Generalizing moving averages for tiling arrays using combined p-value statistics

Modulation of neural gene networks by estradiol in old rhesus macaque females

The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent subcutaneous bioidentical E2 chronic treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (p = 1.6 × 10-51) and upregulation (p = 3.8 × 10-3) of UBE2M across both brain regions provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (p = 1.9 × 10-4; interaction p = 3.5 × 10-2) of LTBR in the PFC provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2's potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step toward understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.

Epigenetic footprints: Investigating placental DNA methylation in the context of prenatal exposure to phenols and phthalates

BACKGROUND

Endocrine disrupting compounds (EDCs) such as phthalates and phenols can affect placental functioning and fetal health, potentially via epigenetic modifications. We investigated the associations between pregnancy exposure to synthetic phenols and phthalates estimated from repeated urine sampling and genome wide placental DNA methylation.

METHODS

The study is based on 387 women with placental DNA methylation assessed with Infinium MethylationEPIC arrays and with 7 phenols, 13 phthalates, and two non-phthalate plasticizer metabolites measured in pools of urine samples collected twice during pregnancy. We conducted an exploratory analysis on individual CpGs (EWAS) and differentially methylated regions (DMRs) as well as a candidate analysis focusing on 20 previously identified CpGs. Sex-stratified analyses were also performed.

RESULTS

In the exploratory analysis, when both sexes were studied together no association was observed in the EWAS. In the sex-stratified analysis, 114 individual CpGs (68 in males, 46 in females) were differentially methylated, encompassing 74 genes (36 for males and 38 for females). We additionally identified 28 DMRs in the entire cohort, 40 for females and 42 for males. Associations were mostly positive (for DMRs: 93% positive associations in the entire cohort, 60% in the sex-stratified analysis), with the exception of several associations for bisphenols and DINCH metabolites that were negative. Biomarkers associated with most DMRs were parabens, DEHP, and DiNP metabolite concentrations. Some DMRs encompassed imprinted genes including APC (associated with parabens and DiNP metabolites), GNAS (bisphenols), ZIM2;PEG3;MIMT1 (parabens, monoethyl phthalate), and SGCE;PEG10 (parabens, DINCH metabolites). Terms related to adiposity, lipid and glucose metabolism, and cardiovascular function were among the enriched phenotypes associated with differentially methylated CpGs. The candidate analysis identified one CpG mapping to imprinted LGALS8 gene, negatively associated with ethylparaben.

CONCLUSIONS

By combining improved exposure assessment and extensive placental epigenome coverage, we identified several novel genes associated with the exposure, possibly in a sex-specific manner.

Methylation analysis by targeted bisulfite sequencing in large for gestational age (LGA) newborns: the LARGAN cohort

BACKGROUND

In 1990, David Barker proposed that prenatal nutrition is directly linked to adult cardiovascular disease. Since then, the relationship between adult cardiovascular risk, metabolic syndrome and birth weight has been widely documented. Here, we used the TruSeq Methyl Capture EPIC platform to compare the methylation patterns in cord blood from large for gestational age (LGA) vs adequate for gestational age (AGA) newborns from the LARGAN cohort.

RESULTS

We found 1672 differentially methylated CpGs (DMCs) with a nominal p < 0.05 and 48 differentially methylated regions (DMRs) with a corrected p < 0.05 between the LGA and AGA groups. A systems biology approach identified several biological processes significantly enriched with genes in association with DMCs with FDR < 0.05, including regulation of transcription, regulation of epinephrine secretion, norepinephrine biosynthesis, receptor transactivation, forebrain regionalization and several terms related to kidney and cardiovascular development. Gene ontology analysis of the genes in association with the 48 DMRs identified several significantly enriched biological processes related to kidney development, including mesonephric duct development and nephron tubule development. Furthermore, our dataset identified several DNA methylation markers enriched in gene networks involved in biological pathways and rare diseases of the cardiovascular system, kidneys, and metabolism.

CONCLUSIONS

Our study identified several DMCs/DMRs in association with fetal overgrowth. The use of cord blood as a material for the identification of DNA methylation biomarkers gives us the possibility to perform follow-up studies on the same patients as they grow. These studies will not only help us understand how the methylome responds to continuum postnatal growth but also link early alterations of the DNA methylome with later clinical markers of growth and metabolic fitness.

Prenatal exposure to triclosan assessed in multiple urine samples and placental DNA methylation

A previous study reported positive associations of maternal urinary concentrations of triclosan, a synthetic phenol with widespread exposure in the general population, with placental DNA methylation of male fetuses. Given the high number of comparisons performed in -omic research, further studies were needed to validate and extend on these findings. Using a cohort of male and female fetuses with repeated maternal urine samples to assess exposure, we studied the associations between triclosan and placental DNA methylation. We assessed triclosan concentrations in two pools of 21 urine samples collected among 395 women from the SEPAGES cohort. We used Infinium Methylation EPIC arrays to measure DNA methylation in placental biopsies collected at delivery. We performed a candidate study restricted to a set of candidate CpGs (n = 500) identified in a previous work as well as an exploratory epigenome-wide association study to investigate the associations between triclosan and differentially methylated probes and regions. Analyses were conducted on the whole population and stratified by child's sex. Mediation analysis was performed to test whether heterogeneity of placental tissue may mediate the observed associations. In the candidate approach, we confirmed 18 triclosan-associated genes when both sexes were considered. After stratification for child's sex, triclosan was associated with 72 genes in females and three in males. Most of the associations were positive and several CpGs mapped to imprinted genes: FBRSL1, KCNQ1, RHOBTB3, and SMOC1. A mediation effect by placental tissue heterogeneity was identified for most of the observed associations. In the exploratory analysis, we identified a few isolated associations in the sex-stratified analysis. In line with a previous study on male placentas, our approach revealed several positive associations between triclosan exposure and placental DNA methylation. Several identified loci mapped to imprinted genes.

Epigenome-Wide Associations of Placental DNA Methylation and Behavioral and Emotional Difficulties in Children at 3 Years of Age

The placenta is a key organ for fetal and brain development. Its epigenome can be regarded as a biochemical record of the prenatal environment and a potential mechanism of its association with the future health of the fetus. We investigated associations between placental DNA methylation levels and child behavioral and emotional difficulties, assessed at 3 years of age using the Strengths and Difficulties Questionnaire (SDQ) in 441 mother-child dyads from the EDEN cohort. Hypothesis-driven and exploratory analyses (on differentially methylated probes (EWAS) and regions (DMR)) were adjusted for confounders, technical factors, and cell composition estimates, corrected for multiple comparisons, and stratified by child sex. Hypothesis-driven analyses showed an association of cg26703534 (AHRR) with emotional symptoms, and exploratory analyses identified two probes, cg09126090 (intergenic region) and cg10305789 (PPP1R16B), as negatively associated with peer relationship problems, as well as 33 DMRs, mostly positively associated with at least one of the SDQ subscales. Among girls, most associations were seen with emotional difficulties, whereas in boys, DMRs were as much associated with emotional than behavioral difficulties. This study provides the first evidence of associations between placental DNA methylation and child behavioral and emotional difficulties. Our results suggest sex-specific associations and might provide new insights into the mechanisms of neurodevelopment.

An evaluation of the genome-wide false positive rates of common methods for identifying differentially methylated regions using illumina methylation arrays

Differentially methylated regions (DMRs) are genomic regions with specific methylation patterns across multiple loci that are associated with a phenotype. We examined the genome-wide false positive (GFP) rates of five widely used DMR methods: comb-p, Bumphunter, DMRcate, mCSEA and coMethDMR using both Illumina HumanMethylation450 (450 K) and MethylationEPIC (EPIC) data and simulated continuous and dichotomous null phenotypes (i.e., generated independently of methylation data). coMethDMR provided well-controlled GFP rates (~5%) except when analysing skewed continuous phenotypes. DMRcate generally had well-controlled GFP rates when applied to 450 K data except for the skewed continuous phenotype and EPIC data only for the normally distributed continuous phenotype. GFP rates for mCSEA were at least 0.096 and comb-p yielded GFP rates above 0.34. Bumphunter had high GFP rates of at least 0.35 across conditions, reaching as high as 0.95. Analysis of the performance of these methods in specific regions of the genome found that regions with higher correlation across loci had higher regional false positive rates on average across methods. Based on the false positive rates, coMethDMR is the most recommended analysis method, and DMRcate had acceptable performance when analysing 450 K data. However, as both could display higher levels of FPs for skewed continuous distributions, a normalizing transformation of skewed continuous phenotypes is suggested. This study highlights the importance of genome-wide simulations when evaluating the performance of DMR-analysis methods.

Permutation-based significance analysis reduces the type 1 error rate in bisulfite sequencing data analysis of human umbilical cord blood samples

DNA methylation patterns are largely established in-utero and might mediate the impacts of in-utero conditions on later health outcomes. Associations between perinatal DNA methylation marks and pregnancy-related variables, such as maternal age and gestational weight gain, have been earlier studied with methylation microarrays, which typically cover less than 2% of human CpG sites. To detect such associations outside these regions, we chose the bisulphite sequencing approach. We collected and curated clinical data on 200 newborn infants; whose umbilical cord blood samples were analysed with the reduced representation bisulphite sequencing (RRBS) method. A generalized linear mixed-effects model was fit for each high coverage CpG site, followed by spatial and multiple testing adjustment of P values to identify differentially methylated cytosines (DMCs) and regions (DMRs) associated with clinical variables, such as maternal age, mode of delivery, and birth weight. Type 1 error rate was then evaluated with a permutation analysis. We discovered a strong inflation of spatially adjusted P values through the permutation analysis, which we then applied for empirical type 1 error control. The inflation of P values was caused by a common method for spatial adjustment and DMR detection, implemented in tools comb-p and RADMeth. Based on empirically estimated significance thresholds, very little differential methylation was associated with any of the studied clinical variables, other than sex. With this analysis workflow, the sex-associated differentially methylated regions were highly reproducible across studies, technologies, and statistical models.

Pregnancy exposure to phthalates and DNA methylation in male placenta - An epigenome-wide association study

BACKGROUND

Exposure to phthalates during pregnancy may alter DNA methylation in the placenta, a crucial organ for the growth and development of the fetus.

OBJECTIVES

We studied associations between urinary concentrations of phthalate biomarkers during pregnancy and placental DNA methylation.

METHODS

We measured concentrations of 11 phthalate metabolites in maternal spot urine samples collected between 22 and 29 gestational weeks in 202 pregnant women. We analyzed DNA methylation levels in placental tissue (fetal side) collected at delivery. We first investigated changes in global DNA methylation of repetitive elements Alu and LINE-1. We then performed an adjusted epigenome-wide association study using IlluminaHM450 BeadChips and identified differentially methylated regions (DMRs) associated with phthalate exposure.

RESULTS

Monobenzyl phthalate concentration was inversely associated with placental methylation of Alu repeats. Moreover, all phthalate biomarkers except for monocarboxy-iso-octyl phthalate and mono(2-ethyl-5-hydroxyhexyl) phthalate were associated with at least one DMR. All but three DMRs showed increased DNA methylation with increased phthalate exposure. The largest identified DMR (22 CpGs) was positively associated with monocarboxy-iso-nonyl phthalate and encompassed heat shock proteins (HSPA1A, HSPA1L). The remaining DMRs encompassed transcription factors and nucleotide exchange factors, among other genes.

CONCLUSIONS

This is the first description of genome-wide modifications of placental DNA methylation in association with pregnancy exposure to phthalates. Our results suggest epigenetic mechanisms by which exposure to these compounds could affect fetal development. Of interest, four identified DMRs had been previously associated with maternal smoking, which may suggest particular sensitivity of these genomic regions to the effect of environmental contaminants.

Genome-wide methylation and expression analyses reveal the epigenetic landscape of immune-related diseases for tobacco smoking

BACKGROUND

Smoking is a major causal risk factor for lung cancer, chronic obstructive pulmonary disease (COPD), cardiovascular disease (CVD), and is the main preventable cause of deaths in the world. The components of cigarette smoke are involved in immune and inflammatory processes, which may increase the prevalence of cigarette smoke-related diseases. However, the underlying molecular mechanisms linking smoking and diseases have not been well explored. This study was aimed to depict a global map of DNA methylation and gene expression changes induced by tobacco smoking and to explore the molecular mechanisms between smoking and human diseases through whole-genome bisulfite sequencing (WGBS) and RNA-sequencing (RNA-seq).

RESULTS

We performed WGBS on 72 samples (36 smokers and 36 nonsmokers) and RNA-seq on 75 samples (38 smokers and 37 nonsmokers), and cytokine immunoassay on plasma from 22 males (9 smokers and 13 nonsmokers) who were recruited from the city of Jincheng in China. By comparing the data of the two groups, we discovered a genome-wide methylation landscape of differentially methylated regions (DMRs) associated with smoking. Functional enrichment analyses revealed that both smoking-related hyper-DMR genes (DMGs) and hypo-DMGs were related to synapse-related pathways, whereas the hypo-DMGs were specifically related to cancer and addiction. The differentially expressed genes (DEGs) revealed by RNA-seq analysis were significantly enriched in the "immunosuppression" pathway. Correlation analysis of DMRs with their corresponding gene expression showed that genes affected by tobacco smoking were mostly related to immune system diseases. Finally, by comparing cytokine concentrations between smokers and nonsmokers, we found that vascular endothelial growth factor (VEGF) was significantly upregulated in smokers.

CONCLUSIONS

In sum, we found that smoking-induced DMRs have different distribution patterns in hypermethylated and hypomethylated areas between smokers and nonsmokers. We further identified and verified smoking-related DMGs and DEGs through multi-omics integration analysis of DNA methylome and transcriptome data. These findings provide us a comprehensive genomic map of the molecular changes induced by smoking which would enhance our understanding of the harms of smoking and its relationship with diseases.

Pregnancy exposure to synthetic phenols and placental DNA methylation - An epigenome-wide association study in male infants from the EDEN cohort

In utero exposure to environmental chemicals, such as synthetic phenols, may alter DNA methylation in different tissues, including placenta - a critical organ for fetal development. We studied associations between prenatal urinary biomarker concentrations of synthetic phenols and placental DNA methylation. Our study involved 202 mother-son pairs from the French EDEN cohort. Nine phenols were measured in spot urine samples collected between 22 and 29 gestational weeks. We performed DNA methylation analysis of the fetal side of placental tissues using the IlluminaHM450 BeadChips. We evaluated methylation changes of individual CpGs in an adjusted epigenome-wide association study (EWAS) and identified differentially methylated regions (DMRs). We performed mediation analysis to test whether placental tissue heterogeneity mediated the association between urinary phenol concentrations and DNA methylation. We identified 46 significant DMRs (≥5 CpGs) associated with triclosan (37 DMRs), 2,4-dichlorophenol (3), benzophenone-3 (3), methyl- (2) and propylparaben (1). All but 2 DMRs were positively associated with phenol concentrations. Out of the 46 identified DMRs, 7 (6 for triclosan) encompassed imprinted genes (APC, FOXG1, GNAS, GNASAS, MIR886, PEG10, SGCE), which represented a significant enrichment. Other identified DMRs encompassed genes encoding proteins responsible for cell signaling, transmembrane transport, cell adhesion, inflammatory, apoptotic and immunological response, genes encoding transcription factors, histones, tumor suppressors, genes involved in tumorigenesis and several cancer risk biomarkers. Mediation analysis suggested that placental cell heterogeneity may partly explain these associations. This is the first study describing the genome-wide modifications of placental DNA methylation associated with pregnancy exposure to synthetic phenols or their precursors. Our results suggest that cell heterogeneity might mediate the effects of triclosan exposure on placental DNA methylation. Additionally, the enrichment of imprinted genes within the DMRs suggests mechanisms by which certain exposures, mainly to triclosan, could affect fetal development.

Statistical and bioinformatic analysis of hemimethylation patterns in non-small cell lung cancer

Background

DNA methylation is an epigenetic event involving the addition of a methyl-group to a cytosine-guanine base pair (i.e., CpG site). It is associated with different cancers. Our research focuses on studying non-small cell lung cancer hemimethylation, which refers to methylation occurring on only one of the two DNA strands. Many studies often assume that methylation occurs on both DNA strands at a CpG site. However, recent publications show the existence of hemimethylation and its significant impact. Therefore, it is important to identify cancer hemimethylation patterns.

Methods

In this paper, we use the Wilcoxon signed rank test to identify hemimethylated CpG sites based on publicly available non-small cell lung cancer methylation sequencing data. We then identify two types of hemimethylated CpG clusters, regular and polarity clusters, and genes with large numbers of hemimethylated sites. Highly hemimethylated genes are then studied for their biological interactions using available bioinformatics tools.

Results

In this paper, we have conducted the first-ever investigation of hemimethylation in lung cancer. Our results show that hemimethylation does exist in lung cells either as singletons or clusters. Most clusters contain only two or three CpG sites. Polarity clusters are much shorter than regular clusters and appear less frequently. The majority of clusters found in tumor samples have no overlap with clusters found in normal samples, and vice versa. Several genes that are known to be associated with cancer are hemimethylated differently between the cancerous and normal samples. Furthermore, highly hemimethylated genes exhibit many different interactions with other genes that may be associated with cancer. Hemimethylation has diverse patterns and frequencies that are comparable between normal and tumorous cells. Therefore, hemimethylation may be related to both normal and tumor cell development.

Conclusions

Our research has identified CpG clusters and genes that are hemimethylated in normal and lung tumor samples. Due to the potential impact of hemimethylation on gene expression and cell function, these clusters and genes may be important to advance our understanding of the development and progression of non-small cell lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-07990-7.

Association between Breastfeeding and DNA Methylation over the Life Course: Findings from the Avon Longitudinal Study of Parents and Children (ALSPAC)

Background: Breastfeeding is associated with short and long-term health benefits. Long-term effects might be mediated by epigenetic mechanisms, yet the literature on this topic is scarce. We performed the first epigenome-wide association study of infant feeding, comparing breastfed vs non-breastfed children. We measured DNA methylation in children from peripheral blood collected in childhood (age 7 years, N = 640) and adolescence (age 15–17 years, N = 709) within the Accessible Resource for Integrated Epigenomic Studies (ARIES) project, part of the larger Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. Cord blood methylation (N = 702) was used as a negative control for potential pre-natal residual confounding. Results: Two differentially-methylated sites presented directionally-consistent associations with breastfeeding at ages 7 and 15–17 years, but not at birth. Twelve differentially-methylated regions in relation to breastfeeding were identified, and for three of them there was evidence of directional concordance between ages 7 and 15–17 years, but not between birth and age 7 years. Conclusions: Our findings indicate that DNA methylation in childhood and adolescence may be predicted by breastfeeding, but further studies with sufficiently large samples for replication are required to identify robust associations.

Epigenome-wide association study reveals a molecular signature of response to phylloquinone (vitamin K1) supplementation

Evidence suggests there are roles for vitamin K in various chronic disease outcomes, but population-level diet and supplement recommendations are difficult to determine due to high levels of variability in measures of status and response to intake compared to other nutrients. In this preliminary investigation, a blood-based epigenome-wide association study (EWAS) comparing responders and non-responders to phylloquinone (vitamin K1) supplementation (NCT00183001) was undertaken in order to better understand the molecular underpinnings of this observed variability. Responders (n = 24) and non-responders (n = 24) were identified in a prior 3-year phylloquinone supplementation trial based on their changes in plasma phylloquinone concentrations. Differential DNA methylation was identified in multiple regions with previously unknown relationships to phylloquinone absorption and metabolism, such as at the TMEM263 locus. A hypothesis-driven analysis of lipid-related genes highlighted a site in the NPC1L1 gene, supplementing existing evidence for its role in phylloquinone absorption. Furthermore, an EWAS for baseline plasma phylloquinone concentrations revealed a strong correlation between the epigenomic signatures of phylloquinone baseline status and response to supplementation. This work can guide future epigenomic research on vitamin K and contributes to the development of more personalized dietary recommendations for vitamin K.

An evaluation of supervised methods for identifying differentially methylated regions in Illumina methylation arrays

Epigenome-wide association studies (EWASs) have become increasingly popular for studying DNA methylation (DNAm) variations in complex diseases. The Illumina methylation arrays provide an economical, high-throughput and comprehensive platform for measuring methylation status in EWASs. A number of software tools have been developed for identifying disease-associated differentially methylated regions (DMRs) in the epigenome. However, in practice, we found these tools typically had multiple parameter settings that needed to be specified and the performance of the software tools under different parameters was often unclear. To help users better understand and choose optimal parameter settings when using DNAm analysis tools, we conducted a comprehensive evaluation of 4 popular DMR analysis tools under 60 different parameter settings. In addition to evaluating power, precision, area under precision-recall curve, Matthews correlation coefficient, F1 score and type I error rate, we also compared several additional characteristics of the analysis results, including the size of the DMRs, overlap between the methods and execution time. The results showed that none of the software tools performed best under their default parameter settings, and power varied widely when parameters were changed. Overall, the precision of these software tools were good. In contrast, all methods lacked power when effect size was consistent but small. Across all simulation scenarios, comb-p consistently had the best sensitivity as well as good control of false-positive rate.

DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure

BACKGROUND

Epigenome-wide association studies using DNA methylation have the potential to uncover novel biomarkers and mechanisms of cardiovascular disease (CVD) risk. However, the direction of causation for these associations is not always clear, and investigations to-date have often failed to replicate at the level of individual loci.

METHODS

Here, we undertook module- and region-based DNA methylation analyses of incident CVD in the Women's Health Initiative (WHI) and Framingham Heart Study Offspring Cohort (FHS) in order to find more robust epigenetic biomarkers for cardiovascular risk. We applied weighted gene correlation network analysis (WGCNA) and the Comb-p algorithm to find methylation modules and regions associated with incident CVD in the WHI dataset.

RESULTS

We discovered two modules whose activation correlated with CVD risk and replicated across cohorts. One of these modules was enriched for development-related processes and overlaps strongly with epigenetic aging sites. For the other, we showed preliminary evidence for monocyte-specific effects and statistical links to cumulative exposure to traditional cardiovascular risk factors. Additionally, we found three regions (associated with the genes SLC9A1, SLC1A5, and TNRC6C) whose methylation associates with CVD risk.

CONCLUSIONS

In sum, we present several epigenetic associations with incident CVD which reveal disease mechanisms related to development and monocyte biology. Furthermore, we show that epigenetic modules may act as a molecular readout of cumulative cardiovascular risk factor exposure, with implications for the improvement of clinical risk prediction.

Transcriptional profiling analysis predicts potential biomarkers for glaucoma: HGF, AKR1B10 and AKR1C3

Glaucoma results in damage to the optic nerve and vision loss. The aim of this study was to screen more accurate biomarkers and targets for glaucoma. The datasets E-GEOD-7144 and E-MEXP-3427 were screened for differently expressed genes (DEGs) by significance analysis of microarrays. Functional and pathway enrichment analysis were processed. Pathway relationship networks and gene co-expression networks were constructed. DEGs of disease and treatment with the same symbols were of interest. RT-qPCR was processed to verify the expression of key DEGs. A total of 1,019 DEGs of glaucoma were identified and 93 DEGs in transforming growth factor-β1 (TGF-β1) and TGF-β1-2 treatment cases compared with the normal control group. Pathway relationship network of glaucoma was constructed with 25 nodes. The pathway relationship network of TGF-β1 and -2 treatment groups was constructed with 11 nodes. Glaucoma-related DEGs in GO terms and pathways were inserted and 180 common DEGs were obtained. Then, gene co-expression network of glaucoma-related DEGs was constructed with 91 nodes. Furthermore, DEGs of TGF-β1 and -2 treated glaucoma in GO terms and pathways were inserted, and 29 common DEGs were identified. Based on these DEGs, gene co-expression network was constructed with 12 nodes and 16 edges. Finally, a total of 6 important DEGs of disease and treatment were inserted and obtained. They were HGF, AKR1B10, AKR1C3, PPAP2B, INHBA and BCAT1. The expression of HGF, AKR1B10 and AKR1C3 was decreased in glaucoma samples and treatment samples. In conclusion, HGF, AKR1B10 and AKR1C3 may be key genes for glaucoma diagnosis and treatment.

Genome-wide analysis of the nucleus accumbens identifies DNA methylation signals differentiating low/binge from heavy alcohol drinking

Alcohol-use disorders encompass a range of drinking levels and behaviors, including low, binge, and heavy drinking. In this regard, investigating the neural state of individuals who chronically self-administer lower doses of alcohol may provide insight into mechanisms that prevent the escalation of alcohol use. DNA methylation is one of the epigenetic mechanisms that stabilizes adaptations in gene expression and has been associated with alcohol use. Thus, we investigated DNA methylation, gene expression, and the predicted neural effects in the nucleus accumbens core (NAcc) of male rhesus macaques categorized as "low" or "binge" drinkers, compared to "alcohol-naïve" and "heavy" drinkers based on drinking patterns during a 12-month alcohol self-administration protocol. Using genome-wide CpG-rich region enrichment and bisulfite sequencing, the methylation levels of 2.6 million CpGs were compared between alcohol-naïve (AN), low/binge (L/BD), and heavy/very heavy (H/VHD) drinking subjects (n = 24). Through regional clustering analysis, we identified nine significant differential methylation regions (DMRs) that specifically distinguished ANs and L/BDs, and then compared those DMRs among H/VHDs. The DMRs mapped to genes encoding ion channels, receptors, cell adhesion molecules, and cAMP, NF-κβ and Wnt signaling pathway proteins. Two of the DMRs, linked to PDE10A and PKD2L2, were also differentially methylated in H/VHDs, suggesting an alcohol-dose independent effect. However, two other DMRs, linked to the CCBE1 and FZD5 genes, had L/BD methylation levels that significantly differed from both ANs and H/VHDs. The remaining five DMRs also differentiated L/BDs and ANs. However, H/VHDs methylation levels were not distinguishable from either of the two groups. Functional validation of two DMRs, linked to FZD5 and PDE10A, support their role in regulating gene expression and exon usage, respectively. In summary, the findings demonstrate that L/BD is associated with unique DNA methylation signatures in the primate NAcc, and that the methylation signatures identify synaptic genes that may play a role in preventing the escalation of alcohol use.

Alcohol-dose-dependent DNA methylation and expression in the nucleus accumbens identifies coordinated regulation of synaptic genes

Alterations in DNA methylation have been associated with alcohol exposure and proposed to contribute to continued alcohol use; however, the molecular mechanisms involved remain obscure. We investigated the escalating effects of alcohol use on DNA methylation, gene expression and predicted neural effects in the nucleus accumbens of rhesus macaques that self-administered 4% alcohol for over 12 months. Using an exploratory approach to identify CpG-rich regions, followed by bisulfite sequencing, the methylation levels of 2.7 million CpGs were compared between seven low-binge drinkers and nine heavy-very heavy drinking subjects. We identified 17 significant differential methylation regions (DMRs), including 14 with methylation levels that were correlated with average daily alcohol consumption. The size of the DMRs ranged from 29 to 158 bp (mean=63.7), included 4-19 CpGs per DMR (mean=8.06) and spanned a range of average methylation values from 5 to 34%. Eight of the DMRs mapped to genes implicated in modulating synaptic plasticity. Six of the synaptic genes have not previously been linked to alcohol use. Validation studies of these eight DMRs using bisulfite amplicon sequencing and an expanded set of 30 subjects confirmed the significant alcohol-dose-associated methylation of the DMRs. Expression analysis of three of the DMR-associated genes, LRP5, GPR39 and JAKMIP1, revealed significant correlations between DMR methylation and whole-gene or alternative transcript expression, supporting a functional role in regulating gene expression. Together, these studies suggest that alcohol-associated synaptic remodeling may be regulated and coordinated at the level of DNA methylation.

seqlm: an MDL based method for identifying differentially methylated regions in high density methylation array data

MOTIVATION

One of the main goals of large scale methylation studies is to detect differentially methylated loci. One way is to approach this problem sitewise, i.e. to find differentially methylated positions (DMPs). However, it has been shown that methylation is regulated in longer genomic regions. So it is more desirable to identify differentially methylated regions (DMRs) instead of DMPs. The new high coverage arrays, like Illuminas 450k platform, make it possible at a reasonable cost. Few tools exist for DMR identification from this type of data, but there is no standard approach.

RESULTS

We propose a novel method for DMR identification that detects the region boundaries according to the minimum description length (MDL) principle, essentially solving the problem of model selection. The significance of the regions is established using linear mixed models. Using both simulated and large publicly available methylation datasets, we compare seqlm performance to alternative approaches. We demonstrate that it is both more sensitive and specific than competing methods. This is achieved with minimal parameter tuning and, surprisingly, quickest running time of all the tried methods. Finally, we show that the regional differential methylation patterns identified on sparse array data are confirmed by higher resolution sequencing approaches.

AVAILABILITY AND IMPLEMENTATION

The methods have been implemented in R package seqlm that is available through Github: https://github.com/raivokolde/seqlm

CONTACT

rkolde@gmail.com

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

DNA methylation and childhood asthma in the inner city

BACKGROUND

Epigenetic marks are heritable, influenced by the environment, direct the maturation of T lymphocytes, and in mice enhance the development of allergic airway disease. Thus it is important to define epigenetic alterations in asthmatic populations.

OBJECTIVE

We hypothesize that epigenetic alterations in circulating PBMCs are associated with allergic asthma.

METHODS

We compared DNA methylation patterns and gene expression in inner-city children with persistent atopic asthma versus healthy control subjects by using DNA and RNA from PBMCs. Results were validated in an independent population of asthmatic patients.

RESULTS

Comparing asthmatic patients (n = 97) with control subjects (n = 97), we identified 81 regions that were differentially methylated. Several immune genes were hypomethylated in asthma, including IL13, RUNX3, and specific genes relevant to T lymphocytes (TIGIT). Among asthmatic patients, 11 differentially methylated regions were associated with higher serum IgE concentrations, and 16 were associated with percent predicted FEV1. Hypomethylated and hypermethylated regions were associated with increased and decreased gene expression, respectively (P < 6 × 10(-12) for asthma and P < .01 for IgE). We further explored the relationship between DNA methylation and gene expression using an integrative analysis and identified additional candidates relevant to asthma (IL4 and ST2). Methylation marks involved in T-cell maturation (RUNX3), TH2 immunity (IL4), and oxidative stress (catalase) were validated in an independent asthmatic cohort of children living in the inner city.

CONCLUSIONS

Our results demonstrate that DNA methylation marks in specific gene loci are associated with asthma and suggest that epigenetic changes might play a role in establishing the immune phenotype associated with asthma.

Closed Testing in Pharmaceutical Research: Historical and Recent Developments

In pharmaceutical research, making multiple statistical inferences is standard practice. Unless adjustments are made for multiple testing, the probability of making erroneous determinations of significance increases with the number of inferences. Closed testing is a flexible and easily explained approach to controlling the overall error rate that has seen wide use in pharmaceutical research, particularly in clinical trials settings. In this article, we first give a general review of the uses of multiple testing in pharmaceutical research, with particular emphasis on the benefits and pitfalls of closed testing procedures. We then provide a more technical examination of a class of closed tests that use additive-combination-based and minimum-based p-value statistics, both of which are commonly used in pharmaceutical research. We show that, while the additive combination tests are generally far superior to minimum p-value tests for composite hypotheses, the reverse is true for multiple comparisons using closure-based testing. The loss of power of additive combination tests is explained in terms worst-case "hurdles" that must be cleared before significance can be determined via closed testing. We prove mathematically that this problem can result in the power of a closure-based minimum p-value test approaching 1, while the power of an closure-based additive combination test approaches 0. Finally, implications of these results to pharmaceutical researchers are given.

Genetics of gene expression characterizes response to selective breeding for alcohol preference

Numerous selective breeding experiments have been performed with rodents, in an attempt to understand the genetic basis for innate differences in preference for alcohol consumption. Quantitative trait locus (QTL) analysis has been used to determine regions of the genome that are associated with the behavioral difference in alcohol preference/consumption. Recent work suggests that differences in gene expression represent a major genetic basis for complex traits. Therefore, the QTLs are likely to harbor regulatory regions (eQTLs) for the differentially expressed genes that are associated with the trait. In this study, we examined brain gene expression differences over generations of selection of the third replicate lines of high and low alcohol-preferring (HAP3 and LAP3) mice, and determined regions of the genome that control the expression of these differentially expressed genes (de eQTLs). We also determined eQTL regions (rv eQTLs) for genes that showed a decrease in variance of expression levels over the course of selection. We postulated that de eQTLs that overlap with rv eQTLs, and also with phenotypic QTLs, represent genomic regions that are affected by the process of selection. These overlapping regions controlled the expression of candidate genes (that displayed differential expression and reduced variance of expression) for the predisposition to differences in alcohol consumption by the HAP3/LAP3 mice.

Statistical methods for detecting differentially methylated loci and regions

DNA methylation, the reversible addition of methyl groups at CpG dinucleotides, represents an important regulatory layer associated with gene expression. Changed methylation status has been noted across diverse pathological states, including cancer. The rapid development and uptake of microarrays and large scale DNA sequencing has prompted an explosion of data analytic methods for processing and discovering changes in DNA methylation across varied data types. In this mini-review, we present a compact and accessible discussion of many of the salient challenges, such as experimental design, statistical methods for differential methylation detection, critical considerations such as cell type composition and the potential confounding that can arise from batch effects. From a statistical perspective, our main interests include the use of empirical Bayes or hierarchical models, which have proved immensely powerful in genomics, and the procedures by which false discovery control is achieved.

Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments

Background

Whole-genome bisulfite sequencing currently provides the highest-precision view of the epigenome, with quantitative information about populations of cells down to single nucleotide resolution. Several studies have demonstrated the value of this precision: meaningful features that correlate strongly with biological functions can be found associated with only a few CpG sites. Understanding the role of DNA methylation, and more broadly the role of DNA accessibility, requires that methylation differences between populations of cells are identified with extreme precision and in complex experimental designs.

Results

In this work we investigated the use of beta-binomial regression as a general approach for modeling whole-genome bisulfite data to identify differentially methylated sites and genomic intervals.

Conclusions

The regression-based analysis can handle medium- and large-scale experiments where it becomes critical to accurately model variation in methylation levels between replicates and account for influence of various experimental factors like cell types or batch effects.

A novel method for identification and quantification of consistently differentially methylated regions

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

Hands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformatics

Genomics and proteomics have emerged as key technologies in biomedical research, resulting in a surge of interest in training by investigators keen to incorporate these technologies into their research. At least two types of training can be envisioned in order to produce meaningful results, quality publications and successful grant applications: (1) immediate short-term training workshops and (2) long-term graduate education or visiting scientist programs. We aimed to fill the former need by providing a comprehensive hands-on training course in genomics, proteomics and informatics in a coherent, experimentally-based framework. This was accomplished through a National Heart, Lung, and Blood Institute (NHLBI)-sponsored 10-day Genomics and Proteomics Hands-on Workshop held at National Jewish Health (NJH) and the University of Colorado School of Medicine (UCD). The course content included comprehensive lectures and laboratories in mass spectrometry and genomics technologies, extensive hands-on experience with instrumentation and software, video demonstrations, optional workshops, online sessions, invited keynote speakers, and local and national guest faculty. Here we describe the detailed curriculum and present the results of short- and long-term evaluations from course attendees. Our educational program consistently received positive reviews from participants and had a substantial impact on grant writing and review, manuscript submissions and publications.

Histone deacetylase 6-mediated selective autophagy regulates COPD-associated cilia dysfunction

Chronic obstructive pulmonary disease (COPD) involves aberrant airway inflammatory responses to cigarette smoke (CS) that are associated with epithelial cell dysfunction, cilia shortening, and mucociliary clearance disruption. Exposure to CS reduced cilia length and induced autophagy in vivo and in differentiated mouse tracheal epithelial cells (MTECs). Autophagy-impaired (Becn1+/- or Map1lc3B-/-) mice and MTECs resisted CS-induced cilia shortening. Furthermore, CS increased the autophagic turnover of ciliary proteins, indicating that autophagy may regulate cilia homeostasis. We identified cytosolic deacetylase HDAC6 as a critical regulator of autophagy-mediated cilia shortening during CS exposure. Mice bearing an X chromosome deletion of Hdac6 (Hdac6-/Y) and MTECs from these mice had reduced autophagy and were protected from CS-induced cilia shortening. Autophagy-impaired Becn1-/-, Map1lc3B-/-, and Hdac6-/Y mice or mice injected with an HDAC6 inhibitor were protected from CS-induced mucociliary clearance (MCC) disruption. MCC was preserved in mice given the chemical chaperone 4-phenylbutyric acid, but was disrupted in mice lacking the transcription factor NRF2, suggesting that oxidative stress and altered proteostasis contribute to the disruption of MCC. Analysis of human COPD specimens revealed epigenetic deregulation of HDAC6 by hypomethylation and increased protein expression in the airways. We conclude that an autophagy-dependent pathway regulates cilia length during CS exposure and has potential as a therapeutic target for COPD.

An optimized algorithm for detecting and annotating regional differential methylation

BACKGROUND

DNA methylation profiling reveals important differentially methylated regions (DMRs) of the genome that are altered during development or that are perturbed by disease. To date, few programs exist for regional analysis of enriched or whole-genome bisulfate conversion sequencing data, even though such data are increasingly common. Here, we describe an open-source, optimized method for determining empirically based DMRs (eDMR) from high-throughput sequence data that is applicable to enriched whole-genome methylation profiling datasets, as well as other globally enriched epigenetic modification data.

RESULTS

Here we show that our bimodal distribution model and weighted cost function for optimized regional methylation analysis provides accurate boundaries of regions harboring significant epigenetic modifications. Our algorithm takes the spatial distribution of CpGs into account for the enrichment assay, allowing for optimization of the definition of empirical regions for differential methylation. Combined with the dependent adjustment for regional p-value combination and DMR annotation, we provide a method that may be applied to a variety of datasets for rapid DMR analysis. Our method classifies both the directionality of DMRs and their genome-wide distribution, and we have observed that shows clinical relevance through correct stratification of two Acute Myeloid Leukemia (AML) tumor sub-types.

CONCLUSIONS

Our weighted optimization algorithm eDMR for calling DMRs extends an established DMR R pipeline (methylKit) and provides a needed resource in epigenomics. Our method enables an accurate and scalable way of finding DMRs in high-throughput methylation sequencing experiments. eDMR is available for download at http://code.google.com/p/edmr/.

HAT: a novel statistical approach to discover functional regions in the genome

Tiling arrays are useful for exploring local functions of regions of the genome in an unbiased fashion. The exact determination of those genomic regions based on tiling-array data, e.g., generated by means of hybridization with immunopreciptated DNA-fragments to the arrays is a challenge. Many different statistical methodologies have been developed to find biological relevant regions-of-interest (ROI) by using the quantitative signal intensity of each probe. We previously developed a method called Hypergeometric Analysis of Tiling arrays (HAT) for the analysis of tiling-array data, but it is developed such that it can also be used to study data derived by genome-wide deep sequencing approaches. Here we applied HAT to analyze two publicly available tiling-array data sets. After the detection of statistically significant ROI, these are often used in additional analysis for hypothesis testing. We therefore discuss, by using the results of the tiling-array experiment, pathway and motif analyses.

Epigenetic mechanisms and the development of asthma

Asthma is heritable, influenced by the environment, and modified by in utero exposures and aging; all of these features are also common to epigenetic regulation. Furthermore, the transcription factors that are involved in the development of mature T cells that are critical to the T(H)2 immune phenotype in asthmatic patients are regulated by epigenetic mechanisms. Epigenetic marks (DNA methylation, modifications of histone tails, and noncoding RNAs) work in concert with other components of the cellular regulatory machinery to control the spatial and temporal levels of expressed genes. Technology to measure epigenetic marks on a genomic scale and comprehensive approaches to data analysis have recently emerged and continue to improve. Alterations in epigenetic marks have been associated with exposures relevant to asthma, particularly air pollution and tobacco smoke, as well as asthma phenotypes, in a few population-based studies. On the other hand, animal studies have begun to decipher the role of epigenetic regulation of gene expression associated with the development of allergic airway disease. Epigenetic mechanisms represent a promising line of inquiry that might, in part, explain the inheritance and immunobiology of asthma.

Comb-p: software for combining, analyzing, grouping and correcting spatially correlated P-values

SUMMARY

comb-p is a command-line tool and a python library that manipulates BED files of possibly irregularly spaced P-values and (1) calculates auto-correlation, (2) combines adjacent P-values, (3) performs false discovery adjustment, (4) finds regions of enrichment (i.e. series of adjacent low P-values) and (5) assigns significance to those regions. In addition, tools are provided for visualization and assessment. We provide validation and example uses on bisulfite-seq with P-values from Fisher's exact test, tiled methylation probes using a linear model and Dam-ID for chromatin binding using moderated t-statistics. Because the library accepts input in a simple, standardized format and is unaffected by the origin of the P-values, it can be used for a wide variety of applications.

AVAILABILITY

comb-p is maintained under the BSD license. The documentation and implementation are available at https://github.com/brentp/combined-pvalues.

CONTACT

bpederse@gmail.com

CEDER: accurate detection of differentially expressed genes by combining significance of exons using RNA-Seq

RNA-Seq is widely used in transcriptome studies, and the detection of differentially expressed genes (DEGs) between two classes of individuals, e.g., cases versus controls, using RNA-Seq is of fundamental importance. Many statistical methods for DEG detection based on RNA-Seq data have been developed and most of them are based on the read counts mapped to individual genes. On the other hand, genes are composed of exons and the distribution of reads for the different exons can be heterogeneous. We hypothesize that the detection accuracy of differentially expressed genes can be increased by analyzing individual exons within a gene and then combining the results of the exons. We therefore developed a novel program, termed CEDER, to accurately detect DEGs by combining the significance of the exons. CEDER first tests for differentially expressed exons yielding a p-value for each, and then gives a score indicating the potential for a gene to be differentially expressed by integrating the p-values of the exons in the gene. We showed that CEDER can significantly increase the accuracy of existing methods for detecting DEGs on two benchmark RNA-Seq data sets and simulated datasets.

Detection of differentially expressed segments in tiling array data

MOTIVATION

Tiling arrays have been a mainstay of unbiased genome-wide transcriptomics over the last decade. Currently available approaches to identify expressed or differentially expressed segments in tiling array data are limited in the recovery of the underlying gene structures and require several parameters that are intensity-related or partly dataset-specific.

RESULTS

We have developed TileShuffle, a statistical approach that identifies transcribed and differentially expressed segments as significant differences from the background distribution while considering sequence-specific affinity biases and cross-hybridization. It avoids dataset-specific parameters in order to provide better comparability of different tiling array datasets, based on different technologies or array designs. TileShuffle detects highly and differentially expressed segments in biological data with significantly lower false discovery rates under equal sensitivities than commonly used methods. Also, it is clearly superior in the recovery of exon-intron structures. It further provides window z-scores as a normalized and robust measure for visual inspection.

AVAILABILITY

The R package including documentation and examples is freely available at http://www.bioinf.uni-leipzig.de/Software/TileShuffle/

Hedgehog targets in the Drosophila embryo and the mechanisms that generate tissue-specific outputs of Hedgehog signaling

Paracrine Hedgehog (Hh) signaling regulates growth and patterning in many Drosophila organs. We mapped chromatin binding sites for Cubitus interruptus (Ci), the transcription factor that mediates outputs of Hh signal transduction, and we analyzed transcription profiles of control and mutant embryos to identify genes that are regulated by Hh. Putative targets that we identified included several Hh pathway components, mostly previously identified targets, and many targets that are novel. Every Hh target we analyzed that is not a pathway component appeared to be regulated by Hh in a tissue-specific manner; analysis of expression patterns of pathway components and target genes provided evidence of autocrine Hh signaling in the optic primordium of the embryo. We present evidence that tissue specificity of Hh targets depends on transcription factors that are Hh-independent, suggesting that `pre-patterns' of transcription factors partner with Ci to make Hh-dependent gene expression position specific.