MethylSeqLogo: DNA methylation smart sequence logos.

BACKGROUND: Some transcription factors, MYC for example, bind sites of potentially methylated DNA. This may increase binding specificity as such sites are (1) highly under-represented in the genome, and (2) offer additional, tissue specific information in the form of hypo- or hyper-methylation. Fortunately, bisulfite sequencing data can be used to investigate this phenomenon. METHOD: We developed MethylSeqLogo, an extension of sequence logos which includes new elements to indicate DNA methylation and under-represented dimers in each position of a set binding sites. Our method displays information from both DNA strands, and takes into account the sequence context (CpG or other) and genome region (promoter versus whole genome) appropriate to properly assess the expected background dimer frequency and level of methylation. MethylSeqLogo preserves sequence logo semantics-the relative height of nucleotides within a column represents their proportion in the binding sites, while the absolute height of each column represents information (relative entropy) and the height of all columns added together represents total information RESULTS: We present figures illustrating the utility of using MethylSeqLogo to summarize data from several CpG binding transcription factors. The logos show that unmethylated CpG binding sites are a feature of transcription factors such as MYC and ZBTB33, while some other CpG binding transcription factors, such as CEBPB, appear methylation neutral. CONCLUSIONS: Our software enables users to explore bisulfite and ChIP sequencing data sets-and in the process obtain publication quality figures.

DNA demethylation triggers cell free DNA release in colorectal cancer cells.

BACKGROUND: Liquid biopsy based on cell-free DNA (cfDNA) analysis holds significant promise as a minimally invasive approach for the diagnosis, genotyping, and monitoring of solid malignancies. Human tumors release cfDNA in the bloodstream through a combination of events, including cell death, active and passive release. However, the precise mechanisms leading to cfDNA shedding remain to be characterized. Addressing this question in patients is confounded by several factors, such as tumor burden extent, anatomical and vasculature barriers, and release of nucleic acids from normal cells. In this work, we exploited cancer models to dissect basic mechanisms of DNA release. METHODS: We measured cell loss ratio, doubling time, and cfDNA release in the supernatant of a colorectal cancer (CRC) cell line collection (N = 76) representative of the molecular subtypes previously identified in cancer patients. Association analyses between quantitative parameters of cfDNA release, cell proliferation, and molecular features were evaluated. Functional experiments were performed to test the impact of modulating DNA methylation on cfDNA release. RESULTS: Higher levels of supernatant cfDNA were significantly associated with slower cell cycling and increased cell death. In addition, a higher cfDNA shedding was found in non-CpG Island Methylator Phenotype (CIMP) models. These results indicate a positive correlation between lower methylation and increased cfDNA levels. To explore this further, we exploited methylation microarrays to identify a subset of probes significantly associated with cfDNA shedding and derive a methylation signature capable of discriminating high from low cfDNA releasers. We applied this signature to an independent set of 176 CRC cell lines and patient derived organoids to select 14 models predicted to be low or high releasers. The methylation profile successfully predicted the amount of cfDNA released in the supernatant. At the functional level, genetic ablation of DNA methyl-transferases increased chromatin accessibility and DNA fragmentation, leading to increased cfDNA release in isogenic CRC cell lines. Furthermore, in vitro treatment of five low releaser CRC cells with a demethylating agent was able to induce a significant increase in cfDNA shedding. CONCLUSIONS: Methylation status of cancer cell lines contributes to the variability of cfDNA shedding in vitro. Changes in methylation pattern are associated with cfDNA release levels and might be exploited to increase sensitivity of liquid biopsy assays.

Methylation patterns at the adjacent CpG sites within enhancers are a part of cell identity.

BACKGROUND: It is generally accepted that methylation status of CpG sites spaced up to 50 bp apart is correlated, and accumulation of locally disordered methylation at adjacent CpG sites is involved in neoplastic transformation, acting in similar way as stochastic accumulation of mutations. RESULTS: We used EPIC microarray data from 596 samples, representing 12 healthy tissue and cell types, as well as 572 blood cancer specimens to analyze methylation status of adjacent CpG sites across human genome, and subsequently validated our findings with NGS and Sanger sequencing. Our analysis showed that there is a subset of the adjacent CpG sites in human genome, with cytosine at one CpG site methylated and the other devoid of methyl group. These loci map to enhancers that are targeted by families of transcription factors involved in cell differentiation. Moreover, our results suggest that the methylation at these loci differ between alleles within a cell, what allows for remarkable level of heterogeneity of methylation patterns. However, different types of specialized cells acquire only one specific and stable pattern of methylation at each of these loci and that pattern is to a large extent lost during neoplastic transformation. CONCLUSIONS: We identified a substantial number of adjacent CpG loci in human genome that display remarkably stable and cell type specific methylation pattern. The methylation pattern at these loci appears to reflect different methylation of alleles in cells. Furthermore, we showed that changes of methylation status at those loci are likely to be involved in regulation of the activity of enhancers and contribute to neoplastic transformation.

Sex differences in DNA methylation variations according to ART conception-evidence from the Norwegian mother, father, and child cohort study.

Previous studies have shown cord-blood DNA methylation differences in newborns conceived using assisted reproductive technologies (ART) compared to those conceived naturally. However, whether these ART-related DNA methylation differences vary with children's sex is unknown. We hypothesize that the DNA methylation differences in cord blood between ART-conceived and naturally conceived newborns also varies by the sex of the child, with distinct patterns of differential methylation present in males and females. We investigated sex differences in cord-blood DNA methylation variation according to conception by ART using the Illumina MethylationEPIC platform, comparing 456 ART-conceived versus 507 naturally-conceived girls, and 503 ART-conceived and 473 naturally-conceived boys. We identified 37 differentially methylated CpGs according to ART-conception among girls, and 70 differentially methylated CpGs according to ART-conception among boys, when we used a 1% false discovery rate to account for multiple testing. Ten CpGs were differentially methylated according to conception by ART in both sexes. Among the genes that were associated with these CpGs, we found the BRCA1; NBR2 gene (two CpGs) was hypermethylated in girls while the APC2 (two CpGs) and NECAB3;ACTL10, (four CpGs) related to cellular signaling were hypomethylated in boys. These findings confirm the presence of sex-specific epigenetic differences, illustrating the nuanced impact of ART on the fetal epigenome. There is a need for further explorations into the implications for sex-specific developmental trajectories and health outcomes in ART-conceived children.

Identifying the "stripe" transcription factors and cooperative binding related to DNA methylation.

DNA methylation plays a critical role in gene regulation by modulating the DNA binding of transcription factors (TFs). This study integrates TFs' ChIP-seq profiles with WGBS profiles to investigate how DNA methylation affects protein interactions. Statistical methods and a 5-letter DNA motif calling model have been developed to characterize DNA sequences bound by proteins, while considering the effects of DNA modifications. By employing these methods, 79 significant universal "stripe" TFs and cofactors (USFs), 2360 co-binding protein pairs, and distinct protein modules associated with various DNA methylation states have been identified. The USFs hint a regulatory hierarchy within these protein interactions. Proteins preferentially bind to non-CpG sites in methylated regions, indicating binding affinity is not solely CpG-dependent. Proteins involved in methylation-specific USFs and cobinding pairs play essential roles in promoting and sustaining DNA methylation through interacting with DNMTs or inhibiting TET binding. These findings underscore the interplay between protein binding and methylation, offering insights into epigenetic regulation in cellular biology.

Mediation analysis in longitudinal study with high-dimensional methylation mediators.

Mediation analysis has been widely utilized to identify potential pathways connecting exposures and outcomes. However, there remains a lack of analytical methods for high-dimensional mediation analysis in longitudinal data. To tackle this concern, we proposed an effective and novel approach with variable selection and the indirect effect (IE) assessment based on both linear mixed-effect model and generalized estimating equation. Initially, we employ sure independence screening to reduce the dimension of candidate mediators. Subsequently, we implement the Sobel test with the Bonferroni correction for IE hypothesis testing. Through extensive simulation studies, we demonstrate the performance of our proposed procedure with a higher F$_{1}$ score (0.8056 and 0.9983 at sample sizes of 150 and 500, respectively) compared with the linear method (0.7779 and 0.9642 at the same sample sizes), along with more accurate parameter estimation and a significantly lower false discovery rate. Moreover, we apply our methodology to explore the mediation mechanisms involving over 730 000 DNA methylation sites with potential effects between the paternal body mass index (BMI) and offspring growing BMI in the Shanghai sleeping birth cohort data, leading to the identification of two previously undiscovered mediating CpG sites.

Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework.

DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual's age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci's biological significance.

Genome-wide methylation profiling reveals extracellular vesicle DNA as an ex vivo surrogate of cancer cell-derived DNA.

Extracellular vesicle-derived DNA (evDNA) encapsulates the complete genome and mutational status of cells; however, whether cancer cell-derived evDNA mirrors the epigenetic features of parental genomic DNA remains uncertain. This study aimed to assess and compare the DNA methylation patterns of evDNA from cancer cell lines and primary cancer tissues with those of the nuclear genomic DNA. We isolated evDNA secreted by two cancer cell lines (HCT116 and MDA-MB-231) from various subcellular compartments, including the nucleus and cytoplasm. Additionally, we obtained evDNA and nuclear DNA (nDNA) from the primary cancer tissues of colon cancer patients. We conducted a comprehensive genome-wide DNA methylation analysis using the Infinium Methylation EPIC BeadChip, examining > 850,000 CpG sites. Remarkable similarities were observed between evDNA and nDNA methylation patterns in cancer cell lines and patients. This concordance extended to clinical cancer tissue samples, showcasing the potential utility of evDNA methylation patterns in deducing cellular origin within heterogeneous populations through methylation-based deconvolution. The observed concordance underscores the potential of evDNA as a noninvasive surrogate marker for discerning tissue origin, particularly in cancer tissues, offering a promising future for cancer diagnostics. This finding enhances our understanding of cellular origins and would help develop innovative diagnostic and therapeutic strategies for cancer.

The role of DNA methylation in chondrogenesis of human iPSCs as a stable marker of cartilage quality.

BACKGROUND: Lack of insight into factors that determine purity and quality of human iPSC (hiPSC)-derived neo-cartilage precludes applications of this powerful technology toward regenerative solutions in the clinical setting. Here, we set out to generate methylome-wide landscapes of hiPSC-derived neo-cartilages from different tissues-of-origin and integrated transcriptome-wide data to identify dissimilarities in set points of methylation with associated transcription and the respective pathways in which these genes act. METHODS: We applied in vitro chondrogenesis using hiPSCs generated from two different tissue sources: skin fibroblasts and articular cartilage. Upon differentiation toward chondrocytes, these are referred to as hFiCs and hCiC, respectively. Genome-wide DNA methylation and RNA sequencing datasets were generated of the hiPSC-derived neo-cartilages, and the epigenetically regulated transcriptome was compared to that of neo-cartilage deposited by human primary articular cartilage (hPAC). RESULTS: Methylome-wide landscapes of neo-cartilages of hiPSCs reprogrammed from two different somatic tissues were 85% similar to that of hPACs. By integration of transcriptome-wide data, differences in transcriptionally active CpGs between hCiC relative to hPAC were prioritized. Among the CpG-gene pairs lower expressed in hCiCs relative to hPACs, we identified genes such as MGP, GDF5, and CHAD enriched in closely related pathways and involved in cartilage development that likely mark phenotypic differences in chondrocyte states. Vice versa, among the CpG-gene pairs higher expressed, we identified genes such as KIF1A or NKX2-2 enriched in neurogenic pathways and likely reflecting off target differentiation. CONCLUSIONS: We did not find significant variation between the neo-cartilages derived from hiPSCs of different tissue sources, suggesting that application of a robust differentiation protocol such as we applied here is more important as compared to the epigenetic memory of the cells of origin. Results of our study could be further exploited to improve quality, purity, and maturity of hiPSC-derived neo-cartilage matrix, ultimately to realize introduction of sustainable, hiPSC-derived neo-cartilage implantation into clinical practice.

Applicability of epigenetic age models to next-generation methylation arrays.

BACKGROUND: Epigenetic clocks are mathematical models used to estimate epigenetic age based on DNA methylation at specific CpG sites. As new methylation microarrays are developed and older models discontinued, existing epigenetic clocks might become obsolete. Here, we explored the effects of the changes introduced in the new EPICv2 DNA methylation array on existing epigenetic clocks. METHODS: We tested the performance of four epigenetic clocks on the probeset of the EPICv2 array using a dataset of 10,835 samples. We developed a new epigenetic age prediction model compatible across the 450 k, EPICv1, and EPICv2 microarrays and validated it on 2095 samples. We estimated technical noise and intra-subject variation using two datasets with repeated sampling. We used data from (i) cancer survivors who had undergone different therapies, (ii) breast cancer patients and controls, and (iii) an exercise-based interventional study, to test the ability of our model to detect alterations in epigenetic age acceleration in response to theoretically antiaging interventions. RESULTS: The results of the four epiclocks tested are significantly distorted by the EPICv2 probeset, causing an average difference of up to 25 years. Our new model produced highly accurate chronological age predictions, comparable to a state-of-the-art epiclock. The model reported the lowest epigenetic age acceleration in normal populations, as well as the lowest variation across technical replicates and repeated samples from the same subjects. Finally, our model reproduced previous results of increased epigenetic age acceleration in cancer patients and in survivors treated with radiation therapy, and no changes from exercise-based interventions. CONCLUSION: Existing epigenetic clocks require updates for full EPICv2 compatibility. Our new model translates the capabilities of state-of-the-art epigenetic clocks to the EPICv2 platform and is cross-compatible with older microarrays. The characterization of epigenetic age prediction variation provides useful metrics to contextualize the relevance of epigenetic age alterations. The analysis of data from subjects influenced by radiation, cancer, and exercise-based interventions shows that despite being good predictors of chronological age, neither a pathological state like breast cancer, a hazardous environmental factor (radiation), nor exercise (a beneficial intervention) caused significant changes in the values of the "epigenetic age" determined by these first-generation models.

PER3 promoter hypermethylation correlates to the progression of pan-cancer.

BACKGROUND: Malignant cells exhibit reduced period circadian regulator 3 (PER3) expression. However, the underlying mechanisms of variations in PER3 expression in cancers and the specific function of PER3 in tumor progression remain poorly understood. RESULTS: We explored multiple public databases, conducted bioinformatics analyses, and performed in vitro and in vivo experiments for validation. We found PER3 expression was decreased in most types of cancers, and PER3 downregulation was associated with a poor prognosis in 8 types of cancer. PER3 promoter methylation levels were increased in 11 types of cancer. Promoter hypermethylation (CpG islands [CGIs] cg12258811 and cg14204433) correlated with decreased PER3 expression in six cancers (breast invasive carcinoma, colon adenocarcinoma, head and neck squamous cell carcinoma, kidney renal papillary cell carcinoma [KIRP], lung adenocarcinoma [LUAD], and uterine corpus endometrial carcinoma). CGI cg12258811 hypermethylation was associated with reduced survival time and advanced cancer stages. Moreover, the bisulfite pyrosequencing assay confirmed CGI cg12258811 hypermethylation and its negative correlation with PER3 expression. In vitro and in vivo experiments demonstrated that PER3 inhibited KIRP and LUAD progression. Decitabine enhanced PER3 expression and inhibited KIRP cell functions by reducing promoter (cg12258811) methylation level. CONCLUSIONS: Our findings advanced the mechanistic understanding of variations in PER3 expression in cancers and confirmed the tumor-associated function of PER3 hypermethylation and downregulation.

Somatic mosaicism in schizophrenia brains reveals prenatal mutational processes.

Germline mutations modulate the risk of developing schizophrenia (SCZ). Much less is known about the role of mosaic somatic mutations in the context of SCZ. Deep (239×) whole-genome sequencing (WGS) of brain neurons from 61 SCZ cases and 25 controls postmortem identified mutations occurring during prenatal neurogenesis. SCZ cases showed increased somatic variants in open chromatin, with increased mosaic CpG transversions (CpG>GpG) and T>G mutations at transcription factor binding sites (TFBSs) overlapping open chromatin, a result not seen in controls. Some of these variants alter gene expression, including SCZ risk genes and genes involved in neurodevelopment. Although these mutational processes can reflect a difference in factors indirectly involved in disease, increased somatic mutations at developmental TFBSs could also potentially contribute to SCZ.

Host genetics-associated mechanisms in colorectal cancer.

Colorectal cancer (CRC) represents the second leading cause of cancer incidence and the third leading cause of cancer deaths worldwide. There is currently a lack of understanding of the onset of CRC, hindering the development of effective prevention strategies, early detection methods and the selection of appropriate therapies. This article outlines the key aspects of host genetics currently known about the origin and development of CRC. The organisation of the colonic crypts is described. It discusses how the transformation of a normal cell to a cancer cell occurs and how that malignant cell can populate an entire colonic crypt, promoting colorectal carcinogenesis. Current knowledge about the cell of origin of CRC is discussed, and the two morphological pathways that can give rise to CRC, the classical and alternative pathways, are presented. Due to the molecular heterogeneity of CRC, each of these pathways has been associated with different molecular mechanisms, including chromosomal and microsatellite genetic instability, as well as the CpG island methylator phenotype. Finally, different CRC classification systems are described based on genetic, epigenetic and transcriptomic alterations, allowing diagnosis and treatment personalisation.

Are Methylation Patterns in the KALRN Gene Associated with Cognitive and Depressive Symptoms? Findings from the Moli-sani Cohort.

The KALRN gene (encoding kalirin) has been implicated in several neuropsychiatric and neurodegenerative disorders. However, genetic evidence supporting this implication is limited and targeted epigenetic analyses are lacking. Here, we tested associations between epigenetic variation in KALRN and interindividual variation in depressive symptoms (PHQ9) and cognitive (MoCA) performance, in an Italian population cohort (N = 2409; mean (SD) age: 67 (9) years; 55% women). First, we analyzed the candidate region chr3:124584826-124584886 (hg38), within the KALRN promoter, through pyrosequencing of 1385 samples. Then, we widened the investigated region by analyzing 137 CpGs annotated to the whole gene, rescued from epigenome-wide (Illumina EPIC) data from 1024 independent samples from the same cohort. These were tested through stepwise regression models adjusted for age, sex, circulating leukocytes fractions, education, prevalent health conditions and lifestyles. We observed no statistically significant associations with methylation levels in the three CpGs tested through pyrosequencing, or in the gene-wide association analysis with MoCA score. However, we observed a statistically significant association between PHQ9 and cg13549966 (chr3:124106738; ß (Standard Error) = 0.28 (0.08), Bonferroni-corrected p = 0.025), located close to the transcription start site of the gene. This association was driven by a polychoric factor tagging somatic depressive symptoms (ß (SE) = 0.127 (0.064), p = 0.048). This evidence underscores the importance of studying epigenetic variation within the KALRN gene and the role that it may play in brain diseases, particularly in atypical depression, which is often characterized by somatic symptoms.

Differences in DNA Methylation in Genes Involved in Vitamin D Metabolism Are Related to Insulin Requirement in Pregnant Women with Gestational Diabetes Mellitus.

In a previous study performed by our group, pregnant women with Gestational Diabetes (GDM) showed higher vitamin D (VitD) levels in the last trimester, particularly in those requiring insulin. This phenomenon was not linked to factors like season or supplementation. This study aimed to investigate if insulin treatment in GDM affects DNA methylation in VitD metabolism genes. Thirty-two pregnant women were selected, half of whom had GDM, and were divided into insulin-treated and lifestyle groups. The DNA methylation levels in CpGs from 47 VitD metabolism-related genes were analyzed at the diagnostic visit (24-28 weeks) and before delivery. At week 36-38 of pregnancy, twenty-six CpG sites were differentially methylated (DMPs) in the insulin-treated group compared with the control group and the lifestyle group. Twenty-two of these DMPs were not different at the diagnostic visit. Six CpGs (cg18276810 (CTNNB1), cg03919554 (FGFR3), cg03984919 (NCOA1), cg19218509 (ASIP), cg09922639 (SMAD3), and cg25356935 (PDZD3)) showed significant correlations with VitD levels, not only before childbirth, but also in the postpartum period and at one year later. This suggests that insulin treatment in GDM could influence DNA methylation in genes involved in vitamin D metabolism, affecting VitD levels during and after pregnancy. Further research is warranted to elucidate these findings' clinical implications.

Effects of multisuperovulation on the transcription and genomic methylation of oocytes and offspring.

BACKGROUND: Controlled ovarian stimulation is a common skill of assisted reproductive technologies (ARTs). In the clinic, some females would undergo more than one controlled ovarian stimulation cycle. However, few studies have focused on the influence of multi-superovulation on oocytes and offspring. RESULTS: Here, we found that multi-superovulation disrupted the transcriptome of oocytes and that the differentially expressed genes (DEGs) were associated mainly with metabolism and fertilization. The disruption of mRNA degradation via poly (A) size and metabolism might be a reason for the reduced oocyte maturation rate induced by repeated superovulation. Multi-superovulation results in hypo-genomic methylation in oocytes. However, there was an increase in the methylation level of CGIs. The DMRs are not randomly distributed in genome elements. Genes with differentially methylated regions (DMRs) in promoters are enriched in metabolic pathways. With increasing of superovulation cycles, the glucose and insulin tolerance of offspring is also disturbed. CONCLUSIONS: These results suggest that multi-superovulation has adverse effects on oocyte quality and offspring health.

Changes in mouse epidermal DNA methylation during development of squamous cell carcinoma in response to UVR.

Squamous cell carcinoma (SCC) is a common skin cancer, often caused by exposure to ultraviolet radiation (UVR). Recent studies have shown that changes in DNA methylation play a crucial role in the development of cancers. However, methylation patterns of SCC are not well characterised. Identifying biomarkers for the risk of developing SCC could be helpful for early detection and diagnosis and can potentially improve treatment and prevention strategies. This study aimed to investigate methylation changes in the epidermis of mice exposed to UVR for 24 weeks. We examined the DNA methylation levels of 260 199 CpGs using the Illumina Infinium Mouse Methylation BeadChip and studied the epidermis of UVR-exposed and unexposed mice every 4 weeks for 24 weeks (n = 39). We identified CpGs with large differences in methylation levels (ß-values) between UVR-exposed and unexposed mice. We also observed differences in the epigenetic age of these mice. We identified CpGs in Rev, Ipmk, Rad51b, Fgfr2, Fgfr3 and Ctnnb1 that may serve as potential biomarkers for SCC risk and could be helpful for the early detection and prevention of SCC. Further investigations are necessary to determine the biological functions and clinical significance of these CpGs.

Longitudinal association of peripheral blood DNA methylation with liver fat content: distinguishing between predictors and biomarkers.

BACKGROUND: Alterations in DNA methylation (DNAm) have been observed in patients with fatty liver, but whether they are cause or consequence remains unknown. The study aimed to investigate longitudinal association of epigenome-wide DNAm with liver fat content (LFC) in Chinese participants, and explore their temporal relationships. METHODS: Data were obtained from 2 waves over a four-year time period of the Shanghai Changfeng Study (discovery, n = 407 and replication, n = 126). LFC and peripheral blood DNAm were repeatedly measured using quantitative hepatic ultrasonography and the 850 K Illumina EPIC BeadChip, respectively. Longitudinal and cross-sectional epigenome-wide association studies (EWASs) were conducted with linear mixed model and linear regression model, respectively. Meta-analysis was performed using METAL. Cross-lagged panel analysis (CLPA) was carried out to infer temporal relationships between the significant CpGs and LFC. RESULTS: Longitudinal EWAS identified cg11024682 (SREBF1), cg06500161 (ABCG1), cg16740586 (ABCG1), cg15659943 (ABCA1) and cg00163198 (SNX19) significantly associated with LFC with P < 1e-7. Another 6 of the 22 previously reported CpGs were replicated in the present longitudinal EWAS. CLPA showed longitudinal effects of cg11024682 (SREBF1) (ß = 0.14 [0.06, 0.23]), cg16740586 (ABCG1) (ß = 0.17 [0.08, 0.25]), cg06500161 (ABCG1) (ß = 0.12 [0.03, 0.20]), cg17901584 (DHCR24) (ß = -0.10 [-0.18, -0.02]), cg00574958 (CPT1A) (ß = -0.09 [-0.17, -0.01]), cg08309687 (LINC00649) (ß = -0.11 [-0.19, -0.03]), and cg27243685 (ABCG1) (ß = 0.09 [0.01, 0.18]) on subsequent LFC. The effects were attenuated when further adjusting for body mass index. High levels of LFC led to alterations in DNAm of cg15659943 (ABCA1) (ß = 0.13 [0.04, 0.21]), cg07162647 (ß = -0.11 [-0.19, -0.03]), cg06500161 (ABCG1) (ß = 0.10 [0.02, 0.18]), and cg27243685 (ABCG1) (ß = 0.10 [0.02, 0.18]). CONCLUSIONS: Blood DNAm at SREBF1, ABCG1, DHCR24, CPT1A, and LINC00649 may be predictors of subsequent LFC change. The effects of DNAm at SREBF1 and ABCG1 on LFC were partially influenced by obesity. The findings have potential implications in understanding disease pathogenesis and highlight the potential of DNAm for early detection or intervention of fatty liver.

Phylo-Epigenetics in Phylogeny Analyses and Evolution.

Long-standing, continuous blurring and controversies in the field of phylogenetic interspecies relations, associated with insufficient explanations for dynamics and variability of speeds of evolution in mammals, hint at a crucial missing link. It has been suggested that transgenerational epigenetic inheritance and the concealed mechanisms behind play a distinct role in mammalian evolution. Here, a comprehensive sequence alignment approach in hominid species, i.e., Homo sapiens, Homo neanderthalensis, Denisovan human, Pan troglodytes, Pan paniscus, Gorilla gorilla, and Pongo pygmaeus, comprising conserved CpG islands of housekeeping genes, uncover evidence for a distinct variability of CpG dinucleotides. Applying solely these evolutionary consistent and inconsistent CpG sites in a classic phylogenetic analysis, calibrated by the divergence time point of the common chimpanzee (P. troglodytes) and the bonobo or pygmy chimpanzee (P. paniscus), a "phylo-epigenetic" tree has been generated, which precisely recapitulates branch points and branch lengths, i.e., divergence events and relations, as they have been broadly suggested in the current literature, based on comprehensive molecular phylogenomics and fossil records of many decades. It is suggested here that CpG dinucleotide changes at CpG islands are of superior importance for evolutionary developments. These changes are successfully inherited through the germ line, determining emerging methylation profiles, and they are a central component of transgenerational epigenetic inheritance. It is hidden in the DNA, what will happen on it later.