scholarly journals Derivation of Poly-Methylomic Profile Scores for Schizophrenia

2019 ◽  
Author(s):  
Oliver J. Watkeys ◽  
Sarah Cohen-Woods ◽  
Yann Quidé ◽  
Murray J. Cairns ◽  
Bronwyn Overs ◽  
...  

AbstractSchizophrenia (SZ) and bipolar disorder (BD) share numerous clinical and biological features as well as environmental risk factors that may be associated with altered DNA methylation. In this study we sought to construct a Poly-Methylomic Profile Score (PMPS) for SZ, representing the degree of epigenome-wide methylation according to previously published findings; we then examined its association with SZ and BD in an independent sample. DNA methylation for 57 SZ, 59 BD cases and 55 healthy controls (HCs) was quantified using the Illumina 450K methylation beadchip. We constructed five PMPSs for different p-value thresholds using summary statistics reported in a large epigenome-wide schizophrenia case-control association study, weighted by individual CpG effect sizes. All SZ PMPSs were significantly elevated in SZ cases relative to HCs, with the score calculated at the most stringent threshold accounting for the greatest amount of variance in SZ (compared to other PMPSs derived at more inclusivep-value thresholds). However, none of the PMPSs were associated with BD, or a combined cohort of BD and SZ cases relative to HCs. Results demonstrating elevated PMPSs in SZ relative to BD did not survive correction for multiple testing. PMPSs were also not associated with positive or negative symptom severity. That this SZ-derived PMPSs was elevated among SZ, but not BD participants, suggests that epigenome-wide methylation patterns associated with schizophrenia may represent distinct pathophysiology that is yet to be elucidated. Whether this PMPS may be associated with neuroanatomical or other biological endophenotypes relevant to SZ and/or BD remains to be determined.

2021 ◽  
Author(s):  
Sangeetha Muthamilselvan ◽  
Abirami Raghavendran ◽  
Ashok Palaniappan

Abstract Background: Aberrant DNA methylation acts epigenetically to skew the gene transcription rate up or down, with causative roles in the etiology of cancers. However research on the role of DNA methylation in driving the progression of cancers is limited. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer (CRC), and unravelled significant stagewise signposts of CRC progression. Methods: The methylation β - matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with AJCC stages, and analysed for stage-salient genes using multiple approaches involving stage-differentiated linear modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against controls (adjusted p-value <0.001 and |log fold-change of M-value| >2). These results were filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by Kaplan–Meier survival analysis to evaluate the impact of methylation patterns of consensus stage-salient biomarkers on disease prognosis.Results: We found significant genome-wide changes in methylation patterns in cancer cases relative to controls agnostic of stage. Our stage-differentiated analysis yielded the following stage-salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were hypermethylated, indicating down-regulation and signifying a CpG island Methylator Phenotype (CIMP) manifestation. A significant prognostic signature consisting of FBN1 and FOXG1 survived all the steps of our analysis pipeline, and represents a novel early-stage biomarker. Conclusions: We have designed a workflow for stage-differentiated consensus analysis, and identified stage-salient diagnostic biomarkers and an early-stage prognostic biomarker panel. Our studies further yield a novel CIMP-like signature of potential clinical import underlying CRC progression.


2020 ◽  
Vol 79 (OCE2) ◽  
Author(s):  
Jiantao Ma ◽  
Casey Rebholz ◽  
Kim Braun ◽  
Lindsay Reynolds ◽  
Stella Aslibekyan ◽  
...  

AbstractLeukocyte DNA methylation patterns associated with habitual diet may reveal molecular mechanisms involved in the pathogenesis of diet-related chronic diseases and highlight targets for prevention and treatment. We aimed to examine peripheral blood derived leukocyte DNA methylation signatures associated with diet quality. We meta-analyzed epigenome-wide associations between diet quality and DNA methylation levels at over 400,000 cytosine-guanine dinucleotides (CpGs). We conducted analysis primarily in 6,662 European ancestry (EA) participants and secondarily in a group additionally including 3,062 participants of non-European ancestry from five population-based cohort studies. DNA methylation profiles were measured in whole blood, CD4 + T-cells, or CD14 + monocytes. We used food frequency questionnaires to assess habitual intake and constructed two diet quality scores: the Mediterranean-style diet score (MDS) and Alternative Healthy Eating Index (AHEI). Our primary analysis identified 32 diet-associated CpGs, 12 CpGs for MDS and 24 CpGs for AHEI (at FDR < 0.05, corresponding p-values = 1.2×10-6 and 3.1×10-6, respectively) in EA participants. Four of these CpGs were associated with both MDS and AHEI. In addition, Mendelian randomization analysis indicated that seven diet-associated CpGs were causally linked to at least one of the CVD risk factors. For example, hypermethylation of cg11250194 (FADS2), which was associated with higher diet quality scores, was also associated with lower fasting triglycerides concentrations (p-value = 1.5×10-14) and higher high-density lipoprotein cholesterol concentrations (p-value = 1.7×10-8). Transethnic meta-analysis identified nine additional CpGs associated with diet quality (either MDS or AHEI) at FDR < 0.05. Overall quality of habitual diet was associated with differential peripheral leukocyte DNA methylation levels of 32 CpGs in EA participants. The diet-associated CpGs may serve as biomarkers and targets for preventive measures in CVD health. Future studies are warranted to examine diet-associated DNA methylation patterns in larger, ethnically diverse study samples.


2019 ◽  
Author(s):  
Anke Hüls ◽  
Chloe Robins ◽  
Karen N. Conneely ◽  
Rachel Edgar ◽  
Philip L. De Jager ◽  
...  

AbstractObjectiveCognitive decline is a hallmark of dementia; however, the brain epigenetic signature of cognitive decline is unclear. We investigated the associations between brain tissue-based DNA methylation and cognitive trajectory.MethodsWe performed a brain epigenome-wide association study of cognitive trajectory in 636 participants from the Religious Order Study and the Rush Memory and Aging Project (ROS/MAP) using DNA methylation profiles of the dorsal lateral prefrontal cortex (dPFC). To maximize our power to detect epigenetic associations, we used the recently developed Gene Association with Multiple Traits (GAMuT) test to analyze the five measured cognitive domains simultaneously.ResultsWe found an epigenome-wide association for differential methylation of sites in the Claudin-5 (CLDN5) locus and cognitive trajectory (p-value x 9.96 × 10-7), which was robust to adjustment for cell type proportions (p-value = 8.52 x 10-7). This association was primarily driven by association with declines in episodic (p-value = 4.65 x 10-6) and working memory (p-value = 2.54 x 10-7). This association between methylation in CLDN5 and cognitive decline was independent of beta-amyloid and neurofibrillary tangle pathology and present in participants with low levels of neuropathology. In addition, only 13-31% of the association between methylation and cognitive decline was mediated through levels of neuropathology, whereas the major part of the association was independent of it.InterpretationWe identified methylation in CLDN5 as new epigenetic factor associated with cognitive trajectory. Higher levels of methylation in CLDN5 were associated with faster cognitive decline implicating the blood brain barrier in maintenance of cognitive trajectory.


Author(s):  
Ashani Lecamwasam ◽  
Boris Novakovic ◽  
Braydon Meyer ◽  
Elif I Ekinci ◽  
Karen M Dwyer ◽  
...  

Abstract Background We investigated a cross-sectional epigenome-wide association study of patients with early and late diabetes-associated chronic kidney disease (CKD) to identify possible epigenetic differences between the two groups as well as changes in methylation across all stages of diabetic CKD. We also evaluated the potential of using a panel of identified 5′-C-phosphate-G-3′ (CpG) sites from this cohort to predict the progression of diabetic CKD. Methods This cross-sectional study recruited 119 adults. DNA was extracted from blood using the Qiagen QIAampDNA Mini Spin Kit. Genome-wide methylation analysis was performed using Illumina Infinium MethylationEPIC BeadChips (HM850K). Intensity data files were processed and analysed using the minfi and MissMethyl packages for R. We examined the degree of methylation of CpG sites in early versus late diabetic CKD patients for CpG sites with an unadjusted P-value &lt;0.01 and an absolute change in methylation of 5% (n = 239 CpG sites). Results Hierarchical clustering of the 239 CpG sites largely separated the two groups. A heat map for all 239 CpG sites demonstrated distinct methylation patterns in the early versus late groups, with CpG sites showing evidence of progressive change. Based on our differentially methylated region (DMR) analysis of the 239 CpG sites, we highlighted two DMRs, namely the cysteine-rich secretory protein 2 (CRISP2) and piwi-like RNA-mediated gene silencing 1 (PIWIL1) genes. The best predictability for the two groups involved a receiver operating characteristics curve of eight CpG sites alone and achieved an area under the curve of 0.976. Conclusions We have identified distinct DNA methylation patterns between early and late diabetic CKD patients as well as demonstrated novel findings of potential progressive methylation changes across all stages (1–5) of diabetic CKD at specific CpG sites. We have also identified associated genes CRISP2 and PIWIL1, which may have the potential to act as stage-specific diabetes-associated CKD markers, and showed that the use of a panel of eight identified CpG sites alone helps to increase the predictability for the two groups.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Nathalia Mantovani ◽  
Alexandre Defelicibus ◽  
Israel Tojal da Silva ◽  
Maira Ferreira Cicero ◽  
Luiz Claudio Santana ◽  
...  

AbstractDNA methylation is one of the epigenetic modifications that configures gene transcription programs. This study describes the DNA methylation profile of HIV-infected individuals with distinct characteristics related to natural and artificial viremia control. Sheared DNA from circulating mononuclear cells was subjected to target enrichment bisulfite sequencing designed to cover CpG-rich genomic regions. Gene expression was assessed through RNA-seq. Hypermethylation in virologic responders was highly distributed closer to Transcription Start Sites (p-value = 0.03). Hyper and hypomethylation levels within TSS adjacencies varied according to disease progression status (Kruskal–Wallis, p < 0.001), and specific differentially methylated regions associated genes were identified for each group. The lower the promoter methylation, the higher the gene expression in subjects undergoing virologic failure (R = − 0.82, p = 0.00068). Among the inversely correlated genes, those supporting glycolysis and its related pathways were hypomethylated and up-regulated in virologic failures. Disease progression heterogeneity was associated with distinct DNA methylation patterns in terms of rates and distribution. Methylation was associated with the expression of genes sustaining intracellular glucose metabolism in subjects undergoing antiretroviral virologic failure. Our findings highlight that DNA methylation is associated with latency, disease progression, and fundamental cellular processes.


2017 ◽  
Author(s):  
Divya Mehta ◽  
Dagmar Bruenig ◽  
Bruce Lawford ◽  
Wendy Harvey ◽  
Tania Carrillo-Roa ◽  
...  

AbstractAccelerated epigenetic aging, the difference between the DNA methylation-predicted age (DNAm age) and the chronological age, is associated with a myriad of diseases. This study investigates the relationship between epigenetic aging and risk and protective factors of PTSD. Genome-wide DNA methylation analysis was performed in 211 individuals including combat-exposed Australian veterans (discovery cohort, n = 96 males) and trauma-exposed civilian males from the Grady Trauma Project (replication cohort, n = 115 males). Primary measures included the Clinician Administered PTSD Scale for DSM-5 and the Connor-Davidson Resilience Scale (CDRISC). DNAm age prediction was performed using the validated epigenetic clock calculator. Veterans with PTSD had increased PTSD symptom severity (P-value = 3.75 x 10-34) and lower CDRISC scores (P-value = 7.5 x 10-8) than veterans without PTSD. DNAm age was significantly correlated with the chronological age (P-value = 3.3 x 10-6), but DNAm age acceleration was not different between the PTSD and non-PTSD groups (P-value = 0.24). Evaluating potential protective factors, we found that DNAm age acceleration was significantly associated with CDRISC resilience scores in veterans with PTSD, these results remained significant after multiple testing correction (P-value = 0.023; r = 0.32). This finding was also replicated in an independent trauma-exposed civilian cohort (P-value = 0.02; r = 0.23). Post-hoc factor analyses revealed that this association was driven by “self-efficacy” items within the CDRISC (P-value = 0.015). These results suggest that among individuals already suffering from PTSD, some aspects of increased resilience might come at a biological cost.


2020 ◽  
Author(s):  
Ada Admin ◽  
Meriem Ouni ◽  
Sophie Saussenthaler ◽  
Fabian Eichelmann ◽  
Markus Jähnert ◽  
...  

The identification of individuals with a high risk of developing type 2 diabetes (T2D) is fundamental for prevention. Here, we used a translational approach and prediction criteria to identify changes in DNA methylation visible before the development of T2D. <p>Islets of Langerhans were isolated from genetically identical 10-week-old female New Zealand Obese mice which differ in their degree of hyperglycemia and in liver fat content. The application of a semi-explorative approach identified 497 differentially expressed and methylated genes (p-value=6.42e-09, hypergeometric test) enriched in pathways linked to insulin secretion and ECM-receptor interaction. The comparison of mouse data with DNA methylation levels of incident T2D cases from the prospective EPIC-Potsdam cohort, revealed 105 genes with altered DNA methylation at 605 CpG sites which were associated with future T2D. <i>AKAP13</i>, <i>TENM2</i>, <i>CTDSPL</i>, <i>PTPRN2</i> and <i>PTPRS</i> showed the strongest predictive potential (ROC-AUC values 0.62-0.73). Among the new candidates identified in blood cells, 655 CpG sites, located in 99 genes, were differentially methylated in islets of human with T2D. Utilizing correction for multiple testing detected 236 genes with an altered DNA methylation in blood cells and 201 genes in diabetic islets. Thus, the introduced translational approach identified novel putative biomarkers for early pancreatic islet aberrations preceding T2D.</p>


Blood ◽  
2016 ◽  
Vol 128 (22) ◽  
pp. 1521-1521
Author(s):  
Nicole Wong Doo ◽  
Enes Makalic ◽  
Daniel Schmidt ◽  
Jihoon E Joo ◽  
Chol-Hee Jung ◽  
...  

Abstract Background Aberrant DNA methylation is a feature of mature B cell neoplasms (MBCN) however it is not clear whether this is an early or late event in the development of MBCN. We have previously reported that aberrant global methylation patterns are detectable in peripheral blood sampled years prior to diagnosis and now hypothesize that abnormal methylation at specific genomic regions precedes the diagnosis of MBCN. Methods: In our prospective cohort study peripheral blood was collected from healthy participants in the Melbourne Collaborative Cohort Study, (41,514 adult volunteers, recruited from 1990-94). Blood was stored as either dried blood spots, mononuclear cells or buffy coat. New cases of MBCN (including low grade non Hodgkin lymphoma [NHL], high grade NHL, chronic lymphocytic leukaemia and multiple myeloma) diagnosed during follow up until December 2011 were identified by cancer registry linkage. Cases were matched to controls at a 1:1 ratio based on age, gender, ethnicity and DNA source. Genome-wide DNA methylation was measured using the Infinium®HumanMethylation450 BeadChip and M values were derived after background correction, normalization and probe bias correction methods. Differentially methylated probes (DMPs) were identified by conditional logistic regression using case-control status as the outcome to compute odds ratios and p values for the association between methylation difference and occurrence of MBCN. Bonferroni adjustment was used for multiple testing correction. Adjusted conditional logistic regression models were created to control for the white blood cell (WBC) content of peripheral blood samples. Differentially methylated regions (DMRs) were identified using the DMRcate package. DMPs and DMRs were cross-checked against genes associated with MBCN pathogenesis by a review of current literature. Results We identified 438 cases of MBCN, with a median time between blood collection and MBCN diagnosis of 10.6 years. Following Bonferroni p value adjustment, there were 1,215 DMPs of which 81 were more methylated in cases compared to controls. There were 74 promoter-associated DMPs, with increased methylation in: HOXA9 (cg07778029, OR, 1.58; 95% CI, 1.36-1.83[punadj 2.4x10-9]), HOXA11 (cg05977669, OR, 1.62; 95% CI, 1.37-1.92 [punadj 2.0x10-8], cg24446586, OR, 1.57; 95% CI, 1.34-1.85 [punadj 2.4x10-8]), MYADM (cg25693289, OR, 1.64; 95% CI, 1.39-1.92 [punadj 1.59x10-9]). Decreased methylation of DMPs was noted in: ROBO1 (cg24512093, OR, 1.67; 95% CI, 1.39-1.96 [punadj 2.83x10-8]) and IKBKB (cg11283404, OR, 1.54; 95% CI, 1.32-1.82 [punadj 8.66x10-8]).Using a more relaxed p value cut-off (p < 1x10-5 before Bonferroni adjustment), we identified 10,792 DMPs with 17 probes corresponding to nine genes involved in MBCN pathogenesis: CARD11, CD79B, TNFAIP3, NR2F2, NPTX2, NOTCH1, NOTCH2, EP300 and DTX1. Adjustment for cell content did not significantly change the results. Analysis of differentially methylated regions (clusters of differentially methlyated probes) revealed 4,629 DMRs with p <1x10-7. Of these, 1,824 regions were more methylated in cases and included several genes of interest in MBCN pathogenesis (e.g. GATA3, HOXA9, HOXA11, EBF3, SOX11, SOX9). Conclusions This is the first study to report differential DNA methylation patterns detectable in the peripheral blood many years prior to MBCN diagnosis. Increased methylation of promoter-associated probes in HOXA9, HOXA11 and MYADM and decreased methylation of probes in ROBO1 and IKBKB were associated with an increased risk of developing MBCN. These findings suggest that aberrant methylation is a very early event in the pathogenesis of MBCN. Disclosures No relevant conflicts of interest to declare.


2020 ◽  
Vol 13 (S10) ◽  
Author(s):  
Mai Shi ◽  
Stephen Kwok-Wing Tsui ◽  
Hao Wu ◽  
Yingying Wei

Abstract Background DNA methylation is a key epigenetic regulator contributing to cancer development. To understand the role of DNA methylation in tumorigenesis, it is important to investigate and compare differential methylation (DM) patterns between normal and case samples across different cancer types. However, current pan-cancer analyses call DM separately for each cancer, which suffers from lower statistical power and fails to provide a comprehensive view for patterns across cancers. Methods In this work, we propose a rigorous statistical model, PanDM, to jointly characterize DM patterns across diverse cancer types. PanDM uses the hidden correlations in the combined dataset to improve statistical power through joint modeling. PanDM takes summary statistics from separate analyses as input and performs methylation site clustering, differential methylation detection, and pan-cancer pattern discovery. We demonstrate the favorable performance of PanDM using simulation data. We apply our model to 12 cancer methylome data collected from The Cancer Genome Atlas (TCGA) project. We further conduct ontology- and pathway-enrichment analyses to gain new biological insights into the pan-cancer DM patterns learned by PanDM. Results PanDM outperforms two types of separate analyses in the power of DM calling in the simulation study. Application of PanDM to TCGA data reveals 37 pan-cancer DM patterns in the 12 cancer methylomes, including both common and cancer-type-specific patterns. These 37 patterns are in turn used to group cancer types. Functional ontology and biological pathways enriched in the non-common patterns not only underpin the cancer-type-specific etiology and pathogenesis but also unveil the common environmental risk factors shared by multiple cancer types. Moreover, we also identify PanDM-specific DM CpG sites that the common strategy fails to detect. Conclusions PanDM is a powerful tool that provides a systematic way to investigate aberrant methylation patterns across multiple cancer types. Results from real data analyses suggest a novel angle for us to understand the common and specific DM patterns in different cancers. Moreover, as PanDM works on the summary statistics for each cancer type, the same framework can in principle be applied to pan-cancer analyses of other functional genomic profiles. We implement PanDM as an R package, which is freely available at http://www.sta.cuhk.edu.hk/YWei/PanDM.html.


2020 ◽  
Author(s):  
Sangeetha Muthamilselvan ◽  
Abirami Raghavendran ◽  
Ashok Palaniappan

ABSTRACTBackgroundAberrant methylation of DNA acts epigenetically to skew the gene transcription rate up or down. In this study, we have developed a comprehensive computational framework for the stage-differentiated modelling of DNA methylation landscapes in colorectal cancer. Methods: The methylation β - matrix was derived from the public-domain TCGA data, converted into M-value matrix, annotated with sample stages, and analysed for stage-salient genes using multiple approaches involving stage-differentiated linear modelling of methylation patterns and/or expression patterns. Differentially methylated genes (DMGs) were identified using a contrast against control samples (adjusted p-value <0.001 and |log fold-change of M-value| >2). These results were filtered using a series of all possible pairwise stage contrasts (p-value <0.05) to obtain stage-salient DMGs. These were then subjected to a consensus analysis, followed by Kaplan–Meier survival analysis to explore the relationship between methylation and prognosis for the consensus stage-salient biomarkers.ResultsWe found significant genome-wide changes in methylation patterns in cancer samples relative to controls agnostic of stage. Our stage-differentiated analysis yielded the following stage-salient genes: one stage-I gene (FBN1), one stage-II gene (FOXG1), one stage-III gene (HCN1) and four stage-IV genes (NELL1, ZNF135, FAM123A, LAMA1). All the biomarkers were hypermethylated, indicating down-regulation and signifying a CpG island Methylator Phenotype (CIMP) manifestation. A prognostic signature consisting of FBN1 and FOXG1was significantly associated with patient survival (p-value < 0.01) and could be used as a biomarker panel for early-stage CRC prognosis.ConclusionOur workflow for stage-differentiated consensus analysis has yielded stage-salient diagnostic biomarkers as well as an early-stage prognostic biomarker panel. In addition, our studies have affirmed a novel CIMP-like signature in colorectal cancer, urging clinical validation.


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