Profiling DNA Methylation Based on Next-Generation Sequencing Approaches: New Insights and Clinical Applications

Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform

This study describes the development of a resource module that is part of a learning platform named 'NIGMS Sandbox for Cloud-based Learning' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module is designed to facilitate interactive learning of whole-genome bisulfite sequencing (WGBS) data analysis utilizing cloud-based tools in Google Cloud Platform, such as Cloud Storage, Vertex AI notebooks and Google Batch. WGBS is a powerful technique that can provide comprehensive insights into DNA methylation patterns at single cytosine resolution, essential for understanding epigenetic regulation across the genome. The designed learning module first provides step-by-step tutorials that guide learners through two main stages of WGBS data analysis, preprocessing and the identification of differentially methylated regions. And then, it provides a streamlined workflow and demonstrates how to effectively use it for large datasets given the power of cloud infrastructure. The integration of these interconnected submodules progressively deepens the user's understanding of the WGBS analysis process along with the use of cloud resources. Through this module, we can enhance the accessibility and adoption of cloud computing in epigenomic research, speeding up the advancements in the related field and beyond. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [1] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses.

Intricacies of aging and Down syndrome

Down syndrome is the most frequently occurring genetic condition, with a substantial escalation in risk associated with advanced maternal age. The syndrome is characterized by a diverse range of phenotypes, affecting to some extent all levels of organization, and its progeroid nature - early manifestation of aspects of the senile phenotype. Despite extensive investigations, many aspects and mechanisms of the disease remain unexplored. The current review aims to provide an overview of the main causes and manifestations of Down syndrome, while also examining the phenomenon of accelerated aging and exploring potential therapeutic strategies.

Methylated circulating tumor DNA in hepatocellular carcinoma: A comprehensive analysis of biomarker potential and clinical implications

The intricate epigenetic landscape of hepatocellular carcinoma (HCC) is profoundly influenced by alterations in DNA methylation patterns. Understanding these alterations is crucial for unraveling the molecular mechanisms underlying HCC pathogenesis. Methylated circulating tumor DNA (ctDNA) presents itself as an encouraging avenue for biomarker discovery and holds substantial clinical implications in HCC management. This review comprehensively outlines the studies concerning DNA methylation in HCC and underscores the significance of methylated ctDNA within this context. Moreover, a variety of cfDNA methylation-based methodologies, such as 5hmC profiling, bisulfite-based, restriction enzyme-dependent, and enrichment-based methods, provide in-depth insights into the molecular pathology of HCC. Additionally, the integration of methylated ctDNA analysis into clinical practice represents a significant advancement in personalized HCC management. By facilitating cancer screening, prognosis assessment, and treatment response prediction, the utilization of methylated ctDNA signifies a pivotal stride toward enhancing patient care and outcomes in HCC.

Epigenetic Therapies in Triple-Negative Breast Cancer: Concepts, Visions, and Challenges

Breast cancer, the most frequent malignancy in women worldwide, is a molecularly and clinically very heterogeneous disease. Triple-negative breast cancer is defined by the absence of hormone receptor and growth factor receptor ERBB2/HER2 expression. It is characterized by a more aggressive course of disease and a shortage of effective therapeutic approaches. Hallmarks of cancer cells are not only genetic alterations, but also epigenetic aberrations. The most studied and best understood alterations are methylation of the DNA base cytosine and the covalent modification of histone proteins. The reversibility of these covalent modifications make them attractive targets for therapeutic intervention, as documented in numerous ongoing clinical trials. Epidrugs, targeting DNA methylation and histone modifications, might offer attractive new options in treating triple-negative breast cancer. Currently, the most promising options are combination therapies in which the epidrug increases the efficiency of immuncheckpoint inhibitors. This review focusses exclusively on DNA methylation and histone modifications. In reviewing the knowledge about epigenetic therapies in breast cancer, and especially triple-negative breast cancer, the focus is on explaining concepts and raising awareness of what is not yet known and what has to be clarified in the future.

Unveiling microbial diversity: harnessing long-read sequencing technology

Long-read sequencing has recently transformed metagenomics, enhancing strain-level pathogen characterization, enabling accurate and complete metagenome-assembled genomes, and improving microbiome taxonomic classification and profiling. These advancements are not only due to improvements in sequencing accuracy, but also happening across rapidly changing analysis methods. In this Review, we explore long-read sequencing's profound impact on metagenomics, focusing on computational pipelines for genome assembly, taxonomic characterization and variant detection, to summarize recent advancements in the field and provide an overview of available analytical methods to fully leverage long reads. We provide insights into the advantages and disadvantages of long reads over short reads and their evolution from the early days of long-read sequencing to their recent impact on metagenomics and clinical diagnostics. We further point out remaining challenges for the field such as the integration of methylation signals in sub-strain analysis and the lack of benchmarks.

The expanding diagnostic toolbox for rare genetic diseases

Genomic technologies, such as targeted, exome and short-read genome sequencing approaches, have revolutionized the care of patients with rare genetic diseases. However, more than half of patients remain without a diagnosis. Emerging approaches from research-based settings such as long-read genome sequencing and optical genome mapping hold promise for improving the identification of disease-causal genetic variants. In addition, new omic technologies that measure the transcriptome, epigenome, proteome or metabolome are showing great potential for variant interpretation. As genetic testing options rapidly expand, the clinical community needs to be mindful of their individual strengths and limitations, as well as remaining challenges, to select the appropriate diagnostic test, correctly interpret results and drive innovation to address insufficiencies. If used effectively - through truly integrative multi-omics approaches and data sharing - the resulting large quantities of data from these established and emerging technologies will greatly improve the interpretative power of genetic and genomic diagnostics for rare diseases.

Methylation‑sensitive restriction enzyme‑droplet digital PCR assay for the one‑step highly sensitive analysis of DNA methylation hotspots

DNA methylation is an epigenetic modification that plays a key role in several cellular processes mediating the fine regulation of gene expression. Aberrant DNA methylation is observed in a wide range of pathologies, including cancer. Since these DNA modifications are transferred to the cell progenies and are stable over the time, the analysis of DNA methylation status has been proposed for diagnostic and prognostic purposes in cancer. Currently, DNA bisulfite conversion is the gold standard method for the high‑throughput analysis of DNA methylation alterations. However, bisulfite treatment induces DNA fragmentation affecting its quality for the downstream analyses. In this field, it is mandatory to identify novel methods to overcome the limits of conventional approaches. In the present study, the Methylation‑Sensitive Restriction Enzyme‑droplet digital PCR (MSRE‑ddPCR) assay was developed as a novel sensitive method for the analysis of DNA methylation of short genomic regions, combining the MSRE assay with the high‑sensitivity ddPCR and using an exogenous methylation sequence as control. Setup and validation experiments were performed analyzing a methylation hotspot of the Solute Carrier Family 22 Member 17 in DNA samples derived from melanoma cell lines as well as from tissues and serum samples obtained from patients with melanoma and healthy controls. Compared with the standard MSRE approaches, the MSRE‑ddPCR assay is more appropriate for the analysis of DNA methylation (methDNA) in samples with low amounts of DNA (up to 0.651 ng) showing a greater sensitivity. These findings suggested the potential clinical application of MSRE‑ddPCR paving the way to the analysis of other methDNA hotspots in different tumors.

Uncovering Forensic Evidence: A Path to Age Estimation through DNA Methylation

Age estimation is a critical aspect of reconstructing a biological profile in forensic sciences. Diverse biochemical processes have been studied in their correlation with age, and the results have driven DNA methylation to the forefront as a promising biomarker. DNA methylation, an epigenetic modification, has been extensively studied in recent years for developing age estimation models in criminalistics and forensic anthropology. Epigenetic clocks, which analyze DNA sites undergoing hypermethylation or hypomethylation as individuals age, have paved the way for improved prediction models. A wide range of biomarkers and methods for DNA methylation analysis have been proposed, achieving different accuracies across samples and cell types. This review extensively explores literature from the past 5 years, showing scientific efforts toward the ultimate goal: applying age prediction models to assist in human identification.

Computational deconvolution of DNA methylation data from mixed DNA samples

In this review, we provide a comprehensive overview of the different computational tools that have been published for the deconvolution of bulk DNA methylation (DNAm) data. Here, deconvolution refers to the estimation of cell-type proportions that constitute a mixed sample. The paper reviews and compares 25 deconvolution methods (supervised, unsupervised or hybrid) developed between 2012 and 2023 and compares the strengths and limitations of each approach. Moreover, in this study, we describe the impact of the platform used for the generation of methylation data (including microarrays and sequencing), the applied data pre-processing steps and the used reference dataset on the deconvolution performance. Next to reference-based methods, we also examine methods that require only partial reference datasets or require no reference set at all. In this review, we provide guidelines for the use of specific methods dependent on the DNA methylation data type and data availability.

Novel DNA methylation biomarkers for early diagnosis of oral tongue squamous cell carcinoma (OTSCC)

Oral tongue squamous cell carcinoma (OTSCC) is the most common malignancy type among males across the world. However, analysis of molecular markers could be useful in detecting the early-stage OTSCC, which would allow optimal clinical treatments and prolong the survival rate of patients consequently. The study has the objective of detecting the role of salivary biomarkers based on gene promoter hypermethylation. Sample data from 45 OTSCC and normal groups were analyzed to exhibit the methylation levels of salivary biomarkers (TRH, FHIT, MGMT, p16, and RASSF1A). The specificity and sensitivity analysis of methylation biomarkers was conducted in addition to the receiver operating characteristic (ROC) curve for both early-stage and advanced OTSCC stages. Quantitative data findings showed the perfect sensitivity and specificity for TRH, MGMT, p16, and RASSF1A with 100%, and > 90%, respectively. In addition, the results indicated an inefficient area under curves (> 0.7) for these biomarkers to detect the OTSCC. There were no significant differences observed between TRH and FHIT and p16 and MGMT based on the Wilcoxon signed-rank test. The methylation statuses of genes TRH, RASSF1A, p16, and MGMT might become utilized as predictive biomarkers for clinical application in early diagnosis of OTSCC and noninvasive oral cancer screening.

Review of Molecular Technologies for Investigating Canine Cancer

Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.

A signal processing and deep learning framework for methylation detection using Oxford Nanopore sequencing

Oxford Nanopore sequencing can detect DNA methylations from ionic current signal of single molecules, offering a unique advantage over conventional methods. Additionally, adaptive sampling, a software-controlled enrichment method for targeted sequencing, allows reduced representation methylation sequencing that can be applied to CpG islands or imprinted regions. Here we present DeepMod2, a comprehensive deep-learning framework for methylation detection using ionic current signal from Nanopore sequencing. DeepMod2 implements both a bidirectional long short-term memory (BiLSTM) model and a Transformer model and can analyze POD5 and FAST5 signal files generated on R9 and R10 flowcells. Additionally, DeepMod2 can run efficiently on central processing unit (CPU) through model pruning and can infer epihaplotypes or haplotype-specific methylation calls from phased reads. We use multiple publicly available and newly generated datasets to evaluate the performance of DeepMod2 under varying scenarios. DeepMod2 has comparable performance to Guppy and Dorado, which are the current state-of-the-art methods from Oxford Nanopore Technologies that remain closed-source. Moreover, we show a high correlation (r = 0.96) between reduced representation and whole-genome Nanopore sequencing. In summary, DeepMod2 is an open-source tool that enables fast and accurate DNA methylation detection from whole-genome or adaptive sequencing data on a diverse range of flowcell types.

Identification of a DNA-cytosine methyltransferase that impacts global transcription to promote group B streptococcal vaginal colonization

IMPORTANCE

Group B Streptococcus (GBS) colonizes the female reproductive tract (FRT) in one-third of women, and carriage leads to numerous adverse pregnancy outcomes including the preterm premature rupture of membranes, chorioamnionitis, and stillbirth. The presence of GBS in the FRT during pregnancy is also the largest predisposing factor for the transmission of GBS and invasive neonatal diseases, including pneumonia, sepsis, and meningitis. The factors contributing to GBS colonization are still being elucidated. Here, we show for the first time that GBS transcription is regulated by an orphan DNA cytosine methyltransferase (Dcm). Many GBS factors are regulated by Dcm, especially those involved in carbohydrate transport and metabolism. We show that GBS persistence in the FRT is dependent on the catabolism of sugars found on the vaginal mucin MUC5B. Collectively, this work highlights the regulatory importance of a DNA methyltransferase and identifies both host and bacterial factors required for GBS colonization.

Next-Generation sequencing transforming clinical practice and precision medicine

Next-generation sequencing (NGS) has revolutionized the field of genomics and is rapidly transforming clinical diagnosis and precision medicine. This advanced sequencing technology enables the rapid and cost-effective analysis of large-scale genomic data, allowing comprehensive exploration of the genetic landscape of diseases. In clinical diagnosis, NGS has proven to be a powerful tool for identifying disease-causing variants, enabling accurate and early detection of genetic disorders. Additionally, NGS facilitates the identification of novel disease-associated genes and variants, aiding in the development of targeted therapies and personalized treatment strategies. NGS greatly benefits precision medicine by enhancing our understanding of disease mechanisms and enabling the identification of specific molecular markers for disease subtypes, thus enabling tailored medical interventions based on individual characteristics. Furthermore, NGS contributes to the development of non-invasive diagnostic approaches, such as liquid biopsies, which can monitor disease progression and treatment response. The potential of NGS in clinical diagnosis and precision medicine is vast, yet challenges persist in data analysis, interpretation, and protocol standardization. This review highlights NGS applications in disease diagnosis, prognosis, and personalized treatment strategies, while also addressing challenges and future prospects in fully harnessing genomic potential within clinical practice.

Unveiling the genetic and epigenetic landscape of colorectal cancer: new insights into pathogenic pathways

Colorectal cancer (CRC) is a complex disease characterized by genetic and epigenetic alterations, playing a crucial role in its development and progression. This review aims to provide insights into the emerging landscape of these alterations in CRC pathogenesis to develop effective diagnostic tools and targeted therapies. Genetic alterations in signaling pathways such as Wnt/β-catenin, and PI3K/Akt/mTOR are pivotal in CRC development. Genetic profiling has identified distinct molecular subtypes, enabling personalized treatment strategies. Epigenetic modifications, including DNA methylation and histone modifications, also contribute to CRC pathogenesis by influencing critical cellular processes through gene silencing or activation. Non-coding RNAs have emerged as essential players in epigenetic regulation and CRC progression. Recent research highlights the interplay between genetic and epigenetic alterations in CRC. Genetic mutations can affect epigenetic modifications, leading to dysregulated gene expression and signaling cascades. Conversely, epigenetic changes can modulate genetic expression, amplifying or dampening the effects of genetic alterations. Advancements in understanding pathogenic pathways have potential clinical applications. Identifying genetic and epigenetic markers as diagnostic and prognostic biomarkers promises more accurate risk assessment and early detection. Challenges remain, including validating biomarkers and developing robust therapeutic strategies through extensive research and clinical trials. The dynamic nature of genetic and epigenetic alterations necessitates a comprehensive understanding of their temporal and spatial patterns during CRC progression. In conclusion, the genetic and epigenetic landscape of CRC is increasingly being unraveled, providing valuable insights into its pathogenesis. Integrating genetic and epigenetic knowledge holds great potential for improving diagnostics, prognostics, and personalized therapies in CRC. Continued research efforts are vital to translate these findings into clinical practice, ultimately improving patient outcomes.

Cell-free DNA approaches for cancer early detection and interception

Rapid advancements in the area of early cancer detection have brought us closer to achieving the goals of finding cancer early enough to treat or cure it, while avoiding harms of overdiagnosis. We evaluate progress in the development of early cancer detection tests in the context of the current principles for cancer screening. We review cell-free DNA (cfDNA)-based approaches using mutations, methylation, or fragmentomes for early cancer detection. Lastly, we discuss the challenges in demonstrating clinical utility of these tests before integration into routine clinical care.

Molecular mechanisms of multi-omic regulation in breast cancer

Breast cancer is a complex disease that is influenced by the concurrent influence of multiple genetic and environmental factors. Recent advances in genomics and other high throughput biomolecular techniques (-omics) have provided numerous insights into the molecular mechanisms underlying breast cancer development and progression. A number of these mechanisms involve multiple layers of regulation. In this review, we summarize the current knowledge on the role of multiple omics in the regulation of breast cancer, including the effects of DNA methylation, non-coding RNA, and other epigenomic changes. We comment on how integrating such diverse mechanisms is envisioned as key to a more comprehensive understanding of breast carcinogenesis and cancer biology with relevance to prognostics, diagnostics and therapeutics. We also discuss the potential clinical implications of these findings and highlight areas for future research. Overall, our understanding of the molecular mechanisms of multi-omic regulation in breast cancer is rapidly increasing and has the potential to inform the development of novel therapeutic approaches for this disease.

DNA Methylation in the Fields of Prenatal Diagnosis and Early Detection of Cancers

The central objective of the metamorphosis of discovery science into biomedical applications is to serve the purpose of patients and curtail the global disease burden. The journey from the discovery of DNA methylation (DNAm) as a biological process to its emergence as a diagnostic tool is one of the finest examples of such metamorphosis and has taken nearly a century. Particularly in the last decade, the application of DNA methylation studies in the clinic has been standardized more than ever before, with great potential to diagnose a multitude of diseases that are associated with a burgeoning number of genes with this epigenetic alteration. Fetal DNAm detection is becoming useful for noninvasive prenatal testing, whereas, in very preterm infants, DNAm is also shown to be a potential biological indicator of prenatal risk factors. In the context of cancer, liquid biopsy-based DNA-methylation profiling is offering valuable epigenetic biomarkers for noninvasive early-stage diagnosis. In this review, we focus on the applications of DNA methylation in prenatal diagnosis for delivering timely therapy before or after birth and in detecting early-stage cancers for better clinical outcomes. Furthermore, we also provide an up-to-date commercial landscape of DNAm biomarkers for cancer detection and screening of cancers of unknown origin.

Next-Generation Sequencing Technology: Current Trends and Advancements

The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.

Specific Methyl-CpG Configurations Define Cell Identity through Gene Expression Regulation

Cell identity is determined by the chromatin structure and profiles of gene expression, which are dependent on chromatin accessibility and DNA methylation of the regions critical for gene expression, such as enhancers and promoters. These epigenetic modifications are required for mammalian development and are essential for the establishment and maintenance of the cellular identity. DNA methylation was once thought to be a permanent repressive epigenetic mark, but systematic analyses in various genomic contexts have revealed a more dynamic regulation than previously thought. In fact, both active DNA methylation and demethylation occur during cell fate commitment and terminal differentiation. To link methylation signatures of specific genes to their expression profiles, we determined the methyl-CpG configurations of the promoters of five genes switched on and off during murine postnatal brain differentiation by bisulfite-targeted sequencing. Here, we report the structure of significant, dynamic, and stable methyl-CpG profiles associated with silencing or activation of the expression of genes during neural stem cell and brain postnatal differentiation. Strikingly, these methylation cores mark different mouse brain areas and cell types derived from the same areas during differentiation.

The performance of whole genome bisulfite sequencing on DNBSEQ-Tx platform examined by different library preparation strategies

Background

Whole-genome bisulfite sequencing (WGBS) technology can provide comprehensive DNA methylation at a single-base resolution on a genome-wide scale, and is considered to be the gold standard for the detection of 5-methylcytosine (5 mC). However, the International Human Epigenome Consortium propose a full DNA methylome should have at least 30 fold redundant coverage of the reference genome from a single biological replicate. Therefore, it remains cost prohibitive for large-scale studies. To find a solution, the DNBSEQ-Tx sequencing was developed that can generate up to 6 Tb data in a single run for projects involving large-scale sequencing.

Results

In this study, we provided two WGBS library construction methods DNB_PREBSseq and DNB_SPLATseq optimized for the DNBSEQ-Tx sequencer, and demonstrated the performance of these two methods on the DNBSEQ-Tx platform, using the DNA extracted from four different cell lines. We also compared the sequencing data from these two WGBS library construction methods with HeLa cell line data from ENCODE sequenced on Illumina HiSeq X Ten and WGBS data of two other cell lines sequenced on HiSeq2500. Various quality control (QC) analyses such as the base quality scores, methylation-bias (m-bias), and conversion efficiency indicated that the data sequenced on the DNBSEQ-Tx platform met the WGBS-required quality controls. Meanwhile, our data closely resembled the coverage shown by the data generated by the Illumina platform.

Conclusions

Our study showed that with our optimized methods, DNBSEQ-Tx could generate high-quality WGBS data with relatively good stability for large-scale WGBS sequencing applications. Thus, we conclude that DNBSEQ-Tx can be used for a wide range of WGBS research.

Epigenetic analytical approaches in ecotoxicological aquatic research

Environmental epigenetics has become a key research focus in global climate change studies and environmental pollutant investigations impacting aquatic ecosystems. Specifically, triggered by environmental stress conditions, intergenerational DNA methylation changes contribute to biological adaptive responses and survival of organisms to increase their tolerance towards these conditions. To critically review epigenetic analytical approaches in ecotoxicological aquatic research, we evaluated 78 publications reported over the past five years (2016-2021) that applied these methods to investigate the responses of aquatic organisms to environmental changes and pollution. The results show that DNA methylation appears to be the most robust epigenetic regulatory mark studied in aquatic animals. As such, multiple DNA methylation analysis methods have been developed in aquatic organisms, including enzyme restriction digestion-based and methyl-specific immunoprecipitation methods, and bisulfite (in)dependent sequencing strategies. In contrast, only a handful of aquatic studies, i.e. about 15%, have been focusing on histone variants and post-translational modifications due to the lack of species-specific affinity based immunological reagents, such as specific antibodies for chromatin immunoprecipitation applications. Similarly, ncRNA regulation remains as the least popular method used in the field of environmental epigenetics. Insights into the opportunities and challenges of the DNA methylation and histone variant analysis methods as well as decreasing costs of next generation sequencing approaches suggest that large-scale epigenetic environmental studies in model and non-model organisms will soon become available in the near future. Moreover, antibody-dependent and independent methods, such as mass spectrometry-based methods, can be used as an alternative epigenetic approach to characterize global changes of chromatin histone modifications in future aquatic research. Finally, a systematic guide for DNA methylation and histone variant methods is offered for ecotoxicological aquatic researchers to select the most relevant epigenetic analytical approach in their research.

Profiling the epigenetic landscape of the antigen receptor repertoire: the missing epi-immunogenomics data

High-resolution sequencing methods that capture the epigenetic landscape within the T cell receptor (TCR) gene loci are pivotal for a fundamental understanding of the epigenetic regulatory mechanisms of the TCR repertoire. In our opinion, filling the gaps in our understanding of the epigenetic mechanisms regulating the TCR repertoire will benefit the development of strategies that can modulate the TCR repertoire composition by leveraging the dynamic nature of epigenetic modifications.

A Systematic Review of Progress toward Unlocking the Power of Epigenetics in NSCLC: Latest Updates and Perspectives

Epigenetic research has the potential to improve our understanding of the pathogenesis of cancer, specifically non-small-cell lung cancer, and support our efforts to personalize the management of the disease. Epigenetic alterations are expected to have relevance for early detection, diagnosis, outcome prediction, and tumor response to therapy. Additionally, epi-drugs as therapeutic modalities may lead to the recovery of genes delaying tumor growth, thus increasing survival rates, and may be effective against tumors without druggable mutations. Epigenetic changes involve DNA methylation, histone modifications, and the activity of non-coding RNAs, causing gene expression changes and their mutual interactions. This systematic review, based on 110 studies, gives a comprehensive overview of new perspectives on diagnostic (28 studies) and prognostic (25 studies) epigenetic biomarkers, as well as epigenetic treatment options (57 studies) for non-small-cell lung cancer. This paper outlines the crosstalk between epigenetic and genetic factors as well as elucidates clinical contexts including epigenetic treatments, such as dietary supplements and food additives, which serve as anti-carcinogenic compounds and regulators of cellular epigenetics and which are used to reduce toxicity. Furthermore, a future-oriented exploration of epigenetic studies in NSCLC is presented. The findings suggest that additional studies are necessary to comprehend the mechanisms of epigenetic changes and investigate biomarkers, response rates, and tailored combinations of treatments. In the future, epigenetics could have the potential to become an integral part of diagnostics, prognostics, and personalized treatment in NSCLC.

Methylome Imputation by Methylation Patterns

DNA methylation is studied extensively for its relations with several biological processes such as transcriptional regulation. While methylation levels are usually estimated per cytosine or genomic region, additional information on methylation heterogeneity can be obtained when considering stretches of successive cytosines on the same reads; however, the majority of methylomes suffer from low coverage of genomic regions with sequencing depths enough for accurate estimation of methylation heterogeneity using existing methods. Here we describe a probabilistic-based imputation method that makes use of methylation information from neighboring sites to recover partially observed methylation patterns. Our method and software are proven to be faster and more accurate among all evaluated. Ultimately, our method allows for a more streamlined monitoring of epigenetic changes within cellular populations and their putative role in disease.

Monitoring EGFR-lung cancer evolution: a possible beginning of a "methylation era" in TKI resistance prediction

The advances in scientific knowledge on biological therapies of the last two decades have impressively oriented the clinical management of non-small-cell lung cancer (NSCLC) patients. The treatment with tyrosine kinase inhibitors (TKIs) in patients harboring Epidermal Growth Factor Receptor (EGFR)-activating mutations is dramatically associated with an improvement in disease control. Anyhow, the prognosis for this selected group of patients remains unfavorable, due to the innate and/or acquired resistance to biological therapies. The methylome analysis of many tumors revealed multiple patterns of methylation at single/multiple cytosine-phosphate-guanine (CpG) sites that are linked to the modulation of several cellular pathways involved in cancer onset and progression. In lung cancer patients, ever increasing evidences also suggest that the association between DNA methylation changes at promoter/intergenic regions and the consequent alteration of gene-expression signatures could be related to the acquisition of resistance to biological therapies. Despite this intriguing hypothesis, large confirmatory studies are demanded to consolidate and finalize many preliminary observations made in this field. In this review, we will summarize the available knowledge about the dynamic role of DNA methylation in EGFR-mutated NSCLC patients.

MSNet-4mC: learning effective multi-scale representations for identifying DNA N4-methylcytosine sites

MOTIVATION

N4-methylcytosine (4mC) is an essential kind of epigenetic modification that regulates a wide range of biological processes. However, experimental methods for detecting 4mC sites are time-consuming and labor-intensive. As an alternative, computational methods that are capable of automatically identifying 4mC with data analysis techniques become a reasonable option. A major challenge is how to develop effective methods to fully exploit the complex interactions within the DNA sequences to improve the predictive capability.

RESULTS

In this work, we propose MSNet-4mC, a lightweight neural network building upon convolutional operations with multi-scale receptive fields to perceive cross-element relationships over both short and long ranges of given DNA sequences. With strong imbalances in the number of candidates in different species in mind, we compute and apply class weights in the cross-entropy loss to balance the training process. Extensive benchmarking experiments show that our method achieves a significant performance improvement and outperforms other state-of-the-art methods.

AVAILABILITY AND IMPLEMENTATION

The source code and models are freely available for download at https://github.com/LIU-CT/MSNet-4mC, implemented in Python and supported on Linux and Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Omics technologies to understand drug toxicity mechanisms

Drug side effects are an important study subject in pharmacology. Recent omics technologies provide a range of omics data and help to understand the biological mechanisms involved in drug effects. These modern technologies provide significant support to all biological disciplines, including drug toxicology. In this review, we provide an overview the use of omics applications to understand drug side effects at the molecular level. We discuss by available omics technologies, their possible uses, as well as their advantages and limitations.

Applications of massively parallel sequencing in forensic genetics

Massively parallel sequencing, also referred to as next-generation sequencing, has positively changed DNA analysis, allowing further advances in genetics. Its capability of dealing with low quantity/damaged samples makes it an interesting instrument for forensics. The main advantage of MPS is the possibility of analyzing simultaneously thousands of genetic markers, generating high-resolution data. Its detailed sequence information allowed the discovery of variations in core forensic short tandem repeat loci, as well as the identification of previous unknown polymorphisms. Furthermore, different types of markers can be sequenced in a single run, enabling the emergence of DIP-STRs, SNP-STR haplotypes, and microhaplotypes, which can be very useful in mixture deconvolution cases. In addition, the multiplex analysis of different single nucleotide polymorphisms can provide valuable information about identity, biogeographic ancestry, paternity, or phenotype. DNA methylation patterns, mitochondrial DNA, mRNA, and microRNA profiling can also be analyzed for different purposes, such as age inference, maternal lineage analysis, body-fluid identification, and monozygotic twin discrimination. MPS technology also empowers the study of metagenomics, which analyzes genetic material from a microbial community to obtain information about individual identification, post-mortem interval estimation, geolocation inference, and substrate analysis. This review aims to discuss the main applications of MPS in forensic genetics.

CDH1 (E-cadherin) Gene Methylation in Human Breast Cancer: Critical Appraisal of a Long and Twisted Story

Simple Summary

Genes can be inactivated by specific modifications of DNA bases, most often by adding a methyl group to the DNA base cytosine if it is followed by guanosine (CG methylation). This modification prevents gene expression and has been reported for many different genes in nearly all types of cancer. A prominent example is the gene CDH1, which encodes the cell-adhesion molecule E-cadherin. This is an important player in the spreading of tumor cells within the body (metastasis). Particularly in human breast cancer, many different research groups have studied the inactivation of the CDH1 gene via DNA methylation using various methods. Over the last 20 years, different, in part, even contradicting results have been published for the CDH1 gene in breast cancer. This review summarizes the most important publications and explains the bewildering heterogeneity of results through careful analysis of the methods which have been used.

Abstract

Epigenetic inactivation of a tumor suppressor gene by aberrant DNA methylation is a well-established defect in human tumor cells, complementing genetic inactivation by mutation (germline or somatic). In human breast cancer, aberrant gene methylation has diagnostic, prognostic, and predictive potential. A prominent example is the hypermethylation of the CDH1 gene, encoding the adhesion protein E-Cadherin (“epithelial cadherin”). In numerous publications, it is reported as frequently affected by gene methylation in human breast cancer. However, over more than two decades of research, contradictory results concerning CDH1 gene methylation in human breast cancer accumulated. Therefore, we review the available evidence for and against the role of DNA methylation of the CDH1 gene in human breast cancer and discuss in detail the methodological reasons for conflicting results, which are of general importance for the analysis of aberrant DNA methylation in human cancer specimens. Since the loss of E-cadherin protein expression is a hallmark of invasive lobular breast cancer (ILBC), special attention is paid to CDH1 gene methylation as a potential mechanism for loss of expression in this special subtype of human breast cancer. Proper understanding of the methodological basis is of utmost importance for the correct interpretation of results supposed to demonstrate the presence and clinical relevance of aberrant DNA methylation in cancer specimens.

Update on the Molecular Aspects and Methods Underlying the Complex Architecture of FSHD

Despite the knowledge of the main mechanisms involved in facioscapulohumeral muscular dystrophy (FSHD), the high heterogeneity and variable penetrance of the disease complicate the diagnosis, characterization and genotype–phenotype correlation of patients and families, raising the need for further research and data. Thus, the present review provides an update of the main molecular aspects underlying the complex architecture of FSHD, including the genetic factors (related to D4Z4 repeated units and FSHD-associated genes), epigenetic elements (D4Z4 methylation status, non-coding RNAs and high-order chromatin interactions) and gene expression profiles (FSHD transcriptome signatures both at bulk tissue and single-cell level). In addition, the review will also describe the methods currently available for investigating the above-mentioned features and how the resulting data may be combined with artificial-intelligence-based pipelines, with the purpose of developing a multifunctional tool tailored to enhancing the knowledge of disease pathophysiology and progression and fostering the research for novel treatment strategies, as well as clinically useful biomarkers. In conclusion, the present review highlights how FSHD should be regarded as a disease characterized by a molecular spectrum of genetic and epigenetic factors, whose alteration plays a differential role in DUX4 repression and, subsequently, contributes to determining the FSHD phenotype.

Studying Chromatin Epigenetics with Fluorescence Microscopy

Epigenetic modifications of histones (methylation, acetylation, phosphorylation, etc.) are of great importance in determining the functional state of chromatin. Changes in epigenome underlay all basic biological processes, such as cell division, differentiation, aging, and cancerous transformation. Post-translational histone modifications are mainly studied by immunoprecipitation with high-throughput sequencing (ChIP-Seq). It enables an accurate profiling of target modifications along the genome, but suffers from the high cost of analysis and the inability to work with living cells. Fluorescence microscopy represents an attractive complementary approach to characterize epigenetics. It can be applied to both live and fixed cells, easily compatible with high-throughput screening, and provide access to rich spatial information down to the single cell level. In this review, we discuss various fluorescent probes for histone modification detection. Various types of live-cell imaging epigenetic sensors suitable for conventional as well as super-resolution fluorescence microscopy are described. We also focus on problems and future perspectives in the development of fluorescent probes for epigenetics.

Design and experimental validation of OPERA_MET-A panel for deep methylation analysis by next generation sequencing

DNA methylation is the most recognized epigenetic mark that leads to a massive distortion in cancer cells. It has been observed that a large number of DNA aberrant methylation events occur simultaneously in a group of genes, thus providing a growth advantage to the cell in promoting cell differentiation and neoplastic transformation. Due to this reason, methylation profiles have been suggested as promising cancer biomarkers. Here, we designed and performed a first step of validation of a novel targeted next generation sequencing (NGS) panel for methylation analysis, which can simultaneously evaluate the methylation levels at CpG sites of multiple cancer-related genes. The OPERA_MET-A methylation panel was designed using the Ion AmpliSeq™ technology to amplify 155 regions with 125-175 bp mean length and covers a total of 1107 CpGs of 18 cancer-related genes. The performance of the panel was assessed by running commercially available fully methylated and unmethylated control human genomic DNA (gDNA) samples and a variable mixture of them. The libraries were run on Ion Torrent platform and the sequencing output was analyzed using the “methylation_analysis” plugin. DNA methylation calls on both Watson (W) and Crick (C) strands and methylated:unmethylated ratio for each CpG site were obtained. Cell lines, fresh frozen and formalin-fixed paraffin-embedded (FFPE) lung cancer tissues were tested. The OPERA_MET-A panel allows to run a minimum of 6 samples/530 chip to reach an observed mean target depth ≥2,500X (W and C strands) and an average number of mapped reads >750,000/sample. The conversion efficiency, determined by spiking-in unmethylated Lambda DNA into each sample before the bisulfite conversion process, was >97% for all samples. The observed percentage of global methylation for all CpGs was >95% and <5% for fully methylated and unmethylated gDNA samples, respectively, and the observed results for the variable mixtures were in agreement with what was expected. Methylation-specific NGS analysis represents a feasible method for a fast and multiplexed screening of cancer patients by a high-throughput approach. Moreover, it offers the opportunity to construct a more robust algorithm for disease prediction in cancer patients having a low quantity of biological material available.

Benchmarking DNA Methylation Analysis of 14 Alignment Algorithms for Whole Genome Bisulfite Sequencing in Mammals

Whole genome bisulfite sequencing (WGBS) is an essential technique for methylome studies. Although a series of tools have been developed to overcome the mapping challenges caused by bisulfite treatment, the latest available tools have not been evaluated on the performance of reads mapping as well as on biological insights in multiple mammals. Herein, based on the real and simulated WGBS data of 14.77 billion reads, we undertook 936 mappings to benchmark and evaluate 14 wildly utilized alignment algorithms from reads mapping to biological interpretation in humans, cattle and pigs: Bwa-meth, BSBolt, BSMAP, Walt, Abismal, Batmeth2, Hisat_3n, Hisat_3n_repeat, Bismark-bwt2-e2e, Bismark-his2, BSSeeker2-bwt, BSSeeker2-soap2, BSSeeker2-bwt2-e2e and BSSeeker2-bwt2-local. Specifically, Bwa-meth, BSBolt, BSMAP, Bismark-bwt2-e2e and Walt exhibited higher uniquely mapped reads, mapped precision, recall and F1 score than other nine alignment algorithms, and the influences of distinct alignment algorithms on the methylomes varied considerably at the numbers and methylation levels of CpG sites, the calling of differentially methylated CpGs (DMCs) and regions (DMRs). Moreover, we reported that BSMAP showed the highest accuracy at the detection of CpG coordinates and methylation levels, the calling of DMCs, DMRs, DMR-related genes and signaling pathways. These results suggested that careful selection of algorithms to profile the genome-wide DNA methylation is required, and our works provided investigators with useful information on the choice of alignment algorithms to effectively improve the DNA methylation detection accuracy in mammals.

Epigenetic insights in the diagnosis, prognosis, and treatment selection in CRC, an updated review

BACKGROUND/AIM

The gradual accumulation of genetic and epigenetic alterations can lead to the development of colorectal cancer. In the last decade much research has been done to discover how methylation as an epigenetic alteration leads to carcinogenesis. While Methylation is a biological process, it can influence gene expression by affecting the promoter activity. This article reviews the role of methylation in critical pathways in CRC.

METHODS

In this study using appropriate keywords, all research and review articles related to the role of methylation on different cancers were collected and analyzed. Also, existing information on methylation detection methods and therapeutic sensitivity or resistance due to DNA methylation were reviewed.

RESULTS

The results of this survey revealed that while Methylation is a biological process, it can influence gene expression by affecting the promoter activity. Promoter methylation is associated with up or downregulation of genes involved in critical pathways, including cell cycle, DNA repair, and cell adherence. Hence promoter methylation can be used as a molecular tool for early diagnosis, improving treatment, and predicting treatment resistance.

CONCLUSION

Current knowledge on potential methylation biomarkers for diagnosis and prognoses of CRC has also been discussed. Our survey proposes that a multi-biomarker panel is more efficient than a single biomarker in the early diagnosis of CRC.

Computational methods and translational applications for targeted next-generation sequencing platforms

During the past decade, next-generation sequencing (NGS) technologies have become widely adopted in cancer research and clinical care. Common applications within the clinical setting include patient stratification into relevant molecular subtypes, identification of biomarkers of response and resistance to targeted and systemic therapies, assessment of heritable cancer risk based on known pathogenic variants, and longitudinal monitoring of treatment response. The need for efficient downstream processing and reliable interpretation of sequencing data has led to the development of novel algorithms and computational pipelines, as well as structured knowledge bases that link genomic alterations to currently available drugs and ongoing clinical trials. Cancer centers around the world use different types of targeted solid-tissue and blood based NGS assays to analyze the genomic and transcriptomic profile of patients as part of their routine clinical care. Recently, cross-institutional collaborations have led to the creation of large pooled datasets that can offer valuable insights into the genomics of rare cancers.

Challenges in promoter methylation analysis in the new era of translational oncology: a focus on liquid biopsy

Toward the discovery of novel reliable biomarkers, epigenetic alterations have been repeatedly proposed for the diagnosis and the development of therapeutic strategies against cancer. Indeed, for promoter methylation to actively become a tumor marker for clinical use, it must be combined with a highly informative technology evaluated in an appropriate biospecimen. Methodological standardization related to epigenetic research is, in fact, one of the most challenging tasks. Moreover, tissue-based biopsy is being complemented and, in some cases, replaced by liquid biopsy. This review will highlight the advancements made for both pre-analytical and analytical implementation for the prospective use of methylation biomarkers in clinical settings, with particular emphasis on liquid biopsy.

BSImp: Imputing Partially Observed Methylation Patterns for Evaluating Methylation Heterogeneity

DNA methylation is one of the most studied epigenetic modifications that has applications ranging from transcriptional regulation to aging, and can be assessed by bisulfite sequencing (BS-seq) or enzymatic methyl sequencing (EM-seq) at single base-pair resolution. The permutations of methylation statuses given by aligned reads reflect the methylation patterns of individual cells. These patterns at specific genomic locations are sought to be indicative of cellular heterogeneity within a cellular population, which are predictive of developments and diseases; therefore, methylation heterogeneity has potentials in early detection of these changes. Computational methods have been developed to assess methylation heterogeneity using methylation patterns formed by four consecutive CpGs, but the nature of shotgun sequencing often give partially observed patterns, which makes very limited data available for downstream analysis. While many programs are developed to impute genome-wide methylation levels, currently there is only one method developed for recovering partially observed methylation patterns; however, the program needs lots of data to train and cannot be used directly; therefore, we developed a probabilistic-based imputation method that uses information from neighbouring sites to recover partially observed methylation patterns speedily. It is demonstrated to allow for the evaluation of methylation heterogeneity at 15% more regions genome-wide with high accuracy for data with moderate depth. To make it more user-friendly we also provide a computational pipeline for genome-screening, which can be used in both evaluating methylation levels and profiling methylation patterns genomewide for all cytosine contexts, which is the first of its kind. Our method allows for accurate estimation of methylation levels and makes evaluating methylation heterogeneity available for much more data with reasonable coverage, which has important implications in using methylation heterogeneity for monitoring changes within the cellular populations that were impossible to detect for the assessment of development and diseases.

ImprintSeq, a novel tool to interrogate DNA methylation at human imprinted regions and diagnose multilocus imprinting disturbance

PURPOSE

Disruptions of genomic imprinting are associated with congenital imprinting disorders (CIDs) and other disease states, including cancer. CIDs are most often associated with altered methylation at imprinted differentially methylated regions (iDMRs). In some cases, multiple iDMRs are affected causing multilocus imprinting disturbances (MLIDs). The availability of accurate, quantitative, and scalable high-throughput methods to interrogate multiple iDMRs simultaneously would enhance clinical diagnostics and research.

METHODS

We report the development of a custom targeted methylation sequencing panel that covered most relevant 63 iDMRs for CIDs and the detection of MLIDs. We tested it in 70 healthy controls and 147 individuals with CIDs. We distinguished loss and gain of methylation per differentially methylated region and classified high and moderate methylation alterations.

RESULTS

Across a range of CIDs with a variety of molecular mechanisms, ImprintSeq performed at 98.4% sensitivity, 99.9% specificity, and 99.9% accuracy (when compared with previous diagnostic testing). ImprintSeq was highly sensitive for detecting MLIDs and enabled diagnostic criteria for MLID to be proposed. In a child with extreme MLID profile a probable genetic cause was identified.

CONCLUSION

ImprintSeq provides a novel assay for clinical diagnostic and research studies of CIDs, MLIDs, and the role of disordered imprinting in human disease states.

Technological Approaches in the Analysis of Extracellular Vesicle Nucleotide Sequences

Together with metabolites, proteins, and lipid components, the EV cargo consists of DNA and RNA nucleotide sequence species, which are part of the intracellular communication network regulating specific cellular processes and provoking distinct target cell responses. The extracellular vesicle (EV) nucleotide sequence cargo molecules are often investigated in association with a particular pathology and may provide an insight into the physiological and pathological processes in hard-to-access organs and tissues. The diversity and biological function of EV nucleotide sequences are distinct regarding EV subgroups and differ in tissue- and cell-released EVs. EV DNA is present mainly in apoptotic bodies, while there are different species of EV RNAs in all subgroups of EVs. A limited sample volume of unique human liquid biopsy provides a small amount of EVs with limited isolated DNA and RNA, which can be a challenging factor for EV nucleotide sequence analysis, while the additional difficulty is technical variability of molecular nucleotide detection. Every EV study is challenged with its first step of the EV isolation procedure, which determines the EV's purity, yield, and diameter range and has an impact on the EV's downstream analysis with a significant impact on the final result. The gold standard EV isolation procedure with ultracentrifugation provides a low output and not highly pure isolated EVs, while modern techniques increase EV's yield and purity. Different EV DNA and RNA detection techniques include the PCR procedure for nucleotide sequence replication of the molecules of interest, which can undergo a small-input EV DNA or RNA material. The nucleotide sequence detection approaches with their advantages and disadvantages should be considered to appropriately address the study problem and to extract specific EV nucleotide sequence information with the detection using qPCR or next-generation sequencing. Advanced next-generation sequencing techniques allow the detection of total EV genomic or transcriptomic data even at the single-molecule resolution and thus, offering a sensitive and accurate EV DNA or RNA biomarker detection. Additionally, with the processes where the EV genomic or transcriptomic data profiles are compared to identify characteristic EV differences in specific conditions, novel biomarkers could be discovered. Therefore, a suitable differential expression analysis is crucial to define the EV DNA or RNA differences between conditions under investigation. Further bioinformatics analysis can predict molecular cell targets and identify targeted and affected cellular pathways. The prediction target tools with functional studies are essential to help specify the role of the investigated EV-targeted nucleotide sequences in health and disease and support further development of EV-related therapeutics. This review will discuss the biological diversity of human liquid biopsy-obtained EV nucleotide sequences DNA and RNA species reported as potential biomarkers in health and disease and methodological principles of their detection, from human liquid biopsy EV isolation, EV nucleotide sequence extraction, techniques for their detection, and their cell target prediction.

Computational tools for analyzing single-cell data in pluripotent cell differentiation studies

Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation.

DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation

BACKGROUND

Nanopore long-read sequencing technology greatly expands the capacity of long-range, single-molecule DNA-modification detection. A growing number of analytical tools have been developed to detect DNA methylation from nanopore sequencing reads. Here, we assess the performance of different methylation-calling tools to provide a systematic evaluation to guide researchers performing human epigenome-wide studies.

RESULTS

We compare seven analytic tools for detecting DNA methylation from nanopore long-read sequencing data generated from human natural DNA at a whole-genome scale. We evaluate the per-read and per-site performance of CpG methylation prediction across different genomic contexts, CpG site coverage, and computational resources consumed by each tool. The seven tools exhibit different performances across the evaluation criteria. We show that the methylation prediction at regions with discordant DNA methylation patterns, intergenic regions, low CG density regions, and repetitive regions show room for improvement across all tools. Furthermore, we demonstrate that 5hmC levels at least partly contribute to the discrepancy between bisulfite and nanopore sequencing. Lastly, we provide an online DNA methylation database ( https://nanome.jax.org ) to display the DNA methylation levels detected by nanopore sequencing and bisulfite sequencing data across different genomic contexts.

CONCLUSIONS

Our study is the first systematic benchmark of computational methods for detection of mammalian whole-genome DNA modifications in nanopore sequencing. We provide a broad foundation for cross-platform standardization and an evaluation of analytical tools designed for genome-scale modified base detection using nanopore sequencing.

Concordance analysis of microsatellite instability status between polymerase chain reaction based testing and next generation sequencing for solid tumors

Various malignancies exhibit high microsatellite instability (MSI-H) or mismatch repair deficiency (dMMR). The MSI-IVD kit, a polymerase chain reaction (PCR)-based method, was the first tumor-agnostic companion diagnostic to detect MSI status in MSI-H solid tumors. Recently, next-generation sequencing (NGS), which can also detect MSI-H/dMMR, has been made clinically available; however, its real-world concordance with PCR-based testing of MSI-H/dMMR remains to be investigated. The co-primary end points included the positive and negative predictive values of MSI-H/dMMR. A retrospective analysis of 80 patients who had undergone both MSI testing and NGS between July 2015 and March 2021 was conducted. Five patients were confirmed to have MSI-H in both examinations. Among the 75 patients diagnosed as microsatellite stable (MSS) by PCR-based testing, one with pancreatic cancer was diagnosed as having MSI-H after NGS. One patient with pancreatic cancer was diagnosed as having MSS in both tests was found to have a mutation in MLH1 by NGS, which was confirmed as dMMR by IHC staining. NGS had positive and negative predictive values of 100% (5/5) and 98.7% (74/75), respectively, for MSI-H. The concordance between NGS and PCR-based testing was 98.8% (79/80). Thus, NGS can be useful for evaluating MSI/MMR status in clinical practice and can be an important alternative method for detecting MSI-H/dMMR in the future.

A primer on machine learning techniques for genomic applications

High throughput sequencing technologies have enabled the study of complex biological aspects at single nucleotide resolution, opening the big data era. The analysis of large volumes of heterogeneous "omic" data, however, requires novel and efficient computational algorithms based on the paradigm of Artificial Intelligence. In the present review, we introduce and describe the most common machine learning methodologies, and lately deep learning, applied to a variety of genomics tasks, trying to emphasize capabilities, strengths and limitations through a simple and intuitive language. We highlight the power of the machine learning approach in handling big data by means of a real life example, and underline how described methods could be relevant in all cases in which large amounts of multimodal genomic data are available.

Effect of LHX2 gene methylation level and its function on radiotherapy of cervical cancer

Background

Cervical cancer is the most common malignancy of the female reproductive system, for which radiotherapy is one of the main treatments. Gene methylation in cells is an important factor in tumorigenesis, and radiotherapy can change DNA methylation in cells. At the same time, combined with the clinical effect of radiotherapy, key genes of LIM homeobox 2 (LHX2) significantly related to cervical cancer. The LHX2 are LIM-homeobox genes that play important roles in signal transduction, cell differentiation, tissue-specific differentiation, and body formation.

Methods

In this study, bisulfite genomic sequencing (BSP-Seq) technology was used to analyze the methylation level of LHX2 in patients with cervical cancer before and after radiotherapy. In addition, combined with the clinical effect of radiotherapy, the function of LHX2 in siHA and C33A cells were analyzed with the help of overexpression, small interfering RNA (siRNA), cell invasion, and migration ability. The expression level of the migration- and apoptosis-related genes which were affected by LHX2 were tested with quantitative real time polymerase chain reaction (qRT-PCR).

Results

Combined with clinical treatment, methylation level difference, and correlation enrichment analysis, it was found that LHX2 genes were closely related to the occurrence and development of cervical cancer. After 5-aza-2’-deoxycytidine (5-Aza-dC) and radiotherapy, the methylation of LHX2 genes in siHA and C33A squamous cell carcinoma cells was decreased, and the messenger RNA (mRNA) and protein expression levels were relatively increased; meanwhile, the LHX2 could accelerate the ability for cell invasion and migration and inhibited the apoptosis of the cell after treatment with radiotherapy.

Conclusions

The methylation and expression levels of LHX2 genes are closely related to cervical cancer. The methylation level of LHX2 was reduced after radiation therapy. The LHX2 gene has a positive effect on cervical cancer through acceleration of the cell invasion and migration ability and inhibition of cell apoptosis after radiotherapy treatment.

Drug Response-Related DNA Methylation Changes in Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Pharmacotherapy is the most common treatment for schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD). Pharmacogenetic studies have achieved results with limited clinical utility. DNA methylation (DNAm), an epigenetic modification, has been proposed to be involved in both the pathology and drug treatment of these disorders. Emerging data indicates that DNAm could be used as a predictor of drug response for psychiatric disorders. In this study, we performed a systematic review to evaluate the reproducibility of published changes of drug response-related DNAm in SCZ, BD and MDD. A total of 37 publications were included. Since the studies involved patients of different treatment stages, we partitioned them into three groups based on their primary focuses: (1) medication-induced DNAm changes (n = 8); (2) the relationship between DNAm and clinical improvement (n = 24); and (3) comparison of DNAm status across different medications (n = 14). We found that only BDNF was consistent with the DNAm changes detected in four independent studies for MDD. It was positively correlated with clinical improvement in MDD. To develop better predictive DNAm factors for drug response, we also discussed future research strategies, including experimental, analytical procedures and statistical criteria. Our review shows promising possibilities for using BDNF DNAm as a predictor of antidepressant treatment response for MDD, while more pharmacoepigenetic studies are needed for treatments of various diseases. Future research should take advantage of a system-wide analysis with a strict and standard analytical procedure.

Measuring and Modelling the Epithelial- Mesenchymal Hybrid State in Cancer: Clinical Implications

The epithelial-mesenchymal (E/M) hybrid state has emerged as an important mediator of elements of cancer progression, facilitated by epithelial mesenchymal plasticity (EMP). We review here evidence for the presence, prognostic significance, and therapeutic potential of the E/M hybrid state in carcinoma. We further assess modelling predictions and validation studies to demonstrate stabilised E/M hybrid states along the spectrum of EMP, as well as computational approaches for characterising and quantifying EMP phenotypes, with particular attention to the emerging realm of single-cell approaches through RNA sequencing and protein-based techniques.

Circulating cell-free DNA as potential diagnostic tools for amyotrophic lateral sclerosis

DNA methylation has garnered much attention in recent years for its diagnostic potential in multiple conditions including cancer and neurodegenerative diseases. Conversely, advances regarding the potential diagnostic relevance of DNA methylation status have been sparse in the field of amyotrophic lateral sclerosis (ALS) even though patients diagnosed with this condition would significantly benefit from improved molecular assays aimed at furthering the current diagnostic and therapeutic options available. This review will provide an overview of the current diagnostic approaches available for ALS diagnosis and discuss the potential clinical usefulness of DNA methylation. We will also present examples of DNA methylation as a diagnostic tool in various types of cancer and neurodegenerative conditions and expand on how circulating cfDNA methylation may be leveraged for the early detection of ALS. In general, this article will reinforce the importance of cfDNA methylation as diagnostic tools and will further highlight its clinical relevance for persons diagnosed with ALS.