Biology subjects

Hobbs, R.

Publications and source records attributed to Hobbs, R..

3 recordsLinked to original sources

bioRxiv2026

Mutant Isocitrate Dehydrogenase 1 Alters Extracellular Vesicle Secretion and Cargo Composition in Cellular Models

Somatic mutations in isocitrate dehydrogenase 1 (IDH1) define major subtypes of adult diffuse gliomas and occur across a broad spectrum of cancers. These mutations ablate the conventional turnover of isocitrate to alpha-ketoglutarate and confer the neomorphic production of D-2-hydroxyglutarate (D2HG), which drives epigenetic remodeling. Growing evidence suggests this neomorphic activity may also affect how tumor cells communicate; small extracellular vesicles (sEVs) are elevated in gliomas, though how mutant IDH1 shapes sEV production and cargo remains poorly understood. Using glioblastoma and chondrosarcoma cell models, we show expression of mutant IDH1 correlates with a trend of increased sEV secretion, and that D2HG is packaged into sEVs that are taken up by cells. Cell-permeable D2HG treatment dose-dependently increased sEV release from IDH1 wild type cells. Untargeted proteomics showed that mutant IDH1 expression remodeled sEV cargo in a cell type-specific fashion, with mutant chondrosarcoma sEVs enriched in one-carbon metabolism enzymes, and mutant glioma sEVs in lysosomal luminal proteins and endoplasmic reticulum (ER) chaperones. Mutant IDH1 glioma cell transcriptomics suggested expanded ER folding capacity and reduced ribosomal pathways, and sEVs from these cells decreased nascent protein synthesis in IDH1 wild type recipient cells. Together, this suggests sEVs can serve as carriers of mutant IDH1 phenotypes.

cancer biology↗

bioRxiv2026

Extracting Parsimonious Quantitative Predictors of Biological Effectiveness from 'First-Principles' Radiobiology: Application to the Mixed-Quality Problem

Developing parsimonious, mechanism-aware quantitative models that predict how biological effectiveness changes with different modifiers remains, in general, an unsolved problem. Advances in radiobiological research have created a large knowledge base of first-principles mechanistic models of radiation response that, in principle, could accurately predict radiosensitivity across different experimental and clinical conditions. However, in practice these mechanistic models come with an overabundance of parameters, the majority of which are practically unidentifiable and, moreover, likely unnecessary if one simply wishes to predict how radiosensitivity changes for some specific modifier of interest. Nevertheless, determining which few details in the full mechanistic model are relevant for a given purpose, as well as how to remove any other extraneous details, remains a highly non-trivial task. In this study, we demonstrate the potential of model reduction, starting from a detailed mechanistic description, as a systematic strategy for deriving parsimonious, experimentally falsifiable radiobiological descriptors. As a proof-of-concept demonstration, we apply the Manifold Boundary Approximation Method (MBAM) to a Mechanistic Model of DNA Repair and Survival (MEDRAS), for the problem of cell survival prediction following an acute exposure. Our findings reveal that the complete MEDRAS model for an arbitrary mixed-quality exposure can be structurally simplified to a reduced three-parameter model for an effective uniform-quality, named MEDRAS-LPL. Additional MBAM analysis on MEDRAS-LPL identifies two boundaries in parameter space, corresponding to sparsely ionizing and densely ionizing radiation. Mapping of MEDRAS-LPL parameter space on to effective LQ space further demonstrates that parameters close to the sparsely ionizing boundary line up with expectations from the theory of dual radiation, while parameters close to the densely ionizing boundary line up with expectations from a purely linear model based on a target-theory description. Moreover, our formalism predicts enhanced synergistic interactions between sparsely ionizing and densely ionizing radiation beyond the Zaider Rossi model (ZRM) paradigm, in line with empirical observations. The results highlight the potential for using reduced-order models not only for predictive applications but also for generating novel hypotheses that can inform future experimental designs and optimization strategies in radiobiology.

biophysics↗

bioRxiv2021

AUTS2 controls neuronal lineage choice through a novel PRC1-independent complex and BMP inhibition

Despite a prominent risk factor for Neurodevelopmental disorders (NDD), it remains unclear how Autism Susceptibility Candidate 2 (AUTS2) controls the neurodevelopmental program. Our studies investigated the role of AUTS2 in neuronal differentiation and discovered that AUTS2, together with WDR68 and SKI, forms a novel protein complex (AWS) specifically in neuronal progenitors and promotes neuronal differentiation through inhibiting BMP signaling. Genomic and biochemical analyses demonstrated that the AWS complex achieves this effect by recruiting the CUL4 E3 ubiquitin ligase complex to mediate poly-ubiquitination and subsequent proteasomal degradation of phosphorylated SMAD1/5/9. Furthermore, using primary cortical neurons, we observed aberrant BMP signaling and dysregulated expression of neuronal genes upon manipulating the AWS complex, indicating that the AWS-CUL4-BMP axis plays a role in regulating neuronal lineage specification in vivo. Thus, our findings uncover a sophisticated cellular signaling network mobilized by a prominent NDD risk factor, presenting multiple potential therapeutic targets for NDD.

biochemistry↗