Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

Tanushree Tunstall; Jody Phelan; Charlotte Eccleston; Taane G. Clark; Nicholas Furnham

doi:10.3389/fmolb.2021.619403

Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

Tunstall, Tanushree;Phelan, Jody;Eccleston, Charlotte;Clark, Taane G.;Furnham, Nicholas; 2021-07-23 00:00:00 fmolb-08-619403 August 3, 2021 Time: 21:57 # 1 ORIGINAL RESEARCH published: 23 July 2021 doi: 10.3389/fmolb.2021.619403 Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages 1 1 1 1,2 Tanushree Tunstall , Jody Phelan , Charlotte Eccleston , Taane G. Clark and Nicholas Furnham 1 2 Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom Resistance to drugs used to treat tuberculosis disease (TB) continues to remain a public health burden, with missense point mutations in the underlying Mycobacterium tuberculosis bacteria described for nearly all anti-TB drugs. The post-genomics era along with advances in computational and structural biology provide opportunities to understand the interrelationships between the genetic basis and the structural Edited by: consequences of M. tuberculosis mutations linked to drug resistance. Pyrazinamide Arun Prasad Pandurangan, MRC Laboratory of Molecular Biology (PZA) is a crucial ﬁrst line antibiotic currently used in TB treatment regimens. The (LMB), United Kingdom mutational promiscuity exhibited by the pncA gene (target for PZA) necessitates Reviewed by: computational approaches to investigate the genetic and structural basis for PZA Wim Vranken, Vrije University Brussel, Belgium resistance development. We analysed 424 missense point mutations linked to PZA Raghavan Varadarajan, resistance derived from 35K M. tuberculosis clinical isolates sourced globally, which Indian Institute of Science (IISc), India comprised the four main M. tuberculosis lineages (Lineage 1–4). Mutations were *Correspondence: annotated to reﬂect their association with PZA resistance. Genomic measures (minor Nicholas Furnham [email protected] allele frequency and odds ratio), structural features (surface area, residue depth and hydrophobicity) and biophysical effects (change in stability and ligand afﬁnity) of Specialty section: point mutations on pncA protein stability and ligand afﬁnity were assessed. Missense This article was submitted to Biological Modeling and Simulation, point mutations within pncA were distributed throughout the gene, with the majority a section of the journal (>80%) of mutations with a destabilising effect on protomer stability and on ligand Frontiers in Molecular Biosciences afﬁnity. Active site residues involved in PZA binding were associated with multiple Received: 20 October 2020 Accepted: 14 April 2021 point mutations highlighting mutational diversity due to selection pressures at these Published: 23 July 2021 functionally important sites. There were weak associations between genomic measures Citation: and biophysical effect of mutations. However, mutations associated with PZA resistance Tunstall T, Phelan J, Eccleston C, Clark TG and Furnham N (2021) showed statistically signiﬁcant differences between structural features (surface area and Structural and Genomic Insights Into residue depth), but not hydrophobicity score for mutational sites. Most interestingly Pyrazinamide Resistance M. tuberculosis lineage 1 (ancient lineage) exhibited a distinct protein stability proﬁle in Mycobacterium tuberculosis Underlie Differences Between Ancient for mutations associated with PZA resistance, compared to modern lineages. and Modern Lineages. Front. Mol. Biosci. 8:619403. Keywords: Mycobacterium tuberculosis, pncA, nsSNPs, non-synonymous Single Nucleotide Polymorphisms, biophysical effects, thermodynamic stability, mCSM, FoldX doi: 10.3389/fmolb.2021.619403 Frontiers in Molecular Biosciences | www.frontiersin.org 1 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 2 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis in the pncA gene remain the most common mechanism of PZA INTRODUCTION resistance (Khan et al., 2019). Tuberculosis (TB), is a highly infectious and contagious air-borne Advances in whole genome sequencing (WGS) is assisting disease caused by the bacterium Mycobacterium tuberculosis. the proﬁling of M. tuberculosis for drug resistance, lineage Despite its ancient origins and the eﬀorts to develop disease determination and virulence, and presence in a transmission control and prevention measures, the disease continues to cluster (Phelan et al., 2019a), thereby informing clinical cause a global public health burden, with increased drug management and control policies. This is reﬂected in the resistance making control diﬃcult. In 2019, WHO reported WHO recommendation for use of rapid molecular testing around 10 million global cases of TB of which 1.4 million for detecting TB and drug resistant TB (World Health result in death (World Health Organization [WHO], 2020). Organization [WHO], 2020). The use of WGS can uncover new In 2019, 465,000 cases of rifampicin resistant TB (RR-TB), resistance mutations through genome-wide association studies among which 78% cases of multidrug-resistant TB (MDR- (GWAS) and convergent evolution analysis (Phelan et al., 2016; TB, deﬁned as having additional resistance to isoniazid) were Coll et al., 2018). reported. Among these RR/MDR cases, 6% cases were further Furthermore, using protein structure, the biophysical eﬀects of resistant to one ﬂuoroquinolone and one injectable second point polymorphisms can be investigated allowing a mechanistic line drug, leading to extensively drug resistant TB (XDR-TB) understanding of resistance development (Phelan et al., 2016; (World Health Organization [WHO], 2020). Kavvas et al., 2018; Portelli et al., 2018). This approach can The size of the M. tuberculosis genome (reference H37Rv highlight important functional resistance mutations before they strain) is 4.4 Mb, with a high (65%) GC content. The take hold in a population, corroborate drug susceptibility test M. tuberculosis genome is clonal, and consists of seven main results, as well as provide insights in highly polymorphic lineages, which vary by their geographical spread (L1: Indo- candidate loci (e.g., pncA) where many of the putative mutations Oceanic, L2: East Asian, L3: East-Africa-Indian, and L4: Euro- have low frequency. It has been observed that sites with multiple American) (Phelan et al., 2016). The lineages are further classiﬁed mutations (>2) are linked to drug resistance (Comas et al., into ancient (L1, L5–6), modern (L2–4), and intermediate (L7) 2011), but such resistance hotspots may not necessarily lie close strains, with L2 being particularly mobile as evidenced by its to the drug binding site. To this eﬀect, sites with 2 mutations recent spread to Europe and Africa from Asia (Phelan et al., are considered as “emerging” or “budding” resistance hotspots 2016). The M. tuberculosis lineages appear as distinct clades (Portelli et al., 2018). on phylogenetic trees (Coll et al., 2014) and govern disease One assessment of the impact of missense mutations is to transmission and dynamics with phenotypic consequences measure the change in a protein structure’s as well as drug- on clinical severity and drug resistance (Ford et al., 2013; target complex’s physical interactions that contribute to its overall Reiling et al., 2013), including recent reports of lineage-speciﬁc stability. Computational approaches (e.g., the mCSM suite; Pires associations with the latter (Oppong et al., 2019). Drug resistance et al., 2014a, 2016; Pires and Ascher, 2016, 2017; Rodrigues et al., in M. tuberculosis is almost exclusively due to mutations 2019) have been developed to predict the eﬀects of missense [including non-synonymous Single Nucleotide Polymorphisms point mutations on overall protein structure stability, as well (nsSNPs), insertions and deletions (INDELs)] in genes coding as the binding aﬃnity/stability of ligand, protein-protein, and for drug-targets or drug-converting enzymes. Changes in eﬄux protein-nucleic acid interactions within a single framework, pump regulation may also have an impact on the emergence based on either an experimentally resolved structure or derived of resistance (Al-Saeedi and Al-Hajoj, 2017) and putative model. Here we apply such approaches to the eﬀects of missense compensatory mechanisms have been described to overcome point mutations in the pncA gene. In addition, we also analyse ﬁtness impairment that arises during the accumulation of biophysical structural features including surface area, residue resistance conferring mutations (de Vos et al., 2013). Resistance- depth and hydrophobicity for residues and sites associated with associated point mutations have been described for all ﬁrst-line missense point mutations. drugs, including rifampicin, isoniazid and pyrazinamide, as well A crystal structure for pncA from M. tuberculosis has as for several second-line and newer drugs (ﬂuoroquinolones, been determined as a monomeric enzyme of 186 amino acids bedaquiline) (Somoskovi et al., 2001; Boonaiam et al., 2010; (19.6 kDa) (Petrella et al., 2011). The structure comprises a 6- Segala et al., 2012), but knowledge is still incomplete. stranded parallel beta sheets, with helices on either side forming Pyrazinamide (PZA) is a crucial antibiotic used in WHO a single a/b domain with a metal cofactor (iron, Fe2+) binding recommended combination therapies in the front-line treatment site formed of D49, H51, H57, and H71. The substrate binding of TB. It is a pro-drug which is activated by the amidase cavity in MtPncA is small, approximately 10 Å deep and 7 Å activity of the enzyme pyrazinamidase/nicotinamidase (PZase; wide. It consists of highly conserved residues F13 and W68 MtPncA) encoded by the pncA gene, converting PZA to its active that are essential in substrate binding with Y103 and H137 form of pyrazinoic acid (POA). Despite its indispensable status limiting access to this cavity (Petrella et al., 2011). The catalytic in TB treatment, PZA’s exact mode of action remains poorly triad consisting of C138, D8, K96 is indicative of a cysteine- understood. Other genes (rpsA and panD) have been implicated based catalytic mechanism (Petrella et al., 2011). Leveraging in PZA resistance (Dookie et al., 2018) with a recent study this crystal structure, we developed an in silico framework suggesting that PZA exerts its antibacterial activity by acting as to assess the biophysical impact of pncA mutations and their a target degrader of panD, blocking the synthesis of coenzyme A resistance risk as determined by GWAS. In this study, we attempt (targeted by POA) (Gopal et al., 2020). Despite this, mutations to understand PZA resistance by exploring the relationship Frontiers in Molecular Biosciences | www.frontiersin.org 2 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 3 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis between the genomic features and the biophysical consequences the ligand orientation generated by molecular docking performed of stability and aﬃnity of nsSNPs, and how this is reﬂected in by Karmakar et al. (2018) and comparing interaction of both diﬀerences between M. tuberculosis lineages. poses with active site residues through an Arpeggio (Jubb et al., 2017) analysis (Supplementary Figure 4). Ligand extraction and protonation were carried out using MATERIALS AND METHODS UCSF Chimera, version 1.11 (Pettersen et al., 2004) while identiﬁcation of rotatable bonds was carried out in Autodock SNP Dataset tools (available as part of MGL tools, version 1.5.6) (Morris et al., The dataset consists of 35,944 M. tuberculosis isolates, which 2009) where protonation of the ligand is speciﬁcally required has been described recently (Napier et al., 2020). In brief, it by Autodock Vina (Trott and Olson, 2009). Similarly, protein encompasses all the main lineages (1, 5, and 6, ancient; 2, 3, extraction and explicit removal of solvent were carried out in and 4, modern; 7 intermediate), and drug susceptibility testing UCSF Chimera, version 1.11 (Pettersen et al., 2004), and other across 8 ﬁrst-and second-line anti-TB drugs. Across these isolates, steps in the overall protein preparation process were carried out mutations in the pncA coding region with non-synonymous in Autodock tools (part of MGL tools, version 1.5.6) (Morris et al., amino acid changes (nsSNPs) were extracted. These nsSNPs were 2009). All the required parameters to perform docking needed to further annotated for their link with drug resistance as deﬁned by be included in a conﬁguration ﬁle. their presence in the TB-Proﬁler mutation database (Phelan et al., 2019b). Initial analysis aimed at understanding the structure and In silico Predictions: mCSM DUET, FoldX, characterising the active site, followed by in silico predictions to mCSM-lig quantify the enthalpic and entropic eﬀects of GWAS-identiﬁed The computational tools based on mutation cut-oﬀ scanning nsSNPs on the pncA protein structure. Subsequently, additional matrix, primarily mCSM DUET (Pires et al., 2014a) and mCSM- metadata relating to the clinical isolates were studied in relation lig (Pires et al., 2016) were used to investigate the structural to the structural eﬀects of mutations. The general methodology eﬀects of nsSNPs within the pncA target protein. The eﬀects workﬂow followed in this analysis is similar to the one described of nsSNPs within pncA were analysed with respect to protein previously (Portelli et al., 2018). stability (DUET and FoldX (Schymkowitz et al., 2005) and ligand aﬃnity (mCSM-lig). The consequences of these eﬀects were to Drug and Target: Structural Data investigate change in protein fold and function, and eﬀect on In the absence of a drug (PZA) and target (pncA) complex, mechanism of PZA drug activation, respectively. Results from respective individual structures were obtained from RSCB PDB mCSM-lig (Pires et al., 2016) return both ligand aﬃnity and database (Berman et al., 2000). The crystal structure of pncA in DUET scores, hence only mCSM-lig was run to obtain both the M. tuberculosis is available as PDB entry 3PL1 (Petrella et al., outputs simultaneously. 2011), while the structure of PZA was extracted from PDB entry A semi-automated pipeline was constructed for mCSM and 3R55 (Singh et al., 2011). The molecular motion of pncA was FoldX to submit and extract results for multiple mutations analysed by Normal Mode Analysis using the DynaMut tool consecutively using python and shell scripts. Both tools require (Rodrigues et al., 2018) (Supplementary Figure 1). wild type structure, chain ID and a list of nsSNPs in the X <POS> Y format (X: wild type residue; <POS> : position, Y: Protein-Ligand Docking: Autodock Vina mutant residue). The residue symbols (X and Y) are speciﬁed as The pncA-PZA complex was generated using the software one letter amino acid code. DUET and FoldX estimate mutational AutoDock Vina, version 1.1.2 (Trott and Olson, 2009). Autodock impact as a change in Gibbs Free energy (11G) in Kcal/mol. Vina is an open-source, freely available molecular modelling The classiﬁcation of mutational impact based on 11G from platform to perform protein-ligand docking. Docking was carried these methods are categorised in opposing ways. For example, out with default settings and guided by the positioning of 11G < 0 of a SNP is classiﬁed as a “destabilising” according to the ligand within the active site as descried by Petrella et al. DUET, while the same is classiﬁed as “stabilising” according to (2011). The complex was generated to facilitate downstream FoldX. analyses by mCSM-lig (Pires et al., 2016) Autodock Vina returns The mutational impact on ligand aﬃnity is calculated as a log bound conformations with their respective predicted binding fold change between wild type and mutant binding aﬃnities. In aﬃnity values. The prediction of binding aﬃnity (strength of addition to SNP identiﬁers, mCSM-lig requires the ligand aﬃnity the ligand interaction with its target) is based on one of several of the wild-type protein to be speciﬁed in nano Molar (nM) for scoring functions, which rank the poses in increasing order aﬃnity change calculations. Since the binding aﬃnity returned of predicted binding aﬃnity. Binding free energy is calculated by AutoDock Vina, version 1.1.2 (Trott and Olson, 2009) is in using a semi-empirical force ﬁeld, combining experimental and Kcal/mol, these needed to be converted to nM via Eq. 1 (below). knowledge-based information. The docking poses were visualised The binding aﬃnity for PZA in nM was 0.9911. and inspected in UCSF Chimera 1.13 (Pettersen et al., 2004) according to the occupation of search space and diversity of pose 1G D RTlnK: (1) conformations (Supplementary Figure 2). The top two binding poses were closely matched with the conformations generated Equation 1: Calculation of binding free energy, 1G, where R 1 1 by Karmakar et al. (2018) and Petrella et al. (2011), respectively is the gas constant, 1.987 cal K mol and T is the absolute (Supplementary Figure 3). The best pose was chosen considering temperature, 298 K. Adapted from Morris et al. (1998). Frontiers in Molecular Biosciences | www.frontiersin.org 3 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 4 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis The mCSM suite of tools (Pires et al., 2014a, 2016; Pires odds ratio (OR), and similar to a GWAS approach, adjusted and Ascher, 2017; Rodrigues et al., 2019) are based on odds ratio (aOR) were estimated using logistic regression models graph-based measures at an atomic level along with machine with a kinship matrix adjusting for a random eﬀect representing learning (ML) tools for predicting enthalpic and entropic the SNP-based relationships between samples (e.g., the lineage- eﬀects of stability. mCSM achieves this broadly by generating based population structure) (Zhou and Stephens, 2012; Coll et al., a signature encompassing the wild-type milieu and change in 2018). P-values were estimated using Fisher and Wald test for pharmacophore properties upon mutation (Pires et al., 2014b). unadjusted and adjusted ORs, respectively. Owing to the inter-atomic distance pattern within mCSM describing the wild-type residue environment, novel parameters Statistical Analyses like residue depth and long-range interactions are implicitly Data was analysed using non-parametric statistical tests. considered. In this manner, mCSM is able to characterise For assessing correlations, Spearman correlation values both local and global eﬀects of missense point mutations. The were calculated. For comparing lineage distributions, the mutational change at the atomic level is considered by using a Kolmogorov-Smirnov (KS) test was used. Statistical signiﬁcance change in the “pharmacophore count” vector, thus obviating the thresholds used are P < 0.05, P < 0.01, P < 0.001, need to have explicit mutant structure. All mCSM tools (Pires P < 0.0001). et al., 2014a, 2016; Pires and Ascher, 2016, 2017; Rodrigues et al., 2019) use the atomic changes, while DUET (Pires et al., Data Visualisation 2014a) is an ensemble method combining methods of mCSM All plots were generated using R statistical software, stability (Pires et al., 2014b) and SDM (Worth et al., 2011; version 4.0.2 (R Core Team, 2014). Protein and ligand Pandurangan et al., 2017). FoldX, however is an empirical-based structures were generated using UCSF Chimera, version prediction tool which summarises the change in stability between 1.11 (Pettersen et al., 2004). mutant and wild type protein structures using a combination of energy terms based on fundamental intramolecular interactions (Schymkowitz et al., 2005). RESULTS Other Structural Parameters Analysing the pncA Molecular Motion Additional structural parameters for wild type structure were and pncA-PZA Complex also included in the analysis. These were: Accessible (ASA) and Molecular motion in pncA was analysed by Normal Mode Relative Surface Area (RSA), residue depth (RD), hydrophobicity Analysis (NMA). Regions undergoing the greatest movement values according to the Kyte-Doolittle scale (KD). The DSSP were limited to residues in loop regions and mainly concentrated programme (Kabsch and Sander, 1983; Touw et al., 2015) was to loop 60–66, followed by loop residues 39–41 and 111–113. run to extract the ASA and RSA values, while RD values Residues at site 165–167 within helix 164–178 showed the least calculated as described by Chakravarty and Varadarajan (1999) ﬂexibility (Supplementary Figure 1). The frequency of mutations were calculated using the depth server available at http://cospi. in these variable regions was most prominent for sites 62–63 iiserpune.ac.in/depth. The KD values were fetched from the (>2 mutations) while the other sites were limited to at most two expasy server (Artimo et al., 2012) available at https://web.expasy. mutations (Figure 1). Mutations within the most ﬂexible region org/protscale/. (residues 60–66) of pncA showed mixed eﬀects in relation to their association with PZA resistance with the single mutation at site 64 Data Normalisation: DUET, FoldX, and related to PZA resistance. Sites 39 and 40 within the other highly mCSM-lig ﬂexible region 39–41 were not associated with any mutations in The DUET (Pires et al., 2014a), FoldX (Schymkowitz et al., 2005), our study, while the two mutations at site 41 were not associated and mCSM-lig (Pires et al., 2016) scores associated with each SNP with PZA resistance. The region 111–113 is associated with single were subsequently normalised between the range of1 and 1. For mutations at sites 111 and 112 which are not linked to PZA mCSM-lig analyses, data was ﬁltered according to distance from resistance, while site 113 was not associated with any mutations in interacting site and only residues within a distance of 10 Å of the our study. Sites 165–167, which form part of the helix (164–178), ligand (PZA) were considered for all ligand aﬃnity analyses. are the most stable according to NMA. Two residues (A165 and D166) within this helix were not associated with any mutations Minor Allele Frequency and Odds Ratio in our study, while a single mutation at site T167 was not Calculations: SNP Dataset associated with PZA drug resistance (Supplementary Figure 1 Across the M. tuberculosis isolates tested for PZA drug and Supplementary Table 1). Docking with AutoDock vina susceptibility data, we performed association analysis to estimate (Trott and Olson, 2009) generated nine diﬀerent conformations the risk of resistance for SNP alleles. For each nsSNP, minor allele as per default settings. In six of these poses, the aromatic ring frequency (MAF) and odds ratio (OR) were calculated in relation of PZA was oriented towards the substrate binding residue to all samples tested for PZA susceptibility. MAF is the average W68 (Supplementary Figures 2A,B). The top two poses (1 occurrence of a given nsSNP, and OR is the measure of association and 2) returned by Vina were similar to previous molecular of a given nsSNP with PZA resistance. In addition to unadjusted docking studies (Petrella et al., 2011; Karmakar et al., 2018) Frontiers in Molecular Biosciences | www.frontiersin.org 4 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 5 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 1 | Logo plot showing sites with multiple missense point mutations and association with Odds Ratio. Sites associated with multiple (>2) missense point mutations (i.e., nsSNPs). A total of 386 mutations corresponding to 113 positions on the pncA protein structure were associated with multiple nsSNPs. The horizontal axis in (A,B) show the position numbers of sites with multiple nsSNPs, while part (C) shows the wild-type residues for each position. The vertical axis in (A) represents Odds Ratio (OR) where letters denote mutant residues which are proportional to their corresponding OR highlighting the most resistant mutation at each site and overall. Part (B) shows each mutant residue at a given position, highlighting nsSNP diversity by position. The wild-type and mutant residues are coloured according to the amino acid properties as denoted. Positions marked in yellow form the catalytic triad, residues in blue and teal are involved in substrate binding, those in green are involved in hydrogen binding while the ones in purple are involved in the iron centre coordination. The ﬁgure is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms; pncA, pyrazinamidase. (Supplementary Figure 3). A follow-up Arpeggio analysis (Jubb a total of 13,914 samples tested for PZA drug susceptibility, et al., 2017) indicated that pose 1 when compared to pose 2, a minority of those were found to be resistant (n = 2,379, has more H-bonds (4 vs. 1), fewer aromatic contacts (3 vs. 13), 17.1%) (Table 1). However, the burden of PZA resistance among and greater Van der Waals interactions (3 vs. 1) (Supplementary Figures 4A,B). Therefore, model with pose 1 was chosen to form the pncA-PZA complex (Supplementary Figure 5). TABLE 1 | Number of samples analysed. Genomics Data SNP data from 35,944 M. tuberculosis clinical isolates tested for Item name Total number (%) drug susceptibility to a range of ﬁrst and second line drugs were Clinical isolates/samples 35,944 obtained (Napier et al., 2020). Among these, 39% (n = 13,914) Samples classiﬁed Susceptible 23,256 (64.7) of these isolates were tested for PZA drug susceptibility. The Drug resistant (DR) 5,008 (13.9) isolates were collected from over 30 diﬀerent countries and Multi-drug resistant (MDR) 6,691 (18.6) represented the 4 main M. tuberculosis lineages (L1, n = 144; Extreme drug resistant (XDR) 989 (2.8) L2, n = 1,886; L3, n = 190; L4, n = 2213) (Supplementary Samples tested for PZA drug 13,914 Figure 6). In order to infer whether the ancestral pncA susceptibility sequences for each lineage diﬀered, we quantiﬁed the number Resistant 2,379 (17.1) of samples without any mutations in each lineage. The majority Samples with nsSNPs in the protein 4,731 (13.2) of isolates in L1–L4 had an identical pncA sequence as the coding region of pncA H37Rv reference indicating that the ancestral sequences for Susceptible 184 (3.9) these lineages do not diﬀer. The majority were pan susceptible Drug resistant (DR) 632 (13.4) (n = 23,256, 64.7%), with the remainder MDR-TB (n = 6,691, Multi-drug resistant (MDR) 3,290 (69.5) 18.6%), XDR-TB (n = 989, 2.8%), or another type of resistance Extreme drug resistant (XDR) 625 (13.2) referred to as DR-TB (n = 5,008, 13.9%) (Table 1). From the Samples with pncA nsSNPs tested for 2,289 (48.4) list, only nsSNPs within the protein coding region of pncA PZA drug susceptibility (n = 4,731, 13.2%) were considered for our analyses (Table 1). Samples with pncA nsSNPs resistant to 1,677 (73.3) PZA The majority of these were MDR-TB (n = 3,290, 69.5%) followed Unique nsSNPs: No. of sites 424 nsSNPs: 151 sites by relatively equal numbers of XDR-TB and DR-TB (n = 625, 13.2% and n = 632, 13.4%, respectively), while only a small Summary of clinical isolates from genome-wide analysis. PZA, pyrazinamide; percentage were susceptible (n = 184, 3.9%) (Table 1). From nsSNPs, non-synonymous Single Nucleotide Polymorphisms. Frontiers in Molecular Biosciences | www.frontiersin.org 5 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 6 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 2 | Barplots showing number of mutations and sites associated with protein stability and ligand afﬁnity. (A) Number of nsSNPs categorised as destabilising (n = 359) and stabilising (n = 65) according to DUET protein stability. (B) Frequency of sites associated with the number of nsSNPs, where horizontal axis denotes the number of nsSNPs and vertical axis denotes the total number of sites/positions corresponding to the number of nsSNPs. (C) Barplot showing the number of nsSNPs categorised as destabilising (n = 168) and stabilising (n = 33) according to mCSM ligand afﬁnity where sites lie within 10Å of ligand. (D) Frequency of sites associated with the number of nsSNPs, where horizontal axis denotes the number of nsSNPs and vertical axis denotes the total number of sites/positions corresponding to the number of nsSNPs. The ﬁgure is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms. samples containing nsSNPs in the protein coding region was high the others (197/424; denoted as OM) were assumed to have (n = 1,677, 73.3%) (Table 1). weak or no links. Genomic measures like minor allele frequency Across the 4,731 isolates, 424 distinct nsSNPs corresponding (MAF) and odds ratio (OR) were obtained for a total of 322 to 151 distinct amino acid positions on the pncA structure were nsSNPs, with adjusted OR (aOR) estimated for a total of 163 identiﬁed (Figures 2A,B). A total of 201 nsSNPs corresponding nsSNPs. Across the majority of these nsSNPs, the MAFs were low to 54 amino acid changes were within 10 Å of the ligand binding (median: 0.02% range: 0.01–2.11%) (Supplementary Figure 7A). site (Figures 2C,D). The majority of these nsSNP mutations Similarly, when considering ORs, the majority of the nsSNPs had have been annotated as being linked to PZA resistance within high ORs (median: 9.70, range: 0.22–414.61) (Supplementary the TBProﬁler tool (227/424). The majority of these nsSNP Figure 7D). When looking at the distribution of MAF and OR mutations have been annotated as being linked to PZA resistance within mutations associated with PZA resistance (DM) and other within the TBProﬁler tool (227/424; denoted as DM), while mutations (OM) (Supplementary Figures 7B,E), DM mutations Frontiers in Molecular Biosciences | www.frontiersin.org 6 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 7 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 3 | Mutational landscape of pncA structure (3PL1) coloured by positions linked to pyrazinamide drug (PZA) resistance. Panels (A,B) show all mutational positions in orange while mutational positions in (C,D) are further coloured by mutations classed as either drug resistant mutations (purple) or “other mutations” (blue), while sites linked to mutations belonging to either category are coloured in pink. The right panels (B,D) depict the corresponding structure rotated by 180 . The ligand (PZA) is shown as ball and stick within the active site denoted by the red circle. The ﬁgure is rendered using UCSF Chimera (version 1.14). pncA, pyrazinamidase. were associated with signiﬁcantly higher (P < 0.0001) MAF and point mutation (Table 2 and Figures 1B, 5C). All active site and OR (Supplementary Figures 7C,F). hydrogen-bond forming residues with the ligand were associated with multiple mutations (2) (Figure 1B), thus representing the high diversity of mutations present within pncA. Despite this, there appears to be some degree of clustering around positions Understanding Mutational Effects on 4–14, 46–97, 132–143 involving the active site and metal centre pncA Stability and PZA Binding Afﬁnity residues (Figure 5C). The 424 nsSNPs mapped onto the crystal structure of pncA The biophysical eﬀect of mutations on protomer stability, revealed that mutational landscape of pncA appears distributed estimated as 11G (Kcal/mol), was measured using DUET (Pires (Figures 3A,B) throughout the structure. Sites linked to drug et al., 2014a) and FoldX (Schymkowitz et al., 2005), while resistant mutations were predominant around the PZA binding (active) site, while sites exclusively linked to mutations classed in mutational impact on ligand aﬃnity was measured using mCSM- the “other” category are distal to the active site (Figures 3C,D, 4). lig (Pires et al., 2016) (see section “Materials and Methods”). Furthermore, active site residues were associated with a multiple Assessing mutational eﬀects on protein stability as measured by Frontiers in Molecular Biosciences | www.frontiersin.org 7 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 8 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 4 | Comparison of structural features between Drug resistance (DM) and other mutations (OM) of pncA gene mutations according to (A) DUET protein stability (11G), (B) FoldX stability (11G), and (C) Ligand Afﬁnity. A total of 424 nsSNPs for DUET and FoldX (DM, n = 227, OM, n = 197), while a total of 201 nsSNPs (DM, n = 129 OM, n = 72) lying within 10 Å of PZA for ligand afﬁnity were included in the analysis. DM and OM mutations were compared using Wilcoxon rank-sum (unpaired) and statistical signiﬁcance indicated as: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). The ﬁgure is generated using R statistical software (version 4.0.2). ns, non-synonymous Single Nucleotide Polymorphisms; pnca, pyrazinamidase; PZA, pyrazinamide; Å, Angstroms; 11G, Change in Gibbs free energy in Kcal/mol; ASA, Accessible Surface Area; RSA, Relative surface Area; RD, Residue Depth; KD, Kyte-Doolittle Hydrophobicity values. DUET, nearly 85% had a destabilising eﬀect (n = 359) compared 49, 72, 78, 96, 102, 103, 105, 134, 137, 138, and 162 associated to nearly 15% mutations with stabilising eﬀects (n = 47) as shown with mutations resulting in mixed stability impact. Position 163 in Figure 2A. When assessing ligand aﬃnity, 47.4% (n = 201) comprised only of mutations with stabilising eﬀects (Figure 5D). SNP mutations were present within 10 Å of the PZA binding site The budding resistance hotspot active site residue I133 contained (Figure 2C). Similar to DUET stability eﬀects, the majority (84%; both mutations with destabilising eﬀect for protein stability n = 168) of nsSNPs were destabilising while 16% (n = 27) were (Figure 5C), while stabilising for ligand aﬃnity (Figure 5D). stabilising for ligand binding aﬃnity (Figure 2C). More than Similarly, for budding resistance hotspots, majority of the nsSNPs 50% of the mutational positions were associated with multiple were associated with destabilising eﬀects. For protein stability, nsSNPs for both protein stability (n = 113) and ligand aﬃnity 9/33 sites had mutations with mixed stability (positions 15, 32, 61, (n = 49) (Figures 2B,D). The average protein stability and ligand 66, 76, 114, 127, 153, and 161) (Figure 5C), while only position 20 aﬃnity eﬀects of all mutations mapped onto the pncA structure showed mixed stability eﬀects for ligand aﬃnity (Figure 5D). (Figures 5A,B), highlight mutations with opposing eﬀects for protein stability and ligand aﬃnity. These eﬀects are pronounced Mutations With Extreme Effects for active site residues (I133, A134, H137, C138) (Figures 5C,D). Mutations with extreme eﬀects on protein stability and aﬃnity There were 80 sites within pncA associated with multiple are summarised in Table 3. Overall, the most destabilising nsSNPs (>2) (Figures 1B, 2B) which included all active residues mutation according to DUET was L4S, where a change from except I133 which was associated with 2 mutations (Figure 1B). a hydrophobic to a polar residue may contribute to disruption Sites with 2 nsSNPs are considered to be budding resistance of local conformation (Table 3). The closest most destabilising hotspots (n = 33 for protein stability, n = 7 for ligand aﬃnity). mutational eﬀect on protein stability was from A134D (wild- A total of 57 nsSNPs within 5 Å of PZA were considered type residue involved in hydrogen bonding) (Table 3), likely to be within the ﬁrst shell of residues lining the active site resulting in electrostatic and steric clashes due to a change (Table 2). While majority of the mutational sites associated with in charge and volume aﬀecting the overall stability negatively. more than two mutations comprise of destabilising mutations, The most stabilising mutation on protomer stability was from positions 1, 2, 10, 12, 43, 46, 51, 57, 63, 67, 69, 78, 82, active site residue Y103D, while the closest such mutation was 92, 96, 100, 104, 105, 129, 135–138, 142, 149, 164, 168, and C138R (Table 3). The stabilising eﬀect of these mutations on the 174 comprised of both stabilising and destabilising mutations protein stability and ligand aﬃnity is thought to result from the (Figure 5C). Similarly, for ligand aﬃnity, most mutational sites electrostatic interactions working favourably for sites lying within had destabilising mutational eﬀects, with positions 7, 8, 13, 27, 5 Å of the ligand. The most destabilising mutation according Frontiers in Molecular Biosciences | www.frontiersin.org 8 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 9 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis Frontiers in Molecular Biosciences | www.frontiersin.org 9 July 2021 | Volume 8 | Article 619403 TABLE 2 | Mutations close to the active site of PZA. S. No. Mutation Mutation MAF OR P-value OR P-Wald DUET DUET Distance mCSM- Ligand Foldx Foldx ASA RSA Hydro Residue class (%) adjusted 11G outcome to lig outcome 11G outcome phobicity depth ligand (log (Å) afﬁnity) 1 A134D Others 0.01 2.42 1.00E+00 NA NA 2.98 D 3.05 0.58 S 1.03 D 10 0.08 1.87 6.77 2 A134G Others NA NA NA NA NA 1.62 D 3.05 0.38 D 1.29 S 10 0.08 1.87 6.77 3 A134P Others 0.01 9.70 1.71E-01 NA NA 1.43 D 3.05 0.08 S 5.20 S 10 0.08 1.87 6.77 4 A134T Others NA NA NA NA NA 1.93 D 3.05 0.88 S 0.94 S 10 0.08 1.87 6.77 5 A134V Drug 0.04 19.43 3.68E-03 1.53 3.07E-05 0.41 D 3.05 0.12 S 1.46 S 10 0.08 1.87 6.77 associated 6 I133S Others 0.01 9.70 1.71E-01 NA NA 3.22 D 3.05 0.58 S 3.30 D 3 0.02 1.97 7.90 7 I133T Drug 0.32 6.44 2.90E-09 0.86 4.86E-03 2.79 D 3.05 0.70 S 1.58 D 3 0.02 1.97 7.90 associated 8 D8A Drug 0.01 19.41 2.92E-02 NA NA 0.51 D 3.22 3.27 D 0.54 D 5 0.03 1.63 9.48 associated 9 D8G Drug 0.08 48.69 1.95E-07 1.25 4.42E-02 0.85 D 3.22 3.45 D 1.89 D 5 0.03 1.63 9.48 associated 10 D8E Drug 0.03 14.56 1.74E-02 1.19 1.46E-01 0.79 D 3.22 0.01 S 1.90 D 5 0.03 1.63 9.48 associated 11 D8N Drug 0.05 29.16 1.49E-04 1.24 7.10E-03 1.18 D 3.22 1.66 D 1.26 S 5 0.03 1.63 9.48 associated 12 C138G Others NA NA NA NA NA 0.02 D 3.28 0.01 D 1.12 D 12 0.07 1.17 6.70 13 C138S Drug NA NA NA NA NA 0.00 D 3.28 0.81 S 0.23 S 12 0.07 1.17 6.70 associated 14 C138W Others NA NA NA NA NA 1.05 D 3.28 0.94 S 1.72 S 12 0.07 1.17 6.70 15 C138Y Drug NA NA NA NA NA 0.52 D 3.28 0.91 S 0.57 S 12 0.07 1.17 6.70 associated 16 C138R Drug 0.09 116.96 6.10E-10 1.74 4.08E-12 0.10 S 3.28 0.35 S 2.12 S 12 0.07 1.17 6.70 associated 17 H137N Others 0.01 2.42 1.00E+00 NA NA 0.19 S 3.42 0.12 D 0.40 D 84 0.38 1.40 4.60 18 H137P Drug NA NA NA NA NA 0.37 S 3.42 0.77 D 2.19 D 84 0.38 1.40 4.60 associated 19 H137Y Others 0.01 2.42 1.00E+00 NA NA 0.86 S 3.42 0.01 D 0.34 D 84 0.38 1.40 4.60 20 H137R Drug 0.03 4.85 1.38E-01 0.56 1.21E-04 0.27 D 3.42 0.47 S 0.49 D 84 0.38 1.40 4.60 associated (Continued) fmolb-08-619403 August 3, 2021 Time: 21:57 # 10 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis Frontiers in Molecular Biosciences | www.frontiersin.org 10 July 2021 | Volume 8 | Article 619403 TABLE 2 | Continued S. No. Mutation Mutation MAF OR P-value OR P-Wald DUET DUET Distance mCSM- Ligand Foldx Foldx ASA RSA Hydro Residue class (%) adjusted 11G outcome to lig outcome 11G outcome phobicity depth ligand (log (Å) afﬁnity) 21 D49G Drug 0.05 29.16 1.49E-04 1.66 4.38E-08 1.16 D 3.45 3.46 D 0.46 D 7 0.04 1.53 7.89 associated 22 D49A Drug 0.04 58.33 2.49E-05 1.67 3.17E-06 0.45 D 3.45 3.35 D 2.07 S 7 0.04 1.53 7.89 associated 23 D49N Drug 0.06 77.84 7.23E-07 1.51 3.14E-04 1.68 D 3.45 1.93 D 0.33 S 7 0.04 1.53 7.89 associated 24 D49Y Drug 0.01 9.70 1.71E-01 NA NA 0.74 D 3.45 1.86 D 2.67 S 7 0.04 1.53 7.89 associated 25 D49E Drug 0.02 9.70 7.77E-02 NA NA 0.47 D 3.45 0.25 S 0.70 S 7 0.04 1.53 7.89 associated 26 A102R Others 0.01 2.42 1.00E+00 NA NA 0.70 D 3.50 0.17 S 4.13 D 10 0.08 0.03 5.51 27 A102P Others 0.06 14.58 5.08E-04 0.66 5.33E-04 1.25 D 3.50 0.23 D 0.62 S 10 0.08 0.03 5.51 28 A102V Others 0.06 2.43 1.88E-01 0.91 3.00E-01 0.25 D 3.50 0.16 D 1.91 S 10 0.08 0.03 5.51 29 A102T Drug 0.01 19.41 2.92E-02 1.75 4.98E-04 0.72 D 3.50 0.88 S 2.03 S 10 0.08 0.03 5.51 associated 30 F13C Others 0.01 1.21 1.00E+00 0.64 4.31E-03 2.32 D 3.55 0.49 D 2.70 D 24 0.10 0.60 6.93 31 F13I Drug 0.03 14.56 1.74E-02 NA NA 1.76 D 3.55 0.45 D 0.89 D 24 0.10 0.60 6.93 associated 32 F13L Drug 0.06 34.04 2.89E-05 1.37 2.29E-03 2.03 D 3.55 0.43 D 1.10 D 24 0.10 0.60 6.93 associated 33 F13V Others 0.01 1.21 1.00E+00 NA NA 2.57 D 3.55 0.56 D 1.40 D 24 0.10 0.60 6.93 34 F13S Drug 0.03 1.62 5.28E-01 0.60 3.07E-04 3.10 D 3.55 0.22 S 2.59 D 24 0.10 0.60 6.93 associated 35 K96E Drug 0.08 107.17 3.58E-09 1.75 2.79E-06 2.12 D 3.98 0.67 D 6.92 D 8 0.03 1.87 5.96 associated 36 K96Q Drug 0.03 4.85 1.38E-01 0.64 1.17E-01 1.32 D 3.98 0.08 D 1.04 D 8 0.03 1.87 5.96 associated 37 K96T Drug 0.09 58.47 6.68E-09 1.84 2.25E-13 0.86 D 3.98 0.57 D 3.54 D 8 0.03 1.87 5.96 associated 38 K96M Others 0.01 19.41 2.92E-02 NA NA 0.41 S 3.98 1.03 D 0.27 D 8 0.03 1.87 5.96 39 K96N Drug 0.01 2.42 1.00E+00 NA NA 1.16 D 3.98 0.33 S 2.61 D 8 0.03 1.87 5.96 associated (Continued) fmolb-08-619403 August 3, 2021 Time: 21:57 # 11 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis Frontiers in Molecular Biosciences | www.frontiersin.org 11 July 2021 | Volume 8 | Article 619403 TABLE 2 | Continued S. No. Mutation Mutation MAF OR P-value OR P-Wald DUET DUET Distance mCSM- Ligand Foldx Foldx ASA RSA Hydro Residue class (%) adjusted 11G outcome to lig outcome 11G outcome phobicity depth ligand (log (Å) afﬁnity) 40 K96R Drug 0.11 19.49 1.66E-07 1.43 2.16E-06 0.17 D 3.98 0.08 S 0.74 S 8 0.03 1.87 5.96 associated 41 H71D Drug 0.01 9.70 1.71E-01 NA NA 2.69 D 4.18 2.50 D 5.75 D 5 0.02 0.77 6.25 associated 42 H71N Drug NA NA NA NA NA 2.67 D 4.18 1.34 D 0.64 D 5 0.02 0.77 6.25 associated 43 H71P Others 0.01 4.85 3.13E-01 NA NA 2.36 D 4.18 2.89 D 3.26 D 5 0.02 0.77 6.25 44 H71Q Drug 0.01 19.41 2.92E-02 1.75 2.12E-04 2.29 D 4.18 1.73 D 1.12 D 5 0.02 0.77 6.25 associated 45 H71R Drug 0.05 1.94 3.42E-01 0.88 2.01E-01 1.93 D 4.18 0.83 D 1.52 S 5 0.02 0.77 6.25 associated 46 H71Y Drug 0.18 25.67 4.52E-13 1.48 5.50E-08 0.46 D 4.18 1.96 D 1.78 S 5 0.02 0.77 6.25 associated 47 H57D Drug 0.73 166.91 2.08E-72 1.24 1.05E-01 1.85 D 4.56 1.28 D 1.83 D 16 0.07 1.30 5.63 associated 48 H57P Drug 0.03 38.85 8.53E-04 1.55 1.16E-02 1.23 D 4.56 2.12 D 0.15 D 16 0.07 1.30 5.63 associated 49 H57Q Others NA NA NA NA NA 1.29 D 4.56 0.95 D 0.85 D 16 0.07 1.30 5.63 50 H57R Drug 0.19 254.92 1.02E-20 1.48 9.69E-09 1.17 D 4.56 0.28 D 1.25 D 16 0.07 1.30 5.63 associated 51 H57L Drug NA NA NA NA NA 0.06 D 4.56 1.92 D 1.11 S 16 0.07 1.30 5.63 associated 52 H57Y Drug 0.02 29.13 4.99E-03 2.08 7.92E-06 0.41 S 4.56 1.16 D 0.15 S 16 0.07 1.30 5.63 associated 53 W68C Drug 0.04 24.29 7.49E-04 1.75 1.67E-04 1.45 D 4.97 1.58 D 2.68 D 45 0.16 1.10 5.49 associated 54 W68G Drug 0.14 87.93 2.36E-13 1.58 7.39E-11 2.57 D 4.97 2.13 D 4.04 D 45 0.16 1.10 5.49 associated 55 W68L Drug NA NA NA NA NA 1.62 D 4.97 2.24 D 0.19 D 45 0.16 1.10 5.49 associated 56 W68R Drug 0.20 132.41 4.03E-20 1.50 4.26E-09 1.61 D 4.97 0.58 D 0.08 D 45 0.16 1.10 5.49 associated 57 W68S Drug 0.01 9.70 1.71E-01 NA NA 2.67 D 4.97 1.04 D 2.65 D 45 0.16 1.10 5.49 associated Fifty-seven mutations (nsSNPs) lying within 5 Å of PZA and the corresponding GWAS measures of minor allele frequency (MAF), Odds Ratio (OR), P-values, adjusted OR (aOR), and P-values from Wald test corresponding to aORs, along with structural measures of distance to ligand, DUET, FoldX, ligand afﬁnity values and effect. Wild type residues for mutations highlighted and marked in green are considered to participate in hydrogen bonding, those in yellow form the catalytic triad, residues in teal (and blue) are involved in substrate binding, while the residues in purple are involved in the iron centre. The columns are coloured to highlight the most signiﬁcant column attribute with deeper colours denoting the greatest effects. The dark colours in MAF, OR, and aOR columns indicate the highest values, while P-values are coloured with the darkest colour showing the most signiﬁcant values. Values in the DUET, mCSM-lig, and FoldX columns are coloured according to the extent of their respective effects with red indicating destabilising and blue denoting stabilising effects. nsSNPs, non-synonymous Single Nucleotide Polymorphisms; PZA, pyrazinamide; GWAS, Genome-Wide Association Studies. D, Destabilising; S, Stabilising. fmolb-08-619403 August 3, 2021 Time: 21:57 # 12 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 5 | Protein stability and ligand afﬁnity effects of nsSNPs on pncA structure and by position. Mutational impact of nsSNPs on the pncA protein structure coloured by average (A) DUET Protein stability (n = 424) and (B) ligand afﬁnity (n = 201). Barplots (C,D) showing the frequency of mutations within the pncA gene. The horizontal axis shows the mutational positions within pncA and the vertical axis shows the frequency of mutations. Positions on the horizontal axis are coloured to denote the active site residues: green (residues involved in hydrogen bonding with PZA), yellow (catalytic triad), blue and teal (substrate binding), purple (iron centre). For a given position, each corresponding mutation (nsSNP) is coloured by the level of stability according to (C) DUET(n = 424) and (D) Ligand afﬁnity (n = 201) where the horizontal axis denotes amino acid positions in pnca, and is restricted to positions lying within 10 Å of PZA for ligand afﬁnity. Destabilising mutations are depicted in red and stabilising mutations in blue, where colour intensity reﬂects the extent of effect, ranging from 1 (most destabilising) to + 1 (most stabilising). The structural ﬁgures (A,B) are rendered using UCSF Chimera (version 1.14). The barplot ﬁgure (C,D) is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms; PZA, pyrazinamide; pncA, pyrazinamidase. Interestingly, mutation A134P was the most stabilising according to ligand aﬃnity was D49G located at 3.5 Å (Table 3). The to FoldX, while the same was estimated to have a destabilising three subsequent destabilising mutations for ligand aﬃnity were eﬀect according to DUET (Table 3). All mutations except A134D also all within 5 Å of PZA binding site namely D8G (3 Å), and A134P were associated with PZA drug resistance (Table 3). D49A (3.5 Å), and D8A (3 Å) (Supplementary Table 1), all arising likely due to the loss of charge and volume interfering with ligand interaction. The mutation with the greatest stabilising Relating Structural and GWAS Analyses eﬀect on ligand aﬃnity was G162D, located at8 Å, i.e. outside The minor allele frequencies for the 424 nsSNPs were mapped the ﬁrst shell of inﬂuence (>5 Å) from the ligand. This is onto their corresponding amino acid positions of the pncA possibly due to the resulting electrostatic eﬀects and increase in gene (Supplementary Figure 8). Position 10 had the highest volume, which may favour hydrogen bond formation with nearby cumulative minor allele frequency (MAF, 2.3%), followed by residues and PZA binding, thereby increasing aﬃnity (Table 3). position 7 (1.2%), position 57 (1.0%), position 51 (0.6%), The closest most stabilising mutational impact on ligand aﬃnity and position 14 (0.5%). The risk of PZA resistance from the was due to mutation A134P, though this was a marginal eﬀect alleles at each SNP was estimated by calculating ORs and P-values (Table 3). The most destabilising mutation according to FoldX using a GWAS approach. Additionally, adjusted OR (aOR) was C72W, which is located far away from the active site (27 Å). which accounted for the confounding eﬀects of lineage were also Frontiers in Molecular Biosciences | www.frontiersin.org 12 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 13 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis TABLE 3 | Mutations with extreme effects. Mutational effects Mutation Mutation class MAF (%) OR P-value Distance Stability 11G Ligand afﬁnity to ligand (Å) Highest OR H51D Drug-associated 0.30 414.61 4.49E-33 5.66 2.2 1.82 Most frequent mutation Q10P Drug-associated 2.11 156.23 1.28E-207 6.02 0.63 1.77 Most deStabilising for protein L4S Drug-associated 0.25 28.46 5.63E-18 15.33 3.87 1.08 stability (DUET) Closest destabilising for protein A134D Others 0.007 2.43 1.00 3.05 2.98 0.58 stability (DUET) Most stabilising for protein Y103D Others 0.22 142.33 1.24E-21 5.42 1.18 0.85 stability (DUET) Closest stabilising for protein C138R Drug-associated 0.09 116.96 6.09E-10 3.28 0.10 0.35 stability (DUET) Most destabilising for ligand D49G Drug-associated 0.05 29.16 0.0001 3.45 1.16 3.46 afﬁnity Closest destabilising for ligand D8G Drug-associated 0.08 48.69 1.95E-07 3.22 0.85 3.45 afﬁnity Most stabilising for ligand G162D Drug-associated 0.03 38.85 0.0008 8.32 1.04 2.23 afﬁnity Closest stabilising for ligand A134P Others 0.007 9.70 1.71E-01 3.05 1.43 0.08 afﬁnity Most destabilising for protein C72W Drug-associated 0.01 19.41 0.03 7.05 27.46 – stability (Foldx) Most stabilising for protein A134P Others 0.007 9.70 1.71E-01 3.05 5.2 – stability (Foldx) Mutations (nsSNPs) with extreme effects on odds ratio, frequency, thermodynamic stability, and ligand afﬁnity. For ligand afﬁnity, only mutations lying within 10 Å of PZA (pyrazinamide) were considered. nsSNPs, non-synonymous Single Nucleotide Polymorphisms; Å, Angstroms; MAF, minor allele frequency; OR, Odds Ratio; 11G, Change in Gibbs free energy in Kcal/mol. FIGURE 6 | Correlation between biophysical effects and GWAS measures of Odds Ratio (OR), P-values (P) and minor allele frequency (MAF). Pairwise correlations between MAF, negative log10 P-value [-Log(P)], Log10 (OR) and (A) Protein stability (DUET) and FoldX for 424 nsSNPs, (B) Ligand afﬁnity of 201 nsSNPs (lying within 10 Å of PZA). The upper panel in both plots include the pairwise Spearman correlation values along with their statistical signiﬁcance (*P < 0.05, **P < 0.01, ***P < 0.001). The points in the lower panel represent nsSNPs, coloured according to respective stability effects: (A) nsSNPs with destabilising effect for DUET and ligand afﬁnity are coloured red, while for FoldX these appear in blue, (B) nsSNPs with stabilising effect for DUET and ligand afﬁnity appear in blue, while for FoldX these appear in red. The diagonal plots display the histogram of the corresponding parameter. The ﬁgure is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms; PZA, pyrazinamide; Units for DUET, FoldX and Ligand Afﬁnity (Kcal/mol). Frontiers in Molecular Biosciences | www.frontiersin.org 13 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 14 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 7 | Density distribution of M. tuberculosis lineages. A total of 4,433 samples belonging to Lineages 1–4, containing 419‘pncA mutations were considered. The horizontal axis shows the DUET stability values (1, most destabilising) to blue (C1, most stabilising). while the vertical axis shows the density distribution of M. tuberculosis lineages coloured by mutation class as either DM (associated with pyrazinamide resistance in orange) or OM (not associated with pyrazinamide drug resistance which appear in grey). DM mutations comprise of a total of 3,565 samples contributing to 226 mutations, while 868 samples contributing to 193 mutations formed part of the OM mutation class. The ﬁgure is generated using R statistical software (version 4.0.2). Abbreviations used: nsSNPs: non-synonymous Single Nucleotide Polymorphisms, pncA: pyrazinamidase. analysed (Supplementary Figure 9). The majority of nsSNPs Supplementary Figure 8, and Supplementary Table 1), with were linked to increased likelihood of being resistant to PZA most of these positions being present in the metal binding (OR > 1). For unadjusted ORs, this was 96% (310/322), while and active sites. for aOR, it was 75% (122/163). Wild type position 51 had the When assessing sites in relation to mutational diversity, 3 0 highest unadjusted OR (> 350, P < 10 ), followed by positions active site residues were among the highest, with residues 1 9 57, 120 (OR > 250, P < 10 ), and subsequently by positions 10, H51, H57, H71, K96 associated with six distinct mutations, 1 0 103, 68, 135, 138, 96, and 180 (OR > 100; P < 10 ) (Figure 1A, followed by F13, D49, W68, A134, C138 with ﬁve mutation Frontiers in Molecular Biosciences | www.frontiersin.org 14 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 15 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis each, while residues D8, Y103, H137 were associated with values. The diﬀerence in structural features were most prominent four distinct mutations and residues I133 associated with two when all 424 SNP mutations were considered (P < 0.0001) distinct mutations (Figure 1B). The dominant eﬀect of a highly (Figures 4A,B) with lesser signiﬁcance for ligand aﬃnity frequent mutation (Q10P; MAF = 2.1%, OR = 156.23) in the (P < 0.05), ASA (P < 0.01), and RSA and RD (P < 0.001) population compared to two other mutations observed at the values when 201 nsSNPs lying within 10 Å were considered same position namely Q10R (MAF = 0.13%, OR = 83.01) and (Figure 4C). Mutations associated with PZA resistance have Q10H (MAF = 0.08%, OR = 107.17) (Supplementary Table 1), lower DUET (Figure 4A, top left) but higher FoldX stability makes position 10 prominent in terms of MAF (Supplementary changes (Figure 4B, bottom left), and lower binding aﬃnity Figure 8) while sites involved in the catalytic activity and iron (Figure 4C, second from bottom left) compared to OM. metal centre are more prominent with respect to SNP diversity Additionally, it also appears that that while drug mutations (Supplementary Figure 8). These results suggest that mutations need not necessarily occur at the hydrophobic sites (KD values, at these structurally and functionally important sites are likely P > 0.05), they tend to lie buried indicated by higher RD values, under selective pressure exerted by the drug resulting in this and consequently lower surface area (ASA and RSA) compared observed mutational diversity. to OM (Figures 4A,B). The relationship between structural measures of stability and OR was visualised as a bubble plot indicating that mutations Distinct Stability Proﬁle for Drug associated with greater resistance (high OR) tend not to have Mutations and Lineage 1 extreme eﬀects (Supplementary Figure 10). Furthermore, this A total of 419 nsSNPs are lineage speciﬁc (L1: 74; L2: 277; L3: relationship along with MAF, OR, and P-values was assessed 104; L4: 311). The greatest diversity of nsSNPs was observed through Spearman correlations (Figures 6A,B). MAF was in L3 (54.7%), followed by L1 (51.4%) and Lineage 2 (14.7%) strongly correlated with P-values for all 424 mutations (r = 0.78, with L4 showing the lowest diversity (14.1%) despite containing P < 0.001) and 201 mutations lying with 10 Å of PZA (r = 0.84, the highest number of samples (Supplementary Figure 6). P < 0.001) (Figures 6A,B). As expected, OR and P-values were Statistical analysis of the DUET 11G distributions revealed strongly correlated (r = 0.9, P < 0.001) for all 424 nsSNPs and signiﬁcant diﬀerences between all lineages except between 201 nsSNPs close to PZA binding site (Figures 6A,B). FoldX L3 and L4. Lineage diﬀerences for DUET 11G were most stability and DUET stability values showed moderate correlation prominent between L2 and L4 (P < 0.0001), followed by (r = 0.45, P < 0.001). The negative sign for the DUET and L1 and L4 (P < 0.001) (Supplementary Table 2A). Within FoldX associations is expected since stability changes measured each lineage, mutational distributions were signiﬁcantly diﬀerent by these tools have opposite signs (i.e., 11G < 0: destabilising in between DM and OM mutation classes (P < 0.0001) except DUET vs. stabilising in FoldX). FoldX 11G values showed weak L3 (Supplementary Table 2B). Interestingly, a distinct stability but signiﬁcant correlations with OR (r = 0.23, P < 0.001), and proﬁle was observed for DM mutations within L1. Mutations P-values (r = 0.18, P < 0.01) (Figure 6A), while DUET 11G and associated with drug resistance showed a marked peak around the ligand aﬃnity showed weak and insigniﬁcant association with OR extreme end (0.75 DUET 11G) of the destabilising spectrum (r = 0.1, P > 0.05) (Figures 1B, 6A), including adjusted OR (Figure 7) within L1. (Supplementary Figures 9A, 8B). When considering aOR and its relationship with stability and other structural features [i.e., Accessible (ASA), Relative DISCUSSION Surface Area (RSA), residue depth (RD), and hydrophobicity values (KD)], there was high correlation (r > 0.6, P < 0.05) Genetic mutations including nsSNPs present within drug-targets with adjusted and unadjusted ORs (Supplementary Figure 9A). and their activating genes are the main drivers of resistance DUET 11G showed moderate positive correlation between development in TB (Schön et al., 2017). The motivation for ASA and RSA (r > 0.6, P < 0.05), while moderately investigating the missense mutations within the protein coding negative correlation with RD (r0.5, P < 0.05), and weak region only of the pncA gene was to enable understanding negative correlation with KD values (r0.2, P < 0.05) of the phenotypic mutational eﬀects in relation to PZA (Supplementary Figure 9A). The same structural features, resistance development. While the exact molecular mechanisms however, did not demonstrate correlation with either of PZA resistance are yet to be fully elucidated, the binding FoldX 11G (Supplementary Figure 9A) or ligand aﬃnity pocket of PZA and its key interactions are well known and (Supplementary Figure 9B). characterised (Petrella et al., 2011; Ali et al., 2020; Sheik Amamuddy et al., 2020; Khan et al., 2021). This knowledge was used to guide the molecular docking of PZA to generate Structural Differences in Drug the pncA-PZA complex in the absence of an experimentally Associated Mutations solved structure of the bound complex in Mtb. While docking Comparing stability eﬀect (DUET and FoldX), ligand aﬃnity, generates a variety of ligand conformations (poses), choosing ligand distance, and other structural features (ASA, RSA, RD, the “best” pose is based on considerations around key molecular KD) between mutations associated with PZA drug resistance interactions formed by the ligand, interaction energy of the (DM) and other mutations (OM), revealed statistically signiﬁcant docked complex and subject expertise. Using these guides, diﬀerences (P < 0.05) between all features except hydrophobicity docking pose 1 was chosen due to its molecular interactions Frontiers in Molecular Biosciences | www.frontiersin.org 15 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 16 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis with known key residues and close alignment with previously 2014; Whitﬁeld et al., 2015). While two other genes, rpsA published studies (Karmakar et al., 2018; Ali et al., 2020; Khan and panD have also been linked to PZA resistance, a clear et al., 2021). In addition, we analysed the top two docking link between rpsA and PZA resistance is lacking (Shi et al., poses using the mCSM pipeline (Supplementary Figure 3). 2011; Alexander et al., 2012; Simons et al., 2013; Tan et al., The resulting mutational eﬀects on pncA stability and ligand 2014) although there is increasing evidence to support panDs aﬃnity did not diﬀer between poses indicating the small association with PZA resistance (Pandey et al., 2016; Werngren diﬀerences in pose did not aﬀect downstream analysis. It also et al., 2017; Gopal et al., 2020). In our analysis, there were suggests that due to the small size of the PZA molecule, the only a few samples with rpsA and panD mutations, therefore orientation of the aromatic ring within the cavity may have limiting attempts at assessing their synergistic relationship with more ﬂexibility in its orientation and interaction with the PZA resistance. Mutations within the pncA gene and its promoter neighbouring residues, but without drastically impacting the remain the most common route to PZA resistance (Dookie molecular interactions for global protomer stability and ligand et al., 2018) (Khan et al., 2019). Nearly 70% of the MDR binding aﬃnity. isolates and 13% XDR isolates had nsSNPs in the pncA coding The molecular motion of pncA assessed by NMA was region. The burden of pncA mutations in the MDR and XDR visualised to understand the mutational eﬀects with regard isolates was lower in our analysis compared to 88.0% and to ﬂexibility (Supplementary Figure 1). Sites displaying high 20% observed by Pang et al. (2017). In another study, 70% mutational frequency or association with drug resistance of the MDR isolates, and signiﬁcantly higher i.e., 96% of XDR mutations were not located in regions with high ﬂexibility, with isolates harboured pncA mutations including nsSNPs (Allana large molecular motions mainly restricted to the loop region 60– et al., 2017). An alternative route to resistance for pncA as 66. This suggests the molecular motion in pncA does not interfere a non-essential gene encoding an enzyme that transforms a with PZA binding as active site residues were not associated with prodrug to drug would be by INDELs or mutations leading to high ﬂuctuations. premature stop codons resulting in the protein being degraded Normal mode analysis shows large scale molecular motions. on translation. A recent report analysing the pncAc.85_86insG Molecular dynamics (MD) studies oﬀer insights into the frameshift mutation using structural and biophysical analysis ﬁner grained atomic motions and are an excellent way to showed the mutation resulted in a truncated and incomplete investigate molecular mechanisms. However, these studies are protein lacking the active site pocket (Karmakar et al., 2018). computationally intensive and are diﬃcult to scale for studying Despite this obvious route to resistance, only 1% samples hundreds of mutations. A recent MD study on a subset of in our dataset showed INDELs and stop codons, compared mutations found within our dataset analysed seven pncA nsSNPs to 13% of samples that showed missense point mutations (F94L, F94S, K96N, K96R, G97C, G97D, and G97S) showed in pncA. This is consistent with the knowledge that nsSNPs that these destabilising mutations altered the binding pocket, in pncA remain the major route to resistance for PZA allowing increased PZA ﬂexibility (Khan et al., 2021). All seven (Khan et al., 2019). mutations were associated with PZA resistance and also showed Destabilising eﬀects are considered detrimental to the destabilising eﬀects in our study. A similar study of destabilising downstream protein function (via disruption of drug aﬃnity, mutations R123P, T76P, H7R associated with PZA resistance nucleic acid aﬃnity or overall complex stability) and are thus showed that the mechanism of resistance could be through given higher consideration in classifying mutations (Wylie and increasing the ﬂexibility of the region they are located in, Shakhnovich, 2011). In our analysis, around 85% of mutations thereby changing the binding pocket volume (Ali et al., 2020). were destabilising for overall protein stability as well as complex Another MD study of mutations P54L and H57P showed that aﬃnity. It is thought that the resistant phenotype is imparted they decrease overall stability along with reduced ligand aﬃnity either through aﬀecting protein folding, instability of the PZase leading to PZA resistance (Mehmood et al., 2019). All of these protein, prevention of coenzyme complex (Gopal et al., 2016) observations are concordant with our analysis. or loss of virulence factor synthesis (Gopal et al., 2016). Destabilising eﬀects of nsSNPs are thought to be the main Further, this is thought to come without a high bacterial ﬁtness reason for impeding protein function through directly eﬀecting cost since pncA is primarily an activator of the PZA drug. protomer stability or ligand aﬃnity. However, large stabilising This is similar to a recent observation reported in the katG eﬀects can have an equally deleterious impact on protein gene (target for the anti-TB pro-drug, isoniazid) with a high function through rigidiﬁcation, impeding ﬂexibility and dynamic proportion of destabilising mutations (Portelli et al., 2018). molecular motions. This has been implicated more generally Also, a higher proportion 60% (n = 253) of SNP mutations within a disease context (Gerasimavicius et al., 2020) and more showed electrostatic changes compared to 35% reported by speciﬁcally in PZA resistance (Rajendran and Sethumadhavan, Portelli et al. (2018). This likely due to the larger sample size of 2014). It oﬀers an explanation for the observance of the stabilising our dataset. mutation site 103. Drug associated mutations at this site (Y103C, All active site residues appear to be under drug selection Y103H, and Y103S) could result from the rigidiﬁcation of the pressures due to multiple mutations (>2) associated with these binding pocket leading to reduced binding aﬃnity measured as with the exception of I133, considered to be an emerging or destabilising PZA aﬃnity. budding-resistance hotspot. In our analyses, there were 22 such Mutations within pnca are scattered along the entire gene sites while 83 sites within pncA associated with > 2 nsSNPs linked length observed in studies (Stoﬀels et al., 2012; Miotto et al., to PZA drug resistance (categorised as DM). However mutations Frontiers in Molecular Biosciences | www.frontiersin.org 16 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 17 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis were not restricted to the active site, with less than 50% resistant extreme changes in stability or aﬃnity parameters (Figure 6). variants lying within 10 Å of the active site of PZA, indicating the One possible explanation is that the ﬁtness landscape is gene and possible role of distal residues in resistance development (Portelli function speciﬁc, optimised diﬀerently for genes directly coding et al., 2018). Mutations associated with drug resistance tend to for drug targets and for non-essential genes like pncA. Another have lower stability, lie buried within the structure with lesser major consideration is that resistance is often acquired through surface area as shown by Karmakar et al. (2020). a stepwise ordinal accumulation of mutations (Woodford and Our study compares results from two diﬀerent computational Ellington, 2007; Ismail et al., 2019). The genetic background can stability predictors: mCSM and FoldX (Schymkowitz et al., dramatically inﬂuence ﬁtness eﬀects associated with mutations 2005). Unsurprisingly, most mutations were found to have a (Wong, 2017). Consequently, the mutational impact diﬀers when destabilising eﬀect (Supplementary Figure 11). FoldX reported occurring against a sequence background of extant resistant 85% (vs.80% estimated by DUET) nsSNPs with destabilising mutations, a phenomenon known as epistasis (Wong, 2017). eﬀect. The range for absolute 11G values was greater for FoldX Since resistance development is a balanced interplay between (median: 2.0; range: 5.2, 27.46) compared to DUET (median: ﬁtness eﬀects and cost of resistance, epistasis warrants due 0.1; range: 3.9, 1.2). There was however, 77% agreement consideration in eﬀorts to understand and limit the evolution of between FoldX and DUET outcomes (data not shown). multi-drug resistance. Interestingly, drug associated mutations displayed higher FoldX The use of mCSM suite of tools has the advantage of 11G predictions compared to mCSM-DUET 11G predictions. studying global (protein stability) as well as local eﬀects (ligand A possible explanation for this is the diﬀerences in the underlying aﬃnity, protein-protein interaction, and protein nucleic-acid parameters the diﬀerent methods use. FoldX constructs mutant interaction). Additionally, it also provides the methodological structures by mutating the target residue and searching for the consistency for comparing molecular eﬀects and beneﬁts optimal conformation by iteratively altering the position of the application of machine learning methods (ML) to explore neighbouring side chains. The stability of the mutant structure greater mechanistic details. While computationally intensive, is estimated using an empirical force ﬁeld made of several ML methods would beneﬁt from using tools such as DynaMut energy terms. This compares to DUET where estimates of the (Rodrigues et al., 2018) which account for protein molecular structural eﬀects are based on diﬀerences between the wild-type motions when estimating mutational eﬀect on protein stability. environment and pharmacophore atomic changes resulting from Additionally methods which consider anti-symmetric properties the mutation, without the need to generate mutant structures. of mutational impact i.e., 11G (A ! B) = 11G (B ! A) With this in mind, it appears that the DM mutations have like DeepDDG (Cao et al., 2019) and INPS-MD (Savojardo et al., larger local perturbations in the mutated region considered 2016) have the potential to build robust predictive models and by FoldX, resulting in higher 11G predictions compared to improve the “learning” capability of ML methods in the context the lesser eﬀects of surface area considered by DUET. Drug of machine learning. resistance mutations displaying smaller surface area compared Mtb lineages have been associated with virulence, disease to their susceptible counterparts were also observed in recent transmission, drug resistance, and clinical outcome (Ford et al., studies investigating nsSNPs in Mtb genes (Portelli et al., 2018; 2013; Reiling et al., 2013; Novais et al., 2017; Correa-Macedo et al., Karmakar et al., 2020) indicating the role of compensatory 2019; Oppong et al., 2019; McHenry et al., 2020). Lineage speciﬁc mutations, alleviating any ﬁtness penalty in the development of diﬀerences between lineages 2 and 4 have recently been noted in the drug resistance phenotype. The extent of the contribution the development of TB drug resistance, especially related to MDR of surface area in these methods is reﬂected in the observation and XDR strains (Oppong et al., 2019). Our study highlighted of moderate correlations between DUET and structural features, the most signiﬁcant diﬀerences between L2 and L4 with respect and the weaker associations between FoldX and structural to protomer stability demonstrating the biophysical phenotypic features (Supplementary Figure 9A). Structural associations for manifestation of these underlying genotypic changes. The ligand aﬃnity were also observed to be weak (Supplementary observance of a distinct peak for destabilising mutations related Figure 9B) most likely due to the role of factors involved in short- to drug resistance within L1 suggests that the extreme mutational range interactions (like Van der Waal’s forces) not considered consequences of such mutations in the “ancient” lineage 1 may in our analysis. A similar view emerged in the recent study by be rapidly giving way to other “modern” M. tuberculosis lineages Karmakar et al. (2020) where no signiﬁcant diﬀerences were linked to MDR and XDR-TB and virulence. observed for PZA binding aﬃnity. Our study is based on a well-characterised clinical dataset It has been suggested that frequently occurring mutations may sourced globally from over 35 K clinical isolates, and leverages not confer extreme changes in biophysical stability measures, the availability of robust metadata (lineage, geography, DST, etc.) with mild stability eﬀects oﬀering local ﬁtness advantages for each isolate. We show that the framework used in our work (Portelli et al., 2018). Our data presented us with the opportunity allows us to investigate the interrelationships between genomic to test this theory empirically by assessing relationships of features from GWAS analysis and the biophysical measures of stability with GWAS measures of MAF, OR, and P-values. At nsSNPs, helping to contextualise the underlying bacterial ﬁtness a glance, it appears that mutations with high OR tend be and mutational landscape. The need to consider multiple stability less extreme in their impact on protein stability and ligand predictors with diﬀerent underlying principles to validate these aﬃnity (Supplementary Figure 10). However, we did not ﬁnd associations has also been highlighted. Lineage associations of any signiﬁcant association with high frequency mutations and drug resistance, and their biophysical consequences, require Frontiers in Molecular Biosciences | www.frontiersin.org 17 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 18 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis further investigation and the functional characteristics of the manuscript. All authors contributed to the manuscript and mutations should be validated in future experiments. We hope approved the submitted version. such a framework can be used to understand and inform therapeutic and stewardship eﬀorts. FUNDING TT was supported by a BBSRC Ph.D. studentship (Grant No: DATA AVAILABILITY STATEMENT BB/S507544/1). JP was funded by a Newton Institutional Links Grant (British Council. 261868591). CE was funded by the All pre-generated TB-proﬁler results were downloaded for all Medical Research Council UK (Grant No. MR/T000171/1). isolates from tbdr.lshtm.ac.uk/sra. TC was funded by the Medical Research Council UK (Grant No. MR/M01360X/1, MR/N010469/1, MR/R025576/1, and MR/R020973/1) and BBSRC (Grant No. BB/R013063/1). AUTHOR CONTRIBUTIONS TT was responsible for the molecular docking, integrating SUPPLEMENTARY MATERIAL genomics and structural data, data analysis, and writing the initial draft. JP made available and generated the genomics data The Supplementary Material for this article can be found results. CE provided the FoldX pipeline. 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Epistasis and the evolution of antimicrobial resistance. Front. practice. No use, distribution or reproduction is permitted which does not comply Microbiol. 8:246. with these terms. Frontiers in Molecular Biosciences | www.frontiersin.org 20 July 2021 | Volume 8 | Article 619403 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Frontiers in Molecular Biosciences Unpaywall http://www.deepdyve.com/lp/unpaywall/structural-and-genomic-insights-into-pyrazinamide-resistance-in-GYfyGnKGJc

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DOI: 10.3389/fmolb.2021.619403
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Abstract

fmolb-08-619403 August 3, 2021 Time: 21:57 # 1 ORIGINAL RESEARCH published: 23 July 2021 doi: 10.3389/fmolb.2021.619403 Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages 1 1 1 1,2 Tanushree Tunstall , Jody Phelan , Charlotte Eccleston , Taane G. Clark and Nicholas Furnham 1 2 Department of Infection Biology, London School of Hygiene and Tropical Medicine, London, United Kingdom, Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom Resistance to drugs used to treat tuberculosis disease (TB) continues to remain a public health burden, with missense point mutations in the underlying Mycobacterium tuberculosis bacteria described for nearly all anti-TB drugs. The post-genomics era along with advances in computational and structural biology provide opportunities to understand the interrelationships between the genetic basis and the structural Edited by: consequences of M. tuberculosis mutations linked to drug resistance. Pyrazinamide Arun Prasad Pandurangan, MRC Laboratory of Molecular Biology (PZA) is a crucial ﬁrst line antibiotic currently used in TB treatment regimens. The (LMB), United Kingdom mutational promiscuity exhibited by the pncA gene (target for PZA) necessitates Reviewed by: computational approaches to investigate the genetic and structural basis for PZA Wim Vranken, Vrije University Brussel, Belgium resistance development. We analysed 424 missense point mutations linked to PZA Raghavan Varadarajan, resistance derived from 35K M. tuberculosis clinical isolates sourced globally, which Indian Institute of Science (IISc), India comprised the four main M. tuberculosis lineages (Lineage 1–4). Mutations were *Correspondence: annotated to reﬂect their association with PZA resistance. Genomic measures (minor Nicholas Furnham [email protected] allele frequency and odds ratio), structural features (surface area, residue depth and hydrophobicity) and biophysical effects (change in stability and ligand afﬁnity) of Specialty section: point mutations on pncA protein stability and ligand afﬁnity were assessed. Missense This article was submitted to Biological Modeling and Simulation, point mutations within pncA were distributed throughout the gene, with the majority a section of the journal (>80%) of mutations with a destabilising effect on protomer stability and on ligand Frontiers in Molecular Biosciences afﬁnity. Active site residues involved in PZA binding were associated with multiple Received: 20 October 2020 Accepted: 14 April 2021 point mutations highlighting mutational diversity due to selection pressures at these Published: 23 July 2021 functionally important sites. There were weak associations between genomic measures Citation: and biophysical effect of mutations. However, mutations associated with PZA resistance Tunstall T, Phelan J, Eccleston C, Clark TG and Furnham N (2021) showed statistically signiﬁcant differences between structural features (surface area and Structural and Genomic Insights Into residue depth), but not hydrophobicity score for mutational sites. Most interestingly Pyrazinamide Resistance M. tuberculosis lineage 1 (ancient lineage) exhibited a distinct protein stability proﬁle in Mycobacterium tuberculosis Underlie Differences Between Ancient for mutations associated with PZA resistance, compared to modern lineages. and Modern Lineages. Front. Mol. Biosci. 8:619403. Keywords: Mycobacterium tuberculosis, pncA, nsSNPs, non-synonymous Single Nucleotide Polymorphisms, biophysical effects, thermodynamic stability, mCSM, FoldX doi: 10.3389/fmolb.2021.619403 Frontiers in Molecular Biosciences | www.frontiersin.org 1 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 2 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis in the pncA gene remain the most common mechanism of PZA INTRODUCTION resistance (Khan et al., 2019). Tuberculosis (TB), is a highly infectious and contagious air-borne Advances in whole genome sequencing (WGS) is assisting disease caused by the bacterium Mycobacterium tuberculosis. the proﬁling of M. tuberculosis for drug resistance, lineage Despite its ancient origins and the eﬀorts to develop disease determination and virulence, and presence in a transmission control and prevention measures, the disease continues to cluster (Phelan et al., 2019a), thereby informing clinical cause a global public health burden, with increased drug management and control policies. This is reﬂected in the resistance making control diﬃcult. In 2019, WHO reported WHO recommendation for use of rapid molecular testing around 10 million global cases of TB of which 1.4 million for detecting TB and drug resistant TB (World Health result in death (World Health Organization [WHO], 2020). Organization [WHO], 2020). The use of WGS can uncover new In 2019, 465,000 cases of rifampicin resistant TB (RR-TB), resistance mutations through genome-wide association studies among which 78% cases of multidrug-resistant TB (MDR- (GWAS) and convergent evolution analysis (Phelan et al., 2016; TB, deﬁned as having additional resistance to isoniazid) were Coll et al., 2018). reported. Among these RR/MDR cases, 6% cases were further Furthermore, using protein structure, the biophysical eﬀects of resistant to one ﬂuoroquinolone and one injectable second point polymorphisms can be investigated allowing a mechanistic line drug, leading to extensively drug resistant TB (XDR-TB) understanding of resistance development (Phelan et al., 2016; (World Health Organization [WHO], 2020). Kavvas et al., 2018; Portelli et al., 2018). This approach can The size of the M. tuberculosis genome (reference H37Rv highlight important functional resistance mutations before they strain) is 4.4 Mb, with a high (65%) GC content. The take hold in a population, corroborate drug susceptibility test M. tuberculosis genome is clonal, and consists of seven main results, as well as provide insights in highly polymorphic lineages, which vary by their geographical spread (L1: Indo- candidate loci (e.g., pncA) where many of the putative mutations Oceanic, L2: East Asian, L3: East-Africa-Indian, and L4: Euro- have low frequency. It has been observed that sites with multiple American) (Phelan et al., 2016). The lineages are further classiﬁed mutations (>2) are linked to drug resistance (Comas et al., into ancient (L1, L5–6), modern (L2–4), and intermediate (L7) 2011), but such resistance hotspots may not necessarily lie close strains, with L2 being particularly mobile as evidenced by its to the drug binding site. To this eﬀect, sites with 2 mutations recent spread to Europe and Africa from Asia (Phelan et al., are considered as “emerging” or “budding” resistance hotspots 2016). The M. tuberculosis lineages appear as distinct clades (Portelli et al., 2018). on phylogenetic trees (Coll et al., 2014) and govern disease One assessment of the impact of missense mutations is to transmission and dynamics with phenotypic consequences measure the change in a protein structure’s as well as drug- on clinical severity and drug resistance (Ford et al., 2013; target complex’s physical interactions that contribute to its overall Reiling et al., 2013), including recent reports of lineage-speciﬁc stability. Computational approaches (e.g., the mCSM suite; Pires associations with the latter (Oppong et al., 2019). Drug resistance et al., 2014a, 2016; Pires and Ascher, 2016, 2017; Rodrigues et al., in M. tuberculosis is almost exclusively due to mutations 2019) have been developed to predict the eﬀects of missense [including non-synonymous Single Nucleotide Polymorphisms point mutations on overall protein structure stability, as well (nsSNPs), insertions and deletions (INDELs)] in genes coding as the binding aﬃnity/stability of ligand, protein-protein, and for drug-targets or drug-converting enzymes. Changes in eﬄux protein-nucleic acid interactions within a single framework, pump regulation may also have an impact on the emergence based on either an experimentally resolved structure or derived of resistance (Al-Saeedi and Al-Hajoj, 2017) and putative model. Here we apply such approaches to the eﬀects of missense compensatory mechanisms have been described to overcome point mutations in the pncA gene. In addition, we also analyse ﬁtness impairment that arises during the accumulation of biophysical structural features including surface area, residue resistance conferring mutations (de Vos et al., 2013). Resistance- depth and hydrophobicity for residues and sites associated with associated point mutations have been described for all ﬁrst-line missense point mutations. drugs, including rifampicin, isoniazid and pyrazinamide, as well A crystal structure for pncA from M. tuberculosis has as for several second-line and newer drugs (ﬂuoroquinolones, been determined as a monomeric enzyme of 186 amino acids bedaquiline) (Somoskovi et al., 2001; Boonaiam et al., 2010; (19.6 kDa) (Petrella et al., 2011). The structure comprises a 6- Segala et al., 2012), but knowledge is still incomplete. stranded parallel beta sheets, with helices on either side forming Pyrazinamide (PZA) is a crucial antibiotic used in WHO a single a/b domain with a metal cofactor (iron, Fe2+) binding recommended combination therapies in the front-line treatment site formed of D49, H51, H57, and H71. The substrate binding of TB. It is a pro-drug which is activated by the amidase cavity in MtPncA is small, approximately 10 Å deep and 7 Å activity of the enzyme pyrazinamidase/nicotinamidase (PZase; wide. It consists of highly conserved residues F13 and W68 MtPncA) encoded by the pncA gene, converting PZA to its active that are essential in substrate binding with Y103 and H137 form of pyrazinoic acid (POA). Despite its indispensable status limiting access to this cavity (Petrella et al., 2011). The catalytic in TB treatment, PZA’s exact mode of action remains poorly triad consisting of C138, D8, K96 is indicative of a cysteine- understood. Other genes (rpsA and panD) have been implicated based catalytic mechanism (Petrella et al., 2011). Leveraging in PZA resistance (Dookie et al., 2018) with a recent study this crystal structure, we developed an in silico framework suggesting that PZA exerts its antibacterial activity by acting as to assess the biophysical impact of pncA mutations and their a target degrader of panD, blocking the synthesis of coenzyme A resistance risk as determined by GWAS. In this study, we attempt (targeted by POA) (Gopal et al., 2020). Despite this, mutations to understand PZA resistance by exploring the relationship Frontiers in Molecular Biosciences | www.frontiersin.org 2 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 3 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis between the genomic features and the biophysical consequences the ligand orientation generated by molecular docking performed of stability and aﬃnity of nsSNPs, and how this is reﬂected in by Karmakar et al. (2018) and comparing interaction of both diﬀerences between M. tuberculosis lineages. poses with active site residues through an Arpeggio (Jubb et al., 2017) analysis (Supplementary Figure 4). Ligand extraction and protonation were carried out using MATERIALS AND METHODS UCSF Chimera, version 1.11 (Pettersen et al., 2004) while identiﬁcation of rotatable bonds was carried out in Autodock SNP Dataset tools (available as part of MGL tools, version 1.5.6) (Morris et al., The dataset consists of 35,944 M. tuberculosis isolates, which 2009) where protonation of the ligand is speciﬁcally required has been described recently (Napier et al., 2020). In brief, it by Autodock Vina (Trott and Olson, 2009). Similarly, protein encompasses all the main lineages (1, 5, and 6, ancient; 2, 3, extraction and explicit removal of solvent were carried out in and 4, modern; 7 intermediate), and drug susceptibility testing UCSF Chimera, version 1.11 (Pettersen et al., 2004), and other across 8 ﬁrst-and second-line anti-TB drugs. Across these isolates, steps in the overall protein preparation process were carried out mutations in the pncA coding region with non-synonymous in Autodock tools (part of MGL tools, version 1.5.6) (Morris et al., amino acid changes (nsSNPs) were extracted. These nsSNPs were 2009). All the required parameters to perform docking needed to further annotated for their link with drug resistance as deﬁned by be included in a conﬁguration ﬁle. their presence in the TB-Proﬁler mutation database (Phelan et al., 2019b). Initial analysis aimed at understanding the structure and In silico Predictions: mCSM DUET, FoldX, characterising the active site, followed by in silico predictions to mCSM-lig quantify the enthalpic and entropic eﬀects of GWAS-identiﬁed The computational tools based on mutation cut-oﬀ scanning nsSNPs on the pncA protein structure. Subsequently, additional matrix, primarily mCSM DUET (Pires et al., 2014a) and mCSM- metadata relating to the clinical isolates were studied in relation lig (Pires et al., 2016) were used to investigate the structural to the structural eﬀects of mutations. The general methodology eﬀects of nsSNPs within the pncA target protein. The eﬀects workﬂow followed in this analysis is similar to the one described of nsSNPs within pncA were analysed with respect to protein previously (Portelli et al., 2018). stability (DUET and FoldX (Schymkowitz et al., 2005) and ligand aﬃnity (mCSM-lig). The consequences of these eﬀects were to Drug and Target: Structural Data investigate change in protein fold and function, and eﬀect on In the absence of a drug (PZA) and target (pncA) complex, mechanism of PZA drug activation, respectively. Results from respective individual structures were obtained from RSCB PDB mCSM-lig (Pires et al., 2016) return both ligand aﬃnity and database (Berman et al., 2000). The crystal structure of pncA in DUET scores, hence only mCSM-lig was run to obtain both the M. tuberculosis is available as PDB entry 3PL1 (Petrella et al., outputs simultaneously. 2011), while the structure of PZA was extracted from PDB entry A semi-automated pipeline was constructed for mCSM and 3R55 (Singh et al., 2011). The molecular motion of pncA was FoldX to submit and extract results for multiple mutations analysed by Normal Mode Analysis using the DynaMut tool consecutively using python and shell scripts. Both tools require (Rodrigues et al., 2018) (Supplementary Figure 1). wild type structure, chain ID and a list of nsSNPs in the X <POS> Y format (X: wild type residue; <POS> : position, Y: Protein-Ligand Docking: Autodock Vina mutant residue). The residue symbols (X and Y) are speciﬁed as The pncA-PZA complex was generated using the software one letter amino acid code. DUET and FoldX estimate mutational AutoDock Vina, version 1.1.2 (Trott and Olson, 2009). Autodock impact as a change in Gibbs Free energy (11G) in Kcal/mol. Vina is an open-source, freely available molecular modelling The classiﬁcation of mutational impact based on 11G from platform to perform protein-ligand docking. Docking was carried these methods are categorised in opposing ways. For example, out with default settings and guided by the positioning of 11G < 0 of a SNP is classiﬁed as a “destabilising” according to the ligand within the active site as descried by Petrella et al. DUET, while the same is classiﬁed as “stabilising” according to (2011). The complex was generated to facilitate downstream FoldX. analyses by mCSM-lig (Pires et al., 2016) Autodock Vina returns The mutational impact on ligand aﬃnity is calculated as a log bound conformations with their respective predicted binding fold change between wild type and mutant binding aﬃnities. In aﬃnity values. The prediction of binding aﬃnity (strength of addition to SNP identiﬁers, mCSM-lig requires the ligand aﬃnity the ligand interaction with its target) is based on one of several of the wild-type protein to be speciﬁed in nano Molar (nM) for scoring functions, which rank the poses in increasing order aﬃnity change calculations. Since the binding aﬃnity returned of predicted binding aﬃnity. Binding free energy is calculated by AutoDock Vina, version 1.1.2 (Trott and Olson, 2009) is in using a semi-empirical force ﬁeld, combining experimental and Kcal/mol, these needed to be converted to nM via Eq. 1 (below). knowledge-based information. The docking poses were visualised The binding aﬃnity for PZA in nM was 0.9911. and inspected in UCSF Chimera 1.13 (Pettersen et al., 2004) according to the occupation of search space and diversity of pose 1G D RTlnK: (1) conformations (Supplementary Figure 2). The top two binding poses were closely matched with the conformations generated Equation 1: Calculation of binding free energy, 1G, where R 1 1 by Karmakar et al. (2018) and Petrella et al. (2011), respectively is the gas constant, 1.987 cal K mol and T is the absolute (Supplementary Figure 3). The best pose was chosen considering temperature, 298 K. Adapted from Morris et al. (1998). Frontiers in Molecular Biosciences | www.frontiersin.org 3 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 4 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis The mCSM suite of tools (Pires et al., 2014a, 2016; Pires odds ratio (OR), and similar to a GWAS approach, adjusted and Ascher, 2017; Rodrigues et al., 2019) are based on odds ratio (aOR) were estimated using logistic regression models graph-based measures at an atomic level along with machine with a kinship matrix adjusting for a random eﬀect representing learning (ML) tools for predicting enthalpic and entropic the SNP-based relationships between samples (e.g., the lineage- eﬀects of stability. mCSM achieves this broadly by generating based population structure) (Zhou and Stephens, 2012; Coll et al., a signature encompassing the wild-type milieu and change in 2018). P-values were estimated using Fisher and Wald test for pharmacophore properties upon mutation (Pires et al., 2014b). unadjusted and adjusted ORs, respectively. Owing to the inter-atomic distance pattern within mCSM describing the wild-type residue environment, novel parameters Statistical Analyses like residue depth and long-range interactions are implicitly Data was analysed using non-parametric statistical tests. considered. In this manner, mCSM is able to characterise For assessing correlations, Spearman correlation values both local and global eﬀects of missense point mutations. The were calculated. For comparing lineage distributions, the mutational change at the atomic level is considered by using a Kolmogorov-Smirnov (KS) test was used. Statistical signiﬁcance change in the “pharmacophore count” vector, thus obviating the thresholds used are P < 0.05, P < 0.01, P < 0.001, need to have explicit mutant structure. All mCSM tools (Pires P < 0.0001). et al., 2014a, 2016; Pires and Ascher, 2016, 2017; Rodrigues et al., 2019) use the atomic changes, while DUET (Pires et al., Data Visualisation 2014a) is an ensemble method combining methods of mCSM All plots were generated using R statistical software, stability (Pires et al., 2014b) and SDM (Worth et al., 2011; version 4.0.2 (R Core Team, 2014). Protein and ligand Pandurangan et al., 2017). FoldX, however is an empirical-based structures were generated using UCSF Chimera, version prediction tool which summarises the change in stability between 1.11 (Pettersen et al., 2004). mutant and wild type protein structures using a combination of energy terms based on fundamental intramolecular interactions (Schymkowitz et al., 2005). RESULTS Other Structural Parameters Analysing the pncA Molecular Motion Additional structural parameters for wild type structure were and pncA-PZA Complex also included in the analysis. These were: Accessible (ASA) and Molecular motion in pncA was analysed by Normal Mode Relative Surface Area (RSA), residue depth (RD), hydrophobicity Analysis (NMA). Regions undergoing the greatest movement values according to the Kyte-Doolittle scale (KD). The DSSP were limited to residues in loop regions and mainly concentrated programme (Kabsch and Sander, 1983; Touw et al., 2015) was to loop 60–66, followed by loop residues 39–41 and 111–113. run to extract the ASA and RSA values, while RD values Residues at site 165–167 within helix 164–178 showed the least calculated as described by Chakravarty and Varadarajan (1999) ﬂexibility (Supplementary Figure 1). The frequency of mutations were calculated using the depth server available at http://cospi. in these variable regions was most prominent for sites 62–63 iiserpune.ac.in/depth. The KD values were fetched from the (>2 mutations) while the other sites were limited to at most two expasy server (Artimo et al., 2012) available at https://web.expasy. mutations (Figure 1). Mutations within the most ﬂexible region org/protscale/. (residues 60–66) of pncA showed mixed eﬀects in relation to their association with PZA resistance with the single mutation at site 64 Data Normalisation: DUET, FoldX, and related to PZA resistance. Sites 39 and 40 within the other highly mCSM-lig ﬂexible region 39–41 were not associated with any mutations in The DUET (Pires et al., 2014a), FoldX (Schymkowitz et al., 2005), our study, while the two mutations at site 41 were not associated and mCSM-lig (Pires et al., 2016) scores associated with each SNP with PZA resistance. The region 111–113 is associated with single were subsequently normalised between the range of1 and 1. For mutations at sites 111 and 112 which are not linked to PZA mCSM-lig analyses, data was ﬁltered according to distance from resistance, while site 113 was not associated with any mutations in interacting site and only residues within a distance of 10 Å of the our study. Sites 165–167, which form part of the helix (164–178), ligand (PZA) were considered for all ligand aﬃnity analyses. are the most stable according to NMA. Two residues (A165 and D166) within this helix were not associated with any mutations Minor Allele Frequency and Odds Ratio in our study, while a single mutation at site T167 was not Calculations: SNP Dataset associated with PZA drug resistance (Supplementary Figure 1 Across the M. tuberculosis isolates tested for PZA drug and Supplementary Table 1). Docking with AutoDock vina susceptibility data, we performed association analysis to estimate (Trott and Olson, 2009) generated nine diﬀerent conformations the risk of resistance for SNP alleles. For each nsSNP, minor allele as per default settings. In six of these poses, the aromatic ring frequency (MAF) and odds ratio (OR) were calculated in relation of PZA was oriented towards the substrate binding residue to all samples tested for PZA susceptibility. MAF is the average W68 (Supplementary Figures 2A,B). The top two poses (1 occurrence of a given nsSNP, and OR is the measure of association and 2) returned by Vina were similar to previous molecular of a given nsSNP with PZA resistance. In addition to unadjusted docking studies (Petrella et al., 2011; Karmakar et al., 2018) Frontiers in Molecular Biosciences | www.frontiersin.org 4 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 5 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 1 | Logo plot showing sites with multiple missense point mutations and association with Odds Ratio. Sites associated with multiple (>2) missense point mutations (i.e., nsSNPs). A total of 386 mutations corresponding to 113 positions on the pncA protein structure were associated with multiple nsSNPs. The horizontal axis in (A,B) show the position numbers of sites with multiple nsSNPs, while part (C) shows the wild-type residues for each position. The vertical axis in (A) represents Odds Ratio (OR) where letters denote mutant residues which are proportional to their corresponding OR highlighting the most resistant mutation at each site and overall. Part (B) shows each mutant residue at a given position, highlighting nsSNP diversity by position. The wild-type and mutant residues are coloured according to the amino acid properties as denoted. Positions marked in yellow form the catalytic triad, residues in blue and teal are involved in substrate binding, those in green are involved in hydrogen binding while the ones in purple are involved in the iron centre coordination. The ﬁgure is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms; pncA, pyrazinamidase. (Supplementary Figure 3). A follow-up Arpeggio analysis (Jubb a total of 13,914 samples tested for PZA drug susceptibility, et al., 2017) indicated that pose 1 when compared to pose 2, a minority of those were found to be resistant (n = 2,379, has more H-bonds (4 vs. 1), fewer aromatic contacts (3 vs. 13), 17.1%) (Table 1). However, the burden of PZA resistance among and greater Van der Waals interactions (3 vs. 1) (Supplementary Figures 4A,B). Therefore, model with pose 1 was chosen to form the pncA-PZA complex (Supplementary Figure 5). TABLE 1 | Number of samples analysed. Genomics Data SNP data from 35,944 M. tuberculosis clinical isolates tested for Item name Total number (%) drug susceptibility to a range of ﬁrst and second line drugs were Clinical isolates/samples 35,944 obtained (Napier et al., 2020). Among these, 39% (n = 13,914) Samples classiﬁed Susceptible 23,256 (64.7) of these isolates were tested for PZA drug susceptibility. The Drug resistant (DR) 5,008 (13.9) isolates were collected from over 30 diﬀerent countries and Multi-drug resistant (MDR) 6,691 (18.6) represented the 4 main M. tuberculosis lineages (L1, n = 144; Extreme drug resistant (XDR) 989 (2.8) L2, n = 1,886; L3, n = 190; L4, n = 2213) (Supplementary Samples tested for PZA drug 13,914 Figure 6). In order to infer whether the ancestral pncA susceptibility sequences for each lineage diﬀered, we quantiﬁed the number Resistant 2,379 (17.1) of samples without any mutations in each lineage. The majority Samples with nsSNPs in the protein 4,731 (13.2) of isolates in L1–L4 had an identical pncA sequence as the coding region of pncA H37Rv reference indicating that the ancestral sequences for Susceptible 184 (3.9) these lineages do not diﬀer. The majority were pan susceptible Drug resistant (DR) 632 (13.4) (n = 23,256, 64.7%), with the remainder MDR-TB (n = 6,691, Multi-drug resistant (MDR) 3,290 (69.5) 18.6%), XDR-TB (n = 989, 2.8%), or another type of resistance Extreme drug resistant (XDR) 625 (13.2) referred to as DR-TB (n = 5,008, 13.9%) (Table 1). From the Samples with pncA nsSNPs tested for 2,289 (48.4) list, only nsSNPs within the protein coding region of pncA PZA drug susceptibility (n = 4,731, 13.2%) were considered for our analyses (Table 1). Samples with pncA nsSNPs resistant to 1,677 (73.3) PZA The majority of these were MDR-TB (n = 3,290, 69.5%) followed Unique nsSNPs: No. of sites 424 nsSNPs: 151 sites by relatively equal numbers of XDR-TB and DR-TB (n = 625, 13.2% and n = 632, 13.4%, respectively), while only a small Summary of clinical isolates from genome-wide analysis. PZA, pyrazinamide; percentage were susceptible (n = 184, 3.9%) (Table 1). From nsSNPs, non-synonymous Single Nucleotide Polymorphisms. Frontiers in Molecular Biosciences | www.frontiersin.org 5 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 6 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 2 | Barplots showing number of mutations and sites associated with protein stability and ligand afﬁnity. (A) Number of nsSNPs categorised as destabilising (n = 359) and stabilising (n = 65) according to DUET protein stability. (B) Frequency of sites associated with the number of nsSNPs, where horizontal axis denotes the number of nsSNPs and vertical axis denotes the total number of sites/positions corresponding to the number of nsSNPs. (C) Barplot showing the number of nsSNPs categorised as destabilising (n = 168) and stabilising (n = 33) according to mCSM ligand afﬁnity where sites lie within 10Å of ligand. (D) Frequency of sites associated with the number of nsSNPs, where horizontal axis denotes the number of nsSNPs and vertical axis denotes the total number of sites/positions corresponding to the number of nsSNPs. The ﬁgure is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms. samples containing nsSNPs in the protein coding region was high the others (197/424; denoted as OM) were assumed to have (n = 1,677, 73.3%) (Table 1). weak or no links. Genomic measures like minor allele frequency Across the 4,731 isolates, 424 distinct nsSNPs corresponding (MAF) and odds ratio (OR) were obtained for a total of 322 to 151 distinct amino acid positions on the pncA structure were nsSNPs, with adjusted OR (aOR) estimated for a total of 163 identiﬁed (Figures 2A,B). A total of 201 nsSNPs corresponding nsSNPs. Across the majority of these nsSNPs, the MAFs were low to 54 amino acid changes were within 10 Å of the ligand binding (median: 0.02% range: 0.01–2.11%) (Supplementary Figure 7A). site (Figures 2C,D). The majority of these nsSNP mutations Similarly, when considering ORs, the majority of the nsSNPs had have been annotated as being linked to PZA resistance within high ORs (median: 9.70, range: 0.22–414.61) (Supplementary the TBProﬁler tool (227/424). The majority of these nsSNP Figure 7D). When looking at the distribution of MAF and OR mutations have been annotated as being linked to PZA resistance within mutations associated with PZA resistance (DM) and other within the TBProﬁler tool (227/424; denoted as DM), while mutations (OM) (Supplementary Figures 7B,E), DM mutations Frontiers in Molecular Biosciences | www.frontiersin.org 6 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 7 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 3 | Mutational landscape of pncA structure (3PL1) coloured by positions linked to pyrazinamide drug (PZA) resistance. Panels (A,B) show all mutational positions in orange while mutational positions in (C,D) are further coloured by mutations classed as either drug resistant mutations (purple) or “other mutations” (blue), while sites linked to mutations belonging to either category are coloured in pink. The right panels (B,D) depict the corresponding structure rotated by 180 . The ligand (PZA) is shown as ball and stick within the active site denoted by the red circle. The ﬁgure is rendered using UCSF Chimera (version 1.14). pncA, pyrazinamidase. were associated with signiﬁcantly higher (P < 0.0001) MAF and point mutation (Table 2 and Figures 1B, 5C). All active site and OR (Supplementary Figures 7C,F). hydrogen-bond forming residues with the ligand were associated with multiple mutations (2) (Figure 1B), thus representing the high diversity of mutations present within pncA. Despite this, there appears to be some degree of clustering around positions Understanding Mutational Effects on 4–14, 46–97, 132–143 involving the active site and metal centre pncA Stability and PZA Binding Afﬁnity residues (Figure 5C). The 424 nsSNPs mapped onto the crystal structure of pncA The biophysical eﬀect of mutations on protomer stability, revealed that mutational landscape of pncA appears distributed estimated as 11G (Kcal/mol), was measured using DUET (Pires (Figures 3A,B) throughout the structure. Sites linked to drug et al., 2014a) and FoldX (Schymkowitz et al., 2005), while resistant mutations were predominant around the PZA binding (active) site, while sites exclusively linked to mutations classed in mutational impact on ligand aﬃnity was measured using mCSM- the “other” category are distal to the active site (Figures 3C,D, 4). lig (Pires et al., 2016) (see section “Materials and Methods”). Furthermore, active site residues were associated with a multiple Assessing mutational eﬀects on protein stability as measured by Frontiers in Molecular Biosciences | www.frontiersin.org 7 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 8 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 4 | Comparison of structural features between Drug resistance (DM) and other mutations (OM) of pncA gene mutations according to (A) DUET protein stability (11G), (B) FoldX stability (11G), and (C) Ligand Afﬁnity. A total of 424 nsSNPs for DUET and FoldX (DM, n = 227, OM, n = 197), while a total of 201 nsSNPs (DM, n = 129 OM, n = 72) lying within 10 Å of PZA for ligand afﬁnity were included in the analysis. DM and OM mutations were compared using Wilcoxon rank-sum (unpaired) and statistical signiﬁcance indicated as: *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001). The ﬁgure is generated using R statistical software (version 4.0.2). ns, non-synonymous Single Nucleotide Polymorphisms; pnca, pyrazinamidase; PZA, pyrazinamide; Å, Angstroms; 11G, Change in Gibbs free energy in Kcal/mol; ASA, Accessible Surface Area; RSA, Relative surface Area; RD, Residue Depth; KD, Kyte-Doolittle Hydrophobicity values. DUET, nearly 85% had a destabilising eﬀect (n = 359) compared 49, 72, 78, 96, 102, 103, 105, 134, 137, 138, and 162 associated to nearly 15% mutations with stabilising eﬀects (n = 47) as shown with mutations resulting in mixed stability impact. Position 163 in Figure 2A. When assessing ligand aﬃnity, 47.4% (n = 201) comprised only of mutations with stabilising eﬀects (Figure 5D). SNP mutations were present within 10 Å of the PZA binding site The budding resistance hotspot active site residue I133 contained (Figure 2C). Similar to DUET stability eﬀects, the majority (84%; both mutations with destabilising eﬀect for protein stability n = 168) of nsSNPs were destabilising while 16% (n = 27) were (Figure 5C), while stabilising for ligand aﬃnity (Figure 5D). stabilising for ligand binding aﬃnity (Figure 2C). More than Similarly, for budding resistance hotspots, majority of the nsSNPs 50% of the mutational positions were associated with multiple were associated with destabilising eﬀects. For protein stability, nsSNPs for both protein stability (n = 113) and ligand aﬃnity 9/33 sites had mutations with mixed stability (positions 15, 32, 61, (n = 49) (Figures 2B,D). The average protein stability and ligand 66, 76, 114, 127, 153, and 161) (Figure 5C), while only position 20 aﬃnity eﬀects of all mutations mapped onto the pncA structure showed mixed stability eﬀects for ligand aﬃnity (Figure 5D). (Figures 5A,B), highlight mutations with opposing eﬀects for protein stability and ligand aﬃnity. These eﬀects are pronounced Mutations With Extreme Effects for active site residues (I133, A134, H137, C138) (Figures 5C,D). Mutations with extreme eﬀects on protein stability and aﬃnity There were 80 sites within pncA associated with multiple are summarised in Table 3. Overall, the most destabilising nsSNPs (>2) (Figures 1B, 2B) which included all active residues mutation according to DUET was L4S, where a change from except I133 which was associated with 2 mutations (Figure 1B). a hydrophobic to a polar residue may contribute to disruption Sites with 2 nsSNPs are considered to be budding resistance of local conformation (Table 3). The closest most destabilising hotspots (n = 33 for protein stability, n = 7 for ligand aﬃnity). mutational eﬀect on protein stability was from A134D (wild- A total of 57 nsSNPs within 5 Å of PZA were considered type residue involved in hydrogen bonding) (Table 3), likely to be within the ﬁrst shell of residues lining the active site resulting in electrostatic and steric clashes due to a change (Table 2). While majority of the mutational sites associated with in charge and volume aﬀecting the overall stability negatively. more than two mutations comprise of destabilising mutations, The most stabilising mutation on protomer stability was from positions 1, 2, 10, 12, 43, 46, 51, 57, 63, 67, 69, 78, 82, active site residue Y103D, while the closest such mutation was 92, 96, 100, 104, 105, 129, 135–138, 142, 149, 164, 168, and C138R (Table 3). The stabilising eﬀect of these mutations on the 174 comprised of both stabilising and destabilising mutations protein stability and ligand aﬃnity is thought to result from the (Figure 5C). Similarly, for ligand aﬃnity, most mutational sites electrostatic interactions working favourably for sites lying within had destabilising mutational eﬀects, with positions 7, 8, 13, 27, 5 Å of the ligand. The most destabilising mutation according Frontiers in Molecular Biosciences | www.frontiersin.org 8 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 9 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis Frontiers in Molecular Biosciences | www.frontiersin.org 9 July 2021 | Volume 8 | Article 619403 TABLE 2 | Mutations close to the active site of PZA. S. No. Mutation Mutation MAF OR P-value OR P-Wald DUET DUET Distance mCSM- Ligand Foldx Foldx ASA RSA Hydro Residue class (%) adjusted 11G outcome to lig outcome 11G outcome phobicity depth ligand (log (Å) afﬁnity) 1 A134D Others 0.01 2.42 1.00E+00 NA NA 2.98 D 3.05 0.58 S 1.03 D 10 0.08 1.87 6.77 2 A134G Others NA NA NA NA NA 1.62 D 3.05 0.38 D 1.29 S 10 0.08 1.87 6.77 3 A134P Others 0.01 9.70 1.71E-01 NA NA 1.43 D 3.05 0.08 S 5.20 S 10 0.08 1.87 6.77 4 A134T Others NA NA NA NA NA 1.93 D 3.05 0.88 S 0.94 S 10 0.08 1.87 6.77 5 A134V Drug 0.04 19.43 3.68E-03 1.53 3.07E-05 0.41 D 3.05 0.12 S 1.46 S 10 0.08 1.87 6.77 associated 6 I133S Others 0.01 9.70 1.71E-01 NA NA 3.22 D 3.05 0.58 S 3.30 D 3 0.02 1.97 7.90 7 I133T Drug 0.32 6.44 2.90E-09 0.86 4.86E-03 2.79 D 3.05 0.70 S 1.58 D 3 0.02 1.97 7.90 associated 8 D8A Drug 0.01 19.41 2.92E-02 NA NA 0.51 D 3.22 3.27 D 0.54 D 5 0.03 1.63 9.48 associated 9 D8G Drug 0.08 48.69 1.95E-07 1.25 4.42E-02 0.85 D 3.22 3.45 D 1.89 D 5 0.03 1.63 9.48 associated 10 D8E Drug 0.03 14.56 1.74E-02 1.19 1.46E-01 0.79 D 3.22 0.01 S 1.90 D 5 0.03 1.63 9.48 associated 11 D8N Drug 0.05 29.16 1.49E-04 1.24 7.10E-03 1.18 D 3.22 1.66 D 1.26 S 5 0.03 1.63 9.48 associated 12 C138G Others NA NA NA NA NA 0.02 D 3.28 0.01 D 1.12 D 12 0.07 1.17 6.70 13 C138S Drug NA NA NA NA NA 0.00 D 3.28 0.81 S 0.23 S 12 0.07 1.17 6.70 associated 14 C138W Others NA NA NA NA NA 1.05 D 3.28 0.94 S 1.72 S 12 0.07 1.17 6.70 15 C138Y Drug NA NA NA NA NA 0.52 D 3.28 0.91 S 0.57 S 12 0.07 1.17 6.70 associated 16 C138R Drug 0.09 116.96 6.10E-10 1.74 4.08E-12 0.10 S 3.28 0.35 S 2.12 S 12 0.07 1.17 6.70 associated 17 H137N Others 0.01 2.42 1.00E+00 NA NA 0.19 S 3.42 0.12 D 0.40 D 84 0.38 1.40 4.60 18 H137P Drug NA NA NA NA NA 0.37 S 3.42 0.77 D 2.19 D 84 0.38 1.40 4.60 associated 19 H137Y Others 0.01 2.42 1.00E+00 NA NA 0.86 S 3.42 0.01 D 0.34 D 84 0.38 1.40 4.60 20 H137R Drug 0.03 4.85 1.38E-01 0.56 1.21E-04 0.27 D 3.42 0.47 S 0.49 D 84 0.38 1.40 4.60 associated (Continued) fmolb-08-619403 August 3, 2021 Time: 21:57 # 10 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis Frontiers in Molecular Biosciences | www.frontiersin.org 10 July 2021 | Volume 8 | Article 619403 TABLE 2 | Continued S. No. Mutation Mutation MAF OR P-value OR P-Wald DUET DUET Distance mCSM- Ligand Foldx Foldx ASA RSA Hydro Residue class (%) adjusted 11G outcome to lig outcome 11G outcome phobicity depth ligand (log (Å) afﬁnity) 21 D49G Drug 0.05 29.16 1.49E-04 1.66 4.38E-08 1.16 D 3.45 3.46 D 0.46 D 7 0.04 1.53 7.89 associated 22 D49A Drug 0.04 58.33 2.49E-05 1.67 3.17E-06 0.45 D 3.45 3.35 D 2.07 S 7 0.04 1.53 7.89 associated 23 D49N Drug 0.06 77.84 7.23E-07 1.51 3.14E-04 1.68 D 3.45 1.93 D 0.33 S 7 0.04 1.53 7.89 associated 24 D49Y Drug 0.01 9.70 1.71E-01 NA NA 0.74 D 3.45 1.86 D 2.67 S 7 0.04 1.53 7.89 associated 25 D49E Drug 0.02 9.70 7.77E-02 NA NA 0.47 D 3.45 0.25 S 0.70 S 7 0.04 1.53 7.89 associated 26 A102R Others 0.01 2.42 1.00E+00 NA NA 0.70 D 3.50 0.17 S 4.13 D 10 0.08 0.03 5.51 27 A102P Others 0.06 14.58 5.08E-04 0.66 5.33E-04 1.25 D 3.50 0.23 D 0.62 S 10 0.08 0.03 5.51 28 A102V Others 0.06 2.43 1.88E-01 0.91 3.00E-01 0.25 D 3.50 0.16 D 1.91 S 10 0.08 0.03 5.51 29 A102T Drug 0.01 19.41 2.92E-02 1.75 4.98E-04 0.72 D 3.50 0.88 S 2.03 S 10 0.08 0.03 5.51 associated 30 F13C Others 0.01 1.21 1.00E+00 0.64 4.31E-03 2.32 D 3.55 0.49 D 2.70 D 24 0.10 0.60 6.93 31 F13I Drug 0.03 14.56 1.74E-02 NA NA 1.76 D 3.55 0.45 D 0.89 D 24 0.10 0.60 6.93 associated 32 F13L Drug 0.06 34.04 2.89E-05 1.37 2.29E-03 2.03 D 3.55 0.43 D 1.10 D 24 0.10 0.60 6.93 associated 33 F13V Others 0.01 1.21 1.00E+00 NA NA 2.57 D 3.55 0.56 D 1.40 D 24 0.10 0.60 6.93 34 F13S Drug 0.03 1.62 5.28E-01 0.60 3.07E-04 3.10 D 3.55 0.22 S 2.59 D 24 0.10 0.60 6.93 associated 35 K96E Drug 0.08 107.17 3.58E-09 1.75 2.79E-06 2.12 D 3.98 0.67 D 6.92 D 8 0.03 1.87 5.96 associated 36 K96Q Drug 0.03 4.85 1.38E-01 0.64 1.17E-01 1.32 D 3.98 0.08 D 1.04 D 8 0.03 1.87 5.96 associated 37 K96T Drug 0.09 58.47 6.68E-09 1.84 2.25E-13 0.86 D 3.98 0.57 D 3.54 D 8 0.03 1.87 5.96 associated 38 K96M Others 0.01 19.41 2.92E-02 NA NA 0.41 S 3.98 1.03 D 0.27 D 8 0.03 1.87 5.96 39 K96N Drug 0.01 2.42 1.00E+00 NA NA 1.16 D 3.98 0.33 S 2.61 D 8 0.03 1.87 5.96 associated (Continued) fmolb-08-619403 August 3, 2021 Time: 21:57 # 11 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis Frontiers in Molecular Biosciences | www.frontiersin.org 11 July 2021 | Volume 8 | Article 619403 TABLE 2 | Continued S. No. Mutation Mutation MAF OR P-value OR P-Wald DUET DUET Distance mCSM- Ligand Foldx Foldx ASA RSA Hydro Residue class (%) adjusted 11G outcome to lig outcome 11G outcome phobicity depth ligand (log (Å) afﬁnity) 40 K96R Drug 0.11 19.49 1.66E-07 1.43 2.16E-06 0.17 D 3.98 0.08 S 0.74 S 8 0.03 1.87 5.96 associated 41 H71D Drug 0.01 9.70 1.71E-01 NA NA 2.69 D 4.18 2.50 D 5.75 D 5 0.02 0.77 6.25 associated 42 H71N Drug NA NA NA NA NA 2.67 D 4.18 1.34 D 0.64 D 5 0.02 0.77 6.25 associated 43 H71P Others 0.01 4.85 3.13E-01 NA NA 2.36 D 4.18 2.89 D 3.26 D 5 0.02 0.77 6.25 44 H71Q Drug 0.01 19.41 2.92E-02 1.75 2.12E-04 2.29 D 4.18 1.73 D 1.12 D 5 0.02 0.77 6.25 associated 45 H71R Drug 0.05 1.94 3.42E-01 0.88 2.01E-01 1.93 D 4.18 0.83 D 1.52 S 5 0.02 0.77 6.25 associated 46 H71Y Drug 0.18 25.67 4.52E-13 1.48 5.50E-08 0.46 D 4.18 1.96 D 1.78 S 5 0.02 0.77 6.25 associated 47 H57D Drug 0.73 166.91 2.08E-72 1.24 1.05E-01 1.85 D 4.56 1.28 D 1.83 D 16 0.07 1.30 5.63 associated 48 H57P Drug 0.03 38.85 8.53E-04 1.55 1.16E-02 1.23 D 4.56 2.12 D 0.15 D 16 0.07 1.30 5.63 associated 49 H57Q Others NA NA NA NA NA 1.29 D 4.56 0.95 D 0.85 D 16 0.07 1.30 5.63 50 H57R Drug 0.19 254.92 1.02E-20 1.48 9.69E-09 1.17 D 4.56 0.28 D 1.25 D 16 0.07 1.30 5.63 associated 51 H57L Drug NA NA NA NA NA 0.06 D 4.56 1.92 D 1.11 S 16 0.07 1.30 5.63 associated 52 H57Y Drug 0.02 29.13 4.99E-03 2.08 7.92E-06 0.41 S 4.56 1.16 D 0.15 S 16 0.07 1.30 5.63 associated 53 W68C Drug 0.04 24.29 7.49E-04 1.75 1.67E-04 1.45 D 4.97 1.58 D 2.68 D 45 0.16 1.10 5.49 associated 54 W68G Drug 0.14 87.93 2.36E-13 1.58 7.39E-11 2.57 D 4.97 2.13 D 4.04 D 45 0.16 1.10 5.49 associated 55 W68L Drug NA NA NA NA NA 1.62 D 4.97 2.24 D 0.19 D 45 0.16 1.10 5.49 associated 56 W68R Drug 0.20 132.41 4.03E-20 1.50 4.26E-09 1.61 D 4.97 0.58 D 0.08 D 45 0.16 1.10 5.49 associated 57 W68S Drug 0.01 9.70 1.71E-01 NA NA 2.67 D 4.97 1.04 D 2.65 D 45 0.16 1.10 5.49 associated Fifty-seven mutations (nsSNPs) lying within 5 Å of PZA and the corresponding GWAS measures of minor allele frequency (MAF), Odds Ratio (OR), P-values, adjusted OR (aOR), and P-values from Wald test corresponding to aORs, along with structural measures of distance to ligand, DUET, FoldX, ligand afﬁnity values and effect. Wild type residues for mutations highlighted and marked in green are considered to participate in hydrogen bonding, those in yellow form the catalytic triad, residues in teal (and blue) are involved in substrate binding, while the residues in purple are involved in the iron centre. The columns are coloured to highlight the most signiﬁcant column attribute with deeper colours denoting the greatest effects. The dark colours in MAF, OR, and aOR columns indicate the highest values, while P-values are coloured with the darkest colour showing the most signiﬁcant values. Values in the DUET, mCSM-lig, and FoldX columns are coloured according to the extent of their respective effects with red indicating destabilising and blue denoting stabilising effects. nsSNPs, non-synonymous Single Nucleotide Polymorphisms; PZA, pyrazinamide; GWAS, Genome-Wide Association Studies. D, Destabilising; S, Stabilising. fmolb-08-619403 August 3, 2021 Time: 21:57 # 12 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 5 | Protein stability and ligand afﬁnity effects of nsSNPs on pncA structure and by position. Mutational impact of nsSNPs on the pncA protein structure coloured by average (A) DUET Protein stability (n = 424) and (B) ligand afﬁnity (n = 201). Barplots (C,D) showing the frequency of mutations within the pncA gene. The horizontal axis shows the mutational positions within pncA and the vertical axis shows the frequency of mutations. Positions on the horizontal axis are coloured to denote the active site residues: green (residues involved in hydrogen bonding with PZA), yellow (catalytic triad), blue and teal (substrate binding), purple (iron centre). For a given position, each corresponding mutation (nsSNP) is coloured by the level of stability according to (C) DUET(n = 424) and (D) Ligand afﬁnity (n = 201) where the horizontal axis denotes amino acid positions in pnca, and is restricted to positions lying within 10 Å of PZA for ligand afﬁnity. Destabilising mutations are depicted in red and stabilising mutations in blue, where colour intensity reﬂects the extent of effect, ranging from 1 (most destabilising) to + 1 (most stabilising). The structural ﬁgures (A,B) are rendered using UCSF Chimera (version 1.14). The barplot ﬁgure (C,D) is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms; PZA, pyrazinamide; pncA, pyrazinamidase. Interestingly, mutation A134P was the most stabilising according to ligand aﬃnity was D49G located at 3.5 Å (Table 3). The to FoldX, while the same was estimated to have a destabilising three subsequent destabilising mutations for ligand aﬃnity were eﬀect according to DUET (Table 3). All mutations except A134D also all within 5 Å of PZA binding site namely D8G (3 Å), and A134P were associated with PZA drug resistance (Table 3). D49A (3.5 Å), and D8A (3 Å) (Supplementary Table 1), all arising likely due to the loss of charge and volume interfering with ligand interaction. The mutation with the greatest stabilising Relating Structural and GWAS Analyses eﬀect on ligand aﬃnity was G162D, located at8 Å, i.e. outside The minor allele frequencies for the 424 nsSNPs were mapped the ﬁrst shell of inﬂuence (>5 Å) from the ligand. This is onto their corresponding amino acid positions of the pncA possibly due to the resulting electrostatic eﬀects and increase in gene (Supplementary Figure 8). Position 10 had the highest volume, which may favour hydrogen bond formation with nearby cumulative minor allele frequency (MAF, 2.3%), followed by residues and PZA binding, thereby increasing aﬃnity (Table 3). position 7 (1.2%), position 57 (1.0%), position 51 (0.6%), The closest most stabilising mutational impact on ligand aﬃnity and position 14 (0.5%). The risk of PZA resistance from the was due to mutation A134P, though this was a marginal eﬀect alleles at each SNP was estimated by calculating ORs and P-values (Table 3). The most destabilising mutation according to FoldX using a GWAS approach. Additionally, adjusted OR (aOR) was C72W, which is located far away from the active site (27 Å). which accounted for the confounding eﬀects of lineage were also Frontiers in Molecular Biosciences | www.frontiersin.org 12 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 13 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis TABLE 3 | Mutations with extreme effects. Mutational effects Mutation Mutation class MAF (%) OR P-value Distance Stability 11G Ligand afﬁnity to ligand (Å) Highest OR H51D Drug-associated 0.30 414.61 4.49E-33 5.66 2.2 1.82 Most frequent mutation Q10P Drug-associated 2.11 156.23 1.28E-207 6.02 0.63 1.77 Most deStabilising for protein L4S Drug-associated 0.25 28.46 5.63E-18 15.33 3.87 1.08 stability (DUET) Closest destabilising for protein A134D Others 0.007 2.43 1.00 3.05 2.98 0.58 stability (DUET) Most stabilising for protein Y103D Others 0.22 142.33 1.24E-21 5.42 1.18 0.85 stability (DUET) Closest stabilising for protein C138R Drug-associated 0.09 116.96 6.09E-10 3.28 0.10 0.35 stability (DUET) Most destabilising for ligand D49G Drug-associated 0.05 29.16 0.0001 3.45 1.16 3.46 afﬁnity Closest destabilising for ligand D8G Drug-associated 0.08 48.69 1.95E-07 3.22 0.85 3.45 afﬁnity Most stabilising for ligand G162D Drug-associated 0.03 38.85 0.0008 8.32 1.04 2.23 afﬁnity Closest stabilising for ligand A134P Others 0.007 9.70 1.71E-01 3.05 1.43 0.08 afﬁnity Most destabilising for protein C72W Drug-associated 0.01 19.41 0.03 7.05 27.46 – stability (Foldx) Most stabilising for protein A134P Others 0.007 9.70 1.71E-01 3.05 5.2 – stability (Foldx) Mutations (nsSNPs) with extreme effects on odds ratio, frequency, thermodynamic stability, and ligand afﬁnity. For ligand afﬁnity, only mutations lying within 10 Å of PZA (pyrazinamide) were considered. nsSNPs, non-synonymous Single Nucleotide Polymorphisms; Å, Angstroms; MAF, minor allele frequency; OR, Odds Ratio; 11G, Change in Gibbs free energy in Kcal/mol. FIGURE 6 | Correlation between biophysical effects and GWAS measures of Odds Ratio (OR), P-values (P) and minor allele frequency (MAF). Pairwise correlations between MAF, negative log10 P-value [-Log(P)], Log10 (OR) and (A) Protein stability (DUET) and FoldX for 424 nsSNPs, (B) Ligand afﬁnity of 201 nsSNPs (lying within 10 Å of PZA). The upper panel in both plots include the pairwise Spearman correlation values along with their statistical signiﬁcance (*P < 0.05, **P < 0.01, ***P < 0.001). The points in the lower panel represent nsSNPs, coloured according to respective stability effects: (A) nsSNPs with destabilising effect for DUET and ligand afﬁnity are coloured red, while for FoldX these appear in blue, (B) nsSNPs with stabilising effect for DUET and ligand afﬁnity appear in blue, while for FoldX these appear in red. The diagonal plots display the histogram of the corresponding parameter. The ﬁgure is generated using R statistical software (version 4.0.2). nsSNPs, non-synonymous Single Nucleotide Polymorphisms; PZA, pyrazinamide; Units for DUET, FoldX and Ligand Afﬁnity (Kcal/mol). Frontiers in Molecular Biosciences | www.frontiersin.org 13 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 14 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis FIGURE 7 | Density distribution of M. tuberculosis lineages. A total of 4,433 samples belonging to Lineages 1–4, containing 419‘pncA mutations were considered. The horizontal axis shows the DUET stability values (1, most destabilising) to blue (C1, most stabilising). while the vertical axis shows the density distribution of M. tuberculosis lineages coloured by mutation class as either DM (associated with pyrazinamide resistance in orange) or OM (not associated with pyrazinamide drug resistance which appear in grey). DM mutations comprise of a total of 3,565 samples contributing to 226 mutations, while 868 samples contributing to 193 mutations formed part of the OM mutation class. The ﬁgure is generated using R statistical software (version 4.0.2). Abbreviations used: nsSNPs: non-synonymous Single Nucleotide Polymorphisms, pncA: pyrazinamidase. analysed (Supplementary Figure 9). The majority of nsSNPs Supplementary Figure 8, and Supplementary Table 1), with were linked to increased likelihood of being resistant to PZA most of these positions being present in the metal binding (OR > 1). For unadjusted ORs, this was 96% (310/322), while and active sites. for aOR, it was 75% (122/163). Wild type position 51 had the When assessing sites in relation to mutational diversity, 3 0 highest unadjusted OR (> 350, P < 10 ), followed by positions active site residues were among the highest, with residues 1 9 57, 120 (OR > 250, P < 10 ), and subsequently by positions 10, H51, H57, H71, K96 associated with six distinct mutations, 1 0 103, 68, 135, 138, 96, and 180 (OR > 100; P < 10 ) (Figure 1A, followed by F13, D49, W68, A134, C138 with ﬁve mutation Frontiers in Molecular Biosciences | www.frontiersin.org 14 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 15 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis each, while residues D8, Y103, H137 were associated with values. The diﬀerence in structural features were most prominent four distinct mutations and residues I133 associated with two when all 424 SNP mutations were considered (P < 0.0001) distinct mutations (Figure 1B). The dominant eﬀect of a highly (Figures 4A,B) with lesser signiﬁcance for ligand aﬃnity frequent mutation (Q10P; MAF = 2.1%, OR = 156.23) in the (P < 0.05), ASA (P < 0.01), and RSA and RD (P < 0.001) population compared to two other mutations observed at the values when 201 nsSNPs lying within 10 Å were considered same position namely Q10R (MAF = 0.13%, OR = 83.01) and (Figure 4C). Mutations associated with PZA resistance have Q10H (MAF = 0.08%, OR = 107.17) (Supplementary Table 1), lower DUET (Figure 4A, top left) but higher FoldX stability makes position 10 prominent in terms of MAF (Supplementary changes (Figure 4B, bottom left), and lower binding aﬃnity Figure 8) while sites involved in the catalytic activity and iron (Figure 4C, second from bottom left) compared to OM. metal centre are more prominent with respect to SNP diversity Additionally, it also appears that that while drug mutations (Supplementary Figure 8). These results suggest that mutations need not necessarily occur at the hydrophobic sites (KD values, at these structurally and functionally important sites are likely P > 0.05), they tend to lie buried indicated by higher RD values, under selective pressure exerted by the drug resulting in this and consequently lower surface area (ASA and RSA) compared observed mutational diversity. to OM (Figures 4A,B). The relationship between structural measures of stability and OR was visualised as a bubble plot indicating that mutations Distinct Stability Proﬁle for Drug associated with greater resistance (high OR) tend not to have Mutations and Lineage 1 extreme eﬀects (Supplementary Figure 10). Furthermore, this A total of 419 nsSNPs are lineage speciﬁc (L1: 74; L2: 277; L3: relationship along with MAF, OR, and P-values was assessed 104; L4: 311). The greatest diversity of nsSNPs was observed through Spearman correlations (Figures 6A,B). MAF was in L3 (54.7%), followed by L1 (51.4%) and Lineage 2 (14.7%) strongly correlated with P-values for all 424 mutations (r = 0.78, with L4 showing the lowest diversity (14.1%) despite containing P < 0.001) and 201 mutations lying with 10 Å of PZA (r = 0.84, the highest number of samples (Supplementary Figure 6). P < 0.001) (Figures 6A,B). As expected, OR and P-values were Statistical analysis of the DUET 11G distributions revealed strongly correlated (r = 0.9, P < 0.001) for all 424 nsSNPs and signiﬁcant diﬀerences between all lineages except between 201 nsSNPs close to PZA binding site (Figures 6A,B). FoldX L3 and L4. Lineage diﬀerences for DUET 11G were most stability and DUET stability values showed moderate correlation prominent between L2 and L4 (P < 0.0001), followed by (r = 0.45, P < 0.001). The negative sign for the DUET and L1 and L4 (P < 0.001) (Supplementary Table 2A). Within FoldX associations is expected since stability changes measured each lineage, mutational distributions were signiﬁcantly diﬀerent by these tools have opposite signs (i.e., 11G < 0: destabilising in between DM and OM mutation classes (P < 0.0001) except DUET vs. stabilising in FoldX). FoldX 11G values showed weak L3 (Supplementary Table 2B). Interestingly, a distinct stability but signiﬁcant correlations with OR (r = 0.23, P < 0.001), and proﬁle was observed for DM mutations within L1. Mutations P-values (r = 0.18, P < 0.01) (Figure 6A), while DUET 11G and associated with drug resistance showed a marked peak around the ligand aﬃnity showed weak and insigniﬁcant association with OR extreme end (0.75 DUET 11G) of the destabilising spectrum (r = 0.1, P > 0.05) (Figures 1B, 6A), including adjusted OR (Figure 7) within L1. (Supplementary Figures 9A, 8B). When considering aOR and its relationship with stability and other structural features [i.e., Accessible (ASA), Relative DISCUSSION Surface Area (RSA), residue depth (RD), and hydrophobicity values (KD)], there was high correlation (r > 0.6, P < 0.05) Genetic mutations including nsSNPs present within drug-targets with adjusted and unadjusted ORs (Supplementary Figure 9A). and their activating genes are the main drivers of resistance DUET 11G showed moderate positive correlation between development in TB (Schön et al., 2017). The motivation for ASA and RSA (r > 0.6, P < 0.05), while moderately investigating the missense mutations within the protein coding negative correlation with RD (r0.5, P < 0.05), and weak region only of the pncA gene was to enable understanding negative correlation with KD values (r0.2, P < 0.05) of the phenotypic mutational eﬀects in relation to PZA (Supplementary Figure 9A). The same structural features, resistance development. While the exact molecular mechanisms however, did not demonstrate correlation with either of PZA resistance are yet to be fully elucidated, the binding FoldX 11G (Supplementary Figure 9A) or ligand aﬃnity pocket of PZA and its key interactions are well known and (Supplementary Figure 9B). characterised (Petrella et al., 2011; Ali et al., 2020; Sheik Amamuddy et al., 2020; Khan et al., 2021). This knowledge was used to guide the molecular docking of PZA to generate Structural Differences in Drug the pncA-PZA complex in the absence of an experimentally Associated Mutations solved structure of the bound complex in Mtb. While docking Comparing stability eﬀect (DUET and FoldX), ligand aﬃnity, generates a variety of ligand conformations (poses), choosing ligand distance, and other structural features (ASA, RSA, RD, the “best” pose is based on considerations around key molecular KD) between mutations associated with PZA drug resistance interactions formed by the ligand, interaction energy of the (DM) and other mutations (OM), revealed statistically signiﬁcant docked complex and subject expertise. Using these guides, diﬀerences (P < 0.05) between all features except hydrophobicity docking pose 1 was chosen due to its molecular interactions Frontiers in Molecular Biosciences | www.frontiersin.org 15 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 16 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis with known key residues and close alignment with previously 2014; Whitﬁeld et al., 2015). While two other genes, rpsA published studies (Karmakar et al., 2018; Ali et al., 2020; Khan and panD have also been linked to PZA resistance, a clear et al., 2021). In addition, we analysed the top two docking link between rpsA and PZA resistance is lacking (Shi et al., poses using the mCSM pipeline (Supplementary Figure 3). 2011; Alexander et al., 2012; Simons et al., 2013; Tan et al., The resulting mutational eﬀects on pncA stability and ligand 2014) although there is increasing evidence to support panDs aﬃnity did not diﬀer between poses indicating the small association with PZA resistance (Pandey et al., 2016; Werngren diﬀerences in pose did not aﬀect downstream analysis. It also et al., 2017; Gopal et al., 2020). In our analysis, there were suggests that due to the small size of the PZA molecule, the only a few samples with rpsA and panD mutations, therefore orientation of the aromatic ring within the cavity may have limiting attempts at assessing their synergistic relationship with more ﬂexibility in its orientation and interaction with the PZA resistance. Mutations within the pncA gene and its promoter neighbouring residues, but without drastically impacting the remain the most common route to PZA resistance (Dookie molecular interactions for global protomer stability and ligand et al., 2018) (Khan et al., 2019). Nearly 70% of the MDR binding aﬃnity. isolates and 13% XDR isolates had nsSNPs in the pncA coding The molecular motion of pncA assessed by NMA was region. The burden of pncA mutations in the MDR and XDR visualised to understand the mutational eﬀects with regard isolates was lower in our analysis compared to 88.0% and to ﬂexibility (Supplementary Figure 1). Sites displaying high 20% observed by Pang et al. (2017). In another study, 70% mutational frequency or association with drug resistance of the MDR isolates, and signiﬁcantly higher i.e., 96% of XDR mutations were not located in regions with high ﬂexibility, with isolates harboured pncA mutations including nsSNPs (Allana large molecular motions mainly restricted to the loop region 60– et al., 2017). An alternative route to resistance for pncA as 66. This suggests the molecular motion in pncA does not interfere a non-essential gene encoding an enzyme that transforms a with PZA binding as active site residues were not associated with prodrug to drug would be by INDELs or mutations leading to high ﬂuctuations. premature stop codons resulting in the protein being degraded Normal mode analysis shows large scale molecular motions. on translation. A recent report analysing the pncAc.85_86insG Molecular dynamics (MD) studies oﬀer insights into the frameshift mutation using structural and biophysical analysis ﬁner grained atomic motions and are an excellent way to showed the mutation resulted in a truncated and incomplete investigate molecular mechanisms. However, these studies are protein lacking the active site pocket (Karmakar et al., 2018). computationally intensive and are diﬃcult to scale for studying Despite this obvious route to resistance, only 1% samples hundreds of mutations. A recent MD study on a subset of in our dataset showed INDELs and stop codons, compared mutations found within our dataset analysed seven pncA nsSNPs to 13% of samples that showed missense point mutations (F94L, F94S, K96N, K96R, G97C, G97D, and G97S) showed in pncA. This is consistent with the knowledge that nsSNPs that these destabilising mutations altered the binding pocket, in pncA remain the major route to resistance for PZA allowing increased PZA ﬂexibility (Khan et al., 2021). All seven (Khan et al., 2019). mutations were associated with PZA resistance and also showed Destabilising eﬀects are considered detrimental to the destabilising eﬀects in our study. A similar study of destabilising downstream protein function (via disruption of drug aﬃnity, mutations R123P, T76P, H7R associated with PZA resistance nucleic acid aﬃnity or overall complex stability) and are thus showed that the mechanism of resistance could be through given higher consideration in classifying mutations (Wylie and increasing the ﬂexibility of the region they are located in, Shakhnovich, 2011). In our analysis, around 85% of mutations thereby changing the binding pocket volume (Ali et al., 2020). were destabilising for overall protein stability as well as complex Another MD study of mutations P54L and H57P showed that aﬃnity. It is thought that the resistant phenotype is imparted they decrease overall stability along with reduced ligand aﬃnity either through aﬀecting protein folding, instability of the PZase leading to PZA resistance (Mehmood et al., 2019). All of these protein, prevention of coenzyme complex (Gopal et al., 2016) observations are concordant with our analysis. or loss of virulence factor synthesis (Gopal et al., 2016). Destabilising eﬀects of nsSNPs are thought to be the main Further, this is thought to come without a high bacterial ﬁtness reason for impeding protein function through directly eﬀecting cost since pncA is primarily an activator of the PZA drug. protomer stability or ligand aﬃnity. However, large stabilising This is similar to a recent observation reported in the katG eﬀects can have an equally deleterious impact on protein gene (target for the anti-TB pro-drug, isoniazid) with a high function through rigidiﬁcation, impeding ﬂexibility and dynamic proportion of destabilising mutations (Portelli et al., 2018). molecular motions. This has been implicated more generally Also, a higher proportion 60% (n = 253) of SNP mutations within a disease context (Gerasimavicius et al., 2020) and more showed electrostatic changes compared to 35% reported by speciﬁcally in PZA resistance (Rajendran and Sethumadhavan, Portelli et al. (2018). This likely due to the larger sample size of 2014). It oﬀers an explanation for the observance of the stabilising our dataset. mutation site 103. Drug associated mutations at this site (Y103C, All active site residues appear to be under drug selection Y103H, and Y103S) could result from the rigidiﬁcation of the pressures due to multiple mutations (>2) associated with these binding pocket leading to reduced binding aﬃnity measured as with the exception of I133, considered to be an emerging or destabilising PZA aﬃnity. budding-resistance hotspot. In our analyses, there were 22 such Mutations within pnca are scattered along the entire gene sites while 83 sites within pncA associated with > 2 nsSNPs linked length observed in studies (Stoﬀels et al., 2012; Miotto et al., to PZA drug resistance (categorised as DM). However mutations Frontiers in Molecular Biosciences | www.frontiersin.org 16 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 17 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis were not restricted to the active site, with less than 50% resistant extreme changes in stability or aﬃnity parameters (Figure 6). variants lying within 10 Å of the active site of PZA, indicating the One possible explanation is that the ﬁtness landscape is gene and possible role of distal residues in resistance development (Portelli function speciﬁc, optimised diﬀerently for genes directly coding et al., 2018). Mutations associated with drug resistance tend to for drug targets and for non-essential genes like pncA. Another have lower stability, lie buried within the structure with lesser major consideration is that resistance is often acquired through surface area as shown by Karmakar et al. (2020). a stepwise ordinal accumulation of mutations (Woodford and Our study compares results from two diﬀerent computational Ellington, 2007; Ismail et al., 2019). The genetic background can stability predictors: mCSM and FoldX (Schymkowitz et al., dramatically inﬂuence ﬁtness eﬀects associated with mutations 2005). Unsurprisingly, most mutations were found to have a (Wong, 2017). Consequently, the mutational impact diﬀers when destabilising eﬀect (Supplementary Figure 11). FoldX reported occurring against a sequence background of extant resistant 85% (vs.80% estimated by DUET) nsSNPs with destabilising mutations, a phenomenon known as epistasis (Wong, 2017). eﬀect. The range for absolute 11G values was greater for FoldX Since resistance development is a balanced interplay between (median: 2.0; range: 5.2, 27.46) compared to DUET (median: ﬁtness eﬀects and cost of resistance, epistasis warrants due 0.1; range: 3.9, 1.2). There was however, 77% agreement consideration in eﬀorts to understand and limit the evolution of between FoldX and DUET outcomes (data not shown). multi-drug resistance. Interestingly, drug associated mutations displayed higher FoldX The use of mCSM suite of tools has the advantage of 11G predictions compared to mCSM-DUET 11G predictions. studying global (protein stability) as well as local eﬀects (ligand A possible explanation for this is the diﬀerences in the underlying aﬃnity, protein-protein interaction, and protein nucleic-acid parameters the diﬀerent methods use. FoldX constructs mutant interaction). Additionally, it also provides the methodological structures by mutating the target residue and searching for the consistency for comparing molecular eﬀects and beneﬁts optimal conformation by iteratively altering the position of the application of machine learning methods (ML) to explore neighbouring side chains. The stability of the mutant structure greater mechanistic details. While computationally intensive, is estimated using an empirical force ﬁeld made of several ML methods would beneﬁt from using tools such as DynaMut energy terms. This compares to DUET where estimates of the (Rodrigues et al., 2018) which account for protein molecular structural eﬀects are based on diﬀerences between the wild-type motions when estimating mutational eﬀect on protein stability. environment and pharmacophore atomic changes resulting from Additionally methods which consider anti-symmetric properties the mutation, without the need to generate mutant structures. of mutational impact i.e., 11G (A ! B) = 11G (B ! A) With this in mind, it appears that the DM mutations have like DeepDDG (Cao et al., 2019) and INPS-MD (Savojardo et al., larger local perturbations in the mutated region considered 2016) have the potential to build robust predictive models and by FoldX, resulting in higher 11G predictions compared to improve the “learning” capability of ML methods in the context the lesser eﬀects of surface area considered by DUET. Drug of machine learning. resistance mutations displaying smaller surface area compared Mtb lineages have been associated with virulence, disease to their susceptible counterparts were also observed in recent transmission, drug resistance, and clinical outcome (Ford et al., studies investigating nsSNPs in Mtb genes (Portelli et al., 2018; 2013; Reiling et al., 2013; Novais et al., 2017; Correa-Macedo et al., Karmakar et al., 2020) indicating the role of compensatory 2019; Oppong et al., 2019; McHenry et al., 2020). Lineage speciﬁc mutations, alleviating any ﬁtness penalty in the development of diﬀerences between lineages 2 and 4 have recently been noted in the drug resistance phenotype. The extent of the contribution the development of TB drug resistance, especially related to MDR of surface area in these methods is reﬂected in the observation and XDR strains (Oppong et al., 2019). Our study highlighted of moderate correlations between DUET and structural features, the most signiﬁcant diﬀerences between L2 and L4 with respect and the weaker associations between FoldX and structural to protomer stability demonstrating the biophysical phenotypic features (Supplementary Figure 9A). Structural associations for manifestation of these underlying genotypic changes. The ligand aﬃnity were also observed to be weak (Supplementary observance of a distinct peak for destabilising mutations related Figure 9B) most likely due to the role of factors involved in short- to drug resistance within L1 suggests that the extreme mutational range interactions (like Van der Waal’s forces) not considered consequences of such mutations in the “ancient” lineage 1 may in our analysis. A similar view emerged in the recent study by be rapidly giving way to other “modern” M. tuberculosis lineages Karmakar et al. (2020) where no signiﬁcant diﬀerences were linked to MDR and XDR-TB and virulence. observed for PZA binding aﬃnity. Our study is based on a well-characterised clinical dataset It has been suggested that frequently occurring mutations may sourced globally from over 35 K clinical isolates, and leverages not confer extreme changes in biophysical stability measures, the availability of robust metadata (lineage, geography, DST, etc.) with mild stability eﬀects oﬀering local ﬁtness advantages for each isolate. We show that the framework used in our work (Portelli et al., 2018). Our data presented us with the opportunity allows us to investigate the interrelationships between genomic to test this theory empirically by assessing relationships of features from GWAS analysis and the biophysical measures of stability with GWAS measures of MAF, OR, and P-values. At nsSNPs, helping to contextualise the underlying bacterial ﬁtness a glance, it appears that mutations with high OR tend be and mutational landscape. The need to consider multiple stability less extreme in their impact on protein stability and ligand predictors with diﬀerent underlying principles to validate these aﬃnity (Supplementary Figure 10). However, we did not ﬁnd associations has also been highlighted. Lineage associations of any signiﬁcant association with high frequency mutations and drug resistance, and their biophysical consequences, require Frontiers in Molecular Biosciences | www.frontiersin.org 17 July 2021 | Volume 8 | Article 619403 fmolb-08-619403 August 3, 2021 Time: 21:57 # 18 Tunstall et al. Pyrazinamide Resistance in Mycobacterium tuberculosis further investigation and the functional characteristics of the manuscript. All authors contributed to the manuscript and mutations should be validated in future experiments. We hope approved the submitted version. such a framework can be used to understand and inform therapeutic and stewardship eﬀorts. FUNDING TT was supported by a BBSRC Ph.D. studentship (Grant No: DATA AVAILABILITY STATEMENT BB/S507544/1). JP was funded by a Newton Institutional Links Grant (British Council. 261868591). CE was funded by the All pre-generated TB-proﬁler results were downloaded for all Medical Research Council UK (Grant No. MR/T000171/1). isolates from tbdr.lshtm.ac.uk/sra. TC was funded by the Medical Research Council UK (Grant No. MR/M01360X/1, MR/N010469/1, MR/R025576/1, and MR/R020973/1) and BBSRC (Grant No. BB/R013063/1). AUTHOR CONTRIBUTIONS TT was responsible for the molecular docking, integrating SUPPLEMENTARY MATERIAL genomics and structural data, data analysis, and writing the initial draft. JP made available and generated the genomics data The Supplementary Material for this article can be found results. CE provided the FoldX pipeline. 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Epistasis and the evolution of antimicrobial resistance. Front. practice. No use, distribution or reproduction is permitted which does not comply Microbiol. 8:246. with these terms. Frontiers in Molecular Biosciences | www.frontiersin.org 20 July 2021 | Volume 8 | Article 619403

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Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

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Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

Structural and Genomic Insights Into Pyrazinamide Resistance in Mycobacterium tuberculosis Underlie Differences Between Ancient and Modern Lineages

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