Dominant NARS1 mutations causing axonal Charcot-Marie-Tooth disease expand NARS1-associated diseases

Pathogenic variants in six aminoacyl-tRNA synthetase (ARS) genes are implicated in neurological disorders, most notably inherited per- ipheral neuropathies. ARSs are enzymes that charge tRNA molecules with cognate amino acids. Pathogenic variants in asparaginyl- tRNA synthetase (NARS1) cause a neurological phenotype combining developmental delay, ataxia and demyelinating peripheral neur- opathy. NARS1 has not yet been linked to axonal Charcot–Marie–Tooth disease. Exome sequencing of patients with inherited periph- eral neuropathies revealed three previously unreported heterozygous NARS1 variants in three families. Clinical and electrophysiological details were assessed. We further characterized all three variants in a yeast complementation model and used a knock-in mouse model to study variant p.Ser461Phe. All three variants (p.Met236del, p.Cys342Tyr and p.Ser461Phe) co-segregate with the sensorimotor axonal neuropathy phenotype. Yeast complementation assays show that none of the three NARS1 variants support wild-type yeast growth when tested in isolation (i.e. in the absence of a wild-type copy of NARS1), consistent with a loss-of-function effect. Similarly, the homo- zygous knock-in mouse model (p.Ser461Phe/Ser472Phe in mouse) also demonstrated loss-of-function characteristics. We present three previously unreported NARS1 variants segregating with a sensorimotor neuropathy phenotype in three families. Functional studies in yeast and mouse support variant pathogenicity. Thus, NARS1 is the seventh ARS implicated in dominant axonal Charcot–Marie–Tooth disease, further stressing that all dimeric ARSs should be evaluated for Charcot–Marie–Tooth disease

An in vitro neurogenetics platform for precision disease modeling in the mouse.

The power and scope of disease modeling can be markedly enhanced through the incorporation of broad genetic diversity. The introduction of pathogenic mutations into a single inbred mouse strain sometimes fails to mimic human disease. We describe a cross-species precision disease modeling platform that exploits mouse genetic diversity to bridge cell-based modeling with whole organism analysis. We developed a universal protocol that permitted robust and reproducible neural differentiation of genetically diverse human and mouse pluripotent stem cell lines and then carried out a proof-of-concept study of the neurodevelopmental gene DYRK1A. Results in vitro reliably predicted the effects of genetic background on Dyrk1a loss-of- function phenotypes in vivo. Transcriptomic comparison of responsive and unresponsive strains identified molecular pathways conferring sensitivity or resilience to Dyrk1a1A loss and highlighted differential messenger RNA isoform usage as an important determinant of response. This cross-species strategy provides a powerful tool in the functional analysis of candidate dis- ease variants identified through human genetic studies

Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report.

Rare diseases (RD) have a prevalence of not more than 1/2000 persons in the European population, and are characterised by the difficulty experienced in obtaining a correct and timely diagnosis. According to Orphanet, 72.5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)\u27s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases with other submitted cases and with known RD in Orphanet. Three complementary approaches based on phenotypic similarity calculations using the Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) were developed; genomic data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplary cases discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity calculations. Diagnostic hypotheses were validated in 42.1% of them and needed further exploration in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort

Lack of effect from genetic deletion of Hdac6 in a humanized mouse model of CMT2D.

BACKGROUND: Inhibition of HDAC6 has been proposed as a broadly applicable therapeutic strategy for Charcot-Marie-Tooth disease (CMT). Inhibition of HDAC6 increases the acetylation of proteins important in axonal trafficking, such as α-tubulin and Miro, and has been shown to be efficacious in several preclinical studies using mouse models of CMT. AIMS: Here, we sought to expand on previous preclinical studies by testing the effect of genetic deletion of Hdac6 on mice carrying a humanized knockin allele of Gars1, a model of CMT-type 2D. METHODS: Gars1 RESULTS: The genetic deletion of Hdac6 increased α-tubulin acetylation in the sciatic nerves of both wild-type and Gars1 INTERPRETATION: Our results differ from those of two previous studies that demonstrated the benefit of the HDAC6 inhibitor tubastatin A in mouse models of CMT2D. While we cannot fully explain the different outcomes, our results offer a counterexample to the benefit of inhibiting HDAC6 in CMT2D, suggesting additional research is necessary

Development of primary osteoarthritis during aging in genetically diverse UM-HET3 mice.

BACKGROUND: Primary osteoarthritis (OA) occurs without identifiable underlying causes such as previous injuries or specific medical conditions. Age is a major contributing factor to OA, and as one ages, various joint tissues undergo gradual change, including degeneration of the articular cartilage, alterations in subchondral bone (SCB) morphology, and inflammation of the synovium. METHODS: We investigated the prevalence of primary OA in aged, genetically diverse UM-HET3 mice. Articular cartilage (AC) integrity and SCB morphology were assessed in 182 knee joints of 22-25 months old mice using the Osteoarthritis Research Society International (OARSI) scoring system and micro-CT, respectively. Additionally, we explored the effects of methylene blue (MB) and mitoquinone (MitoQ), two agents that affect mitochondrial function, on the prevalence and progression of OA during aging. RESULTS: Aged UM-HET3 mice showed a high prevalence of primary OA in both sexes. Significant positive correlations were found between cumulative AC (cAC) scores and synovitis in both sexes, and osteophyte formation in female mice. Ectopic chondrogenesis did not show significant correlations with cAC scores. Significant direct correlations were found between AC scores and inflammatory markers in chondrocytes, including matrix metalloproteinase-13, inducible nitric oxide synthase, and the NLR family pyrin domain containing-3 inflammasome in both sexes, indicating a link between OA severity and inflammation. Additionally, markers of cell cycle arrest, such as p16 and β-galactosidase, also correlated with AC scores. In male mice, no significant correlations were found between SCB morphology traits and cAC scores, while in female mice, significant correlations were found between cAC scores and tibial SCB plate bone mineral density. Notably, MB and MitoQ treatments influenced the disease\u27s progression in a sex-specific manner. MB treatment significantly reduced cAC scores at the medial knee joint, while MitoQ treatment reduced cAC scores, but these did not reach significance. CONCLUSIONS: Our study provides comprehensive insights into the prevalence and progression of primary OA in aged UM-HET3 mice, highlighting the sex-specific effects of MB and MitoQ treatments. The correlations between AC scores and various pathological factors underscore the multifaceted nature of OA and its association with inflammation and subchondral bone changes

An interactive web application for exploring systemic lupus erythematosus blood transcriptomic diversity.

In the field of complex autoimmune diseases such as systemic lupus erythematosus (SLE), systems immunology approaches have proven invaluable in translational research settings. Large-scale datasets of transcriptome profiling have been collected and made available to the research community in public repositories, but remain poorly accessible and usable by mainstream researchers. Enabling tools and technologies facilitating investigators\u27 interaction with large-scale datasets such as user-friendly web applications could promote data reuse and foster knowledge discovery. Microarray blood transcriptomic data from the LUPUCE cohort (publicly available on Gene Expression Omnibus, GSE49454), which comprised 157 samples from 62 adult SLE patients, were analyzed with the third-generation (BloodGen3) module repertoire framework, which comprises modules and module aggregates. These well-characterized samples corresponded to different levels of disease activity, different types of flares (including biopsy-proven lupus nephritis), different auto-antibody profiles and different levels of interferon signatures. A web application was deployed to present the aggregate-level, module-level and gene-level analysis results from LUPUCE dataset. Users can explore the similarities and heterogeneity of SLE samples, navigate through different levels of analysis, test hypotheses and generate custom fingerprint grids and heatmaps, which may be used in reports or manuscripts. This resource is available via this link: https://immunology-research.shinyapps.io/LUPUCE/. This web application can be employed as a stand-alone resource to explore changes in blood transcript profiles in SLE, and their relation to clinical and immunological parameters, to generate new research hypotheses

Biallelic variants in CSMD1 are implicated in a neurodevelopmental disorder with intellectual disability and variable cortical malformations.

CSMD1 (Cub and Sushi Multiple Domains 1) is a well-recognized regulator of the complement cascade, an important component of the innate immune response. CSMD1 is highly expressed in the central nervous system (CNS) where emergent functions of the complement pathway modulate neural development and synaptic activity. While a genetic risk factor for neuropsychiatric disorders, the role of CSMD1 in neurodevelopmental disorders is unclear. Through international variant sharing, we identified inherited biallelic CSMD1 variants in eight individuals from six families of diverse ancestry who present with global developmental delay, intellectual disability, microcephaly, and polymicrogyria. We modeled CSMD1 loss-of-function (LOF) pathogenesis in early-stage forebrain organoids differentiated from CSMD1 knockout human embryonic stem cells (hESCs). We show that CSMD1 is necessary for neuroepithelial cytoarchitecture and synchronous differentiation. In summary, we identified a critical role for CSMD1 in brain development and biallelic CSMD1 variants as the molecular basis of a previously undefined neurodevelopmental disorder

Kdm1a safeguards the topological boundaries of PRC2-repressed genes and prevents aging-related euchromatinization in neurons.

Kdm1a is a histone demethylase linked to intellectual disability with essential roles during gastrulation and the terminal differentiation of specialized cell types, including neurons, that remains highly expressed in the adult brain. To explore Kdm1a\u27s function in adult neurons, we develop inducible and forebrain-restricted Kdm1a knockouts. By applying multi-omic transcriptome, epigenome and chromatin conformation data, combined with super-resolution microscopy, we find that Kdm1a elimination causes the neuronal activation of nonneuronal genes that are silenced by the polycomb repressor complex and interspersed with active genes. Functional assays demonstrate that the N-terminus of Kdm1a contains an intrinsically disordered region that is essential to segregate Kdm1a-repressed genes from the neighboring active chromatin environment. Finally, we show that the segregation of Kdm1a-target genes is weakened in neurons during natural aging, underscoring the role of Kdm1a safeguarding neuronal genome organization and gene silencing throughout life

Node-degree aware edge sampling mitigates inflated classification performance in biomedical random walk-based graph representation learning.

MOTIVATION: Graph representation learning is a family of related approaches that learn low-dimensional vector representations of nodes and other graph elements called embeddings. Embeddings approximate characteristics of the graph and can be used for a variety of machine-learning tasks such as novel edge prediction. For many biomedical applications, partial knowledge exists about positive edges that represent relationships between pairs of entities, but little to no knowledge is available about negative edges that represent the explicit lack of a relationship between two nodes. For this reason, classification procedures are forced to assume that the vast majority of unlabeled edges are negative. Existing approaches to sampling negative edges for training and evaluating classifiers do so by uniformly sampling pairs of nodes. RESULTS: We show here that this sampling strategy typically leads to sets of positive and negative examples with imbalanced node degree distributions. Using representative heterogeneous biomedical knowledge graph and random walk-based graph machine learning, we show that this strategy substantially impacts classification performance. If users of graph machine-learning models apply the models to prioritize examples that are drawn from approximately the same distribution as the positive examples are, then performance of models as estimated in the validation phase may be artificially inflated. We present a degree-aware node sampling approach that mitigates this effect and is simple to implement. AVAILABILITY AND IMPLEMENTATION: Our code and data are publicly available at https://github.com/monarch-initiative/negativeExampleSelection