BackgroundMultidrug/extensively drug-resistant tuberculosis (M/XDR-TB) is a major public health problem, and early detection is important for preventing its spread. This study aimed to demonstrate the distribution of genetic site mutation associated with drug resistance in M/XDR-TB in the northern Thai population.MethodsThirty-four clinical MTB isolates from M/XDR-TB patients in the upper northern region of Thailand, who had been identified for drug susceptibility using the indirect agar proportion method from 2005 to 2012, were examined for genetic site mutations of katG, inhA, and ahpC for isoniazid (INH) drug resistance and rpoB for rifampicin (RIF) drug resistance. The variables included the baseline characteristics of the resistant gene, genetic site mutations, and drug susceptibility test results.ResultsAll 34 isolates resisted both INH and RIF. Thirty-two isolates (94.1%) showed a mutation of at least 1 codon for katG, inhA, and ahpC genes. Twenty-eight isolates (82.4%) had a mutation of at least 1 codon of rpoB gene. The katG, inhA, ahpC, and rpoB mutations were detected in 20 (58.7%), 27 (79.4%), 13 (38.2%), and 28 (82.3%) of 34 isolates. The 3 most common mutation codons were katG 315 (11/34, 35.3%), inhA 14 (11/34, 32.4%), and inhA 114 (11/34, 32.4%). For this population, the best genetic mutation test panels for INH resistance included 8 codons (katG 310, katG 340, katG 343, inhA 14, inhA 84, inhA 86, inhA 114, and ahpC 75), and for RIF resistance included 6 codons (rpoB 445, rpoB 450, rpoB 464, rpoB 490, rpoB 507, and rpoB 508) with a sensitivity of 94.1% and 82.4%, respectively.ConclusionThe genetic mutation sites for drug resistance in M/XDR-TB are quite variable. The distribution of these mutations in a certain population must be studied before developing the specific mutation test panels for each area. The results of this study can be applied for further molecular M/XDR-TB diagnosis in the upper northern region of Thailand.
Antimicrobial resistance (AMR) is a major threat to global health. Improving laboratory capacity for AMR detection is critically important for patient health outcomes and population level surveillance. We aimed to estimate the financial cost of setting up and running a microbiology laboratory for organism identification and antimicrobial susceptibility testing as part of an AMR surveillance programme. Financial costs for setting up and running a microbiology laboratory were estimated using a top-down approach based on resource and cost data obtained from three clinical laboratories in the Mahidol Oxford Tropical Medicine Research Unit network. Costs were calculated for twelve scenarios, considering three levels of automation, with equipment sourced from either of the two leading manufacturers, and at low and high specimen throughput. To inform the costs of detection of AMR in existing labs, the unit cost per specimen and per isolate were also calculated using a micro-costing approach. Establishing a laboratory with the capacity to process 10,000 specimens per year ranged from $254,000 to $660,000 while the cost for a laboratory processing 100,000 specimens ranged from $394,000 to $887,000. Excluding capital costs to set up the laboratory, the cost per specimen ranged from $22–31 (10,000 specimens) and $11–12 (100,000 specimens). The cost per isolate ranged from $215–304 (10,000 specimens) and $105–122 (100,000 specimens). This study provides a conservative estimate of the costs for setting up and running a microbiology laboratory for AMR surveillance from a healthcare provider perspective. In the absence of donor support, these costs may be prohibitive in many low- and middle- income country (LMIC) settings. With the increased focus on AMR detection and surveillance, the high laboratory costs highlight the need for more focus on developing cheaper and cost-effective equipment and reagents so that laboratories in LMICs have the potential to improve laboratory capacity and participate in AMR surveillance.
Background: Extensively drug resistant tuberculosis (XDR-TB) is a serious problem in public health and XDR-TB patients usually develop from multi-drug resistance tuberculosis (MDR-TB) and pre-XDR-TB. The rapid molecular test for drug susceptibility testing (DST) can be used for early detection to prevent XDR-TB. Methods: We examined 34 clinical Mycobacterium tuberculosis (M. tuberculosis) isolates from MDR/XDR-TB patients in the upper north of Thailand that were identified with drug susceptibility profiles by indirect agar proportion method from 2005-2012. Our study investigated the genetic mutations in gyrA for ofloxacin resistance and rrs for kanamycin resistance. The genetic mutations and drug susceptibility test results were analyzed using the exact test. Results: The majority of the ofloxacin resistance was detected in gyrA 21, gyrA 70, gyrA 87, gyrA 102, gyrA 162, and gyrA 187 were at 0%, 12.5%, 37.5%, 0%, 50.0% and 25.0% sensitivity, respectively, and at 96.2, 96.2%, 20.1%, 96.2%, 57.7% and 61.5% specificity, respectively. Kanamycin resistance was found in rrs 512, rrs 241, rrs 223, rrs 414 and rrs 408 at 16.7%, 0%, 0%, 16.7% and 16.7% sensitivity, respectively, and at 96.4%, 92.9%, 82.1%, 82.1% and 71.4% specificity, respectively. This study found no significant correlation between gyrA mutations and ofloxacin resistance and also no correlation between the rrs gene and kanamycin resistance. Conclusion: These primer sequences and PCR products in our study such as gyrA and rrs might be unsuitable to detect ofloxacin and kanamycin resistance in the upper How to cite this paper:
Background: Molecular diagnosis based on the detection of mutations conferring genetic drug resistance is useful for early diagnosis and treatment of Pre-XDR and XDR-TB patients. However, the study of mutation as a marker to predict Pre-XDR and XDR-TB is rare. Methods: Thirty-four Mycobacterium tuberculosis (MTB) isolates from MDR, Pre-XDR and XDR-TB patients in the upper north of Thailand, who had been identified for drug susceptibility using the indirect agar proportion method from 2005-2012, were examined for genetic site mutations of katG, inhA, and ahpC for isoniazid (INH) drug resistance, rpoB for rifampicin (RIF) drug resistance, gyrA for ofloxacin (OFX), and rrs for kanamycin (KAN). Associations between resistant genes and Pre-XDR and XDR-TB in the MDR patients were performed using exact probability tests. Univariable logistic regression was used to quantify the strength of association between the gene mutation with Mycobacterium tuberculosis and the prevalence of Pre-XDR and XDR-TB in the MDR patients. Results: The mutations in the region of the rpoB gene at codon 445 (C445T) in the Pre-XDR or XDR-TB patients were significantly 20.6 times more prevalent among the MDR-TB patients. The inhA gene mutation at codon 114 (T114G) was also significantly 8.1 times more prevalent. Conclusion: The findings can be used to predict the odds of Pre-XDR and XDR-TB in MDR-TB patients, as a guide for prevention and treatments.
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