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Myotonic dystrophy type 2 (DM2) is a multisystemic disorder caused by a (CCTG) in intron 1 of the gene. The CCTG repeat tract is part of a complex (TG) (TCTG) (CCTG) (NCTG) (CCTG) motif generally interrupted in healthy range alleles. Here we report our 14-year experience of DM2 postnatal genetic testing in a total of 570 individuals. The DM2 locus has been analyzed by a combination of SR-PCR, TP-PCR, LR-PCR, and Sanger sequencing of alleles. DM2 molecular diagnosis has been confirmed in 187/570 samples analyzed (32.8%) and is mainly associated with the presence of myotonia in patients. This set of alleles showed unimodal distribution with 25 different alleles ranging from 108 to 168 bp, in accordance with previous studies on European populations. The most frequent alleles consisted of 138, 134, 140, and 136 bps with an overall locus heterozygosity of 90%. Sequencing of 103 unexpanded alleles in DM2-positive patients revealed that (CCTG)(NCTG)(CCTG) and (CCTG)(NCTG)(CCTG) are the most common interruption motifs. We also characterized five premutated alleles with (CCTG) repetitions from = 36 to = 53. However, the molecular and clinical consequences in our cohort of samples are not unequivocal. Data that emerged from this study are representative of the Italian population and are useful tools for National and European centers offering DM2 genetic testing and counseling.
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http://dx.doi.org/10.3389/fgene.2021.668094 | DOI Listing |
Mol Plant
September 2025
National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China. Electronic address:
This study introduces Multi-Dimensional Environment (MDE) zoning to enhance maize resilience and improve stagnant yields in China amidst climate change. Utilizing comprehensive environmental and yield data, MDE zoning accurately identifies areas for targeted, climate-adaptive breeding. The tool provides a flexible framework for updates using annual variety testing and daily environmental data, optimizing production and resource allocation.
View Article and Find Full Text PDFKidney Blood Press Res
August 2025
Primary hyperoxaluria type 1 (PH1) is a rare autosomal recessive disorder caused by a deficiency of the hepatic peroxisomal enzyme alanine-glyoxylate aminotransferase (AGT), which catalyses the conversion of glyoxylate to glycine, resulting in increased oxalate production. The clinical consequences of the progressive build up of oxalates include nephrocalcinosis, nephrolithiasis, chronic kidney disease and ultimately renal failure with extra-renal involvement. The diagnosis of PH1 is challenging due to the non-specific nature of its symptoms and the need for costly genetic testing.
View Article and Find Full Text PDFPublic Health Genomics
August 2025
Introduction: Genetic testing for health-related purposes is now offered in some workplace wellness programs, with notable ethical, legal, and social implications. However, little is known about employee perspectives on workplace genetic testing (wGT).
Methods: We surveyed a large, diverse national sample of 2,000 employed adults (mean age=43 years; 51% female).
J Neural Transm (Vienna)
September 2025
Department of Biomedical and Neuromotor Sciences, University of Bologna, Bologna, 40139, Italy.
Multisystem proteinopathy 1 (MSP1) is a rare autosomal dominant disorder caused by mutations in the valosin-containing protein (VCP) gene typically presenting with inclusion body myopathy (IBM), Paget's disease of bone (PDB), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS). Parkinsonism is a rare feature of MSP1, occurring in 3-4% of cases, with limited post-mortem evidence suggesting neuronal synucleinopathy. We report a case of VCP-related parkinsonism providing the first in vivo demonstration of phosphorylated alpha-synuclein deposition in skin biopsy, a highly sensitive and specific in vivo biomarker of synucleinopathy.
View Article and Find Full Text PDFImmunol Res
September 2025
Department of Immunology and Allergy, Faculty of Medicine, Necmettin Erbakan University, Konya, Türkiye.
Background: Variants of uncertain significance (VUS) represent a major diagnostic challenge in the interpretation of genetic testing results, particularly in the context of inborn errors of immunity such as severe combined immunodeficiency (SCID). The inconsistency among computational prediction tools often necessitates expensive and time-consuming wet-lab analyses.
Objective: This study aimed to develop disease-specific, multi-class machine learning models using in silico scores to classify SCID-associated genetic variants and improve the interpretation of VUS.