Single Strain High-Depth NGS Reveals High rDNA (ITS-LSU) Variability in the Four Prevalent Pathogenic Species of the Genus Candida.
Claudia ColabellaDebora Casagrande PierantoniLaura CorteLuca RosciniAngela ContiMatteo BassettiCarlo TasciniVincent RobertGianluigi CardinaliPublished in: Microorganisms (2021)
Ribosomal RNA in fungi is encoded by a series of genes and spacers included in a large operon present in 100 tandem repeats, normally in a single locus. The multigene nature of this locus was somehow masked by Sanger sequencing, which produces a single sequence reporting the prevalent nucleotide of each site. The introduction of next generation sequencing led to deeper knowledge of the individual sequences (reads) and therefore of the variants between the same DNA sequences located in different tandem repeats. In this framework, NGS sequencing of the rDNA region was used to elucidate the extent of intra- and inter-genomic variation at both the strain and species level. Specifically, the use of an innovative NGS technique allowed the high-throughput high-depth sequencing of the ITS1-LSU D1/D2 amplicons of 252 strains belonging to four opportunistic yeast species of the genus Candida. Results showed the presence of a large extent of variability among strains and species. These variants were differently distributed throughout the analyzed regions with a higher concentration within the Internally Transcribed Spacer (ITS) region, suggesting that concerted evolution was not able to totally homogenize these sequences. Both the internal variability and the SNPs between strain can be used for a deep typing of the strains and to study their ecology.
Keyphrases
- copy number
- single cell
- high throughput
- escherichia coli
- genetic diversity
- genome wide
- candida albicans
- circulating tumor
- healthcare
- optical coherence tomography
- biofilm formation
- single molecule
- emergency department
- dna methylation
- pseudomonas aeruginosa
- staphylococcus aureus
- cell free
- cystic fibrosis
- transcription factor
- electronic health record
- bioinformatics analysis
- plant growth