X-linked Recessive Chondrodysplasia Punctata (CDPX1) is due to a defect in arylsulfatase E (ARSE), located on Xp22.3. Neither the substrate nor function of the encoded warfarin-sensitive arylsulfatase has been identified and molecular analysis remains the only confirmatory diagnostic test. Nevertheless, the majority of patients evaluated have not had identifiable mutations in ARSE, and thus far 23 patients have been reported. The major clinical features in these patients are also present in a group now recognized as phenocopies, due to vitamin K deficiency in early gestation or maternal autoimmune disease. We evaluated the ARSE gene in 11 patients who met clinical criteria for CDPX1. We amplified all exons and intronic flanking sequence from each patient, and investigated suspected deletions or rearrangements by southern analysis. We identified mutations in seven individuals. Of the remainder, three had maternal conditions that further expand the phenocopy group. Thus, this group might represent a proportion of the mutation-negative patients in previous studies. We extracted clinical information from all prior reports over the past decade and show that there are few distinguishing features on examination between these two groups of patients. This study supports heterogeneity for CDPX1-like phenotypes and sorting these out will help to define the biological pathway and genetic contributors.
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http://dx.doi.org/10.1002/ajmg.a.32159 | DOI Listing |
Am J Physiol Regul Integr Comp Physiol
January 2025
Department of Thoracic Surgery, Guangxi Academy of Medical Sciences and the People's Hospital of Guangxi Zhuang Autonomous Region.
We aimed to explore the role of Amino acid metabolism (AAM) and identify biomarkers for prognosis management and treatment of lung adenocarcinoma. Differentially expressed genes (DEGs) associated with AAM in lung adenocarcinoma were selected from public databases. Samples were clustered into varying subtypes using ConsensusClusterPlus based on gene levels.
View Article and Find Full Text PDFACS Chem Biol
January 2025
Institute of Molecular Medicine, Shanghai Key Laboratory for Nucleic Acid Chemistry and Nanomedicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China.
As an important receptor in a host's immune and metabolic systems, NOD1 is usually activated by Gram-negative bacteria having -diaminopimelic acid (-DAP) in their peptidoglycan (PGN). But some atypical Gram-positive bacteria also contain -DAP in their PGN, giving them the potential to activate NOD1. The prevalence of -DAP-type Gram-positive bacteria in the gut, however, remains largely unknown.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Life Science Institute, University of Michigan, Ann Arbor, MI, USA.
Cell lineage analysis is primarily undertaken to understand cell fate specification and diversification along a cell lineage tree. Built with dual repressible markers, twin-spot mosaic analysis with repressible cell markers (MARCM) labels the two daughter cells made by a common precursor in distinct colors. The power of twin-spot MARCM to systematically subdivide complex lineages is exemplified in studies of Drosophila neural stem-cell lineages.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Charité Universitätsmedizin Berlin, Berlin, Germany.
A key goal of biology is to understand the origin of the many cell types that can be observed during diverse processes such as development, regeneration, and disease. Single-cell RNA-sequencing (scRNA-seq) is commonly used to identify cell types in a tissue or organ. However, organizing the resulting taxonomy of cell types into lineage trees to understand the origins of cell states and relationships between cells remains challenging.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm.
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