Wade, E. | Search bioRxiv

bioRxiv2022

The fidelity of transcription in human cells

Accurate transcription is required for the faithful expression of genetic information. To provide insight into the molecular mechanisms that control the fidelity of transcription, we analyzed the landscape of transcription errors in human embryonic stem cells. These measurements provide the first reasonable estimate of the fidelity of transcription in human cells and identify multiple genetic and epigenetic factors that control its accuracy. In addition, we developed a new reporter mouse to identify cell types and tissues that commit these errors the most. These experiments revealed that CA1 and dentate gyrus neurons are highly sensitive to transcriptional mutagenesis, lending new support to the hypothesis that transcription errors play a role in the progression of Alzheimers disease. Taken together, these experiments provide unprecedented insight into the fidelity of gene expression in human cells and the molecular mechanisms that govern the central dogma of life.

genetics↗

bioRxiv2021

Key kinematic features in early training predict performance of adult female mice in a single pellet reaching and grasping task

Detailed analyses of overly trained animal models have been long employed to decipher foundational features of skilled motor tasks and their underlying neurobiology. However, initial trial-and-error features that ultimately give rise to skilled, stereotypic movements, and the underlying neurobiological basis of flexibility in learning, to stereotypic movement in adult animals are still unclear. Knowledge obtained from addressing these questions is crucial to improve quality of life in patients affected by movement disorders. We sought to determine if known kinematic parameters of skilled movement in humans could predict learning of motor efficiency in mice during the single pellet reaching and grasping assay. Mice were food restricted to increase motivation to reach for a high reward food pellet. Their attempts to retrieve the pellet were recorded for 10 minutes a day for continuous 4 days. Individual successful and failed reaches for each mouse were manually tracked using Tracker Motion Analysis Software to extract time series data and kinematic features. We found the number of peaks and time to maximum velocity were strong predictors of individual variation in failure and success, respectively. Overall, our approach validates the use of select kinematic features to describe fine motor skill acquisition in mice and establishes peaks and time to maximum velocity as predictive measure of natural variation in motion efficiency in mice. This manually curated dataset, and kinematic parameters would be useful in comparing with pose estimation generated from deep learning approaches.

animal behavior and cognition↗