Infectious diseases have posed significant health challenges throughout human history. While the advent of antibiotics and other antimicrobials was aimed to address this concern, the persistent and widespread use of these agents has led to the emergence of antimicrobial resistance (AMR) and numerous multiple drug-resistant organisms (MDROs).

AMR occurs when bacteria, virus, fungi and parasites change over time and no longer respond to existing treatments.  In 2019 the World Health Organization (WHO) described it as one of the top ten global threats to public health – a threat to which science is playing catch-up in its efforts to mitigate.

Close to 5 million lives are lost annually due to drug-resistant infections.2 Projections suggest that without intervention, global deaths attributable to AMR could reach 10 million by 2050 (the same as cancer deaths).3 In addition to high mortality and morbidity, the economic toll of AMR is substantial. In fact, the World Bank estimates that AMR could result in US$ 1 trillion additional healthcare costs by 2050, and US$ 1 – $3.4 trillion gross domestic product (GDP) losses per year by 2030.4

Mechanisms of Antimicrobial Resistance

The biochemical and genetic mechanisms that cause AMR, fall into four categories: inactivation of the antimicrobial molecule; target modification; active drug efflux; and drug uptake limitation. These mechanisms are summarized below and in Table 1.

• Inactivation of Antimicrobial Molecules: Microorganisms produce enzymes that deactivate drugs by either destroying them or adding specific chemical components, rendering the antimicrobial ineffective at its target site. These modifying enzymes, catalyzing reactions like acetylation, phosphorylation, and adenylation, induce steric hindrance, diminishing the drug’s affinity for its target and raising the bacterial Minimum Inhibitory Concentration (MIC). β-lactam resistance exemplifies this, employing β-lactamases to break amide bonds in the β-lactam ring, rendering the antimicrobial ineffective. Over 1000 β-lactamases have been identified, with more expected as bacterial evolution continues.

• Target Modification is a key resistance mechanism involving alterations to the antimicrobial target site, impeding proper binding of the antimicrobial molecule. These sites are crucial for cellular functions during antimicrobial action. Mutational changes on the target site can reduce inhibition susceptibility while preserving essential cellular functions. In some cases, inducing resistance through modifying target structures may require additional cellular changes. An example is the acquisition of penicillin-binding transpeptidase (PBP2a) in methicillin-resistant Staphylococcus aureus.
• Active Drug Efflux: Efflux pumps, found in families like the major facilitator superfamily (MFS), small multidrug resistance family (SMR), resistance-nodulation cell division family (RND), ATP-binding cassette family (ABC), and multidrug and toxic compound extrusion family (MATE), have the capacity to expel antimicrobial agents rapidly from the bacterial cell. This expulsion mechanism significantly contributes to multidrug resistance.
• Drug Uptake Limitation: Bacteria vary in their ability to limit drug uptake. The outer membrane composition in organisms like gram-negative bacteria slows antimicrobial penetration. Mycobacteria’s lipid-rich outer membrane hinders hydrophilic drug entry. Organisms lacking a cell wall, such as Mycoplasma, are inherently resistant to cell wall-targeting agents. Biofilm formation protects against immune system attacks and provides defense against antimicrobial agents.

Origins of Antimicrobial Resistance

Microorganisms demonstrate genetic plasticity, enabling them to evolve resistance mechanisms against environmental threats, including antimicrobial agents. The development of antimicrobial resistance involves various processes:

1. Mutational Resistance: Susceptible microbial populations undergo mutations in genes affecting drug activity, facilitating cell survival in the presence of antimicrobial agents.
2. Spontaneous Mutations: Random mutation events, arising from replication errors or incorrect DNA strand repairs, contribute to antimicrobial resistance.
3. Hypermutations: Certain bacterial populations enter transient states of elevated mutation rates under prolonged non-lethal antibiotic pressure. Mutators in this context confer selective advantages.
4. Adaptive Mutagenesis: Mutations occur in slowly dividing or non-dividing cells under non-lethal selective pressure, leading to the development of resistant mutants.
5. Horizontal Gene Transfer (HGT): Bacterial evolution is influenced by the acquisition of foreign materials through HGT mechanisms, such as transformation, conjugation, and integrons. These processes contribute to the development of antimicrobial resistance.

Addressing Antimicrobial Resistance through Metagenomic Next Generation Sequencing 

Metagenomic Next-Generation Sequencing (mNGS) provides unbiased, culture-independent diagnosis and surveillance of resistance mechanisms. Recognized as an indispensable tool, mNGS provides a complete genomic sequence and unparalleled structural detail on individual traits within a population, which contributes to more reliable microbial identification, definitive phylogenetic relationships, and a comprehensive catalog of traits. Additionally, mNGS can also be used for outbreak investigations, microorganism-agnostic infectious disease diagnosis, especially for novel pathogens and appropriate treatment selection.   

Understanding the mechanisms and origins of antimicrobial resistance is crucial in developing effective strategies to mitigate its impact.  Learn how our Pathogen Real-Time Identification by Sequencing (PaRTI-Seq™) complete mNGS assay provides rapid and accurate identification of resistant pathogens and invaluable insights for the management of AMR.  (https://micronbrane.com/products/)

Sources

1. Antimicrobial Resistance Collaborators. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet; 399(10325): P629-655. DOI: https://doi.org/10.1016/S0140-6736(21)02724-0
2. Dadgostar P. Antimicrobial Resistance: Implications and Costs. Infect Drug Resist. 2019;12:3903-3910 https://doi.org/10.2147/IDR.S234610
3. Antimicrobial Resistance Collaborators. (2022). Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet; 399(10325): P629-655. DOI: https://doi.org/10.1016/S0140-6736(21)02724-0
4. The World Bank. 2024 Drug-Resistant Infections: A Threat to Our Economic future. Accessed March 10, 2024 https://www.worldbank.org/en/topic/health/publication/drug-resistant-infections-a-threat-to-our-economic-future
5. D’Costa, V. M., McGrann, K. M., Hughes, D. W., and Wright, G. D. Sampling the antibiotic resistome. Science. 2006;311,374–377.
6. Jim O’Neill. Tackling drug-resistant infections globally: final report and recommendations the review on antimicrobial resistance chaired. [Online] 2016. Available from: https://amr-review.org/sites/default/files/160518_Final paper_with cover.pdf. 
7. Founou RC, Founou LL, Essack SY. Clinical and economic impact of antibiotic resistance in developing countries: a systematic review and meta-analysis. PLoS One. [Online]. 2017;12:e0189621. Available from: https//doi.org/10.1371/journal.pone.0189621
8. Shrestha P, Cooper BS, Coast J, et al. Enumerating the economic cost of antimicrobial resistance per antibiotic consumed to inform the evaluation of interventions affecting their use. Antimicrob Resist Infect Control. [Online]. 2018;7(1):98. Available from: doi:10.1186/s13756-018-0384-3
9. Martinez JL. General principles of antibiotic resistance in bacteria. Drug Discov Today. 2014;11:33–39.
10. Cox G, Wright GD. Intrinsic antibiotic resistance: mechanisms, origins, challenges and solutions. Int J Med Microbiol. 2013;303:287–292.
11. Davies J. Where have all the antibiotics gone? Can J Infect Dis Med Microbiol. 2006;17:287–90.
12. Wilson DN. Ribosome-targeting antibiotics and mechanisms of bacterial resistance. Nat Rev Microbiol. 2014;12:35–48.
13. G. Spratt. Resistance to antibiotics mediated by target alterations. Science. 1994;264:388–393.
14. Bush K. The ABCD’s of β-lactamase nomenclature. [Internet]. 2013;19:549–559. Available from: https://doi.org/10.1007/s10156-013-0640-7
15. Bush K, Jacoby GA. Updated functional classification of β-lactamases. Antimicrob Agents Chemother. [Internet]. 2010;54:969–976. Available from: https://doi.org/10.1128/AAC.01009-09
16. Warinner, C., Rodrigues, J. F., Vyas, R., Trachsel, C., Shved, N., Grossmann, J., et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 2014;46:336–344.
17. J. Piddock, R. Wise. Induction of the SOS response in Escherichia coli by 4-quinolone antimicrobial agents. FEMS Microbiol. Lett. 1987;41:289–294.
18. Coculescu BI. Antimicrobial resistance induced by genetic changes. J Med Life. 2009;2:114–123.
19. Aminov, R.I. Mackie. Evolution and ecology of antibiotic resistance genes. FEMS Microbiol. Lett. 2007;271:147– 161.
20. Angers A., Petrillo M., Patak A., Querci M., Van den Eede G. The role and implementation of next-generation sequencing technologies in the coordinated action plan against antimicrobial resistance. Joint Research Center. 2017;4-7.
21. Schmidt, K., Mwaigwisya, S., Crossman, L.C., Doumith, M., Munroe, D., Pires, C., Khan, A.M., Woodford, N., Saunders, N.J., Wain, J., et al. Identification of bacterial pathogens and antimicrobial resistance directly from clinical urines by nanopore-based metagenomic sequencing. J. Antimicrob. Chemother. 2017;72:104–114.
22. Köser et al. (2014). Whole-genome sequencing to control antimicrobial resistance. Trends Genet. 30(9):401–7.
23. Han et al. (2019). mNGS in clinical microbiology laboratories: on the road to maturity. Crit Rev Microbiol 45(5-6):668-85.
24. Balloux et al. (2018), From Theory to Practice: Translating WGS into the Clinic. Trends in Microbiology 26(2).
25. Chen et al. (2021). Novel Human Cell Depletion Method For Rapid Pathogen ID by NGS. Labroots Microbiol. Week 10.13140/RG.2.2.23888.64007.
26. CDC (2019). Antibiotic Resistance Threats in the US. Atlanta, GA: U.S. HHS Dept, cdc.gov/drugresistance/biggest-threats.html.

Nucleic acid contamination compromises mNGS data analysis which can have alarming repercussions.  In fact, a study published in the ASM Journal, “Major data analysis errors invalidate cancer microbiome findings” joins a growing body of evidence illuminating another troubling phenomenon in metagenomic Next-Generation Sequencing (mNGS) — database contamination. 

Types of database contamination

More must be done to understand and mitigate the issues with metagenomic data analysis such as:

• Mis-labeling human or host DNA as microbial genomes1
• Sequencing reads mis-aligned to other microbial species2
• Incomplete reference genomes, especially for emerging species
• Errors in MAG construction (QC, assembly, binning or annotations)
• Other computational errors

In metagenomic research, database contamination can ruin study outcomes as in the above paper and many more, it also slows the adoption of mNGS use in clinical settings.  What characteristics would high-quality databases have?

High-Fidelity Metagenomic Data Analysis

To avoid the errors that may invalidate findings and allow mNGS to be used in the first line in clinical microbiology, analysis programs, need the following:

• State-of-the-art computational and QC algorithms
• Extensive and ongoing curation of the taxonomic assignments of individual pathogens
• Proper benchmarking of microbes with unusual sequence homologies and/or close taxonomic relationships 
• Quick and effective tracking of potentially mis-annotated and/or misrepresented pathogenic species

PaRTI-Cular™ is a bioinformatics web app four years in the making from Micronbrane Medical. The company’s strategic emphasis is on removing technical and cost barriers for the widespread use of mNGS in microbiology.  The web application was specifically developed to avoid genome mis-classification, taxonomic irregularities, erroneous variant calls and other fatal errors. 

PaRTI-Cular streamlines the analysis of metagenomic Next-Generation Sequencing (mNGS) data using our Pathogen Real-Time Identification by Sequencing (PaRTI-Seq) assay. The web app is a deeply curated, up-to-date genome database for over 1400 microorganisms. The software also automatically conducts data quality checks, removal of host genome reads, background noise cut-offs, pathogen identification.  We also developed a proprietary reference database so the platform can deliver abundance distributions and other types of statistical analyses.

With PaRTI-Cular you do not need any programming or special computers to run analyses. In fact, you can upload sample data and get results, from any computer, anywhere in the world. With the RUO version the following functionality is available:

• PaRTI-Cular analysis applies preset parameters developed by Micronbrane Medical using Burrow-Wheeler Aligner (BWA) mapping tools. Mapping parameters such as read length, identity percentages, etc., were tested in various low biomass human clinical samples.
• Data is only mapped to the curated database of 1400 pathogens (no other species are supported at this point.)
• Sequencing output is transfer and PaRTI-Cular analysis uses Micronbrane Medical’s web services hosted by AWS in Singapore

The process is simple:

The next PaRTI-Cular launch will include expanded functionality for clinical settings, including:

1. Various QC check for run validity and data quality
2. Validated cutoff value will be implemented in the pipeline to report potential positives identified from the samples
3. Approval process for the final report
4. Data and analysis can be housed in user’s own AWS account and data center of selection

Please let us know if you would like to try PaRTI-Cular and be part of its development for both research and clinical use.  

Sources

1. Breitwieser FP, Pertea M, Zimin AV, Salzberg SL. 2019. Human contamination in bacterial genomes has created thousands of spurious proteins. Genome Res 29:954–960
2. Steinegger M, Salzberg SL. 2020. Terminating contamination: large-scale search identifies more than 2,000,000 contaminated entries in genbank. Genome Biol 21:115.