{'fraction_non_alphanumeric': 0.020251778872468526, 'fraction_numerical': 0.005473453749315818, 'mean_word_length': 5.770370370370371, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Identification of Mycobacterium Species by Multiple-Fluorescence PCR–Single-Strand Conformation Polymorphism Analysis of the 16S rRNA Gene

ABSTRACT Identification of mycobacteria to the species level by growth-based methodologies is a process that has been fraught with difficulties due to the long generation times of mycobacteria. There is an increasing incidence of unusual nontuberculous mycobacterial infections, especially in patients with concomitant immunocompromised states, which has led to the discovery of new mycobacterial species and the recognition of the pathogenicity of organisms that were once considered nonpathogens. Therefore, there is a need for rapid and sensitive techniques that can accurately identify all mycobacterial species. Multiple-fluorescence-based PCR and subsequent single-strand conformation polymorphism (SSCP) analysis (MF-PCR-SSCP) of four variable regions of the 16S rRNA gene were used to identify species-specific patterns for 30 of the most common mycobacterial human pathogens and environmental isolates. The species-specific SSCP patterns generated were then entered into a database by using BioNumerics, version 1.5, software with a pattern-recognition capability, among its multiple uses. Patient specimens previously identified by 16S rRNA gene sequencing were subsequently tested by this method and were identified by comparing their patterns with those in the reference database. Fourteen species whose SSCP patterns were included in the database were correctly identified. Five other test organisms were correctly identified as unique species or were identified by their closest relative, as they were not in the database. We propose that MF-PCR-SSCP offers a rapid, specific, and relatively inexpensive identification tool for the differentiation of mycobacterial species.