The variability of SMCHD1 gene in FSHD patients: evidence of new mutations.

In this study, we investigated the sequence of (Structural Maintenance of Chromosomes flexible Hinge Domain containing 1) SMCHD1 gene in a cohort of clinically defined FSHD (facioscapulohumeral muscular dystrophy) patients in order to assess the distribution of SMCHD1 variants, considering the D4Z4 fragment size in terms of repeated units (RUs; short fragment: 1-7 RU, borderline: 8-10RU and normal fragment: >11RU). The analysis of SMCHD1 revealed the presence of 82 variants scattered throughout the introns, exons and 3'untranslated region (3'UTR) of the gene. Among them, 64 were classified as benign polymorphisms and 6 as VUS (variants of uncertain significance). Interestingly, seven pathogenic/likely pathogenic variants were identified in patients carrying a borderline or normal D4Z4 fragment size, namely c.182_183dupGT (p.Q62Vfs*48), c.2129dupC (p.A711Cfs*11), c.3469G>T (p.G1157*), c.5150_5151delAA (p.K1717Rfs*16) and c.1131+2_1131+5delTAAG, c.3010A>T (p.K1004*), c.853G>C (p.G285R). All of them were predicted to disrupt the structure and conformation of SMCHD1, resulting in the loss of GHKL-ATPase and SMC hinge essential domains. These results are consistent with the FSHD symptomatology and the Clinical Severity Score (CSS) of patients. In addition, five variants (c.*1376A>C, rs7238459; c.*1579G>A, rs559994; c.*1397A>G, rs150573037; c.*1631C>T, rs193227855; c.*1889G>C, rs149259359) were identified in the 3'UTR region of SMCHD1, suggesting a possible miRNA-dependent regulatory effect on FSHD-related pathways. The present study highlights the clinical utility of next-generation sequencing (NGS) platforms for the molecular diagnosis of FSHD and the importance of integrating molecular findings and clinical data in order to improve the accuracy of genotype-phenotype correlations.

D4Z4 Methylation Levels Combined with a Machine Learning Pipeline Highlight Single CpG Sites as Discriminating Biomarkers for FSHD Patients.

The study describes a protocol for methylation analysis integrated with Machine Learning (ML) algorithms developed to classify Facio-Scapulo-Humeral Dystrophy (FSHD) subjects. The DNA methylation levels of two D4Z4 regions (DR1 and DUX4-PAS) were assessed by an in-house protocol based on bisulfite sequencing and capillary electrophoresis, followed by statistical and ML analyses. The study involved two independent cohorts, namely a training group of 133 patients with clinical signs of FSHD and 150 healthy controls (CTRL) and a testing set of 27 FSHD patients and 25 CTRL. As expected, FSHD patients showed significantly reduced methylation levels compared to CTRL. We utilized single CpG sites to develop a ML pipeline able to discriminate FSHD subjects. The model identified four CpGs sites as the most relevant for the discrimination of FSHD subjects and showed high metrics values (accuracy: 0.94, sensitivity: 0.93, specificity: 0.96). Two additional models were developed to differentiate patients with lower D4Z4 size and patients who might carry pathogenic variants in FSHD genes, respectively. Overall, the present model enables an accurate classification of FSHD patients, providing additional evidence for DNA methylation as a powerful disease biomarker that could be employed for prioritizing subjects to be tested for FSHD.

Comparative analysis of antigen and molecular tests for the detection of Sars-CoV-2 and related variants: A study on 4266 samples.

OBJECTIVES: The present study compared the performance of the Lumipulse G Sars-CoV-2 Ag kit with the TaqPath COVID-19 RT-PCR CE IVD kit. METHODS: The study was conducted on 4266 naso-oropharyngeal swabs. Samples were subjected to antigen RT-PCR tests for the detection of Sars-CoV-2 and related variants. Statistical analyses were conducted in R software. RESULTS: We found 503 positives (including 138 H69-V70 deletion carriers) and 3763 negatives by RT-PCR, whereas 538 positives and 3728 negatives were obtained by antigen testing. We achieved empirical and binormal AU-ROCs of 0.920 and 0.990, accuracy of 0.960, sensitivity of 0.866, specificity of 0.973, positive and negative predictive values of 0.810 and 0.980. We obtained a positive correlation between viral loads and antigen levels (R2 = 0.81), finding a complete concordance for high viral loads (log10 copies/mL > 5.4). Antigen levels > 222 pg/mL were found to be reliable in assigning positive samples (p < 0.01). Concerning variant carriers, antigen test detected them with the same accuracy as other positive samples. CONCLUSIONS: Molecular and antigen tests should be evaluated regarding the prevalence of the area. In case of low prevalence, antigen testing can be employed as a first-line screening for the timely identification of affected individuals with high viral load, also if carriers of Sars-CoV-2 variants.