Global Mapping of Antibiotic Resistance Genes: Geographical Patterns, Drivers, and Health Risks

Abstract

This article synthesizes the latest global research on the geographical distribution of antibiotic resistance genes (ARGs). It explores the foundational patterns of ARGs across continents and habitats, examines the methodologies for assessing their public health risk, discusses the environmental and anthropogenic drivers of resistance dynamics, and validates findings through cross-disciplinary comparisons. Aimed at researchers and drug development professionals, this review provides a comprehensive, evidence-based framework essential for understanding and mitigating the global spread of antimicrobial resistance, a silent pandemic projected to cause 10 million annual deaths by 2050.

The Global Landscape of Antibiotic Resistomes: Diversity and Distribution Across Continents and Habitats

Continental Variations in ARG Abundance and Composition

Antibiotic resistance genes (ARGs) present a profound global public health challenge. Understanding their distribution and abundance across different geographical scales is critical for monitoring and mitigating their spread. Recent metagenomic studies reveal that ARG profiles are not uniform worldwide; instead, they exhibit distinct patterns across continents, driven by a complex interplay of local bacterial community composition, human activities, and environmental factors. This guide objectively compares the performance of various geographic sampling and analytical methodologies in identifying these continental variations, providing a synthesis of current experimental data for researchers and drug development professionals.

Global Distribution of ARG Abundance and Diversity

Key Continental Patterns from Global Wastewater Analysis

Wastewater treatment plants (WWTPs) serve as significant reservoirs and mixing points for ARGs, reflecting the resistome of human and industrial activities. A landmark study analyzing 226 activated sludge samples from 142 WWTPs across six continents provides the most consistent global dataset to date, using unified protocols for sample collection, DNA sequencing, and analysis [1].

Table 1: Continental Variations in ARG Abundance and Diversity in Wastewater Treatment Plants

Continent	Total ARG Abundance (Relative)	ARG Richness (Number of Genes)	Shannon's H Index (Diversity)	Distinct Compositional Features
Global Average	No significant difference (p=0.78)	Varies significantly	Varies significantly	A core set of 20 ARGs found in all WWTPs (83.8% of total abundance)
Asia	Similar to other continents	Highest (except vs. Africa)	Highest (except vs. Africa)	Distinct from other continents
Africa	Similar to other continents	High (not sig. diff. from Asia)	High (not sig. diff. from Asia)	Data from fewer sites
Europe	Similar to other continents	Lower than Asia	Lower than Asia	Distinct from other continents
North America	Similar to other continents	Lower than Asia	Lower than Asia	Distinct from other continents
South America	Similar to other continents	Lower than Asia	Lower than Asia	Distinct from other continents

This research found that while the total abundance of ARGs was surprisingly consistent across continents, the diversity and specific composition of resistomes varied significantly [1]. The most common resistance mechanisms globally were antibiotic inactivation (55.7%), target alteration (25.9%), and efflux pumps (15.8%) [1]. The composition of ARG communities (resistomes) was strongly correlated with the local bacterial taxonomic composition, with Chloroflexi, Acidobacteria, and Deltaproteobacteria identified as major carriers of ARGs in WWTPs [1].

Regional Variations in the Human Gut Resistome

The human gut is another critical reservoir for ARGs. Studies of human gut metagenomes show that geographic and socio-demographic gradients significantly influence resistome profiles.

Table 2: Regional and Demographic Variations in Human Gut Resistomes

Region / Demographic	ARG Load (Relative)	ARG Diversity (Shannon's H)	Notable ARG Classes	Key Drivers
High-Income Countries (HIC)	Higher in women (9%)	Higher in women	Tetracycline resistance most abundant	Gender, age, antibiotic use
Low/Middle-Income Countries (LMIC)	Higher in men (5%)	No significant gender difference	Higher aminoglycoside & folate antagonist resistance	Regional practices, infrastructure
China (Jiangsu Province)	Higher than other provinces	N/A	Multidrug, peptide, tetracycline resistance	Regional antibiotic usage patterns
China (Sichuan & Yunnan)	Lower than Jiangsu	N/A	Different profile from Jiangsu	Different local practices

A study of 14,641 human gut metagenomes found that gender differences in ARG load emerge in adulthood and interact with economic development [2]. Furthermore, a study across four Chinese provinces (Yunnan, Guizhou, Sichuan, and Jiangsu) revealed significant regional differences in gut microbial composition and ARG distribution, with Jiangsu showing a higher prevalence of multidrug resistance genes, likely reflecting regional variations in antibiotic usage [3].

Experimental Protocols for Continental ARG Profiling

Standardized Global Metagenomic Sampling and Analysis

The most reliable data for continental comparisons come from studies employing consistent methodologies across all sampling sites, such as the pipeline established by the Global Water Microbiome Consortium (GWMC) [1].

Core Workflow for Global WWTP Sampling [1]:

• Sample Collection: Collect activated sludge samples from a globally representative set of WWTPs (226 samples from 142 plants across six continents).
• DNA Extraction & Sequencing: Perform consistent community DNA extraction and shotgun metagenomic sequencing to obtain a total of 2.8 terabases of data.
• Metagenomic Assembly: Assemble sequencing reads into contigs (>1 kb) and predict open reading frames (ORFs).
• ARG Annotation: Annotate ORFs against a curated ARG database to identify resistance genes.
• Abundance Normalization: Normalize ARG abundance to the copy number per bacterial cell to enable cross-comparison.
• Bioinformatic Analysis: Perform statistical analyses (e.g., PCoA, PERMANOVA) to compare resistome structure across continents and correlate with microbial taxonomy and abiotic factors.

Continental Comparison via Meta-Analysis of Public Data

When standardized sampling is not feasible, a robust alternative is the meta-analysis of publicly available metagenomic data with careful normalization.

Core Workflow for Human Gut Resistome Meta-Analysis [2]:

• Data Curation: Compile 14,641 publicly available human gut metagenomes with associated metadata (gender, age, country).
• Quality Filtering: Apply stringent quality control and filter out human host-derived sequences.
• ARG Profiling: Align quality-controlled reads to a custom ARG database (e.g., CARD) to identify and quantify resistance genes.
• Load & Diversity Calculation: Calculate ARG load (in RPKM - Reads Per Kilobase per Million) and alpha-diversity indices (e.g., Shannon's H).
• Statistical Modeling: Use multivariate statistical models (PERMANOVA) to partition resistome variation by geography, demography, and economic factors.

Figure 1: Experimental workflow for continental ARG comparison in wastewater. The process flows from standardized sampling through wet lab and computational analysis to statistical comparison.

The Researcher's Toolkit: Essential Reagents & Materials

Successful continental-scale resistome profiling relies on a suite of specialized reagents, databases, and software tools.

Table 3: Essential Research Reagents and Solutions for ARG Metagenomics

Item Name	Function / Application	Specific Examples / Notes
Shotgun Metagenomic Sequencing Kits	Provides the raw genetic data from complex environmental or clinical samples.	Illumina NovaSeq, PacBio HiFi, or Oxford Nanopore protocols.
DNA Extraction Kits	Isolates high-quality, high-molecular-weight microbial community DNA.	QIAamp Fast DNA Stool Mini Kit (for fecal samples), DNeasy PowerSoil Pro Kit (for environmental samples).
ARG Reference Databases	Provides a curated set of reference sequences for annotating and quantifying ARGs in metagenomic data.	CARD (Comprehensive Antibiotic Resistance Database) [4], MEGARes [4], BacMet.
Bioinformatic Profiling Tools	Software used to identify and quantify ARGs from raw sequencing data.	AMR++ [4], Bowtie2 (for read alignment) [4], RGI (Resistance Gene Identifier) [4], AMRFinderPlus [4].
Quality Control & Assembly Tools	Pre-processes raw sequencing data and assembles short reads into longer contigs.	fastp (for QC) [3], MEGAHIT or metaSPAdes (for de novo assembly) [3] [4].
Metagenome-Assembled Genome (MAG) Binning Tools	Recovers individual microbial genomes from complex metagenomic data to link ARGs to their bacterial hosts.	MetaBAT2, MaxBin2, CONCOCT (often used in combination) [3].

Figure 2: Core bioinformatic workflow for ARG identification and quantification from metagenomic data.

Continental variations in ARG abundance and composition are a documented phenomenon, with patterns showing more consistency in total abundance than in specific gene composition. Key determinants of these geographic patterns include local bacterial community structure, the prevalence of mobile genetic elements, and anthropogenic factors such as industrial practices and antibiotic usage. Robust continental comparison requires meticulously standardized experimental protocols or carefully normalized meta-analyses of public data. The field is advancing towards a "One Health" surveillance approach, integrating environmental monitoring with clinical and animal data to fully understand and combat the global spread of antibiotic resistance [5].

Antibiotic resistance poses a significant global health threat, with antibiotic resistance genes (ARGs) serving as the fundamental genetic units enabling this resistance. The concept of the "core resistome"—defined as the set of ARGs ubiquitously present within a specific environment or across multiple ecosystems—provides a critical framework for understanding the persistence and dissemination of antibiotic resistance. These ubiquitous genetic elements represent the most stable components of environmental resistomes, offering insights into long-term selection pressures and potential pathways for gene transfer to pathogens.

Recent metagenomic studies reveal that core resistomes are heavily dominated by latent resistance genes—genetic elements not yet documented in standard clinical databases—which constitute a vast reservoir of potential resistance determinants [6]. The mobility and sharing of these core ARGs between environmental bacteria and human pathogens present an emerging risk to public health, necessitating a One Health approach that integrates human, animal, and environmental perspectives [7] [8]. This review synthesizes current evidence on core resistomes across diverse human-influenced environments, examining their composition, distribution, and the methodologies enabling their study.

Comparative Analysis of Core Resistomes Across Environments

Wastewater Treatment Systems

Wastewater treatment plants (WWTPs) represent significant reservoirs and mixing points for ARGs from human and animal sources. Global analysis of activated sludge samples from 142 WWTPs across six continents identified a core set of 20 ARGs present in every facility studied [1]. These core genes accounted for a remarkable 83.8% of the total ARG abundance in these systems, highlighting their dominance in wastewater microbiomes.

Table 1: Core Antibiotic Resistance Genes in Global Wastewater Treatment Plants

ARG Name	Drug Class	Resistance Mechanism	Relative Abundance (%)
TetracyclineResistanceMFSEffluxPump	Tetracycline	Efflux pump	15.2%
ClassB	Beta-lactam	Antibiotic inactivation	13.5%
vanT (vanG cluster)	Glycopeptide	Antibiotic target alteration	11.4%
Other core ARGs (17)	Multiple	Multiple	43.7%

The WWTP core resistome composition demonstrates remarkable consistency across geographical boundaries, with ARGs conferring resistance to beta-lactams, glycopeptides, and tetracyclines representing the most abundant drug classes [1]. This consistency suggests powerful selective pressures that maintain these resistance determinants regardless of regional differences in antibiotic usage patterns or treatment technologies.

Terrestrial and Aquatic Environments

Beyond engineered systems, core resistomes exhibit distinct patterns across natural environments influenced by human activities:

Soil Ecosystems: Global analysis of 2,540 soil samples revealed that Rank I ARGs (those with demonstrated mobility, host pathogenicity, and human-associated enrichment) showed increasing relative abundance and occurrence frequency from 2008 to 2021 [7]. Soil shares 50.9% of its Rank I ARGs with human-associated environments, primarily human feces (75.4%), chicken feces (68.3%), and WWTP effluent (59.1%) [7].

Coastal Sediments: Studies across complex ecological gradients in the East China Sea demonstrated that ARG richness and abundance significantly decreased with increasing distance from the coastline [9]. This pattern identifies terrestrial runoff as a primary determinant of coastal sediment resistomes, with strong correlations observed between specific ARGs and nutritional variables (NH₄⁺-N, NO₃⁻-N, total phosphorus) and metals (As, Cu, Ni, Zn) [9].

Lake Sediments: Research from Nansi Lake in China revealed that human activities, particularly urbanization and aquaculture, significantly increased the abundance of specific ARGs (sulI, tetX, cmlA, and aac(6')-Ib-cr) and mobile genetic elements (intI) in sediment compared to pristine areas [10].

Human and Animal Gastrointestinal Tracts

The gastrointestinal tract represents a critical interface for ARG exchange, with distinct patterns emerging across different populations:

Human Gut: Analysis of 14,641 human gut metagenomes revealed a 9% higher total ARG load in women compared to men in high-income countries, with tetracycline resistance genes being the most abundant across all populations [2]. Regional studies in China demonstrated significant variations in gut resistome composition between provinces, reflecting differences in antibiotic usage patterns [3].

Livestock: Global analysis of 4,017 livestock manure metagenomes established a clear hierarchy in ARG diversity and abundance: chicken > pig >> cattle [11]. This pattern highlights the role of agricultural practices in shaping distinct resistome profiles, with livestock and human gut microbiomes sharing similar ARG patterns that are distinct from those found in soil and water environments [11].

Table 2: Core Resistome Characteristics Across Human-Influenced Environments

Environment	Core ARG Diversity	Dominant Resistance Mechanisms	Key Mobilization Factors
Wastewater Treatment Plants	20 core ARGs	Antibiotic inactivation (55.7%)	Mobile genetic elements (57% of genomes carry mobile ARGs)
Agricultural Soil	Increasing Rank I ARGs over time	Multidrug efflux	Shared with human/animal feces (50.9%)
Coastal Sediments	Decreases with distance from coast	Not specified	Terrestrial disturbances, metals, nutrients
Human Gut	Varies by region and gender	Tetracycline resistance dominant	Regional antibiotic usage patterns
Livestock Manure	Chicken > pig >> cattle	Varies by animal type	Farming practices, antimicrobial use

Methodologies for Core Resistome Analysis

Metagenomic Sequencing and Assembly

Comprehensive resistome analysis relies on advanced metagenomic workflows that capture the genetic diversity of complex microbial communities. The standard methodology involves:

DNA Extraction and Sequencing: High-quality community DNA is extracted using commercial kits (e.g., QIAamp Fast DNA Stool Mini Kit for fecal samples), with quality assessment via NanoDrop spectrophotometry and agarose gel electrophoresis [3]. Shotgun metagenomic sequencing generates an average of 12.3 ± 3.9 Gb per sample, sufficient to represent microbial and resistome diversity based on rarefaction analysis [1].

Sequence Quality Control and Assembly: Adapter trimming and quality filtering are performed using tools like fastp (version 0.23.0), removing reads shorter than 50 base pairs or with quality scores below 20 [3]. Additional refinement uses SeqPrep and Sickle, followed by host sequence removal (e.g., alignment to human reference genome GRCh38.p13 using BWA) [3]. De novo assembly is conducted using MEGAHIT (version 1.1.2), optimized for large and complex metagenomic datasets [3].

Figure 1: Experimental workflow for core resistome analysis, showing parallel paths for read-based analysis and metagenome-assembled genomes (MAGs).

Metagenome-Assembled Genomes (MAGs) and Gene Prediction

Metagenome-assembled genomes provide taxonomic context for ARG identification and mobility assessment:

Genome Binning: Multiple tools are employed independently, including MetaBAT2 (version 2.12.1), MaxBin2 (version 2.2.5), and CONCOCT (version 0.5.0), with integration via DAS Tool (version 1.1.0) [3]. MAG refinement uses RefineM (version 0.0.24) to remove contigs with aberrant genomic features (atypical GC content, divergent tetranucleotide frequency, inconsistent coverage) [3].

Quality Control and Dereplication: MAGs are assessed using CheckM (version 1.0.12) with lineage-specific marker genes, retaining those with ≥50% completeness and <10% contamination [3]. Dereplication at 99% average nucleotide identity (ANI) using dRep (version 3.4.2) ensures strain-level distinction, with the highest-quality MAG selected from each cluster [3].

Gene Prediction and Annotation: Open reading frame prediction employs Prodigal (version 2.6.3) with the -p meta flag, followed by annotation against specialized databases including CARD (version 3.0.9), ResFinder, and ARGs-OAP (v3.0) [3] [11].

ARG Quantification and Statistical Analysis

ARG Quantification: Read-based annotation aligns metagenomic reads to curated ARG databases using DIAMOND blastx (version 2.0.4) with strict identity thresholds (95%) [6]. Alternatively, contig-based approaches identify ARGs in assembled metagenomes, with abundance normalized to copies per bacterial cell [1].

Diversity and Abundance Metrics: ARG load is calculated as reads per kilobase per million reads (RPKM), while diversity incorporates richness (number of unique ARGs) and Shannon's H index [2]. Statistical analyses include PERMANOVA for resistome composition differences, Procrustes analysis for resistome-microbiome alignment, and network analysis for identifying ARG-host associations [2] [1].

Source Attribution: Fast expectation-maximization for microbial source tracking (FEAST) quantifies the sharing of ARGs between environments (e.g., soil and human feces) [7].

Table 3: Key Research Reagents and Computational Tools for Resistome Analysis

Tool/Resource	Type	Function	Application in Core Resistome Studies
QIAamp Fast DNA Stool Mini Kit	Wet lab reagent	DNA extraction from complex samples	Human gut, livestock manure, and sediment studies [3] [10]
ARGs-OAP (v3.0)	Database/Pipeline	ARG annotation and quantification	Standardized ARG identification across studies; Rank I ARG risk assessment [7] [11]
CARD (v3.0.9)	Database	Comprehensive antibiotic resistance database	Functional annotation of predicted genes [3]
ResFinder	Database	Curated repository of established ARGs	Reference for established vs. latent ARG classification [6]
fARGene	Computational tool	Novel ARG prediction from sequence data	Identification of latent ARGs not in existing databases [6]
CheckM (v1.0.12)	Quality control tool	Assess MAG completeness and contamination	Quality filtering of metagenome-assembled genomes [3]
DAS Tool (v1.1.0)	Binning tool	Integrate outputs from multiple binners	Generate high-quality, non-redundant MAG sets [3]
GTDB-Tk (v2.3.0)	Taxonomic tool	Taxonomic classification of MAGs	Link ARGs to specific taxonomic groups [3]

Core resistomes represent stable, ubiquitous genetic elements that persist across diverse human-influenced environments, functioning as potential reservoirs for emerging clinical resistance. The consistent identification of specific ARG subsets across wastewater, agricultural, and human gut environments highlights the interconnected nature of antibiotic resistance dissemination. Critically, latent ARGs—those not yet documented in clinical databases—dominate these core resistomes, suggesting current surveillance efforts capture only a fraction of the true resistance potential in environmental microbiomes [6].

Future research priorities should include standardized methodological approaches to enable direct cross-study comparisons, expanded geographical sampling to address current biases toward high-income countries, and integrated "One Health" surveillance that simultaneously tracks core resistomes across human, animal, and environmental compartments. The development of refined risk assessment frameworks, such as the Rank I ARG classification system [7], will help prioritize resistance determinants with the greatest potential for human health impact. As metagenomic methodologies continue to evolve, so too will our understanding of the core resistome and its implications for global antibiotic resistance management.

Antibiotic resistance poses an urgent global health threat, with antibiotic resistance genes (ARGs) serving as the fundamental genetic mechanisms enabling bacteria to survive antibiotic treatment. Understanding the distribution, diversity, and drivers of these genes across different environments is crucial for public health interventions and antibiotic stewardship policies. This guide provides a systematic comparison of ARG profiles across four critical habitats: wastewater treatment plants (WWTPs), soil, oceans, and the human gut, synthesizing recent global-scale metagenomic evidence to reveal their distinctive resistance signatures and interconnections within the One Health framework.

Global Distribution of Antibiotic Resistance Genes

Table 1: Antibiotic Resistance Genes Across Major Habitats

Habitat	Total ARG Abundance	Richness/Diversity	Dominant ARG Types	Key Bacterial Hosts	Mobility Potential
Wastewater Treatment Plants (WWTPs)	High (83.8% from 20 core genes) [1]	179 different ARGs across 15 drug classes [1]	Tetracycline (15.2%), Beta-lactam (46.5%), Glycopeptide (24.5%) [1]	Chloroflexi, Acidobacteria, Deltaproteobacteria [1]	High (57% of genomes carry putatively mobile ARGs) [1]
Soil	Lower than human gut, similar to WWTP effluent [12]	1739 subtypes total, 175 Rank I gene types [12]	Multidrug efflux pumps; Increasing mph(A), aadA, mef(B) [12]	Diverse environmental taxa; Escherichia, Bacteroides (for Rank I ARGs) [12] [13]	Moderate (Horizontal transfer crucial for connectivity) [12]
Oceans	Variable, higher near anthropogenic sources [14] [15]	High diversity, mostly previously unclassified genes [14]	Beta-lactamases (blaOXA-48, blaCTX-M-1), sulfonamides (sul1), tetracycline (tetA) [15]	Pelagibacter, Prochlorococcus, Vibrio (44% in abundant marine taxa) [14]	Unknown, but present in diverse marine bacteria [14]
Human Gut	Highest among habitats [12] [16]	149 gene types across populations [16]	Tetracycline (TetQ, TetW, TetO), vancomycin (vanB operon), Macrolide (MacB) [16]	Bacteroides, Escherichia, Clostridium [13]	High (frequent horizontal transfer observed) [17]

Geographical and Anthropogenic Influences

Geographical factors significantly impact ARG distribution, particularly in WWTPs and soil environments. A global analysis of 226 activated sludge samples from 142 WWTPs across six continents revealed that ARG composition differs significantly across continents and is distinct from other habitats [1]. While total ARG abundance showed no significant continental differences, mean ARG richness and Shannon's H index were significantly higher in Asia than most other continents [1].

In soil environments, Rank I ARGs (those characterized by host pathogenicity, gene mobility, and enrichment in human-associated environments) have shown a significant increase over time from 2008 to 2021, with both relative abundance (r = 0.89) and occurrence frequency (r = 0.83) demonstrating strong temporal increases [12]. This suggests growing anthropogenic impact on soil resistomes, with human feces (75.4%), chicken feces (68.3%), and WWTP effluent (59.1%) being the largest contributors to soil Rank I ARGs based on fast expectation-maximization for microbial source tracking (FEAST) analysis [12].

Ocean environments show significant regional variations, with the Mediterranean Sea exhibiting higher levels of multiple ARGs suggesting substantial anthropogenic impact, while even remote areas like the Arctic Ocean show presence of multiple ARGs around the Svalbard Islands [15]. The pervasive distribution highlights the global reach of antibiotic resistance contamination.

Human gut resistomes demonstrate a bimodal distribution between Western and non-Western populations, with ARG families either detected in many countries or very few [18]. Surprisingly, clinically relevant ARGs like carbapenemases (except cfiA) remain rare in global gut microbiomes, with only 8 out of 14,229 individuals harboring them in their gut microbiomes [18].

Methodologies for Resistome Analysis

Experimental Workflows

Diagram: Metagenomic Analysis of Habitat Resistomes

Key Experimental Protocols

Global WWTP Sampling and Metagenomic Analysis

The Global Water Microbiome Consortium (GWMC) established standardized protocols for global resistome comparison [1]. Researchers collected 226 activated sludge samples from 142 WWTPs across six continents, sequencing community DNA to obtain 2.8 terabases total (average 12.3 ± 3.9 Gb per sample) [1]. The analysis pipeline included: (1) assembly of 36,147,212 contigs >1 kb from filtered metagenomic reads; (2) prediction of 34,860,381 non-redundant open reading frames; (3) annotation of ARG sequences against reference databases; and (4) normalization to ARG copy number per bacterial cell for cross-comparison [1].

Functional Metagenomics for Novel ARG Discovery

Marine environment studies employed functional metagenomics to identify novel resistance genes [14]. This approach involved: (1) collecting 8-16 liters of seawater through sequential filtration; (2) extracting and purifying environmental DNA; (3) sonic shearing to 3 kb fragments; (4) cloning into pZE21 plasmid vector; (5) transforming into E. coli host; (6) plating on antibiotic-containing media (ampicillin, tetracycline, sulfadimethoxine, or nitrofurantoin); and (7) sequencing resistant clones [14]. This method identified that 44% of marine ARGs were found in abundant marine taxa including Pelagibacter, Prochlorococcus, and Vibrio [14].

Rank I ARG Risk Assessment in Soil

The soil resistome study implemented a risk-based framework focusing on "Rank I ARGs" [12]. The protocol included: (1) compiling 3,965 metagenomic samples (2,540 soil + 1,425 other habitats); (2) analyzing ARGs using ARGs-OAP v3.2.2 with SARG3.0_S database; (3) excluding multidrug efflux pumps to avoid mis-annotations; (4) applying the "connectivity" metric evaluating cross-habitat ARG sharing through sequence similarity and phylogenetic analysis; and (5) using FEAST for microbial source tracking [12]. This revealed that soil shares 60.1% of total ARGs and 50.9% of Rank I ARGs with other habitats [12].

Inter-Habitat Connectivity and Transfer

Resistance Exchange Networks

Diagram: ARG Connectivity Between Habitats

Research Reagent Solutions

Table 2: Essential Research Tools for Resistome Analysis

Reagent/Tool	Primary Function	Application Examples	Key Features
SARG Database	ARG annotation and classification	Soil Rank I ARG identification; habitat comparison [12]	Excludes transcriptional regulators, point mutations; curated Rank I ARGs
CARD (Comprehensive Antibiotic Resistance Database)	Reference database for ARG detection	Human gut resistome analysis; clinical ARG screening [18]	Includes resistance mechanisms, ontologies, and detection models
ARGs-OAP Pipeline	Metagenomic ARG analysis	Global soil and WWTP resistome profiling [12]	Standardized processing from sequencing data to ARG quantification
FEAST (Fast Expectation-Maximization Microbial Source Tracking)	Source attribution of ARGs	Soil ARG source tracking (human feces, WWTP, livestock) [12]	Estimates contributions of source environments to sink resistomes
Functional Metagenomic Libraries	Novel ARG discovery	Marine resistome exploration; uncharacterized resistance genes [14]	pZE21 plasmid system; heterologous expression in E. coli
Metagenomic Assembly-based Host Tracking	ARG host identification	Linking ARGs to bacterial hosts in gut and pristine environments [13]	Connects ARG contigs to taxonomic markers in assembled genomes

Discussion and Research Implications

The comparative analysis of habitat-specific resistomes reveals that while each environment maintains distinctive ARG signatures, substantial connectivity exists between habitats—particularly through wastewater systems and soil. WWTPs serve as critical interception points for preventing ARG dissemination, receiving inputs from human gut microbiomes and discharging effluents that impact soil and marine environments. The human gut represents a significant ARG reservoir, with specific taxonomic restrictions for clinically relevant genes that have not yet widely spread across diverse commensal microbiota [18]. Soil environments are experiencing increasing ARG risk over time, functioning as both sinks and potential sources within the One Health framework [12]. Marine environments, including remote oceans, now show pervasive ARG contamination, demonstrating the global reach of antibiotic resistance [15].

Future research should prioritize longitudinal studies tracking specific ARG transfer events between habitats, develop more sensitive detection methods for low-abundance clinically relevant genes, and establish standardized protocols for cross-habitat resistome surveillance. The findings underscore the necessity of integrated approaches addressing antibiotic resistance across environmental boundaries, clinical practice, and agricultural use to effectively mitigate this global health threat.

Antimicrobial resistance (AMR) presents an urgent global public health threat, directly responsible for an estimated 1.27 million deaths annually and contributing to nearly 5 million more [19] [20]. The proliferation of antibiotic resistance genes (ARGs) across diverse environments undermines the efficacy of modern medicine, rendering common infections increasingly difficult to treat and elevating risks associated with surgical procedures, cancer chemotherapy, and other medical interventions [19]. Understanding the drivers behind ARG emergence and dissemination is fundamental to developing effective containment strategies.

This review examines the compelling evidence supporting population density and antibiotic use as pivotal anthropogenic factors shaping the global resistome. Through geographical comparisons of ARG distribution across clinical, environmental, and human gut microbiota studies, we analyze how human activities create hotspots for resistance development and transmission. The complex interplay between these drivers within the One Health framework—encompassing human, animal, and environmental sectors—provides critical insights for coordinated intervention strategies to mitigate the AMR crisis.

Methodological Approaches in Resistome Research

Core Analytical Techniques

Research comparing antibiotic resistance across geographical regions relies on standardized methodologies to ensure valid comparisons. The field primarily utilizes high-throughput molecular techniques that provide comprehensive insights into resistome diversity, abundance, and dynamics.

Table 1: Core Methodologies for Geographical ARG Comparison

Method	Application	Key Outputs	Considerations
Shotgun Metagenomic Sequencing	Profiling total ARG diversity and abundance in complex samples [1]	ARG richness, relative abundance, composition	Requires substantial sequencing depth (∼12.3 Gb/sample); enables assembly of contigs and ORF prediction
High-Throughput Quantitative PCR (HT-qPCR)	Targeted quantification of specific ARG types across many samples [21]	Absolute ARG copy numbers (e.g., copies/gram or copies/cell)	Limited to known ARG sequences; highly sensitive for quantification
Metagenome-Assembled Genomes (MAGs)	Linking ARGs to specific microbial hosts and assessing mobility potential [1] [3]	ARG carrier taxa, phylogenetic assignment, mobile genetic element associations	Dependent on assembly quality; requires high completeness (>50%) and low contamination (<10%)
Statistical Analysis (PCoA, PERMANOVA)	Identifying geographical patterns and testing significant differences in resistome composition [1] [22]	Spatial clustering, significance of regional differences	Reveals continental-scale separation when applied at gene level

Experimental Workflow for Geographical Comparisons

The following diagram illustrates a standardized research workflow for comparing antibiotic resistomes across geographical regions:

Figure 1: Experimental workflow for geographical ARG comparison studies

Essential Research Reagents and Solutions

Table 2: Key Research Reagent Solutions for Resistome Studies

Reagent/Solution	Function	Application Examples
CTAB Lysis Buffer	DNA extraction from complex matrices	Soil, sludge, and fecal sample processing [21]
QIAamp Fast DNA Stool Mini Kit	High-quality DNA extraction from fecal samples	Human gut microbiome studies [3]
TruSeq DNA PCR-Free Sample Preparation Kit	Library construction for metagenomic sequencing	Illumina platform sequencing [21]
SmartChip Real-time PCR System	High-throughput ARG quantification	Simultaneous detection of 330+ ARGs across samples [21]
FEAST Algorithm	Microbial source tracking	Attributing ARG origins across habitats [22]
CheckM Lineage Markers	Assessing MAG quality	Evaluating completeness/contamination of assembled genomes [3]

Geographical Patterns of Antibiotic Resistance

Global Clinical Resistance Patterns

Surveillance data from the World Health Organization reveals substantial geographical variation in clinical antibiotic resistance patterns. The 2025 GLASS report indicates disturbing trends, with one in six laboratory-confirmed bacterial infections globally showing resistance to antibiotic treatments in 2023 [23]. Resistance increased in over 40% of pathogen-antibiotic combinations monitored between 2018-2023, with an average annual increase of 5-15% [23].

Table 3: Regional Patterns in Clinical Antibiotic Resistance

Geographical Region	Resistance Prevalence	Key Pathogens & Resistance Patterns
WHO South-East Asia & Eastern Mediterranean	1 in 3 reported infections resistant [23]	High gram-negative resistance; >40% E. coli and >55% K. pneumoniae resistant to 3rd-gen cephalosporins
African Region	1 in 5 infections resistant [23]	Extremely high resistance rates; >70% E. coli and K. pneumoniae resistant to 3rd-gen cephalosporins
European Region	Variable resistance rates [24]	MRSA rates range from <5% (Scandinavia) to >50% (Romania)
United States	2.8+ million resistant infections annually [20]	Significant healthcare-associated infections; 18% reduction in deaths achieved pre-COVID

Environmental Resistome Distributions

Wastewater treatment plants (WWTPs) serve as critical convergence points for anthropogenic ARGs, receiving wastewater from homes, hospitals, and pharmaceutical facilities [1]. A global analysis of 226 activated sludge samples from 142 WWTPs across six continents revealed that while total ARG abundance showed no significant continental differences, ARG composition differed significantly across continents [1]. A core set of 20 ARGs was present in all WWTPs analyzed, accounting for 83.8% of total ARG abundance, with tetracycline, beta-lactam, and glycopeptide resistance genes being most abundant [1].

Beyond WWTPs, ARGs contaminate diverse marine environments, with the Mediterranean Sea exhibiting higher levels of multiple ARGs, suggesting significant anthropogenic impact [15]. Even remote environments like the Arctic Ocean show the presence of multiple ARGs around the Svalbard Islands, demonstrating the pervasive nature of antibiotic resistance [15].

Soil represents another significant ARG reservoir, with studies showing increasing connectivity between soil and clinical resistomes over time. Analysis of 3,965 metagenomic datasets revealed that soil shares 60.1% of total ARGs and 50.9% of Rank I (high-risk) ARGs with other habitats, primarily human feces (75.4%), chicken feces (68.3%), and WWTP effluent (59.1%) [22].

Human Gut Resistome Variations

Geographical variations in the human gut resistome reflect regional differences in antibiotic usage patterns. A study of 119 fecal samples from four Chinese provinces revealed significant regional differences in gut ARG profiles, with ARGs conferring resistance to multidrug agents, peptides, tetracyclines, glycopeptides, and aminoglycosides being more prevalent in Jiangsu province compared to Sichuan and Yunnan [3]. This distribution pattern likely reflects regional differences in antibiotic usage, with areas of higher consumption exhibiting enriched resistance gene profiles in gut microbiota.

Population Density as a Key Driver

Evidence from Multiple Environments

The relationship between population density and antibiotic resistance development operates through multiple mechanisms. A seminal 2003 study demonstrated that antibiotic consumption expressed as defined daily dose per km² (DSD) correlated strongly with resistance prevalence (Pearson correlation coefficients 0.86-1.00), while consumption per capita (DID) showed poor correlation (-0.93 to 0.87) [25]. This finding suggests population density is a critical factor in resistance epidemiology, likely through increased transmission opportunities in crowded environments.

In wastewater environments, studies have identified strong correlations between bacterial community structure and resistome composition (Procrustes analysis: M² = 0.74, p < 0.001) [1], indicating that human-influenced microbial ecosystems develop characteristic resistance profiles. The abundance of mobile genetic elements positively correlates with ARG abundance in these systems, with 57% of 1,112 recovered high-quality genomes containing putatively mobile ARGs [1], facilitating rapid horizontal transfer in dense microbial communities.

Conceptual Framework of Density-Driven Resistance

The relationship between population density, antibiotic use, and resistance development involves complex interactions across multiple subsystems:

Figure 2: Conceptual framework of density-driven resistance development

Antibiotic Use as a Selective Pressure

Usage Patterns and Resistance Development

Antibiotic consumption varies dramatically between regions, with some countries consuming more than three times the antibiotics per capita compared to others [25]. This selective pressure directly enriches for resistant strains and ARGs across environments. In agricultural settings, nearly half of China's annual antibiotic production is used in livestock and poultry sectors [21], creating significant selective pressure that disseminates resistance through food chains and environmental contamination.

The gut microbiome serves as a key interface where antibiotic selective pressure shapes resistomes. Geographical variations in gut ARG profiles directly reflect regional antibiotic usage patterns, with provinces exhibiting higher usage showing enriched resistance to multiple drug classes [3]. These gut resistomes can subsequently transfer to pathogens, creating a reservoir for clinical resistance development.

Temporal Trends in Resistance Gene Propagation

Analysis of global soil metagenomes from 2008 to 2021 reveals alarming temporal trends in environmental resistomes. While total ARG abundance in soil remained time-independent, the relative abundance of Rank I (high-risk) ARGs increased significantly (r = 0.89, p < 0.001), as did their occurrence frequency (r = 0.83, p < 0.001) [22]. This suggests that human-associated, clinically relevant ARGs are increasingly establishing in environmental reservoirs, creating a feedback loop that perpetuates resistance.

Specific high-risk ARGs showing marked increases in soil environments include mph(A), APH(3')-Ia, AAC(6')-le-APH(2")-la, ANT(6)Ia, aadA, APH(6)-Id, aadA10, mef(B), and APH(3")-Ib, with NDM-19 first detected in soil samples in 2021 [22]. The spread of these carbapenemase genes into environmental reservoirs is particularly concerning given their ability to confer resistance to last-resort antibiotics.

Interconnection of Anthropogenic Drivers

The relationship between population density and antibiotic use creates synergistic effects that accelerate resistance development. Dense human populations typically exhibit higher antibiotic consumption due to increased disease transmission and healthcare infrastructure, while simultaneously providing more opportunities for resistant bacteria to disseminate through human-to-human contact, contaminated water, and food supplies [25] [19].

This interconnection is particularly evident in wastewater systems, where ARG composition strongly correlates with bacterial taxonomic composition, with Chloroflexi, Acidobacteria, and Deltaproteobacteria identified as major ARG carriers [1]. Resistome variations in these systems appear to be driven by a complex combination of stochastic processes and deterministic abiotic factors, including those associated with human population density and activities.

The One Health framework recognizes that effective resistance containment requires coordinated action across human, animal, and environmental sectors [19]. The Quadripartite Joint Secretariat on Antimicrobial Resistance (FAO, UNEP, WHO, and WOAH) exemplifies this approach, working to drive multi-stakeholder engagement in AMR mitigation across all relevant sectors [19].

From Metagenomics to Risk Quantification: Frameworks for Assessing ARG Health Impact

Antimicrobial resistance (AMR) represents one of the most serious global health threats of the 21st century, projected to cause 10 million annual deaths by 2050 if left unchecked [26] [27]. Tackling this crisis requires robust surveillance methods capable of accurately monitoring antibiotic resistance genes (ARGs) across diverse environments, from clinical settings to natural ecosystems. Within the framework of geographical comparison of ARG research, two advanced detection methods have emerged as fundamental tools: High-Throughput Quantitative Polymerase Chain Reaction (HT-qPCR) and Shotgun Metagenomic Sequencing (SMS) [28] [29]. This guide provides an objective comparison of these technologies, supporting researchers in selecting appropriate methodologies for mapping the global resistome.

Technical Comparison: HT-qPCR versus Shotgun Metagenomics

Fundamental Principles and Workflows

HT-qPCR utilizes specialized platforms containing nanoliter-scale reaction wells (e.g., WaferGen SmartChip with 5,184 wells) to simultaneously amplify and quantify hundreds of predefined ARG targets using specific primer sets [30] [31]. This method provides absolute quantification of ARG abundance normalized to bacterial 16S rRNA genes, enabling highly sensitive detection of low-abundance resistance determinants [29].

Shotgun Metagenomics employs next-generation sequencing to randomly fragment and sequence all DNA in a sample without target-specific amplification [32] [33]. The resulting sequences are then computationally aligned against comprehensive ARG databases (e.g., CARD, SARG) to identify known and novel resistance genes, while also providing information about the broader genetic context, including mobile genetic elements and bacterial hosts [26] [34].

Comparative Performance Metrics

Table 1: Key methodological characteristics of HT-qPCR and Shotgun Metagenomics

Parameter	HT-qPCR	Shotgun Metagenomics
Detection Basis	Targeted amplification of known genes	Untargeted sequencing of all DNA
Number of Targets	Typically 100-400 predefined ARGs [27] [31]	Potentially all ARGs in databases (thousands) [27]
Sensitivity	High (can detect rare targets ~10-4 ARGs/16S rRNA) [30]	Moderate (requires sufficient sequencing depth) [30]
Quantification Approach	Absolute quantification (copies/16S rRNA) [29]	Relative abundance (reads mapped to databases) [29]
Novel Gene Discovery	Not possible (dependent on primers) [28]	Possible through database mining [28]
Genetic Context Information	Limited to co-amplification of associated elements [29]	Comprehensive (plasmids, integrons, transposons) [26] [33]
Turnaround Time	Rapid (1-2 days after DNA extraction) [28]	Longer (includes library prep and bioinformatics) [28]
Cost per Sample	Moderate [27]	Higher [27]

Table 2: Experimental findings from comparative studies

Study Context	HT-qPCR Results	Shotgun Metagenomics Results	Correlation Between Methods
Wastewater Samples (4 locations)	Detected ARGs for 11 antibiotic classes; most abundant: aminoglycoside, MDR, MLSB, tetracycline, beta-lactams [26]	Similar ARG classes identified; strong correlation for most antibiotic classes [26]	Strong correlation for majority of antibiotic classes; discrepancies due to primer mismatches or low coverage [26]
Coastal Sediments (Kuwait)	122 elements detected (100 ARGs, 5 integrons, 18 MGEs); dominant: beta-lactams > aminoglycoside > tetracycline [27]	402 ARGs, 1,567 plasmid sequences, 168 integrons detected [27]	SMS detected 3x more ARGs and 12x more plasmid sequences; HT-qPCR provided quantitative data [27]
Aquaculture Environments	28 out of 31 targeted ARGs/MGEs detected [29]	314 ARG subtypes detected; only 18 overlapped with HT-qPCR targets [29]	Both methods captured similar spatial trends despite limited target overlap [29]

Experimental Protocols

Standardized Workflow for Comparative Resistome Studies

The following workflow represents a harmonized approach for implementing both HT-qPCR and shotgun metagenomics in geographical ARG comparison studies, synthesized from multiple methodological papers [26] [27] [33].

Detailed Methodological Components

Sample Collection and DNA Extraction

For geographical comparison studies, consistent sample collection and processing is critical. Composite wastewater samples should be collected over 24-hour periods using autosamplers, with 250 mL transferred to sterile polypropylene bottles on ice [26]. For sediment samples, the 0-2 cm layer should be collected using sterile equipment [33]. DNA extraction typically employs commercial kits like the PowerSoil DNA Isolation Kit with modifications for different sample matrices [26] [27]. DNA quality should be verified using spectrophotometric methods (NanoDrop) and fluorometric quantification (Qubit) [27].

HT-qPCR Analysis Protocol

The HT-qPCR workflow utilizes platforms such as the WaferGen SmartChip Real-time PCR system with 5,184 nanoliter-scale wells [30]. Key steps include:

• Reaction Setup: 100 nL reactions containing 1× SmartChip green gene expression master mix, 300 nM primers, and approximately 2 ng/µL DNA [27] [31].
• Amplification Parameters: Cycling conditions optimized for the specific primer sets with detection threshold set at CT of 27 [26].
• Data Processing: Relative abundance calculation using the 2^(-ΔCT) method where ΔCT = CT(target gene) - CT(16S rRNA gene) [26].
• Quality Control: PCR efficiency values typically range between 1.8-2.1, with melting curve analysis to verify amplification specificity [26].

Shotgun Metagenomics Protocol

The shotgun metagenomics approach follows these standardized steps:

• Library Preparation: TruSeq DNA PCR-free library preparation is recommended to avoid amplification biases [26] [28].
• Sequencing: High-throughput sequencing on Illumina platforms (NovaSeq) to generate sufficient coverage (typically 3-10 Gb per sample) [33].
• Bioinformatic Analysis:
  • Quality control of reads using FastQC and Trimmomatic
  • Assembly using metaSPAdes or similar tools [32]
  • ARG annotation using CARD's Resistance Gene Identifier (RGI) or SARG database with thresholds of 90% sequence identity and 90% coverage [26] [34]
  • Taxonomic classification using Kraken/Bracken [32]
• Mobile Genetic Element Analysis: Identification of plasmids, integrons, and insertion sequences using dedicated databases [27].

Geographical Applications of ARG Detection Methods

Regional Case Studies

Both HT-qPCR and shotgun metagenomics have been successfully deployed in diverse geographical contexts to map antibiotic resistomes:

Urban Aquatic Systems: In Japan, HT-qPCR characterized ARG pollution profiles across 24 urban rivers, detecting 9-53 target genes per sample with abundances increasing following wastewater treatment plant effluent discharge [31]. Similarly, shotgun metagenomics applied to Lake Victoria in Kenya revealed nine high-risk ARG families from the WHO priority list, with Proteobacteria identified as the primary antibiotic-resistant phylum (53% relative abundance) [33].

Marine Environments: A global marine study quantifying ARG distribution across the Atlantic, Arctic, and Indian Oceans, the Mediterranean Sea, and the Persian Gulf found sul1 to be ubiquitous, with the Mediterranean Sea exhibiting higher levels of multiple ARGs suggesting significant anthropogenic impact [15]. The Arctic Ocean around the Svalbard Islands also showed unexpected ARG presence, demonstrating the pervasive nature of antibiotic resistance even in remote regions [15].

Wastewater Treatment Plants: Comparative assessment in Kuwaiti coastal sediments receiving emergency waste discharges demonstrated HT-qPCR's identification of approximately 100 ARGs versus 402 ARGs detected via shotgun metagenomics, highlighting the complementary nature of both methods for comprehensive environmental surveillance [27].

Methodological Considerations for Geographical Studies

When designing geographical comparison studies of ARGs, researchers should consider:

• Sample Heterogeneity: Environmental samples from different geographical regions may exhibit varying levels of inhibitors that affect DNA extraction efficiency and subsequent analysis [28].
• Database Biases: Shotgun metagenomics relies heavily on reference databases which may underrepresent ARG diversity in understudied geographical regions [29].
• Normalization Approaches: HT-qPCR typically uses 16S rRNA gene normalization, while metagenomics may use reads per million or genome equivalents, complicating cross-method comparisons [29].
• Spatial Scaling: HT-qPCR enables broader geographical coverage due to lower costs, while shotgun metagenomics provides deeper mechanistic insights at selected locations [28].

Research Reagent Solutions

Table 3: Essential research reagents and materials for HT-qPCR and shotgun metagenomics

Category	Specific Products/Kits	Application	Key Features
DNA Extraction	PowerSoil DNA Isolation Kit [26] [27]	Environmental DNA extraction	Effective for difficult samples; standardized for diverse matrices
HT-qPCR Platforms	WaferGen SmartChip Real-time PCR System [26] [30]	High-throughput ARG quantification	5,184 nanoliter wells; 384+ primer sets
HT-qPCR Master Mix	SmartChip Green Gene Expression Master Mix [27]	Amplification in HT-qPCR	Optimized for nanoliter-scale reactions
Sequencing Kits	TruSeq DNA PCR-Free Library Prep [26]	Metagenomic library preparation	Avoids amplification bias; maintains representation
Sequencing Platforms	Illumina NovaSeq [33]	Shotgun metagenomics	High throughput; appropriate for complex samples
Bioinformatic Tools	CARD RGI [26], ARGs-OAP [34], metaSPAdes [32]	ARG annotation & analysis	Specialized databases and algorithms for resistome profiling

HT-qPCR and shotgun metagenomics offer complementary approaches for geographical comparison of antibiotic resistance genes. HT-qPCR provides sensitive, quantitative data for predefined targets across extensive sample sets, making it ideal for spatial surveillance and time-series studies. Shotgun metagenomics enables comprehensive discovery of known and novel ARGs with functional context, providing mechanistic insights into resistance dissemination. For robust geographical ARG assessment, an integrated approach leveraging both methods' strengths provides the most complete understanding of resistome dynamics across diverse ecosystems and regions. Future methodological developments should focus on standardized protocols, expanded reference databases, and bioinformatic tools that facilitate cross-study comparisons to effectively monitor the global spread of antibiotic resistance.

The global spread of antibiotic resistance genes (ARGs) represents one of the most significant public health challenges of the 21st century, with bacterial antimicrobial resistance directly responsible for over 1.27 million deaths annually [35] [22]. The "One Health" concept recognizes that the interconnectedness of human, animal, and environmental health is crucial for understanding and containing antibiotic resistance [36]. Within this framework, environmental compartments serve as massive reservoirs for both known and novel ARGs, creating an extensive gene pool from which pathogens can acquire resistance mechanisms [1] [36]. To effectively prioritize resources and interventions, researchers have developed sophisticated risk assessment frameworks that evaluate the potential health threats posed by different ARGs beyond mere abundance detection [35].

Contemporary risk assessment has evolved to focus on the likelihood that environmental ARGs will confound clinical treatment of bacterial infections in humans [35]. This approach moves past simple quantification to evaluate the complex pathways through which ARGs can transfer from environmental bacteria to human pathogens. The integration of human accessibility, mobility, and pathogenicity as core indicators provides a standardized methodology for comparing ARG risks across different geographical regions and environmental compartments [35] [37]. This framework enables researchers and public health officials to identify the most threatening ARGs and prioritize them for monitoring and containment efforts, ultimately guiding the development of targeted interventions to mitigate the global spread of antibiotic resistance [36].

Core Components of the Risk Assessment Framework

Quantitative Indicators for ARG Risk Evaluation

The risk assessment framework for ARGs integrates four principal indicators that collectively determine the potential threat to human health. These indicators evaluate the complete pathway from environmental detection to clinical impact, enabling a systematic approach to risk prioritization.

• Human Accessibility: This indicator measures the potential for ARGs to transfer from environmental reservoirs to the human microbiome. It is quantitatively assessed by evaluating the abundance and prevalence of ARGs in human-associated habitats compared to environmental sources [35]. Genes with higher abundance and prevalence in human gut microbiomes pose greater accessibility risks. Research analyzing 4,572 metagenomic samples across six habitats demonstrated that only 1,714 of 2,561 detected ARGs were present in human habitats, with most showing limited accessibility (average abundances <50 RPKM per sample and prevalence <10%) [35]. The gene tetQ, which confers tetracycline resistance, demonstrated particularly high human accessibility [35].

• Mobility: Mobility evaluates the potential for ARGs to transfer between bacterial cells via horizontal gene transfer (HGT). This indicator is assessed by detecting the presence of ARGs on mobile genetic elements (MGEs) such as plasmids, transposases, and integrons [35] [3]. The co-localization of ARGs with MGEs significantly increases dissemination risk. A global wastewater study found that 57% of 1,112 recovered high-quality bacterial genomes contained putatively mobile ARGs, highlighting the substantial role of HGT in resistance spread [1]. Mobility risk is further elevated when ARGs are located on plasmids, as observed in grazing-disturbed natural environments where plasmid-carried ARGs increased significantly [38].

• Pathogenicity: This component assesses the clinical relevance of ARG host bacteria by identifying whether ARGs are located in pathogenic taxa [35]. The highest risk occurs when ARGs and virulence factors (VFs) are found within the same open reading frame (ORF), defining the host bacterium as a potential drug-resistant pathogen [38]. Metagenomic analyses can identify these drug-resistant pathogens by co-localization of ARGs and virulence factors in genetic sequences. Studies have shown that grazing disturbance increases the relative abundance of drug-resistant pathogens, with pathogens like Acinetobacter baumannii ACICU detected exclusively in disturbed environments [38].

• Clinical Availability: This indicator evaluates the relationship between ARG expression and the clinical usage of corresponding antibiotics [35]. ARGs that confer resistance to widely used antibiotic classes (e.g., beta-lactams, tetracyclines, aminoglycosides) pose higher treatment challenges. The framework considers both current antibiotic usage patterns and the development of new antibiotics for clinical use [35].

Table 1: Core Components of the ARG Risk Assessment Framework

Risk Indicator	Definition	Measurement Approach	High-Risk Example
Human Accessibility	Potential for ARG transfer from environment to humans	Abundance/prevalence in human-associated habitats vs. environmental sources	tetQ gene shows high abundance in human gut microbiomes [35]
Mobility	Potential for horizontal gene transfer between bacteria	Presence on mobile genetic elements (plasmids, transposons, integrons)	57% of wastewater bacterial genomes contain mobile ARGs [1]
Pathogenicity	Association of ARGs with human pathogens	Co-localization with virulence factors in pathogenic taxa	Acinetobacter baumannii ACICU carrying ARGs [38]
Clinical Availability	Relevance to currently used antibiotics	Correlation with clinically important antibiotic classes	ARGs conferring resistance to beta-lactams and tetracyclines [35]

Integration of Risk Indicators

The comprehensive risk assessment integrates these four indicators to calculate an overall health risk score for individual ARGs. This integrated approach revealed that approximately 23.78% of the 2,561 ARGs detected in global metagenomic analyses pose a significant health risk, with multidrug resistance genes (MDRGs) representing particularly high threats [35]. The integration of these factors enables the identification of Rank I ARGs - those characterized by confirmed host pathogenicity, demonstrated gene mobility, and enrichment in human-associated environments [22]. These high-priority genes become primary targets for surveillance and intervention efforts within the One Health framework.

Geographical Comparison of ARG Risks

Global Distribution Patterns

Geographical factors significantly influence the distribution and risk profiles of antibiotic resistance genes, creating distinct regional patterns in ARG abundance, diversity, and composition. Understanding these geographical variations is essential for developing targeted surveillance and intervention strategies.

• Continental Variations in WWTP Resistomes: A global analysis of 226 activated sludge samples from 142 wastewater treatment plants across six continents revealed significant structural differences in resistomes at the gene level [1]. While total ARG abundance showed no significant difference across continents, ARG richness and Shannon diversity index were significantly higher in Asia than most other continents [1]. Principal coordinates analysis (PCoA) demonstrated strong regional separation of resistomes, with distinct continental signatures observed in ARG composition [1]. These findings suggest that both localized evolutionary pressures and dispersal limitations contribute to geographical structuring of ARGs.

• Region-Specific Risk Patterns: The human accessibility of ARGs varies substantially across geographical regions due to differences in infrastructure, antibiotic use regulations, and environmental conditions. A study of 14,641 human gut metagenomes from 32 countries revealed a 9% higher ARG load in women compared to men in high-income countries, while this trend was reversed in low- and middle-income countries (LMICs) [2]. Additionally, the abundance of specific ARG classes differed significantly between economic regions, with tetracycline resistance genes dominating in high-income countries, while aminoglycoside resistance genes and folate pathway antagonist genes were more abundant in LMICs [2].

• Anthropogenic Impact on ARG Distribution: Population density and associated anthropogenic activities strongly correlate with ARG abundance and risk profiles. Analyses of samples from high-intensity (>58 people/km²) versus low-intensity population regions revealed significantly higher total ARG abundance in high-intensity regions, with 671 ARGs detected exclusively in these environments [35]. Shared ARG patterns between human-associated habitats and built environments further demonstrate the role of anthropogenic factors in ARG dissemination, with built environments sharing the most ARGs (1,460) with human habitats [35].

Table 2: Geographical Distribution of ARG Risks Across Environments

Geographical Region	Distinct ARG Features	Key Risk Factors	Notable Findings
Asia	Higher ARG richness and diversity in WWTPs [1]	Population density, anthropogenic activity	Significant differences in resistome structure compared to other continents [1]
High-Income Countries	Higher tetracycline resistance genes; 9% higher ARG load in women [2]	Clinical antibiotic usage patterns	Median ARG load (RPKM): 560 for women, 515 for men [2]
Low-Middle Income Countries	Higher aminoglycoside and folate pathway antagonist genes [2]	Infrastructure limitations, regulation	Different gender patterns in ARG load compared to high-income countries [2]
Mediterranean Sea	Multiple ARGs in single samples [15]	Anthropogenic impact from coastal activities	Higher levels of multiple ARGs suggest significant human impact [15]
Arctic Ocean	Presence of multiple ARGs in remote areas [15]	Long-range transport, climate change	Detection near Svalbard Islands shows pervasive spread [15]

Temporal Trends in ARG Risks

Longitudinal analyses reveal concerning trends in environmental ARG risks, particularly the increasing prevalence of high-risk ARGs in soil environments. The relative abundance of Rank I ARGs in global soils showed a significant increase over time (r=0.89, p<0.001), as did their occurrence frequency (r=0.83, p<0.001) [22]. This trend persisted even after normalizing for sampling biases, indicating a genuine increase in soil ARG risk potential. Specific high-risk ARG subtypes showing consistent increases include mph(A), APH(3')-Ia, AAC(6')-le-APH(2")-la, ANT(6)Ia, aadA, APH(6)-Id, aadA10, mef(B), and APH(3")-Ib, with the first detection of NMD-19 in soil samples in 2021 [22]. These temporal patterns highlight the expanding reservoir of clinically relevant ARGs in environmental compartments.

Experimental Protocols for ARG Risk Assessment

Metagenomic Analysis Workflow

Comprehensive risk assessment of antibiotic resistance genes relies on standardized metagenomic protocols that enable comparative analyses across different geographical regions and habitat types. The following workflow represents the integrated methodology derived from multiple large-scale studies:

• Sample Collection and Preservation: Consistent sampling methods are critical for meaningful geographical comparisons. For wastewater studies, activated sludge samples should be collected from multiple treatment plants across target regions [1]. For human gut microbiome studies, fecal samples must be immediately transported on dry ice and stored at -80°C to preserve DNA integrity [3]. Geographical metadata (GPS coordinates, altitude, habitat type) should be systematically recorded for all samples [3].

• DNA Extraction and Quality Control: Total genomic DNA is extracted using commercial kits optimized for different sample types (e.g., FastDNA SPIN Kit for Soil, QIAamp Fast DNA Stool Mini Kit) [3] [38]. DNA quality and quantity should be assessed using spectrophotometry (NanoDrop) and gel electrophoresis, with host DNA removal (e.g., alignment to human reference genome GRCh38) for human-associated samples [3].

• Sequencing and Assembly: Shotgun metagenomic sequencing should be performed on Illumina platforms with sufficient depth (typically 12-15 Gb per sample) to capture rare ARGs [1]. Quality filtering includes adapter trimming, removal of short reads (<50 bp), and quality score filtering (Q>20) using tools like fastp [3]. De novo assembly is performed using MEGAHIT or similar assemblers optimized for metagenomic data [3].

• ARG Annotation and Quantification: Quality-filtered reads are aligned to ARG databases such as the Comprehensive Antibiotic Research Database (CARD) using optimized parameters [35] [3]. Normalization should be performed using copies per cell rather than RPKM to enable cross-study comparisons, calculated by considering the average bacterial genome size and total sequencing depth [36].

• Host Identification and Mobility Assessment: Contig-based analysis identifies ARG hosts by examining co-localization with phylogenetic markers [35]. Mobility potential is assessed by detecting MGEs near ARG sequences using specialized databases [3]. High-quality metagenome-assembled genomes (MAGs) should be reconstructed using tools like MetaBAT2, MaxBin2, and CONCOCT, followed by dereplication [3].

• Risk Prioritization: ARGs are ranked according to the integrated risk framework, considering human accessibility, mobility, pathogenicity, and clinical relevance [35]. Priority Rank I ARGs are identified based on host pathogenicity, demonstrated mobility, and human-associated enrichment [22].

Quantitative Source Tracking

Microbial source tracking analysis using tools like FEAST (Fast Expectation-maximization for Microbial Source Tracking) enables quantification of ARG sharing between habitats [22]. This approach revealed that soil shares 60.1% of total ARGs and 50.9% of Rank I ARGs with other habitats, with human feces (75.4%), chicken feces (68.3%), and WWTP effluent (59.1%) being the largest contributors to soil Rank I ARGs [22]. This methodology is particularly valuable for understanding geographical ARG transmission pathways.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents for ARG Risk Assessment Studies

Reagent/Material	Specific Example	Function in ARG Risk Assessment
DNA Extraction Kit	FastDNA SPIN Kit for Soil; QIAamp Fast DNA Stool Mini Kit [3] [38]	High-quality metagenomic DNA extraction from diverse sample types
Quality Control Tools	NanoDrop spectrophotometer; Agarose gel electrophoresis [3]	Assessment of DNA quality and quantity before sequencing
Sequence Quality Tool	fastp (v0.23.0) [3]	Adapter trimming, quality filtering, and preprocessing of raw reads
Metagenomic Assembler	MEGAHIT (v1.1.2) [3]	De novo assembly of contigs from quality-filtered reads
Binning Tools	MetaBAT2 (v2.12.1); MaxBin2 (v2.2.5); CONCOCT (v0.5.0) [3]	Reconstruction of metagenome-assembled genomes (MAGs) from contigs
Dereplication Tool	dRep (v3.4.2) [3]	Clustering of MAGs at 99% ANI threshold for strain-level similarity
ARG Database	Comprehensive Antibiotic Research Database (CARD v3.0.9) [35] [3]	Reference database for annotation of antibiotic resistance genes
Taxonomic Classifier	GTDB-Tk (v2.3.0) [3]	Taxonomic classification of MAGs using Genome Taxonomy Database
MGE Database	MGEs90 database [3]	Identification of mobile genetic elements associated with ARGs
Source Tracking Tool	FEAST [22]	Quantification of ARG sharing between different habitats

The risk assessment framework integrating human accessibility, mobility, pathogenicity, and clinical availability provides a standardized methodology for evaluating and comparing antibiotic resistance gene threats across geographical regions. This approach represents a significant advancement over simple abundance-based measurements, enabling prioritization of the approximately 24% of ARGs that pose genuine health risks [35]. The geographical comparisons facilitated by this framework reveal distinct continental signatures in resistome composition and varying risk profiles between high-income and low-middle-income countries [1] [2].

Future developments in ARG risk assessment will likely focus on enhanced standardization of metagenomic methods, particularly universal quantification units (ARG copy per cell) and absolute quantification approaches that enable more accurate geographical and temporal comparisons [36]. The integration of long-read sequencing technologies will improve the recovery of genetic context for ARGs, enhancing mobility assessments and host attribution [36]. Furthermore, the establishment of environmental reference samples will help distinguish true biological variations from technical artifacts in geographical surveillance [36].

As global surveillance networks expand, the integrated risk assessment framework will play an increasingly vital role in identifying emerging resistance threats, tracking their geographical spread, and guiding targeted interventions. The development of regulatory standards based on risk assessment findings will ultimately support the formulation of evidence-based policies to mitigate the global spread of antibiotic resistance across the One Health continuum [36].

Identifying High-Risk 'Rank I' ARGs for Targeted Surveillance

Antimicrobial resistance (AMR) poses a critical global health threat, with certain antibiotic resistance genes (ARGs) presenting substantially higher risks due to their mobility, host pathogenicity, and clinical relevance. This comparison guide evaluates frameworks for identifying "Rank I" ARGs—those representing the most immediate threats to human health. We analyze experimental data and methodologies that enable researchers to prioritize high-risk ARGs for surveillance, focusing on their distribution across geographical regions and environments. The integration of metagenomic approaches with standardized risk assessment frameworks provides powerful tools for tracking current resistance threats and guiding public health interventions.

The identification of high-risk ARGs requires systematic frameworks that evaluate multiple risk factors. Not all ARGs pose equivalent threats to human health, necessitating prioritization strategies for effective surveillance and intervention [39]. Rank I ARGs represent the highest risk category—genes that are enriched in human-associated environments, located on mobile genetic elements (MGEs), and already present in known bacterial pathogens [39]. These genes have the greatest potential to contribute to multidrug resistance in pathogens that can cause untreatable infections.

Several risk assessment frameworks have been developed to classify ARGs based on their potential impact on human health. The omics-based framework proposed by Zhang et al. has gained significant traction for its practical implementation and validation against known clinical threats [39]. This approach evaluates ARGs through a decision tree that considers: (1) enrichment in human-associated environments compared to pristine environments, (2) gene mobility potential based on association with MGEs, and (3) presence in ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) [39]. Through this methodology, only approximately 3% of all known ARGs qualify as Rank I or "current threats" [39].

Alternative frameworks have expanded on these core concepts. Some researchers incorporate additional factors such as clinical availability of corresponding antibiotics and human accessibility—the potential for ARGs to transfer from environmental compartments to humans [40]. These multidimensional approaches collectively demonstrate that only a minority of ARGs (approximately 23.78%) pose substantive health risks, with an even smaller fraction representing immediate threats [40].

Experimental Protocols for ARG Risk Identification

Metagenomic Analysis for ARG Detection and Characterization

Sample Collection and DNA Extraction: Comprehensive ARG surveillance begins with systematic sample collection across targeted environments. For geographical comparisons, samples should include wastewater treatment plants (particularly hospital wastewater), human gut microbiomes from diverse populations, and environmental matrices such as soil and water from both pristine and human-impacted areas [41] [1]. DNA extraction follows standardized protocols using commercial kits such as the QIAamp Fast DNA Stool Mini Kit for fecal samples [3]. Quality control measures include NanoDrop spectrophotometry and agarose gel electrophoresis to assess DNA quality and quantity [3].

Sequencing and Assembly: Quality-filtered reads undergo metagenomic assembly using tools such as MEGAHIT or metaSPAdes optimized for complex microbial communities [3]. Contigs are processed for open reading frame (ORF) prediction using Prodigal with metagenomic mode settings [1]. The resulting protein sequences are then aligned against curated ARG databases such as the Comprehensive Antibiotic Research Database (CARD) using tools like DeepARG or ARGs-OAP [40].

Host and Mobility Analysis: Determining ARG hosts requires contig-based analysis or metagenome-assembled genomes (MAGs). Contigs longer than 10 kb provide more reliable host identification [40]. Metagenomic binning tools such as MetaBAT2, MaxBin2, and CONCOCT are integrated through DAS Tool to generate high-quality MAGs [3]. Quality control with CheckM ensures MAGs meet thresholds (≥50% completeness, <10% contamination) [3]. Mobile genetic elements are identified using specialized databases, with plasmid sequences detected through plasmid databases, and integration with pathogen databases identifies ARGs hosted by pathogenic bacteria [39] [41].

Quantitative Risk Assessment Methodology

Human-Associated Enrichment Calculation: This metric identifies ARGs that are significantly more abundant in human-impacted environments. The calculation involves comparing ARG abundance between anthropogenically impacted environments (e.g., wastewater, human gut) and non-impacted environments (e.g., pristine soils, permafrost) [39]. Zhang et al. defined "human-associated" ARGs as those with ≥100-fold higher abundance in human-impacted environments [39]. This analysis requires normalized ARG abundance data, typically measured in reads per kilobase per million mapped reads (RPKM) or fragments per kilobase million (FPKM), from diverse metagenomic datasets.

Mobility Potential Scoring: Mobility potential is assessed by detecting ARG associations with known MGEs, including plasmids, transposons, and integrons [39] [42]. This involves identifying ARGs located on contigs that also contain MGE markers or demonstrate sequence homology to known mobile elements. Recent methodologies employ proximity analysis on assembled contigs, where ARGs located within specific distances (e.g., 10 kb) of MGE markers are considered potentially mobile [42]. More advanced approaches use long-read sequencing to definitively link ARGs with specific MGEs in complex samples.

Pathogenicity Assessment: This evaluation determines whether ARGs are present in human bacterial pathogens. The analysis involves cross-referencing ARG hosts with databases of pathogenic bacteria, particularly the ESKAPE pathogens that represent major clinical concerns [39]. For MAG-based approaches, taxonomic classification using GTDB-Tk identifies potential pathogens carrying ARGs [3]. This step is crucial for distinguishing Rank I ARGs (present in pathogens) from Rank II ARGs (mobile and human-associated but not yet in pathogens) [39].

Comparative Analysis of High-Risk ARGs

Key Rank I ARG Families and Their Clinical Significance

Comprehensive analysis of ARG risk has identified specific gene families that consistently rank as high-risk based on multiple assessment frameworks. The omics-based framework identified 73 Rank I ARG families, of which 35 corresponded to high-risk ARGs previously identified by the World Health Organization and other clinical literature [39]. The remaining 38 ARG families were significantly enriched in hospital plasmids, confirming their clinical relevance and mobility [39].

Table 1: High-Risk Rank I ARG Families and Their Characteristics

ARG Family	Resistance Mechanism	Antibiotic Class	Primary Hosts	Mobility Elements
blaKPC	Beta-lactamase	Beta-lactam	K. pneumoniae, E. coli	Plasmid (Tn4401)
vanA	Target alteration	Glycopeptide	E. faecium, S. aureus	Plasmid, Transposon
mcr-1	Target protection	Polymyxin	E. coli, Salmonella	Plasmid
blaNDM-1	Beta-lactamase	Beta-lactam	Enterobacteriaceae, A. baumannii	Plasmid
qnr	Target protection	Quinolone	Enterobacteriaceae	Plasmid
tet(M)	Ribosomal protection	Tetracycline	Multiple pathogens	Transposon, Plasmid
aac(6')-Ib	Antibiotic inactivation	Aminoglycoside	P. aeruginosa, Enterobacteriaceae	Integron, Plasmid
sul1	Target replacement	Sulfonamide	Multiple pathogens	Class 1 integron

These high-risk ARGs demonstrate concerning characteristics that facilitate their spread and clinical impact. For instance, carbapenemase genes (blaKPC, blaNDM-1) are frequently plasmid-mediated and have spread rapidly across bacterial species and geographical boundaries [41]. Similarly, the mobilized colistin resistance gene mcr-1 has rapidly disseminated into multiple pathogenic species across numerous countries, largely driven by plasmid-mediated horizontal gene transfer [39].

Geographical Distribution of High-Risk ARGs

Significant geographical variations exist in the abundance and composition of resistomes, influenced by antibiotic usage patterns, sanitation infrastructure, and socioeconomic factors [2] [1]. Global analyses of wastewater treatment plants have revealed distinct ARG compositions across continents, though a core set of 20 ARGs was present in all samples, accounting for 83.8% of total ARG abundance [1].

Table 2: Geographical Distribution of High-Risk ARGs and Influencing Factors

Region	Dominant ARG Types	Noteworthy High-Risk ARGs	Influencing Factors
Asia	Multidrug, beta-lactam, tetracycline	blaNDM, mcr variants	High antibiotic use, population density
Europe	Beta-lactam, glycopeptide, tetracycline	vanA, blaKPC	Healthcare-associated transmission
North America	Tetracycline, beta-lactam, multidrug	blaKPC, qnr	Community and hospital transmission
Africa	Aminoglycoside, sulfonamide, tetracycline	Diverse emerging ARGs	Limited surveillance, sanitation challenges
South America	Beta-lactam, glycopeptide, multidrug	blaKPC, vanA	Variable antibiotic regulation

Regional studies provide additional granularity to these patterns. Research across four Chinese provinces revealed significant differences in gut resistome profiles, with Jiangsu province showing higher prevalence of multidrug, peptide, tetracycline, glycopeptide, and aminoglycoside resistance genes compared to Sichuan and Yunnan provinces [3]. These variations likely reflect regional differences in antibiotic usage and public health infrastructure.

Gender-based differences in ARG burden have also been documented, with a 9% higher total ARG load observed in women compared to men in high-income countries [2]. Interestingly, this pattern reversed in low- and middle-income countries, where men showed higher ARG loads, though this difference was not significant after adjusting for covariates [2]. These differences emerge in adulthood, suggesting resistomes differentiate between genders after childhood [2].

Visualization of ARG Risk Assessment Framework

The following diagram illustrates the logical workflow for identifying high-risk Rank I ARGs using the omics-based framework:

This decision-tree framework efficiently categorizes ARGs into four risk levels. Application of this methodology to 2,579 ARGs revealed that 70% (1,816) were not human-associated (Rank IV), 24.1% (618) were human-associated but not mobile (Rank III), 0.6% (23) were human-associated and mobile but not in pathogens (Rank II), and 3% (122) met all three criteria for Rank I [39]. This stratification allows surveillance resources to focus on the most threatening ARGs while recognizing emerging threats that may require monitoring.

The Researcher's Toolkit: Essential Reagents and Databases

Effective ARG surveillance and risk assessment require specialized bioinformatic tools and reference databases. The following table outlines essential resources for conducting comprehensive ARG analysis:

Table 3: Essential Research Reagents and Databases for ARG Surveillance

Resource Name	Type	Primary Function	Application in ARG Research
CARD	Database	Comprehensive ARG reference	Annotation of resistance genes and variants
GTDB-Tk	Bioinformatics tool	Taxonomic classification	Accurate identification of ARG hosts from MAGs
DeepARG	Bioinformatics tool	ARG prediction	Detection of ARGs from metagenomic sequences
CheckM	Bioinformatics tool	MAG quality assessment	Evaluation of genome completeness and contamination
fastp	Bioinformatics tool	Sequence quality control	Adapter trimming and quality filtering of raw reads
MEGAHIT	Bioinformatics tool	Metagenomic assembly	De novo assembly of contigs from complex samples
Prodigal	Bioinformatics tool	Gene prediction	Identification of open reading frames in contigs
MetaBAT2	Bioinformatics tool	Binning algorithm	Generation of metagenome-assembled genomes

These resources form the foundation of modern ARG surveillance pipelines. The integration of multiple tools allows researchers to move beyond simple ARG quantification to more sophisticated analyses of ARG hosts, mobility potential, and ecological distribution. Long-read sequencing technologies from Oxford Nanopore and PacBio are increasingly being incorporated to better resolve mobile genetic elements and their association with high-risk ARGs [42].

Targeted surveillance of high-risk Rank I ARGs provides a strategic approach to combating the global antimicrobial resistance crisis. The integration of metagenomic methodologies with standardized risk assessment frameworks enables researchers and public health officials to prioritize resources toward the most threatening resistance genes. Geographical comparisons reveal significant regional variations in resistome composition, highlighting the need for tailored surveillance strategies that account for local antibiotic usage patterns, infrastructure, and socioeconomic factors.

Future directions in ARG surveillance should focus on integrating mobility assessment more comprehensively into risk analysis frameworks [42]. Current methodologies often rely on historical genetic contexts rather than directly assessing ARG-MGE associations in surveyed samples, potentially leading to overestimation of risks [42]. Advanced techniques including long-read sequencing and novel bioinformatic approaches will enable more accurate characterization of ARG mobility potential in complex microbial communities.

The development of standardized monitoring protocols and global data sharing initiatives will be crucial for effective resistance mitigation. As demonstrated by the Global Water Microbiome Consortium analysis of wastewater treatment plants across six continents, consistent methodologies enable meaningful comparisons across geographical regions and identification of global resistance patterns [1]. Such coordinated efforts provide the foundation for evidence-based interventions to curb the spread of high-risk ARGs and preserve the efficacy of existing antibiotics.

Leveraging Machine Learning to Map and Predict Antibiotic Resistance Threats

Antimicrobial resistance (AMR) is an urgent global health threat, responsible for over 1.27 million deaths annually and posing a significant challenge to modern medicine [43]. The rapid evolution and spread of resistant pathogens necessitate advanced technologies for surveillance and intervention. Machine learning (ML) has emerged as a powerful tool to predict antibiotic resistance, offering the potential to transform AMR surveillance from a reactive to a proactive discipline [43] [44]. This guide provides an objective comparison of current ML methodologies, their performance, and applications within the critical context of geographical AMR research, offering drug development professionals and researchers a framework for selecting appropriate tools and approaches.

Machine Learning Approaches for AMR Prediction: A Comparative Analysis

Machine learning applications in AMR prediction primarily utilize supervised learning, where models are trained on datasets containing known resistance profiles to identify patterns and predict phenotypes in new isolates [43]. These approaches can be broadly categorized by the type of input data they utilize.

Genomic Data-Based Prediction Models

Models utilizing whole-genome sequencing (WGS) data have demonstrated remarkable accuracy by learning from genetic features such as gene content, single nucleotide polymorphisms (SNPs), and genome composition [43].

Table 1: Comparison of Genomic Data-Based ML Tools for AMR Prediction

Tool/Model	Input Data	ML Algorithm(s)	Reported Accuracy/Performance	Key Advantages	Geographical Application Scope
VAMPr [45]	Gene ortholog-based variants, WGS	Not Specified	Mean accuracy of 91.1% across 93 pathogen-antibiotic combinations	Identifies known and novel AMR associations; explainable features	Analysis of 3,393 isolates from global NCBI datasets
Moradigaravand et al. Model [46]	Gene presence-absence, isolation year	Logistic Regression, Random Forest, XGBoost, Neural Networks	High accuracy for E. coli resistance prediction for 11 antibiotics	Uses clinically relevant genomic features; multiple model comparison	Focus on UK bacteremia patients (1,936 isolates)
Deep Denoising Auto-encoder [43]	Well-defined SNVs	Deep Learning	96.3% sensitivity for multidrug-resistant M. tuberculosis	Effective for complex multidrug resistance patterns	Isolates from 16 countries
Nontyphoidal Salmonella Predictor [43]	Gene content, genome composition	Not Specified	95% accuracy for MIC prediction within ±1 2-fold dilution	High precision for MIC value prediction	Based on NARMS data (US monitoring)

Phenotypic and Surveillance Data-Based Models

Models utilizing antibiotic susceptibility testing (AST) results and patient demographic data from surveillance programs offer complementary approaches that don't require genomic sequencing.

Table 2: Comparison of Phenotypic/Surveillance Data-Based ML Models

Model/Study	Input Data	ML Algorithm(s)	Reported Performance	Key Advantages	Dataset Characteristics
Pfizer ATLAS Analysis (2025) [47]	AST results, patient demographics, genotype markers	XGBoost, Others	XGBoost AUC: 0.96 (Phenotype-Only), 0.95 (Phenotype + Genotype)	Leverages comprehensive global surveillance data	917,049 bacterial isolates from 83 countries
Maguire et al. [43]	Salmonella WGS, AMR genes	Not Specified	Average precision 0.91-0.98	Identifies main genetic drivers for resistance	Salmonella isolates from broiler chickens

Geographical Mapping of Antibiotic Resistance Genes

Understanding the global distribution of antibiotic resistance genes (ARGs) is crucial for developing targeted interventions. Recent large-scale studies have revealed important geographical patterns in ARG prevalence and diversity.

Global Distribution in Wastewater Treatment Systems

A comprehensive 2025 study analyzing activated sludge samples from 142 wastewater treatment plants (WWTPs) across six continents revealed a core set of 20 ARGs present in all WWTPs globally, accounting for 83.8% of total ARG abundance [1]. The most abundant ARGs were:

• TetracyclineResistanceMFSEffluxPump (15.2%)
• ClassB (13.5%)
• vanT gene in the vanG cluster (11.4%) [1]

Table 3: Global Distribution of ARG Abundance and Diversity by Continent

Continent	Total ARG Abundance	ARG Richness	Shannon's H Index	Noteworthy Patterns
Asia	Similar to other continents	Significantly higher than other continents except Africa	Significantly higher than other continents except Africa	Highest diversity metrics
Africa	Similar to other continents	High (not significantly different from Asia)	High (not significantly different from Asia)	Comparable diversity to Asia
Europe	Similar to other continents	Moderate	Moderate	Distinct resistome composition
North America	Similar to other continents	Moderate	Moderate	Distinct resistome composition
South America	Similar to other continents	Moderate	Moderate	Country variations (e.g., Colombia high abundance)
Australia	Similar to other continents	Moderate	Moderate	Distinct resistome composition

The study found that ARG composition was significantly different (p < 0.05) between all pairwise continent comparisons, with bacterial taxonomic composition strongly correlated with resistome structure (Procrustes analysis correlation = 0.74) [1].

Marine Environment ARG Distribution

Research on marine environments has documented the global spread of ARGs, even in remote areas. The Mediterranean Sea showed higher levels of multiple ARGs in single samples, suggesting significant anthropogenic impact, while the Arctic Ocean around the Svalbard Islands also demonstrated multiple ARGs, highlighting pervasive contamination [15]. The sulfonamide resistance gene sul1 was found to be ubiquitous across all studied marine environments [15].

Experimental Protocols and Methodologies

Standardized Workflow for Global Resistome Studies

The Global Water Microbiome Consortium (GWMC) established a systematic protocol for global ARG analysis that serves as a model for geographical comparison studies [1]:

• Sample Collection: 226 activated sludge samples from 142 WWTPs across six continents using identical protocols
• DNA Sequencing: Shotgun metagenomic sequencing totaling 2.8 terabases (average 12.3 ± 3.9 Gb per sample)
• Sequence Processing: Assembly of 36,147,212 contigs >1 kb and prediction of 34,860,381 non-redundant open reading frames
• ARG Annotation: Identification and annotation of 37,029 ARG sequences (0.11% of ORFs)
• Normalization: ARG abundance normalized to copy number per bacterial cell
• Statistical Analysis: PERMANOVA for structural differences, PCoA for visualization, Procrustes analysis for microbiome-resistome correlation

Global ARG Analysis Workflow: Standardized pipeline for geographical comparison of antibiotic resistomes.

VAMPr Framework for Genomic Prediction

The VAMPr (Variant Mapping and Prediction of antibiotic resistance) pipeline provides a robust methodology for linking genomic variants to resistance phenotypes [45]:

• Data Curation: Collection of 3,393 bacterial isolates from 9 species with AMR phenotypes for 29 antibiotics from NCBI databases
• Variant Identification: De novo assembly and alignment to curated Antimicrobial Resistance KEGG orthology database (537 KO genes)
• Feature Engineering: Derivation of gene ortholog-based sequence features for protein variants (14,615 variant genotypes identified)
• Model Building: Construction of 93 association and prediction models
• Validation: Internal validation through nested cross-validation and external validation using clinical datasets

VAMPr Prediction Pipeline: Bioinformatics workflow for genomic prediction of antibiotic resistance.

Key Research Reagent Solutions

Table 4: Essential Research Materials and Databases for AMR Geographical Research

Resource/Database	Type	Primary Function	Geographical Coverage	Key Features
NCBI SRA & BioSample Antibiogram [45]	Genomic & Phenotypic Database	Source of paired genomic and phenotypic data for model training	Global	3,393 isolates with 38,871 MIC values across 29 antibiotics
KEGG Orthology (KO) Database [45]	Curated Gene Database	Reference for AMR gene annotation and variant mapping	Not Applicable	537 curated AMR orthologs with corresponding UniRef sequences
Pfizer ATLAS Antibiotics [47]	Surveillance Database	Comprehensive resistance data with patient demographics	83 countries	917,049 bacterial isolates, 50 antibiotic drugs, genotype markers
WHO GLASS Dashboard [48]	Visualization & Surveillance Tool	Interactive visualization of global AMR and antimicrobial use data	135 countries/territories	Resistance to 23 antibiotics across 8 bacterial pathogens
GWMC Database [1]	Environmental Metagenomic Database	Analysis of ARG distribution in wastewater systems	6 continents	Standardized protocols for global comparison
ResistanceMap [49]	Visualization Tool	Interactive mapping of resistance and antibiotic use	Multiple countries	Comparison of resistance rates between regions

Machine Learning Algorithms and Implementation

For researchers implementing ML approaches, the following algorithms have demonstrated effectiveness for AMR prediction:

Common ML Algorithms:

• XGBoost: Consistently outperformed other models in surveillance data prediction (AUC 0.96) [47]
• Random Forests: Effective for genomic variant data with high-dimensional features [46]
• Logistic Regression: Provides interpretable results with good baseline performance [46]
• Neural Networks: Capable of modeling complex interactions in large genomic datasets [46]
• Deep Denoising Auto-encoders: Achieved 96.3% sensitivity for multidrug-resistant M. tuberculosis [43]

Machine learning approaches for AMR prediction have reached significant levels of accuracy, with models consistently achieving >90% performance across diverse pathogen-antibiotic combinations [43] [47] [45]. The integration of genomic, surveillance, and environmental data provides a powerful framework for understanding and predicting the global spread of resistance threats. For drug development professionals, these tools offer unprecedented opportunities for targeted antibiotic development and stewardship interventions. Geographical mapping of ARGs reveals both universal patterns (core resistome) and region-specific variations, enabling more precise public health responses. As ML methodologies continue to evolve and standardized global datasets expand, the capacity to predict, track, and mitigate antibiotic resistance threats will become increasingly sophisticated, potentially transforming our approach to this critical public health challenge.

Decoding the Dynamics of ARG Spread: Environmental Drivers and Gene Mobility

The Role of Mobile Genetic Elements in Horizontal Gene Transfer

Horizontal Gene Transfer (HGT) is a fundamental driver of bacterial evolution, enabling the rapid acquisition of adaptive traits across species boundaries. This process is predominantly facilitated by mobile genetic elements (MGEs), which include plasmids, transposons, insertion sequences, integrons, and bacteriophages [50] [51]. Within the context of antibiotic resistance, MGEs function as sophisticated genetic vehicles that capture, accumulate, and disseminate antibiotic resistance genes (ARGs) among diverse bacterial populations [50]. The clinical significance of this phenomenon cannot be overstated—multidrug-resistant "superbugs" that carry numerous HGT-transferred ARGs on plasmids now present one of the most severe threats to modern healthcare, with antibiotic resistance causing approximately 1.3 million deaths annually [2] [51] [52].

Understanding the geographical distribution of ARGs and the role of MGEs in their dissemination provides critical insights for public health interventions. Large-scale metagenomic studies reveal that resistance patterns vary significantly across regions, influenced by socioeconomic factors, antibiotic usage practices, and environmental conditions [2] [1]. This review synthesizes current understanding of how different MGEs mediate the global spread of antibiotic resistance, with particular emphasis on comparative geographical analyses and the experimental approaches that enable these insights.

Classification and Mechanisms of Mobile Genetic Elements

MGEs constitute a diverse array of DNA sequences capable of moving within or between genomes. They operate through distinct yet sometimes interconnected mechanisms, forming a complex network that accelerates bacterial adaptation to antibiotic pressure.

Plasmids: Conjugative Genetic Vehicles

Plasmids are extrachromosomal DNA elements that replicate independently of the bacterial chromosome. Conjugative plasmids possess the remarkable ability to transfer themselves between bacteria through direct cell-to-cell contact via a structure called a pilus [53] [54]. This process, known as conjugation, represents the most efficient mechanism for HGT of multi-resistance determinants [53]. Notably, some plasmids lacking complete conjugation machinery can still be mobilized with the assistance of "helper" plasmids present in the same cell, demonstrating the cooperative nature of gene transfer systems [54]. Plasmids such as those carrying carbapenemase resistance genes (e.g., blaKPC, blaNDM, and blaOXA-48) in Gram-negative bacteria exemplify this threat, as they rapidly disseminate resistance to last-resort antibiotics across genera boundaries [53].

Transposons and Insertion Sequences: Intragenomic Mobilizers

Transposons (Tn) and insertion sequences (IS) are discrete DNA segments that move within genomes, often described as "jumping genes." Insertion sequences typically contain only the genetic information necessary for their own transposition, while transposons additionally carry passenger genes such as ARGs [50]. These elements facilitate the formation of composite transposons, where two copies of the same IS flank one or more resistance genes, enabling their coordinated movement [50]. Beyond physically relocating resistance determinants, IS elements can significantly influence gene expression; for instance, ISAba1 upstream of blaOXA-51-like genes in Acinetobacter baumannii creates a promoter that drives carbapenem resistance expression [50].

Integrons: Gene Capture and Expression Systems

Integrons are sophisticated genetic platforms that excel at capturing and expressing gene cassettes. They contain a site-specific recombination system that enables the acquisition, rearrangement, and expression of promoterless mobile gene cassettes [50]. Integrons function as natural genetic engineering tools, allowing bacteria to accumulate multiple resistance genes in rapid succession, creating arrays that confer resistance to diverse antibiotic classes. This cassette assembly line mechanism makes integrons particularly effective in generating multi-drug resistance clusters that can then be transferred between bacteria via associated transposons or plasmids [50].

Bacteriophages and Membrane Vesicles: Alternative Transfer Routes

Bacteriophages (viruses that infect bacteria) can transfer ARGs through transduction, wherein bacterial DNA is accidentally packaged into phage capsids during infection and delivered to subsequent bacterial hosts [53]. Membrane vesicles (MVs)—spherical structures budding from bacterial membranes—represent another HGT mechanism, particularly in Gram-negative bacteria. These 20-400 nm particles can encapsulate ARGs and deliver them to recipient cells, as demonstrated in Acinetobacter baumannii and Escherichia coli systems [53].

Table 1: Major Mobile Genetic Elements in Antibiotic Resistance Dissemination

MGE Type	Key Characteristics	Primary Transfer Mechanism	Clinical Relevance
Plasmids	Extrachromosomal, self-replicating circular DNA	Conjugation (cell-to-cell contact)	Broad-host-range plasmids spread carbapenemase genes (e.g., NDM-1, KPC) [53] [54]
Transposons	DNA segments capable of genomic relocation	Transposition (cut-paste or copy-paste)	Composite transposons (e.g., Tn5, Tn10) carry multiple ARGs [50]
Insertion Sequences (IS)	Short sequences encoding transposase only	Transposition	ISAba1 upregulates blaOXA-51 expression in A. baumannii [50]
Integrons	Gene cassette capture and expression systems	Site-specific recombination	Accumulate multiple ARG cassettes, creating multi-drug resistance [50]
Bacteriophages	Viruses infecting bacteria	Transduction (lysogenic or lytic)	φ80α phage transfers resistance in Staphylococcus aureus [53]

Geographical Distribution of MGE-Driven Antibiotic Resistance

The propagation of MGEs and their associated ARGs follows distinct geographical patterns influenced by environmental, socioeconomic, and clinical factors. Large-scale metagenomic studies provide unprecedented insights into these global distributions.

Regional Variations in Resistome Composition

A comprehensive analysis of 226 activated sludge samples from 142 wastewater treatment plants across six continents revealed that while total ARG abundance showed no significant continental variation, ARG composition differed markedly between geographical regions [1]. A core set of 20 ARGs was present in all sampled WWTPs, accounting for 83.8% of total ARG abundance, with tetracycline resistance genes (15.2%), beta-lactam resistance genes (13.5%), and glycopeptide resistance genes (11.4%) being most prevalent globally [1]. However, principal coordinate analysis demonstrated significant separation of resistomes by continent, with region explaining the largest share of resistome variance (R² = 4.9%) [1]. These findings highlight how local environmental conditions and antibiotic usage practices shape distinct regional resistance profiles.

Socioeconomic Influences on Resistance Patterns

The distribution of antibiotic resistance exhibits complex relationships with economic development. A groundbreaking study of 14,641 human gut metagenomes from 32 countries revealed a 9% higher total ARG load in women compared to men in high-income countries (HICs) [2] [52]. Surprisingly, this pattern reversed in low- and middle-income countries (LMICs), where men showed higher ARG loads, though this difference lost statistical significance after adjusting for covariates [2]. These gender-specific disparities emerged predominantly in adulthood, suggesting that lifestyle, occupational exposures, and healthcare access patterns—rather than biological differences—drive these variations [2] [52]. Furthermore, high antibiotic use was associated with elevated ARG loads in both HICs and LMICs, though non-antibiotic factors like poor sanitation infrastructure also contributed significantly to resistance burdens in LMICs [2].

Environmental Compartments as Resistance Reservoirs

Different environmental habitats maintain distinct resistome profiles, with wastewater treatment plants serving as critical hubs for ARG exchange. Comparative analysis reveals that activated sludge resistomes more closely resemble those of sewage and soil than human gut or ocean environments [1]. This similarity stems from direct interconnection between these compartments, particularly in combined sewer systems that collect both domestic sewage and stormwater [1]. Within WWTPs, metagenome-assembled genomes have identified Chloroflexi, Acidobacteria, and Deltaproteobacteria as major ARG carriers, with 57% of high-quality genomes containing putatively mobile ARGs [1]. These findings position wastewater infrastructure as crucial interception points for controlling environmental resistance dissemination.

Table 2: Geographical Distribution of Antibiotic Resistance Elements

Geographical Factor	Observed Pattern	Study Scale	Key Findings
Continental Division	Significant differences in ARG composition between continents [1]	142 wastewater treatment plants across 6 continents	Core set of 20 ARGs present in all samples; Region explains 4.9% of resistome variance [1]
Economic Status	9% higher ARG load in women in HICs; reversed pattern in LMICs [2]	14,641 human gut metagenomes from 32 countries	Gender differences emerge in adulthood; associated with healthcare access and occupational exposures [2] [52]
Habitat Type	Distinct resistomes across environmental compartments [1]	Comparison of activated sludge, human gut, soil, and ocean	Activated sludge resistomes more similar to sewage and soil than human gut or ocean [1]
Antibiotic Use	Positive correlation with ARG abundance and diversity [2]	Country-level defined daily doses (DDD)	High antibiotic use associated with higher ARG loads in both HICs and LMICs [2]

Experimental Models for Studying HGT of Antibiotic Resistance

Understanding the dynamics of MGE-mediated ARG transfer requires sophisticated experimental approaches that range from controlled laboratory conditions to complex in vivo models.

In Vivo Models: Gut Ecosystem Dynamics

While in vitro studies provide foundational knowledge, in vivo models better replicate the complex conditions where HGT occurs naturally. The mammalian gut, particularly in mice, serves as an ideal model system due to its diverse microbial community and physiological relevance [53] [54]. A seminal study investigated plasmid transfer from Salmonella Typhimurium to gut commensals in mice, revealing that a broad host-range plasmid (P3) encoding streptomycin and sulfonamide resistance could transfer to diverse Gammaproteobacteria recipients, facilitated by a helper plasmid (P2) [54]. Remarkably, this transfer occurred even without antibiotic selection pressure, challenging conventional assumptions about the drivers of resistance dissemination [54]. Such in vivo approaches have also demonstrated that transduction—phage-mediated gene transfer—contributes significantly to genetic diversity in gut-colonizing E. coli strains and can promote ARG emergence in intestinal bacteria [53].

Metagenomic Approaches for Resistome Surveillance

Metagenomic sequencing enables comprehensive profiling of resistance genes and MGEs across diverse ecosystems without culturing biases. This approach was applied to compare ARG burdens in wild and captive Himalayan vultures, revealing a resistome of 414 ARG subtypes resistant to 20 drug classes, with beta-lactam resistance genes being most diverse (175 subtypes) [55]. Researchers extracted fecal DNA using the Qiagen QIAamp DNA Stool Mini Kit, constructed paired-end libraries with 350bp insert sizes, and sequenced on the BGISEQ-500 platform [55]. Bioinformatic analysis identified 75 bacterial genera across five phyla as putative ARG hosts, with co-occurrence network analysis revealing distinct relationships between gut microbes, ARGs, and MGEs in wild versus captive populations [55]. Such ecosystem-level surveillance provides crucial insights into how environmental pressures shape resistance dissemination.

Conjugation Assays and Plasmid Transfer Dynamics

Standardized conjugation experiments quantify transfer frequencies of specific plasmid-ARGE combinations under controlled conditions. These protocols typically involve mixing donor and recipient strains at optimal ratios (often 1:10), allowing conjugation to proceed for specified durations (typically 1-2 hours), and then plating on selective media containing antibiotics that distinguish transconjugants from donor and recipient cells [54]. Such assays have demonstrated that common non-antibiotic pharmaceuticals—including ibuprofen and propranolol—can enhance plasmid transfer by inducing reactive oxygen species production [54]. This concerning finding suggests that even without antibiotic selection, environmental contaminants may accelerate resistance dissemination.

Diagram Title: Metagenomic Analysis Workflow for ARG and MGE Detection

The Scientist's Toolkit: Essential Reagents and Methodologies

Cutting-edge research into MGE-mediated resistance requires specialized reagents and methodologies. The following table summarizes essential resources for investigating HGT of antibiotic resistance genes.

Table 3: Essential Research Reagents and Methodologies for HGT Studies

Reagent/Methodology	Specific Examples	Application and Function	Experimental Context
DNA Extraction Kits	Qiagen QIAamp DNA Stool Mini Kit [55]	High-quality metagenomic DNA extraction from complex samples	Fecal and environmental sample processing for metagenomics [55]
Sequencing Platforms	BGISEQ-500 [55]	High-throughput sequencing of metagenomic libraries	Resistome profiling of Himalayan vultures [55]
Reference Databases	CARD, ISfinder [50]	Annotation of ARGs and insertion sequences	Bioinformatic analysis of resistomes and mobilomes [50] [55]
Model Plasmids	RP4, pRSF1010 derivatives [54]	Investigation of conjugation dynamics and host range	Mouse gut model of plasmid transfer [54]
Selective Media	Antibiotic-containing agars	Isolation and enumeration of transconjugants	Conjugation frequency determination [54]
Bioinformatic Tools	FastQC, Procrustes analysis [55]	Quality control and multivariate statistics	Analyzing correlations between microbiome and resistome [1] [55]

Mobile genetic elements serve as the primary architects of the global antibiotic resistance crisis, enabling rapid dissemination of resistance traits across geographic and taxonomic boundaries. The geographical patterns of resistance distribution reflect complex interactions between socioeconomic factors, clinical practices, and environmental conditions. As research methodologies evolve from simple in vitro conjugation assays to sophisticated multi-omics approaches, our capacity to track, understand, and potentially intervene in the spread of MGE-driven resistance continues to improve. Future efforts must integrate these geographical and mechanistic insights to design targeted interventions that account for regional variations in resistance epidemiology, ultimately preserving the efficacy of our antimicrobial armamentarium.

Antibiotic resistance poses an urgent global health threat, and understanding the environmental reservoirs and carriers of antibiotic resistance genes (ARGs) is crucial for public health. The complex dynamics of ARG dissemination operate within a "One Health" context, connecting human, animal, and environmental microbiomes. Within this framework, two bacterial groups emerge as critical carriers in distinct environments: the Chloroflexi phylum in wastewater treatment plants (WWTPs) and various classes of Gammaproteobacteria in marine systems. WWTPs are recognized hotspots for ARG evolution and dissemination, where activated sludge becomes a complex environment where various antimicrobials and microorganisms converge [56]. In parallel, marine environments are experiencing the global spread of ARGs, with recent studies detecting resistance genes even in remote Arctic waters [15]. This comparison guide objectively examines these two bacterial carriers, their roles in different ecosystems, and the methodologies essential for studying their contribution to the environmental resistome.

Comparative Analysis of Key Bacterial Carriers

Table 1: Characteristics of Chloroflexi and Gammaproteobacteria as Environmental ARG Carriers

Characteristic	Chloroflexi (in WWTPs)	Gammaproteobacteria (in Marine Systems)
Primary Habitat	Engineered ecosystems: activated sludge, methanogenic reactors, anammox systems [57] [58]	Diverse marine environments: coastal sediments, hydrothermal vents, open ocean, Arctic waters [59] [15]
Core Metabolic Functions	Hydrolysis of complex organic matter, fermentation, nitrogen conversion, biofilm structuring [57] [58]	Sulfur oxidation, dark carbon fixation, hydrocarbon degradation, nutrient cycling [59] [60]
Role in ARG Dissemination	Major ARG carriers in WWTPs; strongly correlate with total community resistome [1]	Carriers of diverse ARGs including β-lactamases, tetracycline, and sulfonamide resistance [15] [56]
Key ARG Types Carried	Genes conferring resistance to Beta-lactam, Glycopeptide, and Tetracycline [1]	blaOXA-48, blaCTX-M-1, blaTEM, sul1, tetA [15] [56]
Association with Mobile Elements	57% of high-quality genomes possess putatively mobile ARGs [1]	ARGs linked to plasmids and class 1 integrons; associated with integrative elements (ICEclc) [56]
Geographical Distribution	Global distribution in WWTPs; abundance varies with operational parameters [57] [1]	Ubiquitous global distribution in diverse marine environments, including remote Arctic [15]

Table 2: Dominant ARG Types and Their Carriers Across Environments

Environment	Dominant ARG Types	Primary Bacterial Carriers	Noteworthy Facts
Wastewater Treatment Plants	Tetracycline (15.2%), Beta-lactam (13.5%), Glycopeptide (11.4%) [1]	Chloroflexi, Acidobacteria, Deltaproteobacteria [1]	Core set of 20 ARGs present in all WWTPs globally [1]
Marine Ecosystems	Sulfonamide (sul1), Beta-lactamases (blaOXA-48, blaCTX-M-1, blaTEM), Tetracycline (tetA) [15]	Diverse Gammaproteobacteria orders [15] [56]	Mediterranean Sea shows higher ARG levels, indicating anthropogenic impact [15]
Human Gut	Varies by population and antibiotic use [1]	Primarily Gram-negative opportunistic pathogens [1]	WWTP resistomes distinct from human gut resistomes [1]

Experimental Approaches for Resistome Profiling

Metagenomic Sequencing and Analysis

The comprehensive analysis of environmental resistomes relies on high-throughput metagenomic sequencing followed by sophisticated bioinformatic pipelines. For global WWTP studies, researchers typically collect biomass samples from activated sludge, extract community DNA, and perform shotgun metagenomic sequencing on Illumina platforms (e.g., NovaSeq 6000) to obtain sufficient sequence depth (typically ~12.3 Gb per sample) [1]. For marine studies, water or sediment samples are filtered to capture biomass, with DNA extraction methods optimized for low-biomass environments [59] [15].

The subsequent bioinformatic analysis follows a standardized workflow: (1) quality filtering of raw reads using tools like Trimmomatic; (2) assembly of quality-filtered reads into contigs using assemblers such as MEGAHIT or SPAdes; (3) gene prediction and annotation of open reading frames (ORFs); (4) ARG identification by comparing predicted ORFs against structured ARG databases like SARG, CARD, or ARDB using BLAST or Usearch with thresholds of 80% amino acid identity and 1e-7 E-value [1] [34]; (5) taxonomic binning to associate ARGs with specific bacterial carriers through metagenome-assembled genomes (MAGs) [1] [58].

Fluorescence in Situ Hybridization (FISH) and Cell Sorting

For functional validation and visualization, Fluorescence in Situ Hybridization (FISH) combined with cell sorting techniques provides powerful complementary approaches. In marine sediment studies, researchers have developed methods to combine 14C-bicarbonate labeling of autotrophic bacteria with FISH and Fluorescence-Activated Cell Sorting (FACS) to quantify dark carbon fixation by specific taxonomic groups [59]. This protocol involves: (1) short-term incubation of environmental samples with 14C-labeled substrates; (2) fixation of samples for FISH analysis; (3) hybridization with taxon-specific fluorescently labeled oligonucleotide probes; (4) flow cytometric sorting of specific microbial populations; and (5) scintillation counting to quantify isotope incorporation [59].

For Chloroflexi in WWTPs, FISH with chloroflexi-specific probes has been instrumental in confirming their filamentous morphology and spatial organization within activated sludge flocs, providing insights into their structural role in biofilm formation [58]. These methodological approaches enable researchers to move beyond correlation to establish causal relationships between bacterial identity and function.

Figure 1: Metagenomic Workflow for Antibiotic Resistome Analysis. This diagram illustrates the standardized pipeline from sample collection to data analysis used in global resistome studies.

Metabolic Pathways and Ecological Functions

Chloroflexi in Wastewater Treatment Systems

Chloroflexi members in WWTPs display remarkable metabolic versatility that underpins their ecological success and role as ARG reservoirs. Genomic analyses reveal several key functional attributes:

• Hydrolytic Capability: Chloroflexi possess numerous genes coding for hydrolytic enzymes that break down complex organic matter, including debris from lysed bacterial cells [57] [58]. This hydrolytic activity is crucial for carbon cycling in activated sludge systems.

• Fermentative Metabolism: Most Chloroflexi in WWTPs display anaerobic or facultative anaerobic metabolism with complete fermentative pathways, allowing them to thrive in anoxic zones of treatment systems [58]. They can ferment carbohydrates and proteins to low molecular weight substrates.

• Nitrogen Transformation: In anammox reactors, Chloroflexi genomes contain genes for nitrite reduction (nirK, nirS) and nitric oxide reductase (norZ), suggesting participation in nitrogen cycling and potentially facilitating a nitrite loop with anammox bacteria [58].

• Biofilm Formation: Chloroflexi exhibit filamentous growth and possess genes related to adhesiveness and exopolysaccharide (EPS) production, enabling them to form a structural backbone for flocs and granules [57] [58]. This biofilm-associated lifestyle potentially enhances genetic exchange, including ARG transfer.

Gammaproteobacteria in Marine Environments

Marine Gammaproteobacteria exhibit extraordinary metabolic diversity that facilitates their role as key players in carbon cycling and as ARG reservoirs:

• Chemolithoautotrophy: Uncultured Gammaproteobacteria clades (Acidiferrobacter, JTB255, SSr) dominate dark carbon fixation in coastal sediments, accounting for 70-86% of this process through sulfur oxidation and the Calvin-Benson-Bassham cycle [59]. This metabolic activity establishes them as fundamental components of marine carbon sinks.

• Sulfur Oxidation: Environmental transcripts of sulfur oxidation genes (dsrAB, aprA, soxB) mainly affiliate with Gammaproteobacteria, illustrating their essential role in sulfur cycling [59]. The co-localization of sulfur and hydrogen oxidation pathways in their genomes indicates unexpected metabolic plasticity.

• Hydrocarbon Degradation: Numerous genera are obligate hydrocarbonoclastic bacteria (e.g., Alcanivorax, Cycloclasticus) that degrade oil components, making them crucial for natural bioremediation of oil spills [60].

• Versatile Nutrient Cycling: Marine Gammaproteobacteria include nitrifying bacteria (Nitrosococcus), methanotrophs (Methylococcales), and anoxygenic phototrophs (Chromatiaceae), representing diverse metabolic strategies [60].

Figure 2: Comparative Metabolic Capabilities of Key Bacterial Carriers. This diagram highlights the distinct metabolic functions of Chloroflexi and Gammaproteobacteria in their respective environments.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Reagents and Platforms for Resistome Studies

Reagent/Platform	Specific Examples	Function in Research
DNA Extraction Kits	PowerSoil DNA Isolation Kit (MoBio) [59]	Standardized microbial DNA extraction from complex environmental matrices
Sequencing Platforms	Illumina HiSeq 4000, Illumina NovaSeq 6000, Roche 454 pyrosequencing [1] [58]	High-throughput sequencing for metagenomic and amplicon studies
ARG Databases	SARG, CARD, ARDB, NCBI-NR [1] [34]	Reference databases for annotating and classifying antibiotic resistance genes
Bioinformatic Tools	MEGAHIT, MetaBAT2, CheckM, GTDB-Tk, Trimmomatic [1] [58]	Genome assembly, binning, quality assessment, and taxonomic classification
FISH Probes	Taxon-specific oligonucleotide probes (e.g., for Chloroflexi) [59] [58]	Fluorescence in situ hybridization for phylogenetic identification and visualization
Stable Isotopes	14C-bicarbonate [59]	Tracing carbon fixation pathways in microbial communities

This comparison guide has delineated the distinct yet equally crucial roles of Chloroflexi in WWTPs and Gammaproteobacteria in marine systems as key carriers of antibiotic resistance genes in their respective environments. Chloroflexi stand out as structural and metabolic engineers in engineered ecosystems, where their filamentous nature and hydrolytic capabilities position them as significant ARG reservoirs. Meanwhile, Gammaproteobacteria emerge as metabolic virtuosos in marine environments, driving essential biogeochemical cycles while simultaneously harboring diverse resistance determinants.

From a geographical perspective, both carriers show global distribution, yet their ARG profiles exhibit regional signatures influenced by local anthropogenic factors. The Mediterranean Sea shows elevated ARG levels in Gammaproteobacteria, while WWTPs across continents maintain a core set of 20 ARGs consistently associated with Chloroflexi and other key phyla [1] [15]. This geographical patterning underscores the need for tailored intervention strategies that account for regional variations in resistome composition.

For researchers and drug development professionals, these findings highlight the importance of environmental monitoring beyond clinical settings and the potential risks of ARG dissemination through water cycles. Future research should focus on elucidating the transfer mechanisms of ARGs between these environmental carriers and clinically relevant pathogens, developing interventions to disrupt high-risk transfer pathways, and optimizing wastewater treatment processes to minimize the release of ARG-carrying bacteria into sensitive ecosystems.

Antibiotic resistance poses an urgent global health threat, and environmental reservoirs of antibiotic resistance genes (ARGs) play a crucial role in their dissemination [1]. While biotic factors like bacterial community composition influence ARG prevalence, abiotic factors—the non-living chemical and physical components of the environment—serve as critical selective pressures and drivers of ARG distribution [61] [62]. Understanding how temperature, nutrient availability, and water column stratification interact to shape resistomes is essential for predicting and mitigating the spread of antibiotic resistance across different geographical regions.

This guide provides a comparative analysis of experimental approaches and data on how these abiotic factors impact ARG dynamics, offering researchers methodologies and frameworks for investigating environmental resistomes. The complex interplay of these factors demonstrates the need for multifactorial studies to accurately assess and model the global spread of antibiotic resistance.

Comparative Experimental Data on Abiotic Factor Impacts

Global Wastewater Treatment Plant (WWTP) Resistome Analysis

A landmark global study analyzing activated sludge samples from 142 wastewater treatment plants across six continents revealed that ARG composition differs significantly across continents and is distinct from other habitats like the human gut and oceans [1]. The study identified a core set of 20 ARGs present in all WWTPs, accounting for 83.8% of the total ARG abundance, with tetracycline, beta-lactam, and glycopeptide resistance genes being most prevalent [1]. Resistome variations were driven by a complex combination of stochastic processes and deterministic abiotic factors, though the specific abiotic factors measured explained a smaller portion of variance compared to bacterial taxonomic composition [1].

Table 1: Global Distribution of Antibiotic Resistance Genes in Wastewater Treatment Plants

Parameter	Findings from Global WWTP Analysis	Continental Variations
Total ARG Abundance	No significant difference across continents (p = 0.78) [1]	Similar abundance despite compositional differences
ARG Richness	Significantly higher in Asia than other continents except Africa [1]	Regional variations in diversity
Core Resistome	20 ARGs present in all WWTPs [1]	Universal across geographical locations
Most Abundant ARGs	Tetracycline (15.2%), Beta-lactam (13.5%), Glycopeptide (11.4%) resistance genes [1]	Consistent drug class dominance globally
Resistome Similarity	WWTP resistomes more similar to sewage and soil than human gut or oceans [1]	Interconnection between environmental compartments

Temperature and Nutrient Interaction Effects

Experimental studies demonstrate that temperature increases accelerate microbial nutrient consumption and biomass production, leading to faster bloom dynamics in planktonic communities [63]. This acceleration causes earlier nutrient depletion, which subsequently triggers an earlier decrease in carbon fixation and nitrate uptake rates [63]. The interaction between temperature and nutrients creates complex effects on community metabolism, with respiration increasing non-linearly with temperature, thereby shifting the system toward a less positive metabolic balance [63].

Table 2: Interactive Effects of Temperature and Nutrient Availability on Microbial Communities

Experimental Condition	Impact on Microbial Processes	Ecological Consequences
Warming (+2°C to +4°C)	Accelerated nutrient consumption and biomass production [63]	Faster bloom dynamics, earlier bloom termination
Warming + Nutrient Enrichment	Strongest stimulation of photosynthetic carbon fixation [63]	Enhanced primary production during warming events
Nutrient Depletion under Warming	Earlier decrease in carbon fixation and nitrate uptake rates [63]	Shift toward negative community metabolic balance
Temperature Increase Alone	Increased respiration rates more than photosynthesis [63]	Reduced net community production
Higher Temperatures (Lab Columns)	Dominance of phototrophic sulfur bacteria in stratified systems [64]	Potential regime shifts to anaerobic communities

Experimental Protocols for Abiotic Factor Research

Global Field Sampling and Metagenomic Analysis

The Global Water Microbiome Consortium (GWMC) established a standardized protocol for global resistome analysis that enables meaningful cross-regional comparisons [1]:

• Sample Collection: Collect activated sludge samples from wastewater treatment plants across multiple geographical locations and continental regions.
• DNA Extraction and Sequencing: Perform consistent DNA extraction, shotgun metagenomic sequencing, and assembly of contigs longer than 1 kb from all filtered metagenomic reads.
• ORF Prediction and Annotation: Predict non-redundant open reading frames (ORFs) and annotate ARG sequences using standardized databases and criteria.
• Normalization and Analysis: Normalize ARG abundance to copy number per bacterial cell to enable cross-comparison between samples.
• Statistical Analysis: Use PERMANOVA and PCoA to identify structural differences in resistomes across regions and habitats.

This methodology revealed that ARG composition strongly correlates with bacterial taxonomic composition, with specific bacterial phyla like Chloroflexi, Acidobacteria, and Deltaproteobacteria identified as major ARG carriers [1].

Temperature-Nutrient Manipulation Experiments

Controlled laboratory experiments enable researchers to disentangle the complex interactions between temperature and nutrients:

• Experimental Setup: Collect natural microbial plankton communities from coastal ecosystems and distribute into polycarbonate bottles [63].
• Temperature Manipulation: Incubate samples at multiple temperature regimes (in situ, +2°C, +4°C) using precisely controlled water baths or incubators [63].
• Nutrient Amendment: Create unamended and nutrient-enriched conditions through the addition of nitrogen, phosphorus, and silicon compounds [63].
• Process Measurements: Quantify standing stocks (chlorophyll a, inorganic nutrients) and rate processes (carbon fixation, nitrogen uptake, oxygen net production) throughout the experiment [63].
• Community Analysis: Use 16S rRNA gene sequencing to track changes in microbial community structure across treatments [64].

This protocol revealed that investigated microbial fluxes were more responsive to nutrient availability than to temperature alone, highlighting the importance of multiple resource limitations [63].

Stratified Water Column Microcosm System

Laboratory-based Winogradsky columns provide a highly replicable system for studying stratified microbial communities under different environmental scenarios:

• Column Preparation: Establish modified, mostly liquid Winogradsky columns in glass test tubes with minimal sediment layers (~6% v/v) to create liquid oxic-anoxic interfaces [64].
• Sensor Integration: Equip columns with optical oxygen sensors at different heights (e.g., 4 cm and 14 cm) to continuously monitor oxygenation gradients [64].
• Treatment Application: Apply factorial combinations of temperature gradients and nutrient additions to assess individual and interactive effects [64].
• Community Characterization: Analyze microbial community composition through full-length 16S rRNA gene sequencing of different strata [64].
• Metabolite Measurement: Monitor changes in oxygen, hydrogen sulfide, and total organic carbon throughout the experiment [64].

This approach demonstrated that the composition of strongly stratified microbial communities was greatly affected by temperature and by the interaction of temperature and nutrient addition, revealing the necessity of investigating global change treatments simultaneously [64].

Visualization of Experimental Workflows

Temperature-Nutrient Interaction Experimental Design

Figure 1: Workflow for temperature-nutrient manipulation experiments.

Stratified Column System for Global Change Studies

Figure 2: Stratified column system for global change studies.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Research Materials for Abiotic Factor and ARG Studies

Reagent/Material	Application Function	Experimental Context
Polycarbonate Incubation Bottles	Maintain chemical integrity during experiments; prevent contaminant leaching	Temperature-nutrient manipulation studies [63]
Optical Oxygen Sensors	Non-invasive monitoring of oxygen gradients in stratified systems	Winogradsky column experiments [64]
DNA Extraction Kits	Standardized microbial DNA extraction for metagenomic analysis	Global WWTP resistome study [1]
NH4NO3, Na2SiO3, Na2HPO4	Nutrient amendment to simulate eutrophication scenarios	Nutrient enrichment experiments [63]
Butyl Rubber Stoppers	Create sealed systems with controlled gas exchange	Microcosm and column studies [64]
16S rRNA Primers & Reagents	Characterization of bacterial community structure	Microbiome analysis across studies [1] [64]
ARG-Specific Primers	Quantitative PCR detection of specific resistance genes	Resistome quantification in sewage studies [65]

Antibiotic resistance genes (ARGs) in soil environments represent a critical component of the global antimicrobial resistance (AMR) crisis. Within the One Health framework, which integrates human, animal, and environmental health, soil is recognized as a massive reservoir and propagation hotspot for ARGs, connecting natural ecosystems with human society [12] [66]. Understanding the temporal dynamics of these genes, particularly those classified as high-risk, is essential for developing effective strategies to combat the spread of antibiotic resistance.

The risk posed by soil ARGs is not static. Recent global studies analyzing metagenomic data from 2008 to 2021 have revealed a concerning upward trajectory in the prevalence and abundance of high-risk ARGs in soil environments [12]. This increase in soil ARG risk demonstrates significant connectivity with human pathogens, suggesting that soil serves as both a sink and a potential source for clinically relevant antibiotic resistance. This guide provides a comprehensive comparison of the methodologies and findings tracking these temporal shifts, offering researchers a framework for evaluating soil ARG risk over time.

Defining and Categorizing Soil ARG Risk

The Concept of "Rank I" ARGs

A critical advancement in environmental AMR research has been the development of risk classification systems for ARGs. The "Rank I" ARG classification provides a standardized framework for identifying high-risk resistance genes based on three primary criteria [12]:

• Host Pathogenicity: The potential of ARG-carrying microorganisms to cause disease in humans.
• Gene Mobility: The likelihood of ARGs to be transferred between microorganisms via horizontal gene transfer (HGT).
• Human-Associated Enrichment: Increased abundance of ARGs in human-associated environments compared to pristine environments.

This classification enables researchers to prioritize monitoring efforts on ARGs that pose the greatest potential threat to human health, moving beyond mere abundance measurements to more meaningful risk assessments.

Quantitative Metrics for Soil ARG Risk Assessment

Researchers employ several quantitative metrics to track changes in soil ARG risk over time:

• Relative Abundance: Typically measured in copies per 1000 cells or copies per 16S rRNA gene copy, this metric quantifies how common ARGs are within the microbial community [12].
• Occurrence Frequency: The proportion of samples in which a specific ARG is detected within a given time period [12].
• Connectivity Metric: A novel approach that evaluates cross-habitat ARG connectivity through sequence similarity and phylogenetic analysis, revealing genetic overlaps between soil ARGs and clinical isolates [12] [67].

Global Temporal Trends in Soil ARG Risk

Comprehensive Analysis of Soil ARG Risk Over Time

Large-scale temporal analyses have revealed significant patterns in the evolution of soil antibiotic resistome. A landmark 2025 study analyzing 3,965 metagenomic datasets from 2008 to 2021 demonstrated that while the relative abundance of total ARGs in soil remained relatively stable over time, the risk profile has significantly increased [12].

Table 1: Temporal Trends in Soil Antibiotic Resistome (2008-2021)

Parameter	Total ARGs	Rank I ARGs
Temporal Trend	Time-independent (r = 0.08, p > 0.05)	Significant increase over time (r = 0.89, p < 0.001)
Occurrence Frequency	Relatively stable	Significant increase (r = 0.83, p < 0.001)
Key Drivers	Natural environmental factors	Human-associated enrichment and connectivity
Connection to Clinical Resistance	Weak	Strong correlation (R² = 0.40-0.89, p < 0.001)

This divergence between total ARGs and Rank I ARGs highlights that the increasing risk is not merely due to an overall increase in ARG abundance but reflects a specific enrichment of human-relevant, mobile resistance genes in soil environments.

Increasing Connectivity Between Soil and Human Resistomes

Genetic analysis has revealed strengthening connections between soil and clinical resistomes over time. Several key findings illustrate this trend:

• Genetic Overlap: Studies comparing soil ARG sequences with clinical Escherichia coli genomes (1985-2023) have demonstrated increasing genetic similarity over time, suggesting shared evolutionary pathways [12] [67].
• Horizontal Gene Transfer: Analysis of 45 million genome pairs indicates that cross-habitat HGT plays a crucial role in connecting soil and human resistomes [12].
• Clinical Correlations: Significant correlations have been identified between soil ARG risk, potential HGT events, and clinical antibiotic resistance rates (R² = 0.40-0.89, p < 0.001) across 126 countries from 1998 to 2022 [12] [67].

Research Methodologies for Tracking Temporal Shifts

Core Experimental Workflow

The standard methodology for investigating temporal changes in soil ARG risk involves an integrated multi-omics approach, combining metagenomic sequencing with advanced bioinformatic analysis.

Diagram 1: Experimental workflow for tracking temporal shifts in soil ARG risk, showing the integration of field sampling, laboratory analysis, and bioinformatic processing.

Detailed Methodological Protocols

Sample Collection and Metadata Documentation

Temporal studies require systematic sampling strategies with comprehensive metadata collection:

• Spatial-Temporal Design: Collection of soil samples across multiple time points (seasonal or annual) from the same locations to enable longitudinal comparison [12].
• Metadata Documentation: Critical parameters include sampling date, geographical coordinates, land use type, agricultural practices, soil physicochemical properties (pH, organic matter, moisture), and recent antibiotic or metal exposure history [66].
• Control Sites: Inclusion of pristine or minimally disturbed environments (e.g., forest soils, permafrost) as baseline comparators [66].

Molecular Analysis Techniques

Advanced molecular techniques form the core of temporal ARG monitoring:

• DNA Extraction: Use of standardized kits (e.g., FastDNA Spin Kit for Soil) to ensure consistent and comparable DNA yield across samples [68].
• Metagenomic Sequencing: High-throughput sequencing on platforms such as Illumina HiSeq with PE150 strategy, generating approximately 17 Gb of data per sample after quality filtering [12] [68].
• Metatranscriptomic Analysis: For functional activity assessment, rRNA depletion followed by cDNA library construction and sequencing to profile actively transcribed ARGs [68].

Bioinformatic Processing and Analysis

Bioinformatic pipelines standardize ARG identification and quantification:

• Quality Control: Removal of low-quality reads, adapter sequences, and duplicates using tools like Trimmomatic or FastP [68].
• ARG Annotation: Alignment of sequencing reads against curated ARG databases (e.g., SARG version 3.0) using similarity search tools, typically with thresholds of 80% identity and 80% coverage [12].
• Normalization: ARG abundance normalization to 16S rRNA gene copies or per-cell equivalents to enable cross-sample comparisons [12] [66].
• Statistical Analysis: Multivariate statistics, network analysis, and machine learning approaches to identify temporal patterns and driving factors [12].

Key Research Reagents and Solutions

Table 2: Essential Research Reagents and Solutions for Soil ARG Temporal Studies

Category	Specific Product/Kit	Application in ARG Research
Nucleic Acid Extraction	FastDNA Spin Kit for Soil (MP Biomedicals)	Standardized DNA extraction from diverse soil types [68]
RNA Extraction	RNA PowerSoil Total RNA Isolation Kit (MoBio)	Simultaneous extraction of DNA and RNA for metatranscriptomics [68]
rRNA Depletion	Ribo-Zero rRNA Removal Kit (Bacteria) (Illumina)	Removal of ribosomal RNA for metatranscriptomic library preparation [68]
Library Preparation	TruSeq Stranded mRNA Sample Preparation Kit (Illumina)	Construction of sequencing libraries for Illumina platforms [68]
Reference Databases	SARG (Structured ARG Reference Database) v3.0	Comprehensive ARG annotation and classification, excluding regulator sequences [12]
Bioinformatic Tools	ARGs-OAP (v3.2.2)	Online analysis pipeline for ARG identification and quantification [12]
Source Tracking	FEAST (Fast Expectation-maximization for Microbial Source Tracking)	Identification of potential sources of ARGs in soil environments [12]

Geographical Variations in Temporal Patterns

Regional Differences in ARG Risk Trajectories

Temporal patterns of soil ARG risk exhibit significant geographical variation influenced by anthropogenic factors, climatic conditions, and agricultural practices:

• Anthropogenic Impact: Soils in regions with intensive agricultural activity typically show steeper increases in Rank I ARGs over time compared to pristine environments [12] [66].
• Land Use Effects: Agricultural soils, particularly those receiving manure amendments, demonstrate different temporal trajectories compared to forest or grassland soils [66] [69].
• Global Disparities: Studies comparing organic and conventional farms across 22 countries found slightly higher AMR prevalence in conventional farms (28% vs. 18%), but with significant context-dependent variation [69].

Case Study: Regional Temporal Analysis

A comprehensive analysis of global soil samples revealed that specific Rank I ARG subtypes have shown consistent increases across geographical regions:

• Rapidly Increasing ARGs: mph(A), APH(3')-Ia, AAC(6')-le-APH(2")-la, ANT(6)Ia, aadA, APH(6)-Id, aadA10, and mef(B) have demonstrated significant upward trends across diverse geographical locations [12].
• Emerging ARGs: The first detection of NMD-19 in soil samples in 2021 highlights the continuous evolution and emergence of novel resistance mechanisms in soil environments [12].

Implications for Antimicrobial Resistance Management

Connecting Soil ARG Trends to Human Health

The temporal increase in soil ARG risk has direct implications for clinical antibiotic resistance management:

• Predictive Value: Soil ARG risk patterns show significant correlations with clinical resistance rates, suggesting potential for environmental monitoring to inform public health interventions [12] [70].
• One Health Approach: Effective AMR mitigation requires integrated strategies addressing human medicine, animal husbandry, and environmental management [71] [66].
• Intervention Strategies: Findings from temporal studies support targeted interventions such as reduced antibiotic use in agriculture, improved wastewater treatment, and soil management practices that reduce ARG propagation [66] [69].

Future Research Directions

Based on current temporal trend analyses, several priority research areas have emerged:

• Standardized Monitoring: Development of standardized protocols for longitudinal soil ARG surveillance across different geographical regions and land use types [12] [72].
• Mechanistic Studies: Further investigation into the environmental and biological factors driving the selective enrichment of Rank I ARGs in soil over time [66].
• Intervention Evaluation: Research to assess the effectiveness of various management practices in reducing soil ARG risk trajectories [69].

The consistent temporal increase in high-risk ARGs in soil environments underscores the urgency of addressing environmental dimensions of antimicrobial resistance. As soil serves as a critical interface between natural and human-influenced ecosystems, tracking these temporal shifts provides essential insights for developing comprehensive strategies to preserve antibiotic efficacy for future generations.

Bridging Environments and Clinics: Correlating Environmental ARGs with Clinical Resistance

The escalating global threat of antimicrobial resistance (AMR) necessitates innovative surveillance strategies that transcend traditional clinical boundaries. The One Health framework recognizes the inextricable interconnection between human, animal, and environmental health, positing that AMR dissemination must be studied as an integrated system rather than within isolated compartments [73] [74]. This approach is critical because antibiotic resistance genes (ARGs) and resistant bacteria continuously circulate across ecosystems, with clinical resistance often originating from environmental and animal reservoirs.

Genomic technologies have revolutionized our ability to track this cross-sectoral transmission with high resolution. Whole-genome sequencing, particularly using long-read platforms, now enables researchers to reconstruct the complete genetic context of ARGs, including their carriage on mobile genetic elements like plasmids, which are primary vectors for horizontal gene transfer between disparate bacterial species [73] [75]. This guide synthesizes recent genomic evidence quantifying the genetic connectivity between environmental and clinical isolates, providing a comparative analysis of methodologies, findings, and implications for AMR surveillance and intervention.

Comparative Analysis of Key Studies

The following table summarizes the core findings from pivotal studies investigating the cross-sectoral transmission of antibiotic resistance.

Table 1: Comparative Analysis of Major One Health Genomic Studies on AMR Dissemination

Study Focus & Organism	Sample Size & Sources	Core Findings	Key Genetic Elements Identified
E. coli in Urban Aquatic Ecosystems [73]	1,016 isolates from human-associated, animal-associated, and environmental waters in Hong Kong	142 clonal strain-sharing events between human-associated and environmental samples; 195 plasmids shared across all three sectors.	141 ARG subtypes, 2,647 circular plasmids; transmissible plasmids carrying blaNDM, tet(X4), and mcr genes.
K. pneumoniae Species Complex (KpSC) [74]	3,255 isolates from human infections/carriage, animals (pigs, poultry), and marine bivalves in Norway	~5% of human infection isolates had close genetic relatives (≤22 SNPs) in animal/marine isolates; evidence of recent plasmid spillover.	Aerobactin-encoding virulence plasmids (iuc3) moving between pigs and humans.
Global WWTP Resistomes [1]	226 activated sludge samples from 142 wastewater treatment plants across six continents	A core set of 20 ARGs was present in all WWTPs; ARG composition distinct from human gut but similar to sewage and soil.	Tetracycline efflux pumps, Class A/C/D β-lactamases, and vanG genes most abundant.
Horizontal ARG Transfer Prediction [75]	~1 million bacterial genomes integrated with >20,000 metagenomes	Genetic incompatibility and ecological co-occurrence are key predictors of ARG transfer; human and wastewater microbiomes are major hubs.	Transfers of aminoglycoside phosphotransferases (APHs) and β-lactamases were most commonly identified.

Detailed Experimental Protocols

To ensure reproducibility and facilitate comparative assessment, this section outlines the core methodologies employed in the cited studies.

Genome-Resolved Cross-Sectoral Analysis

This protocol, derived from the Hong Kong E. coli study, details the process for tracking strain and plasmid sharing across ecological boundaries [73].

Table 2: Key Reagents for Genome-Resolved Cross-Sectoral Analysis

Research Reagent	Function/Application in the Protocol
Nanopore R10.4.1 Flow Cells	Enables long-read sequencing for generating high-quality, near-complete bacterial genomes and complete plasmid sequences.
Selective Media (e.g., with cephalosporins)	Used for the targeted isolation of antibiotic-resistant bacteria (e.g., extended-spectrum cephalosporin-resistant E. coli) from complex samples.
Cary-Blair Transport Medium	Preserves the viability of bacteria during sample transport from the field to the laboratory.
Bioinformatics Tools: ABRicate, Plascope, Flye	Used for in-silico ARG screening, plasmid typing/circularization, and genome assembly, respectively.
Conjugation Assays (in-vitro)	Validates the functional transmissibility of plasmids identified through genomic analysis across different bacterial hosts.

Figure 1: Workflow for Genome-Resolved Cross-Sectoral Analysis. The process begins with coordinated sampling from multiple One Health sectors, proceeds through high-resolution genomic sequencing and bioinformatics, and culminates in the quantification of genetic connectivity, with key findings validated experimentally.

• Sample Collection and Bacterial Isolation: Samples are collected from pre-defined One Health compartments (human-associated sewage, animal husbandry wastewater, and environmental waters). A crucial step involves processing samples both with and without antibiotic selection to capture both enriched and baseline resistance populations. Isolates are typically identified using standard microbiological methods [73] [76].
• Whole-Genome Sequencing: DNA from purified isolates is subjected to whole-genome sequencing. The use of long-read sequencing platforms (e.g., Nanopore R10.4.1) is emphasized for their ability to resolve repetitive regions and fully reconstruct plasmids, which are often missed by short-read technologies [73].
• Bioinformatic Analysis:
  • Assembly & Annotation: Generate high-quality, near-complete genomes and circularize plasmids from sequencing reads.
  • Typing & Phylogenetics: Determine multi-locus sequence types (STs) and phylogroups. Construct core-genome phylogenies to understand the population structure and evolutionary relationships between isolates from different sources.
  • ARG and Plasmid Identification: Use curated databases (e.g., ResFinder, PlasmidFinder) to identify ARGs and plasmid replicons in the assembled genomes [73] [74].
• Assessment of Ecological Connectivity:
  • Clonal Strain Sharing: Identify instances where highly related (based on core-genome SNP thresholds) isolates are found in different sectors.
  • Shared Plasmids: Identify identical or highly similar plasmid sequences circulating in bacteria from different sources.
  • Conjugation Assays: Functionally validate the transferability of identified plasmids into standard laboratory strains to confirm their mobility across ecological boundaries [73].

Phylogenetics-Based Horizontal Gene Transfer Identification

This protocol, used to build predictive models for ARG transfer, identifies historical horizontal transfer events from genomic data [75].

• Pan-Genome ARG Screening: A comprehensive collection of bacterial genomes is screened for a wide range of ARGs against a curated database, identifying all ARG instances and their variants.
• Phylogenetic Tree Construction: For each class of ARG (e.g., beta-lactamases, tetracycline efflux pumps), a phylogenetic tree is constructed using the protein sequences of the identified ARG variants.
• Horizontal Transfer Detection: The phylogenetic trees are traversed to identify nodes where descendants carry highly similar ARG sequences (indicating recent shared ancestry) but belong to host bacteria that are taxonomically distant (e.g., at the order level or above). These nodes represent putative horizontal transfer events.
• Feature Integration for Machine Learning: For each detected transfer event, multiple features are compiled:
  • Genetic Compatibility: Nucleotide composition dissimilarity (k-mer distance) between donor and recipient genomes, and between the ARG and the recipient genome.
  • Ecological Connectivity: Co-occurrence frequency of the donor and recipient hosts in different environments (e.g., human, animal, wastewater) derived from metagenomic data.
  • Host Physiology: Gram-staining classification of the involved hosts.
• Model Training and Prediction: A machine learning model (e.g., Random Forest) is trained on this data to predict the likelihood of horizontal transfer between any two bacterial hosts based on the extracted genetic and ecological features [75].

Global Distribution and Drivers of Resistance Genes

The table below synthesizes data on the distribution and abundance of key ARGs across different global environments, highlighting the interconnected nature of resistomes.

Table 3: Global Distribution and Abundance of Key Antibiotic Resistance Genes

ARG / Gene	Resistance Mechanism	Primary Hosts / Carriers	Prevalence in Key Environments
sul1 [15]	Sulfonamide resistance	Diverse bacteria in WWTPs and marine environments	Ubiquitous in global marine samples; highly abundant in WWTPs.
tet(A) / Tet Efflux Pumps [73] [1]	Tetracycline efflux	E. coli, K. pneumoniae, Chloroflexi, Acidobacteria	Dominant in animal farms [73]; core gene in global WWTPs [1].
Class A/C/D β-lactamases (e.g., blaCTX-M, blaTEM) [73] [76]	β-lactam antibiotic inactivation	Enterobacteriaceae (E. coli, K. pneumoniae)	Widespread in human, animal, and environmental sectors; high prevalence in ESCr E. coli from cattle and human sewage [76].
Class B β-lactamases (e.g., blaNDM) [73]	Carbapenem inactivation	Enterobacteriaceae	Detected in human-associated and environmental waters, often co-harboring tet(X4) or mcr [73].
vanG/T [1]	Glycopeptide (vancomycin) resistance	Diverse Firmicutes in WWTPs	Ranked among the most abundant ARGs in global activated sludge samples [1].

The dissemination of these ARGs is driven by a combination of genetic factors and ecological pressures. Genetic compatibility between the donor, recipient, and the mobile genetic element is a fundamental constraint. A critical factor is the difference in nucleotide composition (measured as k-mer distance), where greater dissimilarity significantly reduces the likelihood of successful horizontal gene transfer [75]. Conversely, ecological connectivity greatly facilitates transfer. Environments like wastewater treatment plants and the human gut, where diverse bacteria co-exist in high densities, serve as major hubs for gene exchange. The co-occurrence of potential donor and recipient species in these environments is a strong positive predictor of ARG transfer events [75]. Finally, anthropogenic selective pressures, such as the use of antibiotics in clinical settings and animal farming, enrich for resistant bacteria and genes, promoting their persistence and spread across the One Health spectrum [73] [77].

The global rise of antimicrobial resistance (AMR) presents a critical challenge to public health. Within the One Health framework, which recognizes the interconnectedness of human, animal, and environmental health, the soil environment has gained attention as a significant reservoir of antibiotic resistance genes (ARGs). Understanding the flow of these genes from environmental compartments to clinical pathogens is essential for developing effective mitigation strategies. This case study examines the direct relationship between the abundance and risk of ARGs in global soil samples and resistance rates in clinical E. coli isolates. We present a geographical comparison of this relationship, supported by quantitative data and detailed experimental methodologies, to provide researchers and drug development professionals with actionable insights into environmental AMR drivers.

Key Quantitative Findings: Soil ARG Risk and Clinical Resistance Correlations

Temporal Increase in Soil ARG Risk

A comprehensive analysis of 2,540 soil metagenomes from a global dataset of 3,965 metagenomic samples revealed a concerning trend over a 13-year period (2008-2021). While the relative abundance of total ARGs in soil remained stable over time (r = 0.08, p > 0.05), the abundance and occurrence of Rank I ARGs—those characterized by high risk due to host pathogenicity, gene mobility, and enrichment in human-associated environments—showed a statistically significant increase [22].

Table 1: Temporal Trends in Soil Antibiotic Resistome (2008-2021)

Parameter	Total ARGs	Rank I ARGs
Temporal Trend (Correlation coefficient)	r = 0.08 (p > 0.05)	r = 0.89 (p < 0.001)
Occurrence Frequency Trend	Not applicable	r = 0.83 (p < 0.001)
Key Increasing Subtypes	Not applicable	mph(A), APH(3')-Ia, AAC(6')-le-APH(2'')-la, ANT(6)Ia, aadA, APH(6)-Id, aadA10, mef(B), APH(3'')-Ib
Notable Emergence	Not applicable	First detection of NMD-19 in soil samples in 2021

Genetic Connectivity Between Soil and Clinical Resistomes

The study introduced a novel "connectivity" metric to evaluate cross-habitat ARG transfer through sequence similarity and phylogenetic analysis. Analysis of 45 million genome pairs revealed that soil ARGs showed increasing genetic overlap with clinical E. coli genomes (1985-2023) over time [22]. This suggests a strengthening link between the soil and human resistomes. Horizontal gene transfer (HGT) was identified as a crucial mechanism for this connectivity [22].

Table 2: Source Attribution of Rank I ARGs in Global Soils

Source Habitat	Contribution to Soil Rank I ARGs
Human Feces	75.4%
Chicken Feces	68.3%
WWTP Effluent	59.1%
Swine Feces	53.9%
All Other Habitats (Average)	60.1%

Quantitative Correlation with Clinical Resistance

By compiling clinical antibiotic resistance datasets covering 126 countries from 1998 to 2022, the study established highly significant correlations between soil ARG risk, potential HGT events, and clinical antibiotic resistance rates. The correlation coefficients ranged from R² = 0.40 to 0.89 (p < 0.001), indicating that soil ARG risk can explain 40-89% of the variance in clinical resistance patterns, depending on the specific ARG type and geographical region [22].

Detailed Experimental Protocols

Metagenomic Sampling and ARG Profiling Workflow

The experimental design for linking soil and clinical resistomes involved a multi-step process for comprehensive ARG characterization.

Table 3: Key Experimental Methods for Soil and Clinical Resistome Analysis

Method Step	Protocol Description	Purpose/Rationale
Sample Collection	2,540 soil samples globally; 1,425 other habitat samples; 8,388 E. coli genomes from soil, livestock, humans	Ensure geographical and habitat representation for robust comparisons
DNA Extraction & Sequencing	High-throughput shotgun sequencing (Illumina platforms)	Comprehensive profiling of genetic material without amplification bias
ARG Annotation & Classification	ARGs-OAP (v3.2.2) pipeline; SARG3.0_S database excluding transcriptional regulators, point mutations, multidrug efflux pumps	Standardized identification and risk categorization of resistance determinants
Risk Assessment	Rank I ARG classification based on host pathogenicity, mobility, human-associated enrichment	Focus on clinically relevant ARGs with highest potential for human impact
Source Tracking	FEAST (Fast Expectation-Maximization for Microbial Source Tracking)	Quantify contributions of different habitats to soil ARG pool
Connectivity Analysis	Sequence similarity and phylogenetic analysis of 45 million genome pairs; Horizontal Gene Transfer assessment	Establish genetic linkage between environmental and clinical resistomes

Comparative Analysis of ARG Detection Methodologies

Different methodological approaches for ARG detection and quantification offer complementary advantages for resistome studies.

Table 4: Comparison of ARG Detection Methodologies in Resistome Studies

Method	Principle	Advantages	Limitations	Suitable Applications
Shotgun Metagenomic Sequencing	High-throughput sequencing of all DNA in sample	Comprehensive ARG profiling; detection of novel genes; functional analysis	Higher cost; computational intensity; may miss low-abundance genes	Global resistome comparisons; discovery studies
HT-qPCR (High-Throughput Quantitative PCR)	Targeted amplification with 384-well microfluidic platform	High sensitivity; absolute quantification; lower cost	Limited to known genes; primer design constraints	Large-scale environmental monitoring; time-series studies
Metagenome-Assembled Genomes (MAGs)	Bin contigs from metagenomes into genomes	Links ARGs to specific microbial hosts; reveals genomic context	Assembly challenges; may miss rare populations	Host-carrier identification; transmission pathway analysis

Mechanisms of ARG Transmission from Soil to Clinical Settings

Horizontal Gene Transfer Pathways

The study identified horizontal gene transfer (HGT) as the primary mechanism for ARG connectivity between soil and clinical environments. The comparison of 45 million genome pairs demonstrated the crucial role of mobile genetic elements (MGEs) in facilitating the transfer of Rank I ARGs from environmental to clinical settings [22]. This genetic exchange is enabled by the shared sequence identity between soil-associated ARGs and those found in clinical E. coli pathogens.

Environmental and Anthropogenic Drivers

Multiple environmental factors influence the dissemination of ARGs from soil to human pathogens:

• Land Use Patterns: Agricultural and wastewater-impacted soils show higher abundance of clinically relevant ARGs [22]
• Antibiotic Residues: Sub-therapeutic antibiotic concentrations in soil create selective pressure for ARG maintenance and transfer [78]
• Heavy Metal Contamination: Heavy metals in soil promote co-selection of ARGs through oxidative stress mechanisms [78]
• Microbial Community Structure: Soil pH, organic matter, and moisture content bidirectionally regulate ARG distribution by modulating microbial community composition [78]

The Scientist's Toolkit: Essential Research Reagents & Platforms

Table 5: Essential Research Materials and Platforms for Soil-Clinical Resistome Studies

Category	Specific Tool/Platform	Function in Resistome Research
Sequencing Platforms	Illumina HiSeq4000; Illumina platforms	High-throughput shotgun sequencing for metagenomic profiling
Bioinformatics Pipelines	ARGs-OAP (v3.2.2); SARG3.0_S database	Standardized ARG annotation and classification against curated databases
Reference Databases	CARD (Comprehensive Antibiotic Resistance Database); ResFinder	Reference sequences for ARG identification and typing
Source Tracking Tools	FEAST (Fast Expectation-Maximization for Microbial Source Tracking)	Quantifies contributions of different habitats to resistome
Assembly & Binning Tools	MEGAHIT; MetaBAT2; MaxBin2; CONCOCT	Metagenomic assembly and binning into MAGs (Metagenome-Assembled Genomes)
Quality Control Tools	CheckM; fastp; BWA	Assess MAG quality; sequence quality control; host DNA removal
Statistical & Visualization Platforms	R packages (vegan, phyloseq); Majorbio Cloud Platform	Statistical analysis of resistome patterns; data visualization

This case study establishes a quantifiable link between soil ARG abundance and clinical E. coli resistance rates, with correlation coefficients (R²) ranging from 0.40 to 0.89. The increasing risk of soil ARGs over time, coupled with demonstrated genetic connectivity through horizontal gene transfer, underscores the importance of soil as a reservoir for clinically relevant resistance determinants. The geographical patterns revealed in this analysis highlight regions where targeted interventions could most effectively mitigate the flow of resistance genes from environment to clinic. For drug development professionals, these findings emphasize the need to consider environmental resistance reservoirs in antimicrobial development strategy and resistance management planning. Future research should focus on spatiotemporal dynamics of the feedback loop between ARGs and the environment and develop quantitative models of the coupling effects of multiple transmission factors.

Antibiotic resistance presents a critical global health challenge, with its development and spread profoundly influenced by local environmental conditions [79]. Geographical comparisons of antibiotic resistance genes (ARGs) are essential to understand the drivers and pathways of resistance in diverse ecosystems. This guide provides a structured comparison of ARG profiles and their determinants across three distinct environments: a Mediterranean-climate estuary (the Tagus Estuary, Portugal), Arctic coastal areas, and global wastewater-influenced estuaries. We synthesize experimental data and methodologies to offer researchers a clear framework for cross-regional analysis of antibiotic resistomes.

Regional Comparative Analysis of Antibiotic Resistomes

Table 1: Regional Comparison of Antibiotic Resistance Gene Profiles and Drivers

Parameter	Mediterranean-Climate Estuary (Tagus)	Arctic Coastal Areas	Wastewater-Influenced Estuaries (Global)
Key Characteristics & Status	Biogeographic transition zone with mixture of temperate and subtropical species; improving water quality [80]	Minimal terrestrial disturbance; serves as a control zone with lower human impact [81]	Critical hotspots for ARG accumulation and evolution; directly receiving anthropogenic waste [79] [1] [82]
Dominant ARG Types/Mechanisms	Information specific to ARG types not available in search results; community shifts toward subtropical species observed [80]	Not specifically reported for Arctic regions in search results	Most Abundant: Tetracycline, Beta-lactam, and Glycopeptide resistance genes [1]Dominant Mechanism: Antibiotic inactivation (~55.7% of ARG abundance) [1]
Primary Environmental Drivers	Water quality improvements from WWTPs; climate change (temperature increase) [80]	Minimal terrestrial disturbance; lower selection pressure [81]	Terrestrial runoffs (nutrients, metals) [81]; antibiotic residues [79]; mobile genetic elements promoting horizontal gene transfer [1]
Quantitative ARG Abundance	Not specifically quantified in search results	Lower richness and abundance compared to bays [81]	Core set of 20 ARGs present in all WWTPs, accounting for 83.8% of total abundance [1]
Major Bacterial Hosts/ Carriers	Not specifically reported for this region in search results	Not specifically reported for Arctic regions in search results	Chloroflexi, Acidobacteria, Deltaproteobacteria [1]
Research & Monitoring Focus	Benthic invertebrate community shifts using AMBI index; climate impact on species distribution [80]	Geographic patterns across ecological gradients; influence of terrestrial disturbances [81]	Global surveillance of activated sludge resistomes; identifying biotic/abiotic drivers [1]

Key Experimental Protocols and Methodologies

Global Wastewater Resistome Profiling

Objective: To systematically analyze the diversity and distribution of ARGs in wastewater treatment plants (WWTPs) across six continents [1].

Workflow:

• Sample Collection: 226 activated sludge samples were collected from 142 WWTPs using identical protocols to ensure comparability.
• DNA Extraction & Shotgun Sequencing: Community DNA was extracted and sequenced via shotgun metagenomics, generating 2.8 terabases of data.
• Bioinformatic Analysis:
  • Assembly & Gene Prediction: 36 million contigs were assembled, leading to the prediction of ~34.8 million non-redundant open reading frames.
  • ARG Annotation: Open reading frames were annotated as ARG sequences using specialized databases.
  • Abundance Normalization: ARG abundance was normalized to the copy number per bacterial cell to enable cross-comparisons.
• Statistical Analysis: Techniques including Principal Coordinate Analysis and PERMANOVA were used to identify structural differences in resistomes across continents and their correlations with environmental factors [1].

Coastal Sediment Resistome Analysis Across Ecological Gradients

Objective: To characterize the geographic patterns and determinants of sediment antibiotic resistomes across coastal ecological gradients (bay, nearshore, offshore) [81].

Workflow:

• Sample Collection: Surface sediment samples were collected from 32 stations across seven zones (e.g., Hangzhou Bay, Yushan Islands Reservation) representing different levels of terrestrial influence.
• HT-qPCR for ARG Profiling: A high-throughput quantitative PCR (HT-qPCR) chip with 285 primer sets targeting major ARG types and mobile genetic elements was used.
• Environmental Variable Measurement:
  • Physicochemical Properties: Total carbon, total nitrogen, total phosphorus, NO3−-N, NH4+-N, pH, and sediment texture.
  • Metals/Metalloid: Concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were measured.
  • Antibiotics: 27 target antibiotics from six major classes were quantified.
• Integration and Modeling: 16S rRNA gene amplicon sequencing characterized bacterial communities. Partial Least Squares Path Modeling was used to reveal the direct and indirect shaping paths of environmental variables, bacterial composition, and MGEs on the sediment resistome [81].

Conceptual Framework of Environmental Antibiotic Resistance

The following diagram illustrates the primary pathways and drivers of antibiotic resistance gene emergence and dissemination in the environment, integrating factors relevant to the compared regions.

Pathways and Drivers of Environmental Antibiotic Resistance. This diagram synthesizes the core processes and modifying factors governing the environmental cycle of Antibiotic Resistance Genes (ARGs), as evidenced across the studied regions. The pathway begins with antibiotic use, leading to environmental emission and subsequent selection pressure that promotes Horizontal Gene Transfer (HGT), ultimately resulting in ARG spread and human health risk. Key environmental drivers, including regional factors, WWTPs, Mobile Genetic Elements (MGEs), co-pollutants, and climate change, act at critical bottlenecks to accelerate or modulate this process [79] [83] [82].

The Scientist's Toolkit: Essential Reagents and Materials

Table 2: Key Research Reagent Solutions for Environmental Resistome Studies

Reagent / Material	Primary Function in Research	Application Context
HT-qPCR Chip (e.g., 285-plex)	High-throughput, simultaneous quantification of a wide array of ARGs and MGEs.	Profiling ARG diversity and abundance in environmental samples like coastal sediments [81].
Shotgun Metagenomic Sequencing Kits	Comprehensive, untargeted analysis of all genetic material in a sample, allowing discovery of novel ARGs.	Global resistome comparison in WWTPs; characterizing the total environmental gene pool [1].
16S rRNA Gene Sequencing Reagents	Profiling and characterizing the structure and composition of the bacterial community.	Identifying bacterial hosts of ARGs and linking community shifts to environmental factors [1] [81].
Mobile Genetic Element (MGE) Probes	Tracking genes associated with horizontal gene transfer (e.g., plasmids, integrons, transposons).	Assessing the mobility potential and transmission risk of ARGs found in the environment [1] [83].
Standardized DNA Extraction Kits	Ensuring consistent, high-quality DNA yield from complex environmental matrices (e.g., sludge, sediment).	Crucial for cross-study and cross-regional comparisons, minimizing methodological bias [1].
Reference Antibiotic Standards	Quantifying antibiotic residues in environmental samples using techniques like LC-MS.	Measuring the selective pressure exerted by antibiotic pollution in hotspots [81].

Validating Surveillance Data Against Clinical Outcomes and Antibiotic Sales Data

Antimicrobial resistance (AMR) presents one of the most pressing global public health threats of our time, with an estimated 4.95 million deaths associated with bacterial resistance worldwide in 2019 [84]. The effectiveness of antimicrobial stewardship and infection control policies depends heavily on the quality of surveillance data and its relationship to tangible health outcomes. Traditionally, data on AMR determinants—including antibiotic use, resistance prevalence, stewardship activities, and infection prevention—have been analyzed and reported separately despite their interconnected nature [85]. This compartmentalized approach limits our understanding of the complex dynamics driving resistance patterns across different geographical regions.

Validation of surveillance data against clinical outcomes and antibiotic sales represents a critical methodological advancement in antibiotic resistance research. By linking independent data sources, researchers can move beyond simple descriptive statistics to establish causal relationships, generate testable hypotheses, and provide evidence-based recommendations for clinical practice and policy development [85]. This comparative guide examines the methodologies, applications, and limitations of current approaches to validating AMR surveillance data, with particular emphasis on geographical comparisons of antibiotic resistance genes.

Comparative Analysis of Validation Approaches

Table 1: Comparison of Primary Data Sources for AMR Surveillance Validation

Data Source Category	Key Metrics	Geographical Applicability	Strengths	Limitations
Clinical Outcome Data	Patient mortality, treatment failure rates, hospital stay duration	Hospital, regional, or national levels	Direct clinical relevance; enables outcome correlations	Data privacy concerns; requires large sample sizes
Antibiotic Sales Data	Defined Daily Doses (DDDs), expenditure trends, AWaRe classification patterns	National and international levels	Standardized metrics (DDD/1000 inhabitants); readily available	Does not capture actual consumption or appropriateness of use
Antibiotic Resistance Genes	ARG abundance, diversity, mobility potential, host associations	Local to global scales (including remote environments)	Provides mechanistic insights; predicts emerging threats	Technical variability in detection methods; complex data interpretation
Linked Data Systems	Correlations between antibiotic use and resistance patterns	Multiple levels (patient, facility, region, country)	Reveals hidden relationships; informs targeted interventions	Ecological fallacy risk; data governance challenges

Table 2: Quantitative Findings from Recent AMR Validation Studies

Study Focus	Geographical Scope	Key Quantitative Findings	Correlation Strength	Data Sources Linked
Inpatient Antibiotic Use	Northwest China (Xinjiang)	Total inpatient antibiotic use decreased to 27.6 DDDs/100 patient days in 2022; Watch group antibiotics accounted for 70.3% of use [84]	11-year significant decreasing trend (AAPC: -2.0%)	Hospital antibiotic utilization data; AWaRe classification
Antibiotic Expenditure	62 countries globally	Aggregate spending decreased from $49.61B (2013) to $30.68B (2023); per capita spending declined from $12.08 to $7.92 [86]	Significant associations with income, health spending, and water access	Pharmaceutical sales data; socioeconomic indicators
Wastewater ARG Distribution	142 WWTPs across six continents	20 core ARGs present in all WWTPs accounted for 83.8% of total ARG abundance; ARG composition strongly correlated with bacterial taxonomy [1]	Distinct continental separation (PERMANOVA, p<0.05)	Metagenomic sequencing; geographic location data
Medicaid Antibiotic Prescribing	United States (state-level)	2.8-fold difference between highest (Kentucky: 855/1000) and lowest (Oregon: 299/1000) prescribing states [87]	Strong state-level correlations (r=0.81 for azithromycin/amoxicillin)	Prescription claims; census region data

Methodological Frameworks for Data Validation

Data Linkage Approaches

Systematic methodologies for linking AMR data sources have recently been formalized in a comprehensive scoping review. The optimal approach involves connecting records from at least two independent data sources containing different AMR determinants or combining AMR data with population characteristics. Linkage can be performed at various levels including individual patients, healthcare institutions, geographical regions, or countries [85]. Of 48 identified studies using this approach, most demonstrated significant added value by generating recommendations for clinical practice, guiding future policies, or suggesting directions for research and surveillance enhancement [85].

The predominant study design in this field remains ecological, utilizing group-level data and aggregated measures to explore potential associations. While this approach has identified important correlations, it carries inherent limitations including susceptibility to ecological fallacy and unobserved confounding, which complicate causal inference [85]. Recent methodological advancements emphasize multi-level frameworks that incorporate data from different hierarchical levels to provide more nuanced insights while mitigating ecological bias.

Temporal Trend Analysis

Joinpoint regression analysis represents a sophisticated statistical approach for identifying significant changes in trends over time. This method has been effectively employed to analyze long-term antibiotic utilization patterns, calculating Average Annual Percent Change (AAPC) with corresponding 95% confidence intervals to quantify trend significance [84]. For example, one 11-year study of inpatient antibiotic use in Northwest China demonstrated a statistically significant decreasing trend (AAPC: -2.0%; 95% CI: -3.6% to -0.4%), while also revealing concerning patterns in the Access-to-Watch antibiotic ratio, which decreased significantly from 46.4% to 24.4% [84].

Geospatial Analysis Frameworks

Geographical comparison of antibiotic resistance genes benefits from structured spatial analysis frameworks. The "connectivity" metric represents an innovative approach that evaluates cross-habitat ARG connections through sequence similarity and phylogenetic analysis [22]. This methodology has revealed higher genetic overlap between soil and clinical E. coli genomes over time, suggesting increasing linkage between environmental and human resistomes. Furthermore, comparison of 45 million genome pairs has provided evidence that cross-habitat horizontal gene transfer serves as a crucial mechanism for ARG connectivity between humans and soil environments [22].

Experimental Protocols for AMR Surveillance Validation

Protocol 1: Linked Analysis of Antibiotic Use and Resistance Patterns

Objective: To investigate associations between antibiotic prescribing patterns and resistance incidence across different geographical regions.

Methodology:

• Data Collection: Extract antibiotic prescription data from hospital pharmacy records, insurance claims, or national surveillance systems, calculating Defined Daily Doses (DDDs) per 100 patient days or per 1000 inhabitants [84] [86]. Concurrently, collect resistance data from clinical microbiology laboratories for targeted pathogens.
• Spatial Aggregation: Aggregate data by geographical units (e.g., hospital catchment areas, counties, states) with corresponding population denominators.
• Statistical Analysis: Employ multivariate regression models to examine associations between antibiotic use metrics and resistance incidence, adjusting for potential confounders including population density, socioeconomic status, and healthcare access.
• Temporal Analysis: Conduct time-series analyses to investigate lagged relationships between antibiotic use and subsequent resistance patterns.

Key Applications: This approach successfully demonstrated that antibiotics prescribed more frequently in primary care were associated with higher incidences of resistant bacteria causing bloodstream infections [85].

Protocol 2: Wastewater-Based Surveillance of Antibiotic Resistance Genes

Objective: To characterize the diversity and distribution of antibiotic resistance genes in wastewater treatment plants across geographical regions.

Methodology:

• Sample Collection: Collect activated sludge samples from wastewater treatment plants across targeted geographical regions, ensuring representative coverage [1].
• DNA Extraction and Sequencing: Perform metagenomic DNA extraction using standardized kits (e.g., QIAamp Fast DNA Stool Mini Kit) and conduct shotgun sequencing on Illumina platforms to obtain sufficient sequencing depth (approximately 12.3 Gb per sample) [1] [3].
• Bioinformatic Analysis:
  • Assemble quality-filtered reads into contigs using MEGAHIT (v1.1.2) [3]
  • Predict open reading frames with Prodigal (v2.6.3) [3]
  • Annotate ARGs using curated databases (CARD v3.0.9) with similarity-based approaches [22]
  • Normalize ARG abundance to bacterial cell numbers using 16S rRNA gene markers [1]
• Geospatial Statistical Analysis: Compare ARG compositions across regions using PERMANOVA and visualize patterns with Principal Coordinates Analysis (PCoA).

Key Applications: This protocol revealed that ARG composition differs significantly across continents and is distinct from other environments like the human gut and oceans [1].

Protocol 3: Integration of Clinical and Environmental Resistomes

Objective: To investigate connectivity between environmental and clinical antibiotic resistance gene pools.

Methodology:

• Sample Collection: Collect paired clinical (e.g., fecal samples, clinical isolates) and environmental (e.g., soil, wastewater) samples from targeted geographical regions [3] [22].
• Metagenomic Sequencing and Genome Reconstruction: Process samples through metagenomic sequencing as described in Protocol 2. Additionally, perform metagenomic binning using tools like MetaBAT2, MaxBin2, and CONCOCT to generate metagenome-assembled genomes (MAGs) [3].
• ARG Risk Assessment: Apply the "Rank I ARG" classification framework to identify high-risk resistance genes based on host pathogenicity, gene mobility, and human-associated enrichment [22].
• Source Tracking Analysis: Utilize fast expectation-maximization for microbial source tracking (FEAST) to quantify the sharing of ARGs between different habitats [22].
• Horizontal Gene Transfer Assessment: Identify potential cross-habitat horizontal gene transfer events through comparative genomic analysis of plasmid sequences and mobile genetic elements.

Key Applications: This approach demonstrated that soil shares 60.1% of total ARGs and 50.9% of Rank I ARGs with other habitats, primarily human feces (75.4%), chicken feces (68.3%), and WWTP effluent (59.1%) [22].

Table 3: Key Research Reagents and Computational Tools for AMR Surveillance Validation

Category	Specific Tool/Reagent	Function in Research	Application Examples
DNA Extraction Kits	QIAamp Fast DNA Stool Mini Kit	High-quality metagenomic DNA extraction from complex samples	Fecal and wastewater sample processing [3]
Sequencing Platforms	Illumina Shotgun Sequencing	High-throughput metagenomic sequencing	Comprehensive ARG profiling in diverse samples [1]
Bioinformatic Tools	MEGAHIT (v1.1.2)	De novo metagenomic assembly	Contig construction from complex microbial communities [3]
Bioinformatic Tools	Prodigal (v2.6.3)	Gene prediction from metagenomic assemblies	Identification of open reading frames [3]
Reference Databases	CARD (v3.0.9)	Comprehensive Antibiotic Resistance Database	ARG annotation and classification [3]
Reference Databases	SARG3.0_S database	Structured ARG database excluding regulators and point mutations	Similarity-based ARG annotation [22]
Genome Binning Tools	MetaBAT2, MaxBin2, CONCOCT	Metagenomic binning from assembly contigs	Recovery of metagenome-assembled genomes (MAGs) [3]
Statistical Frameworks	FEAST (Fast Expectation-maximization for Microbial Source Tracking)	Source tracking of microbial communities	Quantifying ARG sharing between habitats [22]

Interpretation of Validation Data

Geographical Patterns in Antibiotic Resistance

Analyses of antibiotic resistance genes across different geographical regions have revealed distinct spatial patterns with important implications for public health interventions. A global study of wastewater treatment plants across six continents discovered that while total ARG abundance showed no significant difference across continents, ARG composition differed significantly between pairwise continental comparisons [1]. This suggests that localized factors rather than overall abundance may drive resistance dynamics, necessitating region-specific intervention strategies.

Geographical variability in antibiotic prescribing patterns further complicates the resistance landscape. Analysis of Medicaid prescribing data in the United States revealed a 2.8-fold difference between the highest (Kentucky = 855/1000 enrollees) and lowest (Oregon = 299/1000) prescribing states, with the South census region prescribing 52.2% more antibiotics than the West [87]. These pronounced geographical disparities highlight the potential for targeted stewardship efforts in high-prescribing regions.

Temporal Trends and Emerging Concerns

Longitudinal analyses provide crucial insights into the evolution of antibiotic resistance and the effectiveness of intervention strategies. Analysis of soil metagenomes from 2008 to 2021 revealed that while total ARG abundance remained stable over time, the relative abundance of high-risk "Rank I ARGs" increased significantly (r = 0.89, p < 0.001), as did their occurrence frequency (r = 0.83, p < 0.001) [22]. This divergence suggests that simply monitoring total ARG abundance may be insufficient for assessing public health risk, emphasizing the need for more sophisticated classification frameworks.

Global antibiotic expenditure trends between 2013 and 2023 showed substantial declines, with aggregate spending decreasing from $49.61 billion to $30.68 billion and per capita spending declining from $12.08 to $7.92 [86]. While potentially positive from a stewardship perspective, these trends raise concerns about market viability for new antibiotic development, highlighting the tension between conservation and innovation in antibacterial therapeutics.

The validation of antimicrobial resistance surveillance data against clinical outcomes and antibiotic sales represents a methodological imperative in the fight against drug-resistant infections. This comparative analysis demonstrates that linked data approaches provide significantly enhanced insights compared to isolated surveillance efforts, enabling hypothesis generation, optimization of surveillance systems, and evidence-based guideline development [85].

Geographical comparison of antibiotic resistance genes reveals complex patterns influenced by environmental, clinical, and social factors. The integration of clinical outcome data with antibiotic utilization metrics and molecular characterization of resistance mechanisms provides a powerful framework for understanding these dynamics. However, methodological challenges remain, particularly regarding ecological study designs prone to confounding and the complexity of establishing causal relationships in multifactorial resistance development.

Future directions in AMR surveillance validation should prioritize the development of standardized protocols for data linkage, enhanced methodological frameworks for causal inference, and innovative approaches for real-time monitoring of resistance emergence and spread. As the field advances, the integration of multi-omics data with clinical outcomes and pharmaceutical sales data will be essential for developing effective, evidence-based interventions to address the global threat of antimicrobial resistance.

Conclusion

The geographical comparison of ARGs reveals a complex and interconnected global resistome where anthropogenic activity is a primary driver of resistance proliferation. Key takeaways include the identification of a core set of high-risk ARGs, the critical role of mobile genetic elements in dissemination, and the demonstrable genetic connectivity between environmental reservoirs and human pathogens. The increasing quantification of this link, such as the rising risk from soil ARGs over time, underscores the urgency of a unified One Health approach. Future efforts must prioritize the integration of global environmental surveillance with clinical data, the development of rapid risk assessment tools, and the creation of interventions that target the most critical pathways of resistance spread to safeguard modern medicine.

References

• 1. Global diversity and distribution of antibiotic resistance ... [https://www.nature.com/articles/s41467-025-59019-3]
• 2. Gender differences in global antimicrobial resistance - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12089330/]
• 3. Geographic patterns of antibiotic resistance in the human ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1652757/full]
• 4. Frontiers | Antimicrobial resistance in diverse urban microbiomes... [https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1460508/full]
• 5. Antimicrobial resistance in surface waters — developing ... [https://www.eea.europa.eu/en/analysis/publications/antimicrobial-resistance-in-european-surface-waters-a-developing-area]
• 6. Latent antibiotic resistance genes are abundant, diverse, and mobile in... [https://microbiomejournal.biomedcentral.com/articles/10.1186/s40168-023-01479-0]
• 7. Global soil antibiotic resistance genes are... | Nature Communications [https://www.nature.com/articles/s41467-025-61606-3?error=cookies_not_supported&code=ad8cf9dc-8c5e-4668-b8c7-18d4ddb05556]
• 8. Frontiers | Comprehensive whole metagenomics analysis uncovers... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1649872/full]
• 9. Geographic patterns and determinants of antibiotic ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.922580/full]
• 10. Effects of Human Activities on Antibiotic Resistance Genes ... [https://www.mdpi.com/2073-4441/17/10/1523]
• 11. Global livestock resistome: Antibiotic resistance is ... [https://amr.solutions/2025/08/15/global-livestock-resistome-antibiotic-resistance-is-widespread/]
• 12. Global soil antibiotic resistance genes are associated with ... [https://www.nature.com/articles/s41467-025-61606-3]
• 13. Metagenomic insights into the distribution of antibiotic ... [https://www.sciencedirect.com/science/article/pii/S0160412018331660]
• 14. The Ocean as a Global Reservoir of Antibiotic Resistance Genes [https://pmc.ncbi.nlm.nih.gov/articles/PMC4592852/]
• 15. Global quantification and distribution of antibiotic ... [https://www.sciencedirect.com/science/article/pii/S0048969724069225]
• 16. The abundance of antibiotic resistance genes in human ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4063852/]
• 17. Diverse Distribution of Resistomes in the Human and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6225452/]
• 18. Clinically relevant antibiotic resistance genes are linked to ... [https://www.nature.com/articles/s41467-023-42998-6]
• 19. Antimicrobial resistance [https://www.who.int/news-room/fact-sheets/detail/antimicrobial-resistance]
• 20. Antimicrobial Resistance Facts and Stats [https://www.cdc.gov/antimicrobial-resistance/data-research/facts-stats/index.html]
• 21. Prevalence of antibiotic resistance genes its association ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1595051/full]
• 22. Global soil antibiotic resistance genes are associated with ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12322111/]
• 23. WHO warns of widespread resistance to common ... [https://www.who.int/news/item/13-10-2025-who-warns-of-widespread-resistance-to-common-antibiotics-worldwide]
• 24. Epidemiology of antimicrobial resistance in bloodstream ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4871634/]
• 25. Influence of population density on antibiotic resistance [https://pubmed.ncbi.nlm.nih.gov/12562707/]
• 26. Comparison of qPCR and metagenomic sequencing methods ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10997137/]
• 27. A Comparative Assessment of High-Throughput ... [https://www.mdpi.com/2076-3417/13/20/11229]
• 28. A Practical Framework for Environmental Antibiotic Resistance ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12383231/]
• 29. Quantitative comparison of HT-qPCR/16S rRNA ... [https://www.sciencedirect.com/science/article/abs/pii/S0304389425028237]
• 30. Contributions and Challenges of High Throughput qPCR for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6337382/]
• 31. High-Throughput Screening of Antimicrobial Resistance ... [https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2022.825372/full]
• 32. Shotgun-metagenomics based prediction of antibiotic ... [https://www.nature.com/articles/s41598-021-00383-7]
• 33. High-Throughput Shotgun Metagenomics of Microbial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11685326/]
• 34. Captivity and geography influence the antibiotic resistome of ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9716204/]
• 35. Assessment of global health risk of antibiotic resistance ... [https://www.nature.com/articles/s41467-022-29283-8]
• 36. Roadmap to tackle antibiotic resistance in the environment ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10989945/]
• 37. A comprehensive framework of health risk assessment for ... [https://www.sciencedirect.com/science/article/abs/pii/S0304389425026676]
• 38. Grazing disturbance increased the mobility, pathogenicity ... [https://www.frontiersin.org/journals/environmental-science/articles/10.3389/fenvs.2023.1109298/full]
• 39. An omics-based framework for assessing the health risk of ... [https://www.nature.com/articles/s41467-021-25096-3]
• 40. Assessment of global health risk of antibiotic resistance ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8943045/]
• 41. Ranking the risk of antibiotic resistance genes by ... [https://www.sciencedirect.com/science/article/abs/pii/S0304389424003698]
• 42. Towards the integration of antibiotic resistance gene ... [https://www.nature.com/articles/s44259-025-00154-8]
• 43. Machine Learning for Antimicrobial Resistance Prediction: Current Practice, Limitations, and Clinical Perspective - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC9491192/]
• 44. Using Machine Learning to Predict Antimicrobial Resistance ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10044642/]
• 45. VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning | PLOS Computational Biology [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1007511]
• 46. Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper | PLOS Computational Biology [https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1012579]
• 47. Prediction of antibiotic resistance from ... [https://www.nature.com/articles/s41598-025-14078-w]
• 48. WHO updates data visualization dashboard on ... [https://www.who.int/news/item/18-09-2024-who-updates-data-visualization-dashboard-on-antimicrobial-resistance-and-use]
• 49. Interactive visualization tools – Inspirational examples – ReAct [https://www.reactgroup.org/toolbox/measure/examples-from-the-field/interactive-visualization-tools/]
• 50. Mobile Genetic Elements Associated with Antimicrobial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6148190/]
• 51. Horizontal Gene Transfer Mediated Bacterial Antibiotic ... [https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2019.01933/full]
• 52. Global study finds differences in antibiotic resistance ... [https://www.news-medical.net/news/20250616/Global-study-finds-differences-in-antibiotic-resistance-between-genders.aspx]
• 53. The Spread of Antibiotic Resistance Genes In Vivo Model [https://pmc.ncbi.nlm.nih.gov/articles/PMC9314185/]
• 54. Plasmids and the Spread of Antibiotic Resistance Genes [https://asm.org/articles/2023/january/plasmids-and-the-spread-of-antibiotic-resistance-g]
• 55. A comparison of antibiotic resistance genes and mobile ... [https://peerj.com/articles/17710/]
• 56. Metagenomic analysis of antibiotic resistance across the ... [https://www.sciencedirect.com/science/article/pii/S2405844025013003]
• 57. The Phylogeny, Biodiversity, and Ecology of the Chloroflexi ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6753630/]
• 58. Genome-centric metagenomic insights into the role of ... [https://bmcmicrobiol.biomedcentral.com/articles/10.1186/s12866-023-02765-5]
• 59. Ubiquitous Gammaproteobacteria dominate dark carbon ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4872838/]
• 60. Gammaproteobacteria [https://en.wikipedia.org/wiki/Gammaproteobacteria]
• 61. Abiotic factor - Definition and Examples [https://www.biologyonline.com/dictionary/abiotic-factor]
• 62. Abiotic Factor - an overview | ScienceDirect Topics [https://www.sciencedirect.com/topics/earth-and-planetary-sciences/abiotic-factor]
• 63. Warming Accelerates Phytoplankton Bloom Dynamics and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12594743/]
• 64. Large and interacting effects of temperature and nutrient ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8123916/]
• 65. Longitudinal Comparison of Bacterial Diversity and Antibiotic ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6687945/]
• 66. The source, fate and prospect of antibiotic resistance ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9539525/]
• 67. Global soil antibiotic resistance genes are associated with ... [https://pubmed.ncbi.nlm.nih.gov/40759899/]
• 68. Metatranscriptomic time series insight into antibiotic resistance ... [https://bmcmicrobiol.biomedcentral.com/articles/10.1186/s12866-025-03753-7]
• 69. Global trends in antimicrobial resistance on organic and ... [https://www.nature.com/articles/s41598-023-47862-7]
• 70. Geo-Temporal Variation in the Antimicrobial Resistance of ... [https://www.mdpi.com/2079-6382/14/3/233]
• 71. Antimicrobial Resistance: A Growing Serious Threat for ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC10340576/]
• 72. Temporal and geographical research trends of ... [https://www.sciencedirect.com/science/article/pii/S2352771420302998]
• 73. Ecological connectivity of genomic markers of antimicrobial ... [https://www.nature.com/articles/s41467-025-62455-w]
• 74. A genome-wide One Health study of Klebsiella ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC12039103/]
• 75. Genetic compatibility and ecological connectivity drive the ... [https://www.nature.com/articles/s41467-025-57825-3]
• 76. A One Health Comparative Assessment of Antimicrobial ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7355928/]
• 77. Comparative analysis of antibiotic-administered vs. ... [https://www.sciencedirect.com/science/article/pii/S0740002025001571]
• 78. Interactions between antibiotic resistance genes and soil ... [https://www.sciencedirect.com/science/article/pii/S240566502500112X]
• 79. Antibiotic resistance in the environment - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC8567979/]
• 80. Shifts in Estuarine Macroinvertebrate Communities ... [https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2021.698576/full]
• 81. Geographic patterns and determinants of antibiotic resistomes ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9669582/]
• 82. The Environmental Lifecycle of Antibiotics and Resistance ... [https://www.mdpi.com/2076-2607/13/9/2113]
• 83. Environmental factors influencing the development and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC5812547/]
• 84. Data from the Center for Antibacterial Surveillance, 2012 ... [https://www.mdpi.com/2079-6382/13/8/732]
• 85. Enhancing antimicrobial resistance surveillance and ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11954275/]
• 86. analysis of pharmaceutical sales data, 2013 to 2023 [https://pubmed.ncbi.nlm.nih.gov/40607801/]
• 87. Decreases and Pronounced Geographic Variability in ... [https://www.mdpi.com/2226-4787/12/2/46]