Thomas Vogt

This study uses prime editing to identify and characterize MutSβ inhibitors that reduce (CAG)n repeat expansion.

The paper develops URSA, a method to quantify DNA repair activity at the CAG repeat tract in HD, showing it correlates with somatic instability and can be measured rapidly in blood cells.

The paper develops targeted mass spectrometry assays to quantify DNA repair and handling proteins in Huntington's disease models and finds HTT influences their abundance and interactions.

The paper identifies the proline-rich domain (PRD) of HTT exon 1 as a modifier of neuronal toxicity in Huntington's disease, showing that the human PRD exacerbates while the mouse PRD mitigates toxicity.

The paper compares somatic CAG expansion and siRNA responses in C57BL/6 and FVB BAC-CAG HD mouse models, finding comparable expansion and MSH3-targeted siRNA efficacy.

The paper uses genetic perturbations to identify modifiers of HD-associated transcriptomic networks and pathology in mice and patient-derived neurons.

This study investigates how different HTT-lowering therapies affect somatic instability in HD mice, finding that transcriptional repression and CAG repeat binding reduce SI without lowering HTT levels.

The paper develops an ultrasensitive immunoassay to detect S421 phosphorylation of huntingtin in HD models and nonhuman primate CSF, linking it to disease progression and identifying PRKACA as a kinase involved.

This study shows that combining RNAi to silence MSH3 and HTT reduces huntingtin inclusions and restores gene expression in HD mice.

This study identifies a FAN1 mutation linked to faster Huntington's disease progression and shows it disrupts PCNA-mediated FAN1 assembly on DNA.

This study compares the effects of HTT knockout and CAG repeat expansion in cortical neurons, identifying shared and distinct biological pathways involved in HD.

This study identifies Msh3 and Pms1 as genetic drivers of CAG-repeat migration in HD mice, leading to selective striatal and cortical pathogenesis.

The paper analyzes rare coding variants in smoking and drinking behavior using exome chip data, finding that they contribute to phenotypic variation but require larger samples for individual variant associations.