Association of CNVs with methylation variation

Epigenetic diversity of genes with copy number variations among natural populations of the three-spined stickleback

Duplicated genes provide the opportunity for evolutionary novelty and adaptive divergence. In many cases, having more gene copies increases gene expression, which might facilitate adaptation to stressful or novel environments. Conversely, overexpression or misexpression of duplicated genes can be detrimental and subject to negative selection. In this scenario, newly duplicate genes may evade purifying selection if they are epigenetically silenced, at least temporarily, leading them to persist in populations as copy number variations (CNVs). In animals and plants, younger gene duplicates tend to have higher levels of DNA methylation and lower levels of gene expression, suggesting epigenetic regulation could promote the retention of gene duplications via expression repression or silencing. Here, we test the hypothesis that DNA methylation variation coincides with young duplicate genes that are segregating as CNVs in six populations of the three-spined stickleback that span a salinity gradient from 4 to 30 PSU. Using reduced-representation bisulfite sequencing, we found DNA methylation and CNV differentiation outliers rarely overlapped. Whereas lineage-specific genes and young duplicates were found to be highly methylated, just two gene CNVs showed a significant association between promoter methylation level and copy number, suggesting that DNA methylation might not interact with CNVs in our dataset. If most new duplications are regulated for dosage by epigenetic mechanisms, our results do not support a strong contribution from DNA methylation soon after duplication. Instead, our results are consistent with a preference to duplicate genes that are already highly methylated.

Exportin 4 DNA promoter methylation in liver fibrosis

A role for exportin 4 (XPO4) in the pathogenesis of liver fibrosis was recently identified. We sought to determine changes in hepatic XPO4 promoter methylation levels during liver fibrosis. The quantitative real-time RT-PCR technique was used to quantify the mRNA level of XPO4. Additionally, pyrosequencing was utilized to assess the promoter methylation status of XPO4. The methylation rate of the XPO4 promoter was significantly increased with fibrosis in human and mouse models, while XPO4 mRNA expression negatively correlated with methylation of its promoter. DNA methyltransferases (DNMTs) levels (enzymes that drive DNA methylation) were upregulated in patients with liver fibrosis compared to healthy controls and in hepatic stellate cells upon transforming growth factor beta (TGFβ) stimulation. The DNA methylation inhibitor 5-Aza or specific siRNAs for these DNMTs led to restoration of XPO4 expression. The process of DNA methylation plays a crucial role in the repression of XPO4 transcription in the context of liver fibrosis development.

BISCUIT: an efficient, standards-compliant tool suite for simultaneous genetic and epigenetic inference in bulk and single-cell studies

Data from both bulk and single-cell whole-genome DNA methylation experiments are under-utilized in many ways. This is attributable to inefficient mapping of methylation sequencing reads, routinely discarded genetic information, and neglected read-level epigenetic and genetic linkage information. We introduce the BISulfite-seq Command line User Interface Toolkit (BISCUIT) and its companion R/Bioconductor package, biscuiteer, for simultaneous extraction of genetic and epigenetic information from bulk and single-cell DNA methylation sequencing. BISCUIT's performance, flexibility and standards-compliant output allow large, complex experimental designs to be characterized on clinical timescales. BISCUIT is particularly suited for processing data from single-cell DNA methylation assays, with its excellent scalability, efficiency, and ability to greatly enhance mappability, a key challenge for single-cell studies. We also introduce the epiBED format for single-molecule analysis of coupled epigenetic and genetic information, facilitating the study of cellular and tissue heterogeneity from DNA methylation sequencing.

Epiallelic variation of non-coding RNA genes and their phenotypic consequences

Epigenetic variations contribute greatly to the phenotypic plasticity and diversity. Current functional studies on epialleles have predominantly focused on protein-coding genes, leaving the epialleles of non-coding RNA (ncRNA) genes largely understudied. Here, we uncover abundant DNA methylation variations of ncRNA genes and their significant correlations with plant adaptation among 1001 natural Arabidopsis accessions. Through genome-wide association study (GWAS), we identify large numbers of methylation QTL (methylQTL) that are independent of known DNA methyltransferases and enriched in specific chromatin states. Proximal methylQTL closely located to ncRNA genes have a larger effect on DNA methylation than distal methylQTL. We ectopically tether a DNA methyltransferase MQ1v to miR157a by CRISPR-dCas9 and show de novo establishment of DNA methylation accompanied with decreased miR157a abundance and early flowering. These findings provide important insights into the genetic basis of epigenetic variations and highlight the contribution of epigenetic variations of ncRNA genes to plant phenotypes and diversity.

Machine learning combining multi-omics data and network algorithms identifies adrenocortical carcinoma prognostic biomarkers

Background: Rare endocrine cancers such as Adrenocortical Carcinoma (ACC) present a serious diagnostic and prognostication challenge. The knowledge about ACC pathogenesis is incomplete, and patients have limited therapeutic options. Identification of molecular drivers and effective biomarkers is required for timely diagnosis of the disease and stratify patients to offer the most beneficial treatments. In this study we demonstrate how machine learning methods integrating multi-omics data, in combination with system biology tools, can contribute to the identification of new prognostic biomarkers for ACC. Methods: ACC gene expression and DNA methylation datasets were downloaded from the Xena Browser (GDC TCGA Adrenocortical Carcinoma cohort). A highly correlated multi-omics signature discriminating groups of samples was identified with the data integration analysis for biomarker discovery using latent components (DIABLO) method. Additional regulators of the identified signature were discovered using Clarivate CBDD (Computational Biology for Drug Discovery) network propagation and hidden nodes algorithms on a curated network of molecular interactions (MetaBase™). The discriminative power of the multi-omics signature and their regulators was delineated by training a random forest classifier using 55 samples, by employing a 10-fold cross validation with five iterations. The prognostic value of the identified biomarkers was further assessed on an external ACC dataset obtained from GEO (GSE49280) using the Kaplan-Meier estimator method. An optimal prognostic signature was finally derived using the stepwise Akaike Information Criterion (AIC) that allowed categorization of samples into high and low-risk groups. Results: A multi-omics signature including genes, micro RNA's and methylation sites was generated. Systems biology tools identified additional genes regulating the features included in the multi-omics signature. RNA-seq, miRNA-seq and DNA methylation sets of features revealed a high power to classify patients from stages I-II and stages III-IV, outperforming previously identified prognostic biomarkers. Using an independent dataset, associations of the genes included in the signature with Overall Survival (OS) data demonstrated that patients with differential expression levels of 8 genes and 4 micro RNA's showed a statistically significant decrease in OS. We also found an independent prognostic signature for ACC with potential use in clinical practice, combining 9-gene/micro RNA features, that successfully predicted high-risk ACC cancer patients. Conclusion: Machine learning and integrative analysis of multi-omics data, in combination with Clarivate CBDD systems biology tools, identified a set of biomarkers with high prognostic value for ACC disease. Multi-omics data is a promising resource for the identification of drivers and new prognostic biomarkers in rare diseases that could be used in clinical practice.

Construction of a novel prognostic model in skin cutaneous melanoma based on chemokines-related gene signature

Skin cutaneous melanoma, SKCM, is one of the most aggressive treatment-resistant tumours. Despite the fact that the BRAF oncogene and immunological checkpoints such as PD-1/PD-L1 and CTLA-4 have enhanced the therapeutic efficacy of SKCM, the subsequent resistance mechanisms and remedies have raised concerns. Chemokines have a significant role in the immunological milieu of tumor, which may increase the efficacy of checkpoint blockade and serve as a possible therapeutic intervention route. However, there is still no chemokine-based typing and risk model to provide a prognosis and therapeutic efficacy assessment for SKCM patients. In this study, we verified the distinct differences of prognostic stratification as well as immune characteristics between two chemokine-related clusters in SKCM patients. Two clusters of DEGs were discovered to be primarily enriched in B and T cell receptor signaling pathways as well as TNF signaling via NF-kappa-B. Based on 14 prognosis-related DEGs from aforementioned two clusters (CCL8, GBP2, GBP4, SRNG, HLA-DMB, RARRES3, HLA-DQA1, PARP12, APOL3, IRF1, HLA-DRA, UBE2L6, IL2RA and CD38), a chemokine-related 14-gene prognostic model was established. At the same time, researchers explored differences between the low-risk and high-risk groups in clinical traits, the proportion of infiltration of 22 different types of immune cells, and how well medications worked. The risk score model's immunotherapy and prognostic predictions were also confirmed in testing groups. Based on the finding, we can claim that there is a clear link between chemokines and TME in SKCM. The risk score may perform as a trustworthy prediction model, giving therapeutic benefits for both chemotherapy and immunotherapy, as well as being beneficial for clinical decision making in SKCM patients.

Variant Characterization of a Representative Large Pedigree Suggests "Variant Risk Clusters" Convey Varying Predisposition of Risk to Lynch Syndrome

Recently, worldwide incidences of young adult aggressive colorectal cancer (CRC) have rapidly increased. Of these incidences diagnosed as familial Lynch syndrome (LS) CRC, outcomes are extremely poor. In this study, we seek novel familial germline variants from a large pedigree Tunisian family with 12 LS-affected individuals to identify putative germline variants associated with varying risk of LS. Whole-genome sequencing analysis was performed to identify known and novel germline variants shared between affected and non-affected pedigree members. SNPs, indels, and structural variants (SVs) were computationally identified, and their oncological influence was predicted using the Genetic Association of Complex Diseases and Disorders, OncoKB, and My Cancer Genome databases. Of 94 germline familial variants identified with predicted functional impact, 37 SNPs/indels were detected in 28 genes, 2 of which (MLH1 and PRH1-TAS2R14) have known association with CRC and 4 others (PPP1R13B, LAMA5, FTO, and NLRP14) have known association with non-CRC cancers. In addition, 48 of 57 identified SVs overlap with 43 genes. Three of these genes (RELN, IRS2, and FOXP1) have a known association with non-CRC digestive cancers and one (RRAS2) has a known association with non-CRC cancer. Our study identified 83 novel, predicted functionally impactful germline variants grouped in three "variant risk clusters" shared in three familiarly associated LS groups (high, intermediate and low risk). This variant characterization study demonstrates that large pedigree investigations provide important evidence supporting the hypothesis that different "variant risk clusters" can convey different mechanisms of risk and oncogenesis of LS-CRC even within the same pedigree.

7p22.2 Microduplication: A Pathogenic CNV?

Partial duplication of the short arm of chromosome 7 is a rare chromosome rearrangement. The phenotype spectrum associated with this rearrangement is extremely variable even if in the last decade the use of high-resolution microarray technology for the investigation of patients carrying this rearrangement allowed for the identification of the 7p22.1 sub-band causative of this phenotype and to recognize the corresponding 7p22.1 microduplication syndrome. We report two unrelated patients that carry a microduplication involving the 7.22.2 sub-band. Unlike 7p22.1 microduplication carriers, both patients only show a neurodevelopmental disorder without malformations. We better characterized the clinical pictures of these two patients providing insight into the clinical phenotype associated with the microduplication of the 7p22.2 sub-band and support for a possible role of this sub-band in the 7p22 microduplication syndrome.

Rare genetic variants underlie outlying levels of DNA methylation and gene-expression

Testing the effect of rare variants on phenotypic variation is difficult due to the need for extremely large cohorts to identify associated variants given expected effect sizes. An alternative approach is to investigate the effect of rare genetic variants on DNA methylation (DNAm) as effect sizes are expected to be larger for molecular traits compared with complex traits. Here, we investigate DNAm in healthy ageing populations-the Lothian Birth Cohorts of 1921 and 1936-and identify both transient and stable outlying DNAm levels across the genome. We find an enrichment of rare genetic single nucleotide polymorphisms (SNPs) within 1 kb of DNAm sites in individuals with stable outlying DNAm, implying genetic control of this extreme variation. Using a family-based cohort, the Brisbane Systems Genetics Study, we observed increased sharing of DNAm outliers among more closely related individuals, consistent with these outliers being driven by rare genetic variation. We demonstrated that outlying DNAm levels have a functional consequence on gene expression levels, with extreme levels of DNAm being associated with gene expression levels toward the tails of the population distribution. This study demonstrates the role of rare SNPs in the phenotypic variation of DNAm and the effect of extreme levels of DNAm on gene expression.

Extensive intratumor regional epigenetic heterogeneity in clear cell renal cell carcinoma targets kidney enhancers and is associated with poor outcome

BACKGROUND

Clear cell renal cell cancer (ccRCC), the 8th leading cause of cancer-related death in the US, is challenging to treat due to high level intratumoral heterogeneity (ITH) and the paucity of druggable driver mutations. CcRCC is unusual for its high frequency of epigenetic regulator mutations, such as the SETD2 histone H3 lysine 36 trimethylase (H3K36me3), and low frequency of traditional cancer driver mutations. In this work, we examined epigenetic level ITH and defined its relationships with pathologic features, aspects of tumor biology, and SETD2 mutations.

RESULTS

A multi-region sampling approach coupled with EPIC DNA methylation arrays was conducted on a cohort of normal kidney and ccRCC. ITH was assessed using DNA methylation (5mC) and CNV-based entropy and Euclidian distances. We found elevated 5mC heterogeneity and entropy in ccRCC relative to normal kidney. Variable CpGs are highly enriched in enhancer regions. Using intra-class correlation coefficient analysis, we identified CpGs that segregate tumor regions according to clinical phenotypes related to tumor aggressiveness. SETD2 wild-type tumors overall possess greater 5mC and copy number ITH than SETD2 mutant tumor regions, suggesting SETD2 loss contributes to a distinct epigenome. Finally, coupling our regional data with TCGA, we identified a 5mC signature that links regions within a primary tumor with metastatic potential.

CONCLUSION

Taken together, our results reveal marked levels of epigenetic ITH in ccRCC that are linked to clinically relevant tumor phenotypes and could translate into novel epigenetic biomarkers.

Overexpression of PKMYT1 associated with poor prognosis and immune infiltration may serve as a target in triple-negative breast cancer

Breast cancer (BC) is one of the most common malignancies among women worldwide. It is necessary to search for improvement in diagnosis and treatment methods to improve the prognosis. Protein kinase, membrane associated tyrosine/threonine 1 (PKMYT1), a member of the Wee family of protein kinases, has been studied in some tumors except BC. This study has explored that PKMYT1 functional role by bioinformatics methods combined with local clinical samples and experiments. Comprehensive analysis showed that PKMYT1 expression was higher in BC tissues, especially in advanced patients than that in normal breast tissues. The expression of PKMYT1 was an independent determinant for BC patients' prognosis when combined with the clinical features. In addition, based on multi-omics analysis, we found that the PKMYT1 expression was closely relevant to several oncogenic or tumor suppressor gene variants. The analysis of single-cell sequencing indicated that PKMYT1 expression was upregulated in triple-negative breast cancer (TNBC), consistent with the results of bulk RNA-sequencing. High PKMYT1 expression was correlated with a poor prognosis. Functional enrichment analysis revealed that PKMYT1 expression was associated with cell cycle-related, DNA replication-related, and cancer-related pathways. Further research revealed that PKMYT1 expression was linked to immune cell infiltration in the tumor microenvironment. Additionally, loss-of-function experiments in vitro were performed to investigate the role of PKMYT1. TNBC cell lines' proliferation, migration, and invasion were inhibited when PKMYT1 expression was knock-down. Besides, the down-regulation of PKMYT1 induced apoptosis in vitro. As a result, PKMYT1 might be a biomarker for prognosis and a therapeutic target for TNBC.

DNA methylation as a mediator of genetic and environmental influences on Parkinson's disease susceptibility: Impacts of alpha-Synuclein, physical activity, and pesticide exposure on the epigenome

Parkinson's disease (PD) is a neurodegenerative disorder with a complex etiology and increasing prevalence worldwide. As PD is influenced by a combination of genetic and environment/lifestyle factors in approximately 90% of cases, there is increasing interest in identification of the interindividual mechanisms underlying the development of PD as well as actionable lifestyle factors that can influence risk. This narrative review presents an outline of the genetic and environmental factors contributing to PD risk and explores the possible roles of cytosine methylation and hydroxymethylation in the etiology and/or as early-stage biomarkers of PD, with an emphasis on epigenome-wide association studies (EWAS) of PD conducted over the past decade. Specifically, we focused on variants in the SNCA gene, exposure to pesticides, and physical activity as key contributors to PD risk. Current research indicates that these factors individually impact the epigenome, particularly at the level of CpG methylation. There is also emerging evidence for interaction effects between genetic and environmental contributions to PD risk, possibly acting across multiple omics layers. We speculated that this may be one reason for the poor replicability of the results of EWAS for PD reported to date. Our goal is to provide direction for future epigenetics studies of PD to build upon existing foundations and leverage large datasets, new technologies, and relevant statistical approaches to further elucidate the etiology of this disease.

The early-life exposome modulates the effect of polymorphic inversions on DNA methylation

Polymorphic genomic inversions are chromosomal variants with intrinsic variability that play important roles in evolution, environmental adaptation, and complex traits. We investigated the DNA methylation patterns of three common human inversions, at 8p23.1, 16p11.2, and 17q21.31 in 1,009 blood samples from children from the Human Early Life Exposome (HELIX) project and in 39 prenatal heart tissue samples. We found inversion-state specific methylation patterns within and nearby flanking each inversion region in both datasets. Additionally, numerous inversion-exposure interactions on methylation levels were identified from early-life exposome data comprising 64 exposures. For instance, children homozygous at inv-8p23.1 and higher meat intake were more susceptible to TDH hypermethylation (P = 3.8 × 10⁻²²); being the inversion, exposure, and gene known risk factors for adult obesity. Inv-8p23.1 associated hypermethylation of GATA4 was also detected across numerous exposures. Our data suggests that the pleiotropic influence of inversions during development and lifetime could be substantially mediated by allele-specific methylation patterns which can be modulated by the exposome.

Analysis of the relationship between presence of common DNA sequence inversions and DNA methylation patterns suggests a role for environmental exposures (such as food intake) in mediating inversion state-specific methylation patterns.

Epigenetic Signatures Discriminate Patients With Primary Sclerosing Cholangitis and Ulcerative Colitis From Patients With Ulcerative Colitis

Background

Primary sclerosing cholangitis (PSC) is a chronic inflammatory liver disease affecting the intra- and extrahepatic bile ducts, and is strongly associated with ulcerative colitis (UC). In this study, we explored the peripheral blood DNA methylome and its immune cell composition in patients with PSC-UC, UC, and healthy controls (HC) with the aim to develop a predictive assay in distinguishing patients with PSC-UC from those with UC alone.

Methods

The peripheral blood DNA methylome of male patients with PSC and concomitant UC, UC and HCs was profiled using the Illumina HumanMethylation Infinium EPIC BeadChip (850K) array. Differentially methylated CpG position (DMP) and region (DMR) analyses were performed alongside gradient boosting classification analyses to discern PSC-UC from UC patients. As observed differences in the DNA methylome could be the result of differences in cellular populations, we additionally employed mass cytometry (CyTOF) to characterize the immune cell compositions.

Results

Genome wide methylation analysis did not reveal large differences between PSC-UC and UC patients nor HCs. Nonetheless, using gradient boosting we were capable of discerning PSC-UC from UC with an area under the receiver operator curve (AUROC) of 0.80. Four CpG sites annotated to the NINJ2 gene were found to strongly contribute to the predictive performance. While CyTOF analyses corroborated the largely similar blood cell composition among patients with PSC-UC, UC and HC, a higher abundance of myeloid cells was observed in UC compared to PSC-UC patients.

Conclusion

DNA methylation enables discerning PSC-UC from UC patients, with a potential for biomarker development.

A pan-cancer analysis of GINS complex subunit 4 to identify its potential role as a biomarker in multiple human cancers

This study was initiated to explore the expression variation, clinical significance, and biological importance of the GINS complex subunit 4 (GINS4) in different human cancers as a shared biomarker via pan-cancer analysis through different platforms including UALCAN, Kaplan Meier (KM) plotter, TNMplot, GENT2, GEPIA, DriverDBv3, Human Protein Atlas (HPA), MEXPRESS, cBioportal, STRING, DAVID, MuTarge, Enrichr, TIMER, and CTD. Our findings have verified the up-regulation of GINS4 in 24 major subtypes of human cancers, and its overexpression was found to be substantially associated with poor overall survival (OS), relapse-free survival (RFs), and metastasis in ESCA, KIRC, LIHC, LUAD, and UCEC. This suggested that GINS4 plays a significant role in the development and progression of these five cancers. Furthermore, we noticed that GINS4 is also overexpressed in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics. Enrichment analysis revealed the involvement of GINS4 associated genes in a variety of diverse GO and KEGG terms. We also explored few significant correlations between GINS4 expression and promoter methylation, genetic alterations, CNVs, other mutant genes, tumor purity, and immune cells infiltration. In conclusion, our results elucidated that GINS4 can serve as a shared diagnostic, prognostic biomarker, and a potential therapeutic target in ESCA, KIRC, LIHC, LUAD, and UCEC patients with different clinicopathological characteristics.

Harnessing the power of multi-omics data for predicting climate change response

Predicting how species will respond to future climate change is of central importance in the midst of the global biodiversity crisis, and recent work has demonstrated the utility of population genomics for improving these predictions. Here, we suggest a broadening of the approach to include other types of genomic variants that play an important role in adaptation, like structural (e.g. copy number variants) and epigenetic variants (e.g. DNA methylation). These data could provide additional power for forecasting response, especially in weakly structured or panmictic species. Incorporating structural and epigenetic variation into estimates of climate change vulnerability, or maladaptation, may not only improve prediction power but also provide insight into the molecular mechanisms underpinning species' response to climate change.