The role of the angular gyrus in semantic cognition: A synthesis of five functional neuroimaging studies

Semantic knowledge is central to human cognition. The angular gyrus (AG) is widely considered a key brain region for semantic cognition. However, the role of the AG in semantic processing is controversial. Key controversies concern response polarity (activation vs. deactivation) and its relation to task difficulty, lateralization (left vs. right AG), and functional-anatomical subdivision (PGa vs. PGp subregions). Here, we combined the fMRI data of five studies on semantic processing (n = 172) and analyzed the response profiles from the same anatomical regions-of-interest for left and right PGa and PGp. We found that the AG was consistently deactivated during non-semantic conditions, whereas response polarity during semantic conditions was inconsistent. However, the AG consistently showed relative response differences between semantic and non-semantic conditions, and between different semantic conditions. A combined analysis across all studies revealed that AG responses could be best explained by separable effects of task difficulty and semantic processing demand. Task difficulty effects were stronger in PGa than PGp, regardless of hemisphere. Semantic effects were stronger in left than right AG, regardless of subregion. These results suggest that the AG is engaged in both domain-general task-difficulty-related processes and domain-specific semantic processes. In semantic processing, we propose that left AG acts as a "multimodal convergence zone" that binds different semantic features associated with the same concept, enabling efficient access to task-relevant features

Current management of primary mitochondrial disorders in EU countries: the European Reference Networks survey

Background and purpose: Primary mitochondrial diseases (PMDs) are rare diseases for which diagnosis is challenging, and management and training programs are not well defined in Europe. To capture and assess care needs, five different European Reference Networks have conducted an exploratory survey. Methods: The survey covering multiple topics relating to PMDs was sent to all ERNs healthcare providers (HCPs) in Europe. Results: We have collected answers from 220 members based in 24/27 European member states and seven non-European member states. Even though most of the responders are aware of neurogenetic diseases, difficulties arise in the ability to deliver comprehensive genetic testing. While single gene analysis is widely available in Europe, whole exome and genome sequencing are not easily accessible, with considerable variation between countries and average waiting time for results frequently above 6 months. Only 12.7% of responders were happy with the ICD-10 codes for classifying patients with PMDs discharged from the hospital, and more than 70% of them consider that PMDs deserve specific ICD codes to improve clinical management, including tailored healthcare, and for reimbursement reasons. Finally, 90% of responders declared that there is a need for further education and training in these diseases. Conclusions: This survey provides information on the current difficulties in the care of PMDs in Europe. We believe that the results of this survey are important to help rare disease stakeholders in European countries identify key care and research priorities

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

A guide to writing systematic reviews of rare disease treatments to generate FAIR-compliant datasets: Building a Treatabolome

Background: Rare diseases are individually rare but globally affect around 6% of the population, and in over 70% of cases are genetically determined. Their rarity translates into a delayed diagnosis, with 25% of patients waiting 5 to 30 years for one. It is essential to raise awareness of patients and clinicians of existing gene and variant-specific therapeutics at the time of diagnosis to avoid that treatment delays add up to the diagnostic odyssey of rare diseases' patients and their families. Aims: This paper aims to provide guidance and give detailed instructions on how to write homogeneous systematic reviews of rare diseases' treatments in a manner that allows the capture of the results in a computer-accessible form. The published results need to comply with the FAIR guiding principles for scientific data management and stewardship to facilitate the extraction of datasets that are easily transposable into machine-actionable information. The ultimate purpose is the creation of a database of rare disease treatments ("Treatabolome") at gene and variant levels as part of the H2020 research project Solve-RD. Results: Each systematic review follows a written protocol to address one or more rare diseases in which the authors are experts. The bibliographic search strategy requires detailed documentation to allow its replication. Data capture forms should be built to facilitate the filling of a data capture spreadsheet and to record the application of the inclusion and exclusion criteria to each search result. A PRISMA flowchart is required to provide an overview of the processes of search and selection of papers. A separate table condenses the data collected during the Systematic Review, appraised according to their level of evidence. Conclusions: This paper provides a template that includes the instructions for writing FAIR-compliant systematic reviews of rare diseases' treatments that enables the assembly of a Treatabolome database that complement existing diagnostic and management support tools with treatment awareness data

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

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

Correction to: Structural variant calling and clinical interpretation in 6224 unsolved rare disease exomes (<em>European Journal of Human Genetics</em>, (2024), 32, 8, (998-1004), 10.1038/s41431-024-01637-4)

\ua9 The Author(s) 2025.Correction to: European Journal of Human Genetics (2024) 32:998–1004 https://doi.org/10.1038/s41431-024-01637-4, published online 31 May 2024 In the original article, Tables 1 and 2 were swapped during initial production. The incorrectly assigned table order can be seen below: The total number of structural variant calls, the number of evaluated calls, and the diagnostic value increase per ERN. Total number of affected individuals denotes all the affected family members including index cases. Variants detected via paired-end or soft-clipped signal based SV analysis (Manta) in exomes, considered to be causative for the corresponding rare diseases. ERN RND ERN ITHACA ERN NMD ERN GENTURIS Number of affected individuals 2.343 1.892 1.632 357 Number of index patients 2.2 1.821 1.499 340 Known disease genes in gene list 1.82 3.081 611 230 Number of candidate variants, after filtering 798 1.404 1.519 15 Number of samples with SVs, after filtering 487 868 606 15 Number of solved index patients/all affected patients 7 (0.32%)/11 9 (0.49%)/9 6 (0.4%)/9 1 (0.29%)/3 Percentage of causal SVs among investigated SVs 1.37% 0.64% 0.59% 20% The correct table order should have been: Table 1. The total number of structural variant calls, the number of evaluated calls, and the diagnostic value increase per ERN. Total number of affected individuals denotes all the affected family members including index cases. ERN RND ERN ITHACA ERN NMD ERN GENTURIS Number of affected individuals 2.343 1.892 1.632 357 Number of index patients 2.2 1.821 1.499 340 Known disease genes in gene list 1.82 3.081 611 230 Number of candidate variants, after filtering 798 1.404 1.519 15 Number of samples with SVs, after filtering 487 868 606 15 Number of solved index patients/all affected patients 7 (0.32%)/11 9 (0.49%)/9 6 (0.4%)/9 1 (0.29%)/3 Percentage of causal SVs among investigated SVs 1.37% 0.64% 0.59% 20% Variants detected via paired-end or soft-clipped signal based SV analysis (Manta) in exomes, considered to be causative for the corresponding rare diseases. The original article has now been corrected

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

Framework for Multistakeholder Patient Registries in the Field of Rare Diseases:Focus on Neurogenetic Diseases

Progress in genetic diagnosis and orphan drug legislation has opened doors to new therapies in rare neurogenetic diseases (RNDs). Innovative therapies such as gene therapy can improve patients' quality of life but come with academic, regulatory, and financial challenges. Registries can play a pivotal role in generating evidence to tackle these, but their development requires multidisciplinary knowledge and expertise. This study aims to develop a practical framework for creating and implementing patient registries addressing common challenges and maximizing their impact on care, research, drug development, and regulatory decision making with a focus on RNDs. A comprehensive 3-step literature and qualitative research approach was used to develop the framework. A qualitative systematic literature review was conducted, extracting guidance and practices leading to the draft framework. Subsequently, we interviewed representatives of 5 established international RND registries to add learnings from hands-on experiences to the framework. Expert input on the draft framework was sought in digital multistakeholder focus groups to refine the framework. The literature search; interviews with 5 registries; and focus groups with patient representatives (n = 4), clinicians (n = 6), regulators, health technology assessment (HTA) bodies and payers (n = 7), industry representatives (n = 7), and data/information technology (IT) specialists (n = 5) informed development of the framework. It covers the interests of different stakeholders, purposes for data utilization, data aspects, IT infrastructure, governance, and financing of rare disease registries. Key principles include that data should be rapidly accessible, independent, and trustworthy. Governance should involve multiple stakeholders. In addition, data should be highly descriptive, machine-readable, and accessible through a shared infrastructure and not spread over multiple isolated repositories. Sustainable and independent financing of registries is deemed important but remains challenging because of a lack of widely supported funding models. The proposed framework will guide stakeholders in establishing or improving rare disease registries that fulfill requirements of academics and patients as well as regulators, HTA bodies, and commercial parties. There is a need for more clarity regarding quality requirements for registries in regulatory and HTA context. In addition, independent financing models for registries should be developed, as well as well-defined policies on technical uniformity in health data.</p