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Abstract Although first identified over 70 years ago, Klinefelter syndrome (KS) continues to pose substantial diagnostic challenges, as many patients are still misdiagnosed, or remain undiagnosed. In fact, as few as 25% of patients with KS are accurately diagnosed and most of these diagnoses are not made until adulthood. Classic characteristics of KS include small testes, infertility, hypergonadothropic hypogonadism, and cognitive impairment. However, the pathophysiology behind KS is not well understood, although genetic effects are also thought to play a role. For example, recent developments in genetics and genomics point to a fundamental change in our understanding of KS, with global epigenetic and RNA expression changes playing a central role for the phenotype. KS is also associated with more general health markers, including higher morbidity and mortality rates and lower socioeconomic status (which likely affect both morbidity and mortality). In addition, hypogonadism is associated with greater risk of metabolic syndrome, type 2 diabetes, cardiovascular disease, breast cancer, and extragonadal germ cell tumors. Medical treatment typically focuses on testosterone replacement therapy (TRT), although the effects of this therapy have not been studied rigorously, and future studies need to evaluate the effects of TRT on metabolic risk and neurocognitive outcomes. This review presents a comprehensive interdisciplinary examination of recent developments in genetic, endocrine, and neurocognitive science, including the study of animal models. It provides a number of recommendations for improving the effectiveness of research and clinical practice, including neonatal KS screening programs, and a multidisciplinary approach to KS treatment from childhood until senescence. Klinefelter syndrome still poses diagnostic challenges and many cases remain undiagnosed. Here we review new aspects of the syndrome and integrate genetics, neurocognition, and endocrinology.

Most rheumatic and musculoskeletal diseases (RMDs) can be placed along a spectrum of disorders, with autoinflammatory diseases (including monogenic systemic autoinflammatory diseases) and autoimmune diseases (such as systemic lupus erythematosus and antiphospholipid syndrome) representing the two ends of this spectrum. However, although most autoinflammatory diseases are characterized by the activation of innate immunity and inflammasomes and classical autoimmunity typically involves adaptive immune responses, there is some overlap in the features of autoimmunity and autoinflammation in RMDs. Indeed, some ‘mixed-pattern’ diseases such as spondyloarthritis and some forms of rheumatoid arthritis can also be delineated. A better understanding of the pathogenic pathways of autoinflammation and autoimmunity in RMDs, as well as the preferential cytokine patterns observed in these diseases, could help us to design targeted treatment strategies.Most rheumatic and musculoskeletal diseases (RMDs) fall along a spectrum of disorders from autoinflammatory diseases to autoimmune diseases, with ‘mixed-pattern’ RMDs having features of autoinflammation and autoimmunity. A better understanding of the pathogenic pathways of autoinflammation and autoimmunity in RMDs should enhance targeted treatment strategies.

Genome-wide association studies (GWASs) of medication use may contribute to understanding of disease etiology, could generate new leads relevant for drug discovery and can be used to quantify future risk of medication taking. Here, we conduct GWASs of self-reported medication use from 23 medication categories in approximately 320,000 individuals from the UK Biobank. A total of 505 independent genetic loci that meet stringent criteria ( P  < 10 −8 /23) for statistical significance are identified. We investigate the implications of these GWAS findings in relation to biological mechanism, potential drug target identification and genetic risk stratification of disease. Amongst the medication-associated genes are 16 known therapeutic-effect target genes for medications from 9 categories. Two of the medication classes studied are for disorders that have not previously been subject to large GWAS (hypothyroidism and gastro-oesophageal reflux disease). An understanding of the genetic variants associated with medication use may shed light on the underlying biological pathways of disease, and aid in drug development. Here, Wu and colleagues conduct a GWAS for self-reported medication-use in the UK Biobank, finding more than 500 independent variants and many promising leads for future work.

Aimed at students of bone biology and genetics, this second edition includes general introductory chapters on bone biology and genetics. More specific disease orientated chapters comprehensively summarise the clinical, genetic, molecular, animal model, molecular pathology, diagnostic, counselling, and treatment aspects of each disorder. The book is organised into five sections that each emphasise a particular theme, general background to bone biology, general background to genetics and epigenetics, disorders of bone and joint, parathyroid and related disorders, and vitamin D and renal disorders.

RNA is likely to be the most rediscovered macromolecule in biology. Periodically, new non-canonical functions have been ascribed to RNA, such as the ability to act as a catalytic molecule or to work independently from its coding capacity. Recent annotations show that more than half of the transcriptome encodes for RNA molecules lacking coding activity. Here we illustrate how these transcripts affect skeletal muscle differentiation and related disorders. We discuss the most recent scientific discoveries that have led to the identification of the molecular circuitries that are controlled by RNA during the differentiation process and that, when deregulated, lead to pathogenic events. These findings will provide insights that can aid in the development of new therapeutic interventions for muscle diseases.

Hereditary channelopathies, that is, mutations in channel genes that alter channel function and are causal for the pathogenesis of the disease, have been described for several members of the transient receptor potential channel family. Mutations in the TRPV4 gene, encoding a polymodal Ca 2+ permeable channel, are causative for several human diseases, which affect the skeletal system and the peripheral nervous system, with highly variable phenotypes. In this review, we describe the phenotypes of TRPV4 channelopathies and overlapping symptoms. Putative mechanisms to explain the puzzle, and how mutations in the same region of the channel cause different diseases, are discussed and experimental approaches to tackle this surprising problem are suggested. This review describes the phenotypes of human diseases caused by mutations in the TRPV4 calcium channel and discusses the puzzle of how mutations in the same domain of the channel can lead to diverse diseases with different phenotypes.

Disease gene identification is a critical step towards uncovering the molecular mechanisms of diseases and systematically investigating complex disease phenotypes. Despite considerable efforts to develop powerful computing methods, candidate gene identification remains a severe challenge owing to the connectivity of an incomplete interactome network, which hampers the discovery of true novel candidate genes. We developed a network-based machine-learning framework to identify both functional modules and disease candidate genes. In this framework, we designed a semi-supervised non-negative matrix factorization model to obtain the functional modules related to the diseases and genes. Of note, we proposed a disease gene-prioritizing method called MapGene that integrates the correlations from both functional modules and network closeness. Our framework identified a set of functional modules with highly functional homogeneity and close gene interactions. Experiments on a large-scale benchmark dataset showed that MapGene performs significantly better than the state-of-the-art algorithms. Further analysis demonstrates MapGene can effectively relieve the impact of the incompleteness of interactome networks and obtain highly reliable rankings of candidate genes. In addition, disease cases on Parkinson’s disease and diabetes mellitus confirmed the generalization of MapGene for novel candidate gene identification. This work proposed, for the first time, an integrated computing framework to predict both functional modules and disease candidate genes. The methodology and results support that our framework has the potential to help discover underlying functional modules and reliable candidate genes in human disease.

Background Musculoskeletal health among the elderly emerges as a pivotal public health concern against the backdrop of a globally aging population. Creatine, popular within fitness circles, has emerged as a subject of scientific inquiry for its potential benefits on elderly’ musculoskeletal well-being. Despite extensive documentation of its effects on athletic performance, investigations into creatine’s long-term contributions to the musculoskeletal health of the elderly are comparatively limited. Utilizing publicly accessible genetic datasets, this study aimed to explore the potential causal link between creatine levels and a variety of musculoskeletal health markers in the elderly population, such as osteoarthritis (OA), rheumatoid arthritis (RA), osteoporosis (OP), bone-specific alkaline phosphatase, bone mineral density(BMD), serum calcium levels, and reduced hand grip strength in individuals aged 60 and above. Methods Our methodological approach was grounded in Mendelian Randomization (MR) analysis, with a primary emphasis on the Inverse Variance Weighted (IVW) technique, to explore potential causal links under specific assumptions and limitations inherent to MR analysis. Results A significant inverse relationship was observed between creatine levels and reduced hand grip strength in individuals aged 60 and above (OR = 0.64, b=-0.44, p  = 0.01), suggesting higher creatine levels might be beneficial in maintaining hand grip strength. Conversely, analysis of other bone health parameters, including bone-specific alkaline phosphatase, bone mineral density, serum calcium levels, and conditions such as osteoarthritis, rheumatoid arthritis, and osteoporosis, yielded p-values exceeding 0.05, denoting no statistically significant associations. Conclusions This study provides preliminary evidence indicating a link between creatine levels and decreased hand grip strength among the elderly, notably in individuals aged 60 and older. This finding is significant for understanding the potential impact of creatine supplementation on elderly Musculoskeletal health and underscores the need for evidence-based decision-making in nutritional supplementation. To robustly validate these observations, it is essential to conduct future randomized controlled trials with large sample sizes.

BackgroundPrevious reports of distal deletions in chromosome 10q in patients have described distinct facial features combined with other neurodevelopmental abnormalities, including intellectual disability. However, the association of interstitial deletions in chromosome 10q with global developmental delay, musculoskeletal abnormalities, and dysmorphic features has not been previously reported.MethodsGenetic testing using whole exome sequencing (WES) was performed on three patients with neurodevelopmental delay, musculoskeletal abnormalities and dysmorphic features. Sequencing reads were aligned to the human genome build GRCh37/UCSC hg19 and analysed for both sequence and copy number variants.ResultsWES identified similar interstitial deletions in the 10q21.1q21.3 locus in all three patients. The deleted region includes online Mendelian inheritance in man (OMIM)-annotated genes with clinical significance, such as ANK3 (*600465), JMJD1C (*604503), EGR2 (*129010), BICC1 (*614295), ZNF365 (*607818) and TFAM (*600438). Deletion of this region is considered pathogenic and is implicated in the aetiology of the clinical phenotypes observed in these patients.ConclusionsThis is the first report associating interstitial deletions in the 10q21.1q21.3 locus with neurodevelopmental delay, musculoskeletal abnormalities and dysmorphic features. Our findings highlight the clinical significance of this deleted region and suggest possible mechanisms underlying the observed pathological phenotypes.

In silico network-based methods have shown promising results in the field of drug development. Yet, most of networks used in the previous research have not included context information even though biological associations actually do appear in the specific contexts. Here, we reconstruct an anatomical context-specific network by assigning contexts to biological associations using protein expression data and scientific literature. Furthermore, we employ the context-specific network for the analysis of drug effects with a proximity measure between drug targets and diseases. Distinct from previous context-specific networks, intercellular associations and phenomic level entities such as biological processes are included in our network to represent the human body. It is observed that performances in inferring drug-disease associations are increased by adding context information and phenomic level entities. In particular, hypertension, a disease related to multiple organs and associated with several phenomic level entities, is analyzed in detail to investigate how our network facilitates the inference of drug-disease associations. Our results indicate that the inclusion of context information, intercellular associations, and phenomic level entities can contribute towards a better prediction of drug-disease associations and provide detailed insight into understanding of how drugs affect diseases in the human body.