DNA Methylation Variation Research Articles

Associations between DNA methylation and telomere length during early life: Insight from wild zebra finches (Taeniopygia guttata).

Telomere length and DNA methylation (DNAm) are two promising biomarkers of biological age. Environmental factors and life history traits are known to affect variation in both these biomarkers, especially during early life, yet surprisingly little is known about their reciprocal association, especially in natural populations. Here, we explore how variation in DNAm, growth rate, and early-life conditions are associated with telomere length changes during development. We tested these associations by collecting data from wild, nestling zebra finches in the Australian desert. We found that increases in the level of DNAm were negatively correlated with telomere length changes across early life. We also confirm previously documented effects of post hatch growth rate and clutch size on telomere length in a natural ecological context for a species that has been extensively studied in the laboratory. However, we did not detect any effect of ambient temperature during developmental on telomere length dynamics. We also found that the absolute telomere length of wild zebra finches, measured using the in-gel TRF method, was similar to that of captive birds. Our findings highlight exciting new opportunities to link and disentangle potential relationships between DNA based biomarkers of ageing, and of physiological reactions to environmental change.

Revisiting genetic artifacts on DNA methylation microarrays exposes novel biological implications

BackgroundIllumina DNA methylation microarrays enable epigenome-wide analysis vastly used for the discovery of novel DNA methylation variation in health and disease. However, the microarrays’ probe design cannot fully consider the vast human genetic diversity, leading to genetic artifacts. Distinguishing genuine from artifactual genetic influence is of particular relevance in the study of DNA methylation heritability and methylation quantitative trait loci. But despite its importance, current strategies to account for genetic artifacts are lagging due to a limited mechanistic understanding on how such artifacts operate.ResultsTo address this, we develop and benchmark UMtools, an R-package containing novel methods for the quantification and qualification of genetic artifacts based on fluorescence intensity signals. With our approach, we model and validate known SNPs/indels on a genetically controlled dataset of monozygotic twins, and we estimate minor allele frequency from DNA methylation data and empirically detect variants not included in dbSNP. Moreover, we identify examples where genetic artifacts interact with each other or with imprinting, X-inactivation, or tissue-specific regulation. Finally, we propose a novel strategy based on co-methylation that can discern between genetic artifacts and genuine genomic influence.ConclusionsWe provide an atlas to navigate through the huge diversity of genetic artifacts encountered on DNA methylation microarrays. Overall, our study sets the ground for a paradigm shift in the study of the genetic component of epigenetic variation in DNA methylation microarrays.

Association between dietary patterns reflecting one-carbon metabolism nutrients intake before pregnancy and placental DNA methylation

ABSTRACT The preconception period represents an important window for foetal and epigenetic programming. Some micronutrients (B vitamins, choline, betaine, methionine) implicated in one-carbon metabolism (OCM) are essential for major epigenetic processes that take place in early pregnancy. However, few studies have evaluated the implication of the micronutrients in placental DNA methylation. We investigated whether intake of OCM nutrients in the year before pregnancy was associated with placental DNA methylation in the EDEN mother–child cohort. Maternal dietary intake was assessed with a food-frequency questionnaire. Three dietary patterns, ‘varied and balanced diet,’ ‘vegetarian tendency,’ and ‘bread and starchy food,’ were used to characterize maternal OCM dietary intake. The Illumina Infinium HumanMethylation450 BeadChip was used to measure placental DNA methylation of 573 women included in the analyses. We evaluated the association of dietary patterns with global DNA methylation. Then, we conducted an agnostic epigenome-wide association study (EWAS) and investigated differentially methylated regions (DMRs) associated with each dietary pattern. We found no significant association between the three dietary patterns and global DNA methylation or individual CpG sites. DMR analyses highlighted associations between the ‘varied and balanced’ or ‘vegetarian tendency’ pattern and DMRs located at genes previously implicated in functions essential for embryonic development, such as neurodevelopment. The ‘bread and starchy food’ pattern was associated with regions related to genes whose functions involve various metabolic and cell synthesis-related processes. In mainly well-nourished French women without major deficiencies, OCM intake before pregnancy was not associated with major variation in DNA methylation.

Epigenetic Response of Yarrowia lipolytica to Stress: Tracking Methylation Level and Search for Methylation Patterns via Whole-Genome Sequencing.

DNA methylation is a common, but not universal, epigenetic modification that plays an important role in multiple cellular processes. While definitely settled for numerous plant, mammalian, and bacterial species, the genome methylation in different fungal species, including widely studied and industrially-relevant yeast species, Yarrowia lipolytica, is still a matter of debate. In this paper, we report a differential DNA methylation level in the genome of Y. lipolytica subjected to sequential subculturing and to heat stress conditions. To this end, we adopted repeated batch bioreactor cultivations of Y. lipolytica subjected to thermal stress in specific time intervals. To analyze the variation in DNA methylation between stressed and control cultures, we (a) quantified the global DNA methylation status using an immuno-assay, and (b) studied DNA methylation patterns through whole-genome sequencing. Primarily, we demonstrated that 5 mC modification can be detected using a commercial immuno-assay, and that the modifications are present in Y. lipolytica’s genome at ~0.5% 5 mC frequency. On the other hand, we did not observe any changes in the epigenetic response of Y. lipolytica to heat shock (HS) treatment. Interestingly, we identified a general phenomenon of decreased 5 mC level in Y. lipolytica’s genome in the stationary phase of growth, when compared to a late-exponential epigenome. While this study provides an insight into the subculturing stress response and adaptation to the stress at epigenetic level by Y. lipolytica, it also leaves an open question of inability to detect any genomic DNA methylation level (either in CpG context or context-less) through whole-genome sequencing. The results of ONT sequencing, suggesting that 5 mC modification is either rare or non-existent in Y. lipolytica genome, are contradicted with the results of the immunoassay.

Epigenetic differences in an identical genetic background modulate alternative splicing in A. thaliana

How stable and temperature-dependent variations in DNA methylation and nucleosome occupancy influence alternative splicing (AS) remains poorly understood in plants. To answer this, we generated transcriptome, whole-genome bisulfite, and MNase sequencing data for an epigenetic Recombinant Inbred Line (epiRIL) of A. thaliana at normal and cold temperature. For comparative analysis, the same data sets for the parental ecotype Columbia (Col-0) were also generated, whereas for DNA methylation, previously published high confidence methylation profiles of Col-0 were used. Significant epigenetic differences in an identical genetic background were observed between Col-0 and epiRIL lines under normal and cold temperatures. Our transcriptome data revealed that differential DNA methylation and nucleosome occupancy modulate expression levels of many genes and AS in response to cold. Collectively, DNA methylation and nucleosome levels exhibit characteristic patterns around intron-exon boundaries at normal and cold conditions, and any perturbation in them, in an identical genetic background is sufficient to modulate AS in Arabidopsis.

Ten Years of EWAS.

Epigenome‐wide association study (EWAS) has been applied to analyze DNA methylation variation in complex diseases for a decade, and epigenome as a research target has gradually become a hot topic of current studies. The DNA methylation microarrays, next‐generation, and third‐generation sequencing technologies have prepared a high‐quality platform for EWAS. Here, the progress of EWAS research is reviewed, its contributions to clinical applications, and mainly describe the achievements of four typical diseases. Finally, the challenges encountered by EWAS and make bold predictions for its future development are presented.

Increased CG, CHG and CHH methylation at the cycloidea gene in the “Peloria” mutant of Linaria vulgaris

Heritable DNA methylation variation is frequently observed in natural populations of plants, but is thought mostly to be functionally inconsequential. An exception to this is the “Peloria” mutant of Linaria vulgaris, which was originally described by Carl von Linné in 1744. A study in 1999 found that the Peloria phenotype is caused by an epiallele of the L. vulgaris cycloidea homolog Lcyc that showed increased levels of DNA methylation compared to wild-type. The DNA methylation results in silencing of Lcyc, which causes radial flower symmetry in the peloric mutant, whereas wild-type plants have flowers with bilateral symmetry. However, a detailed view of DNA methylation at Lcyc at the single-nucleotide level has not been available.In this study, we investigated DNA methylation at Lcyc and, as a control, at the LvHIRZ gene in wild-type and peloric plants of L. vulgaris using DNA bisulfite treatment coupled to next-generation sequencing. We found strong increases in CHG and CHH methylation at Lcyc, but not LvHIRZ, in Peloria. CG methylation was also increased, but wild-type Lcyc also showed moderate levels of CG methylation. Our results suggest that DNA methylation in all three sequence contexts has been maintained, and potentially transgenerationally inherited, in the peloric L. vulgaris population over decades or even centuries.

Methylation of Host Genes Associated with Coronavirus Infection from Birth to 26 Years.

DNA methylation (DNAm) patterns over time at 1146 CpGs on coronavirus-related genes were assessed to understand whether the varying differences in susceptibility, symptoms, and the outcomes of the SARS-CoV-2 infection in children and young adults could be explained through epigenetic alterations in a host cell’s transcriptional apparatus to coronaviruses. DNAm data from the Isle of Wight birth cohort (IOWBC) at birth, 10, 18, and 26 years of age were included. Linear mixed models with repeated measurements stratified by sex were used to examine temporal patterns, and cluster analysis was performed to identify CpGs following similar patterns. CpGs on autosomes and sex chromosomes were analyzed separately. The association of identified CpGs and expression of their genes were evaluated. Pathway enrichment analyses of the genes was conducted at FDR = 0.05. DNAm at 635 of the 1146 CpGs on autosomes showed statistically significant time effects (FDR = 0.05). The 635 CpGs were classified into five clusters with each representing a unique temporal pattern of DNAm. Of the 29 CpGs on sex chromosomes, DNAm at seven CpGs in males and eight CpGs in females showed time effects (FDR = 0.05). Sex-specific and non-specific associations of DNAm with gene expression were found at 24 and 93 CpGs, respectively. Genes which mapped the 643 CpGs represent 460 biological processes. We suggest that the observed variability in DNAm with advancing age may partially explain differing susceptibility, disease severity, and mortality of coronavirus infections among different age groups.

BMI trajectory in childhood is associated with asthma incidence at young adulthood mediated by DNA methylation

PurposeBody mass index (BMI) is associated with asthma but associations of BMI temporal patterns with asthma incidence are unclear. Previous studies suggest that DNA methylation (DNAm) is associated with asthma status and variation in DNAm is a consequence of BMI changes. This study assessed the direct and indirect (via DNAm) effects of BMI trajectories in childhood on asthma incidence at young adulthood.MethodsData from the Isle of Wight (IoW) birth cohort were included in the analyses. Group-based trajectory modelling was applied to infer latent BMI trajectories from ages 1 to 10 years. An R package, ttscreening, was applied to identify differentially methylated CpGs at age 10 years associated with BMI trajectories, stratified for sex. Logistic regressions were used to further exclude CpGs with DNAm at age 10 years not associated with asthma incidence at 18 years. CpGs discovered via path analyses that mediated the association of BMI trajectories with asthma incidence in the IoW cohort were further tested in an independent cohort, the Avon Longitudinal Study of Children and Parents (ALSPAC).ResultsTwo BMI trajectories (high vs. normal) were identified. Of the 442,474 CpG sites, DNAm at 159 CpGs in males and 212 in females were potentially associated with BMI trajectories. Assessment of their association with asthma incidence identified 9 CpGs in males and 6 CpGs in females. DNAm at 4 of these 15 CpGs showed statistically significant mediation effects (p-value < 0.05). At two of the 4 CpGs (cg23632109 and cg10817500), DNAm completely mediated the association (i.e., only statistically significant indirect effects were identified). In the ALSPAC cohort, at all four CpGs, the same direction of mediating effects were observed as those found in the IoW cohort, although statistically insignificant.ConclusionThe association of BMI trajectory in childhood with asthma incidence at young adulthood is possibly mediated by DNAm.

Comparison of Genome-Wide DNA Methylation Profiles of Human Fetal Tissues Conceived by in vitro Fertilization and Natural Conception.

BackgroundAssisted reproductive technology (ART) might induce adverse pregnancy outcomes and increase the risk of metabolic diseases in offspring’ later life with unknown reasons. Here we evaluated the global methylation level and methylation profile of fetal tissue from elective terminations of pregnancy (ETP) after natural conception and multifetal pregnancy reduction (MFPR) after in vitro fertilization and embryo transfer (IVF-ET).ResultsGlobal methylation levels were comparable between the fetal tissue of ETP after natural conception group and MFPR after IVF-ET group. The methylation levels were lower in the hypermethylated regions of the MFPR group than in the ETP group, while the methylation levels were higher in the hypomethylated regions of the MFPR group. Heatmap visualization and hierarchical clustering of the candidate differentially methylated regions (DMRs) showed differences between the DMRs in the ETP and MFPR samples. We identified 196 differentially methylated regions that matched 164 genes between the ETP and MFPR groups. In the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, skeletal system morphogenesis and diabetes mellitus ranked first. Ingenuity Pathway Analysis (IPA) revealed 8 diseases and functional annotations associated with IVT-ET. In the MFPR group, the final validation showed lower methylation levels in gene bodies of bone morphogenetic protein 4 (BMP4), higher methylation levels in the 1st exon and 5′UTR of thyroid peroxidase (TPO), and higher methylation levels in TSS1500 and TSS200 of interleukin 1 beta (IL1B).ConclusionsART does not alter global DNA methylation level, but influences DNA methylation variation in specific regions of human fetus in the early stage of life. Further studies are warranted to clarify the potential role of DNA methylation alterations in the gene expression profile.

Exposure to polycyclic aromatic hydrocarbons, DNA methylation and heart rate variability among non-current smokers

Polycyclic aromatic hydrocarbons (PAHs) exposure is associated with heart rate variability (HRV) reduction, a widely used marker of cardiovascular autonomic dysfunction. The role of DNA methylation in the relationship between PAHs exposure and decreased HRV is largely unknown. This study aims to explore epigenome-wide DNA methylation changes associated with PAHs exposure and further evaluate their associations with HRV alternations among non-current smokers. We measured 10 mono-hydroxylated PAHs (OH-PAHs) in urine and DNA methylation levels in blood leukocytes among participants from three panels of Chinese non-current smokers (152 in WHZH, 99 in SY, and 53 in COW). We conducted linear regression analyses between DNA methylation and OH-PAHs metabolites with adjustment for age, gender, body mass index, drinking, blood cell counts, and surrogate variables in each panel separately, and combined the results by using inverse-variance weighted fixed-effect meta-analysis to obtain estimates of effect size. The median value of total OH-PAHs ranged from 0.92 × 10−2 in SY panel (62.6% men) to 13.82 × 10−2 μmol/mmol creatinine in COW panel (43.4% men). The results showed that methylation levels of cg18223625 (COL20A1) and cg07805771 (SLC16A1) were significantly or marginally significantly associated with urinary 2-hydroxynaphthalene [β(SE) = 0.431(0.074) and 0.354(0.068), FDR = 0.016 and 0.056, respectively], while methylation level of cg09235308 (PLEC1) was positively associated with urinary total OH-PAHs [β(SE) = 0.478(0.079), FDR = 0.004]. Hypermethylations of cg18223625, cg07805771, and cg09235308 were inversely associated with HRV indices among the WHZH and COW non-current smokers. However, we did not observe significant epigenome-wide associations for the other 9 urinary OH-PAHs. These findings provide new evidence that PAHs exposure is linked to differential DNA methylation, which may help better understand the influences of PAHs exposure on HRV alternations.

Assessment of natural DNA methylation variation and its association with economically important traits in dolichos bean (Lablab purpureus L. Var. Lignosus) using AMP-PCR assay.

As a prelude to exploit DNA methylation-induced variation, we hypothesized the existence of substantial natural DNA methylation variation and its association with economically important traits in dolichos bean, and tested it using amplified methylation polymorphism-polymerase chain reaction (AMP-PCR) assay. DNA methylation patterns such as internal, external, full and non-methylation were amplified in a set of 64 genotypes using 26 customized randomly amplified polymorphic DNA (RAPD) primers containing 5'CCGG3' sequence. The 64 genotypes included 60 germplasm accessions (GA), two advanced breeding lines (ABLs) and two released varieties. The ABLs and released varieties are referred to as improved germplasm accessions (IGA) in this study. The association of DNA methylation patterns with economically important traits such as days to 50% flowering, raceme length, fresh pods plant-1, fresh pod yield plant-1 and 100-fresh seed weight was explored. At least 50 genotypes were polymorphic for DNA methylation patterns at 10 loci generated by seven of the 26 RAPD primers. The GA and IGA differed significantly for total, full and external methylation and the frequency of methylation was higher in GA compared to that in IGA. The genotypes with external methylation produced longer racemes than those with full, internal and non-methylation in that order at polymorphic RAPD-11-242 locus. High pod yielding genotypes had significantly lower frequency of full methylation than low yielding ones. On the contrary, the genotypes that produced heavier fresh seeds harboured higher frequencies of total and externally methylated loci than those that produced lighter fresh seeds.

Abstract 2100: The impact of race on the relationship between poly-CpG DNA methylation and mRNA expression in cancer with application to epigenome-wide association studies

Abstract DNA methylation is a known regulator of mRNA expression. Recent studies in cancer have shown that significant proportions of the variability in mRNA expression can be explained by variation in DNA methylation. However, none of these studies have investigated the influence of race on this relationship. Here, we develop poly-CpG site models of gene expression that facilitate the quantification of race-specific effects on the regulatory connection between methylation and transcriptional gene expression. Paired Illumina 450k methylation and RNA-Seq gene expression data from The Cancer Genome Atlas (TCGA) was used to develop poly-CpG site elastic net penalized Poisson regression models of gene expression that account for race-by-CpG interactions for each of the eight TCGA tumor types with African American (AA) representation (&amp;gt;40 tumors each). Race-specific effects were summarized as the percentage of CpGs in a gene model with retained interaction terms. The method was applied to methylation data from an AA radical prostatectomy prostate cancer cohort (n=261) to generate predicted gene expression estimates that were used to perform a mechanistic epigenome-wide association study (EWAS) of Gleason grade (≤3+4 and ≥4+3 tumors). High percentages of variability in mRNA expression can be explained by poly-CpG site models, incorporating race specific effects, with tumor type ranges of 62-90% overall, 64-87% for AAs, and 64-92% for European Americans (EAs). Race played a significant role in the relationship between methylation and mRNA expression, with 86-97% of gene models containing at least one CpG site with a race-specific effect. Further, the median percentage of CpGs in a gene model with race-specific effects ranging from 11-18%. For these race-specific effects, the vast majority (&amp;gt;99%) had the same direction of association between the CpG site and gene expression in both AAs and EAs but with differing magnitudes. Application of this approach identified unique methylation alterations (97%) associated with Gleason grade compared with single site and comb-p region-based EWAS. In summary, this poly-CpG site modelling approach revealed a significant impact of race in the regulatory relationship between methylation and gene expression. The high percentage of genes -and CpG sites within those genes- demonstrating race-specific effects was a general phenomenon observed across all eight tumor types investigated. In addition to revealing this previously underappreciated contribution of race, the application of the predicted gene expression output from these poly-CpG site models to perform a gene-based EWAS of Gleason grade demonstrated that it provides a) complementary information to commonly used EWAS methods and b) direct expression-based mechanistic interpretation of findings from an AA methylation-based EWAS of prostate cancer. Citation Format: Ian M. Loveless, Yalei Chen, Sudha Sadasivan, Indrani Datta, Nilesh Gupta, Pamela Paris, Jia Li, Benjamin A. Rybicki, Albert M. Levin. The impact of race on the relationship between poly-CpG DNA methylation and mRNA expression in cancer with application to epigenome-wide association studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2100.

Comprehensive data analysis of genomics, epigenomics, and transcriptomics to identify specific biomolecular markers for prostate adenocarcinoma.

BackgroundMultiomics data analysis based on high-throughput sequencing technology has become a hotspot in tumor investigation. The present study aimed to explore prognostic biomarkers via investigating DNA copy number variation (CNV) and methylation variation (MET) data in prostate cancer.MethodsWe obtained the messenger RNA (mRNA) expression, CNV, and methylated data of prostate adenocarcinoma (PRAD) samples via The Cancer Genome Atlas (TCGA)-PRAD cohort. We calculated and assessed the associations between CNV and RNA sequencing (RNA-seq), and between MET and RNA-seq via Pearson correlation coefficients. We then used the “iCluster” package to perform multigroup cluster analysis with CNVcor gene CNV data, METcor gene methylation data, and CNVcor and METcor gene mRNA data. The univariate Cox analysis was used to screen significant hub genes, and multivariate Cox analysis was used to construct risk a model. The nomogram was constructed based on “rms” package, and the immune infiltrating patterns were compared between high- and low-risk groups.ResultsA total of 477 PRAD samples with complete CNV, methylation, mRNA, and matched clinical information were included in our study. A list of 10,073 CNVcor genes and 9841 METcor genes were confirmed with a significance level of P<0.01. We found that CNVcor is more likely to appear on chromosome (chr)8, chr17, and chr10, while METcor is more likely to appear on chr1, chr19, and chr17. Based on the core genes, we finally classified the samples into 4 subtypes, incorporating iC1 (iCluster) (92 samples), iC2 (79 samples), iC3 (165 samples), and iC4 (141 samples). Furthermore, we constructed the prognostic model for PRAD based on the 5 genes (IER3, AOX1, PRKCDBP, UBD, and FBLN5). Nomograms incorporating risk score and other clinical variables were further constructed, and these nomograms exhibited superior predictive ability. We further compared the differential immune infiltrating patterns in 2 risk groups and found significantly low levels of infiltrating cluster of differentiation (CD)8+ T cells in high-risk samples.ConclusionsOur study integrated the multi-omics data to elucidate the molecular features of PRAD and pivotal genes for predicting prognosis.

Abstract 2123: Chromatin profiling of glioblastoma tissues identifies core oncogenic dependency and therapeutic opportunities

Abstract Background: Glioblastoma (GBM) is the most aggressive and therapy-refractory brain tumor in adults. Molecular profiling of GBM on the basis of gene expression, DNA methylation and genomic variations has advanced both cancer research and clinical diagnosis. However, the enhancer architectures and regulatory circuitries governing tumor-intrinsic transcriptional diversity and subtype identity are still elusive. Methods: Chromatin immunoprecipitation followed by sequencing analysis was applied to examine H3K27ac deposition and to map the active regulatory landscapes across 95 GBM biopsies and 12 normal brain tissues. RNA-sequencing analysis was performed to measure transcriptome and to classify transcriptional subtypes. Super-enhancer associated genes and master transcriptional factors were identified. Function of novel cancer associated genes was explored using both patient-derived GBM propagating cells and orthotopic xenograft models. Results: Analyses of differentially regulated enhancers and super-enhancers uncovered previously unrecognized layers of inter-tumor heterogeneity. Integrative analysis of variant enhancer loci and transcriptome identified topographies of transcriptional enhancers and core regulatory circuitries in four molecular subtypes of primary tumors: AC1-mesenchymal, AC1-classical, AC2-proneural and AC3-proneural. Subtype-specific enhancer domains contributed to transcriptional diversity and shaped subtype identity. Moreover, this study reveals novel oncogenic dependency of GBM on various super-enhancer-driven transcriptional factors and druggable targets (e.g., BRD4). Conclusion: Through profiling of transcriptional enhancers, we provide clinically relevant insights into the molecular classification, pathogenesis and therapeutic intervention of GBM. Citation Format: Liang Xu, Ye Chen, Yulun Huang, Edwin Sandanaraj, John S. Yu, Ruby Yu-Tong Lin, Pushkar Dakle, Xin-Yu Ke, Yuk Kien Chong, Lynnette Koh, Anand Mayakonda, Kassoum Nacro, Jeffrey Hill, Mo-Li Huang, Sigal Gery, See Wee Lim, Zhengyun Huang, Ying Xu, Jianxiang Chen, Longchuan Bai, Shaomeng Wang, Hiroaki Wakimoto, Tseng Tsai Yeo, Beng Ti Ang, Markus Müschen, Carol Tang, Tuan Zea Tan, Phillip H. Koeffler. Chromatin profiling of glioblastoma tissues identifies core oncogenic dependency and therapeutic opportunities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2123.

Genetic and in utero environmental contributions to DNA methylation variation in placenta.

Genetic and prenatal environmental factors shape fetal development and cardiometabolic health in later life. A key target of genetic and prenatal environmental factors is the epigenome of the placenta, an organ that is implicated in fetal growth and diseases in later life. This study had two aims: (1) to identify and functionally characterize placental variably methylated regions (VMRs), which are regions in the epigenome with high inter-individual methylation variability; and (2) to investigate the contributions of fetal genetic loci and 12 prenatal environmental factors (maternal cardiometabolic-,psychosocial-, demographic- and obstetric-related) on methylation at each VMR. Akaike's information criterion was used to select the best model out of four models [prenatal environment only, genotype only, additive effect of genotype and prenatal environment (G + E), and their interaction effect (G × E)]. We identified 5850 VMRs in placenta. Methylation at 70% of VMRs was best explained by G × E, followed by genotype only (17.7%), and G + E (12.3%). Prenatal environment alone best explained only 0.03% of VMRs. We observed that 95.4% of G × E models and 93.9% of G + E models included maternal age, parity, delivery mode, maternal depression or gestational weight gain. VMR methylation sites and their regulatory genetic variants were enriched (P < 0.05) for genomic regions that have known links with regulatory functions and complex traits. This study provided a genome-wide catalog of VMRs in placenta and highlighted that variation in placental DNA methylation at loci with regulatory and trait relevance is best elucidated by integrating genetic and prenatal environmental factors, and rarely by environmental factors alone.

Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization

Integrative analysis of histopathology images and genomic data enables the discovery of potential biomarkers and multimodal association patterns. However, few studies have established effective association models for complex diseases, such as sarcoma, by combining histopathological images with multiple genetic variation data. Here, we present an integrative multiple genomic imaging framework called multi-dimensional constrained joint non-negative matrix factorization (MDJNMF) to identify modules related to lung metastasis of sarcomas based on sample-matched whole-solid image, DNA methylation, and copy number variation features. Three types of feature matrices were projected onto a common feature space, in which heterogeneous variables with large coefficients in the same projected direction form a common module. The correlation between image features and genetic variation features is used as network-regularized constraints to improve the module accuracy. Sparsity and orthogonal constraints are utilized to achieve the modular sparse solution. Multi-level analysis indicates that our method effectively discovers biologically functional modules associated with sarcoma or lung metastasis. The representative module reveals a significant correlation between image features and genetic variation features and excavates potential diagnostic biomarkers. In summary, the proposed method provides new clues for identifying association patterns and biomarkers using multiple types of data sources for other diseases.

Identification of key classification features of early cervical squamous cell carcinoma

Despite the tremendous progress in molecular analysis of pan-cancer, little is known regarding molecular classification of cervical squamous cell carcinoma. In this study, we adopted a multi-omics approach to identify potential key classification features of cervical squamous cell carcinoma. Specifically, we analyzed mRNA, and microRNA (miRNA) expression data, as well as DNA methylation and copy number variation in cervical squamous cell carcinoma cases, using datasets obtained from The Cancer Genome Atlas (TCGA). Moreover, we identified molecules in each dimension, as well as integrated and clustered filtered classification features, and used them to distinguish different subtypes. The resulting key classification features were used to establish a classification model for cervical squamous cell carcinoma. Our results revealed two cervical squamous cell carcinoma subtypes, with significant differences across clinical survival levels, as well as 8 key classification features of cervical squamous cell carcinomas. These findings are expected to provide important references for early classification of cervical squamous cell carcinoma and identification of classification markers.

Association of medically assisted reproduction with offspring cord blood DNA methylation across cohorts.

STUDY QUESTIONIs cord blood DNA methylation associated with having been conceived by medically assisted reproduction?SUMMARY ANSWERThis study does not provide strong evidence of an association of conception by medically assisted reproduction with variation in infant blood cell DNA methylation.WHAT IS KNOWN ALREADYMedically assisted reproduction consists of procedures used to help infertile/subfertile couples conceive, including ART. Due to its importance in gene regulation during early development programming, DNA methylation and its perturbations associated with medically assisted reproduction could reveal new insights into the biological effects of assisted reproductive technologies and potential adverse offspring outcomes.STUDY DESIGN, SIZE, DURATIONWe investigated the association of DNA methylation and medically assisted reproduction using a case–control study design (N = 205 medically assisted reproduction cases and N = 2439 naturally conceived controls in discovery cohorts; N = 149 ART cases and N = 58 non-ART controls in replication cohort).PARTICIPANTS/MATERIALS, SETTINGS, METHODSWe assessed the association between medically assisted reproduction and DNA methylation at birth in cord blood (205 medically assisted conceptions and 2439 naturally conceived controls) at >450 000 CpG sites across the genome in two sub-samples of the UK Avon Longitudinal Study of Parents and Children (ALSPAC) and two sub-samples of the Norwegian Mother, Father and Child Cohort Study (MoBa) by meta-analysis. We explored replication of findings in the Australian Clinical review of the Health of adults conceived following Assisted Reproductive Technologies (CHART) study (N = 149 ART conceptions and N = 58 controls).MAIN RESULTS AND THE ROLE OF CHANCEThe ALSPAC and MoBa meta-analysis revealed evidence of association between conception by medically assisted reproduction and DNA methylation (false-discovery-rate-corrected P-value < 0.05) at five CpG sites which are annotated to two genes (percentage difference in methylation per CpG, cg24051276: Beta = 0.23 (95% CI 0.15,0.31); cg00012522: Beta = 0.47 (95% CI 0.31, 0.63); cg17855264: Beta = 0.31 (95% CI 0.20, 0.43); cg17132421: Beta = 0.30 (95% CI 0.18, 0.42); cg18529845: Beta = 0.41 (95% CI 0.25, 0.57)). Methylation at three of these sites has been previously linked to cancer, aging, HIV infection and neurological diseases. None of these associations replicated in the CHART cohort. There was evidence of a functional role of medically assisted reproduction-induced hypermethylation at CpG sites located within regulatory regions as shown by putative transcription factor binding and chromatin remodelling.LIMITATIONS, REASONS FOR CAUTIONSWhile insufficient power is likely, heterogeneity in types of medically assisted reproduction procedures and between populations may also contribute. Larger studies might identify replicable variation in DNA methylation at birth due to medically assisted reproduction.WIDER IMPLICATIONS OF THE FINDINGSNewborns conceived with medically assisted procedures present with divergent DNA methylation in cord blood white cells. If these associations are true and causal, they might have long-term consequences for offspring health.STUDY FUNDING/COMPETING INTERESTS(S)This study has been supported by the US National Institute of Health (R01 DK10324), the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC Grant agreement no. 669545, European Union’s Horizon 2020 research and innovation programme under Grant agreement no. 733206 (LifeCycle) and the NIHR Biomedical Centre at the University Hospitals Bristol NHS Foundation Trust and the University of Bristol. The UK Medical Research Council and Wellcome (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. Methylation data in the ALSPAC cohort were generated as part of the UK BBSRC funded (BB/I025751/1 and BB/I025263/1) Accessible Resource for Integrated Epigenomic Studies (ARIES, http://www.ariesepigenomics.org.uk). D.C., J.J., C.L.R. D.A.L and H.R.E. work in a Unit that is supported by the University of Bristol and the UK Medical Research Council (Grant nos. MC_UU_00011/1, MC_UU_00011/5 and MC_UU_00011/6). B.N. is supported by an NHMRC (Australia) Investigator Grant (1173314). ALSPAC GWAS data were generated by Sample Logistics and Genotyping Facilities at Wellcome Sanger Institute and LabCorp (Laboratory Corporation of America) using support from 23andMe. The Norwegian Mother, Father and Child Cohort Study is supported by the Norwegian Ministry of Health and Care Services and the Ministry of Education and Research, NIH/NIEHS (Contract no. N01-ES-75558), NIH/NINDS (Grant nos. (i) UO1 NS 047537-01 and (ii) UO1 NS 047537-06A1). For this work, MoBa 1 and 2 were supported by the Intramural Research Program of the NIH, National Institute of Environmental Health Sciences (Z01-ES-49019) and the Norwegian Research Council/BIOBANK (Grant no. 221097). This work was partly supported by the Research Council of Norway through its Centres of Excellence funding scheme, Project no. 262700.D.A.L. has received support from national and international government and charity funders, as well as from Roche Diagnostics and Medtronic for research unrelated to this study. The other authors declare no conflicts of interest.TRIAL REGISTRATION NUMBERN/A.

Multi-Omics Marker Analysis Enables Early Prediction of Breast Tumor Progression

Ductal carcinoma in situ (DCIS) is a preinvasive form of breast cancer with a highly variable potential of becoming invasive and affecting mortality of the patients. Due to the lack of accurate markers of disease progression, many women with detected DCIS are currently overtreated. To distinguish those DCIS cases who are likely to require therapy from those who should be left untreated, there is a need for robust and predictive biomarkers extracted from molecular or genetic profiles. We developed a supervised machine learning approach that implements multi-omics feature selection and model regularization for the identification of biomarker combinations that could be used to distinguish low-risk DCIS lesions from those with a higher likelihood of progression. To investigate the genetic heterogeneity of disease progression, we applied this approach to 40 pure DCIS and 259 invasive breast cancer (IBC) samples profiled with genome-wide transcriptomics, DNA methylation, and DNA copy number variation. Feature selection using the multi-omics Lasso-regularized algorithm identified both known genes involved in breast cancer development, as well as novel markers for early detection. Even though the gene expression-based model features led to the highest classification accuracy alone, methylation data provided a complementary source of features and improved especially the sensitivity of correctly classifying DCIS cases. We also identified a number of repeatedly misclassified DCIS cases when using either the expression or methylation markers. A small panel of 10 gene markers was able to distinguish DCIS and IBC cases with high accuracy in nested cross-validation (AU-ROC = 0.99). The marker panel was not specific to any of the established breast cancer subtypes, suggesting that the 10-gene signature may provide a subtype-agnostic and cost-effective approach for breast cancer detection and patient stratification. We further confirmed high accuracy of the 10-gene signature in an external validation cohort (AU-ROC = 0.95), profiled using distinct transcriptomic assay, hence demonstrating robustness of the risk signature.