Research Article: Empirical ways to identify novel Bedaquiline resistance mutations in AtpE

Date Published: May 29, 2019

Publisher: Public Library of Science

Author(s): Malancha Karmakar, Carlos H. M. Rodrigues, Kathryn E. Holt, Sarah J. Dunstan, Justin Denholm, David B. Ascher, Igor Mokrousov.

http://doi.org/10.1371/journal.pone.0217169

Abstract

Clinical resistance against Bedaquiline, the first new anti-tuberculosis compound with a novel mechanism of action in over 40 years, has already been detected in Mycobacterium tuberculosis. As a new drug, however, there is currently insufficient clinical data to facilitate reliable and timely identification of genomic determinants of resistance. Here we investigate the structural basis for M. tuberculosis associated bedaquiline resistance in the drug target, AtpE. Together with the 9 previously identified resistance-associated variants in AtpE, 54 non-resistance-associated mutations were identified through comparisons of bedaquiline susceptibility across 23 different mycobacterial species. Computational analysis of the structural and functional consequences of these variants revealed that resistance associated variants were mainly localized at the drug binding site, disrupting key interactions with bedaquiline leading to reduced binding affinity. This was used to train a supervised predictive algorithm, which accurately identified likely resistance mutations (93.3% accuracy). Application of this model to circulating variants present in the Asia-Pacific region suggests that current circulating variants are likely to be susceptible to bedaquiline. We have made this model freely available through a user-friendly web interface called SUSPECT-BDQ, StrUctural Susceptibility PrEdiCTion for bedaquiline (http://biosig.unimelb.edu.au/suspect_bdq/). This tool could be useful for the rapid characterization of novel clinical variants, to help guide the effective use of bedaquiline, and to minimize the spread of clinical resistance.

Partial Text

Tuberculosis (TB) is the leading cause of infectious disease death worldwide, with over 10 million new cases and 1.6 million deaths in 2017 [1]. A disproportionate burden arises from the estimated 558,000 annual cases of rifampicin resistant TB (RR-TB) with 82% being multi-drug resistant (MDR), which is associated with lengthy, toxic therapy and high rates of mortality [1]. With limited therapeutic options available, especially for MDR-TB and extensively drug-resistant (XDR) TB, the introduction of new treatment options is urgently required. Bedaquiline, a new anti-TB drug with a novel mechanism of action, targeting the c-ring of ATP synthase (AtpE) [2], was approved for treatment for MDR-TB in 2012 [3, 4]. This innovative drug is potent against both actively replicating and dormant bacilli and has been shown to increase culture conversion in patients with MDR-TB [5]. The use of bedaquiline has expanded considerably in recent years, and has been recommended for more routine use in MDR-TB regimens [6], however clinical failures have already been observed [7, 8]. This necessitates a better understanding of how variants result in resistance to aid in the early detection of resistance.

We used a structure-guided approach to understand the protein structure of the drug target AtpE and machine learning to build an empirical tool that could identify likely resistant mutations. The pipeline used to analyze the variants and train a multilayer perceptron neural network algorithm is shown in Fig 1.

Early genomic detection of resistance is crucial for tailoring individual therapy and preventing the onward transmission of resistant infection. This is especially of importance to limit the spread of resistance to bedaquiline, one of the few treatment options for XDR-TB. While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a drug-resistant phenotype, a gap still exists when integrating these predictions and drawing conclusions regarding causality and the strength of associations observed. This is compounded by the need for detailed information regarding the system/protein. The availability of scalable, effective computational methods to assess mutational effects creates new opportunities for developing integrated approaches and deciphering complex genomic background patterns, shedding light on their role in the emergence of a given phenotype and molecular mechanisms of action [19].

This novel computational approach can enhance the impact of genome sequencing in identifying and characterizing variants more accurately and may therefore assist in guiding optimal usage of bedaquiline. The results obtained from our empirical tool is promising and should help facilitate routine genotypic drug susceptibility testing for bedaquiline and stimulate further research to help avoid the emergence of resistance to this new treatment through early detection.

 

Source:

http://doi.org/10.1371/journal.pone.0217169

 

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