Longitudinal analysis of epigenome-wide DNA methylation reveals novel loci associated with BMI change in East Asians.

BACKGROUND: Obesity is a global public health concern linked to chronic diseases such as cardiovascular disease and type 2 diabetes (T2D). Emerging evidence suggests that epigenetic modifications, particularly DNA methylation, may contribute to obesity. However, the molecular mechanism underlying the longitudinal change of BMI has not been well-explored, especially in East Asian populations. METHODS: This study performed a longitudinal epigenome-wide association analysis of DNA methylation to uncover novel loci associated with BMI change in 533 individuals across two Chinese cohorts with repeated DNA methylation and BMI measurements over four years. RESULTS: We identified three novel CpG sites (cg14671384, cg25540824, and cg10848724) significantly associated with BMI change. Two of the identified CpG sites were located in regions previously associated with body shape and basal metabolic rate. Annotation of the top 20 BMI change-associated CpGs revealed strong connections to obesity and T2D. Notably, these CpGs exhibited active regulatory roles and located in genes with high expression in the liver and digestive tract, suggesting a potential regulatory pathway from genome to phenotypes of energy metabolism and absorption via DNA methylation. Cross-sectional and longitudinal EWAS comparisons indicated different mechanisms between CpGs related to BMI and BMI change. CONCLUSION: This study enhances our understanding of the epigenetic dynamics underlying BMI change and emphasizes the value of longitudinal analyses in deciphering the complex interplay between epigenetics and obesity.

Analysis of blood methylation quantitative trait loci in East Asians reveals ancestry-specific impacts on complex traits.

Methylation quantitative trait loci (mQTLs) are essential for understanding the role of DNA methylation changes in genetic predisposition, yet they have not been fully characterized in East Asians (EAs). Here we identified mQTLs in whole blood from 3,523 Chinese individuals and replicated them in additional 1,858 Chinese individuals from two cohorts. Over 9% of mQTLs displayed specificity to EAs, facilitating the fine-mapping of EA-specific genetic associations, as shown for variants associated with height. Trans-mQTL hotspots revealed biological pathways contributing to EA-specific genetic associations, including an ERG-mediated 233 trans-mCpG network, implicated in hematopoietic cell differentiation, which likely reflects binding efficiency modulation of the ERG protein complex. More than 90% of mQTLs were shared between different blood cell lineages, with a smaller fraction of lineage-specific mQTLs displaying preferential hypomethylation in the respective lineages. Our study provides new insights into the mQTL landscape across genetic ancestries and their downstream effects on cellular processes and diseases/traits.

Seq-RBPPred: Predicting RNA-Binding Proteins from Sequence.

RNA-binding proteins (RBPs) can interact with RNAs to regulate RNA translation, modification, splicing, and other important biological processes. The accurate identification of RBPs is of paramount importance for gaining insights into the intricate mechanisms underlying organismal life activities. Traditional experimental methods to predict RBPs require a lot of time and money, so it is important to develop computational methods to predict RBPs. However, the existing approaches for RBP prediction still require further improvement due to unidentified RBPs in many species. In this study, we present Seq-RBPPred (predicting RBPs from sequence), a novel method that utilizes a comprehensive feature representation encompassing both biophysical properties and hidden-state features derived from protein sequences. In the results, comprehensive performance evaluations of Seq-RBPPred its superiority compare with state-of-the-art methods, yielding impressive performance including 0.922 for overall accuracy, 0.926 for sensitivity, 0.903 for specificity, and Matthew's correlation coefficient (MCC) of 0.757 as ascertained from the evaluation of the testing set. The data and code of Seq-RBPPred are available at https://github.com/yaoyao-11/Seq-RBPPred.

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.

Non-coding variants associated with complex traits can alter the motifs of transcription factor (TF)-deoxyribonucleic acid binding. Although many computational models have been developed to predict the effects of non-coding variants on TF binding, their predictive power lacks systematic evaluation. Here we have evaluated 14 different models built on position weight matrices (PWMs), support vector machines, ordinary least squares and deep neural networks (DNNs), using large-scale in vitro (i.e. SNP-SELEX) and in vivo (i.e. allele-specific binding, ASB) TF binding data. Our results show that the accuracy of each model in predicting SNP effects in vitro significantly exceeds that achieved in vivo. For in vitro variant impact prediction, kmer/gkm-based machine learning methods (deltaSVM_HT-SELEX, QBiC-Pred) trained on in vitro datasets exhibit the best performance. For in vivo ASB variant prediction, DNN-based multitask models (DeepSEA, Sei, Enformer) trained on the ChIP-seq dataset exhibit relatively superior performance. Among the PWM-based methods, tRap demonstrates better performance in both in vitro and in vivo evaluations. In addition, we find that TF classes such as basic leucine zipper factors could be predicted more accurately, whereas those such as C2H2 zinc finger factors are predicted less accurately, aligning with the evolutionary conservation of these TF classes. We also underscore the significance of non-sequence factors such as cis-regulatory element type, TF expression, interactions and post-translational modifications in influencing the in vivo predictive performance of TFs. Our research provides valuable insights into selecting prioritization methods for non-coding variants and further optimizing such models.

Epigenome-wide association study on short-, intermediate- and long-term ozone exposure in Han Chinese, the NSPT study.

Epidemiological and epigenetic studies have acknowledged ambient ozone exposure associated with inflammatory and cardiovascular disease. However, the molecular mechanisms still remained unclear, and epigenome-wide analysis in cohort were lacking, especially in Chinese. We included blood-derived DNA methylation for 3365 Chinese participants from the NSPT cohort and estimated individual ozone exposure level of short-, intermediate- and long-term, based on a validated prediction model. We performed epigenome-wide association studies which identified 59 CpGs and 30 DMRs at a strict genome-wide significance (P < 5 ×10-8). We also conducted comparison on the DNA methylation alteration corresponding to different time windows, and observed an enhanced differentiated methylation trend for intermediate- and long-term exposure, while the short-term exposure associated methylation changes did not retain. The targeted genes of methylation alteration were involved in mechanism related to aging, inflammation disease, metabolic syndrome, neurodevelopmental disorders, and oncogenesis. Underlying pathways were enriched in biological activities including telomere maintenance process, DNA damage response and megakaryocyte differentiation. In conclusion, our study is the first EWAS on ozone exposure conducted in large-scale Han Chinese cohort and identified associated DNA methylation change on CpGs and regions, as well as related gene functions and pathways.

Strategies targeting IL-33/ST2 axis in the treatment of allergic diseases.

Interleukin-33 (IL-33) and its receptor Serum Stimulation-2 (ST2, also called Il1rl1) are members of the IL-1 superfamily that plays a crucial role in allergic diseases. The interaction of IL-33 and ST2 mainly activates NF-κB signaling and MAPK signaling via the MyD88/IRAK/TRAF6 module, resulting in the production and secretion of pro-inflammatory cytokines. The IL-33/ST2 axis participates in the pathogenesis of allergic diseases, and therefore serves as a promising strategy for allergy treatment. In recent years, strategies blocking IL-33/ST2 through targeting regulation of IL-33 and ST2 or targeting the molecules involved in the signal transduction have been extensively studied mostly in animal models. These studies provide various potential therapeutic agents other than antibodies, such as small molecules, nucleic acids and traditional Chinese medicines. Herein, we reviewed potential targets and agents targeting IL-33/ST2 axis in the treatment of allergic diseases, providing directions for further investigations on treatments for IL-33 induced allergic diseases.

DNA methylation age acceleration contributes to the development and prediction of non-alcoholic fatty liver disease.

Non-alcoholic fatty liver disease (NAFLD) is prevalent in the aging society. Despite body weight reduction, the prevalence of NAFLD has been increasing with aging for unknown reasons. Here, we investigate the association of DNA methylation age acceleration, a hallmark of aging, with risk of NAFLD. Genome-wide DNA methylation profiles were measured in 95 participants who developed type 2 diabetes during 4-year follow-up, and 356 randomly sampled participants from Shanghai Changfeng Study. DNA methylation age was calculated using the Horvath's method, and liver fat content (LFC) was measured using a quantitative ultrasound method. Subjects with highest tertile of DNA methylation age acceleration (≥ 9.5 years) had significantly higher LFC (7.2% vs 3.1%, P = 0.008) but lower body fat percentage (29.7% vs 33.0%, P = 0.032) than those with lowest tertile of DNA methylation age acceleration (< 4.0 years). After adjustment for age, sex, alcohol drinking, cigarette smoking, BMI, waist circumference, and different type blood cell counts, the risk of NAFLD was still significantly increased in the highest tertile group (OR, 4.55; 95% CI, 1.06-19.61). Even in subjects with similar LFC at baseline, DNA methylation age acceleration was associated with higher increase in LFC (4.0 ± 10.7% vs 0.9 ± 9.5%, P = 0.004) after a median of 4-year follow-up. Further analysis found that 6 CpGs of Horvath age predictors were associated with longitudinal changes in LFC after multivariate adjustment and located on genes that might lead to fat redistribution from peripheral adipose to liver. Combination of the key CpG methylation related to liver fat content with conventional risk factors improves the performance for NAFLD prediction.

Validation of Three European Risk Scores to Predict Long-Term Outcomes for Patients Receiving Cardiac Resynchronization Therapy in an Asian Population.

To validate externally and recalibrate three European risk scores for all-cause mortality and transplantation in patients receiving cardiac resynchronization therapy (CRT) in an Asian population. Data were collected at our institution between January 2010 and December 2017. The primary endpoints were all-cause mortality and heart transplantation. Of the 506 patients who were followed for 2 years, 104 reached the primary endpoint. The Kaplan-Meier event-free survival analysis, stratified according to the three scores, yielded significant results (log-rank test, all P < 0.05), with a good fit between the predicted and observed event rates (Hosmer-Lemeshow goodness-of-fit test, all P > 0.05). The ScREEN score yielded the best discriminatory power for the primary endpoints compared with the VALID-CRT and EAARN scores. ScREEN was the best predictor of all-cause mortality and heart transplantation. Risk scores based on different populations should be selected cautiously. Graphical Abstract.

A method for scoring the cell type-specific impacts of noncoding variants in personal genomes.

A person's genome typically contains millions of variants which represent the differences between this personal genome and the reference human genome. The interpretation of these variants, i.e., the assessment of their potential impact on a person's phenotype, is currently of great interest in human genetics and medicine. We have developed a prioritization tool called OpenCausal which takes as inputs 1) a personal genome and 2) a reference context-specific TF expression profile and returns a list of noncoding variants prioritized according to their impact on chromatin accessibility for any given genomic region of interest. We applied OpenCausal to 6,430 samples across 18 tissues derived from the GTEx project and found that the variants prioritized by OpenCausal are highly enriched for eQTLs and caQTLs. We further propose a strategy to integrate the predicted open scores with genome-wide association studies (GWAS) data to prioritize putative causal variants and regulatory elements for a given risk locus (i.e., fine-mapping analysis). As an initial example, we applied this method to a GWAS dataset of human height and found that the prioritized putative variants and elements are correlated with the phenotype (i.e., heights of individuals) better than others.

Associating divergent lncRNAs with target genes by integrating genome sequence, gene expression and chromatin accessibility data.

Recent RNA knockdown experiments revealed that a dozen divergent long noncoding RNAs (lncRNAs) positively regulate the transcription of genes in cis. Here, to understand the regulatory mechanism of divergent lncRNAs, we proposed a computational model IRDL (Identify the Regulatory Divergent LncRNAs) to associate divergent lncRNAs with target genes. IRDL took advantage of the cross-tissue paired expression and chromatin accessibility data in ENCODE and a dozen experimentally validated divergent lncRNA target genes. IRDL integrated sequence similarity, co-expression and co-accessibility features, battled the scarcity of gold standard datasets with an increasingly learning framework and identified 446 and 977 divergent lncRNA-gene regulatory associations for mouse and human, respectively. We found that the identified divergent lncRNAs and target genes correlated well in expression and chromatin accessibility. The functional and pathway enrichment analysis suggests that divergent lncRNAs are strongly associated with developmental regulatory transcription factors. The predicted loop structure validation and canonical database search indicate a scaffold regulatory model for divergent lncRNAs. Furthermore, we computationally revealed the tissue/cell-specific regulatory associations considering the specificity of lncRNA. In conclusion, IRDL provides a way to understand the regulatory mechanism of divergent lncRNAs and hints at hundreds of tissue/cell-specific regulatory associations worthy for further biological validation.

EnContact: predicting enhancer-enhancer contacts using sequence-based deep learning model.

Chromatin contacts between regulatory elements are of crucial importance for the interpretation of transcriptional regulation and the understanding of disease mechanisms. However, existing computational methods mainly focus on the prediction of interactions between enhancers and promoters, leaving enhancer-enhancer (E-E) interactions not well explored. In this work, we develop a novel deep learning approach, named Enhancer-enhancer contacts prediction (EnContact), to predict E-E contacts using genomic sequences as input. We statistically demonstrated the predicting ability of EnContact using training sets and testing sets derived from HiChIP data of seven cell lines. We also show that our model significantly outperforms other baseline methods. Besides, our model identifies finer-mapping E-E interactions from region-based chromatin contacts, where each region contains several enhancers. In addition, we identify a class of hub enhancers using the predicted E-E interactions and find that hub enhancers tend to be active across cell lines. We summarize that our EnContact model is capable of predicting E-E interactions using features automatically learned from genomic sequences.

DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning.

Interactions between regulatory elements are of crucial importance for the understanding of transcriptional regulation and the interpretation of disease mechanisms. Hi-C technique has been developed for genome-wide detection of chromatin contacts. However, unless extremely deep sequencing is performed on a very large number of input cells, which is technically limited and expensive, current Hi-C experiments do not have high enough resolution to resolve contacts between regulatory elements. Here, we develop DeepTACT, a bootstrapping deep learning model, to integrate genome sequences and chromatin accessibility data for the prediction of chromatin contacts between regulatory elements. DeepTACT can infer not only promoter-enhancer interactions, but also promoter-promoter interactions. In tests based on promoter capture Hi-C data, DeepTACT shows better performance over existing methods. DeepTACT analysis also identifies a class of hub promoters, which are correlated with transcriptional activation across cell lines, enriched in housekeeping genes, functionally related to fundamental biological processes, and capable of reflecting cell similarity. Finally, the utility of chromatin contacts in the study of human diseases is illustrated by the association of IFNA2 to coronary artery disease via an integrative analysis of GWAS data and interactions predicted by DeepTACT.

Gene co-opening network deciphers gene functional relationships.

Genome sequencing technology has generated a vast amount of genomic and epigenomic data, and has provided us a great opportunity to study gene functions on a global scale from an epigenomic view. In the last decade, network-based studies, such as those based on PPI networks and co-expression networks, have shown good performance in capturing functional relationships between genes. However, the functions of a gene and the mechanism of interaction of genes with each other to elucidate their functions are still not entirely clear. Here, we construct a gene co-opening network based on chromatin accessibility of genes. We show that genes related to a specific biological process or the same disease tend to be clustered in the co-opening network. This understanding allows us to detect functional clusters from the network and to predict new functions for genes. We further apply the network to prioritize disease genes for Psoriasis, and demonstrate the power of the joint analysis of the co-opening network and GWAS data in identifying disease genes. Taken together, the co-opening network provides a new viewpoint for the elucidation of gene associations and the interpretation of disease mechanisms.

Mimvec: a deep learning approach for analyzing the human phenome.

BACKGROUND: The human phenome has been widely used with a variety of genomic data sources in the inference of disease genes. However, most existing methods thus far derive phenotype similarity based on the analysis of biomedical databases by using the traditional term frequency-inverse document frequency (TF-IDF) formulation. This framework, though intuitive, not only ignores semantic relationships between words but also tends to produce high-dimensional vectors, and hence lacks the ability to precisely capture intrinsic semantic characteristics of biomedical documents. To overcome these limitations, we propose a framework called mimvec to analyze the human phenome by making use of the state-of-the-art deep learning technique in natural language processing. RESULTS: We converted 24,061 records in the Online Mendelian Inheritance in Man (OMIM) database to low-dimensional vectors using our method. We demonstrated that the vector presentation not only effectively enabled classification of phenotype records against gene ones, but also succeeded in discriminating diseases of different inheritance styles and different mechanisms. We further derived pairwise phenotype similarities between 7988 human inherited diseases using their vector presentations. With a joint analysis of this phenome with multiple genomic data, we showed that phenotype overlap indeed implied genotype overlap. We finally used the derived phenotype similarities with genomic data to prioritize candidate genes and demonstrated advantages of this method over existing ones. CONCLUSIONS: Our method is capable of not only capturing semantic relationships between words in biomedical records but also alleviating the dimensional disaster accompanying the traditional TF-IDF framework. With the approaching of precision medicine, there will be abundant electronic records of medicine and health awaiting for deep analysis, and we expect to see a wide spectrum of applications borrowing the idea of our method in the near future.

Transcript assembly and quantification by RNA-Seq reveals differentially expressed genes between soft-endocarp and hard-endocarp hawthorns.

Hawthorn (Crataegus spp.) is an important pome with a long history as a fruit, an ornamental, and a source of medicine. Fruits of hawthorn are marked by hard stony endocarps, but a hawthorn germplasm with soft and thin endocarp was found in Liaoning province of China. To elucidate the molecular mechanism underlying the soft endocarp of hawthorn, we conducted a de novo assembly of the fruit transcriptome of Crataegus pinnatifida and compared gene expression profiles between the soft-endocarp and the hard-endocarp hawthorn varieties. De novo assembly yielded 52,673 putative unigenes, 20.4% of which are longer than 1,000 bp. Among the high-quality unique sequences, 35,979 (68.3%) had at least one significant match to an existing gene model. A total of 1,218 genes, represented 2.31% total putative unigenes, were differentially expressed between the soft-endocarp hawthorn and the hard-endocarp hawthorn. Among these differentially expressed genes, a number of lignin biosynthetic pathway genes were down-regulated while almost all the flavonoid biosynthetic pathway genes were strongly up-regulated, concomitant with the formation of soft endocarp. In addition, we have identified some MYB and NAC transcription factors that could potentially control lignin and flavonoid biosynthesis. The altered expression levels of the genes encoding lignin biosynthetic enzymes, MYB and NAC transcription factors were confirmed by quantitative RT-PCR. This is the first transcriptome analysis of Crataegus genus. The high quality ESTs generated in this study will aid future gene cloning from hawthorn. Our study provides important insights into the molecular mechanisms underlying soft endocarp formation in hawthorn.