Phenotype and management of neurologic intronic repeat disorders (NIRDs)

Finsterer J.; Rev Neurol (Paris). 2022 Nov 9:S0035-3787(22)00793-7. doi: 10.1016/j.neurol.2022.09.004. Epub ahead of print. PMID: 36371266. 

During recent years an increasing number of neurologic disorders due to expanded tri-, tetra-, penta-, or hexa-nucleotide repeat motifs in introns of various genes have been described (neurologic intronic repeat disorders (NIRDs)). The repeat may be pathogenic in the heterozygous or homozygous form. Repeat lengths vary considerably and can be stable or unstable during transmission to the next generation. The most well-known NIRDs are Friedreich ataxia, spinocerebellar ataxia types-10, -31, and -36, CANVAS, C9Orf72 familial amyotrophic lateral sclerosis (fALS), and myotonic dystrophy-2 (MD2). Phenotypically, NIRDs manifest as mono-organ (e.g. spinocerebellar ataxia type 31) or multi-organ disease (e.g. Friedreich ataxia, myotonic dystrophy-2). A number of other more rare NIRDs have been recently detected. This review aims at summarising and discussing previous findings and recent advances concerning the etiology, pathophysiology, clinical presentation, and therapeutic management of the most common NIRDs.

Blood Transcript Biomarkers Selected by Machine Learning Algorithm Classify Neurodegenerative Diseases including Alzheimer’s Disease

Huseby, C.J.; Delvaux, E.; Brokaw, D.L.; Coleman, P.D.; Biomolecules 2022, 12, 1592. doi:10.3390/biom12111592 

A blood-based screen to distinguish and classify neurodegenerative diseases is especially interesting having low cost, minimal invasiveness, and accessibility to almost any world clinic. In this study, we set out to discover a small set of blood transcripts that can be used to distinguish healthy individuals from those with Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, Friedreich’s ataxia, or frontotemporal dementia. Using existing public datasets, we developed a machine learning algorithm for application on transcripts present in blood and discovered small sets of transcripts that distinguish a number of neurodegenerative diseases with high sensitivity and specificity. We validated the usefulness of blood RNA transcriptomics for the classification of neurodegenerative diseases. Information about features selected for the classification can direct the development of possible treatment strategies.

Prediction of the disease course in Friedreich ataxia

Christian Hohenfeld, Ulrich Terstiege, Imis Dogan, Paola Giunti, Michael H. Parkinson, Caterina Mariotti, Lorenzo Nanetti, Mario Fichera, Alexandra Durr, Claire Ewenczyk, Sylvia Boesch, Wolfgang Nachbauer, Thomas Klopstock, Claudia Stendel, Francisco Javier Rodríguez de Rivera Garrido, Ludger Schöls, Stefanie N. Hayer, Thomas Klockgether, Ilaria Giordano, Claire Didszun, Myriam Rai, Massimo Pandolfo, Holger Rauhut, Jörg B. Schulz & Kathrin Reetz. Sci Rep 12, 19173 (2022). doi:10.1038/s41598-022-23666-z 

 We explored whether disease severity of Friedreich ataxia can be predicted using data from clinical examinations. From the database of the European Friedreich Ataxia Consortium for Translational Studies (EFACTS) data from up to five examinations of 602 patients with genetically confirmed FRDA was included. Clinical instruments and important symptoms of FRDA were identified as targets for prediction, while variables such as genetics, age of disease onset and first symptom of the disease were used as predictors. We used modelling techniques including generalised linear models, support-vector-machines and decision trees. The scale for rating and assessment of ataxia (SARA) and the activities of daily living (ADL) could be predicted with predictive errors quantified by root-mean-squared-errors (RMSE) of 6.49 and 5.83, respectively. Also, we were able to achieve reasonable performance for loss of ambulation (ROC-AUC score of 0.83). However, predictions for the SCA functional assessment (SCAFI) and presence of cardiological symptoms were difficult. In conclusion, we demonstrate that some clinical features of FRDA can be predicted with reasonable error; being a first step towards future clinical applications of predictive modelling. In contrast, targets where predictions were difficult raise the question whether there are yet unknown variables driving the clinical phenotype of FRDA.

C-Path and EFACTS Announce Data Sharing Agreement Making RDCA-DAP the Largest Worldwide Database for Friedreich’s Ataxia

TUCSON, Ariz., Nov. 10, 2022 — Critical Path Institute (C-Path) and the European Friedreich’s Ataxia Consortium for Translational Studies (EFACTS) today announced a data sharing agreement to incorporate patient data into C-Path’s Rare Disease Cures Accelerator-Data and Analytics Platform (RDCA-DAP®) solidifying RDCA-DAP as the largest worldwide database for Friedreich’s ataxia. 

 “The Friedreich’s Ataxia Research Alliance (FARA) partnered with C-Path to create a database that could integrate data from natural history studies and clinical trials, FA Integrated Clinical Database (FA-ICD), and we are really excited that this data is now part of RDCA-DAP and that our EFACTS partners are also contributing such informative data,” said FARA CEO Jennifer Farmer, M.S. “The collaboration with EFACTS goes beyond the contribution of this important data as EFACTS concomitantly joins the steering committee of the C-Path’s taskforce for FA and will inform new research. We look forward to the new insights and clinical trial tools that will be derived from having such a robust database shared with the research community.”

Frataxin deficiency alters gene expression in Friedreich ataxia derived IPSC-neurons and cardiomyocytes

Angulo, M. B., Bertalovitz, A., Argenziano, M. A., Yang, J., Patel, A., Zesiewicz, T., & McDonald, T. V. (2022). Molecular Genetics & Genomic Medicine, 00, e2093. doi:10.1002/mgg3.2093 

 RNA-seq and differential gene expression enrichment analyses demonstrated that frataxin deficiency affected the expression of glycolytic pathway genes in neurons and extracellular matrix pathway genes in cardiomyocytes. Genes in these pathways were differentially expressed when compared to a control and restored to control levels when FRDA cells were supplemented with frataxin.

Clinical management guidelines for Friedreich ataxia: best practice in rare diseases

Corben, L.A., Collins, V., Milne, S. et al. Orphanet J Rare Dis 17, 415 (2022). doi:10.1186/s13023-022-02568-3 

Individuals with Friedreich ataxia (FRDA) can find it difficult to access specialized clinical care. To facilitate best practice in delivering healthcare for FRDA, clinical management guidelines (CMGs) were developed in 2014. However, the lack of high-certainty evidence and the inadequacy of accepted metrics to measure health status continues to present challenges in FRDA and other rare diseases. To overcome these challenges, the Grading of Recommendations Assessment and Evaluation (GRADE) framework for rare diseases developed by the RARE-Bestpractices Working Group was adopted to update the clinical guidelines for FRDA. This approach incorporates additional strategies to the GRADE framework to support the strength of recommendations, such as review of literature in similar conditions, the systematic collection of expert opinion and patient perceptions, and use of natural history data.