MATERIALS AND METHODS: The object of the study were samples of biological substrates (leukocyte mass, saliva, urine) taken from patients who underwent liver and kidney transplantation. Detection of CMV DNA was carried out by a real-time PCR using commercial diagnostic AmpliSense CMV-FL test systems (Central Research Institute for Epidemiology, Moscow, Russia). DNA extraction was performed using DNA-sorb AM and DNA-sorb V kits (Central Research Institute for Epidemiology) in accordance with manufacturer's manual. The quality of the prepared DNA library for sequencing was assessed by means of the QIAxcel Advanced System capillary gel electrophoresis system (QIAGEN, Germany). Alignment and assembly of nucleotide sequences were carried out using CLC Genomics Workbench 5.5 software (CLC bio, USA). The sequencing results were analyzed using BLAST of NCBI server.
RESULTS: CMV DNA samples were selected for genotyping. The two variable genes, UL55(gB) and UL73(gN), were used for CMV genotype determination, which was performed using NGS technology MiSeq sequencer (Illumina, USA). Based on the exploratory studies and analysis of literature sources, primers for genotyping on the UL55(gB) and UL73(gN) genes have been selected and the optimal conditions for the PCR reaction have been defined. The results of sequencing the UL55(gB) and UL73(gN) gene fragments of CMV clinical isolates from recipients of solid organs made it possible to determine the virus genotypes, among which gB2, gN4c, and gN4b were dominant. In some cases, association of two and three CMV genotypes has been revealed.
CONCLUSION: The application of the NGS technology for genotyping cytomegalovirus strains can become one of the main methods of CMV infection molecular epidemiology, as it allows for obtaining reliable results with a significant reduction in research time.
METHODS AND RESULTS: The amplification of genomic DNA with 32 ISSR markers detected an average of 97.64% polymorphism while 35.15% and 51.08% polymorphism per population and geographical zone, respectively. Analysis of molecular variance revealed significant variation within population 75% and between population 25% whereas within region 84% and between region 16%. The Bidillali exposed greater number of locally common band i.e., NLCB (≤ 25%) = 25 and NLCB (≤ 50%) = 115 were shown by Cancaraki while the lowest was recorded as NLCB (≤ 25%) = 6 and NLCB (≤ 50%) = 72 for Roko and Maibergo, accordingly. The highest PhiPT value was noted between Roko and Katawa (0.405*) whereas Nei's genetic distance was maximum between Roko and Karu (0.124). Based on Nei's genetic distance, a radial phylogenetic tree was constructed that assembled the entire accessions into 3 major clusters for further confirmation unrooted NJ vs NNet split tree analysis based on uncorrected P distance exposed the similar result. Principal coordinate analysis showed variation as PC1 (15.04%) > PC2 (5.81%).
CONCLUSIONS: The current study leads to prompting the genetic improvement and future breeding program by maximum utilization and better conservation of existing accessions. The accessions under Cancaraki and Jatau are population documented for future breeding program due to their higher genetic divergence and homozygosity.
METHOD: Targeted sequencing of fourteen genes panel was performed to identify the mutations in 29 OI patients with type I, III, IV and V disease. The mutations were determined using Ion Torrent Suite software version 5 and variant annotation was conducted using ANNOVAR. The identified mutations were confirmed using Sanger sequencing and in silico analysis was performed to evaluate the effects of the candidate mutations at protein level.
RESULTS: Majority of patients had mutations in collagen genes, 48% (n = 14) in COL1A1 and 14% (n = 4) in COL1A2. Type I OI was caused by quantitative mutations in COL1A1 whereas most of type III and IV were due to qualitative mutations in both of the collagen genes. Those with quantitative mutations had milder clinical severity compared to qualitative mutations in terms of dentinogenesis imperfecta (DI), bone deformity and the ability to walk with aid. Furthermore, a few patients (28%, n = 8) had mutations in IFITM5, BMP1, P3H1 and SERPINF1.
CONCLUSION: Majority of our OI patients have mutations in collagen genes, similar to other OI populations worldwide. Genotype-phenotype analysis revealed that qualitative mutations had more severe clinical characteristics compared to quantitative mutations. It is crucial to identify the causative mutations and the clinical severity of OI patients may be predicted based on the types of mutations.