A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia.

Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics

Vaccine-induced, but not natural immunity, against the Streptococcal inhibitor of complement protects against invasive disease

Highly pathogenic emm1 Streptococcus pyogenes strains secrete the multidomain Streptococcal inhibitor of complement (SIC) that binds and inactivates components of the innate immune response. We aimed to determine if naturally occurring or vaccine-induced antibodies to SIC are protective against invasive S. pyogenes infection. Immunisation with full-length SIC protected mice against systemic bacterial dissemination following intranasal or intramuscular infection with emm1 S. pyogenes. Vaccine-induced rabbit anti-SIC antibodies, but not naturally occurring human anti-SIC antibodies, enhanced bacterial clearance in an ex vivo whole-blood assay. SIC vaccination of both mice and rabbits resulted in antibody recognition of all domains of SIC, whereas naturally occurring human anti-SIC antibodies recognised the proline-rich region of SIC only. We, therefore, propose a model whereby natural infection with S. pyogenes generates non-protective antibodies against the proline-rich region of SIC, while vaccination with full-length SIC permits the development of protective antibodies against all SIC domains

Data-derived wearable digital biomarkers predict Frataxin gene expression levels and longitudinal disease progression in Friedreich’s Ataxia

Friedreich’s ataxia (FA) is caused by repression of the Frataxin gene which impacts patients’ motor behaviour. With current gold-standard clinical scales, it requires 18-24 month-long clinical trials to determine if disease-modifying therapies are beneficial. Our approach captures the full-body movement kinematics from human subjects using a wearable motion-capture-suit. We extracted digital behavioural features from the movement data (eight-meter walk (8MW) and nine-hole peg test (9HPT)) that can distinguish FA patients and controls and then used machine learning to combine these features to longitudinally predict two different ‘gold-standard’ clinical scales (SCAFI34 and SARA). These predictions outperformed predictions from the clinical scales (leave-one-subject-out cross-validated R2 using suit features of 8MW and 9HPT tasks: 0.80 and 0.85 vs R2 of 0.47 using SARA). Unlike the clinical scales, our wearable-derived digital features can accurately cross-sectionally predict for each patient their personal FXN gene expression levels (R2 of suit features of 8MW and 9HPT: 0.59 and 0.53 vs R2 of SARA and SCAFI: 0.01 and 0.01), demonstrating the sensitivity of our approach and the importance of FXN levels in FA. Our work demonstrates that data-derived wearable biomarkers have the potential to substantially reduce clinical trial durations