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Genomics for antimicrobial resistance surveillance

Article summarising how genomics can help to control antimicrobial resistance
Picture of a petri dish containing antibiotic discs representing a non-resistant bacterial colony. In the background there are other petri dishes and blood sample vials.
© Canva

Antimicrobial resistance (AMR), which occurs when changes in bacteria cause the drugs used to treat infections to become less effective, has been largely driven by the misuse of antibiotics. The spread of AMR threatens many advances in modern medicine, for example impairing our ability to treat common infectious diseases, such as pneumonia and tuberculosis, and increasing the risks associated with organ transplantation, complex surgery, and cancer chemotherapy. It is a significant problem: in 2019 1.27 million deaths were directly attributed to AMR, with 4.9 million deaths indirectly attributed to AMR. Low-and-middle-income countries have a greater burden of AMR. The SARS-CoV-2 pandemic has demonstrated the potential and capacity for genomic surveillance to monitor and help combat AMR.

Whole genome sequencing (WGS) can be used to monitor resistance against multiple microbes in parallel, help differentiate AMR evolution versus spread; and improve data sharing and create adaptable infrastructure for future pandemic responses.

Table 1 -A summary of use cases for WGS in mitigating the public health impact of antimicrobial resistance (AMR). Source: Genome Medicine

Enhancing the national surveillance of antimicrobial resistance in the Philippines

Trigger Uses of WGS/workflow Main finding Advantages of using WGS
National laboratory-based surveillance had shown increasing AMR prevalence over the previous 10 years, but the understanding of the epidemiology and drivers of AMR was lacking. WGS capability was introduced to the existing surveillance programme. Retrospective sequencing of multi-drug-resistant (MDR) Gram-negative bacteria (GNB) obtained prior to the introduction was undertaken and analysed with phenotypic and epidemiological data to provide baseline data and inform control measures. Drivers of carbapenem resistance at different levels of the healthcare system were identified, including a localised outbreak of plasmid-driven CR-Kp affecting a specific hospital, through the detection of the introduction and country-wide spread of a high-risk epidemic clone, E. coli ST410. Detailed understanding of the epidemiology and drivers of AMR enabled the introduction of effective infection control measures. Data were contributed to international AMR surveillance efforts, improving global coverage.

Investigating an MRSA outbreak in a neonatal unit

Trigger Uses of WGS/workflow Main finding Advantages of using WGS
Phenotypically similar MRSA isolates were identified from patients on a neonatal unit over a 6-month period but could not be linked temporally or geographically, suggesting that the full extent of the outbreak had not been identified. All MRSA isolates obtained from patients on the neonatal unit over a 6-month period underwent WGS regardless of phenotypic characteristics. MRSA isolates with antibiograms similar to the outbreak strain, identified from the community, and screening samples taken elsewhere in the hospital were also sequenced. Two previously excluded isolates were identified as being part of the outbreak by phylogenetic analysis, allowing temporal links between cases to be established. A wide transmission network beyond the neonatal unit was identified. WGS allowed a large number of isolates to be tested and related strains to be accurately identified, thereby enabling full outbreak reconstruction. Combining WGS data with clinical and epidemiological data enabled the identification of outbreak sources and the successful instigation of infection control measures.

Identifying the drivers of AMR in atypical enteropathogenic E. coli (aEPEC) strains isolated from children < 5 years in four sub-Saharan African countries and three South Asian countries

Trigger Uses of WGS/workflow Main finding Advantages of using WGS
The frequency, mechanisms, and drivers of AMR in intestinal isolates of E. coli in children in the community in many countries worldwide were unknown. Phenotypic susceptibility and WGS of isolates were analysed and correlated with antimicrobial use, disease status (symptomatic/asymptomatic), phylogenetic lineage, and geographic location. High rates of AMR were shown, with 65% of isolates resistant to at least 3 antimicrobial drug classes. A diverse range of genetic mechanisms of AMR was shown, with geographic location and the associated antimicrobial use pattern being the strongest predictors of AMR. WGS was used to provide a detailed analysis of AMR across a large geographical area, providing insights into AMR epidemiology, spread, and drivers.

Investigating colistin resistance detected in commensal E. coli in food stock animals in China

Trigger Uses of WGS/workflow Main finding Advantages of using WGS
Routine surveillance had detected a sharp increase in the rates of colistin resistance in colonising bacteria from pigs in China, but the mechanism of this resistance was not known. Conjugation experiments were undertaken to confirm the presence of plasmid-associated, transmissible colistin resistance. WGS of the plasmids was used to identify the gene responsible. The sequence of the plasmid-associated colistin resistance gene was identified and designated mcr-1. The genetic basis of a new, AMR mechanism was identified and described, allowing ongoing surveillance, as well as informing investigation and detection of this emerging threat in other settings.

The SEDRIC genomics working group has made several policy recommendations on how genomics can be harnessed for AMR surveillance:

Increasing detection and control by Health Laboratories

  • Build capacity – including hub and spoke models to overcome differential sequencing costs in different geographical regions
  • Develop new training competencies to develop a new workforce and upskill current staff
  • Invest in AMR genomic surveillance innovations, especially research required to address uncharacterized associations with health outcomes.

Public Health Networks

  • Harmonise and standardise surveillance – by agreeing on a list of bug/drug combinations informed by local needs, develop clinical standards and support pathogen-specific expert review groups for interpretation guidelines.
  • Agree on equitable data sharing and governance – maximise the benefits of open immediate data sharing, taking due consideration around stigmatisation and inequitable data contribution and use.
  • Improve stakeholder interactions and relationships – improved trust and communications and partnerships among stakeholders are important. Policymakers need to define the key questions and researchers and health deliverers should consolidate and advocate clear use cases.

Recommend actions for policymakers and industry

  • Guide implementation
  • Define goals and key outcomes
  • Build key stakeholder relationships

Further reading

WHO Antibiotic resistance

The overlooked pandemic of antimicrobial resistance

Exploiting genomics to mitigate the public health impact of antimicrobial resistance

Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis

What do you think are the main issues in moving to a genomic approach to AMR? Please discuss this with us in the comments.

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