality of each set of genotypes, and compared each set of genotypes to each other and to the 1000 Genomes Phase 3 (1KG) genotypes, a performance benchmark. For 59 genes (MAP59), we also performed theoretical and empirical evaluation of variants deemed medically actionable predispositions. RESULTS: Quality analyses detected sample contamination and increased assay failure along the chip margins. Comparison to benchmark data demonstrated that > 82% of the GSA assays had a PPV of 1. GSA assays targeting transitions, genomic regions of high complexity, and common variants performed better than those targeting transversions, regions of low complexity, and rare variants. Comparison of GSA data to rWGS and 1KG data showed > 99% performance across all measured parameters. Consistent with predictions from prior studies, the GSA detection of variation within the MAP59 genes was 3/261. CONCLUSION: We establish the analytical validity of GSA assays using quality analytics and comparison to benchmark and rWGS data. GSA assays meet the standards of a clinical screen although assays interrogating rare variants, transversions, and variants within low-complexity regions require careful evaluation.

Exposure to Gestational Diabetes Enriches Immune-Related Pathways in the Transcriptome and Methylome of Human Amniocytes.

CONTEXT: Gestational diabetes (GDM) has profound effects on the intrauterine metabolic milieu and is linked to obesity and diabetes in offspring, but the mechanisms driving these effects remain largely unknown. Alterations in DNA methylation and gene expression in amniocytes exposed to GDM in utero represent a potential mechanism leading to metabolic dysfunction later in life. OBJECTIVE: To profile changes in genome-wide DNA methylation and expression in human amniocytes exposed to GDM. DESIGN: A nested case-control study (n = 14 pairs) was performed in amniocytes matched for offspring sex, maternal race/ethnicity, maternal age, gestational age at amniocentesis, and gestational age at birth. Sex-specific genome-wide DNA methylation analysis and RNA-sequencing were completed and differentially methylated regions (DMRs) and gene expression changes were identified. Ingenuity pathway analysis identified biologically relevant pathways enriched after GDM exposure. In silico high-throughput chromosome conformation capture (Hi-C) analysis identified potential chromatin interactions with DMRs. RESULTS: Expression of interferon-stimulated genes was increased in GDM amniocytes, accounting for 6 of the top 10 altered genes (q < 0.05). Enriched biological pathways in GDM amniocytes included pathways involving inflammation, the interferon response, fatty liver disease, monogenic diabetes, and atherosclerosis. Forty-two DMRs were identified in male GDM-exposed amniocytes and 20 in female amniocyte analysis (q < 0.05). Hi-C analysis identified interactions between DMRs and 11 genes with significant expression changes in male amniocytes and 9 in female amniocytes (P < .05). CONCLUSION: In a unique repository of human amniocytes exposed to GDM in utero, transcriptome analysis identified enrichment of inflammation and interferon-related pathways and novel DMRs with potential distal regulatory functions.

In utero Bisphenol A Exposure Is Linked with Sex Specific Changes in the Transcriptome and Methylome of Human Amniocytes.

CONTEXT: Prenatal exposure to bisphenol A (BPA) is linked to obesity and diabetes but the molecular mechanisms driving these phenomena are not known. Alterations in deoxyribonucleic acid (DNA) methylation in amniocytes exposed to BPA in utero represent a potential mechanism leading to metabolic dysfunction later in life. OBJECTIVE: To profile changes in genome-wide DNA methylation and expression in second trimester human amniocytes exposed to BPA in utero. DESIGN: A nested case-control study was performed in amniocytes matched for offspring sex, maternal race/ethnicity, maternal age, gestational age at amniocentesis, and gestational age at birth. Cases had amniotic fluid BPA measuring 0.251 to 23.74 ng/mL. Sex-specific genome-wide DNA methylation analysis and RNA-sequencing (RNA-seq) were performed to determine differentially methylated regions (DMRs) and gene expression changes associated with BPA exposure. Ingenuity pathway analysis was performed to identify biologically relevant pathways enriched after BPA exposure. In silico Hi-C analysis identified potential chromatin interactions with DMRs. RESULTS: There were 101 genes with altered expression in male amniocytes exposed to BPA (q < 0.05) in utero, with enrichment of pathways critical to hepatic dysfunction, collagen signaling and adipogenesis. Thirty-six DMRs were identified in male BPA-exposed amniocytes and 14 in female amniocyte analysis (q < 0.05). Hi-C analysis identified interactions between DMRs and 24 genes with expression changes in male amniocytes and 12 in female amniocytes (P < 0.05). CONCLUSION: In a unique repository of human amniocytes exposed to BPA in utero, sex-specific analyses identified gene expression changes in pathways associated with metabolic disease and novel DMRs with potential distal regulatory functions.

Dysregulation of Neuronal Genes by Fetal-Neonatal Iron Deficiency Anemia Is Associated with Altered DNA Methylation in the Rat Hippocampus.

Early-life iron deficiency results in long-term abnormalities in cognitive function and affective behavior in adulthood. In preclinical models, these effects have been associated with long-term dysregulation of key neuronal genes. While limited evidence suggests histone methylation as an epigenetic mechanism underlying gene dysregulation, the role of DNA methylation remains unknown. To determine whether DNA methylation is a potential mechanism by which early-life iron deficiency induces gene dysregulation, we performed whole genome bisulfite sequencing to identify loci with altered DNA methylation in the postnatal day (P) 15 iron-deficient (ID) rat hippocampus, a time point at which the highest level of hippocampal iron deficiency is concurrent with peak iron demand for axonal and dendritic growth. We identified 229 differentially methylated loci and they were mapped within 108 genes. Among them, 63 and 45 genes showed significantly increased and decreased DNA methylation in the P15 ID hippocampus, respectively. To establish a correlation between differentially methylated loci and gene dysregulation, the methylome data were compared to our published P15 hippocampal transcriptome. Both datasets showed alteration of similar functional networks regulating nervous system development and cell-to-cell signaling that are critical for learning and behavior. Collectively, the present findings support a role for DNA methylation in neural gene dysregulation following early-life iron deficiency.

Predicting the Kinetics of RNA Oligonucleotides Using Markov State Models.

Nowadays different experimental techniques, such as single molecule or relaxation experiments, can provide dynamic properties of biomolecular systems, but the amount of detail obtainable with these methods is often limited in terms of time or spatial resolution. Here we use state-of-the-art computational techniques, namely, atomistic molecular dynamics and Markov state models, to provide insight into the rapid dynamics of short RNA oligonucleotides, to elucidate the kinetics of stacking interactions. Analysis of multiple microsecond-long simulations indicates that the main relaxation modes of such molecules can consist of transitions between alternative folded states, rather than between random coils and native structures. After properly removing structures that are artificially stabilized by known inaccuracies of the current RNA AMBER force field, the kinetic properties predicted are consistent with the time scales of previously reported relaxation experiments.

Stacking in RNA: NMR of Four Tetramers Benchmark Molecular Dynamics.

Molecular dynamics (MD) simulations for RNA tetramers r(AAAA), r(CAAU), r(GACC), and r(UUUU) are benchmarked against 1H-1H NOESY distances and 3J scalar couplings to test effects of RNA torsion parametrizations. Four different starting structures were used for r(AAAA), r(CAAU), and r(GACC), while five starting structures were used for r(UUUU). On the basis of X-ray structures, criteria are reported for quantifying stacking. The force fields, AMBER ff99, parmbsc0, parm99χ_Yil, ff10, and parmTor, all predict experimentally unobserved stacks and intercalations, e.g., base 1 stacked between bases 3 and 4, and incorrect χ, ϵ, and sugar pucker populations. The intercalated structures are particularly stable, often lasting several microseconds. Parmbsc0, parm99χ_Yil, and ff10 give similar agreement with NMR, but the best agreement is only 46%. Experimentally unobserved intercalations typically are associated with reduced solvent accessible surface area along with amino and hydroxyl hydrogen bonds to phosphate nonbridging oxygens. Results from an extensive set of MD simulations suggest that recent force field parametrizations improve predictions, but further improvements are necessary to provide reasonable agreement with NMR. In particular, intramolecular stacking and hydrogen bonding interactions may not be well balanced with the TIP3P water model. NMR data and the scoring method presented here provide rigorous benchmarks for future changes in force fields and MD methods.

Optimization of an AMBER force field for the artificial nucleic acid, LNA, and benchmarking with NMR of L(CAAU).

Locked Nucleic Acids (LNAs) are RNA analogues with an O2'-C4' methylene bridge which locks the sugar into a C3'-endo conformation. This enhances hybridization to DNA and RNA, making LNAs useful in microarrays and potential therapeutics. Here, the LNA, L(CAAU), provides a simplified benchmark for testing the ability of molecular dynamics (MD) to approximate nucleic acid properties. LNA χ torsions and partial charges were parametrized to create AMBER parm99_LNA. The revisions were tested by comparing MD predictions with AMBER parm99 and parm99_LNA against a 200 ms NOESY NMR spectrum of L(CAAU). NMR indicates an A-Form equilibrium ensemble. In 3000 ns simulations starting with an A-form structure, parm99_LNA and parm99 provide 66% and 35% agreement, respectively, with NMR NOE volumes and (3)J-couplings. In simulations of L(CAAU) starting with all χ torsions in a syn conformation, only parm99_LNA is able to repair the structure. This implies methods for parametrizing force fields for nucleic acid mimics can reasonably approximate key interactions and that parm99_LNA will improve reliability of MD studies for systems with LNA. A method for approximating χ population distribution on the basis of base to sugar NOEs is also introduced.