Search arXivSearch

arXiv · 1910.11180

CD44 alternative splicing is a sensor of intragenic DNA methylation in tumors

Abstract

DNA methylation (meDNA) is a suspected modulator of alternative splicing, while splicing in turn is involved in tumour formations nearly as frequently as DNA mutations. Yet, the impact of meDNA on tumorigenesis via its effect on splicing has not been thoroughly explored. Here, we find that HCT116 colon carcinoma cells inactivated for the DNA methylases DNMT1 and DNMT3b undergo a partial epithelial to mesenchymal transition (EMT) associated with alternative splicing of the CD44 transmembrane receptor. The skipping of CD44 variant exons is in part explained by altered expression or splicing of splicing and chromatin factors. A direct effect of meDNA on alternative splicing was sustained by transient depletion of DNMT1 and the methyl-binding genes MBD1, MBD2, and MBD3. Yet, local changes in intragenic meDNA also altered recruitment of MBD1 protein and of the chromatin factor HP1$γ$ known to alter transcriptional pausing and alternative splicing decisions. We further tested if meDNA level has sufficiently strong direct impact on the outcome of alternative splicing to have a predictive value in the MCF10A model for breast cancer progression and in patients with acute lymphoblastic leukemia (B ALL). We found that a small number of differentially spliced genes mostly involved in splicing and signal transduction is systematically correlated with local meDNA. Altogether, our observations suggest that, although DNA methylation has multiple avenues to alternative splicing, its indirect effect may be also mediated through alternative splicing isoforms of these sensors of meDNA.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Eric Batsché, Oriane Mauger, Etienne Kornobis, Benjamin Hopkins, Charlotte Hanmer-Lloyd, Christian Muchardt. 2019-10-24. CD44 alternative splicing is a sensor of intragenic DNA methylation in tumors. https://arxiv.org/abs/1910.11180

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Revolutionizing Genomics with Reinforcement Learning Techniques

In recent years, Reinforcement Learning (RL) has emerged as a powerful tool for solving a wide range of problems, including decision-making and genomics. The exponential growth of raw genomic data over the past two decades has exceeded the capacity of manual analysis, leading to a growing interest in automatic data analysis and processing. RL algorithms are capable of learning from experience with minimal human supervision, making them well-suited for genomic data analysis and interpretation. One of the key benefits of using RL is the reduced cost associated with collecting labeled training data, which is required for supervised learning. While there have been numerous studies examining the applications of Machine Learning (ML) in genomics, this survey focuses exclusively on the use of RL in various genomics research fields, including gene regulatory networks (GRNs), genome assembly, and sequence alignment. We present a comprehensive technical overview of existing studies on the application of RL in genomics, highlighting the strengths and limitations of these approaches. We then discuss potential research directions that are worthy of future exploration, including the development of more sophisticated reward functions as RL heavily depends on the accuracy of the reward function, the integration of RL with other machine learning techniques, and the application of RL to new and emerging areas in genomics research. Finally, we present our findings and conclude by summarizing the current state of the field and the future outlook for RL in genomics.

q-bio.GN

Transcriptomic Models for Immunotherapy Response Prediction Show Limited Cross-cohort Generalisability

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy; yet substantial proportion of patients exhibit intrinsic or acquired resistance, making accurate pre-treatment response prediction a critical unmet need. Transcriptomics-based biomarkers derived from bulk and single-cell RNA sequencing (scRNA-seq) offer a promising avenue for capturing tumour-immune interactions, yet the cross-cohort generalisability of existing prediction models remains unclear.We systematically benchmark nine state-of-the-art transcriptomic ICI response predictors, five bulk RNA-seq-based models (COMPASS, IRNet, NetBio, IKCScore, and TNBC-ICI) and four scRNA-seq-based models (PRECISE, DeepGeneX, Tres and scCURE), using publicly available independent datasets unseen during model development. Overall, predictive performance was modest: bulk RNA-seq models performed at or near chance level across most cohorts, while scRNA-seq models showed only marginal improvements. Pathway-level analyses revealed sparse and inconsistent biomarker signals across models. Although scRNA-seq-based predictors converged on immune-related programs such as allograft rejection, bulk RNA-seq-based models exhibited little reproducible overlap. PRECISE and NetBio identified the most coherent immune-related themes, whereas IRNet predominantly captured metabolic pathways weakly aligned with ICI biology. Together, these findings demonstrate the limited cross-cohort robustness and biological consistency of current transcriptomic ICI prediction models, underscoring the need for improved domain adaptation, standardised preprocessing, and biologically grounded model design.

q-bio.GN

Large Language Model Agents for Evidence Based Genetic Disease Severity Classification

Disease severity classification for genetic conditions is subjective and labor-intensive, creating bottlenecks in genomic screening, where commercial panels vary widely in size and overlap. We developed an autonomous AI agent integrating Reasoning and Acting (ReAct) with Retrieval-Augmented Generation (RAG) to classify 10,211 Human Phenotype Ontology terms. It uses American College of Medical Genetics (ACMG)-endorsed severity guidelines and American College of Obstetricians and Gynecologists (ACOG) quality-of-life criteria to retrieve PubMed literature, generate interpretable reasoning chains, and independently verify claims. At the phenotype level, using expert-curated cohorts, the agent achieved 93.55% accuracy (MCC 0.9237) with 82.6% to 91.4% of claims supported by direct evidence or valid inferences. Gene-level severity was aggregated across 8,738 pairs, identifying 3,283 autosomal recessive pairs with severe or profound presentations. External validation showed 95.2% concordance with Mackenzie's Mission gene list. This system enables standardized panel design by providing reliable, automated classification supported by direct evidence.

q-bio.GN