QSEA-modelling of genome-wide DNA methylation from sequencing enrichment experiments

LCAT in Cancer Biology: Embracing Epigenetic Regulation, Immune Interactions, and Therapeutic Implications

Lecithin cholesterol acyltransferase (LCAT) is a crucial enzyme in high-density lipoprotein (HDL) metabolism that is often dysregulated in cancers, affecting tumor growth and therapy response. We extensively studied LCAT expression in various malignancies, linking it to clinical outcomes and genetic/epigenetic alterations. We analyzed LCAT expression in multiple cancers and used the Cox regression model to correlate it with patient survival metrics, including overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). We also examined the copy number variations (CNVs), single-nucleotide variations (SNVs), DNA methylation, and N6-methyladenosine (m6A) modifications of LCAT and their connections to tumor immune responses and drug sensitivity. LCAT expression varies among cancers and correlates with patient outcomes. Low expression is linked to poor prognosis in low-grade glioma (LGG) and liver hepatocellular carcinoma (LIHC), while high expression is associated with better outcomes in adrenocortical carcinoma (ACC) and colon adenocarcinoma (COAD). In kidney renal papillary cell carcinoma (KIRP) and uterine corpus endometrial carcinoma (UCEC), LCAT CNV and methylation levels are prognostic markers. LCAT interacts with m6A modifiers and immune molecules, suggesting a role in immune evasion and as a biomarker for immunotherapy response. LCAT expression correlates with chemotherapeutic drug IC50 values, indicating potential for predicting treatment response. In ACC and COAD, LCAT may promote tumor growth, while in LGG and LIHC, it may inhibit progression. LCAT expression and activity regulation could be a new cancer therapy target. As a key molecule linking lipid metabolism, immune modulation, and tumor progression, the potential of LCAT in cancer therapy is significant. Our findings provide new insights into the role of LCAT in cancer biology and support the development of personalized treatment strategies.

Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma

BACKGROUND

The current study's objective is to evaluate the molecular genetic mechanisms influencing the biological behavior of hepatocellular carcinoma (HCC) by analyzing the transcriptomic and epigenetic signatures of the tumors.

METHODS

Transcriptomic data were downloaded from the NCBI GEO database. We investigated the expression differences between the GSE46444 (48 cirrhotic tissues versus 88 HCC tissues) and GSE63898 (168 cirrhotic tissues versus 228 HCC tissues) data sets using GEO2R. Differentially expressed genes were evaluated using GO and KEGG metabolic pathway analysis websites. Whole genome bisulfite sequencing (WGBS) and Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) data sets (26 HCC tissues versus 26 adjacent non-tumoral tissues) were also downloaded from the NCBI SRA database. These data sets were analyzed using Bismark and QSEA, respectively. The methylation differences between the groups were assessed using functional enrichment analysis.

RESULTS

In the GSE46444 data set, 80 genes were upregulated, and 315 genes were downregulated in the tumor tissue (HCC tissue) compared to the non-tumor cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were downregulated in the cirrhotic tissue compared to the tumor tissues. WGBS revealed that 20 protein-coding loci were hypermethylated. while the hypomethylated regions were non-protein-coding. The methylated residues of the tumor tissue, non-tumorous cirrhotic tissue, and healthy tissue were comparable. MeDIP-Seq, conducted on tumoral and non-tumoral tissues, identified hypermethylated or hypomethylated areas as protein-coding regions. The functional enrichment analysis indicated that these genes were related to pathways including peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras, and PI3K/AKT signal transduction.

CONCLUSIONS

The investigation of transcriptomic and epigenetic mechanisms identified several genes significant in the biological behavior of HCC. These genes present potential targets for the development of targeted therapy.

Gene body DNA hydroxymethylation restricts the magnitude of transcriptional changes during aging

DNA hydroxymethylation (5hmC), the most abundant oxidative derivative of DNA methylation, is typically enriched at enhancers and gene bodies of transcriptionally active and tissue-specific genes. Although aberrant genomic 5hmC has been implicated in age-related diseases, its functional role in aging remains unknown. Here, using mouse liver and cerebellum as model organs, we show that 5hmC accumulates in gene bodies associated with tissue-specific function and restricts the magnitude of gene expression changes with age. Mechanistically, 5hmC decreases the binding of splicing associated factors and correlates with age-related alternative splicing events. We found that various age-related contexts, such as prolonged quiescence and senescence, drive the accumulation of 5hmC with age. We provide evidence that this age-related transcriptionally restrictive function is conserved in mouse and human tissues. Our findings reveal that 5hmC regulates tissue-specific function and may play a role in longevity.

Tumor detection by analysis of both symmetric- and hemi-methylation of plasma cell-free DNA

Aberrant DNA methylation patterns have been used for cancer detection. However, DNA hemi-methylation, present at about 10% CpG dinucleotides, has been less well studied. Here we show that a majority of differentially hemi-methylated regions (DHMRs) in liver tumor DNA or plasma cells free (cf) DNA do not overlap with differentially methylated regions (DMRs) of the same samples, indicating that DHMRs could serve as independent biomarkers. Furthermore, we analyzed the cfDNA methylomes of 215 samples from individuals with liver or brain cancer and individuals without cancer (controls), and trained machine learning models using DMRs, DHMRs or both. The models incorporated with both DMRs and DHMRs show a superior performance compared to models trained with DMRs or DHMRs, with AUROC being 0.978, 0.990, and 0.983 in distinguishing control, liver and brain cancer, respectively, in a validation cohort. This study supports the potential of utilizing both DMRs and DHMRs for multi-cancer detection.

Prostate cancer detection through unbiased capture of methylated cell-free DNA

Prostate cancer screening using prostate-specific antigen (PSA) has been shown to reduce mortality but with substantial overdiagnosis, leading to unnecessary biopsies. The identification of a highly specific biomarker using liquid biopsies, represents an unmet need in the diagnostic pathway for prostate cancer. In this study, we employed a method that enriches for methylated cell-free DNA fragments coupled with a machine learning algorithm which enabled the detection of metastatic and localized cancers with AUCs of 0.96 and 0.74, respectively. The model also detected 51.8% (14/27) of localized and 88.7% (79/89) of patients with metastatic cancer in an external dataset. Furthermore, we show that the differentially methylated regions reflect epigenetic and transcriptomic changes at the tissue level. Notably, these regions are significantly enriched for biologically relevant pathways associated with the regulation of cellular proliferation and TGF-beta signaling. This demonstrates the potential of circulating tumor DNA methylation for prostate cancer detection and prognostication.

A cfDNA methylation-based tissue-of-origin classifier for cancers of unknown primary

Cancers of Unknown Primary (CUP) remains a diagnostic and therapeutic challenge due to biological heterogeneity and poor responses to standard chemotherapy. Predicting tissue-of-origin (TOO) molecularly could help refine this diagnosis, with tissue acquisition barriers mitigated via liquid biopsies. However, TOO liquid biopsies are unexplored in CUP cohorts. Here we describe CUPiD, a machine learning classifier for accurate TOO predictions across 29 tumour classes using circulating cell-free DNA (cfDNA) methylation patterns. We tested CUPiD on 143 cfDNA samples from patients with 13 cancer types alongside 27 non-cancer controls, with overall sensitivity of 84.6% and TOO accuracy of 96.8%. In an additional cohort of 41 patients with CUP CUPiD predictions were made in 32/41 (78.0%) cases, with 88.5% of the predictions clinically consistent with a subsequent or suspected primary tumour diagnosis, when available (23/26 patients). Combining CUPiD with cfDNA mutation data demonstrated potential diagnosis re-classification and/or treatment change in this hard-to-treat cancer group.

Neurofibromin 1 controls metabolic balance and Notch-dependent quiescence of murine juvenile myogenic progenitors

Patients affected by neurofibromatosis type 1 (NF1) frequently show muscle weakness with unknown etiology. Here we show that, in mice, Neurofibromin 1 (Nf1) is not required in muscle fibers, but specifically in early postnatal myogenic progenitors (MPs), where Nf1 loss led to cell cycle exit and differentiation blockade, depleting the MP pool resulting in reduced myonuclear accretion as well as reduced muscle stem cell numbers. This was caused by precocious induction of stem cell quiescence coupled to metabolic reprogramming of MPs impinging on glycolytic shutdown, which was conserved in muscle fibers. We show that a Mek/Erk/NOS pathway hypersensitizes Nf1-deficient MPs to Notch signaling, consequently, early postnatal Notch pathway inhibition ameliorated premature quiescence, metabolic reprogramming and muscle growth. This reveals an unexpected role of Ras/Mek/Erk signaling supporting postnatal MP quiescence in concert with Notch signaling, which is controlled by Nf1 safeguarding coordinated muscle growth and muscle stem cell pool establishment. Furthermore, our data suggest transmission of metabolic reprogramming across cellular differentiation, affecting fiber metabolism and function in NF1.

MEDIPIPE: an automated and comprehensive pipeline for cfMeDIP-seq data quality control and analysis

SUMMARY

Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) has emerged as a promising liquid biopsy technology to detect cancers and monitor treatments. While several bioinformatics tools for DNA methylation analysis have been adapted for cfMeDIP-seq data, an end-to-end pipeline and quality control framework specifically for this data type is still lacking. Here, we present the MEDIPIPE, which provides a one-stop solution for cfMeDIP-seq data quality control, methylation quantification, and sample aggregation. The major advantages of MEDIPIPE are: (i) ease of implementation and reproducibility with Snakemake containerized execution environments that will be automatically deployed via Conda; (ii) flexibility to handle different experimental settings with a single configuration file; and (iii) computationally efficiency for large-scale cfMeDIP-seq profiling data analysis and aggregation.

AVAILABILITY AND IMPLEMENTATION

This pipeline is an open-source software under the MIT license and it is freely available at https://github.com/pughlab/MEDIPIPE.

Human VDAC pseudogenes: an emerging role for VDAC1P8 pseudogene in acute myeloid leukemia

BACKGROUND

Voltage-dependent anion selective channels (VDACs) are the most abundant mitochondrial outer membrane proteins, encoded in mammals by three genes, VDAC1, 2 and 3, mostly ubiquitously expressed. As 'mitochondrial gatekeepers', VDACs control organelle and cell metabolism and are involved in many diseases. Despite the presence of numerous VDAC pseudogenes in the human genome, their significance and possible role in VDAC protein expression has not yet been considered.

RESULTS

We investigated the relevance of processed pseudogenes of human VDAC genes, both in physiological and in pathological contexts. Using high-throughput tools and querying many genomic and transcriptomic databases, we show that some VDAC pseudogenes are transcribed in specific tissues and pathological contexts. The obtained experimental data confirm an association of the VDAC1P8 pseudogene with acute myeloid leukemia (AML).

CONCLUSIONS

Our in-silico comparative analysis between the VDAC1 gene and its VDAC1P8 pseudogene, together with experimental data produced in AML cellular models, indicate a specific over-expression of the VDAC1P8 pseudogene in AML, correlated with a downregulation of the parental VDAC1 gene.

Plasma cell-free DNA methylome profiling in pre- and post-surgery oral cavity squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous disease that involves multiple anatomic sites, is a leading cause of cancer-related mortality worldwide. Although the utility of noninvasive biomarkers based on circulating cell-free DNA (cfDNA) methylation profiling has been widely recognized, limited studies have been reported so far regarding the dynamics of cfDNA methylome in oral cavity squamous cell carcinoma (OCSCC). It is hypothesized in this study that comparison of methylation profiles in pre- and postsurgery plasma samples will reveal OCSCC-specific prognostic and diagnostic biomarkers. As a strategy to further prioritize tumor-specific targets, top differential methylated regions (DMRs) were called by reanalyzing methylation data from paired tumor and normal tissue collected in the the cancer genome atlas head-neck squamous cell carcinoma (TCGA) head and neck cancer cohort. Matched plasma samples from eight patients with OCSCC were collected at Moffitt Cancer Center before and after surgical resection. Plasma-derived cfDNA was analyzed by cfMBD-seq, which is a high-sensitive methylation profiling assay. Differential methylation analysis was then performed based on the matched samples profiled. In the top 200 HNSCC-specific DMRs detected based on the TCGA data set, a total of 23 regions reached significance in the plasma-based DMR test. The top five validated DMR regions (ranked by the significance in the plasma study) are located in the promoter regions of genes PENK, NXPH1, ZIK1, TBXT, and CDO1, respectively. The genome-wide cfDNA DMR analysis further highlighted candidate biomarkers located in genes SFRP4, SOX1, IRF4, and PCDH17. The prognostic relevance of candidate genes was confirmed by survival analysis using the TCGA data. This study supports the utility of cfDNA-based methylome profiling as a promising noninvasive biomarker source for OCSCC and HNSCC.

Extra-hematopoietic immunomodulatory role of the guanine-exchange factor DOCK2

Stromal cells interact with immune cells during initiation and resolution of immune responses, though the precise underlying mechanisms remain to be resolved. Lessons learned from stromal cell-based therapies indicate that environmental signals instruct their immunomodulatory action contributing to immune response control. Here, to the best of our knowledge, we show a novel function for the guanine-exchange factor DOCK2 in regulating immunosuppressive function in three human stromal cell models and by siRNA-mediated DOCK2 knockdown. To identify immune function-related stromal cell molecular signatures, we first reprogrammed mesenchymal stem/progenitor cells (MSPCs) into induced pluripotent stem cells (iPSCs) before differentiating these iPSCs in a back-loop into MSPCs. The iPSCs and immature iPS-MSPCs lacked immunosuppressive potential. Successive maturation facilitated immunomodulation, while maintaining clonogenicity, comparable to their parental MSPCs. Sequential transcriptomics and methylomics displayed time-dependent immune-related gene expression trajectories, including DOCK2, eventually resembling parental MSPCs. Severe combined immunodeficiency (SCID) patient-derived fibroblasts harboring bi-allelic DOCK2 mutations showed significantly reduced immunomodulatory capacity compared to non-mutated fibroblasts. Conditional DOCK2 siRNA knockdown in iPS-MSPCs and fibroblasts also immediately reduced immunomodulatory capacity. Conclusively, CRISPR/Cas9-mediated DOCK2 knockout in iPS-MSPCs also resulted in significantly reduced immunomodulation, reduced CDC42 Rho family GTPase activation and blunted filopodia formation. These data identify G protein signaling as key element devising stromal cell immunomodulation.

Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds

The data currently described was generated within the EU/FP7 HeCaToS project (Hepatic and Cardiac Toxicity Systems modeling). The project aimed to develop an in silico prediction system to contribute to drug safety assessment for humans. For this purpose, multi-omics data of repeated dose toxicity were obtained for 10 hepatotoxic and 10 cardiotoxic compounds. Most data were gained from in vitro experiments in which 3D microtissues (either hepatic or cardiac) were exposed to a therapeutic (physiologically relevant concentrations calculated through PBPK-modeling) or a toxic dosing profile (IC20 after 7 days). Exposures lasted for 14 days and samples were obtained at 7 time points (therapeutic doses: 2-8-24-72-168-240-336 h; toxic doses 0-2-8-24-72-168-240 h). Transcriptomics (RNA sequencing & microRNA sequencing), proteomics (LC-MS), epigenomics (MeDIP sequencing) and metabolomics (LC-MS & NMR) data were obtained from these samples. Furthermore, functional endpoints (ATP content, Caspase3/7 and O2 consumption) were measured in exposed microtissues. Additionally, multi-omics data from human biopsies from patients are available. This data is now being released to the scientific community through the BioStudies data repository (https://www.ebi.ac.uk/biostudies/).

cfDNA methylome profiling for detection and subtyping of small cell lung cancers

Small cell lung cancer (SCLC) is characterized by morphologic, epigenetic and transcriptomic heterogeneity. Subtypes based upon predominant transcription factor expression have been defined that, in mouse models and cell lines, exhibit potential differential therapeutic vulnerabilities, with epigenetically distinct SCLC subtypes also described. The clinical relevance of these subtypes is unclear, due in part to challenges in obtaining tumor biopsies for reliable profiling. Here we describe a robust workflow for genome-wide DNA methylation profiling applied to both patient-derived models and to patients' circulating cell-free DNA (cfDNA). Tumor-specific methylation patterns were readily detected in cfDNA samples from patients with SCLC and were correlated with survival outcomes. cfDNA methylation also discriminated between the transcription factor SCLC subtypes, a precedent for a liquid biopsy cfDNA-methylation approach to molecularly subtype SCLC. Our data reveal the potential clinical utility of cfDNA methylation profiling as a universally applicable liquid biopsy approach for the sensitive detection, monitoring and molecular subtyping of patients with SCLC.

Sensitive and reproducible cell-free methylome quantification with synthetic spike-in controls

Cell-free methylated DNA immunoprecipitation sequencing (cfMeDIP-seq) identifies genomic regions with DNA methylation, using a protocol adapted to work with low-input DNA samples and with cell-free DNA (cfDNA). We developed a set of synthetic spike-in DNA controls for cfMeDIP-seq to provide a simple and inexpensive reference for quantitative normalization. We designed 54 DNA fragments with combinations of methylation status (methylated and unmethylated), fragment length (80 bp, 160 bp, 320 bp), G + C content (35%, 50%, 65%), and fraction of CpG dinucleotides within the fragment (1/80 bp, 1/40 bp, 1/20 bp). Using 0.01 ng of spike-in controls enables training a generalized linear model that absolutely quantifies methylated cfDNA in MeDIP-seq experiments. It mitigates batch effects and corrects for biases in enrichment due to known biophysical properties of DNA fragments and other technical biases.

Transgenerational Transcriptomic and DNA Methylome Profiling of Mouse Fetal Testicular Germline and Somatic Cells after Exposure of Pregnant Mothers to Tributyltin, a Potent Obesogen

Obesogens such as tributyltin (TBT) are xenobiotic compounds that promote obesity, in part by distorting the normal balance of lipid metabolism. The obesogenic effects of TBT can be observed in directly exposed (F1 and F2 generations) and also subsequent generations (F3 and beyond) that were never exposed. To address the effects of TBT exposure on germ cells, we exposed pregnant transgenic OG2 mouse dams (F0), which specifically express EGFP in germline cells, to an environmentally relevant dose of TBT or DMSO throughout gestation through drinking water. When fed with a high-fat diet, F3 male offspring of TBT-exposed F0 dams (TBT-F3) accumulated much more body fat than did DMSO-F3 males. TBT-F3 males also lost more body fluid and lean compositions than did DMSO-F3 males. Expression of genes involved in transcriptional regulation or mesenchymal differentiation was up-regulated in somatic cells of TBT-F1 (but not TBT-F3) E18.5 fetal testes, and promoter-associated CpG islands were hyper-methylated in TBT-F1 somatic cells. Global mRNA expression of protein-coding genes in F1 or F3 fetal testicular cells was unaffected by F0 exposure to TBT; however, expression of a subset of endogenous retroviruses was significantly affected in F1 and F3. We infer that TBT may directly target testicular somatic cells in F1 testes to irreversibly affect epigenetic suppression of endogenous retroviruses in both germline and somatic cells.

Experimental and Bioinformatic Approaches to Studying DNA Methylation in Cancer

DNA methylation is an essential epigenetic mark. Alterations of normal DNA methylation are a defining feature of cancer. Here, we review experimental and bioinformatic approaches to showcase the breadth and depth of information that this epigenetic mark provides for cancer research. First, we describe classical approaches for interrogating bulk DNA from cell populations as well as more recently developed approaches for single cells and multi-Omics. Second, we focus on the computational analysis from primary data processing to the identification of unique methylation signatures. Additionally, we discuss challenges such as sparse data and cellular heterogeneity.

Altered DNA Methylation Profiles in SF3B1 Mutated CLL Patients

Mutations in splicing factor genes have a severe impact on the survival of cancer patients. Splicing factor 3b subunit 1 (SF3B1) is one of the most frequently mutated genes in chronic lymphocytic leukemia (CLL); patients carrying these mutations have a poor prognosis. Since the splicing machinery and the epigenome are closely interconnected, we investigated whether these alterations may affect the epigenomes of CLL patients. While an overall hypomethylation during CLL carcinogenesis has been observed, the interplay between the epigenetic stage of the originating B cells and SF3B1 mutations, and the subsequent effect of the mutations on methylation alterations in CLL, have not been investigated. We profiled the genome-wide DNA methylation patterns of 27 CLL patients with and without SF3B1 mutations and identified local decreases in methylation levels in SF3B1^mut CLL patients at 67 genomic regions, mostly in proximity to telomeric regions. These differentially methylated regions (DMRs) were enriched in gene bodies of cancer-related signaling genes, e.g., NOTCH1, HTRA3, and BCL9L. In our study, SF3B1 mutations exclusively emerged in two out of three epigenetic stages of the originating B cells. However, not all the DMRs could be associated with the methylation programming of B cells during development, suggesting that mutations in SF3B1 cause additional epigenetic aberrations during carcinogenesis.

Liquid Biopsy of Methylation Biomarkers in Cell-Free DNA

Liquid biopsies, in particular, analysis of cell-free DNA (cfDNA), have emerged as a promising noninvasive diagnostic approach in oncology. Abnormal distribution of DNA methylation is one of the hallmarks of many cancers and methylation changes occur early during carcinogenesis. Systemic analysis of cfDNA methylation profiles is being developed for cancer early detection, monitoring for minimal residual disease (MRD), predicting treatment response and prognosis, and tracing the tissue origin. This review highlights the advantages and disadvantages of ctDNA profiling for noninvasive diagnosis of early-stage cancers and explores recent advances in the clinical application of ctDNA methylation assays. We also summarize the technologies for ctDNA methylation analysis and provide a brief overview of the bioinformatic approaches for analyzing DNA methylation sequencing data.

PWD/Ph-Encoded Genetic Variants Modulate the Cellular Wnt/β-Catenin Response to Suppress Apc Min-Triggered Intestinal Tumor Formation

Genetic predisposition affects the penetrance of tumor-initiating mutations, such as APC mutations that stabilize β-catenin and cause intestinal tumors in mice and humans. However, the mechanisms involved in genetically predisposed penetrance are not well understood. Here, we analyzed tumor multiplicity and gene expression in tumor-prone Apc ^Min/+ mice on highly variant C57BL/6J (B6) and PWD/Ph (PWD) genetic backgrounds. (B6 × PWD) F1 APC ^Min offspring mice were largely free of intestinal adenoma, and several chromosome substitution (consomic) strains carrying single PWD chromosomes on the B6 genetic background displayed reduced adenoma numbers. Multiple dosage-dependent modifier loci on PWD chromosome 5 each contributed to tumor suppression. Activation of β-catenin-driven and stem cell-specific gene expression in the presence of Apc ^Min or following APC loss remained moderate in intestines carrying PWD chromosome 5, suggesting that PWD variants restrict adenoma initiation by controlling stem cell homeostasis. Gene expression of modifier candidates and DNA methylation on chromosome 5 were predominantly cis controlled and largely reflected parental patterns, providing a genetic basis for inheritance of tumor susceptibility. Human SNP variants of several modifier candidates were depleted in colorectal cancer genomes, suggesting that similar mechanisms may also affect the penetrance of cancer driver mutations in humans. Overall, our analysis highlights the strong impact that multiple genetic variants acting in networks can exert on tumor development. SIGNIFICANCE: These findings in mice show that, in addition to accidental mutations, cancer risk is determined by networks of individual gene variants.

Network integration and modelling of dynamic drug responses at multi-omics levels

Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin). Dynamic molecular analysis at in vivo drug exposure levels reveal a network of 175 disease-associated proteins and identify common modules of anthracycline cardiotoxicity in vitro, related to mitochondrial and sarcomere function as well as remodeling of extracellular matrix. These in vitro-identified modules are transferable and are evaluated with biopsies of cardiomyopathy patients. This to our knowledge most comprehensive study on anthracycline cardiotoxicity demonstrates a reproducible workflow for molecular medicine and serves as a template for detecting adverse drug responses from complex omics data.

Using a network propagation approach with integrated multi-omic data, Selevsek et al. develop a reproducible workflow for identifying drug toxicity effects in cellular systems. This is demonstrated with the analysis of anthracycline cardiotoxicity in cardiac microtissues under the effect of multiple drugs.

Bacteria pathogens drive host colonic epithelial cell promoter hypermethylation of tumor suppressor genes in colorectal cancer

BACKGROUND

Altered microbiome composition and aberrant promoter hypermethylation of tumor suppressor genes (TSGs) are two important hallmarks of colorectal cancer (CRC). Here we performed concurrent 16S rRNA gene sequencing and methyl-CpG binding domain-based capture sequencing in 33 tissue biopsies (5 normal colonic mucosa tissues, 4 pairs of adenoma and adenoma-adjacent tissues, and 10 pairs of CRC and CRC-adjacent tissues) to identify significant associations between TSG promoter hypermethylation and CRC-associated bacteria, followed by functional validation of the methylation-associated bacteria.

RESULTS

Fusobacterium nucleatum and Hungatella hathewayi were identified as the top two methylation-regulating bacteria. Targeted analysis on bona fide TSGs revealed that H. hathewayi and Streptococcus spp. significantly correlated with CDX2 and MLH1 promoter hypermethylation, respectively. Mechanistic validation with cell-line and animal models revealed that F. nucleatum and H. hathewayi upregulated DNA methyltransferase. H. hathewayi inoculation also promoted colonic epithelial cell proliferation in germ-free and conventional mice.

CONCLUSION

Our integrative analysis revealed previously unknown epigenetic regulation of TSGs in host cells through inducing DNA methyltransferase by F. nucleatum and H. hathewayi, and established the latter as CRC-promoting bacteria. Video abstract.

MBD-seq - realities of a misunderstood method for high-quality methylome-wide association studies

The majority of methylome-wide association studies (MWAS) have been performed using commercially available array-based technologies such as the Infinium Human Methylation 450K and the Infinium MethylationEPIC arrays (Illumina). While these arrays offer a convenient and relatively robust assessment of the probed sites they only allow interrogation of 2-4% of all CpG sites in the human genome. Methyl-binding domain sequencing (MBD-seq) is an alternative approach for MWAS that provides near-complete coverage of the methylome at similar costs as the array-based technologies. However, despite publication of multiple positive evaluations, the use of MBD-seq for MWAS is often fiercely criticized. Here we discuss key features of the method and debunk misconceptions using empirical data. We conclude that MBD-seq represents an excellent approach for large-scale MWAS and that increased utilization is likely to result in more discoveries, advance biological knowledge, and expedite the clinical translation of methylome-wide research findings.

Cell-Free DNA Methylation Profiling Analysis-Technologies and Bioinformatics

Analysis of circulating nucleic acids in bodily fluids, referred to as “liquid biopsies”, is rapidly gaining prominence. Studies have shown that cell-free DNA (cfDNA) has great potential in characterizing tumor status and heterogeneity, as well as the response to therapy and tumor recurrence. DNA methylation is an epigenetic modification that plays an important role in a broad range of biological processes and diseases. It is well known that aberrant DNA methylation is generalizable across various samples and occurs early during the pathogenesis of cancer. Methylation patterns of cfDNA are also consistent with their originated cells or tissues. Systemic analysis of cfDNA methylation profiles has emerged as a promising approach for cancer detection and origin determination. In this review, we will summarize the technologies for DNA methylation analysis and discuss their feasibility for liquid biopsy applications. We will also provide a brief overview of the bioinformatic approaches for analysis of DNA methylation sequencing data. Overall, this review provides informative guidance for the selection of experimental and computational methods in cfDNA methylation-based studies.

A model of pulldown alignments from SssI-treated DNA improves DNA methylation prediction

Background

Protein pulldown using Methyl-CpG binding domain (MBD) proteins followed by high-throughput sequencing is a common method to determine DNA methylation. Algorithms have been developed to estimate absolute methylation level from read coverage generated by affinity enrichment-based techniques, but the most accurate one for MBD-seq data requires additional data from an SssI-treated Control experiment.

Results

Using our previous characterizations of Methyl-CpG/MBD2 binding in the context of an MBD pulldown experiment, we build a model of expected MBD pulldown reads as drawn from SssI-treated DNA. We use the program BayMeth to evaluate the effectiveness of this model by substituting calculated SssI Control data for the observed SssI Control data. By comparing methylation predictions against those from an RRBS data set, we find that BayMeth run with our modeled SssI Control data performs better than BayMeth run with observed SssI Control data, on both 100 bp and 10 bp windows. Adapting the model to an external data set solely by changing the average fragment length, our calculated data still informs the BayMeth program to a similar level as observed data in predicting methylation state on a pulldown data set with matching WGBS estimates.

Conclusion

In both internal and external MBD pulldown data sets tested in this study, BayMeth used with our modeled pulldown coverage performs better than BayMeth run without the inclusion of any estimate of SssI Control pulldown, and is comparable to – and in some cases better than – using observed SssI Control data with the BayMeth program. Thus, our MBD pulldown alignment model can improve methylation predictions without the need to perform additional control experiments.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-3011-2) contains supplementary material, which is available to authorized users.

RaMWAS: fast methylome-wide association study pipeline for enrichment platforms

Motivation

Enrichment-based technologies can provide measurements of DNA methylation at tens of millions of CpGs for thousands of samples. Existing tools for methylome-wide association studies cannot analyze datasets of this size and lack important features like principal component analysis, combined analysis with SNP data and outcome predictions that are based on all informative methylation sites.

Results

We present a Bioconductor R package called RaMWAS with a full set of tools for large-scale methylome-wide association studies. It is free, cross-platform, open source, memory efficient and fast.

Availability and implementation

Release version and vignettes with small case study at bioconductor.org/packages/ramwas Development version at github.com/andreyshabalin/ramwas.

Supplementary information

Supplementary data are available at Bioinformatics online.

Epigenetic transgenerational inheritance of testis pathology and Sertoli cell epimutations: generational origins of male infertility

Male reproductive health has been in decline for decades with dropping sperm counts and increasing infertility, which has created a significant societal and economic burden. Between the 1970s and now, a general decline of over 50% in sperm concentration has been observed in the population. Environmental toxicant-induced epigenetic transgenerational inheritance has been shown to affect testis pathology and sperm count. Sertoli cells have an essential role in spermatogenesis by providing physical and nutritional support for developing germ cells. The current study was designed to further investigate the transgenerational epigenetic changes in the rat Sertoli cell epigenome and transcriptome that are associated with the onset of testis disease. Gestating female F0 generation rats were transiently exposed during the period of fetal gonadal sex determination to the environmental toxicants, such as dichlorodiphenyltrichloroethane (DDT) or vinclozolin. The F1 generation offspring were bred (i.e. intercross within the lineage) to produce the F2 generation grand-offspring that were then bred to produce the transgenerational F3 generation (i.e. great-grand-offspring) with no sibling or cousin breeding used. The focus of the current study was to investigate the transgenerational testis disease etiology, so F3 generation rats were utilized. The DNA and RNA were obtained from purified Sertoli cells isolated from postnatal 20-day-old male testis of F3 generation rats. Transgenerational alterations in DNA methylation, noncoding RNA, and gene expression were observed in the Sertoli cells from vinclozolin and DDT lineages when compared to the control (vehicle exposed) lineage. Genes associated with abnormal Sertoli cell function and testis pathology were identified, and the transgenerational impacts of vinclozolin and DDT were determined. Alterations in critical gene pathways, such as the pyruvate metabolism pathway, were identified. Observations suggest that ancestral exposures to environmental toxicants promote the epigenetic transgenerational inheritance of Sertoli cell epigenetic and transcriptome alterations that associate with testis abnormalities. These epigenetic alterations appear to be critical factors in the developmental and generational origins of testis pathologies and male infertility.

DMSO induces drastic changes in human cellular processes and epigenetic landscape in vitro

Though clinical trials for medical applications of dimethyl sulfoxide (DMSO) reported toxicity in the 1960s, later, the FDA classified DMSO in the safest solvent category. DMSO became widely used in many biomedical fields and biological effects were overlooked. Meanwhile, biomedical science has evolved towards sensitive high-throughput techniques and new research areas, including epigenomics and microRNAs. Considering its wide use, especially for cryopreservation and in vitro assays, we evaluated biological effect of DMSO using these technological innovations. We exposed 3D cardiac and hepatic microtissues to medium with or without 0.1% DMSO and analyzed the transcriptome, proteome and DNA methylation profiles. In both tissue types, transcriptome analysis detected >2000 differentially expressed genes affecting similar biological processes, thereby indicating consistent cross-organ actions of DMSO. Furthermore, microRNA analysis revealed large-scale deregulations of cardiac microRNAs and smaller, though still massive, effects in hepatic microtissues. Genome-wide methylation patterns also revealed tissue-specificity. While hepatic microtissues demonstrated non-significant changes, findings from cardiac microtissues suggested disruption of DNA methylation mechanisms leading to genome-wide changes. The extreme changes in microRNAs and alterations in the epigenetic landscape indicate that DMSO is not inert. Its use should be reconsidered, especially for cryopreservation of embryos and oocytes, since it may impact embryonic development.

Systems Biology Approaches Toward Understanding Primary Mitochondrial Diseases

Primary mitochondrial diseases form one of the most common and severe groups of genetic disease, with a birth prevalence of at least 1 in 5000. These disorders are multi-genic and multi-phenotypic (even within the same gene defect) and span the entire age range from prenatal to late adult onset. Mitochondrial disease typically affects one or multiple high-energy demanding organs, and is frequently fatal in early life. Unfortunately, to date there are no known curative therapies, mostly owing to the rarity and heterogeneity of individual mitochondrial diseases, leading to diagnostic odysseys and difficulties in clinical trial design. This review aims to discuss recent advances and challenges of systems approaches for the study of primary mitochondrial diseases. Although there has been an explosion in the generation of omics data, few studies have progressed toward the integration of multiple levels of omics. It is evident that the integration of different types of data to create a more complete representation of biology remains challenging, perhaps due to the scarcity of available integrative tools and the complexity inherent in their use. In addition, "bottom-up" systems approaches have been adopted for use in the iterative cycle of systems biology: from data generation to model prediction and validation. Primary mitochondrial diseases, owing to their complex nature, will most likely benefit from a multidisciplinary approach encompassing clinical, molecular and computational studies integrated together by systems biology to elucidate underlying pathomechanisms for better diagnostics and therapeutic discovery. Just as next generation sequencing has rapidly increased diagnostic rates from approximately 5% up to 60% over two decades, more recent advancing technologies are encouraging; the generation of multi-omics, the integration of multiple types of data, and the ability to predict perturbations will, ultimately, be translated into improved patient care.

MeDEStrand: an improved method to infer genome-wide absolute methylation levels from DNA enrichment data

Background

DNA methylation of CpG dinucleotides is an essential epigenetic modification that plays a key role in transcription. Widely used DNA enrichment-based methods offer high coverage for measuring methylated CpG dinucleotides, with the lowest cost per CpG covered genome-wide. However, these methods measure the DNA enrichment of methyl-CpG binding, and thus do not provide information on absolute methylation levels. Further, the enrichment is influenced by various confounding factors in addition to methylation status, for example, CpG density. Computational models that can accurately derive absolute methylation levels from DNA enrichment data are needed.

Results

We developed “MeDEStrand,” a method that uses a sigmoid function to estimate and correct the CpG bias from enrichment results to infer absolute DNA methylation levels. Unlike previous methods, which estimate CpG bias based on reads mapped at the same genomic loci, MeDEStrand processes the reads for the positive and negative DNA strands separately. We compared the performance of MeDEStrand to that of three other state-of-the-art methods “MEDIPS,” “BayMeth,” and “QSEA” on four independent datasets generated using immortalized cell lines (GM12878 and K562) and human primary cells (foreskin fibroblasts and mammary epithelial cells). Based on the comparison of the inferred absolute methylation levels from MeDIP-seq data and the corresponding reduced-representation bisulfite sequencing data from each method, MeDEStrand showed the best performance at high resolution of 25, 50, and 100 base pairs.

Conclusions

The MeDEStrand tool can be used to infer whole-genome absolute DNA methylation levels at the same cost of enrichment-based methods with adequate accuracy and resolution. R package MeDEStrand and its tutorial is freely available for download at https://github.com/jxu1234/MeDEStrand.git.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2574-7) contains supplementary material, which is available to authorized users.

DNA methylation patterns in peripheral blood mononuclear cells from Holstein cattle with variable milk yield

Background

Milk yield for Holstein cows has doubled over five decades due to genetic selection and changes to management, but the molecular mechanisms that facilitated this increase are mostly unknown. Epigenetic modifications to the cattle genome are a plausible molecular mechanism to cause variation in milk yield and our objective was to describe genome-wide DNA methylation patterns in peripheral blood mononuclear cells (PBMC) from mature Holstein dairy cows with variable milk yield.

Results

Whole genome MeDIP-seq was performed following DNA extraction from PBMC of 6 lactating dairy cows from 4 different herds that varied in milk yield from 13,556 kg to 23,105 kg per 305 day lactation. We describe methylation across the genome and for 13,677 protein coding genes. Repetitive element reads were primarily mapped to satellite (36.4%), SINE (29.1%), and LINE (23.7%) regions and the majority (78.4%) of CpG sites were sequenced at least once. DNA methylation was generally low upstream of genes with the nadir occurring 95 bp prior to the transcription start site (TSS). Methylation was lower in the first exon than in later exons, was highest for introns near the intron-exon junctions, and declined downstream as the distance from the gene increased. We identified 72 differentially methylated regions (DMR) between high milk yield cows and their control, and 252 DMR across herd environments.

Conclusions

This reference methylome for cattle with extreme variation in milk yield phenotype provides a resource to more fully evaluate relationships between DNA methylation and phenotype in populations subject to selection. The detection of DMR in cows of varying milk yield suggests potential to exploit epigenetic variation in cattle improvement programs.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5124-9) contains supplementary material, which is available to authorized users.

Epigenomic profiling of non-small cell lung cancer xenografts uncover LRP12 DNA methylation as predictive biomarker for carboplatin resistance

Background

Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed.

Methods

Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort.

Results

Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p < 0.01). Similarly, we find a shorter survival time for patients with LRP12 hypermethylation in the TCGA data set for NSCLC (lung adenocarcinoma).

Conclusions

Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models.

Electronic supplementary material

The online version of this article (10.1186/s13073-018-0562-1) contains supplementary material, which is available to authorized users.

Therapeutic targeting of ependymoma as informed by oncogenic enhancer profiling

Genomic sequencing has driven precision-based oncology therapy; however, genetic drivers remain unknown or non-targetable for many malignancies, demanding alternative approaches to identify therapeutic leads. Ependymomas are chemotherapy-resistant brain tumours, which, despite genomic sequencing, lack effective molecular targets. Intracranial ependymomas are segregated based on anatomical location – supratentorial region (ST) or posterior fossa (PF) – and further divided into distinct molecular subgroups that reflect differences in age of onset, gender predominance, and response to therapy^1–3. The most common and aggressive subgroup, Posterior Fossa Ependymoma Group A (PF-EPN-A), occurs in young children and appears to lack recurrent somatic mutations². Conversely, Posterior Fossa Ependymoma Group B (PF-EPN-B) tumours display frequent large-scale copy number gains and losses yet favourable clinical outcomes^1,3. Greater than 70% of supratentorial ependymomas are defined by highly recurrent gene fusions in the NFκB subunit RELA (ST-EPN-RELA), and less frequently involve fusion of the gene encoding the transcriptional activator YAP1 (ST-EPN-YAP1).^1,3,4 Subependymomas, a distinct histologic variant, can also be found within the ST and PF compartments accounting for the majority of tumours in the molecular subgroups ST-EPN-SE and PF-EPN-SE, respectively¹. Here, we mapped active chromatin landscapes in 42 primary ependymomas in two non-overlapping primary ependymoma cohorts with the goal of identifying essential super enhancer associated genes on which tumour cells were dependent. Enhancer regions revealed putative oncogenes, molecular targets, and pathways, which when subjected to small molecule inhibitor or shRNA treatment, diminished proliferation of patient-derived neurospheres and increased survival in mouse models of ependymomas. Through profiling of transcriptional enhancers, our study provides a framework for target and drug discovery in other cancers recalcitrant to therapeutic development because of their lack of known genetic drivers.

Data on novel DNA methylation changes induced by valproic acid in human hepatocytes

Valproic acid (VPA) is a widely prescribed antiepileptic drug in the world. Despite its pharmacological importance, it may cause liver toxicity and steatosis. However the exact mechanism of the steatosis formation is unknown. The data presented in this DIB publication is used to further investigate the VPA-induced mechanisms of steatosis by analyzing changes in patterns of methylation. Therefore, primary human hepatocytes (PHHs) were exposed to VPA at a concentration which was shown to cause steatosis without inducing overt cytotoxicity. VPA was administered for 5 days daily to PHHs. Furthermore, after 5 days VPA-treatment parts of the PHHs were followed for a 3 days washout. Differentially methylated DNA regions (DMRs) were identified by using the ‘Methylated DNA Immuno-Precipitation - sequencing’ (MeDIP-seq) method. The data presented in this DIB demonstrate induced steatosis pathways by all DMRs during VPA-treatment, covering interesting drug-induced steatosis genes (persistent DMRs upon terminating VPA treatment and the EP300 network). This was illustrated in our associated article (Wolters et al., 2017) [1]. MeDIP-seq raw data are available on ArrayExpress (accession number: E-MTAB-4437).

Nuclear and Mitochondrial DNA Methylation Patterns Induced by Valproic Acid in Human Hepatocytes

Valproic acid (VPA) is one of the most widely prescribed antiepileptic drugs in the world. Despite its pharmacological importance, it may cause liver toxicity and steatosis through mitochondrial dysfunction. The aim of this study is to further investigate VPA-induced mechanisms of steatosis by analyzing changes in patterns of methylation in nuclear DNA (nDNA) and mitochondrial DNA (mtDNA). Therefore, primary human hepatocytes (PHHs) were exposed to an incubation concentration of VPA that was shown to cause steatosis without inducing overt cytotoxicity. VPA was administered daily for 5 days, and this was followed by a 3 day washout (WO). Methylated DNA regions (DMRs) were identified by using the methylated DNA immunoprecipitation–sequencing (MeDIP-seq) method. The nDNA DMRs after VPA treatment could indeed be classified into oxidative stress- and steatosis-related pathways. In particular, networks of the steatosis-related gene EP300 provided novel insight into the mechanisms of toxicity induced by VPA treatment. Furthermore, we suggest that VPA induces a crosstalk between nDNA hypermethylation and mtDNA hypomethylation that plays a role in oxidative stress and steatosis development. Although most VPA-induced methylation patterns appeared reversible upon terminating VPA treatment, 31 nDNA DMRs (including 5 zinc finger protein genes) remained persistent after the WO period. Overall, we have shown that MeDIP-seq analysis is highly informative in disclosing novel mechanisms of VPA-induced toxicity in PHHs. Our results thus provide a prototype for the novel generation of interesting methylation biomarkers for repeated dose liver toxicity in vitro.