- One Health antimicrobial resistance is an approach to understanding antimicrobial resistance as a connected problem involving humans, animals, food systems, and the environment. Antimicrobial resistance does not develop or spread exclusively within hospitals or individual patients. Resistant microorganisms, antimicrobial resistance genes, mobile genetic elements, and selective pressures can move between interconnected ecosystems. The One Health perspective therefore recognizes that human health, animal health, food production, and environmental health are closely linked and that effective antimicrobial resistance research requires consideration of these connections.
- Antimicrobial resistance occurs when microorganisms acquire or develop characteristics that reduce their susceptibility to antimicrobial compounds. These characteristics can result from genetic mutations, acquisition of antimicrobial resistance genes, changes in gene regulation, altered permeability, active efflux, enzymatic drug modification, target modification, and other mechanisms. Horizontal Gene Transfer can further accelerate the distribution of resistance determinants by allowing genetic material to move between microbial populations. One Health research examines how these processes operate across interconnected reservoirs rather than treating each environment as an isolated system.
- The human microbiome is an important component of the One Health antimicrobial resistance landscape. Humans carry diverse microbial communities on the skin, in the gastrointestinal tract, and at other body sites. These communities contain both susceptible and resistant microorganisms as well as mobile genetic elements that can carry accessory genes. The human microbiome can therefore act as a reservoir of antimicrobial resistance genes, while interactions with healthcare environments, food, animals, medications, and the wider environment can influence its composition.
- Antimicrobial exposure is an important selective pressure within human-associated microbial communities. Antibiotic treatment can alter microbial community structure and may influence the relative abundance of organisms carrying resistance determinants. However, antimicrobial exposure is only one factor affecting the human resistome. Diet, geography, age, healthcare exposure, travel, underlying conditions, environmental contact, and microbial community structure can also influence resistance-associated genetic profiles.
- Animals represent another major component of One Health antimicrobial resistance research. Livestock, companion animals, wildlife, and other animal populations contain complex microbial communities that can harbor antimicrobial resistance genes. Antimicrobial use in veterinary medicine and animal production can create selective pressures that influence these communities. Resistant organisms and resistance-associated genetic material may subsequently move between animals, humans, agricultural environments, food systems, and water.
- Agricultural systems create connections among animals, soil, crops, water, manure, and human communities. Manure can contain microorganisms and resistance genes originating from animal-associated microbiomes. When manure is applied to agricultural land, genetic material may enter soil microbial communities and potentially influence the environmental resistome. Runoff and water movement can further connect agricultural environments with rivers, groundwater, and other ecosystems.
- The food system provides additional opportunities for microbial exchange. Food animals, agricultural products, processing environments, workers, equipment, water, and consumers can form interconnected pathways through which microorganisms and genetic material move. Resistant microorganisms may enter food-processing environments or food products, while microbial communities associated with food production can contribute to the broader circulation of resistance determinants.
- Environmental reservoirs are equally important. Soil, freshwater, sediments, wastewater, marine environments, and other ecosystems contain diverse microbial communities with extensive genetic diversity. Some resistance determinants occur naturally in environmental microbial populations, while others may be introduced or enriched through human activities. Environmental ecosystems can therefore serve as reservoirs, mixing zones, and potential pathways for the dissemination of antimicrobial resistance.
- Wastewater is particularly important because it can receive biological material from multiple sources. Municipal wastewater may contain microorganisms and resistance genes associated with human populations, while hospital wastewater can contain additional clinical and healthcare-associated organisms and resistance determinants. Industrial and agricultural wastewater can contribute other microbial and chemical inputs. Wastewater treatment can reduce some biological material, but the effectiveness and mechanisms vary depending on the treatment process and the genetic targets being studied.
- Metagenomics provides an important tool for investigating One Health antimicrobial resistance because it can characterize microbial communities without requiring cultivation of individual organisms. Shotgun Metagenomics can simultaneously provide information about microbial taxonomy, resistance genes, functional capabilities, mobile genetic elements, and other genomic features. This makes it possible to compare resistance-associated genetic profiles across humans, animals, food systems, wastewater, soil, and aquatic environments.
- A One Health metagenomic study begins with careful study design. Researchers need to define which reservoirs are being compared, which biological or environmental questions are being addressed, and how samples will be collected. Sampling should account for spatial and temporal variation, biological replication, technical variation, and relevant metadata. Without appropriate study design, apparent differences in resistance profiles may reflect sampling or technical effects rather than genuine ecological differences.
- Sample collection is particularly important when comparing different One Health compartments. Human stool, animal feces, wastewater, soil, sediment, water, food, and other sample types differ substantially in microbial composition and chemical characteristics. Standardized collection procedures, appropriate preservation, contamination controls, and consistent metadata are necessary when results from multiple environments are intended to be compared.
- DNA extraction can also influence One Health metagenomic comparisons. Different sample types contain different inhibitors, cell structures, and DNA concentrations. Extraction methods that work well for one environment may perform differently in another. Extraction bias can therefore influence the observed microbial and resistance-gene profiles. Consistent procedures and appropriate controls are important for distinguishing biological differences from methodological effects.
- After DNA extraction, sequencing provides the data needed for metagenomic analysis. Short-read sequencing can provide highly accurate sequence information at substantial throughput, while Long-Read Metagenomics can improve the reconstruction of complex genomic structures. Hybrid approaches can combine complementary information from different sequencing technologies. The appropriate approach depends on the research question, sample complexity, available resources, and need for genomic context.
- Metagenomic Quality Control is essential before resistance analysis. Sequencing reads may contain adapter sequences, low-quality regions, technical artifacts, host DNA, contamination, or other unwanted material. Removing or identifying problematic reads can improve downstream Resistance Gene Detection and reduce misleading results. Negative controls and sequencing controls are especially valuable when comparing samples with different microbial biomass or contamination risks.
- Resistance genes can be identified using specialized Antimicrobial Resistance Databases and sequence-based approaches. These resources contain reference sequences and annotations associated with known resistance determinants. Metagenomic analysis can compare sequencing data against these references to identify potential resistance genes. However, a sequence match generally indicates genetic potential rather than directly demonstrating phenotypic resistance.
- Resistance Gene Detection can be performed using read-based or assembly-based approaches. Read-based methods can detect resistance-associated sequences directly from sequencing reads and are often useful for rapid community-level screening. Assembly-based methods can provide longer sequences and potentially more information about genomic context. The two approaches can complement one another, particularly when investigating resistance genes in complex microbial communities.
- Metagenomic Resistome Analysis can then characterize the overall collection of antimicrobial resistance genes within each One Health environment. Researchers can compare resistome composition, resistance-gene classes, abundance, prevalence, and diversity across human, animal, agricultural, food, wastewater, and environmental samples. Such comparisons can help identify reservoirs of particular resistance determinants and reveal patterns associated with specific ecological settings.
- Resistance Gene Abundance provides another quantitative perspective. Relative abundance can describe the proportion of sequencing data associated with a resistance determinant, whereas absolute measurements may incorporate additional information about microbial biomass, cell counts, or external quantitative measurements. Interpretation should account for sequencing depth, compositionality, gene length, database matching, and other methodological factors.
- Resistome Profiling can also examine diversity and structure across samples. Researchers may investigate which resistance determinants occur within a community, how frequently they occur, and how their distributions differ between environments. Alpha diversity can summarize within-sample diversity, while beta diversity can be used to compare resistome composition between samples. Appropriate statistical methods are necessary to determine whether observed differences are robust.
- Taxonomic information adds an important dimension to One Health analysis. Metagenomic Taxonomic Profiling can identify the organisms contributing to a microbial community, while genome-resolved approaches may sometimes associate resistance genes with specific microbial populations. Linking resistance determinants to their potential hosts can help distinguish widespread environmental genes from resistance associated with particular bacterial lineages.
- Genomic context is especially important when studying the movement of resistance. A resistance gene found near plasmid replication genes, transfer-associated genes, transposases, integrases, or other mobility features may have evidence of association with a mobile genetic structure. Metagenomic Assembly, Metagenomic Binning, Metagenome-Assembled Genomes, and Plasmid Reconstruction can help preserve or recover this context.
- Horizontal gene transfer provides a mechanism connecting One Health compartments. Resistance determinants can potentially move among microbial populations through conjugation, transformation, transduction, and other genetic processes. Mobile Genetic Elements such as plasmids, transposons, integrons, insertion sequences, genomic islands, and bacteriophages can contribute to this movement. However, detecting a shared resistance gene or mobile element across environments does not by itself prove that transfer occurred between those specific environments.
- Plasmids are particularly important because some can carry multiple resistance genes and can move between microbial hosts. Metagenomic Plasmid Reconstruction can help identify plasmid-associated resistance and characterize plasmid backbones, accessory genes, and genetic context. Long-read sequencing can improve physical linkage between resistance genes and plasmid sequences, although host assignment and transfer direction can remain difficult to establish.
- One Health analysis can also investigate the movement of resistance genes through wastewater systems. Researchers may compare resistance profiles upstream and downstream of treatment facilities, across different wastewater sources, or between communities with different antimicrobial-use patterns. Such studies can provide information about environmental resistance reservoirs and potential pathways of dissemination, but they should be interpreted alongside hydrology, population characteristics, treatment processes, and other environmental variables.
- Agricultural One Health studies can examine relationships among livestock, manure, soil, crops, water, and surrounding ecosystems. Metagenomic sequencing can characterize resistance genes across these connected reservoirs and investigate whether particular genetic elements are shared between animal-associated and environmental microbial communities. Longitudinal sampling can be especially useful for examining changes associated with management practices or antimicrobial exposure.
- Food-associated studies can similarly investigate resistance across production and processing chains. Researchers may sample animals, raw materials, food products, processing environments, water, surfaces, and other relevant locations. The goal is not simply to identify resistance genes but to understand where they occur, how abundant they are, whether they are associated with mobile elements, and how genetic patterns change along the production pathway.
- Human, animal, and environmental datasets can also be integrated through comparative genomics. Closely related resistance genes, plasmids, or microbial genomes can be compared across reservoirs to investigate shared genetic features. Genome-resolved metagenomics can sometimes identify microbial populations containing similar resistance determinants in different environments. These observations can generate hypotheses about possible connections that can be tested using epidemiological, culture-based, or experimental approaches.
- Temporal sampling is particularly valuable for One Health antimicrobial resistance surveillance. A single sample provides a snapshot, whereas repeated sampling can reveal persistence, emergence, seasonal variation, or changes following interventions. Time-series metagenomics can therefore help distinguish stable resistance reservoirs from transient changes in microbial communities.
- Geographic variation can also be important. Resistance profiles may differ among communities, regions, countries, agricultural systems, healthcare settings, and environmental ecosystems. Differences may reflect antimicrobial use, sanitation, healthcare infrastructure, agricultural practices, environmental conditions, microbial ecology, or patterns of human and animal movement. Comparing regions requires careful attention to standardized sampling and metadata because methodological differences can otherwise obscure genuine geographic patterns.
- Antimicrobial use data can strengthen One Health analyses. Information about antimicrobial exposure in humans, veterinary settings, or agriculture can be integrated with metagenomic resistance measurements. Statistical models can then investigate associations between antimicrobial-use patterns and resistance-gene abundance or prevalence. Such associations should be interpreted cautiously because antimicrobial use can correlate with many other factors that influence microbial communities.
- Metagenomic Statistical Analysis is therefore an important component of One Health research. Researchers may compare resistome composition, differential abundance, microbial diversity, mobile-element profiles, or functional pathways among environments. Multiple-testing correction, effect-size estimation, appropriate normalization, biological replication, and adjustment for relevant covariates can improve the reliability of these comparisons.
- Machine learning can also be used to investigate complex One Health datasets. Models may integrate microbial taxa, resistance genes, mobile elements, environmental measurements, antimicrobial-use data, and other features to classify samples or identify predictors of resistance patterns. However, predictive performance does not necessarily establish causation, and models must be carefully validated across independent populations and environments.
- A major challenge in One Health antimicrobial resistance research is the distinction between presence and risk. Detecting a resistance gene does not automatically mean that it poses an immediate threat to human health. Risk may depend on the gene’s biological activity, host organism, mobility, abundance, genomic context, ability to transfer, environmental persistence, and exposure pathway. Interpretation therefore requires integration of genetic, ecological, epidemiological, and clinical evidence.
- The distinction between genotype and phenotype is particularly important. Metagenomics detects DNA and can identify resistance-associated genes, but it does not directly measure whether an organism is phenotypically resistant under clinically relevant conditions. Culture-based susceptibility testing, isolate sequencing, transcriptomics, proteomics, and experimental validation can provide complementary evidence.
- Metatranscriptomics can help determine whether resistance-associated genes are being expressed, while Metaproteomics can provide evidence about the corresponding proteins. These approaches can add functional information to DNA-based metagenomic observations. Metabolomics can provide another layer by characterizing biochemical changes associated with antimicrobial exposure or microbial community activity.
- The integration of these approaches illustrates the broader value of multi-omics in One Health research. Metagenomics can describe genetic potential, metatranscriptomics can examine gene expression, metaproteomics can investigate proteins, and metabolomics can characterize biochemical outcomes. Together, these measurements can provide a more complete picture of how microbial communities respond to antimicrobial pressures across interconnected ecosystems.
- One Health antimicrobial resistance surveillance can also benefit from standardized analytical pipelines. Consistent sequencing, Quality Control, resistance-gene identification, database selection, abundance estimation, and statistical analysis make results easier to compare across studies. Database versions should be documented because reference collections change over time and can influence the number and identity of detected resistance genes.
- Reference database limitations remain an important source of uncertainty. Known resistance determinants are better represented than novel or highly divergent genes, and environmental microorganisms may contain resistance-associated functions that are poorly characterized. A lack of database matches should therefore not be interpreted as proof that resistance determinants are absent.
- Contamination is another important concern, especially when comparing low-biomass environmental samples with high-biomass samples. DNA from reagents, laboratory environments, human handling, or neighboring samples can influence results. Negative controls and appropriate contamination-detection procedures are essential for identifying signals that may not originate from the biological sample.
- Standardized metadata are equally important. Useful metadata may include sample type, collection location, collection time, host or environmental context, antimicrobial exposure, agricultural practices, treatment processes, environmental conditions, and other variables relevant to the research question. Without adequate metadata, it can be difficult to interpret why resistomes differ between samples.
- One Health research also requires collaboration across disciplines. Microbiologists, genomic scientists, epidemiologists, clinicians, veterinarians, environmental scientists, agricultural researchers, public-health specialists, bioinformaticians, and statisticians may each contribute different forms of evidence. Integrating these perspectives can help connect molecular observations with real-world transmission and public-health questions.
- The One Health framework is especially useful for antimicrobial resistance surveillance because resistance can cross traditional boundaries. A resistance determinant detected in a clinical setting may have environmental or animal-associated reservoirs, while a resistance gene identified in an environmental sample may have implications for human exposure depending on its genetic context and mobility. These relationships are complex and should be investigated through evidence-based surveillance rather than assumed from sequence similarity alone.
- The future of One Health antimicrobial resistance research will increasingly depend on integrated genomic surveillance. Short- and long-read sequencing, genome-resolved metagenomics, improved plasmid reconstruction, mobile-element analysis, expanded resistance databases, and better statistical frameworks can increase the resolution with which resistance reservoirs are characterized. Longitudinal and geographically distributed sampling can further reveal how resistance patterns change over time and across connected environments.
- Improved computational methods may also enable more detailed reconstruction of resistance transmission networks. Instead of analyzing resistance genes independently, researchers may increasingly connect genes with plasmids, mobile elements, microbial hosts, environments, and temporal patterns. Such analyses could help identify potential pathways of resistance dissemination while maintaining the distinction between inferred relationships and experimentally demonstrated transmission.
- One Health antimicrobial resistance is therefore best understood as a connected ecological and evolutionary problem rather than a collection of isolated resistance events. Humans, animals, food systems, wastewater, agriculture, and natural environments contain interacting microbial communities in which resistance genes can persist, change, and potentially move between hosts and ecosystems. Metagenomics provides a powerful way to investigate these reservoirs and connections, particularly when combined with genomic context, statistical analysis, epidemiological information, and complementary experimental methods.
