Speeding up the diagnosis of multidrug-resistant tuberculosis in a high-burden region with the use of a commercial line probe assay

ABSTRACT Objective: To evaluate the rapid diagnosis of multidrug-resistant tuberculosis, by using a commercial line probe assay for rifampicin and isoniazid detection (LPA-plus), in the routine workflow of a tuberculosis reference laboratory. Methods: The LPA-plus was prospectively evaluated on 341 isolates concurrently submitted to the automated liquid drug susceptibility testing system. Results: Among 303 phenotypically valid results, none was genotypically rifampicin false-susceptible (13/13; 100% sensitivity). Two rifampicin-susceptible isolates harboured rpoB mutations (288/290; 99.3% specificity) which, however, were non-resistance-conferring mutations. LPA-plus missed three isoniazid-resistant isolates (23/26; 88.5% sensitivity) and detected all isoniazid-susceptible isolates (277/277; 100% specificity). Among the 38 (11%) invalid phenotypic results, LPA-plus identified 31 rifampicin- and isoniazid-susceptible isolates, one isoniazid-resistant and six as non-Mycobacterium tuberculosis complex. Conclusions: LPA-plus showed excellent agreement (≥91%) and accuracy (≥99%). Implementing LPA-plus in our setting can speed up the diagnosis of multidrug-resistant tuberculosis, yield a significantly higher number of valid results than phenotypic drug susceptibility testing and provide further information on the drug-resistance level

A crowd of BashTheBug volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally, this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is still an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing

Quantitative measurement of antibiotic resistance in Mycobacterium tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach

Abstract: The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis. However, molecular diagnostics to date have focused largely on first-line drugs and predicting susceptibilities in a binary manner (classifying strains as either susceptible or resistant). Here, we used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration (MIC) in 15,211 Mycobacterium tuberculosis clinical isolates from 23 countries across five continents. We identified 492 unique MIC-elevating variants across 13 drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms