Search

Spinocerebellar Ataxia Type 7 (SCA7) is an autosomal dominantly inherited disorder, primarily characterized by cerebellar ataxia and visual loss. SCA7 is caused by a CAG repeat expansion in exon 3 of the ATXN7 gene. We generated human induced pluripotent stem cells (hiPSCs) from peripheral blood-derived erythroblasts from two SCA7 patients (LUMCi051-A,B and LUMCi052-A,B,C) using integration-free episomal vectors. All hiPSC clones express pluripotency factors, show a normal karyotype, and can differentiate into the three germ layers. These lines can be used for in vitro disease modeling and therapy testing.

Background: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. Results: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main . Conclusions: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO pre

Background: Spinocerebellar ataxia type 1 (SCA1) is a neurodegenerative disease caused by a polyglutamine expansion in the ataxin-1 protein resulting in neuropathology including mutant ataxin-1 protein aggregation, aberrant neurodevelopment, and mitochondrial dysfunction. Objectives: Identify SCA1-relevant phenotypes in patient-specific fibroblasts and SCA1 induced pluripotent stem cells (iPSCs) neuronal cultures. Methods: SCA1 iPSCs were generated and differentiated into neuronal cultures. Protein aggregation and neuronal morphology were evaluated using fluorescent microscopy. Mitochondrial respiration was measured using the Seahorse Analyzer. The multi-electrode array (MEA) was used to identify network activity. Finally, gene expression changes were studied using RNA-seq to identify disease-specific mechanisms. Results: Bioenergetics deficits in patient-derived fibroblasts and SCA1 neuronal cultures showed altered oxygen consumption rate, suggesting involvement of mitochondrial dysfunction in SCA1. In SCA1 hiPSC-derived neuronal cells, nuclear and cytoplasmic aggregates were identified similar in localization as aggregates in SCA1 postmortem brain tissue. SCA1 hiPSC-derived neuronal cells showed reduced dendrite length and number of branching points while MEA recordings identified delayed development in network activity in SCA1 hiPSC-derived neuronal cells. Transcriptome analysis identified 1050 differentially expressed genes in SCA1 hiPSC-derived neuronal cells associated with synapse organization and neuron projection guidance, where a subgroup of 151 genes was highly associated with SCA1 phenotypes and linked to SCA1 relevant signaling pathways. Conclusions: Patient-derived cells recapitulate key pathological features of SCA1 pathogenesis providing a valuable tool for the identification of novel disease-specific processes. This model can be used for high throughput screenings to identify compounds, which may prevent or rescue neurodegeneration in this devast

Cytosine-adenine-guanine (CAG) repeat expansions in the coding regions of nine polyglutamine (polyQ) genes (HTT, ATXN1, ATXN2, ATXN3, CACNA1A, ATXN7, ATN1, AR, and TBP) are the cause of several neurodegenerative diseases including Huntington’s disease (HD), six different spinocerebellar ataxias (SCAs), dentatorubral-pallidoluysian atrophy, and spinobulbar muscular atrophy. The expanded CAG repeat length in the causative gene is negatively related to the age-at-onset (AAO) of clinical symptoms. In addition to the expanded CAG repeat length in the causative gene, the normal CAG repeats in the other polyQ genes can affect the AAO, suggesting functional interactions between the polyQ genes. However, there is no detailed assessment of the relationships among polyQ genes in pathologically relevant brain regions. We used gene co-expression analysis to study the functional relationships among polyQ genes in different brain regions using the Allen Human Brain Atlas (AHBA), a spatial map of gene expression in the healthy brain. We constructed co-expression networks for seven anatomical brain structures, as well as a region showing a specific pattern of atrophy in HD patients detected by magnetic resonance imaging (MRI) of the brain. In this HD-associated region, we found that ATN1 and ATXN2 were co-expressed and shared co-expression partners which were enriched for DNA repair genes. We observed a similar co-expression pattern in the frontal lobe, parietal lobe, and striatum in which this relation was most pronounced. Given that the co-expression patterns for these anatomical structures were similar to those for the HD-associated region, our results suggest that their disruption is likely involved in HD pathology. Moreover, ATN1 and ATXN2 also shared many co-expressed genes with HTT, the causative gene of HD, across the brain. Although this triangular relationship among these three polyQ genes may also be dysregulated in other polyQ diseases, stronger co-expression patterns be, Pattern Recognition and Bioinformatics

The use of splice-switching antisense therapy is highly promising, with a wealth of pre-clinical data and numerous clinical trials ongoing. Nevertheless, its potential to treat a variety of disorders has yet to be realized. The main obstacle impeding the clinical translation of this approach is the relatively poor delivery of antisense oligonucleotides to target tissues after systemic delivery. We are a group of researchers closely involved in the development of these therapies and would like to communicate our discussions concerning the validity of standard methodologies currently used in their pre-clinical development, the gaps in current knowledge and the pertinent challenges facing the field. We therefore make recommendations in order to focus future research efforts and facilitate a wider application of therapeutic antisense oligonucleotides.