Nuclear localization of MTHFD2 is required for correct mitosis progression
Nov 12, 2024·
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0 min read
Natalia Pardo-Lorente
Anestis Gkanogiannis
Luca Cozzuto
Antoni Gañez Zapater
Lorena Espinar
Ritobrata Ghose
Jacqueline Severino
Laura García-López
Rabia Gül Aydin
Laura Martin
Maria Victoria Neguembor
Evangelia Darai
Maria Pia Cosma
Laura Batlle-Morera
Julia Ponomarenko
Sara Sdelci
Abstract
Subcellular compartmentalization of metabolic enzymes establishes a unique metabolic environment that elicits specific cellular functions. Indeed, the nuclear translocation of certain metabolic enzymes is required for epigenetic regulation and gene expression control. Here, we show that the nuclear localization of the mitochondrial enzyme methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) ensures mitosis progression. Nuclear MTHFD2 interacts with proteins involved in mitosis regulation and centromere stability, including the methyltransferases KMT5A and DNMT3B. Loss of MTHFD2 induces severe methylation defects and impedes correct mitosis completion. MTHFD2 deficient cells display chromosome congression and segregation defects and accumulate chromosomal aberrations. Blocking the catalytic nuclear function of MTHFD2 recapitulates the phenotype observed in MTHFD2 deficient cells, whereas restricting MTHFD2 to the nucleus is sufficient to ensure correct mitotic progression. Our discovery uncovers a nuclear role for MTHFD2, supporting the notion that translocation of metabolic enzymes to the nucleus is required to meet precise chromatin needs.
Type
Publication
Nature Communications, 15(1), 9529

Authors
Senior AI/ML and genomics practitioner
Senior AI/ML and genomics practitioner with ~15 years building open-source, production-grade tools for large-scale biological data. Maintainer of multiple Bioconductor packages (fastreeR, metabinR, jvecfor), and author of agentic, LLM-driven tooling that runs reproducible bioinformatics workflows from natural-language requests.
Broad multi-omics background spanning genome assembly and annotation, population genomics, large-scale NGS and functional-genomics analysis, and metagenomics, backed by reproducible HPC software and end-to-end program leadership. Currently focused on bringing modern AI — embeddings, deep learning, and LLM-based agents — to making complex omics datasets faster and easier to interrogate.