Ines Joao1, Paula Cristovao2, Liliana Antunes3, Baltazar Nunes4, Luisa Jordao5. 1. National Institute of Health, Dr. Ricardo Jorge, Department of Infectious Diseases, Avenida Padre Cruz, 1649-016 Lisboa, Portugal. Electronic address: ines.joao@insa.min-saude.pt. 2. National Institute of Health, Dr. Ricardo Jorge, Department of Infectious Diseases, Avenida Padre Cruz, 1649-016 Lisboa, Portugal. Electronic address: paula.cristovao@insa.min-saude.pt. 3. National Institute of Health, Dr. Ricardo Jorge, Department of Epidemiology, Avenida Padre Cruz, 1649-016 Lisboa, Portugal. Electronic address: liliana.antunes@insa.min-saude.pt. 4. National Institute of Health, Dr. Ricardo Jorge, Department of Epidemiology, Avenida Padre Cruz, 1649-016 Lisboa, Portugal. Electronic address: Baltazar.Nunes@insa.min-saude.pt. 5. National Institute of Health, Dr. Ricardo Jorge, Department of Infectious Diseases, Avenida Padre Cruz, 1649-016 Lisboa, Portugal. Electronic address: maria.jordao@insa.min-saude.pt.
Abstract
BACKGROUND: Nontuberculous mycobacteria (NTM) are a heterogeneous group of microorganisms with distinct clinical relevance. The treatment of NTM infections depends significantly upon the crucial identification of species at this level. The steady increase of mycobacteria species, the use of time-consuming techniques and the lack of standardized identification methods makes the achievement of this goal a demanding challenge. Additionally, inaccurate diagnosis can lead to therapeutic approaches consistent with Mycobacterium tuberculosis infection that are useless. In the present study, the performance of public databases in the accurate identification of NTM by sequence analysis of 16S rRNA and hsp65 genes were evaluated and compared. An algorithm is proposed to achieve an accurate classification of NTM in the geographic region of Portugal (Western Europe). METHODS: Partial sequencing of 16S rRNA and hsp65 genes of 22 reference strains and 54 clinical isolates was performed. The resulting sequences were analysed by public web databases since their performance is evaluated statistically. The phenotypic characteristics of the isolates were also evaluated. RESULTS: The use of commercial kits allowed the accurate identification of 57.4% of the clinical isolates. This result was improved either by the use of 16S rRNA (75.9%) and hsp65 (88.9%) genes analysis alone or combined (96.3%). CONCLUSIONS: Analysis of 16S rRNA gene alone is insufficient for the accurate identification of NTM. A stepwise algorithm combining 16S rRNA and hsp65 gene analysis by multiple public databases is proposed to identify NTM at the species' level.
BACKGROUND:Nontuberculous mycobacteria (NTM) are a heterogeneous group of microorganisms with distinct clinical relevance. The treatment of NTM infections depends significantly upon the crucial identification of species at this level. The steady increase of mycobacteria species, the use of time-consuming techniques and the lack of standardized identification methods makes the achievement of this goal a demanding challenge. Additionally, inaccurate diagnosis can lead to therapeutic approaches consistent with Mycobacterium tuberculosis infection that are useless. In the present study, the performance of public databases in the accurate identification of NTM by sequence analysis of 16S rRNA and hsp65 genes were evaluated and compared. An algorithm is proposed to achieve an accurate classification of NTM in the geographic region of Portugal (Western Europe). METHODS: Partial sequencing of 16S rRNA and hsp65 genes of 22 reference strains and 54 clinical isolates was performed. The resulting sequences were analysed by public web databases since their performance is evaluated statistically. The phenotypic characteristics of the isolates were also evaluated. RESULTS: The use of commercial kits allowed the accurate identification of 57.4% of the clinical isolates. This result was improved either by the use of 16S rRNA (75.9%) and hsp65 (88.9%) genes analysis alone or combined (96.3%). CONCLUSIONS: Analysis of 16S rRNA gene alone is insufficient for the accurate identification of NTM. A stepwise algorithm combining 16S rRNA and hsp65 gene analysis by multiple public databases is proposed to identify NTM at the species' level.
Authors: Ann Susan Sam; Marilyn Mary Ninan; R Ranjani; Naveen Kumar Devanga Raghupathi; Veeraraghavan Balaji; Joy Sarojini Michael Journal: Future Sci OA Date: 2020-07-30
Authors: Ali Akbar Velayati; Parissa Farnia; Mohadese Mozafari; Donya Malekshahian; Shima Seif; Snaz Rahideh; Mehdi Mirsaeidi Journal: PLoS One Date: 2014-12-08 Impact factor: 3.240