ThreaDNA: predicting DNA mechanics' contribution to sequence selectivity of proteins along whole genomes.

Motivation: Many DNA-binding proteins recognize their target sequences indirectly, by sensing DNA's response to mechanical distortion. ThreaDNA estimates this response based on high-resolution structures of the protein-DNA complex of interest. Implementing an efficient nanoscale modeling of DNA deformations involving essentially no adjustable parameters, it returns the profile of deformation energy along whole genomes, at base-pair resolution, within minutes on usual laptop/desktop computers. Our predictions can also be easily combined with estimations of direct selectivity through a generalized form of position-weight-matrices. The formalism of ThreaDNA is accessible to a wide audience. Results: We demonstrate the importance of indirect readout for the nucleosome as well as the bacterial regulators Fis and CRP. Combined with the direct contribution provided by usual sequence motifs, it significantly improves the prediction of sequence selectivity, and allows quantifying the two distinct physical mechanisms underlying it. Availability and implementation: Python software available at bioinfo.insa-lyon.fr, natively executable on Linux/MacOS systems with a user-friendly graphical interface. Galaxy webserver version available. Contact: sam.meyer@insa-lyon.fr. Supplementary information: Supplementary data are available at Bioinformatics online.

Shape deformation analysis reveals the temporal dynamics of cell-type-specific homeostatic and pathogenic responses to mutant huntingtin.

Loss of cellular homeostasis has been implicated in the etiology of several neurodegenerative diseases (NDs). However, the molecular mechanisms that underlie this loss remain poorly understood on a systems level in each case. Here, using a novel computational approach to integrate dimensional RNA-seq and in vivo neuron survival data, we map the temporal dynamics of homeostatic and pathogenic responses in four striatal cell types of Huntington's disease (HD) model mice. This map shows that most pathogenic responses are mitigated and most homeostatic responses are decreased over time, suggesting that neuronal death in HD is primarily driven by the loss of homeostatic responses. Moreover, different cell types may lose similar homeostatic processes, for example, endosome biogenesis and mitochondrial quality control in Drd1-expressing neurons and astrocytes. HD relevance is validated by human stem cell, genome-wide association study, and post-mortem brain data. These findings provide a new paradigm and framework for therapeutic discovery in HD and other NDs.