Browsing by Author "Pais, Lynn (57209022500)"

Cytoplasmic and nuclear iron-sulfur (Fe-S) enzymes that are essential for genome maintenance and replication depend on the cytoplasmic Fe-S assembly (CIA) machinery for cluster acquisition. The core of the CIA machinery consists of a complex of CIAO1, MMS19 and FAM96B. The physiological consequences of loss of function in the components of the CIA pathway have thus far remained uncharacterized. Our study revealed that patients with biallelic loss of function in CIAO1 developed proximal and axial muscle weakness, fluctuating creatine kinase elevation, and respiratory insufficiency. In addition, they presented with CNS symptoms including learning difficulties and neurobehavioral comorbidities, along with iron deposition in deep brain nuclei, mild normocytic to macrocytic anemia, and gastrointestinal symptoms. Mutational analysis revealed reduced stability of the variants compared with WT CIAO1. Functional assays demonstrated failure of the variants identified in patients to recruit Fe-S recipient proteins, resulting in compromised activities of DNA helicases, polymerases, and repair enzymes that rely on the CIA complex to acquire their Fe-S cofactors. Lentivirus-mediated restoration of CIAO1 expression reversed all patient-derived cellular abnormalities. Our study identifies CIAO1 as a human disease gene and provides insights into the broader implications of the cytosolic Fe-S assembly pathway in human health and disease. Copyright: © 2024, Maio et al.

Purpose: We set out to develop a publicly available tool that could accurately diagnose spinal muscular atrophy (SMA) in exome, genome, or panel sequencing data sets aligned to a GRCh37, GRCh38, or T2T reference genome. Methods: The SMA Finder algorithm detects the most common genetic causes of SMA by evaluating reads that overlap the c.840 position of the SMN1 and SMN2 paralogs. It uses these reads to determine whether an individual most likely has 0 functional copies of SMN1. Results: We developed SMA Finder and evaluated it on 16,626 exomes and 3911 genomes from the Broad Institute Center for Mendelian Genomics, 1157 exomes and 8762 panel samples from Tartu University Hospital, and 198,868 exomes and 198,868 genomes from the UK Biobank. SMA Finder's false-positive rate was below 1 in 200,000 samples, its positive predictive value was greater than 96%, and its true-positive rate was 29 out of 29. Most of these SMA diagnoses had initially been clinically misdiagnosed as limb-girdle muscular dystrophy. Conclusion: Our extensive evaluation of SMA Finder on exome, genome, and panel sequencing samples found it to have nearly 100% accuracy and demonstrated its ability to reduce diagnostic delays, particularly in individuals with milder subtypes of SMA. Given this accuracy, the common misdiagnoses identified here, the widespread availability of clinical confirmatory testing for SMA, and the existence of treatment options, we propose that it is time to add SMN1 to the American College of Medical Genetics list of genes with reportable secondary findings after genome and exome sequencing. © 2024 American College of Medical Genetics and Genomics