ABSTRACT
Novel mutations have been emerging in the genome of severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2); consequently, the evolving of more virulent and treatment resistance strains have the potential to increase transmissibility and mortality rates. The characterization of full-length SARS-CoV-2 genomes is critical for understanding the origin and transmission pathways of the virus, as well as identifying mutations that affect the transmissibility and pathogenicity of the virus. We present an analysis of the mutation pattern and clade distribution of full-length SARS-CoV-2 genome sequences obtained from specimens tested at Gazi University Medical Virology Laboratory. Viral RNA was extracted from nasopharyngeal specimens. Next-generation sequencing libraries were prepared and sequenced on Illumina iSeq 100 platform. Raw sequencing data were processed to obtain full-length genome sequences and variant calling was performed to analyze amino acid changes. Clade distribution was determined to understand the phylogenetic background in relation to global data. A total of 293 distinct mutations were identified, of which 152 missense, 124 synonymous, 12 noncoding, and 5 deletions. The most frequent mutations were P323L (nsp12), D614G (ORF2/S), and 2421C>T (5'-untranslated region) found simultaneously in all sequences. Novel mutations were found in nsp12 (V111A, H133R, Y453C, M626K) and ORF2/S (R995G, V1068L). Nine different Pangolin lineages were detected. The most frequently assigned lineage was B.1.1 (17 sequences), followed by B.1 (7 sequences) and B.1.1.36 (3 sequences). Sequence information is essential for revealing genomic diversity. Mutations might have significant functional implications and analysis of these mutations provides valuable information for therapeutic and vaccine development studies. Our findings point to the introduction of the virus into Turkey through various sources and the subsequent spread of several key variants.
Subject(s)
COVID-19/virology , Coronavirus RNA-Dependent RNA Polymerase/genetics , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/genetics , Adult , COVID-19/epidemiology , COVID-19/transmission , Female , Genome, Viral/genetics , Humans , Male , Mutation , Mutation Rate , Phylogeny , RNA, Viral/genetics , SARS-CoV-2/classification , SARS-CoV-2/isolation & purification , Turkey/epidemiologyABSTRACT
In Coronavirus disease-2019 (COVID-19) cases, hyper inflammation is associated with the severity of the disease. High levels of circulating cytokines were reported in severe COVID-19 patients. Neopterin produced by macrophages on stimulation with interferon-gamma, which is an important cytokine in the antiviral immune response, hence it can be used to predict the severity of disease in COVID-19 cases. In this study, it was aimed to determine the prognostic value of the neopterin for the prediction of severe disease in patients with COVID-19. This single-center, prospective study was conducted in hospitalized COVID-19 patients and healthy volunteers. Severe and mild COVID-19 cases were compared in terms of clinical and laboratory findings as well as serum neopterin levels on hospital admission. To assess the prognostic utility of neopterin between the severe and mild COVID-19 groups, a receiver-operating characteristic (ROC) curve was generated, and the area under the curve (AUC) was calculated. The median serum neopterin level was four times higher in COVID-19 patients than the healthy controls (46 vs. 12 nmol/L; p < .001). The AUC value of serum neopterin was 0.914 (95% confidence interval, 0.85-0.97). The sensitivity and specificity of serum neopterin for the cut-off value of 90 nmol/L to identify severe COVID-19 cases were 100% and 76%, respectively. Serum neopterin levels on hospitalization were significantly higher in severe COVID-19 disease than mild COVID-19 patients. Neopterin levels can be used as an early prognostic biomarker for COVID-19 on admission.
Subject(s)
COVID-19/diagnosis , Interferon-gamma/immunology , Macrophages/immunology , Neopterin/blood , Adult , Biomarkers/blood , Bronchoalveolar Lavage Fluid/cytology , COVID-19/mortality , COVID-19/pathology , Cytokines/blood , Female , Humans , Male , Middle Aged , Prognosis , SARS-CoV-2/immunology , Severity of Illness Index , Young AdultABSTRACT
Background/aim: Pneumonia is the most serious clinical presentation of COVID-19. This study aimed to determine the demographic, clinical, and laboratory findings that can properly predict COVID-19 pneumonia. Materials and methods: This study was conducted in the Gazi University hospital. All hospitalized patients with confirmed and suspected SARS-CoV-2 infection between 16 March 2020 and 30 April 2020 were analyzed retrospectively. COVID-19 patients were separated into two groups, pneumonia and nonpneumonia, and then compared to determine predicting factors for COVID-19 pneumonia. Variables that had a P-value of less than 0.20 and were not correlated with each other were included in the logistic regression model. Results: Of the 247 patients included in the study 58% were female, and the median age was 40. COVID-19 was confirmed in 70.9% of these patients. Among the confirmed COVID-19 cases, 21.4% had pneumonia. In the multivariate analysis male sex (P = 0.028), hypertension (P = 0.022), and shortness of breath on hospital admission (P = 0.025) were significant factors predicting COVID-19 pneumonia. Conclusion: Shortness of breath, male sex, and hypertension were significant for predicting COVID-19 pneumonia on admission. Patients with these factors should be evaluated more carefully for diagnostic procedures, such as thorax CT.