Integrating whole-genome sequencing within the National Antimicrobial Resistance Surveillance Program in the Philippines

Abstract

National networks of laboratory-based surveillance of antimicrobial resistance (AMR) monitor resistance trends and disseminate these data to AMR stakeholders. Whole-genome sequencing (WGS) can support surveillance by pinpointing resistance mechanisms and uncovering transmission patterns. However, genomic surveillance is rare in low- and middle-income countries. Here, we implement WGS within the established Antimicrobial Resistance Surveillance Program of the Philippines via a binational collaboration. In parallel, we characterize bacterial populations of key bug-drug combinations via a retrospective sequencing survey. By linking the resistance phenotypes to genomic data, we reveal the interplay of genetic lineages (strains), AMR mechanisms, and AMR vehicles underlying the expansion of specific resistance phenotypes that coincide with the growing carbapenem resistance rates observed since 2010. Our results enhance our understanding of the drivers of carbapenem resistance in the Philippines, while also serving as the genetic background to contextualize ongoing local prospective surveillance.

Fig. 1. Implementing whole-genome sequencing (WGS) for AMR surveillance in the Philippines.

a ARSP workflow and enhanced detection of high-risk clones by WGS. Isolates collected by sentinel sites are tested for susceptibility to antibiotics (open squares: susceptible, solid squares: resistant). The data are stored as resistance profiles in WHONET and summaries of resistance trends are shared yearly with surveillance stakeholders. WGS of bacterial isolates and interpretation with web applications like Microreact and Pathogenwatch provide information on genetic relatedness (strains), known AMR determinants (mechanisms), and the mobile genetic elements (MGE, vehicles) for their dissemination, thus allowing us to detect high-risk clones. b Detail of trends in antimicrobial resistance in the Philippines. Yearly resistance rates for key bug–drug combinations based on phenotypic data collected by sentinel sites. EBC Enterobacteriaceae (K. pneumoniae, E. coli, Salmonella enterica), PAE P. aeruginosa, ABA A. baumannii (Acinetobacter spp. before the year 2000), SAL Salmonella enterica, SAU S. aureus, ESBL extended-spectrum beta-lactamase production suspected, or non-susceptible to the following antibiotics IPM imipenem, CIP ciprofloxacin, OXA oxacillin, WGS box: period covered by the retrospective sequencing survey.

Fig. 2. Temporal dynamics of carbapenem resistance profiles.

a Yearly carbapenem resistance rates (IPM imipenem, MEM meropenem) for A. baumannii, P. aeruginosa, K. pneumoniae and E. coli. b Relative abundance of carbapenem resistance profiles (non-susceptible to imipenem and/or meropenem). The three-letter antibiotic codes (as in Table 1) indicate that the isolate is non-susceptible (resistant or intermediate). Only carbapenem non-susceptible isolates with complete susceptibility data were included, as indicated by the numbers under the x-axis (N). WGS box: period covered by the retrospective whole-genome sequencing survey.

Fig. 3. Carbapenem resistance profiles are not associated with specific genetic lineages.

Phylogenetic trees showing the major lineages of A. baumannii, P. aeruginosa, K. pneumoniae species complex and E. coli, indicating the position of select sequence types (ST) and clonal groups (CG). KpI K. pneumoniae sensu stricto, KpII K. quasipneumoniae, KpIII K. variicola. Tree ring: select carbapenem resistance profiles from Fig. 2 are shown in colour. The remaining carbapenem resistance profiles are shown in light grey. Other resistance profiles (susceptible to carbapenems) are shown in white for simplicity. The three-letter antibiotic codes are as in Table 1. The pie charts show the relative abundance of the resistance profiles in the retrospective collection of sequenced genomes. The bar charts show the distribution of carbapenemase genes across the key resistance profiles. The scale bars show the number of SNPs per variable site. The data are available at https://microreact.org/project/ARSP_ABA_2013-14 (Aba), https://microreact.org/project/ARSP_PAE_2013-14 (Pae), https://microreact.org/project/ARSP_KPN_2013-14 (Kpn) and https://microreact.org/project/ARSP_ECO_2013-14 (Eco).

Fig. 4. Detection of a plasmid-driven outbreak of K. pneumoniae ST340.

a Phylogenetic tree and linked epidemiological and genotypic data of 24 retrospective ST340 genomes. Origin type defined as either community-acquired infection (CAI) or hospital-acquired infection (HAI). NA ward information not available. The imipenem (IPM) phenotype was either resistant (R) or susceptible (S). The three-letter antibiotic codes in the resistance profiles are as in Table 1. This interactive view is available at https://microreact.org/project/ARSP_KPN_ST340_2013-14/ac2a0920. The maximum-likelihood tree was inferred from 196 SNP positions identified by mapping the genomes to reference CAV1217 (CP018676.1 [https://www.ncbi.nlm.nih.gov/nuccore/CP018676.1]), and masking regions corresponding to mobile genetic elements and recombination. The scale bar shows the number of SNPs per variable site. The full data are available at https://microreact.org/project/ARSP_KPN_ST340_2013-14. b Distribution of 33 isolates from hospital MMH with resistance profile RP-6 by patient age group, sequence types (ST) and NDM plasmid. Short reads of the 33 isolates were mapped to the plasmid sequences and a match was counted when the reads covered at least 95% of the sequence length with at least 5× depth of coverage. ND not detected.

Fig. 5. WGS dissects the circulation of K. pneumoniae ST147 in the Philippines.

Phylogenetic tree and linked epidemiological and genotypic data of 80 ST147 genomes. The imipenem (IPM) phenotype was either resistant (R), intermediate (I) or susceptible (S). The three-letter antibiotic codes in the resistance profiles are as in Table 1. This interactive view is available at https://microreact.org/project/ARSP_KPN_ST147_2013-14/4c33ace7. The maximum-likelihood tree was inferred from 809 SNP positions identified by mapping the genomes to reference MS6671 (LN824133.1 [https://www.ncbi.nlm.nih.gov/nuccore/LN824133.1]) and masking regions corresponding to mobile genetic elements and recombination. The scale bar shows the number of SNPs per variable site. The distribution of plasmids with NDM genes was inferred by mapping the short reads of the genomes to the complete plasmid sequences, and a match was counted when the reads covered at least 95% of the sequence length with at least 5x depth of coverage. The full data are available at https://microreact.org/project/ARSP_KPN_ST147_2013-14.

Fig. 6. Phylogeographic analysis of E. coli ST410 from the Philippines.

a Phylogenetic tree and linked epidemiological and genotypic data of 24 retrospective ST410 genomes. The imipenem (IPM) phenotype was either resistant (R) or susceptible (S). The three-letter antibiotic codes in the resistance profiles are as in Table 1. This interactive view is available at https://microreact.org/project/ARSP_ECO_ST410/088ba65b. b Philippine isolates (orange nodes) in global context. This interactive view is available at https://microreact.org/project/ARSP_ECO_ST410_GLOBAL/c701506e. The maximum-likelihood trees were inferred from 703 (A) and 2851 (B) SNP positions, respectively, identified by mapping the genomes to reference AMA1167 (CP024801.1) and masking regions corresponding to mobile genetic elements and recombination. The scale bars show the number of SNPs per variable site. The distribution of plasmids with carbapenemases genes in (b) was inferred by mapping the short reads of the genomes to the complete plasmid sequences, and a match was counted when the reads covered at least 95% of the sequence length with at least 5× depth of coverage. The full data are available at https://microreact.org/project/ARSP_ECO_ST410 (a) and https://microreact.org/project/ARSP_ECO_ST410_GLOBAL (b).