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. SIMILAR CONTENT BEING VIEWED BY OTHERS WHOLE GENOME SEQUENCING ANALYSIS OF_ MYCOBACTERIUM TUBERCULOSIS_ REVEALS CIRCULATING STRAIN TYPES AND DRUG-RESISTANCE MUTATIONS IN THE

PHILIPPINES Article Open access 23 August 2024 HARMONISATION OF IN-SILICO NEXT-GENERATION SEQUENCING BASED METHODS FOR DIAGNOSTICS AND SURVEILLANCE Article Open access 23 August 2022

NATIONAL GENOMIC SURVEILLANCE INTEGRATING STANDARDIZED QUANTITATIVE SUSCEPTIBILITY TESTING CLARIFIES ANTIMICROBIAL RESISTANCE IN ENTEROBACTERALES Article Open access 05 December 2023

INTRODUCTION Antimicrobial resistance (AMR) is an increasingly serious threat to global public health and the economy that requires concerted action across countries, government sectors and

non-government organizations1. Without AMR containment, an adverse impact on medical costs, global gross domestic product, livestock production and international trade is expected by 2050,

and the sustainable development goals for 2030 are less likely to be attained2. The Global Action Plan developed by the World Health Organization (WHO) to tackle AMR highlights the need to

strengthen our understanding of how resistance develops and spreads, and the underlying resistance mechanisms3. One of the pillars of this objective is the national surveillance system for

AMR. An advanced example of a national surveillance system is the Antimicrobial Resistance Surveillance Program (ARSP), the national laboratory-based surveillance of the Philippine

Department of Health (DOH). The ARSP began in 1988 with eight hospital-based laboratories (sentinel sites) and the Antimicrobial Resistance Surveillance Reference Laboratory (ARSRL) in Metro

Manila and has since expanded to 24 sentinel sites and two gonorrhoea surveillance sites in 16 regions of the country (Supplementary Fig. 1). Surveillance encompasses common pathogens of

public health importance in the Philippines, where infectious diseases represented nine out of the ten leading causes of morbidity4, and four out of the ten leading causes of infant

mortality5 in 2013–2014. Results of culture-based identification and antimicrobial susceptibility are stored centrally at the ARSRL within the clinical microbiology management software

WHONET (Fig. 1a6). A report summarizing the resistance trends is published annually and disseminated to local, national and international surveillance stakeholders7. For policy change,

surveillance data generated by ARSP are used by the DOH to develop clinical practice guidelines and determine the panel of antibiotics to be included in the national formulary. Furthermore,

the ARSP has been contributing data to international AMR surveillance programmes since the 1980s, including the Global Antimicrobial Surveillance System (GLASS)8 since 2015. Importantly, the

ARSP data has informed the Philippine National Action Plan to combat AMR, one of the key requirements for alignment with the WHO Global Action Plan. The Philippines has seen a steady

increase in resistance rates for several key pathogen–antibiotic combinations in the last 10 years, including carbapenem-resistant organisms (Fig. 1b). The genetic mechanisms underlying

carbapenem resistance, one of the biggest therapeutic challenges in the treatment of antimicrobial-resistant infections, include increased upregulation of efflux pumps, decreased uptake by

altered expression/loss of porin function, and acquisition of hydrolytic enzymes—carbapenemases9. Whole-genome sequencing (WGS) of bacterial pathogens can identify distinct clonal lineages

(strains) on phylogenetic trees, known AMR mechanisms (genes or mutations) and the vehicles for the acquired AMR genes (mobile genetic elements, MGEs) which, in turn, enables enhanced

detection and characterization of high-risk clones (Fig. 1a). WGS is routinely used for infectious disease epidemiology in several high-income countries around the world, where it has

improved outbreak investigations and epidemiological surveillance10,11 and enhanced our knowledge of the spread of antimicrobial-resistant strains and their resistance mechanisms12,13.

Elucidation of resistance mechanisms and their context can be critical for effective infection control. For example, upregulation of efflux pumps and loss of porin function by mutation are

vertically transmitted, while acquired carbapenemases carried in transmissible plasmids or integrative conjugative elements have the potential for horizontal dissemination between strains

and species, thus necessitating enhanced infection control measures14. Moreover, diverse carbapenemase classes, variants, flanking MGEs and associated plasmid incompatibility groups have

been described, requiring multiple biochemical and molecular tests for identification. For example, the class B New Delhi metallo-beta-lactamase (NDM) is found worldwide, represented by over

ten different variants that are usually upstream of intact or truncated IS_Aba125_ elements, within plasmid backbones of different incompatibility groups, across multiple lineages of _E.

coli_—as well as of other carbapenem-resistant organisms15,16. WGS can determine the interplay between these different components, i.e. the gene, the vehicle and the strain, thus maximizing

the epidemiological benefit derived from its cost. Implementation of WGS within existing national surveillance systems in low- and middle-income countries (LMICs) has the potential to

enhance local prevention and control of resistant infections in a sustainable and equitable manner. Integration of WGS into routine surveillance of AMR can be facilitated by international

collaboration focused on transfer of expertize and ownership. Our collaboration (See and Sequence, Fig. 1a) aimed to implement WGS within the ARSP via a multi-faceted approach that included

a large initial retrospective sequencing survey, technology transfer, utilization of user-friendly web applications and capacity building in laboratory and bioinformatic procedures for local

prospective sequencing. We linked what has traditionally been used as the operational unit of laboratory surveillance, the resistance profile (RP), with the operational unit of genomic

epidemiology now provided by WGS, genetic relatedness. Here we provide exemplars from the retrospective sequencing survey that highlight how the granular view of strain-gene-vehicle in

carbapenem-resistant populations at the local, national and global operational scales can be leveraged for surveillance of AMR and public health. RESULTS INTEGRATION OF LABORATORY AND

GENOMIC SURVEILLANCE The Philippine ARSP collects bacterial isolates and stores the associated clinical and epidemiological data in the WHONET software6. Antibiograms are stored as RPs,

which represent the diversity of AMR phenotypes in the country. We implemented WGS within the ARSP via ongoing technology transfer, capacity building in and binational collaboration. In

parallel, we conducted a large retrospective sequencing survey to characterize bacterial populations, dissect resistance phenotypes of key bug–drug combinations and provide genomic context

for local prospective surveillance. Here, we focus on the carbapenem-resistant organisms, which have been classified as of critical priority for the development of new antibiotics by the

WHO. OPERATIONAL UNIT OF LABORATORY SURVEILLANCE—INCREASING THE BURDEN OF SPECIFIC RESISTANCE PHENOTYPES Carbapenem-resistant _P. aeruginosa, A. baumannii, K. pneumoniae_ and _E. coli_ were

all first isolated by the ARSP between 1992 and 1994. Since the year 2000, resistance rates to imipenem and meropenem have remained below 30% for all organisms except for _A. baumannii_,

which has seen a steady rise in resistance rates between 2009 and 2017, reaching 56% (Fig. 2a). This coincides with the expansion of two RPs, first a striking expansion of true extremely

drug-resistant (XDR) and possible pan drug-resistant (PDR) RP-1, and later of true-XDR RP-2 (full profiles can be found in Fig. 2b). Yearly resistance rates for _P. aeruginosa_ have

oscillated between 10 and 25% since the year 2000, and have doubled between 2010 and 2013 (Fig. 2a). Two RPs have seen the largest combined expansion between 2011 and 2013, possible-PDR RP-3

and true-XDR RP-4 (Fig. 2b). The yearly resistance rates to imipenem for Enterobacteriaceae were ~3% in 2011 but have since steadily increased to 5% for _E. coli_ and 11% for _K.

pneumoniae_, respectively (Fig. 2a). This coincides with the expansion of three possible-XDR RPs, RP-5, RP-6 and RP-7 (Fig. 2b). The observed expansion of specific RPs could be driven by the

rapid dissemination of a discrete genetic lineage (strain) carrying AMR determinants or, alternatively, of transmissible MGEs, which shuttle resistance determinants across different genetic

lineages. OPERATIONAL UNIT OF GENOMIC SURVEILLANCE—GENETIC DIVERSITY AND MECHANISMS UNDERPINNING CARBAPENEM RESISTANCE PHENOTYPES The sequences of 805 genomes linked to WHONET data were

obtained from isolates belonging to the four bacterial pathogens in the WHO critical list (Table 1), which were collected in 2013 and 2014 by between 13 and 18 ARSP sites. The isolates

sequenced were non-susceptible to carbapenems and/or to extended-spectrum cephalosporins (Table 1), with the exception of _P. aeruginosa_, for which susceptible isolates were available and

also sequenced. The distribution of the carbapenem RPs highlighted in Fig. 2 was not concordant with the major clades observed on the phylogenetic trees (Fig. 3). Instead, the same RP was

usually observed in multiple genetic lineages across the tree. Yet, the distribution of the multi-locus sequence types (STs), a molecular genotyping method inferred from the sequences of

seven house-keeping genes, was largely concordant with the major clades observed on the trees inferred from whole-genome single-nucleotide polymorphisms (SNPs) (Fig. 3), confirming that the

homoplasic distribution of RPs was not an artefact of the tree topology. The distribution of the same carbapenem RPs across multiple genetic lineages was observed both at the national and

local levels (Supplementary Fig. 2), as the number of ST assignments increased with the number of isolates representing a RP. Thus, the genomic data suggest that the expansion of specific

carbapenem RPs in the Philippines is driven by the horizontal dissemination of resistance determinants across diverse genetic backgrounds, rather than by the sole expansion of single

resistant genetic clones, and that resistance to multiple antibiotics is acquired en bloc via MGEs, as previously observed for carbapenem-resistant organisms16. Carbapenemases are a diverse

collection of hydrolytic enzymes16, and we identified representatives of classes A, B and C carbapenemases in the Philippines (Supplementary Fig. 3). Class B NDM-1 and Verona integron-borne

metallo-beta-lactamase VIM-2 were the most prevalent and geographically disseminated in the Enterobacteriaceae and _P. aeruginosa_, respectively, while the same was true for class D OXA-23

in _A. baumannii_. Importantly, different carbapenemase genes/variants (or combinations thereof) were found underlying the same RP in all four organisms (Fig. 3). LOCAL SCALE OF

SURVEILLANCE—PLASMID-DRIVEN HOSPITAL OUTBREAK OF CARBAPENEM-RESISTANT _K. PNEUMONIAE_ Fifty-seven percent (_N_ = 33) of the _K. pneumoniae_ genomes with the possible-XDR resistance phenotype

AMP FOX CAZ CRO FEP IPM AMC TZP GEN AMK CIP SXT (RP-6) were isolated from a single hospital (MMH) but belonged to 12 different STs, with almost half of them (_N_ = 15) placed within ST340

(Supplementary Fig. 4a). The phylogenetic analysis of these 15 genomes in the wider context of ST340 showed three main lineages, with the 15 possible-XDR isolates from MMH forming a tight

cluster (clade III, Fig. 4a). The shorter branch lengths in the imipenem-resistant clade III (6.5 ± 3.7 pairwise SNP differences) compared with the imipenem-susceptible clade II (26.8 ± 6.9

pairwise SNP differences, Mann–Whitney _U_ test _z_-score −6.265, _p_ value = 3.71 × 10−10, Supplementary Fig. 4b), and the isolation dates spanning 12 months, suggest a rapid expansion of

this clone, which coincides with the acquisition of the _bla_NDM-1 gene (Fig. 4a). Epidemiological data showed that the samples were mostly from hospital-acquired infections (HAI, _N_ = 14)

and from neonates (_N_ = 12) (Fig. 4a). These observations triggered a retrospective epidemiological investigation that revealed that ten of the isolates originated from patients of the

neonatal intensive care unit (NICU), all of which had umbilical catheters and were on mechanical ventilators. The remaining cases were either from paediatric wards (_N_ = 4) or the male ward

(_N_ = 1), suggesting wider dissemination of this high-risk clone within the hospital environment. The genetic diversity underlying the 33 possible-XDR isolates from MMH (12 STs) prompted

us to investigate the hypothesis of dissemination of carbapenem resistance in the hospital by a plasmid carrying _bla_NDM-1. We identified a 101,540 bp IncFII plasmid with _bla_NDM-1, _rmtC_

and _sul1_ (p13ARS_MMH0112-3, Table 2, Supplementary Note 1 and Supplementary Fig. 5a, b) in a representative isolate from ST340 clade III. We found the entire p13ARS_MMH0112-3 plasmid

sequence (i.e., ≥95% of the length) represented in 27 genomes from 9 different STs, including 20 samples from 6 different STs isolated from patients under 1 year old (Fig. 4b). A second

plasmid identified in the representative isolate carried 13 AMR genes, including the extended-spectrum beta-lactamase gene _bla_CTX-M-15 (p13ARS_MMH0112-2, Table 2, Supplementary Note 2 and

Supplementary Fig. 5a, c). Altogether, our findings suggest that the burden of carbapenem-resistant _K. pneumoniae_ infections in hospital MMH was largely linked to plasmid p13ARS_MMH0112-3

with _bla_NDM-1, circulating within diverse genetic lineages, and leading to outbreaks in high-risk patient populations. Hospital authorities were informed of these findings, and measures

for infection control were implemented, including designating a separate multi-drug resistance organism room for cohorting, active surveillance upon identification of any new

carbapenem-resistant _K. pneumoniae_ from the NICU, and referral of any new carbapenem-resistant _K. pneumoniae_ from the NICU to ARSRL for sequencing. NATIONAL SCALE OF

SURVEILLANCE—REGIONAL CIRCULATION OF A SUCCESSFUL _K. PNEUMONIAE_ LINEAGE The possible-XDR resistance phenotype AMP FOX CAZ CRO FEP IPM AMC TZP CIP SXT (RP-5) was represented by 76 _K.

pneumoniae_ isolates from 14 different STs. Seventy-one percent (_N_ = 54) belonged to ST147, an international epidemic clone that was found in 11 sentinel sites in all three island groups

in the Philippines. The phylogeny of the ST147 genomes showed that carbapenem non-susceptible isolates (78.8%, _N_ = 63) were found in clades arising from three out of four deep branches of

the tree (Fig. 5), which represent separate groups of the global population (Supplementary Note 3 and Supplementary Fig. 6). Carbapenem resistance was largely explained by the presence of

_bla_NDM-1 in clade IV genomes, while clade III-B showed geographically distinct clusters with either _bla_NDM-1 or _bla_NDM-7 (Fig. 5, Supplementary Note 4 and Supplementary Fig. 7). Five

plasmid sequences obtained from five different isolates representing carbapenem-resistant clusters in clades III and IV showed that the NDM genes were carried within different variants of

the insertion sequence IS_Aba125_ (Supplementary Fig. 8a) on different plasmid backbones, two of which showed high sequence similarity to international plasmids (Table 3 and Supplementary

Figs. 5b and 8b). The distribution of plasmids harbouring _bla_NDM-1 and _bla_NDM-7 matched the strong phylogeographic signal in the terminal branches of the tree (Fig. 5). Within clade

III-B, a cluster of genomes from sentinel sites MMH and STU was characterized by plasmid p14ARS_MMH0055-5 carrying _bla_NDM-7, while another cluster of genomes from VSM and NMC was

distinguished by plasmid p13ARS_VSM0593-1 with _bla_NDM-1. Plasmids p13ARS_GMH0099 and p14ARS_VSM0843-1, both carrying _bla_NDM-1, were found in different subclades of clade IV, one of local

circulation in hospital VSM, and another one showing regional expansion across five sentinel sites (Fig. 5). Taken together, the phylogeographic signal (strains) in combination with the

distribution of _bla_NDM variants (mechanism) and plasmids (vehicle) revealed local and regional patterns of circulation of carbapenem non-susceptible ST147 within the Philippines, which

could not be identified based solely on the RPs and MLST information. Furthermore, comparison with global ST147 genomes indicated that clade IV may be unique to the Philippines

(Supplementary Fig. 6). INTERNATIONAL SCALE OF SURVEILLANCE—HIGH-RISK CLONE OF _E. COLI_ ST410 WITH NDM-1 AND OXA-181 Recent reports of _E. coli_ ST410 international high-risk clones

carrying class D and class B carbapenemases17,18 are particularly alarming in light of their wide geographic distribution and the broad variety of niches this clone can occupy19,20. ST410

was the second most prevalent ST in the retrospective collection of _E. coli_ (13.2%, _N_ = 24), encompassing a large proportion of imipenem-resistant isolates (_N_ = 15), and the three

expanding possible-XDR RP-5,6,7 (Supplementary Fig. 9). The phylogenetic tree of 24 ST410 genomes showed that isolates with the possible-XDR profiles clustered within one clade (Fig. 6a),

which can be further delineated by the distribution of carbapenemase genes and variants into three different clones carrying _bla_NDM-4 (_N_ = 1), _bla_NDM-1 (_N_ = 8) or both _bla_NDM-1 and

_bla_OXA-181 (_N_ = 5). The phylogenetic analysis with global _E. coli_ ST410 genomes confirmed that these are independent clones, as they were found interspersed with international genomes

within a major high-risk lineage (Fig. 6b and Supplementary Note 5). _K. pneumoniae_ carrying _bla_NDM-1 and _bla_OXA-181 have been previously reported21,22, as well as _E. coli_ ST410

harbouring _bla_NDM-5 and _bla_OXA-181 from Egypt23, Denmark and the UK18, but, to our knowledge, this is the first report of _E. coli_ carrying both _bla_NDM-1 and _bla_OXA-181, which is

likely to have disseminated between two sentinel sites (NMC and VSM). We identified five plasmids in the representative strain 14ARS_NMC0074 (Table 4 and Supplementary Fig. 10). The IncX3

plasmid carrying _bla_OXA-181 (p14ARS_NMC0074-5) was identical to plasmid pAMA1167-OXA-181 isolated previously from an _E. coli_ ST410 strain24 (Supplementary Fig. 10). Mapping short reads

to p14ARS_NMC0074-5 showed that this plasmid is the main vehicle of _bla_OXA-181 in the Philippines, as in the international genomes (Fig. 6b18). However, we did not identify any known

plasmids similar to the IncA/C2 plasmid with _bla_NDM-1 (p14ARS_NMC0074-2), though it shared ~90% of its backbone with the IncA/C2 plasmid described above from _K. pneumoniae_ ST147 strain

13ARS_VSM0593 (Supplementary Fig. 10). This plasmid backbone was found in _E. coli_ ST410 genomes from the Philippines, but not in international genomes (Fig. 6b). Altogether, our results

show that that the Philippine _E. coli_ ST410 genomes represent diverse lineages of the global circulating population, with evidence of frequent international introductions. These lineages

are characterized by a diverse repertoire of carbapenemase genes and variants, amassed via a combination of conserved international plasmids and locally circulating plasmids. DISCUSSION

National networks of laboratory-based surveillance are a key pillar within the Global Action Plan to combat AMR, with the reference laboratory playing a central role in the dissemination of

surveillance data to local, national and international stakeholders. The Philippines ARSP surveillance data have shown increasing resistance trends for key bug–drug combinations (Figs. 1b

and 2a). In this study, the analysis of the dynamics of RPs showed the concomitant expansion of specific resistance phenotypes (Fig. 2b). By complementing laboratory data with WGS and

linking the operational units of laboratory and genomic surveillance, we revealed a diversity of genetic lineages, AMR mechanisms and vehicles underlying the expansion of carbapenem

resistance phenotypes (Figs. 1a and 3). The combination of these three components vastly improved our understanding of the drivers of carbapenem resistance in the Philippines at different

geographical scales, from an plasmid-driven local outbreak of _K. pneumoniae_ ST340 (Fig. 4), to the expansion of a _K. pneumoniae_ ST147 clone carrying NDM-1 in an IncA/C2 plasmid without

any close match in international genomes (Fig. 5), to the independent introductions of international epidemic clone _E. coli_ ST410 that can acquire plasmids of local circulation (Fig. 6). A

crucial aspect of AMR surveillance is the detection of the emergence and spread of high-risk clones. In traditional laboratory surveillance, RPs can be analyzed with spatiotemporal

algorithms to detect hospital outbreaks25 or dissemination of phenotypic subpopulations between hospitals26. Cluster detection based on RPs depends on consistent and complete antibiograms

within and across sentinel sites. During the course of our project it became apparent that there were inconsistencies in the panels of antibiotics tested by the sentinel sites (missing data

or different antibiotics tested), which led to the exclusion of samples from the analysis of the dynamics of RPs (Fig. 2). These inconsistencies would also hamper cluster detection based on

RPs, or the early detection of novel emerging RPs that could act as indicators of novel high-risk clones. The joint discussions of these observations by the two collaborating teams led to an

effort to reinforce the standardization of antibiotic testing across the ARSP sentinel sites, which was coordinated by the ARSRL. Even with complete and comprehensive susceptibility

testing, RPs can only provide a coarse view of the spread of AMR, and sometimes lead to clusters of disparate genetic relatedness (Fig. 3 and Supplementary Fig. 2). Integration of WGS into

laboratory-based AMR surveillance can substantially improve the detection of high-risk clones by providing a high-resolution picture of genetic lineages (strains), AMR mechanisms

(genes/mutations) and vehicles (MGEs, Fig. 1a). We identified high-risk clones within known international epidemic clones at the local, national and international scales by linking clonal

relatedness, geographic clustering, epidemiological data and gene and plasmid content with interactive web tools27. At the local scale, we identified a plasmid-driven NICU outbreak of

carbapenem-resistant _K. pneumoniae_. The epidemiological data captured in WHONET was key to support the interpretation of the genomic findings, triggering a retrospective investigation.

This previously undetected outbreak was traced to an IncFII plasmid carrying _bla_NDM-1 in the genetic context of ST340 (Fig. 4a). Yet, the endemic IncFII plasmid was found across multiple

wards, and genetic backgrounds (STs, Fig. 4b), indicating a major role in the burden of carbapenem resistance _K. pneumoniae_ in this hospital. A second IncFIB-IncFII plasmid found only

within the ST340 lineage might have contributed to the persistence and transmission of this clone in the NICU, by carrying several genomic islands with a role in survival in the host and in

the environment (Supplementary Fig. 5). ST340 is a member of the drug-resistant clonal complex 258 and has been reported to cause outbreaks worldwide28. Our findings were disseminated to the

local stakeholders (i.e., hospital) via a forum with NICU staff, paediatricians and ARSRL representatives. Infection control strategies were implemented, which ultimately bolstered the

infection control team of this hospital. Control of healthcare-associated infections is crucial to containing the spread of AMR29. The roadmap from sequence data to actionable data for

infection control and prevention within a hospital has been mapped by multiple use cases30,31 and supported by recent studies on implementation and cost-effectiveness in high-resource

settings32,33. This study makes a case for extending this roadmap to LMICs that have infection control capacity in place. At the national scale, the combined information on strain,

carbapenemase gene/variant and vehicle shows a granular picture of carbapenem-resistant _K. pneumoniae_ ST147 that uncovers the geographic distribution of high-risk clones. ST147 is an

international epidemic clone that causes carbapenem-resistant infections mediated by NDM, OXA-48-like and VIM carbapenemases28,34. Notably, ST147 was not reported in a recent study of _K.

pneumoniae_ from seven healthcare facilities across South and South East Asia35, which did not include the Philippines. Within the seemingly well-established genetic background of clade IV

(Fig. 5), a high-risk clone characterized by the presence of _bla_NDM-1 within plasmid p14ARS_VSM0843-1 displayed local clonal expansion in one sentinel site, while another high-risk clone

characterized by the presence of _bla_NDM-1 within plasmid p13ARS_GMH0099 had also disseminated geographically across five different sites (Fig. 5). Clonal expansion followed by geographical

dissemination is usually attributed to the acquisition of an AMR determinant36. The extent of the geographical dissemination of the different high-risk clones could be attributed to the

different plasmid backbones (vehicle), but differences in the genetic background (strain) could also be at play. The presence of a robust AMR surveillance network such as the ARSP is key for

the detection of geographical dissemination of high-risk clones at a regional/national level, and for the dissemination of the information to national stakeholders. In this respect, the

ARSRL is currently evaluating formats for the inclusion of genomic data into their annual surveillance reports. The reference laboratory directors can also alert hospitals across the country

to establish concerted infection control measures, as well as relay this information to the national DOH for the formulation of evidence-based guidelines. At the global scale, we identified

several high-risk clones of _E. coli_ ST410 circulating the in the Philippines, with the evidence of frequent international transmission, in agreement with a previous report18. Previous

reports of carbapenem-resistant _E. coli_ ST410 from the Western Pacific and South East Asia regions described the presence of _bla_OXA-181 in China17,37, _bla_NDM-1 in Singapore38,

_bla_NDM-5 in South Korea39, or the combination of _bla_NDM-5 and _bla_OXA-181 in Myanmar40 and South Korea39. The repertoire of carbapenemases in the retrospective collection of ST410 from

the Philippines seemed to be in tune with the genes/variants circulating locally, as most isolates carried the prevalent variant _bla_NDM-1. Of note was the high-risk clone carrying both

_bla_NDM-1 and _bla_OXA-181, a combination hitherto unreported in _E. coli_ ST410, and acquired through the combination of a plasmid of local circulation and a conserved international

plasmid, respectively. This suggests that _E. coli_ ST410, a successful pandemic lineage, can not only easily disseminate, but it can also adjust the complement of carbapenemases by

acquiring endemic plasmids along the way. Global AMR surveillance networks, such as the WHO GLASS8, are paramount to detect the emergence and monitor the spread of resistance at the

international level and inform the implementation of targeted prevention and control programmes. The ARSRL has been supplying laboratory surveillance data to GLASS since 2015, and presented

their experience with WGS at a WHO technical consultation on the application of WGS for national, regional and global surveillance in 2020, thus becoming an international contributor in both

fields. Collecting information on AMR that can be rapidly transformed into action requires harmonized standards, especially at the national and international levels8. For laboratory data,

WHONET serves this purpose in the ARSP and in over 2000 clinical, public health, veterinary and food laboratories in over 120 countries worldwide26, while GLASS serves this purpose at the

global level. Genomic data are amenable to standardization29, and national public health agencies41 and international surveillance networks42 are adopting diverse schema to identify and name

genetic lineages. However, a global standard system for defining a cluster has not been implemented beyond the level of discrimination of MLST. Likewise, different databases of AMR

mechanisms43,44,45 may differ in content and nomenclature. Thus, the standardization of genomic data, as well as the provision of platforms, for the uptake of genomic information is

crucially moving forward, and for ongoing AMR surveillance. Containment of AMR at a global level requires an international concerted effort. High-risk clones have the propensity to

disseminate rapidly, and genomics can improve the detection of their emergence and spread. This highlights the increasing need to build equitable partnerships to facilitate ownership

transfer of genomic epidemiology capacity (operational, analytical and interpretational) to enhance national AMR surveillance programmes within low-resource settings. The binational

partnership of this project led to a large retrospective survey of bacterial pathogens that has provided the first in-depth genomic view of the AMR landscape in the Philippines and

established contextual data for ongoing local prospective sequencing. In parallel, through training and transfer of expertize in laboratory procedures and bioinformatics, the open exchange

of data, and collective interpretation of results, we have expanded the existing capacity of a national reference laboratory with WGS focused on action for public health. WGS commenced at

the ARSRL in 2018 with the Illumina MiSeq equipment available locally. A new dedicated bioinformatics server was installed at ARSRL for sequence data storage and analysis. Collective data

interpretation was aided by data sharing via interactive web tools such as Microreact (www.microreact.org)27 and Pathogenwatch (www.pathogen.watch) to leverage the expertize from both CGPS

and ARSRL. The results of the retrospective survey were presented to the representatives of the sentinel sites during the ARSP annual meetings and at international conferences, at first by

the members of CGPS, followed by presentations by the members of the ARSRL in subsequent years46,47. The ARSRL staff conducted its first outbreak investigation using WGS in July 2019, with

sequencing, bioinformatics, and reporting to the DOH conducted locally, with a turnaround time of 8 days from sample receipt to report preparation. While progress has been made, significant

challenges remain to bring genomics into routine surveillance in low-resource settings. These include, but are not limited to, challenges in the supply chain and procurement of WGS reagents

and equipment, vastly differing costs between high- and low-income settings, shortage of skilled local bioinformaticians due to both limited access to training and difficulties in staff

retention, and the lack of platforms to feedback the actionable genomic data to sentinel sites. In addition, it was expected that the timeline from implementation of a disruptive technology

such as WGS to full adoption by all the relevant stakeholders was to exceed the period of one academically funded project. The continued collaboration with the ARSRL (via an NIHR-funded

project) is building on the foundations laid by this study to tackle some of the challenges mentioned above, and to improve on the translation of genomic data into public-health action and

policy. We laid the foundations for the sustainable implementation of WGS and genomic epidemiology within national surveillance networks in LMICs, which can be extended to other locations to

tackle the global challenge of AMR. METHODS ETHICS STATEMENT Ethical approval is not applicable as per the Research Institute for Tropical Medicine—Institutional Review Board. This study

uses archived bacterial samples processed by ARSP. No identifiable data were used in this study. ARSP WORKFLOW AND DATA The ARSP implements AMR surveillance on aerobic bacteria from clinical

specimens. The programme collects culture and antimicrobial susceptibility data from its 24 sentinel sites and 2 gonorrhoeae surveillance sites. The sentinel sites participate in an

external quality assessment scheme conducted by the reference laboratory to ensure quality of laboratory results. Case finding is based on priority specimens sent routinely to the hospitals’

laboratories for clinical purposes, and is thus based on the diagnostic practices of the clinicians, as well as the resources available to the patient to cover the culture and

susceptibility testing. Sentinel sites submit monthly results of all culture and susceptibility tests to the ARSRL via WHONET, a free Windows-based database software for the management and

analysis of microbiology laboratory data with a special focus on the analysis of antimicrobial susceptibility test results6. Multi-drug-resistant phenotypes are classified according to

recommended standard definitions48. Select isolates are referred to the ARSRL for confirmatory testing if they fall in one of the three following categories: (1) organisms of public health

importance regardless of their resistance phenotype, such as _Salmonella enterica_ ser. Typhi, _Streptococcus pneumoniae_ and _Vibrio cholerae_; (2) organisms with emerging resistance, such

as carbapenem resistance; (3) organisms with rare susceptibility patterns, such as vancomycin-resistant _Staphylococcus aureus_ or ceftriaxone-resistant _Neisseria gonorrhoeae_. At the

ARSRL, all referred isolates are re-identified using standardized culture, and susceptibility tested by disc diffusion or minimum inhibitory concentration (MIC) determined using the most

current guidance for antibiotic test panels and breakpoints recommended by the Clinical and Laboratory Standards Institute. Serotyping for _S. pneumoniae, H. influenzae_, Salmonellae and _V.

cholerae_ is conducted to further characterize the isolates. ARSP DATA AND BACTERIAL ISOLATES INCLUDED IN THIS STUDY Phenotypic data collected since 1988 from antimicrobial susceptibility

tests were summarized with WHONET v5.6 to compute yearly resistance rates for key pathogen–antibiotic combinations. Only the first bacterial isolate per patient per species per year was

included in the calculations. RPs derived from the antibiograms were summarized per organism with WHONET v5.66, and their relative abundance visualized with Tableau Desktop. Data collected

between 2009 and 2017 were included in the analysis as the number of sentinel sites collecting data remained relatively stable during this period (22–24 sites). Isolates with missing data

for any antibiotic on the panels listed on Table 1 were excluded. The bacterial pathogens included in the See and Sequence study were those responsible for the majority of

antimicrobial-resistant infections in the Philippines. These included carbapenemase-producing _Pseudomonas aeruginosa_ and _Acinetobacter baumanii_, carbapenemase-producing and ESBL-suspect

_Escherichia coli_ and _Klebsiella pneumoniae_, methicillin-resistant _Staphylococcus aureus_, _Neisseria gonorrhoeae_, non-typhoidal _Salmonella_, and _Salmonella enterica_ ser. Typhi.

Linked epidemiological data included location and date of specimen collection, type of specimen, type of patient (in or outpatient) and sex and age of the patient. We utilized a proxy

definition for infection origin whereby patient first isolates collected in the community or on either of the first 2 days of hospitalization were categorized as community infection

isolates, while isolates collected on hospital day 3 or later were categorized as HAI isolates8. In this article, we focus on carbapenemase-producing organisms. All carbapenemase-producing

_K. pneumoniae_ and _E. coli_ isolates referred to, confirmed, and banked by the ARSRL in 2013–2014 were selected for the retrospective sequencing survey. Approximately 100 isolates of

carbapenemase-producing _P. aeruginosa_ and _A. baumannii_ each were selected according to the following criteria: (1) referred to ARSRL in 2013–2014; (2) complete RP (i.e., no missing

data); (3) overall prevalence of the RP in the ARSP data (including both referred and non-referred isolates); (4) geographical representation of different sentinel sites. The number of

isolates included from each sentinel site was proportional to their relative abundance and estimated from (_n_/_N_) × 100 (rounded up), where _n_ is the total number of isolates from one

site, and _N_ is grand total of isolates; (5) when both invasive and non-invasive isolates representing a combination of RP, sentinel site and year of collection were available, invasive

isolates (i.e., from blood, or cerebrospinal, joint, pleural and pericardial fluids) were given priority. In addition, ~100 isolates of ESBL-producing _E. coli_ and _K. pneumoniae_ each were

included as per the criteria above. Pan-susceptible isolates were only available for _P. aeruginosa_ isolates and were also included in the retrospective sequencing survey. Lyophilisates of

the selected bacterial isolates were re-suspended in 0.5 mL of tryptic soy broth, plated onto MacConkey agar plates and incubated at 35–37 °C for 18–24 h. The species identity of the

revived bacterial isolates was confirmed by characterization of the colony morphology and conventional biochemical tests (e.g., oxidase, motility and sugar fermentation using triple sugar

iron agar test). Phenotypic re-testing was performed on the resuscitated isolates based on the phenotype stored in WHONET. Carbapenemase production in resuscitated isolates of

Enterobacteriaceae was tested with the modified Hodge test, and confirmed using the imipenem MIC method with imipenem Etest strips (BioMerieux). _P. aeruginosa_ and _A. baumanii_ were

screened for metallo-beta-lactamase production using the EDTA synergy test between imipenem disk (10 µg) and EDTA (750 µg/mL), which was confirmed by imipenem/imipenem inhibitor Etest

strips. _E. coli_ and _K. pneumoniae_ isolates were screened for ESBL with the double-disk synergy test between aztreonam (30 µg) and amoxicillin/clavulanic acid (20/10 µg) and confirmed by

using the gradient diffusion method with cefotaxime/cefotaxime with clavulanic acid or ceftazidime/ceftazidime with clavulanic acid Etest strips. WHOLE-GENOME SEQUENCING, ASSEMBLY AND

ANNOTATION _A. baumanni_, _E. coli_, _K. pneumoniae_ and _P. aeruginosa_ bacterial strains were shipped to the Wellcome Sanger Institute streaked onto Nutrient Agar butts contained in

screw-cap cryovials, where DNA was extracted from a single colony of each isolate with the QIAamp 96 DNA QIAcube HT kit and a QIAcube HT (Qiagen; Hilden, Germany). DNA extracts were

multiplexed and sequenced on the Illumina HiSeq platform (Illumina, CA, USA) with 100-bp paired-end reads. The quality of the sequence data was assessed for median base quality >30,

mapping coverage >80% of the length of a reference genome sequence of the same species (_A. baumannii_ ATCC 17978, _E. coli_ UPEC_ST131, _K. pneumoniae_ subsp. _pneumoniae_ Ecl8 and _P.

aeruginosa_ LESB58), and >80% of the sequence reads assigned to the corresponding species with Kraken v0.10.6-a2d113dc8f49 and a reference a database of all viruses, archaea and bacteria

genomes in RefSeq50 and the mouse and human reference. Annotated assemblies were produced using the pipeline described in51. For each sample, sequence reads were used to create multiple

assemblies using VelvetOptimiser v2.2.552 and Velvet v1.253. An assembly improvement step was applied to the assembly with the best N50 and contigs were scaffolded using SSPACE v2.054 and

sequence gaps were filled using GapFiller v1.1155. Automated annotation was performed using PROKKA v1.556 and a genus-specific database from RefSeq50. The quality of the assemblies was

assessed for total size, number of contigs, N50 and GC content appropriate for each species, with all genomes passing quality control characterized by assemblies of <150 contigs and N50 

> 60,000. PHYLOGENETIC ANALYSIS Evolutionary relationships between isolates were inferred from SNPs by mapping the paired-end reads to the reference genomes of _A. baumannii_ A1

(CP010781), _E. coli_ EC958 (HG941718), _K. pneumoniae_ subsp. _pneumoniae_ (AP006725) or _P. aeruginosa_ LESB58 (FM209186), using the Burrows Wheeler aligner v0.7.1257 to produce a BAM

file. PCR duplicate reads were identified using Picard v1.9258 and flagged as duplicates in the BAM file. Variation detection was performed using samtools mpileup v0.1.1959 with parameters

-d 1000 -DSugBf and bcftools v0.1.1960 to produce a BCF file of all variant sites. The option to call genotypes at variant sites was passed to the bcftools call. All bases were filtered to

remove those with uncertainty in the base call. The bcftools variant quality score was required to be >50 (quality < 50) and mapping quality >30 (map_quality < 30). If not all

reads gave the same base call, the allele frequency, as calculated by bcftools, was required to be either 0 for bases called the same as the reference, or 1 for bases called as an SNP (af1 

< 0.95). The majority base call was required to be present in at least 75% of reads mapping at the base (ratio < 0.75), and the minimum mapping depth required was 4 reads, at least two

of which had to map to each strand (depth < 4, depth_strand < 2). Finally, strand_bias was required to be <0.001, map_bias < 0.001 and tail_bias < 0.001. If any of these

filters were not met, the base was called as uncertain. A pseudogenome was constructed by substituting the base call at each site (variant and non-variant) in the BCF file into the reference

genome, and any site called as uncertain was substituted with an N. Insertions with respect to the reference genome were ignored and deletions with respect to the reference genome were

filled with Ns in the pseudogenome to keep it aligned and the same length as the reference genome used for read mapping. For sequence-type-specific phylogenies, MGEs and recombination

regions detected with Gubbins v1.4.1061 were masked from the alignment of pseudogenomes produced by mapping against the genomes of _K. pneumoniae_ strain CAV1217 (ST340, CP018676.1), _K.

pneumoniae_ strain MS6671 (ST147, LN824133.1) or E. coli strain AMA1167 (ST410, CP024801.1). Alignments of only variant positions were inferred with snp-sites v2.4.162 and were also used to

compute pairwise SNP differences between isolates (https://github.com/simonrharris/pairwise_difference_count). Maximum-likelihood phylogenetic trees were generated with RAxML v8.2.863 based

on the generalized time reversible model with GAMMA method of correction for among-site rate variation and 100 bootstrap replications. Trees were midpoint rooted. The phylogenetic trees,

genomic predictions of AMR and genotyping results were visualized together with the ARSP epidemiological data using Microreact27. IN SILICO PREDICTIONS OF AMR DETERMINANTS AND PLASMID

REPLICONS Known antibiotic resistance determinants were identified using Antibiotic Resistance Identification By Assembly v.2.6.1 (ARIBA)64. For acquired genes an in-house curated database

comprising 2316 known resistance gene variants was used (https://figshare.com/s/94437a301288969109c2)65. Full-length assembled matches with identity >90% and coverage >5× were

considered as evidence of the presence of the gene in the query genome. Further manual inspection of the results excluded 13 matches that passed the above filters but that were suspected

low-level contamination. Mutations in _ompK35_ and _ompK36_ genes were detected with ARIBA and the Comprehensive Antimicrobial Resistance Database44 and the output was inspected for

truncations, interruptions and frameshifts. Plasmid replicons were identified with ARIBA and the PlasmidFinder database66. Full-length assembled matches with 100% identity were considered as

evidence of the presence of the replicon gene. IN SILICO MULTI-LOCUS SEQUENCE TYPING Multi-locus STs were determined from assemblies with MLSTcheck v1.00700167 or from sequence reads with

ARIBA64, using species-specific databases hosted at PubMLST (https://pubmlst.org)68 for _E. coli_69, _A. baumannii_70, _P. aeruginosa_71 or at BIGSdb

(https://bigsdb.readthedocs.io/en/latest/#)68 for _K. pneumoniae_72. MLST calls from _P. aeruginosa_ genome sequences were manually curated as we noted the presence of two genes exhibiting

100% sequence identity to different _acsA_ alleles in the pubmlst database (accessed April 2019), which confounded the in silico predictions. This was not privy to the genomes is this study,

since the genome sequence of reference strain NCGM2_S1 (accession AP012280), known to belong to ST235, showed two genes with locus tags NCGM2_1665 (nt position 1808730–1810685) and

NCGM2_0806 (nt positions c879796-881733) that are annotated as _acsA_ and _acsB_, respectively, and are 100% identical to alleles acsA_38 and acsA_225, respectively

(https://pubmlst.org/data/alleles/paeruginosa/acsA.tfa, accessed Apr 2019). Furthermore, the same was only observed for the _acsA_ allele both from assemblies (MLST_check) and raw reads

(ARIBA-MLST), and therefore unlikely the result of contamination with other strains. Allele acsA_38 defines ST235, but allele acsA_225 defines a novel ST. Thus, when two different _acsA_

alleles were detected in a _P. aeruginosa_ genome we selected the one associated with a known ST. CHARACTERIZATION OF CARBAPENEMASE PLASMIDS Select _K. pneumoniae_ (14ARS_CVM0040,

14ARS_MMH0055, 13ARS_GMH0099, 14ARS_VSM0843, 13ARS-VSM0593 and 13ARS_MMH0112) and _E. coli_ (14ARS_NMC0074) isolates were also sequenced with PacBio RS II or Sequel (Pacific Biosciences, CA,

USA) or Oxford Nanopore Gridion (Oxford Nanopore, Oxford, UK) platforms. Hybrid assemblies from short and long reads were obtained with Unicycler v0.4.073 with default parameters, except

for 13ARS_GMH0099 for which the bold assembly mode was used. The sequence of the p13ARS_GMH0099 plasmid was manually curated with Bandage v0.8.174 circularized with Circlator v1.5.375, and

further polished with Quiver (Pacific Biosciences, CA, USA). The presence of AMR genes and plasmid replicons in the plasmid sequences was detected with ResFinder45 and PlasmidFinder66,

respectively. Matches with identity larger than 98% were reported. Detailed annotations of plasmids were obtained with MARA76. Visual comparisons between plasmid sequences were created with

BRIG v1.077 with default BLAST v2.7.0 parameters. The distribution of plasmids across draft genomes was inferred by mapping short reads to the plasmid sequences with smalt v0.7.4 with

identity threshold _y_ = 96% and random alignment of reads with multiple mapping positions of equal score (-r 1). The BAM file was sorted and indexed with samtools v0.1.19–44428cd59, which

was also used to and generate a pileup (mpileup -DSugBf) of the alignment records in BCF format. A pseudo-plasmid sequence in fastq format was constructed with the bcftools view and

vcfutils.pl vcf2fq programmes, and the sequence length coverage (%) of the reference plasmid was computed. REPORTING SUMMARY Further information on research design is available in the Nature

Research Reporting Summary linked to this article. DATA AVAILABILITY Raw sequence data generated during this study have been deposited in the European Nucleotide Archive (ENA) under the

study accession PRJEB17615. Run accessions are provided in the Microreact projects linked throughout the paper and below. The plasmid sequence assemblies generated during this study are

available from Tables 2–4. The resistance database used in this study is available at: https://figshare.com/s/94437a301288969109c2. The multi-locus sequence-type databases used in this study

are available at PubMLST (https://pubmlst.org) or at BIGSdb (https://bigsdb.readthedocs.io/en/latest/#). The data presented in this study are available from the following Microreact

projects: https://microreact.org/project/ARSP_ABA_2013-14 https://microreact.org/project/ARSP_PAE_2013-14 https://microreact.org/project/ARSP_KPN_2013-14

https://microreact.org/project/ARSP_ECO_2013-14 https://microreact.org/project/ARSP_KPN_ST340_2013-14 https://microreact.org/project/ARSP_KPN_ST147_2013-14

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PubMed  PubMed Central  Google Scholar  Download references ACKNOWLEDGEMENTS We are grateful to the members of the Antimicrobial Resistance Surveillance Program that collected bacterial

isolates and linked epidemiological data. The full list can be found in the Supplementary Information (Supplementary Note 6). This work was funded by the Newton Fund, Medical Research

Council (UK) grant MR/N019296/1, Philippine Council for Health Research and Development project number FP160007. J.S. was partially supported by research grants RR025040 and U01CA207167 from

the National Institutes of Health (NIH). S.A. and D.M.A. were additionally supported by the National Institute for Health Research (UK) Global Health Research Unit on genomic Surveillance

of AMR (16_136_111) and by the Centre for Genomic Pathogen Surveillance (http://pathogensurveillance.net). AUTHOR INFORMATION Author notes * Charmian M. Hufano Present address: St. Luke’s

Medical Center—Global City, Taguig, Metro Manila, The Philippines * These authors contributed equally: David M. Aanensen, Celia C. Carlos. AUTHORS AND AFFILIATIONS * Centre for Genomic

Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK Silvia Argimón, Victoria Cohen, Benjamin Jeffrey, Khalil Abudahab & David M. Aanensen * Antimicrobial Resistance Surveillance

Reference Laboratory, Research Institute for Tropical Medicine, Muntinlupa, The Philippines Melissa A. L. Masim, June M. Gayeta, Marietta L. Lagrada, Polle K. V. Macaranas, Marilyn T. Limas,

 Holly O. Espiritu, Janziel C. Palarca, Jeremiah Chilam, Manuel C. Jamoralin Jr., Alfred S. Villamin, Janice B. Borlasa, Agnettah M. Olorosa, Lara F. T. Hernandez, Karis D. Boehme, Charmian

M. Hufano, Sonia B. Sia & Celia C. Carlos * Brigham and Women’s Hospital, Boston, MA, USA John Stelling * University of St Andrews School of Medicine, St Andrews, Scotland, UK Matthew T.

G. Holden * Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK David M. Aanensen Authors * Silvia Argimón View author publications

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also search for this author inPubMed Google Scholar CONTRIBUTIONS C.C.C. and D.M.A. conceived the study. S.A., M.A.L.M., J.M.G., M.L.L., P.K.V.M., V.C., M.T.L., H.O.E., J.C.P., J.C., M.C.J.,

A.S.V., J.B.B., A.M.O., L.F.T.H., K.D.B., B.J., K.A., C.M.H., S.B.S., J.S., M.T.G.H, C.C.C. and D.M.A. performed the data analysis. S.A., M.A.L.M., M.T.G.H., C.C.C. and D.M.A. wrote the

paper. All authors read and approved the paper. CORRESPONDING AUTHORS Correspondence to David M. Aanensen or Celia C. Carlos. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no

competing interests. ADDITIONAL INFORMATION PEER REVIEW INFORMATION _Nature Communications_ thanks the anonymous reviewers for their contribution to the peer review of this work. PUBLISHER’S

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THIS ARTICLE CITE THIS ARTICLE Argimón, S., Masim, M.A.L., Gayeta, J.M. _et al._ Integrating whole-genome sequencing within the National Antimicrobial Resistance Surveillance Program in the

Philippines. _Nat Commun_ 11, 2719 (2020). https://doi.org/10.1038/s41467-020-16322-5 Download citation * Received: 06 March 2020 * Accepted: 25 April 2020 * Published: 01 June 2020 * DOI:

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