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PubMed2026

Innovative advances and clinical applications of cell-free DNA methylation detection technologies.

Advances in DNA methylation detection technologies have promoted disease-related cell-free DNA (cfDNA) analysis. CfDNA methylation profiling has the potential to serve as a promising clinical tool for early disease diagnosis. However, current detection technologies suffer from high costs, complex operational procedures, and insufficient sensitivity for low-input samples. Moreover, the definitive validation of its clinical value still awaits robust evidence from high-quality confirmatory studies. Therefore, this review begins by mapping the historical evolution of cfDNA methylation, followed by a comparison of the traditional approaches and recent breakthroughs in cfDNA methylation analysis. Specifically, this review systematically examines the two major strategies: the ones based on bisulfite-dependent DNA modification and the bisulfite-free methods, including the techniques for whole-genome methylation profiling and methods targeting specific genomic regions. Additionally, to evaluate the clinical application potential of these methods, this review comprehensively describes the details of these technologies, such as sample input requirements and sensing accuracy in detecting clinical samples. The future development of cfDNA methylation detection will focus on clinical translation, integrating technical innovations with the demands for efficient clinical diagnosis. We believe this review will help researchers select methods tailored to sample availability and clinical applicability.

Humans↗

PubMed2025

A Protocol for Detecting DNA Methylation Changes at CpG Sites of Stemness-Related Genes in Aging Stem Cells.

Aging adversely affects the self-renewal and differentiation capabilities of stem cells, which impairs tissue regeneration as well as the homeostasis. Epigenetic mechanisms, specifically DNA methylation, play a key role in the maintenance of pluripotency in stem cells and regulation of pluripotency-related gene expression. Age-related modifications in methylation patterns could influence the expression of genes critical for stem cell potency maintenance, including transcription factors Nanog and Sox2. The following chapter describes a step-by-step bisulfite sequencing protocol for detection of methylation changes in the aging stem cells and provides valuable insights into the stem cells epigenetic profile. Further, the methodology describes the steps of genomic DNA extraction, bisulfite conversion, real-time PCR amplification, and sequencing for an in-depth view of the epigenetic profile derived from aging stem cells.

DNA Methylation↗

PubMed2025

Enhancing the sensitivity of non-invasive cervical cancer detection using CpG methylation haplotype profiling.

DNA methylation is a critical epigenetic modification that regulates gene expression and plays a significant role in cancer development. This methylation signature can be detected in cancer-derived DNA from non-invasive samples, such as plasma, urine or Pap smears. However, in early-stage cancers-when detection is most critical-the concentration of cancer DNA is often low, limiting the sensitivity of current detection methods. Traditional DNA methylation detection techniques, which rely on methylation ratio-based measurements, may obscure subtle variations in methylation patterns, further reducing detection sensitivity. In this study, we analyzed cervical scraping specimens and examined whether detecting cancer-specific methylation patterns in cervical cancer could be enhanced using a Highly Methylated Haplotype (HMH) approach. This novel approach captures highly methylated haplotypes at single-molecule resolution using next-generation sequencing, providing greater detail than conventional methods. HMHs in specific DNA regions are a hallmark of cancer and stand out in contrast to sporadic methylation commonly observed in non-cancerous tissues. We applied HMH profiling to a gene panel of four biomarkers (CA10, DPP10, FMN2, and HAS1) previously validated in cervical cancer studies. At pre-specified cutoffs (99th percentile of normals), haplotype-based scoring achieved 89.9% sensitivity for invasive cancer at high specificity (~ 94-98%), outperforming median (78.0%) and single-CpG (71.6%) methods. For clinically relevant endpoints, the combined panel detected 51-52% of CIN2 + and 66-67% of CIN3 + cases, again exceeding the performance of median- and single-CpG-based scoring methods.These findings demonstrate the potential of HMH to substantially enhance sensitivity in cervical cancer detection, offering a promising approach for non-invasive diagnostics.

Humans↗

PubMed2025

Single-cell multi-omic detection of DNA methylation and histone modifications reconstructs the dynamics of epigenomic maintenance.

DNA methylation and histone modifications encode epigenetic information. Recently, major progress was made to measure either mark at a single-cell resolution; however, a method for simultaneous detection is lacking, preventing study of their interactions. Here, to bridge this gap, we developed scEpi2-seq. Our technique provides a readout of histone modifications and DNA methylation at the single-cell and single-molecule level. Application in a cell line with the FUCCI cell cycle reporter system reveals how DNA methylation maintenance is influenced by the local chromatin context. In addition, profiling of H3K27me3 and DNA methylation in the mouse intestine yields insights into epigenetic interactions during cell type specification. Differentially methylated regions also demonstrated independent cell-type regulation in addition to H3K27me3 regulation, which reinforces that CpG methylation acts as an additional layer of control in facultative heterochromatin.

DNA Methylation↗

PubMed2026

ReMeDy: A Flexible Statistical Framework for Region-Based Detection of DNA Methylation Dysregulation.

Region-based epigenome-wide association studies have demonstrated improved statistical power and biological interpretability compared with probe-wise analyses of DNA methylation data. However, most existing region-based methods characterize methylation dysregulation primarily through changes in mean methylation levels associated with a phenotype of interest. Substantial evidence indicates that phenotype-associated methylation alterations may also manifest through changes in methylation variability or through joint shifts in mean and variability. Despite this, no existing statistical framework jointly models mean-variance methylation changes in a region-based manner. We propose ReMeDy, a flexible statistical framework that uses a hierarchical likelihood approach within a generalized linear model setting to identify differentially methylated regions, variably methylated regions, and regions exhibiting joint differential and variable methylation at a genome-wide scale. Unlike existing models, ReMeDy operates directly on biologically defined co-methylated regions, allowing it to naturally capture spatial correlation inherent in DNA methylation array data, while avoiding reliance on heuristic, user-defined tuning parameters such as smoothing spans and kernel bandwidths that can substantially influence results and introduce subjectivity. Through extensive simulation studies and comprehensive benchmarking against popular models, we demonstrate that ReMeDy maintains false discovery and Type-I error rates at nominal levels while achieving consistently higher statistical power across a wide range of realistic scenarios. Application to population-level DNA methylation data further shows that ReMeDy identifies biologically meaningful regions and pathways implicated in complex human diseases that are not captured by conventional mean-based analyses alone. ReMeDy is implemented as an open-source R package and is freely available at https://github.com/SChatLab/ReMeDy.

DNA Methylation↗

PubMed2025

N6-methyladenine identification using deep learning and discriminative feature integration.

N6-methyladenine (6 mA) is a pivotal DNA modification that plays a crucial role in epigenetic regulation, gene expression, and various biological processes. With advancements in sequencing technologies and computational biology, there is an increasing focus on developing accurate methods for 6 mA site identification to enhance early detection and understand its biological significance. Despite the rapid progress of machine learning in bioinformatics, accurately detecting 6 mA sites remains a challenge due to the limited generalizability and efficiency of existing approaches. In this study, we present Deep-N6mA, a novel Deep Neural Network (DNN) model incorporating optimal hybrid features for precise 6 mA site identification. The proposed framework captures complex patterns from DNA sequences through a comprehensive feature extraction process, leveraging k-mer, Dinucleotide-based Cross Covariance (DCC), Trinucleotide-based Auto Covariance (TAC), Pseudo Single Nucleotide Composition (PseSNC), Pseudo Dinucleotide Composition (PseDNC), and Pseudo Trinucleotide Composition (PseTNC). To optimize computational efficiency and eliminate irrelevant or noisy features, an unsupervised Principal Component Analysis (PCA) algorithm is employed, ensuring the selection of the most informative features. A multilayer DNN serves as the classification algorithm to identify N6-methyladenine sites accurately. The robustness and generalizability of Deep-N6mA were rigorously validated using fivefold cross-validation on two benchmark datasets. Experimental results reveal that Deep-N6mA achieves an average accuracy of 97.70% on the F. vesca dataset and 95.75% on the R. chinensis dataset, outperforming existing methods by 4.12% and 4.55%, respectively. These findings underscore the effectiveness of Deep-N6mA as a reliable tool for early 6 mA site detection, contributing to epigenetic research and advancing the field of computational biology.

Deep Learning↗

PubMed2026

Low-Cost Nucleic-Acid-Based Radial Flow Assay for the Detection of GSTP1 Promoter DNA Methylation in Prostate Cancer.

DNA methylation of the glutathione S-transferase pi 1 (GSTP1) promoter is a widely studied epigenetic biomarker for prostate cancer; however, its direct detection in genomic DNA remains analytically challenging without complex chemical or amplification-based workflows. Here, we report a nucleic acid-based radial flow assay (NABRFA) that enables visual and pattern-based detection of gene-specific DNA methylation using gold nanoparticle (AuNP)-conjugated oligonucleotide probes. Thiol-modified single-stranded DNA probes targeting the GSTP1 CpG island (5'ThG) were conjugated to AuNPs to form stable probe-nanoparticle constructs that retain colloidal stability under high ionic strength conditions (0.5 M NaCl). Upon hybridization with methylation-protected GSTP1 DNA, the resulting AuNP-DNA complexes exhibit hybridization-dependent modulation of transport and retention on a porous nylon membrane, generating characteristic concentric radial patterns. These patterns arise from spatial separation between retained hybridized complexes and outwardly transported unbound probe-functionalized nanoparticles, enabling direct visual discrimination of target presence. The assay demonstrated concentration-dependent pattern evolution, with visual detection achievable down to 1 ng of target DNA and an analytically determined limit of detection of approximately 32 ng, based on image-derived gray value analysis. The human prostate cancer cell line LNCaP, known for GSTP1 promoter hypermethylation, was used as the test model for assay validation. Comparative analysis using methyl-sensitive restriction enzyme-treated native genomic DNA from the human osteosarcoma MG-63 cell line (non-prostate cancer, GSTP1 methylation-negative control) and the human lung fibroblast WI-38 cell line (non-cancerous, GSTP1 methylation-negative control) confirmed assay specificity. By coupling sequence-specific hybridization with transport-mediated nanoparticle pattern formation, NABRFA provides a label-free and conversion-free analytical strategy for detection of methylation-protected GSTP1 DNA using minimal instrumentation. This work establishes a proof-of-concept membrane-based, transport-driven sensing approach for epigenetic biomarker detection and highlights its potential for integration into simplified molecular diagnostic workflows.

Humans↗

PubMed2025

Discovery and performance of DNA methylation panels for cancer detection and classification in blood.

Examining DNA in a liquid biopsy for non-invasive cancer detection relies on identifying dilute signal in a high background. This study aims to identify DNA methylation biomarkers for multi-cancer detection. Utilizing large tissue datasets, we apply novel search algorithms to discover confined biomarker panels capable of distinguishing tumor from normal and determining the tissue of origin. We explore the applicability to blood-based testing using targeted methylation sequencing followed by machine learning classification. We present an 8-marker panel, which successfully predicts tumors across 14 types with a 91% average sensitivity, maintaining a low false positive rate (< 0.04%). Additionally, a panel of 39 CpG sites exhibits accuracies ranging from 69% to 98% for identifying tissue of origin. When tested on 114 patient plasma samples (colon, liver, pancreatic, prostate, and stomach cancer), the 8-marker panel obtains an AUC of 0.78 with a 78% sensitivity among 32 early-stage patients (stage I-II), and 60% overall. Using the 39-marker panel in a multi-class classification model selecting only the best match, 54% of tumor samples were on average correctly assigned to the tissue of origin, and up to 80% when allowing more inclusive criteria. Using a limited set of biomarkers, our work contributes to advancing non-invasive cancer diagnostics.

DNA methylation↗

PubMed2026

DNA methylation biomarkers for early detection of ovarian cancer.

Ovarian cancer (OC) remains difficult to detect at an early stage, and current screening approaches using CA125 and transvaginal ultrasonography have not demonstrated sufficient benefit for population screening. DNA methylation is a promising biomarker class because epigenetic alterations may arise early in tumourigenesis, can be detected in circulating cell-free DNA (cfDNA), and may provide tissue-of-origin information. This review critically evaluates recent evidence on DNA methylation biomarkers for early OC detection. PubMed/MEDLINE, Web of Science, and Scopus were searched for studies published between January 2020 and September 2025, supplemented by selected earlier studies of biological or methodological relevance. Evidence was synthesised across single-gene biomarkers, multi-locus panels, genome-wide signatures, assay platforms, and machine-learning classifiers, with emphasis on early-stage performance, histological representation, comparator populations, analytical methodology, and validation design. Single-gene markers such as BRCA1, RASSF1A, OPCML, HOXA9, and HIC1 show variable performance, while multi-gene and classifier-based approaches generally provide stronger discrimination. However, many studies remain limited by retrospective case-control designs, small FIGO stage I-II subsets, predominance of serous disease, and insufficient prospective validation. Integration with CA125 may improve sensitivity but can reduce specificity, which is critical in low-prevalence screening. Clinical translation will therefore require minimal and reproducible methylation signatures, standardised low-input cfDNA workflows, rigorous external validation, and prospective longitudinal evaluation in intended-use populations.

Humans↗

PubMed1979

Direct detection of methylated cytosine in DNA by use of the restriction enzyme MspI.

The extent of methylation of the internal C in the sequence CCGG in DNA from various eukaryotic sources has been determined using the restriction enzyme MspI known to be specific for this sequence. The methylation of the CCGG sequence is reflected in the restriction pattern obtained by DNA treated with MspI and its isoschizomer HpaII and analyzed by gel electrophoresis. A direct method for detection 5-methylcytosine in the sequence CCGG has been deviced. DNA fragments obtained with MspI were radioactively labeled at their 5' ends and subsequently degraded to the corresponding 5'-deoxyribonucleoside monophosphates. 5 methylcytidylic acid has been found in most of the 5' ends of MspI fragments of calf thymus DNA (about 90%) indicating heavy methylation of the sequence CCGG in calf thymus DNA. The results also reveal a symmetric methylation of both strands at this sequence in calf thymus DNA. In contrast, the CCGG sequence in other eukaryotic DNAs from organisms like Neurospora, Drosophila and Herpes virus proved to be undermethylated at this sequence.

Animals↗

PubMed1976

DNA methylation in adenovirus, adenovirus-transformed cells, and host cells.

DNAs of adenovirus type 2 and type 12 contain low amounts of methylated bases (0.01 and 0.02% N6-methyl-adenine per adenine, if any, and 0.04 and 0.06% 5-methylcytosine per cytosine for type 2 and type 12, respectively), whereas the DNA of the mammalian host cells contains much more 5-methylcytosine (3.57% for human KB cells). The DNA of hamster cells transformed by adenovirus type 12 contains 3.11 and 3.14% 5-methycytosine (HA12/7 and T627 cells, respectively), whereas the DNA from untransformed hamster cells (BHK21 cells) contains 2.22% 5-methylcytosine. In the DNA of human and hamster cells, little, if any, N6-methyladenine was detected. Methylation of DNA was determined by a sensitive method based on two consecutive steps of two-dimensional thin-layer chromatography of the radioactively labeled DNA bases. By this procedure the detection limits of 5-methylcytosine and N6-methyladenine could be lowered to 0.01% per main base.

Adenoviridae↗

PubMed2025

Methylated ARHGAP40 in renal cell carcinoma associated with tumor necrosis and grade: a potential biomarker for non-invasive early detection.

This study investigated the expression, methylation patterns, and clinicopathological implications of ARHGAP40 in renal cell carcinoma (RCC), the most common urinary malignancy. A total of 60 clear cell renal cell carcinomas (ccRCC), 30 papillary renal cell carcinomas (pRCC), 30 chromophobe renal cell carcinomas (chRCC), and 13 other RCC subtypes were enrolled. ARHGAP40 expression was analyzed in both RCC tissues and matched paracancerous normal tissues using immunohistochemistry (IHC). The methylation status of the ARHGAP40 promoter region was assessed in both normal and tumor samples by bisulfite sequencing PCR (BSP). Circulating tumor DNA (ctDNA) extracted from peripheral blood samples of RCC patients (20), patients with benign renal tumors (1), and healthy controls (14) was quantitatively analyzed for methylation using quantitative methylation-specific PCR (qMSP). ARHGAP40 expression was significantly downregulated in RCC compared to matched normal tissues (P < 0.001). This reduced expression correlated with tumor necrosis (P = 0.009) but showed no significant association with age, gender, tumor location, tumor diameter, TNM stage, or vascular invasion. In the ccRCC subtype, ARHGAP40 expression exhibited a progressive decrease with larger tumor diameter (P = 0.045), advancing histological grade (P = 0.032), and tumor necrosis (P = 0.011). The methylation status of ARHGAP40 was consistent with its expression level in both tumor and adjacent normal tissues. Methylated ARHGAP40 DNA was detectable only in RCC patient ctDNA samples. ARHGAP40 is epigenetically silenced in RCC through methylation-mediated downregulation, which correlates with tumor necrosis and grade. The detection of methylated ARHGAP40 in ctDNA holds promise as a potential biomarker for early RCC diagnosis.

Humans↗

PubMed2026

Cell-free DNA methylation biomarkers for the early detection and tumor burden monitoring of gastric cancer.

Development of sensitive biomarkers is required to achieve early detection and tumor burden monitoring in gastric cancer (GC). We performed genome-wide methylation sequencing on 78 tissue and 241 plasma samples from 171 GC patients and 114 healthy controls from two independent clinical centers. Differentially methylated regions (DMRs) were screened using paired GC and normal tissues, and refined through cfDNA profiles with LASSO regression to construct a cfDNA-based biomarker, the GCML-score. The GCML-score, consisting of 13 DMRs, demonstrated excellent diagnostic performance (AUC: 0.95/0.99/0.95 overall and 0.96/0.99/0.82 in early GC for training/internal validation/external validation cohorts). In 12 patients receiving neoadjuvant chemotherapy, dynamic changes in GCML-score were consistent with radiological tumor burden, highlighting its monitoring potential. The GCML-score, derived from genome-wide cfDNA methylation profiling, provides a robust tool for early GC detection and real-time tumor burden monitoring, facilitating improved prognosis and personalized therapeutic strategies.

Journal Article↗

PubMed2026

MethylModes: computationally efficient detection of multimodal distributions in DNA methylation data.

SUMMARY: MethylModes is an R package and Shiny application to identify multimodal distributions in human DNA methylation at individual CpG sites. Multimodal distributions, which can be the result of nearby genetic variation, environmental exposures, or assay artifacts, are susceptible to confounding and important to identify for methylation analysis. MethylModes is easily incorporated into existing quality control pipelines of array-based DNA methylation data. The underlying algorithm uses kernel smoothing of probe-level data to locate the number and location of peaks. The algorithm can be parallelized across probes for efficient implementation at genome-scale. We provide a case study implementation of MethylModes in the Health and Retirement Study as well as the Airwave Health Monitoring Study. AVAILABILITY AND IMPLEMENTATION: MethylModes is available on GitHub at https://github.com/lutiffan/methylModes as an R package wrapping an R Shiny application. We include a toy dataset to validate installation. The codebase is also published on Zenodo at https://doi.org/10.5281/zenodo.17448517.

DNA Methylation↗

PubMed2025

Revealing cancer driver genes through integrative transcriptomic and epigenomic analyses with Moonlight.

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

Humans↗

PubMed1979

Methylated and unmethylated DNA compartments in the sea urchin genome.

Sea urchin (Echinus esculentus) DNA has been separated into high and low molecular weight fractions by digestion with the mCpG-sensitive restriction endonucleases Hpa II, Hha I and Ava I. The separation was due to differences in methylation at the recognition sequences for these enzymes because an mCpG-insensitive isoschizomer of Hpa II (Msp I) digested Hpa II-resistant DNA to low molecular weight, showing that many Hpa II sites were in fact present in this fraction; and because 3H-methyl methionine administered to embryos was incorporated into the high molecular weight Hpa II-, Hha I- and Ava I-resistant fraction, but not significantly into the low molecular weight fraction. The fraction resistant to Hpa II, Hha I and Ava I amounted to about 40% of the total DNA. It consisted of long sequence tracts between 15 and well over 50 kg in length, in which many sites for each of these enzymes were methylated consecutively. The remaining 60% of the genome, (m-), was not significantly methylated. Methylated and unmethylated fractions were considered to be subfractions of the genome because enriched unique sequences from one fraction cross-reassociated poorly with the other fraction and specific sequences were found in either (m+) or (m-) but not in both (see below). Similar (m+) and (m-) compartments were found in embryos, germ cells and adult somatic tissues. Furthermor, we found no evidence for changes in the sequence composition of (m+) or (m-) between sperm, embryo or intestine DNAs, although low levels of exchange would not have been detected. Using cloned Echinus histone DNA, heterologous 5S DNA and ribosomal DNA probes, we have found that each of these gene families belongs to the unmethylated DNA compartment in all the tissues examined. In particular, there was no detectable methylation of histone DNA either in early embryos, which are thought to be actively transcribing the bulk of histone genes, or in sperm and gastrulae, in which most histone genes are not being transcribed. In contrast to these gene families, sequences complementary to an internally repetitious Echinus DNA clone were found primarily in the methylated DNA compartment.

Animals↗

PubMed2026

Epigenetic profiling of circulating cell-free DNA for early detection and minimal residual disease assessment in lung cancer: a focus on DNA methylation.

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

DNA methylation↗

PubMed1977

In vivo alkylation studies with dichlorvos at practical use concentrations.

Twenty male CFE rats were exposed to atmospheres containing 0.064 microgram/l of [Me-14C] dichlorvos (113 Ci/mol) for 12 h. Analysis of the DNA and RNA from the total soft tissues of these rats revealed no methylation of the N7 atom of guanine moieties. The limits of detection of methylation were one methyl group per 6.0 X 10(11) and per 2 X 10(9) nucleotide units for DNA and RNA, respectively. Only 0.000001% of the administered dose would have needed to react with DNA in order to produce detectable methylation of this macromolecule. The exposure period employed in this study (12 h) constituted a significant fraction of the half-life of 7-methylguanine moieties in DNA (3 days). On the basis of this information and the extremely rapid metabolism of dichlorvos in a wide range of mammalian tissues and species it was concluded that dichlorvos does not methylate the nucleic acids of mammalian tissues when it is inhaled continuously at practical use concentrations.

Alkylating Agents↗