Mouse HP1γ regulates TRF1 expression and telomere stability.

AIMS: Telomeric repeat-containing RNAs are long non-coding RNAs generated from the telomeres. TERRAs are essential for the establishment of heterochromatin marks at telomeres, which serve for the binding of members of the heterochromatin protein 1 (HP1) protein family of epigenetic modifiers involved with chromatin compaction and gene silencing. While HP1γ is enriched on gene bodies of actively transcribed human and mouse genes, it is unclear if its transcriptional role is important for HP1γ function in telomere cohesion and telomere maintenance. We aimed to study the effect of mouse HP1γ on the transcription of telomere factors and molecules that can affect telomere maintenance. MAIN METHODS: We investigated the telomere function of HP1γ by using HP1γ deficient mouse embryonic fibroblasts (MEFs). We used gene expression analysis of HP1γ deficient MEFs and validated the molecular and mechanistic consequences of HP1γ loss by telomere FISH, immunofluorescence, RT-qPCR and DNA-RNA immunoprecipitation (DRIP). KEY FINDINGS: Loss of HP1γ in primary MEFs led to a downregulation of various telomere and telomere-accessory transcripts, including the shelterin protein TRF1. Its downregulation is associated with increased telomere replication stress and DNA damage (γH2AX), effects more profound in females. We suggest that the source for the impaired telomere maintenance is a consequence of increased telomeric DNA-RNA hybrids and TERRAs arising at and from mouse chromosomes 18 and X. SIGNIFICANCE: Our results suggest an important transcriptional control by mouse HP1γ of various telomere factors including TRF1 protein and TERRAs that has profound consequences on telomere stability, with a potential sexually dimorphic nature.

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.