How To Optimally Combine Genotypic and Phenotypic Drug Susceptibility Testing Methods for Pyrazinamide

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False-susceptible phenotypic drug-susceptibility testing (DST) results for pyrazinamide due to mutations with MICs close to the critical concentration (CC) confound the classification of pncA resistance mutations, leading to an underestimate of the specificity of genotypic DST. This could be minimized by basing treatment decisions on well-understood mutations and by adopting an area of technical uncertainty for phenotypic DST rather than only testing the CC, as is current practice for the Mycobacterium tuberculosis complex.

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DNA Sequencing Predicts 1st-Line Tuberculosis Drug Susceptibility Profiles

Caroline Allix-Beguec, Irena Arandjelovic, Lijun Bi … (2018)

Background The World Health Organization recommends universal drug susceptibility testing for Mycobacterium tuberculosis complex to guide treatment decisions and improve outcomes. We assessed whether DNA sequencing can accurately predict antibiotic susceptibility profiles for first-line anti-tuberculosis drugs. Methods Whole-genome sequences and associated phenotypes to isoniazid, rifampicin, ethambutol and pyrazinamide were obtained for isolates from 16 countries across six continents. For each isolate, mutations associated with drug-resistance and drug-susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These were predicted to be pan-susceptible if predicted susceptible to isoniazid and to other drugs, or contained mutations of unknown association in genes affecting these other drugs. We simulated how negative predictive value changed with drug-resistance prevalence. Results 10,209 isolates were analysed. The greatest proportion of phenotypes were predicted for rifampicin (9,660/10,130; (95.4%)) and the lowest for ethambutol (8,794/9,794; (89.8%)). Isoniazid, rifampicin, ethambutol and pyrazinamide resistance was correctly predicted with 97.1%, 97.5% 94.6% and 91.3% sensitivity, and susceptibility with 99.0%, 98.8%, 93.6% and 96.8% specificity, respectively. 5,250 (89.5%) drug profiles were correctly predicted for 5,865/7,516 (78.0%) isolates with complete phenotypic profiles. Among these, 3,952/4,037 (97.9%) predictions of pan-susceptibility were correct. The negative predictive value for 97.5% of simulated drug profiles exceeded 95% where the prevalence of drug-resistance was below 47.0%. Conclusions Phenotypic testing for first-line drugs can be phased down in favour of DNA sequencing to guide anti- tuberculosis drug therapy.

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Mycobacterium tuberculosis Pyrazinamide Resistance Determinants: a Multicenter Study

Paolo Miotto, Andrea Cabibbe, Silke Feuerriegel … (2014)

INTRODUCTION Pyrazinamide (PZA) is a key drug in current and future tuberculosis (TB) treatment regimens. It has a high sterilizing capacity in vivo, but it is not active against Mycobacterium tuberculosis complex (MTBC) strains growing at neutral pH (1 – 4). In addition to its crucial role in the standard short-course regimen for TB treatment, PZA is used in the treatment of patients infected with strains that are multidrug resistant (MDR) (resistant to at least isoniazid and rifampin). Here PZA has a strong impact on the success rates of MDR treatment and may allow a shortening of current MDR therapy (5). Finally, PZA is the only first-line drug that will be maintained in all regimens in the near future (6). These new regimens aim at reducing the treatment duration of susceptible, drug-resistant (especially MDR TB and extensively resistant) strain variants. The essential role of PZA underlines the need for accurate and rapid detection of PZA resistance that is very difficult with current phenotypic tests (7). The difficulties with culture-based PZA susceptibility testing result from several factors, including suboptimal test media with unreliable pH and larger inocula that reduce the activity of PZA (8, 9). Furthermore, the critical concentration itself may result in inconsistent results for isolates with a PZA MIC close to this concentration (10). While for isoniazid and rifampin, highly reliable culture-based drug susceptibility testing (DST) techniques and rapid molecular assays such as the line probe assay MTBDRplus (Hain Lifescience GmbH, Nehren, Germany) and the cartridge-based Xpert MTB/RIF assay (Cepheid, Sunnyvale, CA) are available (11), no commercial molecular assays are currently marketed for PZA. Great efforts have been made in understanding molecular resistance mechanisms. PZA is a prodrug that needs to be converted to an active compound, pyrazinoic acid, by the bacterial pyrazinamidase (PZase) (encoded by pncA). Mutations/variations in pncA leading to the loss of PZase activity are the major mechanism leading to PZA resistance (PZAr) (4, 12). However, while high numbers of PZAr cases can be related to inactivation of the PZase, the genetic variants, including single nucleotide polymorphisms (SNPs) and small deletions, are highly diverse and scattered over the full length of the 561 bp of the pncA gene (4, 12). This complicates the development of molecular tests, as no “hot spot region” comprising the majority of mutations is present in the pncA gene, as is present in rpoB for rifampin resistance. Accordingly, future molecular approaches to detect PZAr in clinical isolates need to cover at least a significant number of possible variants, if not the complete gene, to reach a high sensitivity (e.g., using approaches based on classical Sanger sequencing or next-generation genome sequencing). These techniques must be combined with an appropriate interpretation algorithm/database that distinguishes SNPs clearly associated with drug resistance from those for which the impact for developing PZAr is unclear, e.g., due to phylogenic variants found in members of the MTBC (13, 14). In-depth knowledge of the variants found in PZAr strains combined with evidence-based correlation with resistance phenotypes are needed to develop large-scale databases ensuring valid data interpretation. The fact that such a valid data basis is currently lacking represents a substantial limitation for molecular PZA DST. To tackle this question, we performed a large multicenter study assessing pncA sequence variations in 1,950 MTBC pan-susceptible strains and PZAr strains. The strains were classified in phylogenetic lineages to identify variants that are phylogenetically informative but not likely to be involved in PZAr and those that are occurring in strains from different groups and are obviously under positive selection. Using this comprehensive approach, we could catalog 239 high-confidence PZAr mutations and a number of pncA variants most likely not involved in PZAr. RESULTS We studied 1,950 clinical isolates, including 1,142 MDR strains and 483 fully susceptible strains (see Table S1 in the supplemental material). By phenotypic DST, 1,107 clinical isolates were susceptible to PZA, whereas 843 were classified as PZAr. Genotyping data were available for 1,853 isolates (95.0%). Predominant lineages among the strains investigated were Beijing (47.8%), LAM (9.0%), Ural (7.7%), and Haarlem (5.0%). Other lineages found (Ghana, EAI, Delhi/CAS, H37Rv_like, Uganda I and II, West African 1 and 2, S, Cameroon, Sierra Leone 1 and 2, Mycobacterium bovis, H37Rv, UZB_H37Rv_like, New-1, X, CAS, TUR,and Mycobacterium microti vole) each represented less than 5% of isolates (Fig. 1). Six percent were classified as EuroAmerican strains not belonging to a valid lineage described previously (“other undefined,” as described in reference 15), and 5.2% of strains were classified as “unknown,” because it was not possible to assign a defined lineage. FIG 1 Pie chart reporting percentages of lineages for isolates included in the study. Considering the sequencing results, 1,062 (54.5%) isolates were found to be wild type (WT) for the pncA gene, whereas 888 harbored variations in the pncA gene that amounted to a total of 280 genetic variants comprising 67 insertions or deletions (indels) and 213 SNPs (see Table S2 in the supplemental material). The PZase enzymatic activity was available for 251 clinical isolates accounting for 90 different genetic variants. Considering the distribution of the mutations across the entire gene, 73 (39.0%) codons were not affected by mutations, whereas the remaining 114 codons presented one or more mutations (Fig. 2). Only 50 codons showed a frequency of mutation over the mean value of 0.5%, but despite this, a clear hot spot region could not be found; the most frequently affected regions (representing more than 70% of mutated strains) were found at the promoter (positions −13 to −3) and at codons 6 to 15, 50 to 70, 90 to 100, 130 to 145, and 170 to 175 (Fig. 3). FIG 2 Number of different mutations found at each codon. Note that multiple mutations and IS6110 are not included. The broken line indicates mean value. FIG 3 Frequency of mutations found at each codon (calculated with 888 mutated isolates). Note that multiple mutations and IS6110 are not included. The dotted line indicates mean value. For mutations found in both PZA-sensitive (PZAs) and PZAr isolates, enzymatic activity and structural analysis results were used to adjust for possible errors in phenotypic DST whenever possible and to obtain a “revised DST” (included as “DST rev” in Table S1 in the supplemental material). Accordingly, 56 clinical isolates originally reported as PZAs were reclassified as PZAr (Table S1). The final distribution of mutations among revised PZAs and PZAr isolates is summarized in Table 1. To further validate the classification data, we analyzed the homoplastic occurrence of particular mutations (e.g., the emergence in strains of two phylogenetic lineages [16]). As the homoplasy level is rather low in MTBC genomes, this confirms that these mutations are most likely under positive selection and involved in the development of PZAr. TABLE 1 Distribution of mutations among PZAs and PZAr clinical isolates pncA gene No. of isolates (%) PZAs (n = 1,051) PZAr (n = 899) WT 893 a (85.0) 158 (15.0) Mutant 200 (22.2) 699 (77.8) a Includes 19 isolates harboring silent mutations or mutations at the distal region of the promoter (>100 nucleotides upstream of the start codon). Using this procedure, four classes of genetic variants were identified: (i) very high confidence resistance mutations that were found only in PZAr strains (category A), (ii) high-confidence resistance mutations found in more than 70% of PZAr strains (category E), (iii) mutations with an unclear role found in less than 70% in PZAr strains (category D), and (iv) genetic variants (including the wild type) not involved in phenotypic resistance (category B). Table S2 in the supplemental material summarizes these clinically relevant categories; a graphical overview is provided in Fig. 4. FIG 4 Distribution of genetic variants across the four categories identified: (i) very high confidence resistance mutations, (ii) high-confidence resistance mutations, (iii) mutations with an unclear role, and (iv) mutations not involved in phenotypic resistance. The number of isolates belonging to each category is also reported. The inner ring shows the percentages of mutations affecting the structure of the enzyme for each category of genetic variants. PZA-R, PZA resistance. *, including wild-type isolates for the pncA gene. Mutations conferring PZAr at very high confidence. Out of the 280 sequence variants identified in pncA, 239 (85.4%) mutations found in 644 clinical isol ates (644/1,950 [33.0%]) were classified as very high confidence variants associated with phenotypic PZAr (category A) (see Table S2 in the supplemental material). Several mutations affect the catalytic residues and amino acids recruited in the scaffold of the active site or directly/indirectly involved in the coordination of the Fe2+ ion (Asp8Gly/Ala/Glu/Asn, His51Gln/Tyr, His71Arg, Asp49Glu/Asn/Ala, His57Arg/Tyr/Gln/Pro, Trp68Arg/Gly/Cys/Stop/Leu, Gln10Pro/Arg, and His137Pro/Arg/Asp) or residues engaged in the hydrophobic core (Ile6Thr, Val44Gly, Val139Gly/Leu, Met175Thr/Val, and Phe94Cys/Ser/Leu). Out of the 90 variants tested, 87 variants, including nucleotide substitutions at position −11, were also associated with negative PZase activity, and 55 genetic variants (detected in 332 isolates) were found in strains of at least 2 different lineages, indicating homoplasy (data not shown). Table 2 reports the mutations mapping in the most frequently affected regions. TABLE 2 Mutations for PZAr affecting the most frequently affected regions of pncA gene and representing more than 70% of mutated cases Nucleotide change a Result of the mutation b p.S c p.R d No. of cases A-11C Promoter −11 0.01497006 0.98502994 5 A-11G Promoter −11 0.01497006 0.98502994 35 A-11T Promoter −11 0.01497006 0.98502994 1 T-7C Promoter −7 0.01497006 0.98502994 4 T-7G Promoter −7 0.01497006 0.98502994 1 Del-5 → G Promoter (del) 0.01497006 0.98502994 2 ATC6ACC Ile6Thr 0.01497006 0.98502994 3 Del14 → TCATCG FSC 6 (del) 0.01497006 0.98502994 1 GTC7GGC Val7Gly 0.01497006 0.98502994 9 GTC7TTC Val7Phe 0.01497006 0.98502994 2 WT + GTC7GGC WT + Val7Gly 0.01497006 0.98502994 1 GAC8AAC Asp8Asn 0.01497006 0.98502994 3 GAC8GAA Asp8Glu 0.01497006 0.98502994 6 GAC8GCC Asp8Ala 0.01497006 0.98502994 1 GAC8GGC Asp8Gly 0.01497006 0.98502994 9 GTG9GGG Val9Gly 0.01497006 0.98502994 1 CAG10AAG Gln10Lys 0.01497006 0.98502994 2 CAG10CCG Gln10Pro 0.01497006 0.98502994 21 CAG10CGG Gln10Arg 0.01497006 0.98502994 6 GAC12AAC Asp12Asn 0.01497006 0.98502994 GAC12GAG Asp12Glu 0.01497006 0.98502994 2 GAC12GCC Asp12Ala 0.01497006 0.98502994 6 Ins37 → GACT FSC 13 (ins) 0.01497006 0.98502994 1 TTC13TCC Phe13Ser 0.01497006 0.98502994 2 TTC13TTG Phe13Leu 0.01497006 0.98502994 3 TGC14CGC Cys14Arg 0.01497006 0.98502994 1 TGC14TGA Cys14Stop 0.01497006 0.98502994 2 WT + TGC14CGC WT + Cys14Arg 0.01497006 0.98502994 1 Ins44 → C FSC 15 (ins) 0.01497006 0.98502994 1 Del150 → T FSC 50 (del) 0.01497006 0.98502994 1 CAC51CAA His51Gln 0.01497006 0.98502994 7 CAC51CCC His51Pro 0.01497006 0.98502994 2 CAC51CGC His51Arg 0.01497006 0.98502994 4 CAC51TAC His51Tyr 0.01497006 0.98502994 3 CCG54CAG Pro54Gln 0.01497006 0.98502994 4 CCG54CGG Pro54Arg 0.01497006 0.98502994 1 CCG54CTG Pro54Leu 0.01497006 0.98502994 4 CCG54TCG Pro54Ser 0.01497006 0.98502994 1 CAC57CAG His57Gln 0.01497006 0.98502994 1 CAC57CCC His57Pro 0.01497006 0.98502994 1 CAC57CGC His57Arg 0.01497006 0.98502994 14 CAC57GAC His57Asp 0.01497006 0.98502994 10 CAC57TAC His57Tyr 0.01497006 0.98502994 5 WT + CAC57CGC WT + His57Arg 0.01497006 0.98502994 2 TTC58CTC Phe58Leu 0.01497006 0.98502994 7 CCG62CTG Pro62Leu 0.01497006 0.98502994 3 Del186 → C FSC 62 (del) 0.01497006 0.98502994 3 Ins185 → 4 nt FSC 62 (ins) 0.01497006 0.98502994 1 Ins186 → A FSC 62 (ins) 0.01497006 0.98502994 1 GAC63GGC Asp63Gly 0.01497006 0.98502994 4 Ins192 → A FSC 64 (ins) 0.01497006 0.98502994 1 TAT64TAG Tyr64stop 0.01497006 0.98502994 3 Ins193 → A FSC 65 (ins) 0.01497006 0.98502994 1 Ins193 → TATCAGG FSC 65 (ins) 0.01497006 0.98502994 1 TCG67CCG Ser67Pro 0.01497006 0.98502994 2 TGG68CGG Trp68Arg 0.01497006 0.98502994 7 TGG68GGG Trp68Gly 0.01497006 0.98502994 16 TGG68TAG Trp68stop 0.01497006 0.98502994 1 TGG68TGC Trp68Cys 0.01497006 0.98502994 5 TGG68TGT Trp68Cys 0.01497006 0.98502994 1 GAG91TAG Glu91Stop 0.01497006 0.98502994 1 TTC94CTC Phe94Leu 0.01497006 0.98502994 8 TTC94TCC Phe94Ser 0.01497006 0.98502994 3 TTC94TGC Phe94Cys 0.01497006 0.98502994 6 TTC94TTA Phe94Leu 0.01497006 0.98502994 2 TTC94TTG Phe94Leu 0.01497006 0.98502994 1 WT + TTC94CTC WT + Phe94Leu 0.01497006 0.98502994 1 TAC95TAG Tyr95stop 0.01497006 0.98502994 1 AAG96AAC Lys96Asn 0.01497006 0.98502994 1 AAG96ACG Lys96Thr 0.01497006 0.98502994 2 AAG96AGG Lys96Arg 0.01497006 0.98502994 1 AAG96CAG Lys96Gln 0.01497006 0.98502994 1 AAG96GAC Lys96Glu 0.01497006 0.98502994 6 Ins288 → T FSC 96 (ins) 0.01497006 0.98502994 2 Ins288 → 33 nt FSC 96 (ins) 0.01497006 0.98502994 4 Del291 → T FSC 97 (del) 0.01497006 0.98502994 1 GGT97AGT Gly97Ser 0.01497006 0.98502994 6 GGT97GAT Gly97Asp 0.01497006 0.98502994 4 GGT97GCT Gly97Ala 0.01497006 0.98502994 1 TAC99TAA Tyr99stop 0.01497006 0.98502994 2 ACC100CCC Thr100Pro 0.01497006 0.98502994 2 ACC100GCC Thr100Ala 0.01497006 0.98502994 1 GTG130GCG Val130Ala 0.01497006 0.98502994 1 GTG130GGG Val130Gly 0.01497006 0.98502994 1 Ins391 → G FSC 131 (ins) 0.01497006 0.98502994 3 Ins391 → GG FSC 131 (ins) 0.01497006 0.98502994 2 Ins392 → G FSC 131 (ins) 0.01497006 0.98502994 2 Ins392 → GG FSC 131 (ins) 0.01497006 0.98502994 4 Ins393 → G FSC 131 (ins) 0.01497006 0.98502994 2 Ins393 → GG FSC 131 (ins) 0.01497006 0.98502994 1 Ins394 → ATGTGGTCG FSC 131 (ins) 0.01497006 0.98502994 1 TGC131GGTGC FSC 131 (ins) 0.01497006 0.98502994 1 GGT132AGT Gly132Ser 0.01497006 0.98502994 1 GGT132GAT Gly132Asp 0.01497006 0.98502994 1 GGT132GCT Gly132Ala 0.01497006 0.98502994 1 GGT132TGT Gly132Cys 0.01497006 0.98502994 2 ATT133ACT Ile133Thr 0.01497006 0.98502994 17 Del398 → T FSC 133 (del) 0.01497006 0,98502994 1 GCC134GTC Ala134Val 0.01497006 0.98502994 2 ACC135AAC Thr135Asn 0.01497006 0.98502994 3 ACC135CCC Thr135Pro 0.01497006 0.98502994 4 GAT136TAT Asp136Tyr 0.01497006 0.98502994 3 Ins408 → A FSC 136 (ins) 0.01497006 0.98502994 4 CAT137CCT His137Pro 0.01497006 0.98502994 1 CAT137CGT His137Arg 0.01497006 0.98502994 1 CAT137GAT His137Asp 0.01497006 0.98502994 1 TGT138CGT Cys138Arg 0.01497006 0.98502994 3 TGT138TGG Cys138Trp 0.01497006 0.98502994 1 Del417 → G FSC 139 (del) 0.01497006 0.98502994 1 GTG139CTG Val139Leu 0.01497006 0.98502994 3 GTG139GGG Val139Gly 0.01497006 0.98502994 5 CGC140CCC Arg140Pro 0.01497006 0.98502994 1 CAG141CCG Gln141Pro 0.01497006 0.98502994 11 CAG141TAG Gln141stop 0.01497006 0.98502994 1 Ins423 → CAGACGGCGCCAG FSC 141 (ins) 0.01497006 0.98502994 1 ACG142AAG Thr142Lys 0.01497006 0.98502994 1 ACG142ATG Thr142Met 0.01497006 0.98502994 3 ACG142GCG Thr142Ala 0.01497006 0.98502994 3 GCC143GGC Ala143Gly 0.01497006 0.98502994 1 CTG172CCG Leu172Pro 0.01497006 0.98502994 9 Del514 → C FSC 172 (del) 0.01497006 0.98502994 1 Ins516 → CG FSC 172 (ins) 0.01497006 0.98502994 1 ATG175ACG Met175Thr 0.01497006 0.98502994 1 ATG175ATA Met175Ile 0.01497006 0.98502994 10 ATG175GTG Met175Val 0.01497006 0.98502994 6 a A-11C, nucleotide change A to C in position −11; Del-5 → G, deletion of nucleotide G in position −5; ATC6AAC, ATC at codon 7 changed to AAC; WT + GTC7GGC, double pattern wild-type + GTC at codon 7 changed to GGC; Ins37 → GACT, GACT inserted at codon 37. b Promoter −11, nucleotidic mutation affecting the promoter region at position −11; del, deletion; FSC, frameshift codon; ins, insertion. c p.S, probability associated with the susceptible phenotype. d p.R, probability associated with the resistant phenotype. Mutations conferring PZAr at high confidence. Nine genetic variants (32 strains, category E) were found in both PZAr and PZAs isolates, but with a proportion higher than 70% in PZAr strains. These mutations were mainly associated with an increase in free energy and/or structural constraints and were most frequently associated with PZAr (93.5% of cases). We confirmed a reduced but still present PZase activity for some of these variants as a development of faint color during the enzymatic assay. Whereas Leu172Pro was found to be associated with homoplasy, for other substitutions, the number of cases was too low to consider convergent evolution in different lineages. Mutations with an unclear role in conferring PZAr. Five genetic variants (21 cases, category D) were found in both PZAr and PZAs isolates but at a proportion less than 70% in PZAr strains. Two genetic variants (15 cases) showed borderline behavior in terms of structure/free energy variation and enzyme activity. Homoplasy was found for the Val139Ala mutation, thus suggesting a putative role in phenotypic resistance or at least in increasing the MIC. Pro62Arg, Asp63Ala, and Ser65Pro substitutions (6 cases) represent another group of mutations belonging to this ambiguous category. Further characterization of these mutations is needed to better understand their correlation with the PZA phenotype. Mutations not involved in phenotypic resistance. Twenty-seven genetic variants were not associated with PZAr according to our classification. Eighteen mutations (163 cases, category C) were most frequently associated with PZAs (91.4% of cases). It should be noted that the Val21Ala mutation was also found associated with other mutations in category A responsible for PZAr/PZase negativity. Interestingly, all these mutations were found to be associated with single lineages; thus, no homoplasy was observed. Further characterization of these mutations is needed to better understand their role (if any) in PZA susceptibility. The remaining genetic variants (27 cases, category B) did not affect the amino acid sequence of the PZase enzyme. We observed two silent mutations: TCC65TCT (Ser65Ser), GCG38GCC (Ala38Ala). The Ser65Ser silent mutation was found associated with the Delhi/CAS lineage. In some cases, sequencing of the upstream region of pncA allowed the identification of a deletion at position −125 or an insertion at nucleotide −3; however, isolates harboring these genetic variants were found associated with both phenotypic resistance and susceptibility. According to these data, and supported by the lack of homoplasy for these mutations, the indels detected do not represent a marker for PZAr. A total of 1,062 clinical isolates (1,062/1,950 [55.4%]) showed a WT sequence for the PZase enzyme (included in category B), and the sequence was associated with PZAs in more than 80% of cases. Enzymatic assay results were not available for all: 17 isolates (out of 138 tested; 12.3%) gave a negative PZase enzymatic activity, indicating that a WT PZase does not exclude phenotypic resistance a priori. DISCUSSION PZA DST is crucial for successful management of patients with susceptible and drug-resistant TB, especially with MDR TB. Furthermore, future shorter regimens for both drug-resistant and drug-susceptible TB will include PZA as a key drug for achieving both sterilization and prevention of the development of drug resistance to new drugs (17, 18). Thus, reliable PZAr data for clinical isolates are crucial for guiding the clinical management of patients. Phenotypic tests, however, have a long turnaround time, are expensive, and are considered poorly reliable. As a consequence, the design of a molecular test for predicting PZAr is a priority. The development of a rapid molecular PZA DST is hampered by the diverse nature of resistance-associated mutations mainly scattered over the full length of the pncA gene, and by the fact that the impact of individual mutations has not been systematically investigated (4, 12). Therefore, we performed a large-scale study linking pncA sequence diversity with phenotypic, structural biology and population biology data to develop the first encyclopedia of pncA sequence variations linked to either a PZAr or PZAs phenotype. This is likely to pave the way for application of new genome-based sequencing technologies for predicting PZAr, allowing for personalized treatment algorithms. Strikingly, nearly 85% of the genetic variants identified in the pncA gene were associated with phenotypic resistance to PZA and were classified as “high-confidence” PZA resistance mutations. All in-frame and frameshift indel mutations within the coding region were included in this group. We found that nearly 90% of observed mutations are associated with protein structural destabilization that causes loss of enzymatic activity. Equally importantly, we described 27 mutations most likely not involved in PZA resistance that should be “filtered out” in future molecular tests and labeled as not “clinically relevant” (Fig. 4). Only five mutations cannot be classified by our approach and remain without clear association with a resistance or susceptible phenotype. These mutations need further validation for association with PZAr and/or with a specific genetic background by an allelic exchange procedure as performed for other drugs (19). Discrepancies between molecular and phenotypic DST are confusing for clinicians managing patients; 180 isolates investigated here showed discrepant results between phenotypic and genotypic tests (WT pncA gene sequence and resistance by Bactec MGIT 960 DST). It has been reported that the Bactec MGIT 960 mycobacterial detection system may overestimate resistance even in the best laboratory settings (due to changes in the medium pH and/or variability in the inoculum size). Alternatively, a different mechanism of resistance, such as mutations in rpsA, could also be hypothesized for a few cases, although these were not clearly confirmed in clinical isolates (data not shown) (20 – 23). In Fig. S1 in the supplemental material, we modeled the impact of these “discrepant cases” in different hypothetical diagnostic scenarios to provide worst and best performances of pncA sequencing-based assay as follows. If all 180 cases were truly susceptible, the diagnostic accuracy of a molecular test for PZA based on sequence would be 98.77% (95% confidence interval [95% CI], 98.18 to 99.17%) (Fig. S1C) in the range of the rifampin and isoniazid test results (11). If the 180 cases were truly PZAr strains (wrongly predicted as PZAs by pncA gene sequence), the diagnostic accuracy of pncA sequencing in detecting PZAr would be 89.54% (95% CI, 89.21 to 90.82%), in the range of isoniazid resistance (Fig. S1B) (11). Based on our findings, any future molecular test for PZA resistance should be able not only to detect the absence of the wild-type sequence but also to identify the specific SNPs. We found, indeed, a relevant number (10%) of mutations previously not reported as associated with drug resistance (DR) and the degree of variability in terms of indel mutations. In addition, we found mutations not associated with DR, including the previously reported lineage-specific genetic variants (e.g., TCC65TCT in Delhi/CAS) (14). Accordingly, only an assay with the capacity to provide in-depth sequence information could comply with the minimal requirements for a new molecular PZAr test. Fully automated, low-cost medium-density arrays and user-friendly whole-gene/whole-genome sequencing-based approaches will become a reality in the very near future and will be the most suitable assays to fulfill this task. In particular, new next-generation sequencing (NGS)-based diagnostics could represent innovative tools to reduce false PZAr cases and to improve safe and fast detection of drug resistances by molecular DST (24). Our work has generated the minimum sets of mutations that should be included in any molecular test for PZA and provide a start point for a pncA genetic variation encyclopedia needed for the valid interpretation of data generated by massive sequencing approaches. An additional aspect that is highlighted by our study is the great advantage in sharing large data sets generated by several groups. The establishment of a common database combining data from clinical isolates collected in a large number of settings was crucial to improve our understanding the role of pncA gene mutations in determining the PZA susceptibility phenotype of M. tuberculosis. The high number of samples providing sufficient reiteration of less frequent mutations together with the inclusion of different parameters (phenotype, genotype, enzymatic activity, structure, and free energy analyses) in a decision tree allowed us to define specific operational categories of mutations relevant from a clinical point of view. This enabled us to build a user-friendly diagnostic algorithm through the classification of specific SNPs in a shared database collecting more-complex information. These large shared databases of mutations involved in drug resistance could contribute to a better understanding of molecular mechanisms of resistance, improved molecular diagnostics, new diagnostic algorithms, and better public health control of drug-sensitive and drug-resistant TB. MATERIALS AND METHODS Strain selection. Strains were made available by six TB National/Supranational Reference and partner laboratories within the TB-PANNET Consortium to provide wide coverage for most of the lineages observed for the M. tuberculosis complex. Strains were tested for PZA susceptibility and included in the study regardless of testing for other antitubercular drugs. PZA drug susceptibility testing (DST) was performed by using a Bactec MGIT 960 mycobacterial detection system and MGIT 960 PZA kits (BD, Franklin Lakes, NJ, USA) according to the manufacturer’s instructions. A total of 1,950 clinical isolates were incorporated in the database. Whenever available, genotyping information (spoligotyping and/or mycobacterial interspersed repetitive-unit−variable-number tandem-repeat [MIRU-VNTR] typing [25]) were collected. The MIRU-VNTRplus web tool (26, 27) was used to define lineage information (similarity search settings for identification: 0.17; distance measure for MIRU-VNTR: categorical, weighting 1; distance measure for spoligotyping: categorical, weighting 1). pncA gene sequencing. DNA was extracted as described elsewhere (28). The pncA gene (Rv2043c, NCBI gene identifier [ID] 888260), including the proximal promoter region, was amplified. On a subset of samples, the distal promoter region (>100 bp upstream of the start codon) was also included in the amplified region according to the protocol described in reference 29. Amplicons were sequenced with an automated DNA sequencer. The pncA gene sequence of isolates from Samara, Russian Federation, was determined from whole-genome sequencing data as previously described (15). Mutations in the pncA gene were identified by comparison with the wild-type M. tuberculosis H37Rv pncA gene sequence. PZase assay. PZase activity was evaluated as described by Singh et al. (30). Briefly, a Middlebrook 7H9 (BD, Franklin Lakes, NJ, USA) 1.5% agarose containing PZA (Sigma-Aldrich Corporation, Saint Louis, MO, USA) at a final concentration of 400 µg/ml was prepared. Melted PZA agar was distributed in glass tubes by using an agarose base to obtain a semitransparent medium allowing the detection of a faint pink band against a white background. A heavy loopful of actively growing culture was carefully inoculated on the surface of the PZA agar medium and incubated at 37°C for 4 days. One milliliter of ferrous ammonium sulfate (1%) was added to each tube after incubation and observed for 4 h for the appearance of a pink band (positive) in the subsurface agar. PZA-resistant isolates of M. bovis (negative by the PZase test) were used as negative controls, and the PZA-susceptible strain M. tuberculosis H37Rv was used as a positive control. All isolates showing discrepant results (namely, pncA mutant and PZase positive or WT pncA and PZase negative) were retested at 4 and 10 days (31). PZase structure. For each amino acid substitution, we performed an in silico analysis of the free energy variation associated with the specific mutation taking into account an acidic environmental pH (6.0), very close to the one required for PZA activity. The crystal structure of the PZase enzyme determined to 2.2-Å resolution (PDB code 3PL1) (32) was used in conjunction with the program FoldX (33). Mean free energy variation was calculated for triplicates of predicted structures, and based on statistical analysis, a free energy variation greater than 2 kJ/mol was considered to destabilize the enzyme. Frameshifts and mutations affecting the promoter region were not considered. Free energy variation was then integrated with a visual structural analysis in order to identify substitutions tolerated by the free energy term but detrimental for the specific activity of the enzyme. Statistical analysis. For understanding the significance of each mutation, we predicted the DST by fitting a conditional inference tree model considering results of sequencing, activity, and the combination of structure and energy analyses as predictors. In the model, we applied recursive partitioning based on conditional permutation tests. Furthermore, at each step, P values were adjusted for multiplicity by the procedure of Benjamini and Yekutiely (34). The majority of recursive partitioning algorithms introduced since 1963 (35), such as CHAID and CART, yield trees with too many branches and can also fail to pursue branches which can add significantly to the overall fit. This leads to potential drawbacks: overfitting and a selection bias toward covariates with many possible splits or missing values (36, 37). This approach is able to address missing data, since it uses surrogate splits to determine the daughter node where the observations with missing values in the primary split variable are sent (for further details, see references 38 and 39). As output of the model, given an isolate’s profile, a conditional probability of being PZA resistant is given. As a general rule, adjusted P values of less than 0.05 were considered significant. In the model, we applied recursive partitioning based on conditional permutation tests. In fact, when splitting, the use of the conditional distribution of the statistics ensures an unbiased selection of the covariates. This statistical approach prevented overfitting and overgrown trees, and no further pruning or cross-validation was needed. Further details on the rationale used for the analysis is available in the supplemental material. SUPPLEMENTAL MATERIAL Table S1 The Dataset columns in the middle of the table show the database collecting clinical isolate origin, pncA gene sequencing data, standard drug susceptibility testing (DST) results, lineage, PZase enzymatic activity, and free energy variation calculated at pH 6 (in the Notes column, b stands for borderline free energy variation). The Analysis columns at the right show the results interpreted from the data set provided considering consensus among the samples and literature data. Revised DST and enzymatic activity were modified only if consensus was reached. The impact of amino acid substitutions on the structure and stability of the protein were combined in the column labeled “Summary structure+energy.” Finally, each isolate is also classified within the conditional inference tree (p.S, probability of being PZAs; p.R, probability of being PZAr) and the clinically relevant categories (Category column). Table S1, XLSX file, 0.2 MB. Table S2 Summary of the genetic variants observed in this study, together with their frequency and lineage distribution. Mutations are classified according to their clinical relevance (Category column), and an overlap with the conditional inference tree is also reported (probability S and probability R columns). Table S2, XLSX file, 0.04 MB. Figure S1 Diagnostic scenarios Download Figure S1, DOCX file, 0.3 MB Text S1 Supplemental materials and methods Download Text S1, DOCX file, 0.01 MB

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A comprehensive characterization of PncA polymorphisms that confer resistance to pyrazinamide

Adam Yadon, Kashmeel Maharaj, John Adamson … (2017)

Tuberculosis chemotherapy is dependent on the use of the antibiotic pyrazinamide, which is being threatened by emerging drug resistance. Resistance is mediated through mutations in the bacterial gene pncA. Methods for testing pyrazinamide susceptibility are difficult and rarely performed, and this means that the full spectrum of pncA alleles that confer clinical resistance to pyrazinamide is unknown. Here, we performed in vitro saturating mutagenesis of pncA to generate a comprehensive library of PncA polymorphisms resultant from a single-nucleotide polymorphism. We then screened it for pyrazinamide resistance both in vitro and in an infected animal model. We identify over 300 resistance-conferring substitutions. Strikingly, these mutations map throughout the PncA structure and result in either loss of enzymatic activity and/or decrease in protein abundance. Our comprehensive mutational and screening approach should stand as a paradigm for determining resistance mutations and their mechanisms of action.

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Author and article information

Journal

Journal ID (nlm-ta): Antimicrob Agents Chemother

Journal ID (iso-abbrev): Antimicrob. Agents Chemother

Journal ID (hwp): aac

Journal ID (pmc): aac

Journal ID (publisher-id): AAC

Title: Antimicrobial Agents and Chemotherapy

Publisher: American Society for Microbiology (1752 N St., N.W., Washington, DC )

ISSN (Print): 0066-4804

ISSN (Electronic): 1098-6596

Publication date (Electronic preprint): 22 June 2020

Publication date (Electronic): 20 August 2020

Publication date Collection: September 2020

Publication date PMC-release: 20 August 2020

Volume: 64

Issue: 9

Electronic Location Identifier: e01003-20

Affiliations

[a ]Department of Genetics, University of Cambridge, Cambridge, United Kingdom

[b ]Emerging Bacterial Pathogens Unit, IRCCS Ospedale San Raffaele, Milan, Italy

Author notes

Address correspondence to Paolo Miotto, miotto.paolo@ 123456hsr.it .

Citation Köser CU, Cirillo DM, Miotto P. 2020. How to optimally combine genotypic and phenotypic drug susceptibility testing methods for pyrazinamide. Antimicrob Agents Chemother 64:e01003-20. https://doi.org/10.1128/AAC.01003-20.