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Severino, J.

Publications and source records attributed to Severino, J..

2 recordsLinked to original sources

Nuclear MTHFD2 secures mitosis progression by preserving centromere integrity

Subcellular compartmentalization of metabolic enzymes may elicit specific cellular functions by establishing a unique metabolic environment. Indeed, the nuclear translocation of certain metabolic enzymes is required for epigenetic regulation and gene expression control. Here, we reveal that, in cancer cells, the mitochondrial enzyme methylenetetrahydrofolate dehydrogenase 2 (MTHFD2) localizes in the nucleus during the G2-M phase of the cell cycle to secure mitosis progression. Nuclear MTHFD2 interacts with proteins involved in mitosis regulation and centromere stability, including the methyltransferases KMT5A and DNMT3B. Loss of MTHFD2 induces centromere overexpression and severe methylation defects and impedes correct mitosis completion. As a consequence, MTHFD2 deficient cells accumulate chromosomal aberrations arising from chromosome congression and segregation defects. Blocking the catalytic nuclear function of MTHFD2 recapitulates the phenotype observed in MTHFD2 deficient cells, attributing to nuclear MTHFD2 an enzymatic active role in controlling mitosis. Our discovery uncovers a nuclear moonlighting role for the cancer target MTHFD2, and emphasizes that cancer metabolism rewiring may encompass the relocation of metabolic enzymes to alternative subcellular compartments.

cell biology↗

Volumetric bioluminescence imaging of cellular dynamics with deep learning based light-field reconstruction

The application of genetically encoded fluorophores for microscopy has afforded one of the biggest revolutions in the biosciences. Bioluminescence microscopy is an appealing alternative to fluorescence microscopy, because it does not depend on external illumination, and consequently does neither produce spurious background autofluorescence, nor perturb intrinsically photosensitive processes in living cells and animals. The low quantum yield of known luciferases, however, limit the acquisition of high signal-noise images of fast biological dynamics. To increase the versatility of bioluminescence microscopy, we present an improved low-light microscope in combination with deep learning methods to increase the signal to noise ratio in extremely photon-starved samples at millisecond exposures for timelapse and volumetric imaging. We apply our method to image subcellular dynamics in mouse embryonic stem cells, the epithelial morphology during zebrafish development, and DAF-16 FoxO transcription factor shuttling from the cytoplasm to the nucleus under external stress. Finally, we concatenate neural networks for denoising and light-field deconvolution to resolve intracellular calcium dynamics in three dimensions of freely moving Caenorhabditis elegans with millisecond exposure times. This technology is cost-effective and has the potential to replace standard optical microscopy where external illumination is prohibitive.

bioengineering↗