1. Epidemiology and Global Health
  2. Microbiology and Infectious Disease
Download icon

Targeted surveillance strategies for efficient detection of novel antibiotic resistance variants

  1. Allison L Hicks
  2. Stephen M Kissler
  3. Tatum D Mortimer
  4. Kevin C Ma
  5. George Taiaroa
  6. Melinda Ashcroft
  7. Deborah A Williamson
  8. Marc Lipsitch
  9. Yonatan H Grad  Is a corresponding author
  1. Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, United States
  2. Department of Microbiology and Immunology, The University of Melbourne at The Peter Doherty Institute for Infection and Immunity, Australia
  3. Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T.H. Chan School of Public Health, United States
  4. Division of Infectious Diseases, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, United States
Research Article
Cite this article as: eLife 2020;9:e56367 doi: 10.7554/eLife.56367
3 figures, 4 tables, 5 data sets and 3 additional files

Figures

Figure 1 with 1 supplement
The impact of demography-, niche-, and geography-aware sampling on the detection efficiency of genetic resistance variants.

Dot plots showing the detection efficiency (with lines indicating the mean and 95% confidence intervals from 100 simulations) for resistance variants RplD G70D (A–B), 23S rRNA C2611T (C–D), and penA XXXIV (E–F) in datasets 1 and 2. In datasets 1 and 2, targeted sampling was informed by demographic (gender and sexual behavior) and anatomical site of isolate collection (niche) information (A, C, and E), and in datasets 3 and 4, targeted sampling was informed by country or prefecture of sample collection (B, D, and F). Dot colors indicate the sampling approach, and asterisks indicate a significant difference (p<0.05 by Mann-Whitney U test) in detection efficiency between the demography-, niche- or geography-aware approach compared to random sampling (*p<0.05, **p<0.01, ***p<0.001; red asterisks indicate significantly lower detection efficiency of demography- or geography-aware approaches compared to random sampling). Note that sampling simulations were not performed for RplD G70D in datasets 1 and 4 or for penA XXXIV in dataset 3 as prevalence of the variants in these datasets was >10%. n.s., not significant at α = 0.05; M, men; W, women; MSM, men who have sex with men; MSW, men who have sex with women; WSM, women who have sex with men.

Figure 1—figure supplement 1
Isolates from patients with travel-associated gonorrhea are associated with longer terminal branches compared to patients with locally-acquired gonorrhea.

Maximum-likelihood phylogeny produced from the pseudogenome alignment (with predicted regions of recombination removed) of isolates from dataset 2 (A). Patient travel history is indicated by the colored ring in A. Scatter dot plots showing the terminal branch lengths (with lines indicating the mean and 95% confidence intervals) associated with isolates from patients with travel-associated gonorrhea compared to patients with locally-acquired gonorrhea with asterisks indicating a significant difference (p<0.001 by Mann-Whitney U test) in terminal branch lengths between the two groups (B).

Figure 2 with 1 supplement
The impact of phylogeny-aware sampling on the detection efficiency of genetic resistance and diagnostic escape variants.

Scatter dot plots showing the detection efficiency (with lines indicating the mean and 95% confidence intervals from 100 simulations) for resistance variants RplD G70D (A), 23S rRNA C2611T (B), and penA XXXIV (C) in datasets 1–5. Note that sampling simulations were not performed for RplD G70D in datasets 1 and 4 or for penA XXXIV in dataset 3 as prevalence of the variants in these datasets was >10%. Maximum-likelihood phylogenies produced from pseudogenome alignments (with predicted regions of recombination removed) of isolates from dataset 4 (D) and dataset 2 (E). Presence or absence of the 23S rRNA C2611T mutation (in at least 2/4 alleles) and the mosaic penA XXXIV allele is indicated by colored rings. Scatter dot plots showing the detection efficiency (with lines indicating the mean and 95% confidence intervals from 100 simulations) for diagnostic-associated variants 16S rRNA C1209A (F), N. meningitidis-like porA (G), cppB deletion (H), and DR-9A G168A (I) in all datasets in which the variant was present. Dot colors in A–C) and F–I) indicate the sampling approach, and asterisks indicate a significant difference (p<0.05 by Mann-Whitney U test) in detection efficiency between the phylogeny-aware approach compared to random sampling (*p<0.05, **p<0.01, ***p<0.001; red asterisks indicate significantly lower detection efficiency of the phylogeny-aware approach compared to random sampling, and green asterisks indicate significantly higher detection efficiency of the phylogeny-aware approach compared to random sampling). n.s., not significant at α = 0.05.

Figure 2—figure supplement 1
Detection efficiency of clonal group sampling across different similarity thresholds.

Scatter dot plots showing the detection efficiency (with lines indicating the mean and 95% confidence intervals from 100 simulations) for resistance variants RplD G70D (A), 23S rRNA C2611T (B), and penA XXXIV (C) in datasets 1–5 and for diagnostic-associated variants 16S rRNA C1209A (D), N. meningitidis-like porA (E), cppB deletion (F), and DR-9A G168A (G) in all datasets in which the variant was present. Note that sampling simulations were not performed for RplD G70D in datasets 1 and 4 or for penA XXXIV in dataset 3 as prevalence of the variants in these datasets was >10%. Dot colors indicate the sampling approach, and asterisks indicate a significant difference (p<0.05 by Mann-Whitney U test) in detection efficiency between the phylogeny-aware approach compared to random sampling (*p<0.05, **p<0.01, ***p<0.001; red asterisks indicate significantly lower detection efficiency of the phylogeny-aware approach compared to random sampling, and green asterisks indicate significantly higher detection efficiency of the phylogeny-aware approach compared to random sampling). n.s., not significant at α = 0.05.

The impact of genomic background-aware sampling on the detection efficiency of phenotypic resistance variants.

Bar charts showing the proportions of ceftriaxone reduced susceptibility (CRO-RS) isolates, ceftriaxone susceptible (CRO-S) isolates, cefixime resistant (CFX-R) isolates, and cefixime susceptible (CFX-S) isolates with GyrA S91F and GyrA S91 wild-type alleles (A) and with PorB G120 and/or A121 mutations and PorB G120 and A121 wild-type alleles (B) across datasets 1–5. Bar charts showing the number of (C) CRO-RS and (D) CFX-R isolates with each haplotype, along with heatmaps showing the presence or absence of the GyrA S19F mutation, the PorB G120 and/or A121 mutations, and other alleles at loci previously associated with extended spectrum cephalosporin resistance. Bar colors in (C) and (D) indicate the dataset from which the isolates were derived. Scatter dot plots showing the detection efficiency (with lines indicating the mean and 95% confidence intervals from 100 simulations) for CRO-RS (E) and CFX-R (F) in all datasets in which the variant was present. Dot colors in E–F) indicate the sampling approach, and asterisks indicate a significant difference (p<0.05 by Mann-Whitney U test) in detection efficiency between the phylogeny-aware approach compared to random sampling (*p<0.05, **p<0.01, ***p<0.001; green asterisks indicate significantly higher detection efficiency of the genomic background-aware approach compared to random sampling).

Tables

Table 1
Summary of datasets.
DatasetTemporal rangeNisolatesGeographic rangeMetadata availableSRA study ID/Reference
12011–2015896New York, NY, USGender, sexual behavior, anatomical site of isolationERP011192 (Mortimer et al., 2020)
22016–20172186Victoria, AustraliaGender, sexual behavior, anatomical site of isolation, travel history, sex worker statusSRP185594 (Williamson et al., 2019)
320131054EuropeCountry of sample collectionERP010312 (Harris et al., 2018)
42015244JapanPrefecture of sample collectionDRP004052 (Yahara et al., 2018)
52014–2015398New ZealandN/ASRP111927 (Lee et al., 2018b)
Table 2
Summary by dataset of the prevalence and distribution of the genetic markers of resistance and resistance phenotypes tested.
VariantGeneticPhenotypic
RplD G70D23S rRNA C2611T (2–4 alleles)penA XXXIVCRO-RS (≥0.12 μg/mL)CFX-R (>0.25 μg/mL)
DrugAZM (Grad et al., 2016)AZM (Lk et al., 2002)ESCs (Grad et al., 2014)N/AN/A
Prevalence of variant in dataset110.04%*0.11%5.25%1.47%0.11%
21.14%1.24%1.69%0%0%
32.47%0.95%15.68%*1.04%0.76%
411.07%*1.23%0.41%6.56%8.20%
50.75%0.50%2.26%0.25%0%
Phylogenetic D statistic for variant in dataset1−0.1817.50−0.29N/AN/A
2−0.100.46−0.24N/AN/A
30.050.30−0.20N/AN/A
4−0.161.831.81N/AN/A
50.831.12−0.15N/AN/A
  1. *Given the >10% prevalence of RplD G70D in datasets 1 and 4 and penA XXXIV in dataset 3, these variants were excluded from sampling simulations.

    AZM, azithromycin; ESC, extended-spectrum cephalosporin; CRO-RS, ceftriaxone reduced susceptibility; CFX-R, cefixime resistance.

Table 3
Summary of the potential diagnostic escape variants assessed.
VariantDiagnostic assayDocumented association with diagnostic failurePrevalence in dataset
12345
16S rRNA C1209A (four alleles)Aptima GC ComboYes (Guglielmino et al., 2019)0.11%0.09%0%0%0%
N. meningitidis-like porAIn-house (Whiley et al., 2004; Whiley et al., 2005)Yes (Whiley et al., 2011; Golparian et al., 2012)0.11%0.05%0%0%0%
cppB deletionIn-house (Diemert et al., 2002; Van Dyck et al., 2001)Yes (Bruisten et al., 2004)1.12%0.05%0.47%0%7.29%
DR-9A G168ARoche COBAS 4800 CT/NGNo0%0.09%0%0%0%
Table 4
Reference information for the genetic variants assessed.
VariantReference accessionCoordinates of genetic locus in reference entryPosition of mutation in reference locus
RplD G70DNC_011035.12033052–2033672amino acid 70
23S rRNA C2611TNC_011035.11263408–1266305nucleotide 2603
penA XXXIVNZ_LT906440.11588456–1590201N/A (assessed presence/absence of this allele)
16S rRNA C1209ANC_011035.11266903–1268450nucleotide 1192
N. meningitidis-like porANC_011035.1735796–737125N/A (assessed nucleotide similarity across the full locus with a threshold of ≤ 90%)*
cppB deletionLT592149.12912–3553N/A (assessed presence/absence of full locus)
DR-9A G168ANC_011035.1530088–530277nucleotide 168
  1. *Isolates with a porA pseudogene with ≤90% similarity to the NC_011035.1 porA pseudogene were called positive for N. meningitidis-like porA. Note that all such isolates were confirmed to have a porA pseudogene that was ≥92% similar to the N. meningitidis porA (GenBank Accession: GQ173789.1), while all other isolates had ≤89% similarity to the N. meningitidis porA.

Data availability

The source data for all figures and tables are included in available in Supplementary file 1 and/or the NCBI Sequence Read Archive (BioProject numbers indicated in Table 1 and individual sample accession numbers indicated in Supplementary file 1).

The following previously published data sets were used
  1. 1
    NCBI Sequence Read Archive
    1. D Williamson
    2. EPF Chow
    3. C Gorrie
    4. T Seemann
    5. DJ Ingle
    6. N Higgins
    (2019)
    ID SRP185594. Bridging of Neisseria Gonorrhoeae Across Diverse Sexual Networks in the HIV Pre-Exposure Prophylaxis (PrEP) Era: A Clinical and Molecular Epidemiological Study.
  2. 2
    NCBI Sequence Read Archive
    1. SR Harris
    2. MJ Cole
    3. G Spiteri
    4. L Sanchez-Buso
    5. D Golparian
    6. S Jacobsson
    (2018)
    ID ERP010312. Public health surveillance of multidrug-resistant clones of Neisseria gonorrhoeae in Europe: a genomic survey.
  3. 3
    NCBI Sequence Read Archive
    1. K Yahara
    2. SI Nakayama
    3. K Shimuta
    4. KI Lee
    5. M Morita
    6. T Kawahata
    (2018)
    ID DRP004052. Genomic surveillance of Neisseria gonorrhoeae to investigate the distribution and evolution of antimicrobial-resistance determinants and lineages.
  4. 4
    NCBI Sequence Read Archive
    1. RS Lee
    2. T Seemann
    3. H Heffernan
    4. JC Kwong
    5. A Goncalves da Silva
    6. GP Carter
    (2018)
    ID SRP111927. Genomic epidemiology and antimicrobial resistance of Neisseria gonorrhoeae in New Zealand.
  5. 5
    NCBI Sequence Read Archive
    1. TD Mortimer
    (2020)
    ID ERP011192. Using genomics to understand transmission networks of Neisseria gonorrhoeae in New York City.

Additional files

Supplementary file 1

Metadata and resistance variant profiles for isolates assessed in this study.

M, men; W, women; MSM, men who have sex with men; MSW, men who have sex with women; WSM, women who have sex with men; CFX-R, cefixime resistance; CRO-RS, ceftriaxone reduced susceptibility; AZM-R, azithromycin resistance.

https://cdn.elifesciences.org/articles/56367/elife-56367-supp1-v1.xlsx
Supplementary file 2

Dataset bias and targeted sampling results.

(A) Demographic and geographic sampling biases in datasets 1–3. (B) Detection efficiency of random, demography-, niche-, and geography-aware sampling approaches for resistance variants. (C) Detection efficiency of random sampling, as well as preferential sampling of patients that had recently engaged in overseas sex or in sex work, for resistance variants in dataset 2. (D) Detection efficiency of random and phylogeny-aware sampling approaches for resistance variants. (E) Detection efficiency of random and phylogeny-aware sampling approaches for variants associated with diagnostic escape. (F) Detection efficiency of random and genomic background-aware sampling approaches for resistance variants.

https://cdn.elifesciences.org/articles/56367/elife-56367-supp2-v1.docx
Transparent reporting form
https://cdn.elifesciences.org/articles/56367/elife-56367-transrepform-v1.pdf

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)