Methylome Data Research Articles

N 6-Methyladenosine Methylomic Landscape of Ureteral Deficiency in Reflux Uropathy and Obstructive Uropathy.

BackgroundCongenital anomalies of the kidneys and urinary tracts (CAKUT) represent the most prevalent cause for renal failure in children. The RNA epigenetic modification N6-methyladenosine (m6A) methylation modulates gene expression and function post-transcriptionally, which has recently been revealed to be critical in organ development. However, it is uncertain whether m6A methylation plays a role in the pathogenesis of CAKUT. Thus, we aimed to explore the pattern of m6A methylation in CAKUT.MethodsUsing m6A-mRNA epitranscriptomic microarray, we investigated the m6A methylomic landscape in the ureter tissue of children with obstructive megaureter (M group) and primary vesicoureteral reflux (V group).ResultsA total of 228 mRNAs engaged in multiple function-relevant signaling pathways were substantially differential methylated between the “V” and “M” groups. Additionally, 215 RNA-binding proteins that recognize differentially methylated regions were predicted based on public databases. The M group showed significantly higher mRNA levels of m6A readers/writers (YTHDF1, YTHDF2, YTHDC1, YTHDC2 and WTAP) and significantly lower mRNA levels of m6A eraser (FTO) according to real-time PCR. To further investigate the differentially methylated genes, m6A methylome and transcriptome data were integrated to identified 298 hypermethylated mRNAs with differential expressions (265 upregulation and 33 downregulation) and 489 hypomethylated mRNAs with differential expressions (431 upregulation and 58 downregulation) in the M/V comparison.ConclusionThe current results highlight the pathogenesis of m6A methylation in obstructive and reflux uropathy.

Abstract 3051: Commensal microbiota, host DNA methylation and gene expression: A pilot study in colorectal adenomas

Abstract Increasing evidence suggests that gut microbiota plays a critical role in colorectal cancer (CRC) development; however, the underlying mechanism is largely unknown. We investigated the relationship of commensal microbiota with host DNA methylation (DNAm) and gene expression in colorectal adenoma, the major precursor of CRC. This study included 72 participants from the Tennessee Colorectal Polyp Study. Microbiome, DNA methylome, and transcriptome data of fresh frozen conventional adenoma samples were generated using 16S rRNA gene sequencing, HumanMethylation450 BeadChip, and RNA-seq, respectively. Among 35 participants with microbiome and matched DNA methylome data, microbial features were evaluated for their associations with DNAm at CpG sites (CpGs) via linear regression. False-discovery rate (FDR) correction was conducted separately for each microbial feature and significant associations were identified at FDR<0.1. DNAm at microbiome-associated CpGs were then tested for their correlations with expression of flanking genes (500Kb) among 45 subjects with DNA methylome and matched transcriptome data. For genes with expression significantly (FDR<0.1) correlated with DNAm at microbiome-associated CpGs, their differential expression between colorectal adenomas and adenocarcinomas were examined using microarray data from Gene Expression Omnibus (GEO, 363 conventional adenoma samples) and CRC Subtyping Consortium (CRCSC, 2,760 adenocarcinoma samples). The R package “limma” was used to identify significantly differentially expressed genes at FDR<0.1. Four alpha diversity indexes, three beta diversity matrices, and 194 taxa were investigated for their associations with DNAm at 28,081 variable CpGs (variance>0.02 across samples). Faith’s phylogenetic diversity index and abundance of 11 taxa were significantly associated with DNAm at 68 CpGs. Among them, the most significant association was observed between Lactococcus and cg03292388 (β=-0.67, P=4.39×10-9). DNAm at 11 of the 68 CpGs were significantly correlated with expression of 12 genes (551 genes tested). Six of these 12 genes were found in data from GEO and CRCSC, and three showed a significant differential expression between adenomas and adenocarcinomas. Integrating these results revealed potential bacteria-DNAm-gene-adenocarcinoma pathways in which Bacteroides ovatus, three CpGs, and three genes were involved. Specifically, increased abundance of B. ovatus was associated with decreased DNAm at cg19003815 (β=-0.28, P=7.64×10-5), which was correlated with increased expression of RARB (rho=-0.65, P=2.18×10-6). These findings are in line with the higher expression of RARB in adenocarcinomas compared to adenomas (fold-change=1.51, P=8.58×10-10). In conventional adenomas, bacterial-related host DNAm changes may affect expression of nearby genes, which might be implicated in adenoma-carcinoma development. Citation Format: Yaohua Yang, Jirong Long, Martha J. Shrubsole, Qiuyin Cai, Zhiguo Zhao, Fei Ye, Zhigang Li, Xingyi Guo, Bingshan Li, Seth R. Bordenstein, Ken S. Lau, Harvey J. Murff, Reid M. Ness, Robert J. Coffey, Wei Zheng. Commensal microbiota, host DNA methylation and gene expression: A pilot study in colorectal adenomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3051.

Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models.

DNA methylation modification plays a vital role in the pathophysiology of high blood pressure (BP). Herein, we applied three machine learning (ML) algorithms including deep learning (DL), support vector machine, and random forest for detecting high BP using DNA methylome data. Peripheral blood samples of 50 elderly individuals were collected three times at three visits for DNA methylome profiling. Participants who had a history of hypertension and/or current high BP measure were considered to have high BP. The whole dataset was randomly divided to conduct a nested five-group cross-validation for prediction performance. Data in each outer training set were independently normalized using a min–max scaler, reduced dimensionality using principal component analysis, then fed into three predictive algorithms. Of the three ML algorithms, DL achieved the best performance (AUPRC = 0.65, AUROC = 0.73, accuracy = 0.69, and F1-score = 0.73). To confirm the reliability of using DNA methylome as a biomarker for high BP, we constructed mixed-effects models and found that 61,694 methylation sites located in 15,523 intragenic regions and 16,754 intergenic regions were significantly associated with BP measures. Our proposed models pioneered the methodology of applying ML and DNA methylome data for early detection of high BP in clinical practices.

Epigenetically-regulated miR-30a/c-5p directly target TWF1 and hamper ccRCC cell aggressiveness

Clear cell renal cell carcinoma (ccRCC) is highly prone to metastasize and displays an extremely low 5-year survival rate. Not only miRNAs (miRs) are key gene expression regulators but can also be epigenetically modified. Abnormal miR expression has been linked with epithelial-mesenchymal transition (EMT)-driven ccRCC progression. MiR-30a/c-5p were found downregulated in ccRCC and associated with aggressiveness. Herein, we sought to unravel miR-30a/c-5p mechanistic role in ccRCC. RNA sequencing and genome-wide methylome data of ccRCC and normal tissue samples from The Cancer Genome Atlas database were integrated to identify candidate miRs cytosine-phosphate-guanine (CpG) loci deregulated in ccRCC. TargetScan was searched to identify miR putative targets. MiR-30a/c-5p expression and promoter methylation was evaluated in vitro, by PCR. Western blot, functional and luciferase assays were performed after cell transfection with either pre-miR, antimiR, or siRNA against twinfilin-1 (TWF1). Immunohistochemistry (IHC) was performed in ccRCC tissues. We found miR-30c-5p downregulation and aberrant promoter methylation in ccRCC tissues. In vitro studies revealed concomitant miR-30a/c-5p downregulation and increased promoter methylation, as well as a significant re-expression following decitabine treatment. Functional assays demonstrated that both miRs significantly decreased cell aggressiveness and the protein levels of EMT-promoting players, while upregulating epithelial markers, namely Claudin-1 and ZO-1. Importantly, we confirmed TWF1 as a direct target of both miRs, and its potential involvement in epithelial-mesenchymal transition/mesenchymal-epithelial transition regulation. IHC analysis revealed higher TWF1 expression in primary tissues from patients that developed metastases, after surgical treatment. Our results implicate miR-30a/c-5p in ccRCC cells' aggressiveness attenuation by directly targeting TWF1 and hampering EMT.

MeGPS: a multi-omics signature for hepatocellular carcinoma detection integrating methylome and transcriptome data.

Hepatocellular carcinoma (HCC) is a primary malignancy with a poor prognosis. Recently, multi-omics molecular-level measurement enables HCC diagnosis and prognosis prediction, which is crucial for early intervention of personalized therapy to diminish mortality. Here, we introduce a novel strategy utilizing DNA methylation and RNA expression data to achieve a multi-omics gene pair signature (GPS) for HCC discrimination. The immune genes with negative correlations between expression and promoter methylation are enriched in the highly connected cancer-related pathway network, which are considered as the candidates for HCC detection. After that, we separately construct a methylation GPS (mGPS) and an expression GPS (eGPS), and then assemble them as a meGPS with five gene pairs, in which the significant methylation and expression changes occur between HCC tumor and non-tumor groups. Reliable performance has been validated by independent tissue (age, gender and etiology) and blood datasets. This study proposes a procedure for multi-omics GPS identification and develops a novel HCC signature using both methylome and transcriptome data, suggesting potential molecular targets for the detection and therapy of HCC. Models are available at https://github.com/bioinformaticStudy/meGPS.git. Supplementary data are available at Bioinformatics online.

THER-02. Pediatric brain tumor cultures reveal differential susceptibility to four oncolytic viruses

INTRODUCTION: New therapeutic modalities such as Oncolytic viruses (OVs) are considered possible treatment options for pediatric brain tumors (PBTs) either as monotherapy or as adjuvants to immunotherapies. OVs specifically lyse tumor cells and can induce anti-tumor immune responses. Here, we evaluate the oncolytic potency of different clinically relevant OVs against various PBT entities. METHODS: The effect of four different OVs, Reovirus (R124), Newcastle Disease virus (NDV), Adenovirus (DNX-2401) and Herpes simplex virus-1 (rQNestin 34.5v.1), was assessed on patient-derived cell cultures belonging to four different PBT entities. Cell viability 5 days after virus treatment of diffuse midline gliomas (DMG n=6), atypical teratoid rhabdoid tumors (n=4), glioblastomas (n=1) and ependymomas (n=2) was measured using Cell Titer Glo assay to demonstrate the cytotoxic potency of each virus. RESULTS: Our screenings revealed that DNX-2401, rQNestin and NDV could infect and kill the majority of cell cultures (12 out of 13, 11 out of 13 and 11 out of 13, respectively). rQNestin34.5v.1 required lower amounts of infectious particles per cell (MedianEC50: 0.65±2.7) compared to NDV (3.5±1.7) and DNX-2401 (7.5±14.5), with DMGs being more sensitive for rQNestin34.5v.1 than non-DMGs. R124 was effective in only 6 out of 13 cultures, with DMGs being more resistant with EC50 > 100 (5 out of 6) compared to non-DMG cell lines with EC50 < 8 (5 out of 7). CONCLUSION: All cell lines revealed differential susceptibility to the 4 different OVs with at least one effective OV per cell line. Further analysis of transcriptome and methylome data might uncover genes and pathways which correlate with specific OV susceptibility and provide biomarkers for response prediction. Further investigation is ongoing to interpret how differential susceptibility affects OV-induced anti-tumor immunity.

Clioquinol as an inhibitor of JmjC-histone demethylase exhibits common and unique histone methylome and transcriptome between clioquinol and hypoxia

SummaryClioquinol (CQ) is a hypoxic mimicker to activate hypoxia-inducible factor-1α (HIF-1α) by inhibiting HIF-1α specific asparaginyl hypoxylase (FIH-1). The structural similarity of the Jumonji C (JmjC) domain between FIH-1 and JmjC domain-containing histone lysine demethylases (JmjC-KDMs) led us to investigate whether CQ could inhibit the catalytic activities of JmjC-KDMs. Herein, we showed that CQ inhibits KDM4A/C, KDM5A/B, and KDM6B and affects H3K4me3, H3K9me3, and H3K27me3 marks, respectively. An integrative analysis of the histone methylome and transcriptome data revealed that CQ-mediated JmjC-KDM inhibition altered the transcription of target genes through differential combinations of KDMs and transcription factors. Notably, functional enrichment of target genes showed that CQ and hypoxia commonly affected the response to hypoxia, VEGF signaling, and glycolysis, whereas CQ uniquely altered apoptosis/autophagy and cytoskeleton/extracellular matrix organization. Our results suggest that CQ can be used as a JmjC-KDM inhibitor, HIF-α activator, and an alternative therapeutic agent in hypoxia-based diseases.

Enhancer methylation dynamics drive core transcriptional regulatory circuitry in pan-cancer.

Accumulating evidence has demonstrated that enhancer methylation has strong and dynamic regulatory effects on gene expression. Some transcription factors (TFs) can auto- and cross-regulate in a feed-forward manner, and cooperate with their enhancers to form core transcriptional regulatory circuitries (CRCs). However, the elaborated regulatory mechanism between enhancer methylation and CRC remains the tip of the iceberg. Here, we revealed that DNA methylation could drive the tissue-specific enhancer basal transcription and target gene expression in human cancers. By integrating methylome, transcriptome, and 3D genomic data, we identified enhancer methylation triplets (enhancer methylation-enhancer transcription-target gene expression) and dissected potential regulatory patterns within them. Moreover, we observed that cancer-specific core TFs regulated by enhancers were able to shape their enhancer methylation forming the enhancer methylation-driven CRCs (emCRCs). Further parsing of clinical implications showed rewired emCRCs could serve as druggable targets and prognostic risk markers. In summary, the integrative analysis of enhancer methylation regulome would facilitate portraying the cancer epigenomics landscape and developing the epigenetic anti-cancer approaches.

Identification of aberrant transcription termination at specific gene loci with DNA hypomethylated transcription termination sites caused by DNA methyltransferase deficiency.

CpG methylation of genomic DNA is a well-known repressive epigenetic marker in eukaryotic transcription, and DNA methylation of promoter regions is correlated with gene silencing. In contrast to the promoter regions, the function of DNA methylation during transcription termination remains to be elucidated. A recent study revealed that mouse DNA methyltransferase 3a (Dnmt3a) mainly functions in de novo methylation in the promoter and gene body regions, including transcription termination sites (TTSs), during development. To investigate the relationship between DNA methylation overlapping the TTSs and transcription termination, we performed bioinformatics analysis using six pre-existing Dnmt-/- mouse cell datasets: four types of neurons (three Dnmt3a-/- and one Dnmt1-/- mutants) and two types of embryonic fibroblasts (MEFs) (Dnmt3a-/- and Dnmt3b-/- mutants). Combined analyses using methylome and transcriptome data revealed that read counts downstream of hypomethylated TTSs were increased in three types of neurons (two Dnmt3a-/- and one Dnmt1-/- mutants). Among these, an increase in chimeric transcripts downstream of the TTSs was observed in Dnmt3a-/- mature olfactory sensory neurons and Dnmt3a-/- agouti-related peptide (protein)-producing neurons, thereby indicating that read-through occurs in hypomethylated TTSs at specific gene loci in these two mutants. Conversely, in Dnmt3a-/- MEFs, we detected reductions in read counts downstream of hypomethylated TTSs. These results indicate that the hypomethylation of TTSs can both positively and negatively regulate transcription termination, dependent on Dnmt and cell types. This study is the first to identify the aberrant termination of transcription at specific gene loci with DNA hypomethylated TTSs attributable to Dnmt deficiency.

Harmonization of transcriptomic and methylomic analysis in environmental epidemiology studies for potential application in chemical risk assessment.

Recent efforts have posited the utility of transcriptomic-based approaches to understand chemical-related perturbations in the context of human health risk assessment. Epigenetic modification (e.g., DNA methylation) can influence gene expression changes and is known to occur as a molecular response to some chemical exposures. Characterization of these methylation events is critical to understand the molecular consequences of chemical exposures. In this context, a novel workflow was developed to interrogate publicly available epidemiological transcriptomic and methylomic data to identify relevant pathway level changes in response to chemical exposure, using inorganic arsenic as a case study. Gene Set Enrichment Analysis (GSEA) was used to identify causal methylation events that result in concomitant downstream transcriptional deregulation. This analysis demonstrated an unequal distribution of differentially methylated regions across the human genome. After mapping these events to known genes, significant enrichment of a subset of these pathways suggested that arsenic-mediated methylation may be both specific and non-specific. Parallel GSEA performed on matched transcriptomic samples determined that a substantially reduced subset of these pathways are enriched and that not all chemically-induced methylation results in a downstream alteration in gene expression. The resulting pathways were found to be representative of well-established molecular events known to occur in response to arsenic exposure. The harmonization of enriched transcriptional patterns with those identified from the methylomic platform promoted the characterization of plausibly causal molecular signaling events. The workflow described here enables significant gene and methylation-specific pathways to be identified from whole blood samples of individuals exposed to environmentally relevant chemical levels. As future efforts solidify specific causal relationships between these molecular events and relevant apical endpoints, this novel workflow could aid risk assessments by identifying molecular targets serving as biomarkers of hazard, informing mechanistic understanding, and characterizing dose ranges that promote relevant molecular/epigenetic signaling events occuring in response to chemical exposures.

A novel DNA methylation-driver gene signature for long-term survival prediction of hepatitis-positive hepatocellular carcinoma patients.

Abnormal DNA methylation is one of the most general epigenetic modifications in hepatocellular carcinoma (HCC). Recent research showed that DNA methylation was a prognostic indicator of all-cause HCC and nonviral HCC. However, whether DNA methylation-driver genes could be used for predicting survival, the probability of hepatitis-positive HCC remains unclear. In this study, DNA methylation-driver genes (MDGs) were screened by a joint analysis of methylome and transcriptome data of 142 hepatitis-positive HCC patients. Subsequently, a prognostic risk score and nomogram were constructed. Finally, correlation analyses between the risk score and signaling pathways and immunity were conducted by GSVA and CIBERSORT. Through random forest screening and Cox progression analysis, 10 prognostic methylation-driver genes (AC008271.1, C11orf53, CASP8, F2RL2, GBP5, LUCAT1, RP11-114B7.6, RP11-149I23.3, RP11-383 J24.1, and SLC35G2) were screened out. As a result, a prognostic risk score signature was constructed. The independent value of the risk score for prognosis prediction were addressed in the TCGA-HCC and the China-HCC cohorts. Next, clinicopathological features were analyzed and HBV status and histological grade were screened to construct a nomogram together with the risk score. The prognostic efficiency of the nomogram was validated by the calibration curves and the concordance index (C index: 0.829, 95% confidence interval: 0.794-0.864), while its clinical application ability was confirmed by decision curve analysis (DCA). At last, the relationship between the risk score and signaling pathways, as well as the correlations between immune cells were elucidated preliminary. Taken together, our study explored a novel DNA methylation-driver gene risk score signature and an efficient nomogram for long-term survival prediction of hepatitis-positive HCC patients.

Endotyping pediatric obesity–related asthma: Contribution of anthropometrics, metabolism, nutrients, and CD4+ lymphocytes to pulmonary function

Obesity-related complications including visceral fat, metabolic abnormalities, nutrient deficiencies, and immune perturbations are interdependent but have been individually associated with childhood asthma. We sought to endotype childhood obesity-related asthma by quantifying contributions of obesity-related complications to symptoms and pulmonary function. Multiomics analysis using Similarity Network Fusion followed by mediation analysis were performed to quantify prediction of obese asthma phenotype by different combinations of anthropometric, metabolic, nutrient, and TH-cell transcriptome and DNA methylome data sets. Two clusters (n= 28 and 26) distinct in their anthropometric (neck and midarm circumference, waist to hip ratio [WHR], and body mass index [BMI] z score), metabolic, nutrient, and TH-cell transcriptome and DNA methylome footprint predicted 5 or more pulmonary function indices across 7 different data set combinations. Metabolic measures attenuated the association of neck, WHR, and BMI z score with FEV1/forced vital capacity (FVC) ratio and expiratory reserve volume (ERV), of neck, midarm, and BMI z score with functional residual capacity, but only of WHR with inspiratory capacity. Nutrient levels attenuated the association of neck, midarm circumference, and BMI z score with functional residual capacity, and of WHR with FEV1/FVC ratio, ERV, and inspiratory capacity. TH-cell transcriptome attenuated the association of all 4 anthropometric measures with FEV1/FVC ratio, but only of WHR with ERV and inspiratory capacity. The DNA methylome attenuated the association of all 4 anthropometric measures with FEV1/FVC ratio and ERV, but only of WHR with inspiratory capacity. Anthropometric, metabolic, nutrient, and immune perturbations have individual but interdependent contributions to obese asthma phenotype, with the most consistent effect of WHR, highlighting the role of truncal adiposity in endotyping childhood obesity-related asthma.

MS-HRM protocol: a simple and low-cost approach for technical validation of next-generation methylation sequencing data.

DNA methylation is a fundamental epigenetic process and have a critical role in many biological processes. The study of DNA methylation at a large scale ofgenomiclevels is widely conducted by several techniques that are next-generation sequencing (NGS)-based methods. Methylome data revealed by DNA methylation next-generation sequencing (mNGS), should be always verified by another technique which they usually have a high cost. In this study, we offered a low-cost approach to corroborate the mNGS data. In this regard, mNGS was performed on 6 colorectal cancer (case group) and 6 healthy individual colon tissue (control group) samples. An R-script detected differentially methylated regions (DMRs), was further validated by high resolution melting (MS-HRM) analysis. After analyzing the data, the algorithm found 194 DMRs. Two locations with the highest level of methylation difference were verified by MS-HRM, which their results were in accordance with the mNGS. Therefore, in the present study, we suggested MS-HRM as a simple, accurate and low-cost method, useful for confirming methylation sequencing results.

Methylation Subtypes of Primary Prostate Cancer Predict Poor Prognosis.

Patients with prostate cancer experience heterogeneous outcomes after radical prostatectomy. Genomic studies including The Cancer Genome Atlas (TCGA) have reported molecular signatures of prostate cancer, but few studies have assessed the prognostic effects of DNA methylation profiles. We conducted the largest methylome subtyping analysis for primary prostate tumors to date, using methylome data from three patient populations: TCGA, a prostate cancer cohort study conducted at the Fred Hutchinson Cancer Research Center (FH; Seattle, WA), and the Canadian International Cancer Genome Consortium (ICGC) cohort. Four subtypes were detected in the TCGA dataset, then independently assigned to FH and ICGC cohort data. The identified methylation subtypes were assessed for association with cancer prognosis in the above three patient populations. Using a set of hypermethylated CpG sites, four methylation subtypes were identified in TCGA. Compared with subtype 1, subtype 4 had an HR of 2.09 (P = 0.029) for biochemical recurrence (BCR) in TCGA patients. HRs of 2.76 (P = 0.002) for recurrence and 9.73 (P = 0.002) for metastatic-lethal (metastasis or prostate cancer-specific death) outcomes were observed in the FH cohort. A similar pattern of association was noted in the Canadian ICGC cohort, though HRs were not statistically significant. A hypermethylated subtype was associated with an increased hazard of recurrence and mortality in three studies with prostate tumor methylome data. Further molecular work is needed to understand the effect of methylation subtypes on cancer prognosis. This study identified a DNA methylation subtype that was associated with worse prostate cancer prognosis after radical prostatectomy.

A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes.

Understanding molecular features that facilitate aggressive phenotypes in glioblastoma multiforme (GBM) remains a major clinical challenge. Accurate diagnosis of GBM subtypes, namely classical, proneural, and mesenchymal, and identification of specific molecular features are crucial for clinicians for systematic treatment. We develop a biologically interpretable and highly efficient deep learning framework based on a convolutional neural network for subtype identification. The classifiers were generated from high-throughput data of different molecular levels, i.e., transcriptome and methylome. Furthermore, an integrated subsystem of transcriptome and methylome data was also used to build the biologically relevant model. Our results show that deep learning model outperforms the traditional machine learning algorithms. Furthermore, to evaluate the biological and clinical applicability of the classification, we performed weighted gene correlation network analysis, gene set enrichment, and survival analysis of the feature genes. We identified the genotype–phenotype relationship of GBM subtypes and the subtype-specific predictive biomarkers for potential diagnosis and treatment.

Sympatric speciation of the spiny mouse from Evolution Canyon in Israel substantiated genomically and methylomically

SignificanceWhether sympatric speciation (SS) is rare or common is still debated. Two populations of the spiny mouse, Acomys cahirinus, from Evolution Canyon I (EC I) in Israel have been depicted earlier as speciating sympatrically by molecular markers and transcriptome. Here, we investigated SS both genomically and methylomically, demonstrating that the opposite populations of spiny mice are sister taxa and split from the common ancestor around 20,000 years ago without an allopatric history. Mate choice, olfactory receptors, and speciation genes contributed to prezygotic/postzygotic reproductive isolation. The two populations showed different methylation patterns, facilitating adaptation to their local environment. They cope with abiotic and biotic stresses, due to high solar interslope radiation differences. We conclude that our new genomic and methylomic data substantiated SS.

Effects of Cryopreservation on Sperm with Cryodiluent in Viviparous Black Rockfish (Sebastes schlegelii).

Black rockfish is an economically important fish in East Asia. Little mention has been paid to the sperm cryopreservation in black rockfish. In this study, the optimal cryodiluent was selected from 48 combinations by detecting various sperm parameters. Transcriptome and methylome analysis were further performed to explore the molecular mechanism of inevitable cryoinjuries. The results showed that cryopreservation had negative effects on the viability, DNA integrity, mitochondrial activity, total ATPase and LDH of sperm even with optimal cryodiluent (FBS + 15% Gly). Transcriptome and methylome analysis revealed that the expression of 179 genes and methylation of 1266 genes were affected by cryopreservation. These genes were enriched in GO terms of death, G-protein coupled receptor signaling pathway, response to external stimulus and KEGG pathways of phospholipase D signaling pathway and xenobiotic and carbohydrate metabolism pathways. The role of PIK3CA and CCNA2 were highlighted in the protein-protein interaction network, and the sperm quality-related imprinted gene mest was identified among the 7 overlapping genes between transcriptome and methylome. Overall, the cryodiluent for black rockfish sperm was optimized, providing a feasible method for cryopreservation. The transcriptome and methylome data further demonstrated the underlying molecular mechanisms of cryoinjuries, proving clues for improvement of cryopreservation method of black rockfish.

Methylome and Transcriptome-Based Integration Analysis Identified Molecular Signatures Associated With Meningitis Induced by Glaesserella parasuis.

Glaesserella parasuis (G. parasuis) can elicit serious inflammatory responses and cause meningitis in piglets. Previous epigenetic studies have indicated that alterations in host DNA methylation may modify the inflammatory response to bacterial infection. However, to date, genome-wide analysis of the DNA methylome during meningitis caused by G. parasuis infection is still lacking. In this study, we employed an unbiased approach using deep sequencing to profile the DNA methylome and transcriptome from G. parasuis infected porcine brain (cerebrum) and integrated the data to identify key differential methylation regions/sites involved in the regulation of the inflammatory response. Results showed that DNA methylation patterns and gene expression profiles from porcine brain were changed after G. parasuis infection. The majority of the altered DNA methylation regions were found in the intergenic regions and introns and not associated with CpG islands, with only a low percentage occurring at promoter or exon regions. Integrated analysis of the DNA methylome and transcriptome identified a number of inversely and positively correlated genes between DNA methylation and gene expression, following the criteria of |log2FC| > 0.5, |diffMethy| > 0.1, and P < 0.05. Differential expression and methylation of two significant genes, semaphoring 4D (SEMA4D) and von Willebrand factor A domain containing 1 (VWA1), were validated by qRT-PCR and bisulfite sequencing. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses demonstrated that DNA methylation inversely correlated genes in G. parasuis infected porcine brains were mainly involved with cell adhesion molecules (CAMs), bacterial invasion of epithelial cells, RIG-1-like receptor signaling pathways, and hematopoietic cell lineage signaling pathways. In addition, a protein-protein interaction network of differentially methylated genes found potential candidate molecular interactions relevant to the pathology of G. parasuis infection. To the best of our knowledge, this is the first attempt to integrate the DNA methylome and transcriptome data from G. parasuis infected porcine brains. Our findings will help understanding the contribution of genome-wide DNA methylation to the pathogenesis of meningitis in pigs and developing epigenetic biomarkers and therapeutic targets for the treatment of G. parasuis induced meningitis.

Multi-omics data integration reveals link between epigenetic modifications and gene expression in sugar beet (Beta vulgaris subsp. vulgaris) in response to cold

BackgroundDNA methylation is thought to influence the expression of genes, especially in response to changing environmental conditions and developmental changes. Sugar beet (Beta vulgaris ssp. vulgaris), and other biennial or perennial plants are inevitably exposed to fluctuating temperatures throughout their lifecycle and might even require such stimulus to acquire floral competence. Therefore, plants such as beets, need to fine-tune their epigenetic makeup to ensure phenotypic plasticity towards changing environmental conditions while at the same time steering essential developmental processes. Different crop species may show opposing reactions towards the same abiotic stress, or, vice versa, identical species may respond differently depending on the specific kind of stress.ResultsIn this study, we investigated common effects of cold treatment on genome-wide DNA methylation and gene expression of two Beta vulgaris accessions via multi-omics data analysis. Cold exposure resulted in a pronounced reduction of DNA methylation levels, which particularly affected methylation in CHH context (and to a lesser extent CHG) and was accompanied by transcriptional downregulation of the chromomethyltransferase CMT2 and strong upregulation of several genes mediating active DNA demethylation.ConclusionIntegration of methylomic and transcriptomic data revealed that, rather than methylation having directly influenced expression, epigenetic modifications correlated with changes in expression of known players involved in DNA (de)methylation. In particular, cold triggered upregulation of genes putatively contributing to DNA demethylation via the ROS1 pathway. Our observations suggest that these transcriptional responses precede the cold-induced global DNA-hypomethylation in non-CpG, preparing beets for additional transcriptional alterations necessary for adapting to upcoming environmental changes.

Prediction of transcription factors associated with DNA demethylation during human cellular development

DNA methylation of CpG dinucleotides is an important epigenetic modification involved in the regulation of mammalian gene expression, with each type of cell developing a specific methylation profile during its differentiation. Recently, it has been shown that a small subgroup of transcription factors (TFs) might promote DNA demethylation at their binding sites. We developed a bioinformatics pipeline to predict from genome-wide DNA methylation data TFs that promote DNA demethylation at their binding site. We applied the pipeline to International Human Epigenome Consortium methylome data and selected 393 candidate transcription factor binding motifs and associated 383 TFs that are likely associated with DNA demethylation. Validation of a subset of the candidate TFs using an in vitro assay suggested that 28 of 49 TFs from various TF families had DNA-demethylation-promoting activity; TF families, such as bHLH and ETS, contained both TFs with and without the activity. The identified TFs showed large demethylated/methylated CpG ratios and their demethylated CpGs showed significant bias toward hypermethylation in original cells. Furthermore, the identified TFs promoted demethylation of distinct sets of CpGs, with slight overlap of the targeted CpGs among TF family members, which was consistent with the results of a gene ontology (GO) term analysis of the identified TFs. Gene expression analysis of the identified TFs revealed that multiple TFs from various families are specifically expressed in human cells and tissues. Together, our results suggest that a large number of TFs from various TF families are associated with cell-type-specific DNA demethylation during human cellular development.