Molecular analysis of bedaquiline resistance and genetic variations in clinical isolates of multidrug and fluoroquinolones-resistant Mycobacterium tuberculosis

1 Introduction

Tuberculosis (TB) is a highly contagious and lethal disease worldwide, affecting all age groups, especially those with weakened immunity (Khusro et al., 2020). It is caused by Mycobacterium tuberculosis and is latently widespread across the world's population; however, it seems to be a global burden, with India experiencing the highest incidence of TB. As per the worldwide report of WHO on 2020, India has recorded one-fourth of all TB cases worldwide (World Health Organization, 2020). HIV/AIDS patients and members of marginalized communities, and those with weakened immune systems have the greatest impact on the prevalence. It is due to a combination of factors including poor access to healthcare, overcrowding, and economic and social situations (Hargreaves et al., 2011; Khusro et al., 2018a). Approximately 90 % of adults acquire TB each year; comparatively, men are more vulnerable to this problem than women (Khusro et al., 2016; Global Tuberculosis Report, 2022).

In terms of previous TB studies, people with multidrug resistance (MDR) were reported in 132,222 individuals; among these, pre-extensive or extensively drug resistance (XDR) was reported in 25,681 individuals (Global Tuberculosis Report, 2021). In many parts of the world, the incidence of bacillary resistance is potentially expanding, with MDR- and XDR-TB being the major contributing factors (Khusro et al., 2018b).

In addition, it should be noted that M. tuberculosis remains inactive and does not replicate, making it resistant to most primary anti-tuberculosis medications (Koul et al., 2008). Currently, the majority of anti-tubercular treatments involves the use of bedaquiline (BDQ), which has been approved by the FDA. However, adult MDR-TB cases typically receive it as second-line anti-tubercular therapy (Deoghare, 2013; Khusro et al., 2018c). This disease can be prevented and treated, regardless its difficulties. Through early detection screening, timely beginning of suitable treatment, and adherence to medication regimens can considerably decrease TB-related morbidity and mortality (Khusro et al., 2016).

After being inhaled, M. tuberculosis travels to the lung's alveoli, where it comes in contact with alveolar macrophages, which are in charge of engulfing and eliminating infections. However, M. tuberculosis has developed defense against macrophage destruction, enabling it to endure and proliferate within these cells (Cohen et al., 2018). Anti-TB therapies primarily operates by interrupting vital metabolic processes of M. tuberculosis, therefore preventing the bacteria from growing and spreading (Warner, 2014).

The crucial factors that act as virulence determinants are the ESX-1 secretion system and the cell wall components, which enable M. tuberculosis to invade and maintain infection in the host cell. The TB pathogenesis involves the ESX-1 system, which secretes the proteins ESAT-6 and CFP-10, which allow the bacteria to travel across the phagosomal membrane and reach the cytoplasm of the host cell (Forrellad et al., 2013) Similarly, another key virulence determining factor is the cell wall components, which contain mycolic acids, cord factor (trehalose 6,6′-dimycolate), lipoarabinomannan, and sulfatides. These components help the bacterium develop resistance to phagocytosis, prevent the fusion of phagosomes and lysosomes, and weaken the host immune system (Rajni et al., 2011).

Key drugs have been developed to target multiple pathways within M. tuberculosis cells. These pathways include hindering the synthesis of essential cell wall components, inhibiting the onset of RNA synthesis by targeting bacterial RNA polymerase, impeding the enzymes involved in DNA replication and transcription, interfering protein synthesis by focusing on the bacterial ribosome, and disrupting the cellular ATP synthesis mechanism, which is the power hub of the cell (Kohanski et al., 2010). Interestingly, F₁-F₀-ATP synthase enzyme is lead factor for the ATP synthesis mechanism of cells (Zharova et al., 2023) and the c-subunit of this enzyme is targeted by the diarylquinoline inhibitor, the ATP synthase inhibitor. This inhibitor was found to possess remarkable properties in combating mycobacterial replication, as reported in previous studies (Koul et al., 2008).

The significant barrier that renders all of these main treatments useless by developing drug resistance to these anti-TB medications creates a major challenge for treatment providers and researchers. Drug resistance is generally caused by genetic changes that alter drug targets or processes involved in drug activation or metabolism (Algammal et al., 2023). Understanding the genetic basis of medication resistance in M. tuberculosis is very critical for developing effective treatment regimens (Palomino and Martin, 2014). Simultaneously, MDR has increased all over the world and is considered a public health threat. Several recent investigations have reported the emergence of MDR bacterial pathogens from different origins that increase the necessity of the proper use of antibiotics (Algammal et al., 2021; Shafiq et al., 2022). We are required to build effective diagnostic tools and strategies to combat drug-resistant TB. And this requires expanding the prevalent study and mutational analysis across the globe and enriching the TB genetic variants database. As a part of this objective, our study focuses on genetic variants that lead to BDQ resistance.

Bedaquiline was first utilized in PMDT incidents in India on March 21, 2016, according to sources (Yadav, 2016). Whole-genome sequencing revealed that drug resistance is mediated through inefficiency and an increase in the efflux mechanism of the drug target genes pepQ, Rv0678, and atpE. To fully understand the processes of resistance to BDQ, which are required to prevent the establishment of resistance, it is crucial to locate novel variants in BDQ resistance-related genes (Yadav, 2016).

Throughout this research, our focus was to analyze single nucleotide variants associated with BDQ resistance in selected clinical specimens that were not exposed to BDQ. We investigated the BDQ resistance patterns in atpE, pepQ, and Rv0678 genes among the selected MDR + FQ resistant (FQR) isolates. Further in this study, in order to better understand the effectiveness of the drug, structural predictions for pepQ and Rv0678, as well as a molecular docking study with BDQ, were conducted.

2 Materials and methods

3 Results

3.2

3.2 Detection of genetic variations linked to BDQ resistance

We attempted to identify mutations predictive for potential BDQ resistance. In this study, 28 MDR-TB samples were carefully chosen from a collection of 83 samples exhibiting resistance to FQ, as shown in Fig. 1. This selective sampling strategy ensures a focused investigation into the specific subset of MDR + FQR-TB cases within the wider cohort of FQR samples, as shown in Fig. 2. Using H37Rv as a template, atpE, pepQ-S1, pepQ-S2, and Rv0678 genes were amplified. The amplification was verified by the development of clear bands at 373, 483, 735, and 572 bp (Fig. S2: Supplementary material).

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Fig. 1

Mutational analysis of FQR among the H/R-mono-resistant and MDR-TB isolates (H-isoniazid, R-Rifampicin, MDR-Multi drug resistant).

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Fig. 2

Diverse mutational pattern across age group with in MDR + FQR–TB cases.

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Among the total number of cases studied, the age group of affected people was categorized as mentioned in Fig. 2. The subset's most affected age range, according to the examination of mutational trends across a range of age groups, was 45 to 60 years old. According to the mutation analysis, the combination of rpoB, katG, and gyrA gene mutations exists in almost all age groups. In particular, it became apparent that 19 of the 28 cases under investigation had mutations in each of the three genes-rpoB, katG, and gyrA.

The typical procedure involves sequencing the genomes of TB bacteria sourced from patients with no prior exposure to BDQ. Among the chosen samples, most of them lacked mutations in atpE and pepQ genes. However, in two cases, Rv0678 gene exhibited a non-synonymous mutation involving the substitution of G337A across the mutational groups of MDR + FQR subset. This mutation involves the substitution of glutamic acid (E) with lysine (K) at position 113 of Rv0678 gene (Fig. S3: Supplementary material).

3.3

3.3 Molecular docking

3D structure of target genes (pepQ and Rv0678) is shown in Fig. 3. Using AutoDock 4.2 version, the docking study on BDQ with pepQ and Rv0678 was performed. Numerous conformations of the protein-compound complex have been generated by the docking modeling, depending on the conformation with the lowest binding free energy (ΔG) and the best conformations were picked. The Biovia Discovery Studio Visualizer program is used to show the interactions between BDQ and drug target genes.

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Fig. 3

3D-structure of target genes-pepQ and Rv0678.

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Figs. 4 and 5 show solid ribbon model conformation of pepQ and Rv0678 genes. The essential residues are indicated as red lines, and the compound was depicted as blue ball and stick. It also showed the interactions of BDQ at the active binding sites.

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Fig. 4

Bedaquiline (blue colour ball and stick model) interactions with pepQ represented in (A) solid ribbon model with; (B) active site amino acid residues amino acid residues represented in red colour line model; (C) hydrophobic interactions (alkyl and pi-alkyl are shown in light pink colour dotted lines; Amide-pi-Anion are shown in orange), Carbon hydrogen bonds are shown in (light blue colour circles). van der Waals interactions (light green colour circles).

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Fig. 5

Bedaquiline (blue colour ball and stick model) interactions with Rv0678 represented in (A) solid ribbon model with; (B) active site amino acid residues amino acid residues represented in red colour line model; (C) Pi-Anion are shown in orange dotted lines and Pi-sigma are shown in purple dotted lines), Carbon hydrogen bonds are shown in (light blue colour circles). van der Waals interactions (light green colour circles).

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4 Discussion

The efficacy of BDQ in treating TB appears to be reducing in current times, primarily due to the emergence of drug resistance. Longer regimens have no control over alterations in the patients' treatment plans (Singhal et al., 2016; Wu et al., 2021).

FQR occurs primarily due to inappropriate regimen concordance and mutations in the QRDR of gyrA, specifically in the amino acid 74–113 coding region (Kabir et al., 2020). The drug efflux process pumps the drug out of the cell, thus leading to FQR (Singhal et al., 2016).

It was observed that 83/1158 isolates (7.1 %) were FQR. Among all samples examined, INH resistance was found in 75 % of samples (870/1158). Out of 83 FQR isolates, 64 was gyrA mutants and 45 (72.5 %) resistant isolates exhibited mutations at codon 94, resulting in an alteration of the amino acid from Asp to Gly. The remaining 16 isolates (25.8 %) possessed mutations in codon A90V (GCG to GTG), whereas 3 (4.8 %) possessed mutations in codon S91P (TCG to CCG). These findings align with the research conducted by Kabir et al. (2020) and Singhal et al. (2016). Out of 19 gyrase B mutants, 16 resistant isolates (84.2 %) had mutations at codon 538 (asparagine to aspartic acid), while the other 3 (15.8 %) had a mutation at codon 540 (glutamic acid to valine).

The most prevalent mutation, S315T, located in the active site of the katG enzyme, leads to reduced binding affinity to isoniazid and induces a high degree of drug resistance (Rabha et al., 2019). There were 201 MDR and 87 Rifampicin-monoresistant samples. Mutations alter the conformation of the drug binding site and cause resistance to all FQ (Singhal et al., 2016; Kabir et al., 2020).

Since levofloxacin is resistant to the A90V mutation, moxifloxacin can be used at a higher dose. However, neither levofloxacin nor moxifloxacin work if the D94G mutation is present. These results agree with the majority of the earlier investigations (Singhal et al., 2016; Kabir et al., 2020).

A high frequency of A90V mutations was observed in newly identified incidents of TB isolates with FQR; hence, moxifloxacin is still the treatment of choice at higher dosages for these mutations. On the other hand, the D94G mutation is frequently present in cases of therapy failure and recurrence.

Among these 83 isolates, a substitution of Ala with Val was observed in major isolates when the MUT1 probe was present. Our most common pattern was the presence of the MUT3C probe and the lack of WT3, a mutation of Asp to Gly (Kabir et al., 2020).

We found a significant association between MDR-TB and the gyrA mutations among the MDR + FQR subset (Dreyer et al., 2022). Among the selected subset, the age range of 45 to 60 years exhibited the most pronounced prevalence of mutations. The mutations were identified in the genes rpoB, katG, and gyrA. The age group between 31 to 45 years showed a similar level of impact compared to other age groups. In the age range of 45 to 60 years, 46.4 % of the group was affected, while in the 31 to 45 years range, the affected percentage was 36 %.

Variations in Rv0678, atpE, and pepQ play a role as potential determinants of BDQ resistance. Overexpression of the MmpL5 efflux pump gene may result from a mutation in the Rv0678 transcriptional regulatory gene (Hartkoorn et al., 2014). In two distinct samples, the Rv0678 gene displayed nonsynonymous mutations, including the nucleotide substitution of G337A. This mutation alters glutamic acid (E) to lysine (K) at codon position 113 of the Rv0678 gene, generating elevated BDQ resistance. And the pattern was consistent with that reported by the CRyPTIC collection and Sonnenkalb et al. (2023).

Based on the computational analysis results indicating a binding energy of −6.07 kcal/mol and −5.72 kcal/mol and an inhibition constant of 35.46 µM and 63.68 µM in the molecular interaction analysis of pepQ and Rv0678 with BDQ, it can be concluded that there is a favorable interaction between these two genes and BDQ. And this suggests that pepQ has a slightly higher binding affinity for BDQ than Rv0678 does. However, both interactions show that the corresponding proteins and BDQ bind well, implying that BDQ could potentially be effective against targets associated with TB.

We are seeking out the new mutation in the FQR specimens to check whether there is any prior mutation in the drug target genes for BDQ (Deshkar and Shirure, 2022). With enhanced research and development, TB eradication objectives can be achieved by 2030 or 2035 (Global Tuberculosis Report, 2022).

5 Conclusions

The findings revealed a strong occurrence of gyrA gene mutations among the MDR-TB specimens. Nearly all (96.4 %) of the MDR-TB samples exhibited gyrA mutations, while only 3.6 % displayed gyrB mutation among the MDR + FQR subset. The mutation analysis suggests that the highest prevalence of mutations occurred within 45 to 60-year-old age group. Moreover, each case of the MDR + FQR subset in all age categories harbored mutations in rpoB + katG + gyrA gene group. Among the MDR + FQR subset, an E113K mutation in the Rv0678 gene was found, which is potentially linked to BDQ resistance. Investigating unique BDQ mutations in MDR + FQR samples aims to identify variants in drug target genes, facilitating the development of improved probes for rapid diagnosis in TB suspects. The computational study demonstrated a negative binding energy of -6.07 kcal/mol and −5.72 kcal/mol, implying that the interaction between pepQ and BDQ is energetically more beneficial than Rv0678, indicating the potential efficacy of BDQ drug against these genes. These findings provide valuable insights into the potential effectiveness of BDQ as a therapeutic agent targeting pepQ and Rv0678 in the treatment of TB. However, these experimental validations are required to create a database of potential variations to determine the association between effective drug concentrations for treating patients. It will also help us understand how these point mutations alter protein structure and drug binding effectiveness. Thus, consistent surveillance and analysis of resistance patterns play a pivotal role in shaping effective strategies for treating TB.

CRediT authorship contribution statement

Vijayalakshmi Jawaharlal Nehru: Conceptualization, Investigation, Methodology, Writing – original draft, Software. Usharani Brammacharry: Data curation, Project administration, Supervision, Visualization. S.R. Sri Ramkumar: Resources, Visualization. Ameer Khusro: Writing – review & editing, Methodology, Data curation, Conceptualization. Maria Jose Vadakunnel: Data curation, Visualization. Shoba Gunasekaran: Validation, Visualization. Esther David: Resources, Software, Validation. Veeraraghavan Vishnu Priya: Formal analysis, Visualization. Reem M. Aljowaie: Formal analysis, Funding acquisition. Saeedah Musaed Almutairi: Visualization, Funding acquisition.