A novel screening system improves genetic correction by internal exon replacement

Trans-splicing is a powerful approach to reprogram the genome. It can be used to replace 5′, 3′ or internal exons. The latter approach has been characterized by low efficiency, as the requirements to promote internal trans-splicing are largely uncharacterized. The trans-splicing process is induced by engineered ‘RNA trans-splicing molecules’ (RTMs), which target a selected pre-mRNA to be reprogrammed via two complementary binding domains. To facilitate the development of more efficient RTMs for therapeutic applications we constructed a novel fluorescence based screening system. We incorporated exon 52 of the COL17A1 gene into a GFP-based cassette system as the target exon. This exon is mutated in many patients with the devastating skin blistering disease epidermolysis bullosa. In a double transfection assay we were able to rapidly identify optimal binding domains targeted to sequences in the surrounding introns 51 and 52. The ability to replace exon 52 was then evaluated in a more endogenous context using a target containing COL17A1 exon 51–intron 51–exon 52–intron 52–exon 53. Two selected RTMs produced significantly higher levels of GFP expression in up to 61% assayed cells. This novel approach allows for rapid identification of efficient RTMs for internal exon replacement

Mobilise-D insights to estimate real-world walking speed in multiple conditions with a wearable device

This study aimed to validate a wearable device’s walking speed estimation pipeline, considering complexity, speed, and walking bout duration. The goal was to provide recommendations on the use of wearable devices for real-world mobility analysis. Participants with Parkinson’s Disease, Multiple Sclerosis, Proximal Femoral Fracture, Chronic Obstructive Pulmonary Disease, Congestive Heart Failure, and healthy older adults (n = 97) were monitored in the laboratory and the real-world (2.5 h), using a lower back wearable device. Two walking speed estimation pipelines were validated across 4408/1298 (2.5 h/laboratory) detected walking bouts, compared to 4620/1365 bouts detected by a multi-sensor reference system. In the laboratory, the mean absolute error (MAE) and mean relative error (MRE) for walking speed estimation ranged from 0.06 to 0.12 m/s and − 2.1 to 14.4%, with ICCs (Intraclass correlation coefficients) between good (0.79) and excellent (0.91). Real-world MAE ranged from 0.09 to 0.13, MARE from 1.3 to 22.7%, with ICCs indicating moderate (0.57) to good (0.88) agreement. Lower errors were observed for cohorts without major gait impairments, less complex tasks, and longer walking bouts. The analytical pipelines demonstrated moderate to good accuracy in estimating walking speed. Accuracy depended on confounding factors, emphasizing the need for robust technical validation before clinical application. Trial registration: ISRCTN – 12246987