bims-dinmec 2025-07-13 papers

Issue of 2025–07–13
seven papers selected by
Lorena Ancona, Humanitas Research

Mol Neurodegener. 2025 Jul 06. 20(1): 80

Methylome analysis of FTLD patients with TDP-43 pathology identifies epigenetic signatures specific to pathological subtypes.

Cristina T Vicente, Tejasvi Niranjan, Elise Coopman, Júlia Faura, Sara Alidadiani, Claudia Schrauwen, Billie J Matchett, Bavo Heeman, Marleen Van den Broeck, Wouter De Coster, Thuy Nguyen, Julie S Lau, Saurabh Baheti, Tim de Pooter, Peter De Rijk, Mojca Strazisar, Matt Baker, Mariely DeJesus-Hernandez, NiCole A Finch, Cyril Pottier, Marka van Blitterswijk, Yan Asmann, Melissa E Murray, Leonard Petrucelli, Andrew King, Claire Troakes, Safa Al-Sarraj, Robert A Rissman, Annie Hiniker, Margaret Flanagan, Bret M Evers, Charles L White, Carlos Cruchaga, Rudolph Castellani, Jeroen G J van Rooij, Merel O Mol, Harro Seelaar, John C van Swieten, Björn Oskarsson, Robert Ross Reichard, Aivi T Nguyen, Keith A Josephs, Ronald C Petersen, Nilüfer Ertekin-Taner, Bradley F Boeve, Neill R Graff-Radford, Sarah Weckhuysen, Dennis W Dickson, Rosa Rademakers.

BACKGROUND: In the last decade, the importance of DNA methylation in the functioning of the central nervous system has been highlighted through associations between methylation changes and differential expression of key genes involved in aging and neurodegenerative diseases. In frontotemporal lobar degeneration (FTLD), aberrant methylation has been reported in causal disease genes including GRN and C9orf72; however, the genome-wide contribution of epigenetic changes to the development of FTLD remains largely unexplored.
METHODS: We performed reduced representation bisulfite sequencing of matched pairs of post-mortem tissue from frontal cortex (FCX) and cerebellum (CER) from pathologically confirmed FTLD patients with TDP-43 pathology (FTLD-TDP) further divided into five subtypes and including both sporadic and genetic forms (N = 25 pairs per group), and neuropathologically normal controls (N = 42 pairs). Case-control differential methylation analyses were performed, both at the individual CpG level, and in regions of grouped CpGs (differentially methylated regions; DMRs), either including all genomic locations or only gene promoters. Gene Ontology (GO) analyses were then performed using all differentially methylated genes in each group of sporadic patients. Finally, additional datasets were queried to prioritize candidate genes for follow-up.
RESULTS: Using the largest FTLD-TDP DNA methylation dataset generated to date, we identified thousands of differentially methylated CpGs (FCX = 6,520; CER = 7,134) and several hundred DMRs in FTLD-TDP brains (FCX = 134; CER = 219). Of these, less than 10% are shared between pathological subgroups. Combining additional datasets, we identified, validated and replicated hypomethylation of CAMTA1 in TDP-A potentially also impacting additional genes in the locus. GO analysis further implicated DNA methylation in myelination and developmental processes, as well as important disease-relevant mechanisms with subtype specificity such as protein phosphorylation and DNA damage repair in TDP-A, cholesterol biosynthesis in TDP-B, and protein localization in TDP-C.
CONCLUSIONS: We identify methylation changes in all FTLD-TDP patient groups and show that most changes are unique to a specific pathological FTLD-TDP subtype, suggesting that these subtypes not only have distinct transcriptomic and genetic signatures, but are also epigenetically distinct. Our study constitutes an invaluable resource to the community and highlights the need for further studies to profile additional epigenetic layers within each FTLD-TDP pathological subtype.

Keywords: Brain; DNA methylation; Epigenetics; FTLD; FTLD-TDP; Frontotemporal lobar degeneration; Methylome; Neurodegeneration; Pathological subtypes; TDP-43

DOI: https://doi.org/10.1186/s13024-025-00869-2

BMC Cancer. 2025 Jul 08. 25(1): 1153

Evaluating cell-free DNA integrity index as a non-invasive biomarker for neoadjuvant chemotherapy in colorectal cancer patients.

Takuma Iwai, Takeshi Yamada, Kay Uehara, Seiichi Shinji, Akihisa Matsuda, Yasuyuki Yokoyama, Goro Takahashi, Toshimitsu Miyasaka, Shintaro Kanaka, Michihiro Koizumi, Hiroshi Yoshida.

BACKGROUND: Neoadjuvant chemotherapy (NAC) is gaining attention as a treatment for advanced colorectal cancer owing to its potential to improve surgical outcomes and prognosis. However, reliable biomarkers to predict the response to NAC are lacking. We aimed to investigate the predictive value of cell-free DNA (cfDNA) integrity index for NAC response, using machine learning to compensate for the small cohort size.
METHODS: This retrospective study included 31 locally advanced colorectal cancer patients who underwent NAC and surgery at the Nippon Medical School Hospital between 2016 and 2020. Blood samples were collected pre-post-NAC to assess cfDNA levels using quantitative polymerase chain reaction. The cfDNA integrity index was calculated based on the ratio of long to short fragments in the long-interspersed element-1 repeat sequence. Statistical analyses, including random forest modeling, were performed to evaluate the predictive value of the cfDNA integrity index for treatment response.
RESULTS: Of the 31 patients, 19 (61.3%) were classified as responders and 12 (38.7%) as non-responders. The post-NAC cfDNA integrity index was significantly different between the groups (P = 0.002, odds ratio = 16.0). Random forest analysis identified changes in the cfDNA integrity index as the most important predictor of NAC response (%IncMSE: 15.79; IncNodePurity: 2.21), while sex, age, tumor site, and pre-NAC cfDNA levels were not significant predictors.
CONCLUSIONS: Variability in the cfDNA integrity index shows promise as a biomarker for predicting NAC efficacy in colorectal cancer.

Keywords: Cell-free DNA; Colorectal cancer; Liquid biopsy; Neoadjuvant chemotherapy

DOI: https://doi.org/10.1186/s12885-025-14570-6

bioRxiv. 2025 Jul 05. pii: 2025.07.01.662655. [Epub ahead of print]

DiffMethylTools: a toolbox of the detection, annotation and visualization of differential DNA methylation.

Houssemeddine Derbel, Evan Kinnear, Justin Wong, Qian Liu.

DNA methylation is a compulsory and fundamental epigenetic mechanism, and its significant changes (i.e., differential methylation) regulate gene expression, cell-type specification and disease progression without altering the underlying DNA sequence. Differential methylation biomarkers were widely used as inputs for various downstream investigations, and differential methylation could be detected via existing statistical tools by comparing two groups of methyomes (i.e. whole-genome methylation profiles). However, few toolboxes were available to integrate robust detection, annotation and visualization of differential methylation to efficiently streamline methylation investigation. Also, differential methylation detected via tools has poor reproducibility and no tools were tested on long-read methylomes. To address these issues, we introduced DiffMethylTools, an end-to-end solution to eliminate analytical and computational difficulties for differential methylation dissection. Comparison on six datasets including three long-read methylomes demonstrated that DiffMethylTools achieved overall better detection performance of differential methylation than existing tools like MethylKit, DSS, MethylSig, and bsseq. Besides, DiffMethylTools supported versatile input formats for seamless transition from upstream methylation detection tools, and offered diverse annotations and visualizations to facilitate downstream investigations. DiffMethylTools therefore offered a robust, interpretable, and user-friendly solution for differential methylation investigation, benefiting the dissection of methylation's roles in human disease studies.

DOI: https://doi.org/10.1101/2025.07.01.662655

Nat Commun. 2025 Jul 08. 16(1): 6273

High-coverage allele-resolved single-cell DNA methylation profiling reveals cell lineage, X-inactivation state, and replication dynamics.

Nathan J Spix, Walid Abi Habib, Zhouwei Zhang, Emily Eugster, Hsiao-Yun Milliron, David Sokol, KwangHo Lee, Paula A Nolte, Jamie L Endicott, Kelly F Krzyzanowski, Toshinori Hinoue, Jacob Morrison, Benjamin K Johnson, Wanding Zhou, Hui Shen, Peter W Laird.

DNA methylation patterns at crucial short sequence features, such as enhancers and promoters, may convey key information about cell lineage and state. The need for high-resolution single-cell DNA methylation profiling has therefore become increasingly apparent. Existing single-cell whole-genome bisulfite sequencing (scWGBS) studies have both methodological and analytical shortcomings. Inefficient library generation and low CpG coverage mostly preclude direct cell-to-cell comparisons and necessitate the use of cluster-based analyses, imputation of methylation states, or averaging of DNA methylation measurements across large genomic bins. Such summarization methods obscure the interpretation of methylation states at individual regulatory elements and limit our ability to discern important cell-to-cell differences. We report an improved scWGBS method, single-cell Deep and Efficient Epigenomic Profiling of methyl-C (scDEEP-mC), which offers efficient generation of high-coverage libraries. scDEEP-mC allows for cell type identification, genome-wide profiling of hemi-methylation, and allele-resolved analysis of X-inactivation epigenetics in single cells. Furthermore, we combine methylation and copy-number data from scDEEP-mC to identify single, actively replicating cells and profile DNA methylation maintenance dynamics during and after DNA replication. These analyses unlock further avenues for exploring DNA methylation regulation and dynamics and illustrate the power of high-complexity, highly efficient scWGBS library construction as facilitated by scDEEP-mC.

DOI: https://doi.org/10.1038/s41467-025-61589-1

BMC Genomics. 2025 Jul 09. 26(1): 648

CanASM: a comprehensive database for genome-wide allele-specific DNA methylation identification and annotation in cancer.

Jianmei Zhao, Zeyu Zhao, Hongfei Li, Haojie Yu, Hao Lin, Hanqi Chen, Xuecang Li, Di Liu, Yiming Wang, Guohua Wang.

Allele-specific DNA methylation (ASM) provides critical insights into the complex genetic and epigenetic mechanisms regulating gene transcription. Emerging evidence suggests that ASM is particularly enriched in gene enhancer regions, and recent studies have demonstrated that ASM is increased in cancer tissues compared with normal tissues. Despite the increasing recognition of ASM as a potential biomarker in tumorigenesis, systematic resources dedicated to identifying and annotating ASMs in cancer contexts remain limited. In this study, we developed CanASM ( https://bioinfor.nefu.edu.cn/CanASM/ ), the first comprehensive database specifically designed to identify and annotate ASM in cancer. In CanASM, ASM sites identified from bisulfite sequencing (BS-Seq) data across 31 cancer types and their matched normal tissue samples are cataloged. Importantly, CanASM includes extensive regulatory annotations for ASMs, including associated genes, cis-regulatory elements and transcription factor binding colocalizations, transcription factor affinity changes, etc. Users can query and explore ASMs using various parameters, such as single-nucleotide variations (SNVs), chromosomal coordinates, and gene names. The current version of CanASM includes 5,003,877 unique SNV-CpG pairs, including 3,056,776 index SNVs, of which 2,634,406 are single-nucleotide polymorphisms (SNPs), and 4,157,508 CpGs. With an intuitive interface for browsing, querying, analyzing, and downloading, CanASM serves as a valuable resource for researchers investigating cancer-associated genetic variations and epigenetic regulation in cancer.

Keywords: Bioinformatics; DNA methylation; Gene regulation; Single nucleotide variation

DOI: https://doi.org/10.1186/s12864-025-11849-7

Epigenetics. 2025 Dec;20(1): 2528563

Mechanism of crosstalk between DNA methylation and histone acetylation and related advances in diagnosis and treatment of premature ovarian failure.

Jing Li, Qianhui Liao, Yurou Guo, Jiaheng Zhang, Ruyi Zhang, Qiyu Liu, Huiping Liu.

Premature ovarian failure (POF) affects 1-3.5% of women under 40 years of age, characterized by irreversible depletion of the follicular pool and decline in oocyte quality, with its pathogenesis remaining incompletely understood. Current mainstream therapies, such as hormone replacement therapy, only alleviate symptoms, fail to reverse the underlying functional decline, and carry long-term risks, necessitating the exploration of novel strategies targeting the etiology. This review systematically dissects the central role of epigenetic regulation in POF. First, DNA methylation governs female reproductive lifespan by reprogramming the dormant-activation balance of primordial follicles and maintaining epigenetic memory in oocytes. Second, histone modification homeostasis determines ovarian endocrine function by influencing granulosa cell senescence and steroid hormone synthesis. Additionally, non-coding RNAs form regulatory hubs by constructing competing endogenous RNA networks that integrate oxidative stress and developmental signaling pathways. These mechanisms provide new insights into the pathological basis of POF, identify potential biomarkers, and offer a theoretical framework for deciphering targeted intervention strategies and developing precision epigenetic therapies to delay POF progression.

Keywords: DNA methylation; Premature ovarian failure (POF); epigenetic regulation; histone acetylation; non-coding RNA

DOI: https://doi.org/10.1080/15592294.2025.2528563

bioRxiv. 2025 Jul 04. pii: 2025.06.29.662079. [Epub ahead of print]

NANOME: A Nextflow pipeline for haplotype-aware allele-specific consensus DNA methylation detection by nanopore long-read sequencing.

Yang Liu, Hash Brown Taha, Qiuyang Zhang, Ziwei Pan, Christina Chatzipantsiou, Emma Wade, Thatcher Slocum, Lasya Karuturi, Yue Zhao, Shilpita Karmakar, Sheng Li.

Nanopore long-read sequencing has expanded the capacity of long-range, single-base, and single-molecule DNA-methylation (DNAme) detection and haplotype-aware allele-specific epigenetic phasing. Previously, we benchmarked and ranked the robustness of seven computational tools for DNAme detection using nanopore sequencing. The top performers were Megalodon, Nanopolish, DeepSignal and Guppy. However, these algorithms exhibit lower performance at regions with discordant non-singleton DNAme patterns compared to genome-wide regions. Furthermore, long-read sequencing analysis of mammalian genomes requires higher computational resources than next-generation sequencing. To address these issues, we developed a NANOpore Methylation (NANOME) a consensus DNAme predictive model using XGBoost, which integrates the output of Megalodon, Nanopolish, and Deepsignal for analyzing data obtained using Oxford Nanopore Technologies (ONT). NANOME enhanced DNAme detection precision (mean square error) at single-base resolution by 11% and improved accuracy (F1-score) at single-molecule resolution by 2.4% for human B-lymphocyte European cell lines (NA12878). The consensus model also detected ∼200,000 more CpGs than all three tools. Combing variant calling and long-read phasing, NANOME can detect haplotype-aware allele-specific DNAme in known imprinting controls in resolved and previously unresolved regions. We conducted haplotype-aware methylation detection on the T2T genome for dataset NA12878, revealing significant variations in differentially methylated region (DMR) density between gap and non-gap regions. Overall, NANOME represents a significant step forward in DNAme detection and long-range epigenetic phasing, offering a robust and accessible tool for researchers studying the epigenome.

DOI: https://doi.org/10.1101/2025.06.29.662079