Generalized joint hypermobility (GJH) is highly prevalent among patients diagnosed with chronic pain. When GJH is accompanied by pain in ≥4 joints over a period ≥3 months in the absence of other conditions that cause chronic pain, the hypermobility syndrome (HMS) may be diagnosed. In addition, GJH is also a clinical sign that is frequently present in hereditary diseases of the connective tissue, such as the Marfan syndrome, osteogenesis imperfecta, and the Ehlers-Danlos syndrome. However, within the Ehlers-Danlos spectrum, a similar subcategory of patients having similar clinical features as HMS but lacking a specific genetic profile was identified: Ehlers-Danlos syndrome hypermobility type (EDS-HT). Researchers and clinicians have struggled for decades with the highly diverse clinical presentation within the HMS and EDS-HT phenotypes (Challenge 1) and the lack of understanding of the pathological mechanisms that underlie the development of pain and its persistence (Challenge 2). In addition, within the HMS/EDS-HT phenotype, there is a high prevalence of psychosocial factors, which again presents a difficult issue that needs to be addressed (Challenge 3). Despite recent scientific advances, many obstacles for clinical care and research still remain. To gain further insight into the phenotype of HMS/EDS-HT and its mechanisms, clearer descriptions of these populations should be made available. Future research and clinical care should revise and create consensus on the diagnostic criteria for HMS/EDS-HT (Solution 1), account for clinical heterogeneity by the classification of subtypes within the HMS/EDS-HT spectrum (Solution 2), and create a clinical core set (Solution 3).
AimsGenetic hypertrophic cardiomyopathy (HCM) is caused by mutations in sarcomere protein-encoding genes (i.e. genotype-positive HCM). In an increasing number of patients, HCM occurs in the absence of a mutation (i.e. genotype-negative HCM). Mitochondrial dysfunction is thought to be a key driver of pathological remodelling in HCM. Reports of mitochondrial respiratory function and specific disease-modifying treatment options in patients with HCM are scarce.Methods and resultsRespirometry was performed on septal myectomy tissue from patients with HCM (n = 59) to evaluate oxidative phosphorylation and fatty acid oxidation. Mitochondrial dysfunction was most notably reflected by impaired NADH-linked respiration. In genotype-negative patients, but not genotype-positive patients, NADH-linked respiration was markedly depressed in patients with an indexed septal thickness ≥10 compared with <10. Mitochondrial dysfunction was not explained by reduced abundance or fragmentation of mitochondria, as evaluated by transmission electron microscopy. Rather, improper organization of mitochondria relative to myofibrils (expressed as a percentage of disorganized mitochondria) was strongly associated with mitochondrial dysfunction. Pre-incubation with the cardiolipin-stabilizing drug elamipretide and raising mitochondrial NAD+ levels both boosted NADH-linked respiration.ConclusionMitochondrial dysfunction is explained by cardiomyocyte architecture disruption and is linked to septal hypertrophy in genotype-negative HCM. Despite severe myocardial remodelling mitochondria were responsive to treatments aimed at restoring respiratory function, eliciting the mitochondria as a drug target to prevent and ameliorate cardiac disease in HCM. Mitochondria-targeting therapy may particularly benefit genotype-negative patients with HCM, given the tight link between mitochondrial impairment and septal thickening in this subpopulation.
Using an optimized transformation protocol we have studied the possible interactions between transforming plasmid DNA and the Hansenula polymorpha genome. Plasmids consisting only of a pBR322 replicon, an antibiotic resistance marker for Escherichia coli and the Saccharomyces cerevisiae LEU2 gene were shown to replicate autonomously in the yeast at an approximate copy number of 6 (copies per genome equivalent). This autonomous behaviour is probably due to an H. polymorpha replicon-like sequence present on the S. cerevisiae LEU2 gene fragment. Plasmids replicated as multimers consisting of monomers connected in a head-to-tail configuration. Two out of nine transformants analysed appeared to contain plasmid multimers in which one of the monomers contained a deletion. Plasmids containing internal or flanking regions of the genomic alcohol oxidase gene were shown to integrate by homologous single or double cross-over recombination. Both single- and multi-copy (two or three) tandem integrations were observed. Targeted integration occurred in 1-22% of the cases and was only observed with plasmids linearized within the genomic sequences, indicating that homologous linear ends are recombinogenic in H. polymorpha. In the cases in which no targeted integration occurred, double-strand breaks were efficiently repaired in a homology-independent way. Repair of double-strand breaks was precise in 50-68% of the cases. Linearization within homologous as well as nonhomologous plasmid regions stimulated transformation frequencies up to 15-fold.
Huntington’s disease (HD) and various spinocerebellar ataxias (SCA) are autosomal dominantly inherited neurodegenerative disorders caused by a CAG repeat expansion in the disease-related gene1. The impact of HD and SCA on families and individuals is enormous and far reaching, as patients typically display first symptoms during midlife. HD is characterized by unwanted choreatic movements, behavioral and psychiatric disturbances and dementia. SCAs are mainly characterized by ataxia but also other symptoms including cognitive deficits, similarly affecting quality of life and leading to disability. These problems worsen as the disease progresses and affected individuals are no longer able to work, drive, or care for themselves. It places an enormous burden on their family and caregivers, and patients will require intensive nursing home care when disease progresses, and lifespan is reduced. Although the clinical and pathological phenotypes are distinct for each CAG repeat expansion disorder, it is thought that similar molecular mechanisms underlie the effect of expanded CAG repeats in different genes. The predicted Age of Onset (AO) for both HD, SCA1 and SCA3 (and 5 other CAG-repeat diseases) is based on the polyQ expansion, but the CAG/polyQ determines the AO only for 50% (see figure below). A large variety on AO is observed, especially for the most common range between 40 and 50 repeats11,12. Large differences in onset, especially in the range 40-50 CAGs not only imply that current individual predictions for AO are imprecise (affecting important life decisions that patients need to make and also hampering assessment of potential onset-delaying intervention) but also do offer optimism that (patient-related) factors exist that can delay the onset of disease.To address both items, we need to generate a better model, based on patient-derived cells that generates parameters that not only mirror the CAG-repeat length dependency of these diseases, but that also better predicts inter-patient variations in disease susceptibility and effectiveness of interventions. Hereto, we will use a staggered project design as explained in 5.1, in which we first will determine which cellular and molecular determinants (referred to as landscapes) in isogenic iPSC models are associated with increased CAG repeat lengths using deep-learning algorithms (DLA) (WP1). Hereto, we will use a well characterized control cell line in which we modify the CAG repeat length in the endogenous ataxin-1, Ataxin-3 and Huntingtin gene from wildtype Q repeats to intermediate to adult onset and juvenile polyQ repeats. We will next expand the model with cells from the 3 (SCA1, SCA3, and HD) existing and new cohorts of early-onset, adult-onset and late-onset/intermediate repeat patients for which, besides accurate AO information, also clinical parameters (MRI scans, liquor markers etc) will be (made) available. This will be used for validation and to fine-tune the molecular landscapes (again using DLA) towards the best prediction of individual patient related clinical markers and AO (WP3). The same models and (most relevant) landscapes will also be used for evaluations of novel mutant protein lowering strategies as will emerge from WP4.This overall development process of landscape prediction is an iterative process that involves (a) data processing (WP5) (b) unsupervised data exploration and dimensionality reduction to find patterns in data and create “labels” for similarity and (c) development of data supervised Deep Learning (DL) models for landscape prediction based on the labels from previous step. Each iteration starts with data that is generated and deployed according to FAIR principles, and the developed deep learning system will be instrumental to connect these WPs. Insights in algorithm sensitivity from the predictive models will form the basis for discussion with field experts on the distinction and phenotypic consequences. While full development of accurate diagnostics might go beyond the timespan of the 5 year project, ideally our final landscapes can be used for new genetic counselling: when somebody is positive for the gene, can we use his/her cells, feed it into the generated cell-based model and better predict the AO and severity? While this will answer questions from clinicians and patient communities, it will also generate new ones, which is why we will study the ethical implications of such improved diagnostics in advance (WP6).
