AMR prediction based on WGS data

Transcription

1 AMR prediction based on WGS data Valeria Bortolaia, DVM, PhD Research Group for Genomic Epidemiology National Food Institute Technical University of Denmark

2 This lecture How to use WGS for AMR surveillance? Why using WGS for AMR surveillance? How does WGS-based AMR prediction perform? What I would like you to learn today: Assess the feasibility of using WGS data to predict antimicrobial resistance Evaluate the advantages and disadvantages of AMR prediction based on phenotypic and genotypic methods in the context of surveillance

3 How resistance is measured by WGS The sequence data are interrogated for presence/absence of antimicrobial resistance genes and/or specific chromosomal mutations in DNA sequence data. How to interrogate sequence data: alignment/mapping What to interrogate sequence data for: AMR genes/mutations collected in databases

4 The database In the perfect world, it contains a list of: all existing AMR genes AND all existing mutations of chromosomal genes mediating AMR AND all the mutations leading to modified expression of porins and efflux pumps mediating AMR resistance

5 AMR gene databases/software

6 The database In the perfect world, it contains a list of: all existing AMR genes AND all existing mutations of chromosomal genes mediating AMR AND all the mutations leading to modified expression of porins and efflux pumps mediating AMR resistance So why I obtain different results when analyzing the same sequence data using different databases/software?

7 Nat. Rev. Microbiol Context of gene matters! Expression of gene matters! Our knowledge is imperfect! Databases are built with different criteria and by different people and also these two aspects might change over time READ READ READ the documentation accompanying the databases, check the version number and contact the curators whenever in doubt.

8 WHY USING WGS FOR DETECTING AMR IF THERE ARE ALL THESE ISSUES? At least two main reasons: 1. You obtain much more information that is indeed useful 2. The phenotypic methods we rely on are not so perfect

9 Why are you doing AMR monitoring? one of the purposes is for comparisons: Across sectors (animals, food, humans) Across countries (animals, food, humans travel)

10 Different level of information can lead to different conclusions 76.8% chicken meat sample with ESBL EC Overdevest et al % patients rectal swabs with ESBL EC FOODBORNE TRANSFER? 64% patients with bacteremia by CTX-R EC Circumstantial evidence for an animal reservoir for a Substantial part of ESBL genes found in humans

11 Different level of information can lead to different conclusions De Been et al No E. coli clonal spread ESBL plasmid spread

12 How resistance is measured phenotypically MIC determination Measurement of the lowest concentration (µg/ml or mg/l) that inhibits completely growth of the test strain (MIC)

13 How resistance is measured phenotypically Obtained values (MIC) are interpreted using accepted guidelines

14 Different results when different persons read the same MIC plate

15 Different results when testing the same isolate at different laboratories

16 ResFinder 4.0: keys for AMR prediction Gene_accession no. Class Phenotype PMID Mechanism of resistance ant(2'')-ia_1_x04555 Aminoglycoside Gentamicin, Tobramycin Enzymatic modification ant(2'')-ia_2_jf Aminoglycoside Gentamicin, Tobramycin Enzymatic modification ant(2'')-ia_3_x74412 Aminoglycoside Gentamicin, Tobramycin unpublished Enzymatic modification ant(2'')-ia_4_af Aminoglycoside Gentamicin, Tobramycin Enzymatic modification ant(2'')-ia_5_ay Aminoglycoside Gentamicin, Tobramycin Enzymatic modification ant(2'')-ia_6_aj Aminoglycoside Gentamicin, Tobramycin unpublished Enzymatic modification ant(2'')-ia_7_dq Aminoglycoside Gentamicin, Tobramycin Enzymatic modification ant(2'')-ia_8_ay Aminoglycoside Gentamicin, Tobramycin Enzymatic modification ant(2'')-ia_9_hm Aminoglycoside Gentamicin, Tobramycin Enzymatic modification

17 WGS performs similarly to phenotypic methods Sensitivity and specificity comparable to those of routinely used phenotypic methods Rates of very major and major errors within the acceptance limits set by FDA for marketing approval (<1.5% and <3%, respectively)

18 Clinical Escherichia coli ResFinder 4.0 validation unpublished results isolate-antimicrobial combinations tested isolate-antimicrobial combinations showed 100% genotype-phenotype correlation Antimicrobials Number of Number of resistant isolates with no Number of susceptible isolates with genes Genes or mutations responsible for resistance in resistant isolates resistant isolates WGS accurately predicted 99,37% responsible genes bla TEM-1B (n=45); bla CTX-M-15 (n=6); bla TEM-1C (n=4); bla CTX-M-27 (n=2); bla OXA-1 (n=3); bla TEM- 1A (n=2); bla of resistant CTX-M-14 (n=1); bla or susceptible TEM-1D (n=1); bla SHV-1 (n=1); bla DHA-1 (n=1) phenotypic profiles AMP a CZO 14 bla CTX-M-15 (n=6); bla TEM-1B (n=3); bla CTX-M-27 (n=2); bla DHA-1 (n=1); bla CTX-M-14 (n=1); bla TEM- 1C (n=1) 0 1 a CUR and/or CXI 14 bla CTX-M-15 (n=6); bla CTX-M-27 (n=2); bla OXA-1 (n=2); bla DHA-1 (n=1); bla CTX-M-14 (n=1) 2 b 2 ac CPO and/or CTZ and/or CTR 10 bla CTX-M-15 (n=6); bla CTX-M-27 (n=2); bla DHA-1 (n=1); bla CTX-M-14 (n=1) 0 1 a CIP 23 parc S80I, gyra S83L, gyra D87N/D87H d (n=17); parc S80I, gyra S83L, gyra D87N d and aac(6 )-Ib-cr (n=5); qnrs1 (n=1) 0 2 e TRS 37 dfra17 (n=18); dfra14 (n=5); dfra1 (n=3); dfra5 (n=4); dfra7 (n=2); dfra12 (n=2); dfra8 (n=1) accompanied by sul1 (n=10); sul2 (n=10); sul3 (n=1); sul1 and sul2 (n=14) 2 f 2 GEN and/or TOB aac(6 )-Ib-cr (n=5); aac(3)-iid (n=3); aac(3)-iia (n=1); aac(3)-iva (n=1); aac(6 )-Ib-cr and 12 and/or AMI aac(3)-iia (n=1) 1 0 TET 45 tet(a) (n=23); tet(b) (n=22) 0 0

19 ResFinder 4.0 validation unpublished results Foodborne Campylobacter jejuni isolate-antimicrobial combinations tested isolate-antimicrobial combinations showed 100% genotype-phenotype correlation WGS accurately predicted 97.5% of resistant or susceptible phenotypic profiles

20 No gene = No resistance: true? E. coli isolate: no colistin resistance gene found (chromosomal mutation, mcr-1 to -5) MIC = 4 mg/l ECOFF = 2 mg/l Limitation of the MIC method? New colistin resistance gene?

21 Gene = Resistance: true? E. faecium isolate: vana The entire vancomycin resistance (vana) operon is present: vanr, vans, vany, vanz: 100% id vana: 99.9% id vanx: 99.8% id However, this is not a wild-type E. faecium

22 Consensus on when WGS is superior to phenotypic AST Slow-growing bacteria (M. tubercolosis) Knowledge of mechanisms of resistance relevant for taking decisions to implement enhanced control measures The data are in theory available in perpetuity and can be interrogated infinitely with new genes or data sets

23 Troubleshooting before routine use of WGS-based antibiograms Differences within and between bacterial species Validation against larger datasets Differences across antimicrobials Increased knowledge of new resistance alleles; improved surveillance breakpoints

24 A look into the future: Measurement of resistance by WGS GCVs allow correlation between resistance genotypes and MICs GCV Ongoing machine learning projects on WGS-based AMR prediction

25 Take-home messages Neither phenotypic nor genotypic methods are perfect in detecting antimicrobial resistance Genotypic methods can perform in a comparable manner to phenotypic methods in predicting resistance the main limitation is our incomplete knowledge in genetic bases of resistance Genotypic methods can perform in a comparable manner to phenotypic methods in predicting resistance the main limitation is our incomplete knowledge in genetic bases of resistance

26 Acknowledgements Rolf S. Kaas Ana Rita Rebelo Mohammed Nateqi Kathrine Valentini Jensen Gunhild Larsen Hanne Mordhost Frank Aarestrup

27 Thank you for your attention!