Few amino acid positions in rpoB are associated with most of the rifampin resistance in Mycobacterium tuberculosis

Michael P Cummings,Mark R Segal

doi:10.1186/1471-2105-5-137

Abstract

BackgroundMutations in rpoB, the gene encoding the β subunit of DNA-dependent RNA polymerase, are associated with rifampin resistance in Mycobacterium tuberculosis. Several studies have been conducted where minimum inhibitory concentration (MIC, which is defined as the minimum concentration of the antibiotic in a given culture medium below which bacterial growth is not inhibited) of rifampin has been measured and partial DNA sequences have been determined for rpoB in different isolates of M. tuberculosis. However, no model has been constructed to predict rifampin resistance based on sequence information alone. Such a model might provide the basis for quantifying rifampin resistance status based exclusively on DNA sequence data and thus eliminate the requirements for time consuming culturing and antibiotic testing of clinical isolates.ResultsSequence data for amino acid positions 511–533 of rpoB and associated MIC of rifampin for different isolates of M. tuberculosis were taken from studies examining rifampin resistance in clinical samples from New York City and throughout Japan. We used tree-based statistical methods and random forests to generate models of the relationships between rpoB amino acid sequence and rifampin resistance. The proportion of variance explained by a relatively simple tree-based cross-validated regression model involving two amino acid positions (526 and 531) is 0.679. The first partition in the data, based on position 531, results in groups that differ one hundredfold in mean MIC (1.596 μg/ml and 159.676 μg/ml). The subsequent partition based on position 526, the most variable in this region, results in a > 354-fold difference in MIC. When considered as a classification problem (susceptible or resistant), a cross-validated tree-based model correctly classified most (0.884) of the observations and was very similar to the regression model. Random forest analysis of the MIC data as a continuous variable, a regression problem, produced a model that explained 0.861 of the variance. The random forest analysis of the MIC data as discrete classes produced a model that correctly classified 0.942 of the observations with sensitivity of 0.958 and specificity of 0.885.ConclusionsHighly accurate regression and classification models of rifampin resistance can be made based on this short sequence region. Models may be better with improved (and consistent) measurements of MIC and more sequence data.

Highlights

Mutations in rpoB, the gene encoding the β subunit of DNA-dependent RNA polymerase, are associated with rifampin resistance in Mycobacterium tuberculosis
Sequence data for amino acid positions 511–533 of rpoB and associated minimum inhibitory concentration (MIC) of rifampin for different isolates of M. tuberculosis were taken from studies examining rifampin resistance in clinical samples from New York City and throughout Japan
Drug resistance is quantified in terms of minimum inhibitory concentration (MIC), which is defined as the minimum concentration of the antibiotic in a given culture medium below which bacterial growth is not inhibited

Summary

Introduction

Mutations in rpoB, the gene encoding the β subunit of DNA-dependent RNA polymerase, are associated with rifampin resistance in Mycobacterium tuberculosis. Several studies have been conducted where minimum inhibitory concentration (MIC, which is defined as the minimum concentration of the antibiotic in a given culture medium below which bacterial growth is not inhibited) of rifampin has been measured and partial DNA sequences have been determined for rpoB in different isolates of M. tuberculosis. Tree-based statistical methods (see Methods) have generated very accurate models relating amino acid sequence of short (8-mer) peptides to their binding by major histocompatibility complex (MHC) class I molecules with higher accuracy than artificial neural networks [7] Both tree-based models and aggregation of such models through random forests (see Methods) have proven to be quite successful in other problems involving sequence data as covariates such as HIV-1 replication capacity [8] and cytidine to uridine RNA editing in plant mitochondria [9]. The success of tree-based statistical models and random forests in these problems involving covariates derived from sequence data motivated our application of these models to the problem of rifampin resistance in M. tuberculosis

Methods

Results

Discussion

Conclusion