Whole Mitochondrial Genome Analysis in Turkish Patients with Mitochondrial Diseases

Background: Mitochondrial diseases are a clinically heterogeneous group of rare hereditary disorders that are defined by a genetic defect predominantly affecting mitochondrial oxidative phosphorylation. Mitochondrial diseases are caused by mutations of genes encoded by either nuclear DNA or mitochondrial DNA. Hundreds of different mitochondrial DNA point mutations and large-scale mitochondrial DNA rearrangements have been shown to cause mitochondrial diseases including Kearns­Sayre syndrome, Leber's hereditary optic neuropathy, Leigh syndrome, myoclonic epilepsy with ragged-red fibers, mitochondrial encephalopathy lactic acidosis stroke. Aims: To investigate new variants that could be associated with mitochondrial diseases and to determine the effect of mitochondrial DNA mutations on the clinical spectrum. Study Design: Cross-sectional study. Methods: We screened whole mitochondrial DNA genome using next-generation sequencing in 16 patients who are considered to have mitochondrial disease. CentoGene and Mikrogen Genetic Diseases Diagnostic Center's database were used to investigate sequence variants. Detected variants were evaluated in bioinformatic databases to determine pathogenicity and were classified as class 1 (pathogenic), class 2 (likely pathogenic), and class 3 (variant of uncertain significance) according to CentoGene-ACMG database. Results: As a result of the study, 2 patients were diagnosed with Leigh syndrome as previously reported class 1 mutations in MT-ATP6 and MT-ND5 genes. Four variants were identified for the first time in literature and 2 variants, previously reported but with uncertain pathogenic effect, are thought to be associated with mitochondrial disease. Conclusion: Mitochondrial DNA screening should be among the primary clinical tests in patients with suspected mitochondrial disease to rule out DNA-associated mutations.

A model integrating Killer Immunoglobulin-like Receptor (KIR) haplotypes for risk prediction of COVID-19 clinical disease severity.

Associations between inherited Killer Immunoglobulin-like Receptor (KIR) genotypes and the severity of multiple RNA virus infections have been reported. This prospective study was initiated to investigate if such an association exists for COVID-19. In this cohort study performed at Ankara University, 132 COVID-19 patients (56 asymptomatic, 51 mild-intermediate, and 25 patients with severe disease) were genotyped for KIR and ligands. Ankara University Donor Registry (n:449) KIR data was used for comparison. Clinical parameters (age, gender, comorbidities, blood group antigens, inflammation biomarkers) and KIR genotypes across cohorts of asymptomatic, mild-intermediate, or severe disease were compared to construct a risk prediction model based on multivariate binary logistic regression analysis with backward elimination method. Age, blood group, number of comorbidities, CRP, D-dimer, and telomeric and centromeric KIR genotypes (tAA, tAB1, and cAB1) along with their cognate ligands were found to differ between cohorts. Two prediction models were constructed; both included age, number of comorbidities, and blood group. Inclusion of the KIR genotypes in the second prediction model exp (-3.52 + 1.56 age group - 2.74 blood group (type A vs others) + 1.26 number of comorbidities - 2.46 tAB1 with ligand + 3.17 tAA with ligand) increased the predictive performance with a 92.9% correct classification for asymptomatic and 76% for severe cases (AUC: 0.93; P < 0.0001, 95% CI 0.88, 0.99). This novel risk model, consisting of KIR genotypes with their cognate ligands, and clinical parameters but excluding earlier published inflammation-related biomarkers allow for the prediction of the severity of COVID-19 infection prior to the onset of infection. This study is listed in the National COVID-19 clinical research studies database.