An overview of DNA methylation-derived trait score methods and applications

Mapping prenatal predictors and neurobehavioral outcomes of an epigenetic marker of neonatal inflammation - A longitudinal population-based study

BACKGROUND

DNA methylation levels at specific sites can be used to proxy C-reactive protein (CRP) levels, providing a potentially more stable and accurate indicator of sustained inflammation and associated health risk. However, its use has been primarily limited to adults or preterm infants, and little is known about determinants for - or offspring outcomes of - elevated levels of this epigenetic proxy in cord blood. The aim of this study was to comprehensively map prenatal predictors and long-term neurobehavioral outcomes of neonatal inflammation, as assessed with an epigenetic proxy of inflammation in cord blood, in the general pediatric population.

METHODS

Our study was embedded in the prospective population-based Generation R Study (n = 2,394). We created a methylation profile score of CRP (MPS-CRP) in cord blood as a marker of neonatal inflammation and validated it against serum CRP levels in mothers during pregnancy, as well as offspring at birth and in childhood. We then examined (i) which factors (previously associated with sustained inflammation) explain variability in MPS-CRP at birth, including a wide range of prenatal lifestyle and clinical conditions, pro-inflammatory exposures, as well as child genetic liability to elevated CRP levels; and (ii) whether MPS-CRP at birth associates with child neurobehavioral outcomes, including global structural MRI and DTI measures (child mean age 10 and 14 years) as well as psychiatric symptoms over time (Child Behavioral Checklist, at mean age 1.5, 3, 6, 10 and 14 years).

RESULTS

MPS-CRP at birth was validated with serum CRP in cord blood (cut-off > 1 mg/L) (AUC = 0.72). Prenatal lifestyle pro-inflammatory factors explained a small part (i.e., < 5%) of the variance in the MPS-CRP at birth. No other prenatal predictor or the polygenic score of CRP in the child explained significant variance in the MPS-CRP at birth. The MPS-CRP at birth prospectively associated with a reduction in global fractional anisotropy over time on mainly a nominal threshold (β = -0.014, SE = 0.007, p = 0.032), as well as showing nominal associations with structural differences (amygdala [(β = 0.016, SE = 0.006, p = 0.010], cerebellum [(β = -0.007, SE = 0.003, p = 0.036]). However, no associations with child psychiatric symptoms were observed.

CONCLUSION

Prenatal exposure to lifestyle-related pro-inflammatory factors was the only prenatal predictor that accounted for some of the individual variability in MPS-CRP levels at birth. Further, while the MPS-CRP prospectively associated with white matter alterations over time, no associations were observed at the behavioral level. Thus, the relevance and potential utility of using epigenetic data as a marker of neonatal inflammation in the general population remain unclear. In the future, the use of epigenetic proxies for a wider range of immune markers may lend further insights into the relationship between neonatal inflammation and adverse neurodevelopment within the general pediatric population.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

BACKGROUND

Incorporating genomic data into risk prediction has become an increasingly popular approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not.

METHODS

Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts.

RESULTS

The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p=0.006), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD.

CONCLUSION

The inclusion of exposure variables adds to the predictive power of MRS. Classification-based MRS may be useful in predicting risk of future PTSD in populations with anticipated trauma exposure. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting PTSD and, relatedly, improve their performance in independent cohorts.

Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

A review of disease risk prediction methods and applications in the omics era

Risk prediction and disease prevention are the innovative care challenges of the 21st century. Apart from freeing the individual from the pain of disease, it will lead to low medical costs for society. Until very recently, risk assessments have ushered in a new era with the emergence of omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and so on, which potentially advance the ability of biomarkers to aid prediction models. While risk prediction has achieved great success, there are still some challenges and limitations. We reviewed the general process of omics-based disease risk model construction and the applications in four typical diseases. Meanwhile, we highlighted the problems in current studies and explored the potential opportunities and challenges for future clinical practice.

A novel method for cell deconvolution using DNA methylation in PCA space

BACKGROUND

In this study, we present a novel method for reference-based cell deconvolution using data from DNA methylation arrays. Different from existing methods like IDOL-Ext, which operate on probe-level data, our approach represents features in the principal component analysis (PCA) space for cell type deconvolution.

RESULTS

Our method's accuracy in estimating cell compositions is validated across various public datasets, including blood samples from glioma patients. It demonstrates precision comparable to IDOL-Ext, with R2 values ranging from 0.73 to 0.99 for most cell types, while offering improved discrimination between similar cell types, particularly T cell subtypes in glioma patient samples (R2 0.42-0.75 vs. 0.36-0.66 for IDOL-Ext). However, both methods showed lower accuracy for certain cell types, such as memory CD8 T cells in glioma patients (R2 0.42 vs. 0.36 for IDOL-Ext), highlighting the challenges in distinguishing closely related cell populations. We have made this method available as an R package "BloodCellDecon" on GitHub.

CONCLUSIONS

Our study confirms the efficacy of cell type deconvolution in PCA space. The results indicate wide-ranging applicability and potential for adaptation to other forms of genomic data.

DNA Methylation Profiling in Genetically Selected Clarias magur (Hamilton, 1822) Provides Insights into the Epigenetic Regulation of Growth and Development

DNA methylation is an epigenetic alteration that impacts gene expression without changing the DNA sequence affecting an organism's phenotype. This study utilized a reduced representation bisulfite sequencing (RRBS) approach to investigate the patterns of DNA methylation in genetically selected Clarias magur stocks. RRBS generated 249.22 million reads, with an average of 490,120 methylation sites detected in various parts of genes, including exons, introns, and intergenic regions. A total of 896 differentially methylated regions (DMRs) were identified; 356 and 540 were detected as hyper-methylated and hypo-methylated regions, respectively. The DMRs and their association with overlapping genes were explored using whole genome data of magur, which revealed 205 genes in exonic, 210 in intronic, and 480 in intergenic regions. The analysis identified the maximum number of genes enriched in biological processes such as RNA biosynthetic process, response to growth factors, nervous system development, neurogenesis, and anatomical structure morphogenesis. Differentially methylated genes (DMGs) such as myrip, mylk3, mafb, egr3, ndnf, meis2a, foxn3, bmp1a, plxna3, fgf6, sipa1l1, mcu, cnot8, trim55b, and myof were associated with growth and development. The selected DMGs were analyzed using real-time PCR, which showed altered mRNA expression levels. This work offers insights into the epigenetic mechanisms governing growth performance regulation in magur stocks. This work provides a valuable resource of epigenetic data that could be integrated into breeding programs to select high-performing individuals.

Consortium Profile: The Methylation, Imaging and NeuroDevelopment (MIND) Consortium

Epigenetic processes, such as DNA methylation, show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of mental health and other brain-based phenotypes. However, little is known about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research in this area is almost entirely comprised of cross-sectional studies in adults. To bridge this gap, we established the Methylation, Imaging and NeuroDevelopment (MIND) Consortium, which aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies. MIND currently integrates 15 cohorts worldwide, comprising (repeated) measures of DNA methylation in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging across up to five time points over a period of up to 21 years (Npooled DNAm = 11,299; Npooled neuroimaging = 10,133; Npooled combined = 4,914). By triangulating associations across multiple developmental time points and study types, we hope to generate new insights into the dynamic relationships between peripheral DNA methylation and the brain, and how these ultimately relate to neurodevelopmental and psychiatric phenotypes.

DUSP5 regulated by YTHDF1-mediated m6A modification promotes epithelial-mesenchymal transition and EGFR-TKI resistance via the TGF-β/Smad signaling pathway in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) patients have a dismal survival rate because of cancer metastasis and drug resistance. The study aims to identify the genes that concurrently modulate EMT, metastasis and EGFR-TKI resistance, and to investigate the underlying regulatory mechanisms.

METHODS

Cox regression and Kaplan-Meier analyses were applied to identify prognostic oncogenes in LUAD. Gene set enrichment analysis (GSEA) was used to indicate the biological functions of the gene. Wound-healing and Transwell assays were used to detect migratory and invasive ability. EGFR-TKI sensitivity was evaluated by assessing the proliferation, clonogenic survival and metastatic capability of cancer cells with treatment with gefitinib. Methylated RNA immunoprecipitation (MeRIP) and RNA immunoprecipitation (RIP) analyses established the level of m6A modification present on the target gene and the protein's capability to interact with RNA, respectively. Single-sample gene set enrichment (ssGSEA) algorithm used to investigate levels of immune cell infiltration.

RESULTS

Our study identified dual-specificity phosphatase 5 (DUSP5) as a novel and powerful predictor of adverse outcomes for LUAD by using public datasets. Functional enrichment analysis found that DUSP5 was positively enriched in EMT and transforming growth factor-beta (TGF-β) signaling pathway, a prevailing pathway involved in the induction of EMT. As expected, DUSP5 knockdown suppressed EMT via inhibiting the canonical TGF-β/Smad signaling pathway in in vitro experiments. Consistently, knockdown of DUSP5 was first found to inhibit migratory ability and invasiveness of LUAD cells in in vitro and prevent lung metastasis in in vivo. DUSP5 knockdown re-sensitized gefitinib-resistant LUAD cells to gefitinib, accompanying reversion of EMT progress. In LUAD tissue samples, we found 14 cytosine-phosphate-guanine (CpG) sites of DUSP5 that were negatively associated with DUSP5 gene expression. Importantly, 5'Azacytidine (AZA), an FDA-approved DNA methyltransferase inhibitor, restored DUSP5 expression. Moreover, RIP experiments confirmed that YTH N6-methyladenosine RNA binding protein 1 (YTHDF1), a m6A reader protein, could bind DUSP5 mRNA. YTHDF1 promoted DUSP5 expression and the malignant phenotype of LUAD cells. In addition, the DUSP5-derived genomic model revealed the two clusters with distinguishable immune features and tumor mutational burden (TMB).

CONCLUSIONS

Briefly, our study discovered DUSP5 which was regulated by epigenetic modification, might be a potential therapeutic target, especially in LUAD patients with acquired EGFR-TKI resistance.

Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study

Importance

Understanding antidepressant mechanisms could help design more effective and tolerated treatments.

Objective

Identify DNA methylation (DNAm) changes associated with antidepressant exposure.

Design

Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS.

Setting

Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR.

Participants

Participants with DNAm data and self-report/prescription derived antidepressant exposure.

Main Outcomes and Measures

Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions.

Results

The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ).

Conclusions and Relevance

Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments.

3 Key Points

Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

Epigenomic insights into common human disease pathology

The epigenome-the chemical modifications and chromatin-related packaging of the genome-enables the same genetic template to be activated or repressed in different cellular settings. This multi-layered mechanism facilitates cell-type specific function by setting the local sequence and 3D interactive activity level. Gene transcription is further modulated through the interplay with transcription factors and co-regulators. The human body requires this epigenomic apparatus to be precisely installed throughout development and then adequately maintained during the lifespan. The causal role of the epigenome in human pathology, beyond imprinting disorders and specific tumour suppressor genes, was further brought into the spotlight by large-scale sequencing projects identifying that mutations in epigenomic machinery genes could be critical drivers in both cancer and developmental disorders. Abrogation of this cellular mechanism is providing new molecular insights into pathogenesis. However, deciphering the full breadth and implications of these epigenomic changes remains challenging. Knowledge is accruing regarding disease mechanisms and clinical biomarkers, through pathogenically relevant and surrogate tissue analyses, respectively. Advances include consortia generated cell-type specific reference epigenomes, high-throughput DNA methylome association studies, as well as insights into ageing-related diseases from biological 'clocks' constructed by machine learning algorithms. Also, 3rd-generation sequencing is beginning to disentangle the complexity of genetic and DNA modification haplotypes. Cell-free DNA methylation as a cancer biomarker has clear clinical utility and further potential to assess organ damage across many disorders. Finally, molecular understanding of disease aetiology brings with it the opportunity for exact therapeutic alteration of the epigenome through CRISPR-activation or inhibition.

Utilizing epigenetics to study the shared nature of development and biological aging across the lifespan

Recently, biological aging has been quantified in DNA-methylation samples of older adults and applied as so-called "methylation profile scores" (MPSs) in separate target samples, including samples of children. This nascent research indicates that (1) biological aging can be quantified early in the life course, decades before the onset of aging-related disease, (2) is affected by common environmental predictors of childhood development, and (3) shows overlap with "developmental processes" (e.g., puberty). Because the MPSs were computed using algorithms developed in adults, these studies indicate a molecular link between childhood environments, development, and adult biological aging. Yet, if MPSs can be used to connect development and aging, previous research has only traveled one way, deriving MPSs developed in adults and applying them to samples of children. Researchers have not yet quantified epigenetic measures that reflect the pace of child development, and tested whether resulting MPSs are associated with physical and psychological aging. In this perspective I posit that combining measures of biological aging with new quantifications of child development has the power to address fundamental questions about life span: How are development and experience in childhood related to biological aging in adulthood? And what is aging?

Spatial covariation between genetic and epigenetic diversity in wild plant and animal populations: a meta-analysis

Epigenetic variation may be crucial in understanding the structure of wild populations, thereby aiding in their management and conservation. However, the relationship between epigenetic and genetic variation remains poorly understood, especially in wild populations. To address this, we conducted a meta-analysis of studies that examined the genetic and epigenetic structures of wild plant and animal populations. We aimed to determine whether epigenetic variation is spatially independent of genetic variation in the wild and to highlight the conditions under which epigenetic variation might be informative. We show a significant positive correlation between genetic and epigenetic pairwise differentiation, indicating that in wild populations, epigenetic diversity is closely linked to genetic differentiation. The correlation was weaker for population pairs that were weakly differentiated genetically, suggesting that in such cases, epigenetic marks might be independent of genetic marks. Additionally, we found that global levels of genetic and epigenetic differentiation were similar across plant and animal populations, except when populations were weakly differentiated genetically. In such cases, epigenetic differentiation was either higher or lower than genetic differentiation. Our results suggest that epigenetic information is particularly relevant in populations that have recently diverged genetically or are connected by gene flow. Future studies should consider the genetic structure of populations when inferring the role of epigenetic diversity in local adaptation in wild populations. Furthermore, there is a need to identify the factors that sustain the links between genetic and epigenetic diversity to improve our understanding of the interplay between these two forms of variation in wild populations.

Blood-based DNA methylation and exposure risk scores predict PTSD with high accuracy in military and civilian cohorts

Background

Incorporating genomic data into risk prediction has become an increasingly useful approach for rapid identification of individuals most at risk for complex disorders such as PTSD. Our goal was to develop and validate Methylation Risk Scores (MRS) using machine learning to distinguish individuals who have PTSD from those who do not.

Methods

Elastic Net was used to develop three risk score models using a discovery dataset (n = 1226; 314 cases, 912 controls) comprised of 5 diverse cohorts with available blood-derived DNA methylation (DNAm) measured on the Illumina Epic BeadChip. The first risk score, exposure and methylation risk score (eMRS) used cumulative and childhood trauma exposure and DNAm variables; the second, methylation-only risk score (MoRS) was based solely on DNAm data; the third, methylation-only risk scores with adjusted exposure variables (MoRSAE) utilized DNAm data adjusted for the two exposure variables. The potential of these risk scores to predict future PTSD based on pre-deployment data was also assessed. External validation of risk scores was conducted in four independent cohorts.

Results

The eMRS model showed the highest accuracy (92%), precision (91%), recall (87%), and f1-score (89%) in classifying PTSD using 3730 features. While still highly accurate, the MoRS (accuracy = 89%) using 3728 features and MoRSAE (accuracy = 84%) using 4150 features showed a decline in classification power. eMRS significantly predicted PTSD in one of the four independent cohorts, the BEAR cohort (beta = 0.6839, p-0.003), but not in the remaining three cohorts. Pre-deployment risk scores from all models (eMRS, beta = 1.92; MoRS, beta = 1.99 and MoRSAE, beta = 1.77) displayed a significant (p < 0.001) predictive power for post-deployment PTSD.

Conclusion

Results, especially those from the eMRS, reinforce earlier findings that methylation and trauma are interconnected and can be leveraged to increase the correct classification of those with vs. without PTSD. Moreover, our models can potentially be a valuable tool in predicting the future risk of developing PTSD. As more data become available, including additional molecular, environmental, and psychosocial factors in these scores may enhance their accuracy in predicting the condition and, relatedly, improve their performance in independent cohorts.

DNA methylation risk score for type 2 diabetes is associated with gestational diabetes

BACKGROUND

Gestational diabetes mellitus (GDM) and type 2 diabetes mellitus (T2DM) share many pathophysiological factors including genetics, but whether epigenetic marks are shared is unknown. We aimed to test whether a DNA methylation risk score (MRS) for T2DM was associated with GDM across ancestry and GDM criteria.

METHODS

In two independent pregnancy cohorts, EPIPREG (n = 480) and EPIDG (n = 32), DNA methylation in peripheral blood leukocytes was measured at a gestational age of 28 ± 2. We constructed an MRS in EPIPREG and EPIDG based on CpG hits from a published epigenome-wide association study (EWAS) of T2DM.

RESULTS

With mixed models logistic regression of EPIPREG and EPIDG, MRS for T2DM was associated with GDM: odd ratio (OR)[95% CI]: 1.3 [1.1-1.8], P = 0.002 for the unadjusted model, and 1.4 [1.1-1.7], P = 0.00014 for a model adjusted by age, pre-pregnant BMI, family history of diabetes and smoking status. Also, we found 6 CpGs through a meta-analysis (cg14020176, cg22650271, cg14870271, cg27243685, cg06378491, cg25130381) associated with GDM, and some of their methylation quantitative loci (mQTLs) were related to T2DM and GDM.

CONCLUSION

For the first time, we show that DNA methylation marks for T2DM are also associated with GDM, suggesting shared epigenetic mechanisms between GDM and T2DM.

Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction

BACKGROUND

DNA methylation is a form of epigenetic modification that impacts gene expression without modifying the DNA sequence, thereby exerting control over gene function and cellular development. The prediction of DNA methylation is vital for understanding and exploring gene regulatory mechanisms. Currently, machine learning algorithms are primarily used for model construction. However, several challenges remain to be addressed, including limited prediction accuracy, constrained generalization capability, and insufficient learning capacity.

RESULTS

In response to the aforementioned challenges, this paper leverages the similarities between DNA sequences and time series to introduce a time series-based hybrid ensemble learning model, called Multi2-Con-CAPSO-LSTM. The model utilizes multivariate and multidimensional encoding approach, combining three types of time series encodings with three kinds of genetic feature encodings, resulting in a total of nine types of feature encoding matrices. Convolutional Neural Networks are utilized to extract features from DNA sequences, including temporal, positional, physicochemical, and genetic information, thereby creating a comprehensive feature matrix. The Long Short-Term Memory model is then optimized using the Chaotic Accelerated Particle Swarm Optimization algorithm for predicting DNA methylation.

CONCLUSIONS

Through cross-validation experiments conducted on 17 species involving three types of DNA methylation (6 mA, 5hmC, and 4mC), the results demonstrate the robust predictive capabilities of the Multi2-Con-CAPSO-LSTM model in DNA methylation prediction across various types and species. Compared with other benchmark models, the Multi2-Con-CAPSO-LSTM model demonstrates significant advantages in sensitivity, specificity, accuracy, and correlation. The model proposed in this paper provides valuable insights and inspiration across various disciplines, including sequence alignment, genetic evolution, time series analysis, and structure-activity relationships.

A Predictive Tool Based on DNA Methylation Data for Personalized Weight Loss through Different Dietary Strategies: A Pilot Study

BACKGROUND AND AIMS

Obesity is a public health problem. The usual treatment is a reduction in calorie intake and an increase in energy expenditure, but not all individuals respond equally to these treatments. Epigenetics could be a factor that contributes to this heterogeneity. The aim of this research was to determine the association between DNA methylation at baseline and the percentage of BMI loss (%BMIL) after two dietary interventions, in order to design a prediction model to evaluate %BMIL based on methylation data.

METHODS AND RESULTS

Spanish participants with overweight or obesity (n = 306) were randomly assigned to two lifestyle interventions with hypocaloric diets: one moderately high in protein (MHP) and the other low in fat (LF) for 4 months (Obekit study; ClinicalTrials.gov ID: NCT02737267). Basal DNA methylation was analyzed in white blood cells using the Infinium MethylationEPIC array. After identifying those methylation sites associated with %BMIL (p < 0.05 and SD > 0.1), two weighted methylation sub-scores were constructed for each diet: 15 CpGs were used for the MHP diet and 11 CpGs for the LF diet. Afterwards, a total methylation score was made by subtracting the previous sub-scores. These data were used to design a prediction model for %BMIL through a linear mixed effect model with the interaction between diet and total score.

CONCLUSION

Overall, DNA methylation predicts the %BMIL of two 4-month hypocaloric diets and was able to determine which type of diet is the most appropriate for each individual. The results of this pioneer study confirm that epigenetic biomarkers may be further used for precision nutrition and the design of personalized dietary strategies against obesity.

Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.

Biomarkers of aging in frailty and age-associated disorders: State of the art and future perspective

According to the Geroscience concept that organismal aging and age-associated diseases share the same basic molecular mechanisms, the identification of biomarkers of age that can efficiently classify people as biologically older (or younger) than their chronological (i.e. calendar) age is becoming of paramount importance. These people will be in fact at higher (or lower) risk for many different age-associated diseases, including cardiovascular diseases, neurodegeneration, cancer, etc. In turn, patients suffering from these diseases are biologically older than healthy age-matched individuals. Many biomarkers that correlate with age have been described so far. The aim of the present review is to discuss the usefulness of some of these biomarkers (especially soluble, circulating ones) in order to identify frail patients, possibly before the appearance of clinical symptoms, as well as patients at risk for age-associated diseases. An overview of selected biomarkers will be discussed in this regard, in particular we will focus on biomarkers related to metabolic stress response, inflammation, and cell death (in particular in neurodegeneration), all phenomena connected to inflammaging (chronic, low-grade, age-associated inflammation). In the second part of the review, next-generation markers such as extracellular vesicles and their cargos, epigenetic markers and gut microbiota composition, will be discussed. Since recent progresses in omics techniques have allowed an exponential increase in the production of laboratory data also in the field of biomarkers of age, making it difficult to extract biological meaning from the huge mass of available data, Artificial Intelligence (AI) approaches will be discussed as an increasingly important strategy for extracting knowledge from raw data and providing practitioners with actionable information to treat patients.