Congenital muscular dystrophies (CMDs) are early onset disorders of muscle with histological features suggesting a dystrophic process. The congenital muscular dystrophies as a group encompass great clinical and genetic heterogeneity so that achieving an accurate genetic diagnosis has become increasingly challenging, even in the age of next generation sequencing. In this document we review the diagnostic features, differential diagnostic considerations and available diagnostic tools for the various CMD subtypes and provide a systematic guide to the use of these resources for achieving an accurate molecular diagnosis. An International Committee on the Standard of Care for Congenital Muscular Dystrophies composed of experts on various aspects relevant to the CMDs performed a review of the available literature as well as of the unpublished expertise represented by the members of the committee and their contacts. This process was refined by two rounds of online surveys and followed by a three-day meeting at which the conclusions were presented and further refined. The combined consensus summarized in this document allows the physician to recognize the presence of a CMD in a child with weakness based on history, clinical examination, muscle biopsy results, and imaging. It will be helpful in suspecting a specific CMD subtype in order to prioritize testing to arrive at a final genetic diagnosis.

The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4, and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine sulfoxide reductase B1), and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15-kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV), and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing, and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates.

Background: An accurate estimation of the risk of life-threatening (LT) ventricular tachyarrhythmia (VTA) in patients with LMNA mutations is crucial to select candidates for implantable cardioverter-defibrillator implantation. Methods: We included 839 adult patients with LMNA mutations, including 660 from a French nationwide registry in the development sample, and 179 from other countries, referred to 5 tertiary centers for cardiomyopathies, in the validation sample. LTVTA was defined as (1) sudden cardiac death or (2) implantable cardioverter defibrillator–treated or hemodynamically unstable VTA. The prognostic model was derived using the Fine-Gray regression model. The net reclassification was compared with current clinical practice guidelines. The results are presented as means (SD) or medians [interquartile range]. Results: We included 444 patients, 40.6 (14.1) years of age, in the derivation sample and 145 patients, 38.2 (15.0) years, in the validation sample, of whom 86 (19.3%) and 34 (23.4%) experienced LTVTA over 3.6 [1.0–7.2] and 5.1 [2.0–9.3] years of follow-up, respectively. Predictors of LTVTA in the derivation sample were: male sex, nonmissense LMNA mutation, first degree and higher atrioventricular block, nonsustained ventricular tachycardia, and left ventricular ejection fraction (https://lmna-risk-vta.fr). In the derivation sample, C-index (95% CI) of the model was 0.776 (0.711–0.842), and the calibration slope 0.827. In the external validation sample, the C-index was 0.800 (0.642–0.959), and the calibration slope was 1.082 (95% CI, 0.643–1.522). A 5-year estimated risk threshold ≥7% predicted 96.2% of LTVTA and net reclassified 28.8% of patients with LTVTA in comparison with the guidelines-based approach. Conclusions: In comparison with the current standard of care, this risk prediction model for LTVTA in laminopathies significantly facilitated the choice of candidates for implantable cardioverter defibrillators. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT03058185.

The 364 exon TTN gene encodes titin (TTN), the largest known protein, which plays key structural, developmental, mechanical, and regulatory roles in cardiac and skeletal muscles. Prior to next‐generation sequencing (NGS), routine analysis of the whole TTN gene was impossible due to its giant size and complexity. Thus, only a few TTN mutations had been reported and the general incidence and spectrum of titinopathies was significantly underestimated. In the last months, due to the widespread use of NGS, TTN is emerging as a major gene in human‐inherited disease. So far, 127 TTN disease‐causing mutations have been reported in patients with at least 10 different conditions, including isolated cardiomyopathies, purely skeletal muscle phenotypes, or infantile diseases affecting both types of striated muscles. However, the identification of TTN variants in virtually every individual from control populations, as well as the multiplicity of TTN isoforms and reference sequences used, stress the difficulties in assessing the relevance, inheritance, and correlation with the phenotype of TTN sequence changes. In this review, we provide the first comprehensive update of the TTN mutations reported and discuss their distribution, molecular mechanisms, associated phenotypes, transmission pattern, and phenotype–genotype correlations, alongside with their implications for basic research and for human health.