Genetic Mutations Associated with Isoniazid Resistance in <i>Mycobacterium tuberculosis</i>: A Systematic Review

<div><p>Background</p><p>Tuberculosis (TB) incidence and mortality are declining worldwide; however, poor detection of drug-resistant disease threatens to reverse current progress toward global TB control. Multiple, rapid molecular diagnostic tests have recently been developed to detect genetic mutations in <i>Mycobacterium tuberculosis (Mtb)</i> genes known to confer first-line drug resistance. Their utility, though, depends on the frequency and distribution of the resistance associated mutations in the pathogen population. Mutations associated with rifampicin resistance, one of the two first-line drugs, are well understood and appear to occur in a single gene region in >95% of phenotypically resistant isolates. Mutations associated with isoniazid, the other first-line drug, are more complex and occur in multiple <i>Mtb</i> genes.</p><p>Objectives/Methodology</p><p>A systematic review of all published studies from January 2000 through August 2013 was conducted to quantify the frequency of the most common mutations associated with isoniazid resistance, to describe the frequency at which these mutations co-occur, and to identify the regional differences in the distribution of these mutations. Mutation data from 118 publications were extracted and analyzed for 11,411 <i>Mtb</i> isolates from 49 countries.</p><p>Principal Findings/Conclusions</p><p>Globally, 64% of all observed phenotypic isoniazid resistance was associated with the <i>kat</i>G315 mutation. The second most frequently observed mutation, <i>inhA</i>-15, was reported among 19% of phenotypically resistant isolates. These two mutations, <i>katG</i>315 and <i>inhA</i>-15, combined with ten of the most commonly occurring mutations in the <i>inhA</i> promoter and the <i>ahpC-oxyR</i> intergenic region explain 84% of global phenotypic isoniazid resistance. Regional variation in the frequency of individual mutations may limit the sensitivity of molecular diagnostic tests. Well-designed systematic surveys and whole genome sequencing are needed to identify mutation frequencies in geographic regions where rapid molecular tests are currently being deployed, providing a context for interpretation of test results and the opportunity for improving the next generation of diagnostics.</p></div

Number of publications that reported on specific biomarkers (2010–2105).

<p>The counts in this figure represent the number of publications evaluating a specific host biomarker, regardless of specimen. Biomarker combinations are not represented in this graph. The multi-gene classifier studies screened >1000 host transcripts each, with a final data set of ranging from 10–52 host gene transcripts; however, for the purposes of this graph, a single count was entered for each multi-gene classifier study, regardless of the number of transcripts profiled.</p

Cell surface markers evaluated as predictors of bacterial infection ranked by diagnostic parameters: comprehensive review 2010–2015.

<p>Cell surface markers evaluated as predictors of bacterial infection ranked by diagnostic parameters: comprehensive review 2010–2015.</p

Drug resistant specimens by phenotypic DST (n = 16).

<p>Drug resistant specimens by phenotypic DST (n = 16).</p

Performance of Xpert MTB/RIF for the detection of RIF resistance, relative to phenotypic DST.

<p>Performance of Xpert MTB/RIF for the detection of RIF resistance, relative to phenotypic DST.</p

Risk of Bias for 26 Quality Measures: Systematic Review (2010-April 2015).

<p>* Criteria that are specified by both QUADAS tool and Lijmer et al. (1999).</p

Summary of multi-gene classifiers: comprehensive review 2010–2015.

<p>Summary of multi-gene classifiers: comprehensive review 2010–2015.</p

Blood cells and hematologic markers as clinical predictors of bacterial infections ranked by diagnostic performance: comprehensive review 2010–2015.

<p>Blood cells and hematologic markers as clinical predictors of bacterial infections ranked by diagnostic performance: comprehensive review 2010–2015.</p

Performance of Xpert MTB/RIF vs AFB smear microscopy for the exclusion of NTM, using mycobacterial culture as the reference standard.

<p>Performance of Xpert MTB/RIF vs AFB smear microscopy for the exclusion of NTM, using mycobacterial culture as the reference standard.</p

Summary of high-performing host biomarkers with statistically significant findings.

<p>Summary of high-performing host biomarkers with statistically significant findings.</p