Linear Regression Equations To Predict β-Lactam, Macrolide, Lincosamide, and Fluoroquinolone MICs from Molecular Antimicrobial Resistance Determinants in Streptococcus pneumoniae
ABSTRACT
Antimicrobial resistance in Streptococcus pneumoniae represents a threat to public health, and monitoring the dissemination of resistant strains is essential to guiding health policy. Multiple-variable linear regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to antimicrobial MICs for penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim-sulfamethoxazole. Training data sets consisting of Canadian S. pneumoniae isolates obtained from 1995 to 2019 were used to generate multiple-variable linear regression equations for each antimicrobial. The regression equations were then applied to validation data sets of Canadian (n = 439) and U.S. (n = 607 and n = 747) isolates. The MICs for β-lactam antimicrobials were fully explained by amino acid substitutions in motif regions of the penicillin binding proteins PBP1a, PPB2b, and PBP2x. Accuracies of predicted MICs within 1 doubling dilution to phenotypically determined MICs were 97.4% for penicillin, 98.2% for ceftriaxone, 94.8% for erythromycin, 96.6% for clarithromycin, 98.2% for clindamycin, 100% for levofloxacin, and 98.8% for trimethoprim-sulfamethoxazole, with an overall sensitivity of 95.8% and specificity of 98.0%. Accuracies of predicted MICs to the phenotypically determined MICs were similar to those of phenotype-only MIC comparison studies. The ability to acquire detailed antimicrobial resistance information directly from molecular determinants will facilitate the transition from routine phenotypic testing to whole-genome sequencing analysis and can fill the surveillance gap in an era of increased reliance on nucleic acid assay diagnostics to better monitor the dynamics of S. pneumoniae.
INTRODUCTION
Streptococcus pneumoniae is a common Gram-positive microorganism of the human nasopharynx that can cause severe invasive pneumococcal diseases (IPDs), such as bacteremia and meningitis. In this environment, exposure to other commensal bacteria, host immune responses, and sublethal antimicrobial levels increase selective pressures and favor the exchange of genetic material (1–4). The pneumococcal genome also contains numerous insertion sequences and other mobile genetic elements that facilitate the uptake of antimicrobial resistance determinants through horizontal recombination events (2, 5). Due to the ease with which S. pneumoniae can acquire novel antimicrobial resistance determinants, prompt detection of their emergence, dissemination, and dynamics through surveillance systems is essential to public health. The implementation of pediatric vaccination programs has been successful not only in lowering general incidence of IPD, but also by targeting serotypes associated with antimicrobial resistance, achieving a concurrent decrease in overall antimicrobial resistance (6, 7). The proliferation of antimicrobial-resistant isolates of nonvaccine serotypes, however, remains a major global health concern (8, 9).
The antimicrobial resistance mechanisms of S. pneumoniae have been extensively documented and with few exceptions can fully explain the observed antimicrobial phenotypes (10, 11). β-Lactam resistance has been attributed to genetic changes in the transpeptidase domains of penicillin binding proteins (encoded by pbp1a, pbp2b, and pbp2x), macrolide and lincosamide resistance to the presence of 23S rRNA methyltransferases (encoded by ermB and ermTR), the ABC-efflux pump (encoded by mefAE), or the number of 23S rRNA alleles with point mutations in the peptidyltransferase loop of domain V, fluoroquinolone resistance due to genetic changes in quinolone resistance-determining regions (QRDR) of gyrA and/or parC, tetracycline resistance due to the presence of tetM or tetO, chloramphenicol resistance due to the presence of cat, and trimethoprim-sulfamethoxazole resistance caused by genetic mutations in folA and folP (10, 12–24).
Monitoring the dissemination and dynamics of antimicrobial resistance of S. pneumoniae has traditionally relied upon in vitro phenotypic susceptibility testing of bacterial cultures; however, the development of whole-genome sequencing and novel molecular-based techniques to determine antimicrobial resistance can increase efficiency and decrease the labor associated with screening and monitoring strains for surveillance purposes. A EUCAST subcommittee reviewed the existing data on the use of whole-genome sequencing for the prediction of antimicrobial resistance for a number of bacterial species (25). The committee concluded that there was a lack of studies on the utility of whole-genome sequencing for the prediction of antimicrobial resistance in antimicrobials used to treat S. pneumoniae infections. Multiple-variable linear regression modeling has been successfully used to accurately predict MICs of a variety of agents for Neisseria gonorrhoeae (26–28). In this study, we employ a computational statistical approach not only to directly determine antimicrobial MICs from molecular determinants, but also to determine the relative contribution of each determinant to the overall MIC and present simple mathematical equations that can be applied to determine penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim-sulfamethoxazole MIC values for S. pneumoniae.
RESULTS
Regression analysis indicated that the predicted MIC (MICpred) for penicillin depended upon any alteration of the PBP1a-STMK, PBP1a-TSQF, PBP2b-SSNT, PBP2b-QLQPT, and/or PBP2x-LKSG motifs and specific alterations of PBP2x-STMK→SAFK and PBP2x-KDA→EDT or KEA. No isolates were found in the training or validation data sets to have modifications of the PBP1a-SSN, PBP1a-KTG, PBP2b-SVVK, PBP2b-KTG, or PBP2x-SSN motifs (Table 1).
TABLE 1
| PBP1a motifs | PBP2b motifs | PBP2x motifs | No. of isolates with penicillin MIC (mg/L) of: | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.03 | 0.06 | 0.125 | 0.25 | 0.5 | 1 | 2 | 4 | 8 | |||
| SSMK/WT/WT/NTGY | WT/SSNA/AIDTK/WT | SAFK/WT/KEA/VKSG | 2 | 2 | |||||||
| SSMK/WT/WT/NTGY | WT/SSNA/AIDTK/WT | SAMK/WT/EDT/VKSG | 1 | 2 | 7 | 17 | 6 | ||||
| SSMK/WT/WT/NTGY | WT/SSNA/AIDTK/WT | SAMK/WT/EDA/VKSG | 1 | ||||||||
| SAMK/WT/WT/NTGY | WT/SSNA/TVDTK/WT | SAMK/WT/KEA/VKSG | 1 | ||||||||
| SAMK/WT/WT/NTGY | WT/SSNA/AIDTK/WT | SAMK/WT/KEA/VKSG | 1 | 1 | |||||||
| SAMK/WT/WT/NTGY | WT/SSNA/WT/WT | SAMK/WT/KEA/VKSG | 1 | 8 | 28 | 6 | |||||
| SSMK/WT/WT/NTGY | WT/SSNA/SVESK/WT | SAMK/WT/KEA/VKSG | 1 | ||||||||
| SSMK/WT/WT/NTGY | WT/SSNA/WT/WT | SAMK/WT/KEA/VKSG | 1 | 3 | 11 | 11 | 1 | ||||
| SSMK/WT/WT/NTGY | WT/SSNA/WT/WT | SAMK/WT/EDT/VKSG | 1 | ||||||||
| SAMK/WT/WT/NTGY | WT/SSNA/WT/WT | WT/WT/WT/WT | 1 | 1 | 4 | ||||||
| SAMK/WT/WT/NTGY | WT/SSNA/SVESK/WT | WT/WT/WT/WT | 1 | ||||||||
| SAMK/WT/WT/NTGY | WT/SSNA/SVESK/WT | WT/WT/EDA/VKSG | 1 | ||||||||
| SAMK/WT/WT/NTGY | WT/SSNA/WT/WT | SPMK/WT/WT/WT | 1 | ||||||||
| SSMK/WT/WT/NTGY | WT/SSNA/AIDTK/WT | SAMK/WT/KEA/VKSG | 1 | ||||||||
| SSMK/WT/WT/NTGY | WT/SSNS/WT/WT | SAMK/WT/KEA/VKSG | 1 | ||||||||
| SSMK/WT/WT/NTGY | WT/WT/WT/WT | SAMK/WT/KEA/VKSG | 1 | ||||||||
| SSMK/WT/WT/NTGY | WT/SSNA/WT/WT | WT/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/NTGY | WT/SSNA/WT/WT | SAMK/WT/KEA/VKSG | 2 | 4 | 1 | ||||||
| WT/WT/WT/NTGY | WT/SSNA/SVETK/WT | SPMK/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/NTGY | WT/SSNA/WT/WT | WT/WT/WT/WT | 2 | 4 | 16 | 4 | |||||
| WT/WT/WT/NTGY | WT/SSNA/SVESK/WT | WT/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/NTGY | WT/SSNA/SVESK/WT | WT/WT/EDA/VKSG | 3 | ||||||||
| WT/WT/WT/NTGY | WT/YSSN/QQLQP/GKT | WT/WT/WT/VKSG | 1 | ||||||||
| WT/WT/WT/TSQY | WT/YSSN/QQLQP/GKT | WT/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/TSQY | WT/SSNA/SVESK/WT | WT/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/TSQY | WT/SSNA/WT/WT | SAMK/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/TSQY | WT/SSNA/SVESK/WT | SAMK/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/NTGY | WT/WT/WT/WT | WT/WT/WT/WT | 1 | ||||||||
| WT/WT/WT/NTGY | WT/WT/WT/WT | WT/WT/KEA/WT | 1 | ||||||||
| WT/WT/WT/NTGY | WT/WT/WT/WT | SAMK/WT/WT/WT | 1 | 19 | 1 | ||||||
| WT/WT/WT/WT | WT/SSNA/WT/WT | SAMK/WT/KEA/VKSG | 2 | ||||||||
| WT/WT/WT/WT | WT/SSNA/WT/WT | WT/WT/WT/VKSG | 1 | ||||||||
| WT/WT/WT/WT | WT/SSNA/WT/WT | WT/WT/KEA/WT | 4 | 16 | 6 | ||||||
| WT/WT/WT/WT | WT/SSNA/TVDTK/WT | WT/WT/WT/WT | 1 | 1 | |||||||
| WT/WT/WT/WT | WT/SSNA/SVESK/WT | WT/WT/WT/WT | 2 | 9 | 3 | ||||||
| WT/WT/WT/WT | WT/SSNA/WT/WT | SAMK/WT/WT/WT | 2 | 9 | 12 | ||||||
| WT/WT/WT/WT | WT/SSNA/SVESK/WT | SAMK/WT/WT/WT | 1 | 1 | |||||||
| WT/WT/WT/WT | WT/SSNA/WT/WT | WT/WT/KAA/WT | 6 | 3 | |||||||
| WT/WT/WT/WT | WT/SSNA/WT/WT | WT/WT/WT/WT | 1 | 3 | |||||||
| WT/WT/WT/WT | WT/WT/WT/WT | WT/WT/KEA/VKSG | 1 | 1 | |||||||
| WT/WT/WT/WT | WT/WT/WT/WT | WT/WT/KET/VKSG | 3 | 1 | 1 | ||||||
| WT/WT/WT/WT | WT/WT/WT/WT | WT/WT/KEA/WT | 1 | ||||||||
| WT/WT/WT/WT | WT/WT/WT/WT | SAMK/WT/KAA/WT | 4 | 1 | |||||||
| WT/WT/WT/WT | WT/WT/WT/WT | SAMK/WT/WT/WT | 2 | 1 | |||||||
| WT/WT/WT/WT | WT/WT/WT/WT | WT/WT/WT/WT | 279 | 191 | 8 | 2 | 1 | ||||
| Total | 292 | 241 | 64 | 44 | 23 | 21 | 50 | 29 | 8 | ||
a
Unaltered “wild-type” (WT) penicillin binding protein (PBP) motifs correspond to S. pneumoniae R6 (NCBI accession no. AE007317.1: 332863 to 335022, 1494216 to 1496273, and 302261 to 304513; locus tags spr0329, spr1517, and spr0304, respectively). PBP1a represents STMK/SSN/KTG/TSFQ, with amino acid start positions at 370/446/557/574, PBP2b represents SVVK/SSNT/QLQPT/KTG, with start positions at 386/443/565/615, and PBP2x represents STMK/SSN/KDA/LKSG, with start positions at 337/395/505/546 (Fig. 3). Bold font indicates motifs that significantly contribute to the predicted penicillin MIC value through regression analysis.
Equation 1 shows a penicillin MIC regression model:where each molecular determinant has a value of 1 if present or 0 if absent.
(1)
The molecular determinants having the largest effect on penicillin MIC were modifications to the PBP2x-STMK→SAFK, PBP2x-KDA→EDT, and PBP1a-STMK→SAMK/SSMK amino acid motifs producing an adjusted R2 value of 0.893 (see Table S1 in the supplemental material). The PBP2x-KDA→EDT motif with two amino acid changes had a regression coefficient over two times that of the PBP2x-KDA→KEA motif, corresponding to a 2-fold increased contribution to the MICpred increment value. The accuracy of the resultant penicillin MICpred values calculated from the regression equation within 1 doubling dilution to the overall phenotypically determined MIC (MICpheno) values of the Canadian and U.S. data sets was 97.4% (1,748/1,794), with a sensitivity and specificity of 92.8% and 96.5%, respectively (Table 2). There were four (4/1,354 [0.3%]) very major interpretative errors (VMEs; i.e., predicted susceptible but phenotypically resistant) seen in the U.S. data sets, with MICpred values of ≤0.03 mg/liter but MICpheno values ranging from 2 to 8 mg/liter. Alignments of pbp1a, pbp2b, and pbp2x genes from these isolates did not reveal any other major nucleotide differences from other susceptible strains to explain the discrepancy.
TABLE 2
| Antimicrobial | Data seta | No. or % of isolates matching MIC dilution ofb: | SENS (%)c | SPEC (%)c | PPV (%)c | NPV (%)c | % of MIC interpretive errorsd | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No. >−2 | No. −2 | No. −1 | No. 0 | No. +1 | No. +2 | No. >2 | % ±1 | MI | ME | VME | ||||||
| Penicillin | Canada | 0 | 1 | 21 | 405 | 11 | 1 | 0 | 99.5 | 100 | 99.7 | 97.5 | 100 | 2.3 | 0 | 0 |
| USA-1 | 4 | 4 | 38 | 451 | 91 | 12 | 7 | 95.6 | 87.8 | 97.2 | 94.6 | 93.3 | 8.1 | 0.3 | 0.5 | |
| USA-2 | 2 | 3 | 75 | 458 | 198 | 9 | 2 | 97.9 | 94.2 | 87.7 | 96.1 | 82.6 | 8.2 | 0.1 | 0.1 | |
| All | 7 | 8 | 133 | 1,314 | 300 | 22 | 9 | 97.4 | 92.8 | 96.5 | 95.8 | 93.6 | 6.7 | 0.1 | 0.2 | |
| Ceftriaxone | Canada | 0 | 2 | 5 | 422 | 9 | 1 | 0 | 99.3 | 100 | 98.8 | 68.8 | 100 | 1.1 | 0 | 0 |
| USA-1 | 2 | 4 | 20 | 535 | 43 | 2 | 1 | 98.5 | 98.8 | 95.3 | 76.0 | 99.8 | 7.2 | 0 | 0.2 | |
| USA-2 | 1 | 6 | 18 | 586 | 122 | 11 | 3 | 97.2 | 96.5 | 96.5 | 91.0 | 98.7 | 10.0 | 0 | 0.1 | |
| All | 3 | 12 | 43 | 1,543 | 174 | 14 | 4 | 98.2 | 97.2 | 96.7 | 85.2 | 99.5 | 6.9 | 0 | 0.1 | |
| Erythromycin | Canada | NAe | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| USA-1 | 18 | 8 | 13 | 488 | 2 | 0 | 5 | 94.2 | 91.2 | 97.5 | 81.3 | 98.9 | 0.7 | 2.2 | 0.9 | |
| USA-2 | 14 | 15 | 70 | 601 | 41 | 3 | 3 | 95.3 | 99.0 | 99.4 | 99.5 | 98.8 | 0.3 | 0.3 | 0.3 | |
| All | 32 | 23 | 83 | 1,089 | 43 | 3 | 8 | 94.8 | 98.1 | 98.3 | 97.1 | 98.9 | 0.5 | 1.1 | 0.6 | |
| Clarithromycin | Canada | 2 | 1 | 10 | 368 | 46 | 5 | 7 | 96.6 | 100 | 100 | 100 | 100 | 0.2 | 0 | 0 |
| USA-1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
| USA-2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
| Clindamycin | Canada | 0 | 1 | 1 | 437 | 0 | 0 | 0 | 99.8 | 100 | 99.8 | 95.7 | 100 | 0.2 | 0 | 0 |
| USA-1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
| USA-2 | 13 | 3 | 366 | 360 | 0 | 2 | 3 | 97.2 | 97.8 | 97.7 | 93.3 | 99.3 | 0.7 | 1.7 | 0.4 | |
| All | 13 | 4 | 367 | 797 | 0 | 2 | 3 | 98.2 | 98.1 | 98.6 | 93.6 | 99.6 | 0.5 | 1.1 | 0.3 | |
| Levofloxacin | Canada | 0 | 0 | 38 | 389 | 11 | 0 | 0 | 100 | 100 | 100 | 100 | 100 | 0 | 0 | 0 |
| USA-1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
| USA-2 | 0 | 0 | 42 | 702 | 3 | 0 | 100 | 100 | 100 | 100 | 100 | 0 | 0 | 0 | ||
| All | 0 | 0 | 79 | 1,091 | 14 | 0 | 0 | 100 | 100 | 100 | 100 | 100 | 0 | 0 | 0 | |
| Trimethoprim-sulfamethoxazole | Canada | 0 | 0 | 191 | 215 | 32 | 1 | 0 | 99.8 | 100 | 97.2 | 82.5 | 100 | 0.5 | 0 | 0 |
| USA-1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
| USA-2 | 0 | 1 | 121 | 511 | 101 | 11 | 2 | 98.1 | 95.8 | 97.8 | 96.5 | 97.4 | 4.8 | 0 | 0.3 | |
| All | 0 | 1 | 312 | 726 | 133 | 10 | 2 | 98.8 | 96.5 | 97.5 | 94.0 | 98.6 | 4.0 | 0 | 0.2 | |
| Overall | 57 | 49 | 1,027 | 6,928 | 710 | 56 | 33 | 97.8 | 95.8 | 98.0 | 94.2 | 98.6 | 3.4 | 0.3 | 0.2 | |
b
Shown is the number of isolates with MICpred and MICpheno values that differ by the number of doubling dilutions indicated.
c
SENS, SPEC, PPV, and NPV represent sensitivity, specificity, positive predictive value, and negative predictive value, respectively.
d
Shown are the percentages of isolates with minor (MI), major (ME), and very major (VME) interpretative errors for susceptibilities. Detailed totals of minor errors based on interpretative value combinations are available in Table S39 in the supplemental material.
e
NA, no phenotypic MIC available.
Ceftriaxone MIC regression modeling resulted in fewer molecular determinants where there were no contributions to MICpred from PBP2b motif changes, with only the PBP1a-STMK, PBP2x-STMK, PBP2x-KDA, and PBP2x-LKSG motifs having an influence, with an adjusted R2 value of 0.72 (see Table S3 in the supplemental material). The PBP2x-STMK→SAFK motif had the greatest magnitude, with a regression coefficient of 2.7, twice that of modifications to next most influential motif, PBP1a-STMK, which had a regression coefficient of 1.3.
Equation 2 shows the ceftriaxone MIC regression model:where each molecular determinant has a value of 1 if present or 0 if absent.
(2)
The MICpred for ceftriaxone had an overall accuracy of 98.2% to 1 doubling dilution of the MICpheno of the combined validation data sets. The specificity (measure of susceptibility) was 96.7%, and sensitivity (measure of resistance) was 97.2%. The relatively large number of minor (MI) errors for both penicillin (n = 121 [6.7%]) and ceftriaxone (n = 124 [6.9%]) were due to a large number of MICpred values within 1 doubling dilution of the intermediate resistance interpretative breakpoints and the very broad CLSI intermediate resistance interpretative breakpoint range for penicillin, covering 3 doubling dilutions from 0.125 to 1 mg/liter.
Equation 3 shows the erythromycin MIC regression model:where ermB and mefAE molecular determinants have a value of 1 or 0 if present or absent, respectively, “23S rRNA-A2059G” and “23S rRNA-C2611T” are the number of alleles with the point mutation present, and “mefAE promoter” and “mefAE intergenic” have a value of 1 or 0, corresponding to the presence or absence of the −364T substitution or the 99-bp deletion in the intergenic region between mefE and mel, respectively.
(3)
Seven isolates within the validation data sets possessed a predicted dysfunctional ermB gene and were considered ermB negative for regression analysis calculations. Specifically, an ErmB-G41E amino acid substitution was found in five isolates, ErmB-G41K was detected in one isolate, and an ermB adenosine nucleotide deletion at position 629 creating a pseudogene was observed in two isolates (see supplemental validation Data Set S12 in the supplemental material).
The MICpred for erythromycin was greatly influenced by the presence of ermB (coefficient = 9.5), mefAE (coefficient = 5.5), and the A2059G point mutation of 23S rRNA (coefficient = 2.9 for each mutated allele), with lesser contributions from the C2611T 23S rRNA mutation, mefAE-346T mutation, and 99-bp intergenic deletion, to achieve an adjusted R2 value of 0.96 (see Table S4 in the supplemental material). Although the coefficient value for the 23S rRNA-A2059G mutation is relatively low compared to those of the ermB or mefAE determinants, when all four alleles carry the mutation, the magnitude of the coefficient increases considerably to 12. The erythromycin MICpred had an overall accuracy of 94.8% (1,215/1,281) within 1 doubling dilution of the erythromycin MICpheno of the validation data sets and 98% sensitivity and specificity.
Equation 4 shows the clarithromycin MIC regression model:where ermB and mefAE molecular determinants have a value of 1 or 0 if present or absent, respectively, “23S rRNA-A2059G” and “23S rRNA-C2611T” are the number of alleles with the point mutation present, and “mefAE promoter” and “mefAE intergenic” have a value of 1 or 0, corresponding to the presence or absence of the −364T substitution or the 99-bp deletion in the intergenic region between mefE and mel, respectively.
(4)
The regression equation for the clarithromycin MICpred had determinant coefficients similar to those for erythromycin, with ermB, mefAE, and the A2059G 23S rRNA point mutations contributing the most to the overall MIC, with values of 10.8, 5.6, and 1.8, respectively, resulting in an adjusted R2 value of 0.98 (see Table S5 in the supplemental material). Overall MIC accuracy within 1 doubling dilution was 96.6% (424/439), with 100% sensitivity and specificity.
Equation 5 shows the clindamycin MIC regression model:where the ermB molecular determinant has a value of 1 if present or 0 if absent, and the value of “23S rRNA-A2059G” is the number of alleles with the point mutation present.
(5)
The regression model for clindamycin resistance included primarily the presence of ermB and a minor contribution from the 23S rRNA-A2059G mutation and had an adjusted R2 value of 0.97 (see Table S6 in the supplemental material). The C2611T 23S rRNA mutation did not contribute to the model, as only one isolate was present in the training data carrying the mutation in all four alleles, yet only having a MICpheno value of ≤0.125 mg/liter. This determinant was also rare in the validation data, present in two isolates of the USA-1 data set, for which the MICpheno values were not available. This minimal complement of resistance determinants for clindamycin reflected the distribution of MICpheno values observed in the training (see Table S12 in the supplemental material) and validation data sets (see Tables S26 and S27 in the supplemental material), where MICs are polarized with extremely low or very high values. In the USA-2 data set, which had a maximum clindamycin MICpheno value of 2 mg/liter, 0.74% of the isolates had MICpheno values between 0.25 and 2 mg/liter, and of the 439 isolates in the Canadian validation data set, where testing included dilutions up to ≥64 mg/liter, there were no MICpheno values in the range from 0.5 to 16 mg/liter observed. There was a 98.2% (1,164/1,186) overall accuracy between MICpred and MICpheno values, with over 98% specificity and sensitivity.
Equation 6 shows the levofloxacin MIC regression model:where each molecular determinant has a value of 1 if present or 0 if absent.
The MICpred for levofloxacin was predominantly dependent upon GyrA amino acid mutations S81L (n = 2) and S81F (n = 16), having regression coefficients of 3.6 and 2.0, respectively. Other determinants contributing to a lesser extent to the overall MICpred values included the GyrA-S81Y mutations (n = 1) and any mutations at ParC-S79 or ParC-D83. The adjusted R2 value of 0.460 (see Table S7 in the supplemental material) was very low for the model, likely due to very small number of isolates with resistance determinants and corresponding MICpheno values of ≥8 mg/liter (n = 11) compared to the very large number of isolates with no determinants and MICpheno values of ≤2 mg/liter (n = 942) within the training data. Despite the low adjusted R2 value and only two phenotypically levofloxacin-resistant isolates in the validation data, the accuracy of MICpred values compared to MICpheno values within 1 doubling dilution approached 100% (1,185/1,186), with 100% sensitivity and specificity. One isolate was missing the open reading frame corresponding to the gyrA gene in the genome assembly.
Equation 7 shows the trimethoprim-sulfamethoxaxole MIC regression model:where each molecular determinant has a value of 1 if present or 0 if absent.
(7)
Regression modeling for trimethoprim-sulfamethoxazole resistance indicated that disruption of FolP had a regression coefficient of 2.5, suggesting a larger influence on overall MICpred than the I100L FolA determinant, which had a coefficient of 1.6. The adjusted R2 value was 0.798 (see Table S8 in the supplemental material) for the model, and there was 98.8% overall accuracy of MICpred and MICpheno, with a specificity of 97.5% and sensitivity of 96.5%.
The absence or presence of a single molecular determinant for chloramphenicol, doxycycline, and tetracycline resistance was used to assign a MICpred value as less than or greater than the susceptible or resistant interpretation breakpoint, rather than using multiple-variable linear regression analysis. The presence of the cat gene was associated with a MICpred value of ≥8 mg/liter (see Table S15 in the supplemental material), which resulted in 99.8% accuracy with corresponding MICpheno values, with sensitivity, specificity, positive predictive value (PPV), and negative predicted value (NPV) of 53.3%, 100%, 100%, and 98.5%, respectively. The low sensitivity can be attributed to the very abrupt resistance breakpoint between susceptible and resistant at ≤4 mg/liter and ≥8 mg/liter resulting in a relatively large number of possible phenotyping errors that were phenotypically resistant strains (n = 21) without the cat gene among a relatively low overall number of phenotypically resistant isolates (n = 45). Similarly, tetracycline and doxycycline MICs were determined solely by the presence of tetM (tetO was not detected in this study), giving a MICpred value for tetracycline of ≥8 mg/liter and a value for doxycycline of ≥4 mg/liter (see Tables S16 and S17 in the supplemental material). The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value for tetracycline MICpred were 97.6%, 96.2%, 98.1%, 94.1% and 98.8%, those for doxycycline were 98.4%, 95.1%, 99.5%, 95.1% and 99.5%, respectively.
DISCUSSION
Multiple-variable linear regression analysis is a relatively simple, yet powerful tool to determine the dynamics of a specific variables among a complex series of other factors. Determinants of interest affecting the MIC were identified for each antimicrobial by summarizing the phenotypic MIC values and molecular determinant profiles (Table 1; see Tables S9 to S17 in the supplemental material), and the regression model was optimized by removing, combining, and adding back individual factors while examining the effect on the regression model metrics. This analytical strategy has been successfully used to validate predicted MICs from whole-genome sequence data of N. gonorrhoeae (26–28). In this study, multiple-variable linear regression modeling of molecular antimicrobial resistance determinants accurately predicted the MIC values for the β-lactam, macrolide, lincosamide, fluoroquinolone, and folate pathway inhibitor antimicrobials investigated. There was 98% (range, 94.2 to 100%) overall accuracy between the predicted and phenotypically derived MIC values, with 96% (range, 87.8 to 100%) sensitivity and 98% (range, 87.7 to 100%) specificity.
Resistance to β-lactam antimicrobials in S. pneumoniae is associated with changes to the transpeptidase domains of penicillin binding proteins PBP1a, PBP2b, and PBP2x, with particular focus on three amino acid motifs in each protein (24). It has been suggested that changes in PBP2b and PBP2x provide low-level resistance, while high level resistance is achieved with additional changes to PBP1a (29). Although increased resistance to β-lactams caused by altered PBP amino acid motifs has been extensively reported, multiple-variable linear regression analysis identified another possible amino acid motif in each protein that may contribute to overall MIC levels and was able to predict the relative contribution of each mutation to the overall MIC. Multiple-variable linear regression modeling for predicting penicillin MICs identified any changes to two motifs of PBP1a and PBP2b and specific changes to three motifs of PBP2x as significantly contributing to the MIC. The ceftriaxone MIC regression model was simpler, lacking the PBP2b motifs as contributing factors. Penicillin and ceftriaxone models included any changes to the PBP1a-STMK motif and PBP2x-STMK→SAFK, PBP2x-KDA→EDT, and LKSG→VKSG specific motif changes. The regression coefficients for these shared determinants were similar for both penicillin and ceftriaxone MICs, except for PBP2x-STMK→SAFK, which had a 2-fold greater effect on ceftriaxone MICs, reflecting the importance of this mutation to overall resistance reported in other studies (10, 24). The regression models for predicting β-lactam MICs had 98% accuracy to those derived phenotypically, with 0.1% major interpretative errors and 0.2% very major errors. The relatively large number of minor interpretative errors in both penicillin and ceftriaxone MICs could be due to a large number of MICpred values within 1 doubling dilution of the intermediate CLSI resistance interpretative breakpoints, which are very broad for penicillin, covering 3 doubling dilutions from 0.125 to 1 mg/liter. These findings are similar to those from a previous study, which used PBP allelic profiles as a PBP type library to associate phenotypic MICs with specific alleles, giving a similar 98% accuracy within 1 doubling dilution of the phenotypic MIC, with major and very major interpretative errors slightly larger at 3% and 2%, respectively (18).
The greatest contributor to the macrolide and lincosamide MICs was the presence of ermB, which had similar regression coefficients for erythromycin, clarithromycin, and clindamycin, corresponding to 9 to 11 doubling MIC increments. The mefAE coefficients for erythromycin and clarithromycin were also similar, with increment values of about 5 for each antimicrobial, contributing about half as much as ermB to the MICpred values. The 23S rRNA-A2059G point mutation had regression coefficient values of 3 for each mutated allele for erythromycin and 2 for clarithromycin, but contributed much less to the clindamycin MIC, with a coefficient of only 0.5. The C2611T 23S rRNA resistance determinant contributed less than the A2059G determinant, with a value of about 1 for clarithromycin and erythromycin MICs; however, the C2611T mutation was not identified as a significantly contributing factor to increased clindamycin MICs. The G761T mutation, 364 nucleotides upstream (−364T) from the mefE start codon (Fig. 1), had a 2-fold greater influence upon the erythromycin predicted MIC than that for clarithromycin and had a similar influence to the 99-bp intergenic deletion between mefE and mel. Although the −364T mutation may be a considerable distance from the mefE start codon in the macrolide efflux genetic assembly to be located in the promoter region for mefE, there are a number of ATG start codons upstream before mefE, which may suggest that a small regulatory protein is located in this region. Accuracy with phenotypic MIC was best with clindamycin, with 98% accuracy, and both erythromycin and clarithromycin had accuracies of 95% and 97%, respectively, with sensitivities and specificities over 98% for all three antimicrobials. The USA-1 validation data set had an accuracy of 94% and a relatively low sensitivity of 91%, primarily due to 11 isolates with erythromycin MICpheno values of ≤0.5 mg/liter, despite having mefAE or an intact ermB as the sole resistance determinant, which should result in a MICpred value of ≥8 mg/liter, suggesting possible phenotyping errors. Conversely, there were 5 isolates in this data set that lacked any known molecular determinants but were phenotypically erythromycin resistant, having MICs of ≥1 mg/liter. Screening the discrepant genomes with additional molecular antimicrobial resistance determinant query tools ResFinder, ARG-ANNOT, and CARD (30–32) confirmed the genotypes. The observed discrepancies between molecular determinant profiles and expected resistance phenotypes may be due phenotypic reading errors, contamination, mislabeling, DNA sequencing errors, or possibly novel resistance mechanisms.
FIG 1
A single isolate in the training data set, and no isolates in the validation data sets, possessed the ermTR resistance determinant combined with mefAE. The single ermTR-positive isolate had MICpheno values for erythromycin and clarithromycin of ≥256 mg/liter and ≥16 mg/liter, respectively, similar to other isolates having an ermB mefAE genotype. Additional data are required to perform adequate regression analysis for the ermTR resistance determinant; however, speculatively its contribution to overall MICpred may be similar to that of ermB.
Fluoroquinolone resistance has been attributed to the GyrA-S81 and ParC-S79, -D83, and -N91 amino acid substitutions (10, 21). Regression modeling indicated that each of the three GyrA-S81F, -Y, and -L mutations had different contributions to the overall levofloxacin MIC, with the S81L mutation having about twice the effect of S81Y. Any mutation at ParC-D79 or -D83 significantly contributed the overall levofloxacin MICpred; however, a D91 mutation was found in only a single isolate of the training data set, with a MICpheno value of 0.5 mg/liter (susceptible interpretation) and therefore did not contribute significantly during the modeling process. Despite have the lowest adjusted R2 value, the MICpred for levofloxacin had the best accuracy to MICpheno of all the antimicrobials analyzed, with accuracy to within 1 doubling dilution, all percentages of sensitivity and specificity of 100%, and no interpretive errors.
Molecular determinants for sulfamethoxazole-trimethoprim resistance include a simple and well-documented mechanism involving a FolA-I100L amino acid substitution and disruptions of folP, with a resistant phenotype observed when both determinants are present and intermediate when only one is present (10, 33, 34). MICs could be assigned simply as ≥4/76 mg/liter (resistant) when both determinants are present, 1/19 mg/liter or 2/38 mg/liter (intermediate) when only one is present, or ≤0.5/9.5 mg/liter (susceptible) when both determinants are wild type. A similar strategy can be used where the presence of cat in an isolate correlates with a chloramphenicol MICpred value of ≥8 mg/liter (resistant CLSI breakpoint), or tetM or tetO with a tetracycline MICpred value of ≥16 mg/liter. Regression analysis, however, offers insight into the relative magnitudes of each determinant, and in the future, the specific nucleotide insertions of folP may each be investigated to add further refinement to the predictive models to enhance the accuracy and precision of the MIC predictions. Comparison of the magnitudes of the regression coefficients indicates that the FolP disruptions (Fig. 2; see Table S8 in the supplemental material) have a greater influence on MIC than the FolA-I100L mutation.
FIG 2
There was low variability of MIC accuracy values between the validation data sets, suggesting the regression equations are robust and may be applied broadly across testing sites. A regression model developed for penicillin MICs from PBP types described by Metcalf et al. (10, 18, 35) as a simulated training data set (http://www.cdc.gov/streplab/mic-tables.html) generated a regression equation very similar to that attained using the Canadian training data (see Tables S1 and S2 in the supplemental material). The accuracy and precision of the predicted MICs may continually be improved over time, with larger, broader, and more current training data to address consistency of sampling, culturing methods, laboratory testing procedures, interpretation of phenotypic results, geographical variation, and the discovery of novel resistance determinants. Despite some discrepancies, the comparison of MICpred to MICpheno compares favorably to comparison studies of purely phenotypic results. A summary of an interlaboratory quality control program for pneumococcal serotyping and antimicrobial susceptibility testing involving reference laboratories participating in the International Circumpolar Surveillance program had an 97% overall accuracy of tests within 1 doubling dilution of the modal MIC, with erythromycin and clindamycin accuracies of 92% and 89%, respectively (36). Other quality assurance programs that have collated accuracy for phenotypic antimicrobial susceptibility testing included the Canadian National Gonococcal Antimicrobial Susceptibility Comparison Program (37), where the average MIC accuracy ranged from 85.6% to 98.9%, and a 2018 comparison of international antimicrobial proficiency panel results from various Caribbean and South American countries (38) reported an overall accuracy of >90% for some participants, while accuracy among other laboratories ranged from 60.0% to 82.4%.
Limitations of the study include that the accuracy and precision of the MIC prediction based on molecular determinants are largely limited by the training data used to generate the regression equations. The training data may include variability due to the subjective nature of phenotypic testing, where the same phenotypes may not always be observed on repeat testing, molecular resistance profile errors, and the possible presence of as-yet-unidentified resistance factors. Rare resistance determinants need to be present in the training data in sufficient quantities to generate meaningful statistics. While using a large training data set to develop the regression model can resolve some discrepancies, some rare resistance patterns, such as very high β-lactam resistance, are reliant on the availability of a relatively small number of isolates with this phenotype. Furthermore, there may also be some rare resistance determinants that were not present or were present in insufficient numbers to significantly influence the regression model, such as some of the reported PBP motifs, ermTR, or the 23S rRNA point mutations. These limitations can be reduced by increasing the size of the training data with isolates from varied regions of the world and regularly updating the regression models with newly discovered factors and updated coefficient values for currently identified factors. The MIC prediction models described here can be easily regenerated using the molecular markers discussed in this study with local training phenotypic data sets, which may be more applicable to individual laboratory testing environments. This approach also directly identifies the magnitude of antimicrobial resistance determinants specifically contributing to overall MIC without the need for continual curation of allelic databases that infer MIC values.
There is a need for surveillance systems that not only closely track the dissemination of known resistant strains, but also promptly detect novel antimicrobial resistant clones as they emerge to limit their expansion. Over the short term, molecular-based methods may primarily be used for surveillance purposes. As molecular-based genomic techniques become more comprehensive and broadly available to track lineages, antibiotic resistance, and virulence and fitness determinants, the MIC predicting strategy described here may provide a powerful tool to replace traditional phenotypic testing in clinical settings. Mathematical modeling to describe biological systems can fill a surveillance gap in an era of increased reliance on nucleic acid assay diagnostics to monitor the dynamics of S. pneumoniae, and the ability to acquire detailed antimicrobial resistance information directly from molecular information will enhance the monitoring of the dynamics of S. pneumoniae to effectively inform public health interventions to reduce the burden of disease.
MATERIALS AND METHODS
Training and validation data sets and antimicrobial susceptibility testing.
Training data sets (see supplemental training Data Sets S1 to S11 in the supplemental material) consisted of S. pneumoniae isolates collected in Canada from 1995 to 2019 for national surveillance purposes that had both phenotypic antimicrobial susceptibilities as well as molecular characterization data available. Isolates for the penicillin (n = 772), ceftriaxone (n = 772), erythromycin (n = 324), clarithromycin (n = 847), clindamycin (n = 1,356), levofloxacin (n = 1,446), trimethoprim-sulfamethoxazole (n = 1,207), tetracycline (n = 573), doxycycline (n = 938), and chloramphenicol (n = 824) MIC training data sets were selected to provide a broad range of MICs and well-characterized antimicrobial resistance determinants. An additional simulated training data set (n = 4,339) for penicillin MICs was generated from PBP types described by Metcalf et al. (10, 18, 35; http://www.cdc.gov/streplab/mic-tables.html).
Validation data (supplemental validation Data Set D12) included 439 Canadian S. pneumoniae isolates collected during 2019 for which both phenotypic antimicrobial susceptibility and molecular characterization results were available, as well as data previously reported for 534 isolates from Massachusetts, USA, during 2001 to 2007 (USA-1 data set) (39) and 747 isolates collected through a study of the Active Bacterial Core surveillance (ABCs), Centers for Disease Control and Prevention, Atlanta, GA, USA, during 2015 (USA-2 data set) (10).
Testing on the training and validation data sets of Canadian and U.S. isolates of their susceptibility to penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, trimethoprim-sulfamethoxazole, tetracycline, doxycycline, and chloramphenicol was done using the broth microdilution method according to Clinical and Laboratory Standards Institute (CLSI) guidelines (40, 41). Oral penicillin V and meningitis ceftriaxone resistance breakpoint interpretations were used.
Molecular analysis.
Molecular antimicrobial resistance determinants were identified in silico from whole-genome sequencing data by querying reference (“wild-type”) gene nucleotide sequences against assembled contig files using BLAST (42), with the E value cutoff option set to 10e−100 and identifying relevant mutations or the presence or absence of the gene, as appropriate. Penicillin and ceftriaxone resistance determinants included changes to the “wild-type” amino acid SXXK, SXN, and KXG motifs in penicillin binding proteins PBP1a, PBP2b, and PBP2x from S. pneumoniae R6 (NCBI accession no. AE007317.1: 332863 to 335022, 1494216 to 1496273, and 302261 to 304513; locus tags spr0329, spr1517, and spr0304;, respectively) (14, 24). The wild-type PBP1a motifs STMK, SSN, and KTG had amino acid start positions 370, 446, and 557, respectively, the wild-type PBP2a motifs SVVK, SSNT, and KTG started at positions 386, 443 and 615, respectively, and the wild-type PBP2x motifs STMK, SSN, and LKSG started at positions 337, 395, and 546, respectively. An additional motif for each protein was identified through sequence alignment analysis of previously wild-type PBP motif profiles with relatively high MIC values. Novel motifs identified included TSQF, starting at position 574 of PBP1a, QLQPT, starting at position 565 of PBP2b, and KDA, starting at position 505 of PBP2x, bringing the total number of motifs analyzed per protein to four (Fig. 3).
FIG 3
Macrolide and lincosamide resistance determinants included the presence or absence of ermB (NCBI accession no. AB426620.1: 4320 to 5057), ermTR (CP002121.1: 856174 to 856905), mefAE (CP000921.1: 1802511 to 1803728); a G761T nucleotide mutation of the macrolide efflux genetic assembly (mega) sequence of GenBank accession no. AF274320.1 located in the mefAE promoter region 364 bp upstream [−364T] of the start codon (Fig. 1) (43), a 99-bp deletion in the intergenic region between mefE and mel (Fig. 1) (43), and 23S rRNA-A2059G and -C2611T point mutations (Escherichia coli numbering, corresponding to A2061G and C2613T in S. pneumoniae R6 GenBank accession no. AE007317.1, respectively). Alleles of ermB with G41E, G41K, or L63Q amino acid substitutions or an adenosine nucleotide insertion at position 628 conferred a susceptible macrolide phenotype to the strains and therefore were given an ermB-negative genotype. The number of 23S rRNA allele mutations was determined by a custom SNVPhyl workflow (44) using a 23S rRNA allele of S. pneumoniae R6 (GenBank accession no. AE007317.1, locus tag sprr02) as a mapping reference and interrogating the allele counts at nucleotide positions 2061 and 2613 from the resultant variant call files (.vcf). By convention, the locations of the 23S rRNA nucleotide mutations are based on the Escherichia coli coordinates of A2059G and C2611T (19, 20), which correspond to A2061G and C2613T, respectively, of S. pneumoniae R6.
Tetracycline resistance markers included the presence of tetM (AB426620.1: 14972 to 16891) or tetO (FM178797.1: 66 to 1985), and the chloramphenicol resistance determinant in the presence or absence of the cat gene (ICE6BST90: 56517 to 57167). The presence of the GyrA amino acid substitution S81 (AE007317.1: 1095463 to 1097931, locus tag spr1099) and ParC-S79, -D83, and -N91 substitutions (AE007317.1: 752250 to 754721, locus tag 0757) were analyzed as levofloxacin resistance determinants. Trimethoprim-sulfamethoxazole resistance determinants included the FolA-I100L (AE007317.1: 1412861 to 1413367) amino acid substitution and disruptions of folP (AE007317.1: 268022 to 268966) by nucleotide insertions or deletions in the region spanning nucleotide positions 169 to 196 corresponding to amino acid positions 57 to 66 (Fig. 2). Disrupted folP DNA sequences were detected by changes of the conserved “IEE” translated FolP amino acid motif at positions 66 to 68 indicating a shift in the protein reading frame.
Multiple-variable linear regression analysis.
Multiple-variable linear regression analyses (45) were performed using Microsoft Excel 2010 (version 14.0.7151.5001; Microsoft Corp.) to determine the relationship of the molecular antimicrobial resistance determinants contained in an isolate to the phenotypically determined MIC value (MICpheno) for each antimicrobial as previously described (27). The doubling MICpheno values were standardized to exact doubling dilutions (512, 256, 128, 64, 32, 16, 8, 4, 2, 1, 0.5, 0.25, 0.125, 0.0625, 0.03125, 0.015625, 0.0078125, 0.00390625, 0.001953125, and 0.000976563). The exact MICs were then converted to a linear increment scale using the formula phenotypic MIC increment = log2 (standardized MIC) and used as the dependent variable in the regression analysis. Molecular markers were used as independent variables and with presence or absence represented by a value of 1 and 0, respectively, except for the 23S rRNA-A2059G and -C2611T variables, which corresponded to the number of alleles with a respective mutation. A regression model for each antimicrobial was built from a preliminary analysis that included all independent variables followed by stepwise removal of variables with relatively high individual P values of >0.05 and those causing little change in the adjusted coefficient of determination (R2) value (Tables S1 to S8). To simplify the regression equations, variables with multiple possible mutations for a single resistance determinant having similar regression coefficients were collapsed into a single combined variable if the new regression coefficient was similar to the initial separately derived coefficients. For example, two of the possible PBP1a mutations for the STMK motif combined as “SAMK or SSMK” (“any”) had a similar coefficient value to each of the values calculated individually. Nonsignificantly contributing variables removed during the initial stepwise removal molecular determinants were then reintroduced to assess their contribution to the model.
An adjusted R2 value (95% confidence interval) of 0.0 to 0.1 was considered no correlation to very weak correlation, 0.2 to 0.4 was considered weak correlation, 0.5 to 0.7 was considered moderate correlation, 0.8 to 0.9 was considered strong correlation, and >0.9 was considered very strong correlation (27). Predicted MIC values (MICpred) for each antimicrobial were calculated by first calculating the predicted MIC increment by summing the regression intercept and independent variable coefficients for each isolate, rounding fractional values up or down to the nearest whole integer and then converting this value back to a doubling MIC value using the formula predicted MIC value = 2predicted MIC increment. Individual P values of <0.05 for the independent variables at a confidence interval of 95% were considered significant.
Sensitivity (measure of resistance), specificity (measure of susceptibility), positive predictive value and negative predictive value for the MICpred were based on the accuracy to traditional MICpheno values: true positive (TP) having nonsusceptible (resistant or intermediate) predicted and phenotypic MICs, false negative (FN) having susceptible predicted MICs but nonsusceptible phenotypic MICs, true negative (TN) having susceptible predicted and phenotypic MICs, and false positive (FP) having a nonsusceptible predicted MICs and susceptible phenotypic MICs. Calculations were performed as follows: sensitivity (SENS) = TP/(FN + TP) × 100, specificity (SPEC) = TN/(FP + TN) × 100, positive predictive value = TP/(TP + FP), and negative predictive value = TN/(TN + FN) (46). Antimicrobial resistance interpretative errors were defined as minor error (MI) where the MICpred corresponded to intermediate resistance and the MICpheno corresponded to either susceptible or resistance interpretations and vice versa, major error (ME) where the MICpred corresponded to a resistant interpretation and the MICpheno was susceptible, and very major error (VME) where the MICpred was susceptible and MICpheno was resistant.
ACKNOWLEDGMENTS
We thank Angela Yuen, Rozette Mallari, and Shelley Peterson from the Streptococcus and Sexually Transmitted Diseases Unit at NML for laboratory technical assistance, the NML Science Technology Cores and Services Division for genomics infrastructure, software tools, technical support, and guidance, the NML Genomics Core Facility for next-generation sequencing and analytical expertise, the staff of provincial and public health laboratories in Canada for participating in the national laboratory surveillance program, and the CDC Streptococcus Laboratory whole-genome sequencing team for valuable data.
Supplemental Material
REFERENCES
1.
Kadioglu A, Weiser JN, Paton JC, Andrew PW. 2008. The role of Streptococcus pneumoniae virulence factors in host respiratory colonization and disease. Nat Rev Microbiol 6:288–301.
2.
Henriques-Normark B, Blomberg C, Dagerhamn J, Bättig P, Normark S. 2008. The rise and fall of bacterial clones: Streptococcus pneumoniae. Nat Rev Microbiol 6:827–837.
3.
Shelyakin PV, Bochkareva OO, Karan AA, Gelfand MS. 2019. Micro-evolution of three Streptococcus species: selection, antigenic variation, and horizontal gene inflow. BMC Evol Biol 19:83.
4.
Donati C, Hiller NL, Tettelin H, Muzzi A, Croucher NJ, Angiuoli SV, Oggioni M, Dunning Hotopp JC, Hu FZ, Riley DR, Covacci A, Mitchell TJ, Bentley SD, Kilian M, Ehrlich GD, Rappuoli R, Moxon ER, Masignani V. 2010. Structure and dynamics of the pan-genome of Streptococcus pneumoniae and closely related species. Genome Biol 11:R107.
5.
van der Poll T, Opal SM. 2009. Pathogenesis, treatment, and prevention of pneumococcal pneumonia. Lancet 374:1543–1556.
6.
Tomczyk S, Lynfield R, Schaffner W, Reingold A, Miller L, Petit S, Holtzman C, Zansky SM, Thomas A, Baumbach J, Harrison LH, Farley MM, Beall B, McGee L, Gierke R, Pondo T, Kim L. 2016. Prevention of antibiotic-nonsusceptible invasive pneumococcal disease with the 13-valent pneumococcal conjugate vaccine. Clin Infect Dis 62:1119–1125.
7.
Demczuk WHB, Martin I, Desai S, Griffith A, Caron-Poulin L, Lefebvre B, McGeer A, Tyrrell GJ, Zhanel GG, Gubbay J, Hoang L, Levett PN, Van Caeseele P, Raafat Gad R, Haldane D, Zahariadis G, German G, Daley Bernier J, Strudwick L, Mulvey MR. 2018. Serotype distribution of invasive Streptococcus pneumoniae in adults 65 years of age and over after the introduction of childhood 13-valent pneumococcal conjugate vaccination programs in Canada, 2010–2016. Vaccine 36:4701–4707.
8.
Liñares J, Ardanuy C, Pallares R, Fenoll A. 2010. Changes in antimicrobial resistance, serotypes and genotypes in Streptococcus pneumoniae over a 30-year period. Clin Microbiol Infect 16:402–410.
9.
Golden AR, Baxter MR, Davidson RJ, Martin I, Demczuk W, Mulvey MR, Karlowsky JA, Hoban DJ, Zhanel GG, Adam HJ, Canadian Antimicrobial Resistance Alliance (CARA), CANWARD. 2019. Comparison of antimicrobial resistance patterns in Streptococcus pneumoniae from respiratory and blood cultures in Canadian hospitals from 2007–16. J Antimicrob Chemother 74(Suppl 4):iv39–iv47.
10.
Metcalf BJ, Chochua S, Gertz RE, Li Z, Walker H, Tran T, Hawkins PA, Glennen A, Lynfield R, Li Y, McGee L, Beall B, Reingold A, Brooks S, Randel H, Miller L, White B, Aragon D, Barnes M, Sadlowski J, Petit S, Cartter M, Marquez C, Wilson M, Farley M, Thomas S, Tunali A, Baughman W, Harrison L, Benton J, Carter T, Hollick R, Holmes K, Riner A, Holtzman C, Danila R, MacInnes K, Scherzinger K, Angeles K, Bareta J, Butler L, Khanlian S, Mansmann R, Nichols M, Bennett N, Zansky S, Currenti S, McGuire S, Thomas A, Schmidt M, et al. 2016. Using whole genome sequencing to identify resistance determinants and predict antimicrobial resistance phenotypes for year 2015 invasive pneumococcal disease isolates recovered in the United States. Clin Microbiol Infect 22:1002.e1–1002.e8.
11.
Dever LA. 1991. Mechanisms of bacterial resistance to antibiotics. Arch Intern Med 151:886.
12.
Job V, Carapito R, Vernet T, Dessen A, Zapun A. 2008. Common alterations in PBP1a from resistant Streptococcus pneumoniae decrease its reactivity toward β-lactams: structural insights. J Biol Chem 283:4886–4894.
13.
Dessen A, Mouz N, Gordon E, Hopkins J, Dideberg O. 2001. Crystal structure of PBP2x from a highly penicillin-resistant Streptococcus pneumoniae clinical isolate: a mosaic framework containing 83 mutations. J Biol Chem 276:45106–45112.
14.
Diawara I, Nayme K, Katfy K, Barguigua A, Kettani-Halabi M, Belabbes H, Timinouni M, Zerouali K, Elmdaghri N. 2018. Analysis of amino acid motif of penicillin-binding proteins 1a, 2b, and 2x in invasive Streptococcus pneumoniae nonsusceptible to penicillin isolated from pediatric patients in Casablanca, Morocco. BMC Res Notes 11:632.
15.
Hackel M, Lascols C, Bouchillon S, Hilton B, Morgenstern D, Purdy J. 2013. Serotype prevalence and antibiotic resistance in Streptococcus pneumoniae clinical isolates among global populations. Vaccine 31:4881–4887.
16.
Ghuysen JM. 1991. Serine beta-lactamases and penicillin-binding proteins. Annu Rev Microbiol 45:37–67.
17.
Contreras-Martel C, Dahout-Gonzalez C, Martins ADS, Kotnik M, Dessen A. 2009. PBP active site flexibility as the key mechanism for β-lactam resistance in pneumococci. J Mol Biol 387:899–909.
18.
Li Y, Metcalf BJ, Chochua S, Li Z, Gertz RE, Walker H, Hawkins PA, Tran T, Whitney CG, McGee L, Beall BW. 2016. Penicillin-binding protein transpeptidase signatures for tracking and predicting β-lactam resistance levels in Streptococcus pneumoniae. mBio 7:e00756-16.
19.
Depardieu F, Courvalin P. 2001. Mutation in 23S rRNA responsible for resistance to 16-membered macrolides and streptogramins in Streptococcus pneumoniae. Antimicrob Agents Chemother 45:319–323.
20.
Ng LK, Martin I, Liu G, Bryden L. 2002. Mutation in 23S rRNA associated with macrolide resistance in Neisseria gonorrhoeae. Antimicrob Agents Chemother 46:3020–3025.
21.
Tankovic J, Perichon B, Duval J, Courvalin P. 1996. Contribution of mutations in gyrA and parC genes to fluoroquinolone resistance of mutants of Streptococcus pneumoniae obtained in vivo and in vitro. Antimicrob Agents Chemother 40:2505–2510.
22.
Leclercq R. 2002. Mechanisms of resistance to macrolides and lincosamides: nature of the resistance elements and their clinical implications. Clin Infect Dis 34:482–492.
23.
Horinouchi S, Weisblum B. 1982. Nucleotide sequence and functional map of pC194, a plasmid that specifies inducible chloramphenicol resistance. J Bacteriol 150:815–825.
24.
Nichol KA, Zhanel GG, Hoban DJ. 2002. Penicillin-binding protein 1A, 2B, and 2X alterations in Canadian isolates of penicillin-resistant Streptococcus pneumoniae. Antimicrob Agents Chemother 46:3261–3264.
25.
Ellington MJ, Ekelund O, Aarestrup FM, Canton R, Doumith M, Giske C, Grundman H, Hasman H, Holden MTG, Hopkins KL, Iredell J, Kahlmeter G, Köser CU, MacGowan A, Mevius D, Mulvey M, Naas T, Peto T, Rolain J-M, Samuelsen Ø, Woodford N. 2017. The role of whole genome sequencing in antimicrobial susceptibility testing of bacteria: report from the EUCAST Subcommittee. Clin Microbiol Infect 23:2–22.
26.
Eyre DW, De Silva D, Cole K, Peters J, Cole MJ, Grad YH, Demczuk W, Martin I, Mulvey MR, Crook DW, Walker AS, Peto TEA, Paul J. 2017. WGS to predict antibiotic MICs for Neisseria gonorrhoeae. J Antimicrob Chemother 72:1937–1947.
27.
Demczuk W, Martin I, Sawatzky P, Allen V, Lefebvre B, Hoang L, Naidu P, Minion J, VanCaeseele P, Haldane D, Eyre DW, Mulvey MR. 2020. Equations to predict antimicrobial MICs in Neisseria gonorrhoeae using molecular antimicrobial resistance determinants. Antimicrob Agents Chemother 64:e02005-19.
28.
Demczuk W, Martin I, Peterson S, Bharat A, Van Domselaar G, Graham M, Lefebvre B, Allen V, Hoang L, Tyrrell G, Horsman G, Wylie J, Haldane D, Archibald C, Wong T, Unemo M, Mulvey MR. 2016. Genomic epidemiology and molecular resistance mechanisms of azithromycin-resistant Neisseria gonorrhoeae in Canada from 1997 to 2014. J Clin Microbiol 54:1304–1313.
29.
Smith AM, Klugman KP. 1998. Alterations in PBP 1a essential for high-level penicillin resistance in Streptococcus pneumoniae. Antimicrob Agents Chemother 42:1329–1333.
30.
Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, Philippon A, Allesoe RL, Rebelo AR, Florensa AF, Fagelhauer L, Chakraborty T, Neumann B, Werner G, Bender JK, Stingl K, Nguyen M, Coppens J, Xavier BB, Malhotra-Kumar S, Westh H, Pinholt M, Anjum MF, Duggett NA, Kempf I, Nykäsenoja S, Olkkola S, Wieczorek K, Amaro A, Clemente L, Mossong J, Losch S, Ragimbeau C, Lund O, Aarestrup FM. 2020. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother 75:3491–3500.
31.
McArthur AG, Waglechner N, Nizam F, Yan A, Azad MA, Baylay AJ, Bhullar K, Canova MJ, De Pascale G, Ejim L, Kalan L, King AM, Koteva K, Morar M, Mulvey MR, O'Brien JS, Pawlowski AC, Piddock LJV, Spanogiannopoulos P, Sutherland AD, Tang I, Taylor PL, Thaker M, Wang W, Yan M, Yu T, Wright GD. 2013. The comprehensive antibiotic resistance database. Antimicrob Agents Chemother 57:3348–3357.
32.
Gupta SK, Padmanabhan BR, Diene SM, Lopez-Rojas R, Kempf M, Landraud L, Rolain JM. 2014. ARG-annot, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes. Antimicrob Agents Chemother 58:212–220.
33.
Pikis A, Donkersloot JA, Rodriguez WJ, Keith JM. 1998. A conservative amino acid mutation in the chromosome-encoded dihydrofolate reductase confers trimethoprim resistance in Streptococcus pneumoniae. J Infect Dis 178:700–706.
34.
Haasum Y, Ström K, Wehelie R, Luna V, Roberts MC, Maskell JP, Hall LMC, Swedberg G. 2001. Amino acid repetitions in the dihydropteroate synthase of Streptococcus pneumoniae lead to sulfonamide resistance with limited effects on substrate Km. Antimicrob Agents Chemother 45:805–809.
35.
Metcalf BJ, Gertz RE, Jr, Gladstone RA, Walker H, Sherwood LK, Jackson D, Li Z, Law C, Hawkins PA, Chochua S, Sheth M, Rayamajhi N, Bentley SD, Kim L, Whitney CG, McGee L, Beall B. 2016. Strain features and distributions in pneumococci from children with invasive disease before and after 13-valent conjugate vaccine implementation in the USA. Clin Microbiol Infect 22:60.e9–60.e29.
36.
Reasonover A, Zulz T, Bruce MG, Bruden D, Jetté L, Kaltoft M, Lambertsen L, Parkinson A, Rudolph K, Lovgren M. 2011. The International Circumpolar Surveillance Interlaboratory Quality Control Program for Streptococcus pneumoniae, 1999 to 2008. J Clin Microbiol 49:138–143.
37.
Sawatzky P, Liu G, Dillon JAR, Allen V, Lefebvre B, Hoang L, Tyrrell G, Van Caeseele P, Levett P, Martin I. 2015. Quality assurance for antimicrobial susceptibility testing of Neisseria gonorrhoeae in Canada, 2003 to 2012. J Clin Microbiol 53:3646–3649.
38.
Sawatzky P, Martin I, Galarza P, Carvallo MET, Araya Rodriguez P, Cruz OMS, Hernandez AL, Martinez MF, Borthagaray G, Payares D, Moreno JE, Chiappe M, Corredor AH, Thakur SD, Dillon JAR. 2018. Quality assurance for antimicrobial susceptibility testing of Neisseria gonorrhoeae in Latin American and Caribbean countries, 2013–2015. Sex Transm Infect 94:479–482.
39.
Croucher NJ, Finkelstein JA, Pelton SI, Mitchell PK, Lee GM, Parkhill J, Bentley SD, Hanage WP, Lipsitch M. 2013. Population genomics of post-vaccine changes in pneumococcal epidemiology. Nat Genet 45:656–663.
40.
Clinical and Laboratory Standards Institute. 2018. Methods for dilution antimicrobial susceptibility tests for bacteria that grow aerobically, 11th ed. CLSI standard M07. Clinical and Laboratory Standards Institute, Wayne, PA.
41.
Clinical and Laboratory Standards Institute. 2021. Performance standards for antimicrobial susceptiblity testing, 31st ed. CLSI supplement M100. Clinical and Laboratory Standards Institute, Wayne, PA.
42.
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990. Basic local alignment search tool. J Mol Biol 215:403–410.
43.
Wierzbowski AK, Boyd D, Mulvey M, Hoban DJ, Zhanel GG. 2005. Expression of the mef(E) gene encoding the macrolide efflux pump protein increases in Streptococcus pneumoniae with increasing resistance to macrolides. Antimicrob Agents Chemother 49:4635–4640.
44.
Petkau A, Mabon P, Sieffert C, Knox NC, Cabral J, Iskander M, Iskander M, Weedmark K, Zaheer R, Katz LS, Nadon C, Reimer A, Taboada E, Beiko RG, Hsiao W, Brinkman F, Graham M, Van Domselaar G. 2017. SNVPhyl: a single nucleotide variant phylogenomics pipeline for microbial genomic epidemiology. Microb Genom 3:e000116.
45.
Triola MF, Goodman WM, Law R. 2002. Elementary statistics, p 462–521. In Triola MF (ed), Elementary Statistics, 2nd ed. Pearson Education Canada, Inc, Toronto, Ontario, Canada.
46.
Parikh R, Mathai A, Parikh S, Chandra Sekhar G, Thomas R. 2008. Understanding and using sensitivity, specificity and predictive values. Indian J Ophthalmol 56:45–50.
Information & Contributors
Information
Published In
Antimicrobial Agents and Chemotherapy
Volume 66 • Number 1 • 18 January 2022
eLocator: e01370-21
PubMed: 34662197
Copyright
© 2022 Crown. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.
History
Received: 9 July 2021
Returned for modification: 16 August 2021
Accepted: 6 October 2021
Accepted manuscript posted online: 18 October 2021
Published online: 18 January 2022
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2022.
Linear Regression Equations To Predict β-Lactam, Macrolide, Lincosamide, and Fluoroquinolone MICs from Molecular Antimicrobial Resistance Determinants in Streptococcus pneumoniae. Antimicrob Agents Chemother
66:e01370-21.
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