- Strain-level antimicrobial resistance surveillance examines antimicrobial resistance at the level of individual microbial lineages, allowing researchers to distinguish closely related organisms, track resistant strains, investigate their distribution, and study how resistance changes within microbial populations. While broad Antimicrobial Resistance Surveillance can identify resistance genes and describe population-level patterns, strain-level analysis provides greater genomic resolution. It can help determine whether resistance is associated with the expansion of a particular microbial lineage, the acquisition of new resistance determinants, or the movement of resistance genes between otherwise unrelated organisms.
- A microbial strain represents a genetically distinguishable population or lineage within a microbial species. In genomic surveillance, the practical definition of a strain depends on the organism, dataset, and analytical method being used. Some investigations distinguish strains using whole-genome differences, while others rely on combinations of core-genome variation, single nucleotide variants, accessory genes, plasmids, or other genomic characteristics. The appropriate resolution depends on the biological question and the amount of genetic diversity present within the organism being studied.
- Strain-level surveillance is particularly valuable for antimicrobial resistance because resistance can spread through multiple biological processes. A resistant strain may expand within a population and spread between hosts or locations, while an individual resistance gene may move between unrelated strains through Horizontal Gene Transfer. These processes can occur simultaneously. Distinguishing clonal spread from resistance-gene dissemination is therefore one of the central goals of strain-level genomic analysis.
- Study design determines the strength of strain-level conclusions. Outbreak investigations may require dense sampling over a relatively short period, whereas surveillance programs may collect samples over months or years. Geographic studies may compare hospitals, farms, communities, wastewater systems, food production environments, or environmental sites. The sampling strategy should provide sufficient representation of the microbial population and include relevant metadata for interpreting genomic relationships.
- Metadata are essential for strain-level surveillance. Useful information can include collection date, location, sample type, host category, clinical or environmental setting, antimicrobial exposure, healthcare facility, agricultural system, wastewater source, or other factors relevant to the investigation. When genomic relationships are combined with reliable temporal and spatial information, researchers can investigate whether genetically related strains occur in compatible epidemiological contexts.
- Biological replication is also important. A single isolate or metagenomic sample may provide evidence of a resistance-associated strain, but repeated observations provide stronger support for persistence, expansion, or transmission. Longitudinal sampling can reveal whether a strain remains present, disappears, re-emerges, or is replaced by another lineage. Repeated sampling can also help identify evolutionary changes occurring within a strain over time.
- Whole-genome sequencing is one of the primary approaches for strain-level surveillance. Sequencing individual microbial isolates can provide detailed information about chromosomal variation, resistance genes, accessory genes, plasmids, and other genomic features. High-quality genome sequences allow researchers to compare organisms at fine resolution and construct relationships among isolates collected from different hosts, locations, and time points.
- Metagenomic sequencing provides a complementary approach. Instead of requiring individual organisms to be cultured and isolated, metagenomics sequences DNA directly from microbial communities. This can reveal resistance-associated organisms and genetic determinants that may not be readily recovered through culture. However, strain-level resolution is generally more difficult in metagenomic datasets because DNA from multiple organisms is mixed together.
- Metagenomic Quality Control is therefore particularly important when strain-level analysis is performed directly from community sequencing data. Low-quality reads, contamination, host DNA, sequencing errors, and insufficient coverage can make it difficult to distinguish closely related organisms. Reliable quality assessment helps ensure that genomic differences represent biological variation rather than sequencing artifacts.
- Sequencing depth is another major consideration. Closely related strains may differ at only a small number of genomic positions, so insufficient coverage can make those differences difficult to identify confidently. In metagenomic datasets, low-abundance strains may be especially challenging to reconstruct because their DNA can be overwhelmed by sequences from more abundant organisms.
- Short-read sequencing can provide highly accurate data suitable for many strain-level comparisons, particularly when sufficient genome coverage is available. Long-read sequencing can provide longer genomic sequences and improve the reconstruction of repetitive regions, plasmids, and other complex structures. Hybrid sequencing can combine complementary information from short and long reads and may be particularly useful when resistance genes need to be linked to their surrounding genomic context.
- Core-genome analysis focuses on genomic regions shared among members of a microbial population or species. Comparing variation across these conserved regions can provide a robust basis for determining genetic relationships. Core-genome phylogenies can help identify closely related groups and distinguish major lineages within a population.
- Single nucleotide variant analysis can provide even finer resolution when appropriate. Single nucleotide variants are individual nucleotide differences between genomes, and their distribution can be used to estimate genetic relatedness. Closely related strains may differ by relatively few variants, whereas more distant organisms generally contain greater genomic divergence. The interpretation of variant distances depends strongly on the organism, evolutionary rate, recombination, sampling strategy, and analytical method.
- Phylogenetic analysis provides a visual and analytical framework for interpreting strain relationships. A phylogenetic tree can group related microbial genomes and reveal clusters of closely related strains. When sampling dates and locations are incorporated, phylogenetic analysis can help investigate possible patterns of emergence, persistence, geographic movement, or transmission.
- Genomic clustering is another important component of strain-level surveillance. Investigators may define clusters using genetic distances, phylogenetic relationships, core-genome variation, or other criteria. A cluster can indicate that several isolates are genetically similar, but genetic similarity alone does not establish direct transmission. Epidemiological information is needed to determine whether the observed relationship is consistent with a plausible transmission event.
- Temporal information can strengthen genomic interpretation. If closely related resistant strains appear sequentially in compatible locations, the pattern may be consistent with transmission or persistence. However, microbial lineages can remain genetically stable over extended periods, and similar strains may circulate independently in different populations. The timing and structure of sampling therefore influence how confidently genomic relationships can be interpreted.
- Geographic information is similarly important. Strain-level surveillance may identify related organisms across hospitals, cities, farms, wastewater treatment plants, food production facilities, or environmental sites. Geographic patterns can reveal widespread lineages or localized clusters, but movement between locations should not be inferred solely from genetic similarity. Transportation, human movement, animal movement, food distribution, wastewater connections, and environmental pathways may all influence microbial distribution.
- Antimicrobial Resistance Genes can be examined alongside strain relationships. Resistance Gene Detection identifies genetic determinants associated with resistance, while strain-level analysis determines how those determinants are distributed among microbial lineages. If closely related strains share the same resistance profile, this may be consistent with clonal expansion. If unrelated strains contain the same resistance gene, horizontal dissemination of the determinant or a shared mobile element may be a more plausible explanation.
- Genomic context provides further resolution. A resistance gene located on the chromosome may behave differently from one located on a plasmid, transposon, integron, or other Mobile Genetic Element. Comparing the genetic neighborhoods surrounding resistance determinants can help determine whether similar resistance genes are carried within similar genomic structures.
- Plasmid analysis is particularly important when investigating strain-level resistance. Plasmids can carry one or multiple resistance genes and may be shared among different bacterial lineages. Plasmid Reconstruction can help identify plasmid structures, while long-read sequencing can sometimes connect resistance genes to complete or near-complete plasmid sequences. Comparing plasmids across strains can reveal whether resistance is associated with a shared plasmid backbone or whether similar resistance genes occur in different genetic contexts.
- Horizontal Gene Transfer complicates strain-level interpretation because resistance determinants can move independently of the bacterial chromosome. Two unrelated strains may carry the same resistance gene because of a mobile genetic element, while closely related strains may have different resistance profiles because of gene acquisition or loss. Strain-level surveillance should therefore examine both chromosomal relatedness and resistance-gene context.
- Accessory genome analysis can reveal differences between otherwise related strains. Accessory genes may include antimicrobial resistance determinants, virulence-associated genes, metabolic functions, plasmid-associated genes, and other traits that are not present in every member of a species. Comparing accessory genomes can therefore help identify genetic changes associated with adaptation or resistance.
- Recombination can also influence strain-level genomic analysis. DNA exchange between related microorganisms can introduce genomic regions with different evolutionary histories and complicate phylogenetic reconstruction. Analytical methods should therefore account for recombination when appropriate, particularly for organisms in which horizontal exchange is common.
- Resistance phenotypes can be compared with genomic predictions to investigate genotype-phenotype relationships. Whole-genome sequencing may identify known resistance genes or mutations, while antimicrobial susceptibility testing provides direct phenotypic information about how an organism responds to particular antimicrobial agents. Agreement between genomic and phenotypic observations can strengthen interpretation, whereas discrepancies may reveal incomplete databases, novel mechanisms, regulatory effects, or limitations in prediction models.
- Metagenomic approaches can extend strain-level surveillance beyond cultured organisms. Genome-resolved metagenomics can reconstruct partial or near-complete genomes from complex microbial communities. Metagenomic Assembly generates longer DNA sequences, Metagenomic Binning groups sequences according to genomic characteristics, and Metagenome-Assembled Genomes can represent microbial genomes recovered without cultivation. These approaches can help investigate resistant organisms that are difficult to isolate using conventional methods.
- Strain-level reconstruction from metagenomic data remains challenging. Closely related organisms can share large portions of their genomes, making it difficult to determine which sequencing reads belong to which strain. Strain variation within a single microbial species can also cause mixed genomic signals. Advanced computational approaches may use coverage patterns, variant frequencies, genomic composition, and other information to separate coexisting strains.
- Strain-level analysis can be particularly valuable for human-associated microbial populations. Human Resistome surveillance can identify resistance genes within the human microbiome, while strain-level analysis can investigate whether particular resistant lineages persist within individuals or appear across multiple individuals. Longitudinal sampling can reveal changes in microbial populations following antimicrobial exposure or other environmental pressures.
- Hospital surveillance is another major application. Resistant bacterial strains can spread between patients, healthcare environments, equipment, and other reservoirs. Whole-genome sequencing can identify genetically related isolates and support investigations of potential healthcare-associated transmission. Combining strain-level genomic data with patient movement, sampling time, location, and infection-control information can improve outbreak investigation.
- Community surveillance can identify resistant strains circulating outside healthcare environments. Genomic comparisons may reveal whether particular lineages are restricted to hospitals or are also present in community populations. Wastewater and environmental sampling can provide additional information about resistant organisms circulating at the population level.
- Animal and agricultural surveillance can similarly benefit from strain-level resolution. Resistant microorganisms may occur in livestock, poultry, aquaculture systems, manure, agricultural soils, water, and food production environments. Comparing genomes across animals, farms, food products, and environmental samples can help investigate the distribution of resistant lineages and potential connections among agricultural compartments.
- Wastewater can provide a complementary source of strain-level information. Wastewater Resistome analysis primarily examines resistance genes and broader resistance profiles, but genome-resolved approaches may also identify microbial lineages and resistance-associated genetic structures. Because wastewater combines material from many sources, strain attribution can be difficult, but repeated sampling can provide useful information about persistence and population-level trends.
- Environmental strain surveillance can examine resistant microorganisms in soil, freshwater, sediments, marine environments, agricultural landscapes, and other ecosystems. Environmental Antimicrobial Resistance may involve complex microbial communities and multiple resistance reservoirs. Strain-level genomic analysis can help determine whether particular lineages are persistent within an environment or distributed across connected ecosystems.
- Food-associated surveillance can investigate resistant strains along the food chain. Genomic comparisons can determine whether related microbial lineages occur in food production environments, animals, processing facilities, food products, or human-associated samples. Such information can support investigations of potential relationships while recognizing that genetic similarity does not by itself establish a transmission pathway.
- One Health surveillance benefits from combining strain-level information across human, animal, agricultural, food, wastewater, and environmental systems. Closely related microbial strains or shared resistance-associated genetic structures found across multiple sectors may provide evidence of connected reservoirs. However, the strength of these conclusions depends on sampling density, genomic resolution, metadata quality, and the biological characteristics of the organisms involved.
- Antimicrobial exposure can influence strain-level dynamics. Selection pressure may favor resistant lineages, but the success of a resistant strain depends on more than resistance alone. Fitness, compensatory mutations, ecological competition, host factors, antimicrobial exposure, and horizontal gene transfer can all influence whether a strain persists or expands.
- Comparative genomics can identify genetic changes that distinguish resistant and susceptible strains or different variants of the same resistant lineage. Researchers may compare core genomes, accessory genes, resistance determinants, plasmids, mobile elements, mutations, and other genomic features. These comparisons can reveal candidate genetic factors associated with resistance or adaptation.
- Strain-level surveillance can also investigate resistance evolution within a lineage. Repeated sequencing of related isolates may reveal the acquisition or loss of resistance genes, mutations in antimicrobial targets, changes in efflux systems, or other genomic alterations. Longitudinal genomic studies can therefore provide a dynamic view of how resistance changes over time.
- Genomic epidemiology provides the broader framework for interpreting these strain-level observations. A genomic relationship becomes more meaningful when combined with epidemiological evidence. For example, closely related isolates collected from connected locations within a plausible time period may warrant investigation of a potential transmission pathway. Conversely, genetically similar organisms separated by large geographic or temporal distances may represent an established lineage rather than a recent transmission event.
- Statistical analysis can also support strain-level surveillance. Researchers may compare strain prevalence, resistance profiles, genomic clusters, resistance-gene abundance, or lineage distributions across populations. Longitudinal models can investigate changes over time, while multivariable analysis can account for factors such as antimicrobial exposure, geographic location, season, host category, or healthcare setting.
- Machine learning may provide additional analytical capabilities. Large genomic datasets can be used to classify strains, predict resistance phenotypes, identify genomic signatures, detect unusual clusters, or model relationships among genetic and epidemiological variables. However, predictive models should be independently validated because genomic patterns learned from one population may not generalize to another.
- Database quality remains important for strain-level resistance analysis. Resistance databases can identify known resistance genes, while microbial genome databases can provide reference genomes for comparative analysis. Changes in taxonomy, database content, gene nomenclature, and reference sequences can affect results. Surveillance workflows should therefore document database versions and analytical criteria.
- A major challenge is distinguishing true strain differences from technical variation. Sequencing errors, insufficient coverage, contamination, mixed samples, incomplete genomes, assembly artifacts, and inconsistent laboratory procedures can create apparent genomic differences. Standardized laboratory protocols and analytical quality control are essential when very small genetic differences are being interpreted.
- Another challenge is sampling bias. Surveillance rarely captures every member of a microbial population. If only a small number of isolates are sampled, a widespread strain may appear rare, while an intensively sampled location may appear to contain unusually high strain diversity. Sampling density should therefore be considered when interpreting genomic cluster size, prevalence, and geographic distribution.
- Strain-level surveillance also requires careful handling of uncertainty. Genetic distance thresholds should not be treated as universal indicators of transmission because evolutionary rates differ among organisms and settings. The same number of genomic differences can have different epidemiological meanings depending on the organism, sampling interval, recombination, and background population diversity.
- Reproducibility is essential for surveillance programs that track strains over long periods. Laboratories should document sequencing methods, quality thresholds, reference genomes, variant-calling approaches, phylogenetic methods, resistance databases, clustering criteria, and software versions. Consistent analytical procedures allow genomic relationships to be compared over time.
- The future of strain-level antimicrobial resistance surveillance will increasingly combine whole-genome sequencing, metagenomics, long-read sequencing, genome-resolved metagenomics, plasmid reconstruction, and integrated epidemiological datasets. Improved strain-resolution methods may make it easier to identify coexisting lineages within complex microbial communities. Long-read sequencing may improve the connection between chromosomal backgrounds, plasmids, resistance genes, and other mobile elements.
- Real-time or near-real-time genomic surveillance may also become increasingly important. Rapid sequencing and automated analysis could allow emerging resistant lineages to be detected soon after they appear in a population. Integration with genomic epidemiology, antimicrobial susceptibility testing, clinical information, wastewater monitoring, and environmental surveillance could provide a more comprehensive early-warning system.
- Ultimately, strain-level antimicrobial resistance surveillance provides a higher-resolution view of resistance dynamics than population-level resistance measurements alone. It can help distinguish clonal expansion from horizontal resistance-gene dissemination, identify persistent or emerging lineages, investigate potential transmission pathways, and characterize genomic changes associated with resistance. When combined with metagenomics, resistome analysis, genomic epidemiology, epidemiological metadata, and One Health surveillance, strain-level analysis provides a powerful framework for understanding how antimicrobial resistance evolves and spreads.
