Isoform specific Interactome Analysis of Spastin

Hereditary Spastic Paraplegias (HSPs) are a heterogeneous group of inherited neurodegenerative disorders, that are distinguished by an axonopathy of the upper motor neurons and therefore clinically present with a spasticity and weakness of the lower limbs. Complicated forms of the disease can include additional symptoms such as cognitive impairment, ataxia or myopathy (Klebe et al., 2015). HSPs can be inherited in an autosomal recessive, dominant or X-chromosomal manner. The most common form of autosomal dominant HSP is caused by a mutation in the Spastic Paraplegia Gene 4 (SPG4), encoding for the protein spastin, which was first described in 1999 (Bürger et al., 2000, Solowska and Baas, 2015). Four isoforms of the protein, that are shown to differ in their cellular expression levels and localization (Claudiani et al., 2005, Solowska and Baas, 2015), are known to be endogenously expressed. Furthermore, spastin integrates several key pathways of HSP pathogenesis, including membrane shaping, cytoskeleton dynamics as well as intracellular transport and is known to interact with a number of HSP-associated proteins such as Atlastin or REEP1 (Evans et al., 2006, Park et al., 2010). While there have been many postulations about the disease mechanism of SPG4, such as a loss of function of the protein (Solowska et al., 2010) or the toxicity of the truncated M1 spastin isoform (Solowska et al., 2017), it was shown that not all disease-causing mutations can be explained by these theories. Therefore, up to today, the pathomechanism remains uncertain. In this work, a mass spectrometry-based approach was chosen to perform an isoform-specific interactome analysis of spastin. The Flp-InTM T-RexTM system was used to create stable SH-SY5Y overexpression cell lines for the four endogenously expressed spastin isoforms. The tagged protein was then isolated by immunoprecipitation and bound interaction partners were identified by mass spectrometry. Promising interaction candidates were subsequently confirmed in co- immunoprecipitation studies. Abstract 76 Resulting from this workflow, we were able to reveal the two novel protein-protein interaction partners of the SPG4 protein spastin, NUP43 and ATP5A. Our findings indicate an interaction of the longer M1 isoform of spastin with the NUP107-160 complex, a subunit of the nuclear pore complex, that is known to play a major role in the assembly of the nuclear pore complex and is presumed to promote the spindle assembly during mitosis. Surprisingly, another finding was the interaction of spastin with proteins of the F1 subunit of the mitochondrial ATP synthase, such as ATP5A. As an impairment of mitochondrial functions was previously shown for other forms of HSP (e.g. SPG7,13), an affection seems possible. As those novel M1 spastin interactions were identified in a simplified cell model after cell lysis, they will need to be confirmed a second in vivo cell model, for example through co-localization studies. Furthermore, the relevance of these possible spastin interactions in post-mitotic neuronal cells requires further investigation. A better understanding of the spastin function in health and disease will hopefully bring us closer to revealing the disease mechanism in SPG4 and the development of treatment options. Abstrac

Correction:Solving unsolved rare neurological diseases—a Solve-RD viewpoint (European Journal of Human Genetics, (2021), 29, 9, (1332-1336), 10.1038/s41431-021-00901-1)

In the original publication of the article, consortium author lists were missing in the article

Correction to: Solving patients with rare diseases through programmatic reanalysis of genome-phenome data

In the original publication of the article, consortium author lists were missing in the articl

Correction to: Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases

In the original publication of the article, consortium author list was missing in the article

Solving unsolved rare neurological diseases-a Solve-RD viewpoint.

Funder: Durch Princess Beatrix Muscle Fund Durch Speeren voor Spieren Muscle FundFunder: University of Tübingen Medical Faculty PATE programFunder: European Reference Network for Rare Neurological Diseases | 739510Funder: European Joint Program on Rare Diseases (EJP-RD COFUND-EJP) | 44140962

Solving patients with rare diseases through programmatic reanalysis of genome-phenome data.

Funder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health); doi: https://doi.org/10.13039/100011272; Grant(s): 305444, 305444Funder: Ministerio de Economía y Competitividad (Ministry of Economy and Competitiveness); doi: https://doi.org/10.13039/501100003329Funder: Generalitat de Catalunya (Government of Catalonia); doi: https://doi.org/10.13039/501100002809Funder: EC | European Regional Development Fund (Europski Fond za Regionalni Razvoj); doi: https://doi.org/10.13039/501100008530Funder: Instituto Nacional de Bioinformática ELIXIR Implementation Studies Centro de Excelencia Severo OchoaFunder: EC | EC Seventh Framework Programm | FP7 Health (FP7-HEALTH - Specific Programme "Cooperation": Health)Reanalysis of inconclusive exome/genome sequencing data increases the diagnosis yield of patients with rare diseases. However, the cost and efforts required for reanalysis prevent its routine implementation in research and clinical environments. The Solve-RD project aims to reveal the molecular causes underlying undiagnosed rare diseases. One of the goals is to implement innovative approaches to reanalyse the exomes and genomes from thousands of well-studied undiagnosed cases. The raw genomic data is submitted to Solve-RD through the RD-Connect Genome-Phenome Analysis Platform (GPAP) together with standardised phenotypic and pedigree data. We have developed a programmatic workflow to reanalyse genome-phenome data. It uses the RD-Connect GPAP's Application Programming Interface (API) and relies on the big-data technologies upon which the system is built. We have applied the workflow to prioritise rare known pathogenic variants from 4411 undiagnosed cases. The queries returned an average of 1.45 variants per case, which first were evaluated in bulk by a panel of disease experts and afterwards specifically by the submitter of each case. A total of 120 index cases (21.2% of prioritised cases, 2.7% of all exome/genome-negative samples) have already been solved, with others being under investigation. The implementation of solutions as the one described here provide the technical framework to enable periodic case-level data re-evaluation in clinical settings, as recommended by the American College of Medical Genetics

Solve-RD: systematic pan-European data sharing and collaborative analysis to solve rare diseases.

For the first time in Europe hundreds of rare disease (RD) experts team up to actively share and jointly analyse existing patient's data. Solve-RD is a Horizon 2020-supported EU flagship project bringing together >300 clinicians, scientists, and patient representatives of 51 sites from 15 countries. Solve-RD is built upon a core group of four European Reference Networks (ERNs; ERN-ITHACA, ERN-RND, ERN-Euro NMD, ERN-GENTURIS) which annually see more than 270,000 RD patients with respective pathologies. The main ambition is to solve unsolved rare diseases for which a molecular cause is not yet known. This is achieved through an innovative clinical research environment that introduces novel ways to organise expertise and data. Two major approaches are being pursued (i) massive data re-analysis of >19,000 unsolved rare disease patients and (ii) novel combined -omics approaches. The minimum requirement to be eligible for the analysis activities is an inconclusive exome that can be shared with controlled access. The first preliminary data re-analysis has already diagnosed 255 cases form 8393 exomes/genome datasets. This unprecedented degree of collaboration focused on sharing of data and expertise shall identify many new disease genes and enable diagnosis of many so far undiagnosed patients from all over Europe

Twist exome capture allows for lower average sequence coverage in clinical exome sequencing

Background Exome and genome sequencing are the predominant techniques in the diagnosis and research of genetic disorders. Sufficient, uniform and reproducible/consistent sequence coverage is a main determinant for the sensitivity to detect single-nucleotide (SNVs) and copy number variants (CNVs). Here we compared the ability to obtain comprehensive exome coverage for recent exome capture kits and genome sequencing techniques. Results We compared three different widely used enrichment kits (Agilent SureSelect Human All Exon V5, Agilent SureSelect Human All Exon V7 and Twist Bioscience) as well as short-read and long-read WGS. We show that the Twist exome capture significantly improves complete coverage and coverage uniformity across coding regions compared to other exome capture kits. Twist performance is comparable to that of both short- and long-read whole genome sequencing. Additionally, we show that even at a reduced average coverage of 70× there is only minimal loss in sensitivity for SNV and CNV detection. Conclusion We conclude that exome sequencing with Twist represents a significant improvement and could be performed at lower sequence coverage compared to other exome capture techniques

A Solve-RD ClinVar-based reanalysis of 1522 index cases from ERN-ITHACA reveals common pitfalls and misinterpretations in exome sequencing

Purpose Within the Solve-RD project (https://solve-rd.eu/), the European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies aimed to investigate whether a reanalysis of exomes from unsolved cases based on ClinVar annotations could establish additional diagnoses. We present the results of the “ClinVar low-hanging fruit” reanalysis, reasons for the failure of previous analyses, and lessons learned. Methods Data from the first 3576 exomes (1522 probands and 2054 relatives) collected from European Reference Network for Intellectual disability, TeleHealth, Autism and Congenital Anomalies was reanalyzed by the Solve-RD consortium by evaluating for the presence of single-nucleotide variant, and small insertions and deletions already reported as (likely) pathogenic in ClinVar. Variants were filtered according to frequency, genotype, and mode of inheritance and reinterpreted. Results We identified causal variants in 59 cases (3.9%), 50 of them also raised by other approaches and 9 leading to new diagnoses, highlighting interpretation challenges: variants in genes not known to be involved in human disease at the time of the first analysis, misleading genotypes, or variants undetected by local pipelines (variants in off-target regions, low quality filters, low allelic balance, or high frequency). Conclusion The “ClinVar low-hanging fruit” analysis represents an effective, fast, and easy approach to recover causal variants from exome sequencing data, herewith contributing to the reduction of the diagnostic deadlock