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Biology subjects

Houghton, F.

Publications and source records attributed to Houghton, F..

2 recordsLinked to original sources

The penetrant chordoid glioma PRKCA mutation is an oncogenic gain-of-function kinase inactivation eliciting early onset chondrosarcoma in mice.

The penetrant PRKCA D463H mutation, a biomarker and potential driver in chordoid glioma, was found to provoke the development of chondrosarcomas in heterozygous knock-in mice. This mutation entirely abrogates kinase activity, but strikingly no oncogenic phenotype is observed for the related inactivating mutation D463N indicating that the lack of activity is not the driver. In cells, the D463H mutant closely mirrored PKC WT behaviours and retained ATP binding, contrary to the related D463N mutant. Mechanistically, the PKC D463H mutant protein was found to display quantitative alterations to the PKC interactome, enhancing association with epigenetic regulators. This aligned with transcriptomic changes which resembled an augmented PKC expression program, with enhanced BRD4, Myc and TGF{beta} signatures. D463H dependent reduced sensitivity to the BET inhibitors JQ1 and AZD5153 indicates the functional importance of these pathways. The data show that D463H is a dominant gain-of-function oncogenic mutant, operating through a non-catalytic allosteric mechanism. One Sentence SummaryA PKC catalytic inactivating mutation confers gain-of-function properties - a paradigm shift in kinase actions.

cancer biology↗

Deep learning enables accurate soft tissue deformation estimation in vivo

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been instrumental in biomechanics research where measuring in vitro and in vivo tissue deformations help evaluate tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of in vivo images called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and contraction effort, highlighting the potential for StrainNet to be a valuable tool for assessing preventative care, rehabilitation strategies, or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

bioengineering↗