Genomics in Antimicrobial stewardship

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Genomics in Antimicrobial stewardship

Genomics in Antimicrobial stewardship

Alexander Fleming recognized the danger of antibiotic resistance shortly after he discovered penicillin, saying in his 1945 Nobel Lecture:

“There is the danger that the ignorant man may easily underdose himself and make them resistant by exposing his microbes to non-lethal quantities of the drug.”

The World Health Organization considers antibiotic resistance one of the most severe threats to global health.

A report released last month by the European Centre for Disease Prevention and Control (ECDC) and the European Food Safety Authority (EFSA) led the EU commissioner for health and food safety to comment.

“We are entering a world where more common infections become difficult – or even sometimes impossible – to treat.”

Ways to combat antibiotic resistance

There are two main ways to combat antibiotic resistance – developing new antibiotics and using the ones we already have more effectively –and it makes sense to do both.
Genomics is already at the forefront of the race to develop new antimicrobials. Still, stewardship – making the best use of current antibiotics to minimize the spread of resistance – is equally important and is already part of UK guidelines on antibiotic use.
Tactics include not prescribing antibiotics unnecessarily and emphasizing the importance of finishing the course.
And reserving certain drugs as ‘antibiotics of last resort for use in infections that don’t respond to first-line treatments.
It also includes measures outside the medical sphere, such as moving away from routine antibiotic use in livestock.
And encouraging hygiene practices, such as hand-washing, to reduce infections.

Genomics and stewardship?

Already valued in the search for new antimicrobial agents that may enable the development of new drugs, genomics also has great potential in terms of targeted prescribing.
A newly developed genomic test can tell if bacteria sampled from a patient are resistant to two common antibiotics – while the patient waits.
This test – developed at the American University in Washington DC and reported in BMC Infectious Diseases – detects Macrolide efflux gene A (mefA), which can confer resistance to the antibiotics erythromycin and azithromycin.
“The test can detect the gene within ten minutes of assay run-time,” said co-author John Bracht.
The potential advantages of this kind of genomic test are numerous.
Access to quick, easy and cheap tests could help doctors prescribe the right antibiotic first, meaning the patient recovers faster and avoids wasted medicines and unnecessary follow-up appointments.

Genomics and the management of antimicrobial resistance

The targeted use of genomics presents a valuable opportunity to support and enhance the ability of the health services to minimize the ineffective use of antibiotics and antivirals, combat the spread of resistance through improved infection control, and ensure that these vital therapies continue to be effective in the years to come.
Antimicrobial resistance (AMR) is one of the great challenges facing 21st-century medicine.
The development of new antibiotics has slowed rapidly, with few new drugs developing. In addition, current antibiotics are becoming increasingly ineffective at treating some common infections.
The emergence of bacterial infections, such as gonorrhoea that are resistant to the antibiotics of last resort indicates that the number of untreatable infections could increase, leading to the return of a significant death toll from previously easy-to-treat infectious agents.

Antimicrobial resistance – responding to a growing threat

The UK government is adding antibiotic resistance to the National Risk Register of significant potential threats to the nation, and in 2013 launched a five-year strategy outlining.

how it plans to slow the development and spread of antimicrobial resistance.

Measures central to achieving this aim include:

Prompt diagnosis of infections
Rapid identification of pathogen susceptibility to drugs
Appropriate prescribing practice
Robust infection control

Current strategies used to diagnose the presence of infectious pathogens and test their susceptibility to drugs, such as culture techniques or mass spectrometry, remain the quickest and most cost-effective approaches.

Reductions in the incidence of infections caused by antibiotic-resistant organisms, such as MRSA, in healthcare settings demonstrate that hospital infection control has improved significantly.

We will explore how the pathogen whole genome sequencing could underpin these improvements.

Advantages of genomics over conventional testing for HIV treatment choices Genome sequencing

How can genomics contribute to tackling AMR?

Resistance of pathogens to drugs is determined by the presence of specific genes and/or mutations.

There are several key areas where the use of whole genome sequencing (WGS) of pathogens could improve the understanding and management of antimicrobial resistance:

Rapid treatment decisions for slow-growing pathogens

WGS can be quicker than traditional methods for characterizing pathogens, especially those that are very slow growing or difficult to culture.
Earlier detection ensures that patients start receiving the correct treatments much sooner.

Pathogen spread and infection control.

Genomics can track the spread of these genes through a pathogen population and monitor the spread of susceptible and resistant pathogens within human populations, between people, the environment, or animals.

Targeted treatment decisions

Once the presence and nature of resistance are known, this information helps clinicians decide which drug might be the best choice to treat a patient.

Evolving drug resistance within individuals

WGS can also be used for real-time tracking of how an infectious agent responds genetically to a course of treatment, potentially providing early warning of any emergence of resistance.
For example, genomic analysis of HIV in infected patients with rising viral levels alerts clinicians to developing resistance to therapy.
It allows them to choose new combination therapies to which the virus remains susceptible.

Understanding genomic mechanisms of resistance

WGS enables researchers to understand the different genomic mechanisms that lead to resistance.
Which can inform the development of new therapies and guide the development of new point-of-care diagnostic tests.
Rapid treatment decisions Pathogen spread and infection control Targeted treatment decisions.
Pathogen spread and infection control Evolving drug resistance in individuals.
Understanding mechanisms of resistance Genomics and managing antimicrobial resistance.

Use cases for whole-genome sequencing (WGS) in mitigating the public health impact of antimicrobial resistance (AMR)

AMR surveillance

Surveillance is the cornerstone of public health efforts in controlling AMR.
AMR surveillance has traditionally relied on phenotypic AST, but different testing methods, variation in interpretation, the extent to which thresholds are clinically validated, and changes in interpretive guidelines limit standardization.
WGS data overcomes many of these limitations, providing detailed insights that can greatly augment the value of AMR surveillance.
Such data can inform an understanding of AMR evolution and spread, inform control strategies, facilitate the detection of new and emerging threats, and support new diagnostic and therapeutic approaches.
Underlying the expansion of AMR is a dynamic and complex interaction between microbes.
AMR encodes genes and mobile genetic elements that act as vehicles for AMR via horizontal gene transfer.
Once horizontally transferred AMR genes have become chromosomally integrated, clonal expansion can lead to the rapid dissemination of these genes.
This phenomenon has been observed with methicillin-resistant Staphylococcus aureus (MRSA), penicillin-resistant Streptococcus pneumoniae, vancomycin-resistant enterococci, and fluoroquinolone-resistant Clostridium difficile.
The granular resolution afforded by WGS allows inferences on the nature of AMR evolution and dissemination, providing insights that can help contain AMR and protect public health.

International surveillance

Increased accessibility to WGS has significantly enhanced our understanding of AMR’s global evolution and spread.
High-throughput WGS methods have facilitated the sequencing of large, geographically representative collections of isolates, overcoming many of the biases associated with historic, small-scale studies often skewed by the domination of local clonal dissemination.
This paradigm is illustrated by a modified understanding of AMR in E. coli ST131, which has rapidly spread to become a frequent cause of healthcare and community-acquired infection since it was first described in 2008.
ST131 E. coli frequently exhibits cephalosporin (most commonly due to a CTX-M-15 encoding gene) and fluoroquinolone resistance.
The development of AMR in ST131 E. coli was initially speculated to have arisen from frequent and independent acquisitions of mobile genetic elements.
However, a USA study concluded that AMR’s success in ST131 E. coli was associated with a sustained clonal expansion.

comprehensive global study

However, it was not until a more comprehensive global study that the true diversity of AMR in ST131 was revealed, which identified the chromosomal integrations of various resistance genes, the persistence and evolution of mobile elements within sub-lineages, and the sporadic acquisition of several different resistance elements.
As well as enhancing the mechanistic understanding of AMR, this study highlighted the need for multifaceted control strategies that could limit the spread of ST131 and mobile genetic elements to other pathogens.
These studies were foundational for additional work investigating risk factors for ST131 infection and spread.
ST131 is now known to be a common gut commensal, with opportunistic infections occurring mainly in functionally compromised hosts such as the elderly, particularly those having prior antimicrobial use and living in long-term care facilities.

National surveillance

The inclusion of WGS can similarly strengthen national AMR surveillance.
For example, the introduction of WGS into AMR surveillance in the Philippines showed that HGT was key for driving carbapenem resistance in Klebsiella, with mobile genetic element (MGE) acquisition often leading to MDR.
WGS data also identified a localized plasmid-driven outbreak of CR-Kp, where various infection control measures, including patient isolation, were initiated.
By contributing WGS data to the WHO international surveillance network, national surveillance data also enhanced the global understanding of some high-risk clones of interest, notably ST147 CR-Kp, of which a previously uncharacterized clade was described.

Local Surveillance

Healthcare facilities house patients with serious and complex infections in proximity to patients with compromised immunity in settings characterized by high antimicrobial use.
In this context, healthcare-associated infections (HCAIs) frequently involve highly drug-resistant pathogens.
A significant challenge requires rigorous infection prevention and control practices to reduce their incidence.
By exploiting a ‘molecular clock’ (the mutation rate in the genome of a specific organism), WGS can also infer directionality in outbreaks.
This approach was key to understanding an outbreak of MDR Acinetobacter baumannii in a UK hospital treating civilian patients and injured military personnel returning from the Middle East.

Community surveillance and outbreak investigation

WGS also contributes to community public health efforts, such as contact tracing and the detection of secondary cases of TB. Contact tracing is difficult as there is often a long interval between the initial infection and the diagnosis.
Several countries, such as the UK, have established a nationwide WGS database of TB to facilitate the identification of transmission events.
This system enhances contact tracing and informs a wider understanding of TB control.
Using WGS in preference to variable number tandem repeat (MIRU-VNTR) typing has reduced the number of false case clusters.

Understanding the drivers of AMR

Despite broad geographical, symptomatic, and phylogenetic diversity, 65% of the bacterial isolates were resistant to three or more classes of antimicrobials.
In this study, the best predictor of resistance profile was not the presence or absence of clinical symptoms or genetic lineage but the geographical patterns of antimicrobial usage.
On an individual patient level, the likelihood of developing infection with extended-spectrum beta-lactamase-producing bacteria is associated with an increased length of hospital stay before infection, exposure to antimicrobials, and recent overseas travel.

More broadly:

Predictions of MDR GNB infection include male sex, older age, and co-morbidities.
Changes in the human microbiota occur in response to illness, particularly when associated with frequent and/or prolonged antimicrobial exposure.
The Enterobacterales are habitual colonizers of the gastrointestinal tract, where they can act as a major reservoir for mobile AMR genes.
Metagenomic studies have shown that commensal bacteria in healthy individuals help maintain pathogenic bacteria at a low density.
Meaning that carriage is rarely problematic.

Informing vaccination strategies to control AMR

All of these efforts played a major role in the Federal Expanded Program on Immunization’s decision to introduce typhoid conjugate vaccine (TCV) into their national immunization programme, making Pakistan the first country to do so.
Consequently, WGS and phenotypic AMR data directly impacted the decision to introduce TCV and the vaccine introduction strategy itself.
Given the ubiquitous nature of drug-resistant typhoid, the anticipated country demand for TCVs, and the presence of only two WHO prequalified manufacturers of TCV; additional selection criteria may be required.
Such phylodynamic data can inform optimal vaccine introduction strategies for other key highly drug-resistant pathogens when new vaccines become available.

Limitations, challenges, and future directions

Technical limitations

Disease states, and environmental pressures such as starvation.
The threshold number of SNPs above which relatedness is unlikely is therefore highly context-dependent; if not recognized, then selection biases within isolates in collections can lead to false epidemiological inferences.

Challenges in implementing WGS for routine use

The routine, prospective inclusion of WGS into microbiological surveillance has the potential to enhance and strengthen public health efforts to combat AMR greatly.
Still, it comes with significant logistical and financial implications.
Cost-effectiveness is even harder to determine, reflecting marked uncertainties and challenges in estimating the costs for implementing WGS.
Generating accurate, contemporary cost estimates is difficult in the context of the marked decrease in costs that has accompanied the rapid technological advances in recent years.
A problem compounded by the challenge of estimating costs for complex workflows that include several steps, including downstream analysis.
This approach contrasts with many research and surveillance studies that lend themselves to batch processing of samples.

Implementing

However, implementing QA processes for WGS is challenging. Variations in DNA extraction methods and reagents, sequencing technologies, analysis pipelines, and bioinformatic approaches can impact WGS analysis.
Making the standardization and quality assurance methods challenging.
The ability to share, integrate, and compare the vast wealth of data derived from WGS across laboratories and settings.
Time is key to harnessing the power of WGS for global AMR surveillance.
Barriers to data sharing include the reluctance of academics to share data before publication.
It is (a process that is often slow and time-consuming).
When competing interests are at play (for example, tourism), political sensitivities and legal requirements to protect personal data.

More challenges:

And the need for standardized methodological and analytical approaches to generate comparable data.
The Global Microbial Identifier (GMI) initiative is in the early stages of addressing these issues.
Aiming to develop standardized identification and characterization approaches to form a global interactive network of genomic databases.
The absence of validated globally standardized systems for genomic typing.
Defining clusters and determining AMR genes further intensifies the challenges of interpreting genomic data.
Computational and technical challenges.
The collaborative approach to sharing the SARS-CoV-2 genomic data demonstrates that such an approach is possible.
But to date, scalable solutions to genomic data sharing have remained elusive.

Conclusion

The World Health Organization considers antibiotic resistance one of the most serious threats to global health.
Genomics is already at the forefront of the race to develop new antimicrobials.

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