Defects in integrin complex formation promote CHKB-mediated muscular dystrophy.

Phosphatidylcholine (PC) is the major membrane phospholipid in most eukaryotic cells. Bi-allelic loss of function variants in CHKB, encoding the first step in the synthesis of PC, is the cause of a rostrocaudal muscular dystrophy in both humans and mice. Loss of sarcolemma integrity is a hallmark of muscular dystrophies; however, how this occurs in the absence of choline kinase function is not known. We determine that in Chkb -/- mice there is a failure of the α7ß1 integrin complex that is specific to affected muscle. We observed that in Chkb -/- hindlimb muscles there is a decrease in sarcolemma association/abundance of the PI(4,5)P2 binding integrin complex proteins vinculin, and α-actinin, and a decrease in actin association with the sarcolemma. In cells, pharmacological inhibition of choline kinase activity results in internalization of a fluorescent PI(4,5)P2 reporter from discrete plasma membrane clusters at the cell surface membrane to cytosol, this corresponds with a decreased vinculin localization at plasma membrane focal adhesions that was rescued by overexpression of CHKB.

Multimodal imaging of white preretinal lesions in atypical familial exudative vitreoretinopathy: Case report and literature review.

Purpose: To report a rare clinical finding of preretinal granules associated with atypical familial exudative vitreoretinopathy (FEVR) and perform a review of the literature. Observations: An asymptomatic 18-year-old male was referred for unilateral peripheral avascular retina evaluation in association with presumed FEVR. He was first noted to have white preretinal granules on fundus examination at five years of age. The lesions remained unchanged over the subsequent years. Genetic testing did not reveal a pathogenic or likely pathogenic variant in a known FEVR gene. A review of the literature revealed five other cases of FEVR with similar findings. Conclusions and Importance: Literature review suggests preretinal granules may present rarely in FEVR. Negative genetic screening of known FEVR genes in our patient with atypical FEVR suggests either a molecularly distinct etiology supporting the rarity of this association with FEVR or, alternatively, the presence of granules in developmental retinal vascular anomalies that are not specific to FEVR. Future study and genetic testing is necessary to better understand the cause of these preretinal granules and the clinical manifestations of FEVR.

Characterising polymyalgia rheumatica on whole-body 18F-FDG PET/CT: an atlas.

The impact of modern imaging in uncovering the underlying pathology of PMR cannot be understated. Long dismissed as an inflammatory syndrome with links to the large vessel vasculitis giant cell arteritis (GCA), a pathognomonic pattern of musculotendinous inflammation is now attributed to PMR and may be used to confirm its diagnosis. Among the available modalities, 18F-fluorodeoxyglucose (18F-FDG) PET/CT is increasingly recognized for its high sensitivity and specificity, as well as added ability to detect concomitant large vessel GCA and exclude other relevant differentials like infection and malignancy. This atlas provides a contemporary depiction of PMR's pathology and outlines how this knowledge translates into a pattern of findings on whole body 18F-FDG PET/CT that can reliably confirm its diagnosis.

Atrial natriuretic peptide signaling co-regulates lipid metabolism and ventricular conduction system gene expression in the embryonic heart.

It has been shown that atrial natriuretic peptide (ANP) and its high affinity receptor (NPRA) are involved in the formation of ventricular conduction system (VCS). Inherited genetic variants in fatty acid oxidation (FAO) genes are known to cause conduction abnormalities in newborn children. Although the effect of ANP on energy metabolism in noncardiac cell types is well documented, the role of lipid metabolism in VCS cell differentiation via ANP/NPRA signaling is not known. In this study, histological sections and primary cultures obtained from E11.5 mouse ventricles were analyzed to determine the role of metabolic adaptations in VCS cell fate determination and maturation. Exogenous treatment of E11.5 ventricular cells with ANP revealed a significant increase in lipid droplet accumulation, FAO and higher expression of VCS marker Cx40. Using specific inhibitors, we further identified PPARγ and FAO as critical downstream regulators of ANP-mediated regulation of metabolism and VCS formation.

The leucine zipper domain of the transcriptional repressor Opi1 underlies a signal transduction mechanism regulating lipid synthesis.

In Saccharomyces cerevisiae, the transcriptional repressor Opi1 regulates the expression of genes involved in phospholipid synthesis responding to the abundance of the phospholipid precursor phosphatidic acid at the endoplasmic reticulum. We report here the identification of the conserved leucine zipper (LZ) domain of Opi1 as a hot spot for gain of function mutations and the characterization of the strongest variant identified, Opi1N150D. LZ modeling posits asparagine 150 embedded on the hydrophobic surface of the zipper and specifying dynamic parallel homodimerization by allowing electrostatic bonding across the hydrophobic dimerization interface. Opi1 variants carrying any of the other three ionic residues at amino acid 150 were also repressing. Genetic analyses showed that Opi1N150D variant is dominant, and its phenotype is attenuated when loss of function mutations identified in the other two conserved domains are present in cis. We build on the notion that membrane binding facilitates LZ dimerization to antagonize an intramolecular interaction of the zipper necessary for repression. Dissecting Opi1 protein in three polypeptides containing each conserved region, we performed in vitro analyses to explore interdomain interactions. An Opi11-190 probe interacted with Opi1291-404, the C terminus that bears the activator interacting domain (AID). LZ or AID loss of function mutations attenuated the interaction of the probes but was unaffected by the N150D mutation. We propose a model for Opi1 signal transduction whereby synergy between membrane-binding events and LZ dimerization antagonizes intramolecular LZ-AID interaction and transcriptional repression.

Managing Cardiovascular and Cancer Risk Associated with JAK Inhibitors.

Janus kinase inhibitors (JAKi) have enormous appeal as immune-modulating therapies across many chronic inflammatory diseases, but recently this promise has been overshadowed by questions regarding associated cardiovascular and cancer risk emerging from the ORAL Surveillance phase 3b/4 post-marketing requirement randomized controlled trial. In that study of patients with rheumatoid arthritis with existing cardiovascular risk, tofacitinib, the first JAKi registered for chronic inflammatory disease, failed to meet non-inferiority thresholds when compared with tumor necrosis factor inhibitors for both incident major adverse cardiovascular events and incident cancer. While this result was unexpected by many, subsequently published observational data have also supported this finding. Notably, however, such a risk has largely not yet been demonstrated in patients outside the specific clinical situation examined in the trial, even in the face of many studies examining this. Nevertheless, this signal has practically re-aligned approaches to both tofacitinib and other JAKi to varying extents, in other patient populations and contexts: within rheumatoid arthritis, but also in psoriatic arthritis, axial spondyloarthritis, inflammatory bowel disease, atopic dermatitis, and beyond. Application to individual patients can be more challenging but remains important to harness the substantive potential of JAKi to the maximum extent safely possible. This review not only explores the evolution of the regulatory response to the signal, its informing data, biological plausibility, and its impact on guidelines, but also the many factors that clinicians must consider in navigating cardiovascular and cancer risk for their patients considering JAKi as immune-modulating therapy.

Diagnosis of septic arthritis in the acute care setting: the value of routine intra-operative sample culture.

Objective: Diagnosing septic arthritis can be challenging and frequently involves clinical assessment, laboratory investigations and synovial fluid analysis. We sought to determine the utility of synovial aspiration and intra-operative synovial fluid and tissue culture for the accurate diagnosis of septic arthritis. Methods: We carried out a retrospective review of the records of patients referred to a tertiary orthopaedic unit with possible septic arthritis between 2015 and 2019 inclusive, including clinical and laboratory data for this cohort study. Performance characteristics were determined for synovial aspiration, intra-operative synovial fluid and tissue culture in diagnosing expert review-determined true septic arthritis. Concordance between discharge diagnosis, antibiotic prescribing and true septic arthritis was determined. Results: Of 268 patients identified with suspected septic arthritis, 143 underwent both synovial fluid aspiration and intra-operative synovial fluid and tissue biopsy culture. True septic arthritis was not differentiated significantly by laboratory parameters including serum white cell count (WCC), CRP or synovial WCC. Considering only patients with negative pre-operative synovial aspirate cultures, intra-operative samples led to diagnosis of true septic arthritis in 6 of 63 patients [number needed to treat (NNT) 10.5]. For all patients sampled in theatre, positive synovial tissue biopsy was the only evidence of true septic arthritis in six (NNT 23.9). Despite insufficient microbiological evidence, 27 of the 59 patients who did not have septic arthritis received a discharge diagnosis of septic arthritis, 26 of whom were discharged with antibiotics. Conclusion: Intra-operative sample collection, particularly tissue biopsy, increases the likelihood of a true septic arthritis diagnosis. Such measures might help to reduce diagnostic ambiguity in clinical practice and might therefore reduce overtreatment.

Developing a deep learning natural language processing algorithm for automated reporting of adverse drug reactions.

The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5%-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 1.1 million unlabelled clinical documents; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. This model was compared to a version without the pre-training step, and a previously published RoBERTa model pretrained on MIMIC III, which has demonstrated strong performance on other pharmacovigilance tasks. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.955 (95% CI 0.933 - 0.978) for the task of identifying discharge summaries containing ADR mentions, significantly outperforming the two comparator models.

COVID-19 vaccine hesitancy in inflammatory arthritis patients: serial surveys from a large longitudinal national Australian cohort.

OBJECTIVES: To determine COVID-19 vaccine hesitancy rates in inflammatory arthritis patients and identify factors associated with changing vaccine hesitancy over time. METHODS: This investigation was a prospective cohort study of inflammatory arthritis patients from community and public hospital outpatient rheumatology clinics enrolled in the Australian Rheumatology Association Database (ARAD). Two surveys were conducted, one immediately prior to (pre-pandemic) and another approximately 1 year after the start of the pandemic (follow-up). Coronavirus disease 2019 (COVID-19) vaccine hesitancy was measured at follow-up, and general vaccine hesitancy was inferred pre-pandemic; these were used to identify factors associated with fixed and changing vaccine beliefs, including sources of information and broader beliefs about medication. RESULTS: Of the 594 participants who completed both surveys, 74 (12%) were COVID-19 vaccine hesitant. This was associated with pre-pandemic beliefs about medications being harmful (P < 0.001) and overused (P = 0.002), with stronger beliefs resulting in vaccine hesitancy persistent over two time points (P = 0.008, P = 0.005). For those not vaccine hesitant pre-pandemic, the development of COVID-19 vaccine hesitancy was associated with a lower likelihood of seeking out vaccine information from health-care professionals (P < 0.001). COVID-19 vaccine hesitancy was not associated with new influenza vaccine hesitancy (P = 0.138). CONCLUSION: In this study of vaccine beliefs before and during the COVID-19 pandemic, factors associated with COVID-19 vaccine hesitancy in inflammatory arthritis patients varied, depending on vaccine attitudes immediately prior to the start of the pandemic. Fixed beliefs reflected broader views about medications, while fluid beliefs were highly influenced by whether they sought out information from health-care professionals, including rheumatologists.

Artificial intelligence and the future of radiographic scoring in rheumatoid arthritis: a viewpoint.

Rheumatoid arthritis is an autoimmune condition that predominantly affects the synovial joints, causing joint destruction, pain, and disability. Historically, the standard for measuring the long-term efficacy of disease-modifying antirheumatic drugs has been the assessment of plain radiographs with scoring techniques that quantify joint damage. However, with significant improvements in therapy, current radiographic scoring systems may no longer be fit for purpose for the milder spectrum of disease seen today. We argue that artificial intelligence is an apt solution to further improve upon radiographic scoring, as it can readily learn to recognize subtle patterns in imaging data to not only improve efficiency, but can also increase the sensitivity to variation in mild disease. Current work in the area demonstrates the feasibility of automating scoring but is yet to take full advantage of the strengths of artificial intelligence. By fully leveraging the power of artificial intelligence, faster and more sensitive scoring could enable the ongoing development of effective treatments for patients with rheumatoid arthritis.

Severe Familial Exudative Vitreoretinopathy, Congenital Hearing Loss, and Developmental Delay in a Child With Biallelic Variants in FZD4.

Importance: Familial exudative vitreoretinopathy (FEVR) is a nonsyndromic autosomal dominant retinal disorder commonly caused by variants in the FZD4 gene. This study investigates the potential role beyond ocular abnormalities for FZD4 gene variants in patients with FEVR. Objective: To evaluate the role of FZD4 in symptoms beyond those associated with FEVR through a patient with biallelic variants in FZD4. Design, Setting, and Participants: This case series included the DNA testing and phenotyping of 1 patient proband and her parents, combined with signaling assays, to determine the association of patient-derived compound heterozygous variants on FZD4 signaling and biologic function. Main Outcomes and Measures: FZD4 genes were tested using next-generation sequencing and Sanger sequencing. Cell-based assays measured the effect of the variants on FZD4 signaling. Results: The proband presented with absent red reflexes from complete tractional retinal detachments diagnosed at 3 days of age and failed the newborn screening hearing test. Auditory brainstem response at 6 months of age showed bilateral mild to moderate high-frequency sensorineural hearing loss. The patient manifested developmental delays in speech and walking. Intravenous fluorescein angiography (IVFA) of the patient's parents detected stage 1 FEVR. Genetic testing revealed 2 FZD4 variants in the patient, each variant found in 1 parent. Signaling assays confirmed that the presence of both variants was associated with significantly worse signaling activity compared with the heterozygous state. Conclusions and Relevance: Results of this case series suggest that extraocular syndromic FEVR was associated with FZD4 variants. The decrease in FZD4 signaling owing to the biallelic nature of the disease resulted in hearing deficits, developmental delays, and a more severe retinal phenotype.

Artificial Intelligence and Deep Learning for Rheumatologists.

Deep learning has emerged as the leading method in machine learning, spawning a rapidly growing field of academic research and commercial applications across medicine. Deep learning could have particular relevance to rheumatology if correctly utilized. The greatest benefits of deep learning methods are seen with unstructured data frequently found in rheumatology, such as images and text, where traditional machine learning methods have struggled to unlock the trove of information held within these data formats. The basis for this success comes from the ability of deep learning to learn the structure of the underlying data. It is no surprise that the first areas of medicine that have started to experience impact from deep learning heavily rely on interpreting visual data, such as triaging radiology workflows and computer-assisted colonoscopy. Applications in rheumatology are beginning to emerge, with recent successes in areas as diverse as detecting joint erosions on plain radiography, predicting future rheumatoid arthritis disease activity, and identifying halo sign on temporal artery ultrasound. Given the important role deep learning methods are likely to play in the future of rheumatology, it is imperative that rheumatologists understand the methods and assumptions that underlie the deep learning algorithms in widespread use today, their limitations and the landscape of deep learning research that will inform algorithm development, and clinical decision support tools of the future. The best applications of deep learning in rheumatology must be informed by the clinical experience of rheumatologists, so that algorithms can be developed to tackle the most relevant clinical problems.

Mechanism of action and therapeutic route for a muscular dystrophy caused by a genetic defect in lipid metabolism.

CHKB encodes one of two mammalian choline kinase enzymes that catalyze the first step in the synthesis of the membrane phospholipid phosphatidylcholine. In humans and mice, inactivation of the CHKB gene (Chkb in mice) causes a recessive rostral-to-caudal muscular dystrophy. Using Chkb knockout mice, we reveal that at no stage of the disease is phosphatidylcholine level significantly altered. We observe that in affected muscle a temporal change in lipid metabolism occurs with an initial inability to utilize fatty acids for energy via mitochondrial ß-oxidation resulting in shunting of fatty acids into triacyglycerol as the disease progresses. There is a decrease in peroxisome proliferator-activated receptors and target gene expression specific to Chkb-/- affected muscle. Treatment of Chkb-/- myocytes with peroxisome proliferator-activated receptor agonists enables fatty acids to be used for ß-oxidation and prevents triacyglyerol accumulation, while simultaneously increasing expression of the compensatory choline kinase alpha (Chka) isoform, preventing muscle cell injury.

Bi-allelic variants in CHKA cause a neurodevelopmental disorder with epilepsy and microcephaly.

The Kennedy pathways catalyse the de novo synthesis of phosphatidylcholine and phosphatidylethanolamine, the most abundant components of eukaryotic cell membranes. In recent years, these pathways have moved into clinical focus because four of ten genes involved have been associated with a range of autosomal recessive rare diseases such as a neurodevelopmental disorder with muscular dystrophy (CHKB), bone abnormalities and cone-rod dystrophy (PCYT1A) and spastic paraplegia (PCYT2, SELENOI). We identified six individuals from five families with bi-allelic variants in CHKA presenting with severe global developmental delay, epilepsy, movement disorders and microcephaly. Using structural molecular modelling and functional testing of the variants in a cell-based Saccharomyces cerevisiae model, we determined that these variants reduce the enzymatic activity of CHKA and confer a significant impairment of the first enzymatic step of the Kennedy pathway. In summary, we present CHKA as a novel autosomal recessive gene for a neurodevelopmental disorder with epilepsy and microcephaly.