Biology subjects

Basse, M.

Publications and source records attributed to Basse, M..

2 recordsLinked to original sources

bioRxiv2026

Targeting MTHFD2 disrupts mitochondrial redox homeostasis and restores venetoclax sensitivity in acute myeloid leukemia

One-carbon metabolism is frequently dysregulated in human cancer including acute myeloid leukemia. However, the mitochondrial mechanisms by which one-carbon enzymes support leukemia survival and therapeutic response remain incompletely defined. Here, we report that the one-carbon metabolism enzyme MTHFD2 is a critical regulator of acute myeloid leukemia nucleotide metabolism, redox homeostasis, and disease progression. We show that genetic ablation of MTHFD2 suppresses acute myeloid leukemia cell proliferation in vitro and significantly delays leukemia onset in a genetically engineered mouse model, while sparing healthy hematopoietic stem and progenitor cell function. Stable isotope tracing demonstrates that MTHFD2 supports de novo purine synthesis and sustains mitochondrial NADH and NADPH production. Consistent with this role, MTHFD2 inhibition increases mitochondrial superoxide levels, and combined purine supplementation and mitochondrial reactive oxygen species neutralization rescues acute myeloid leukemia cell viability. We also demonstrate that the small-molecule inhibitor DS18561882 directly inhibits mitochondrial MTHFD2 activity and phenocopies genetic deletion. DS18561882 exhibits activity across a cohort of 60 primary AML patient samples, synergizes with venetoclax in treatment-naive acute myeloid leukemia, and restores venetoclax sensitivity in resistant AML models. These findings establish mitochondrial MTHFD2 as a genetically validated, therapeutically targetable metabolic vulnerability in acute myeloid leukemia and support targeting mitochondrial one-carbon metabolism to enhance and restore venetoclax response.

cancer biology↗

bioRxiv2025

Spatial Clustering of Interface Residues Enhances Few-Shot Prediction of Viral Protein Binding

AO_SCPLOWBSTRACTC_SCPLOWPredicting protein binding affinities across large combinatorial mutation spaces remains a critical challenge in molecular biology, particularly for understanding viral evolution and antibody interactions. While combinatorial mutagenesis experiments provide valuable data for training predictive models, they are typically limited due to experimental constraints. This creates a significant gap in our ability to predict the effects of more extensive mutation combinations, such as those observed in emerging SARS-CoV-2 variants. We present PROXICLUST, which strategically combines smaller combinatorial mutagenesis experiments to enable accurate predictions across larger combinatorial spaces. Our approach leverages the spatial proximity of amino acid residues to identify potential epistatic interactions, using these relationships to optimize the design of manageable-sized combinatorial experiments. By combining just two small combinatorial datasets, we achieve accurate binding affinity predictions across substantially larger mutation spaces (R2 {approx} 0.8), with performance strongly correlated with capture of high-order epistatic effects. We validated our method in five different protein-protein interaction datasets, including binding of SARS-CoV-2 receptor binding domain (RBD) to various antibodies and cellular receptors, as well as influenza RBD- antibody interactions. This work provides a practical framework for extending the predictive power of combinatorial mutagenesis beyond current experimental constraints, offering applications in viral surveillance and antibody engineering.

molecular biology↗