Targeting Horizontal Gene Transfer to Combat Antimicrobial Resistance: A Review of Mechanisms, Drivers, and Multi-Omics Strategies

Lili Tang; Wei Yang; Lingqi Yang; You Lv; Jian Zhang

doi:10.2147/IDR.S589962

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Review

Targeting Horizontal Gene Transfer to Combat Antimicrobial Resistance: A Review of Mechanisms, Drivers, and Multi-Omics Strategies

Authors Tang L , Yang W, Yang L, Lv Y, Zhang J

Received 18 December 2025

Accepted for publication 24 March 2026

Published 2 April 2026 Volume 2026:19 589962

DOI https://doi.org/10.2147/IDR.S589962

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Sandip Patil

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Lili Tang,¹ Wei Yang,¹ Lingqi Yang,¹ You Lv,² Jian Zhang¹

¹Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China; ²Information Center of West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China

Correspondence: Jian Zhang, Department of Pharmacy, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China, Email [email protected]

Abstract: The widespread dissemination of antibiotic resistance genes in bacteria primarily relies on horizontal gene transfer (HGT), a phenomenon that has profound implications for global healthcare and animal husbandry. Therefore, elucidating the key mechanisms of HGT is crucial for controlling the global spread of resistance genes. Horizontal gene transfer can occur not only through classical pathways such as conjugation, transformation, and transduction but also involves non-classical mechanisms including gene transfer agents, outer membrane vesicles, and nanotubes. This process is mediated by various mobile genetic elements, such as plasmids, bacteriophages, transposons, integrons, integrative and conjugative elements (ICEs), and integrative and mobilizable elements (IMEs). HGT is typically regulated by a combination of host-specific intrinsic factors and external environmental conditions. To address the spread of resistance, numerous detection and prevention tools targeting this mechanism have been developed. This article focuses on the process of HGT and its associated mobile genetic elements, systematically analyzes key factors influencing this process, summarizes sequencing and bioinformatic technologies used for monitoring HGT, and explores prevention strategies informed by genomic, proteomic, and metabolomic approaches. The aim is to provide a theoretical foundation and practical guidance for the control of drug-resistant bacteria.

Keywords: HGT, ARGs, MGEs, control of drug-resistant bacteria

Introduction

As an ancient cornerstone of Earth’s ecosystems, bacteria have developed unparalleled environmental adaptability through their 3.5-billion-year evolutionary journey, forming complex communities from deep-sea hydrothermal vents to human mucosal surfaces.¹ This ubiquitous presence endows bacteria with a dual role: while commensal species maintain host metabolic homeostasis, pathogenic species such as Mycobacterium tuberculosis and Streptococcus pneumoniae cause approximately 7.7 million annual deaths, accounting for 13.4% of global mortality and representing the second-largest health threat after cardiovascular diseases.^2,3 The 20th-century discovery of antibiotics temporarily shifted the balance of power between humans and pathogens—for instance, β-lactams irreversibly bind penicillin-binding proteins (PBPs) to block peptidoglycan cross-linking, while quinolones inhibit DNA gyrase to trigger chromosomal fragmentation.^4,5 These “chemical weapons” dramatically reduced infection-related mortality. Yet beneath this triumph lurked a crisis: excessive antibiotic use in medical, livestock, and agricultural sectors accelerated bacterial evolution, enabling their survival through the development of resistance.^6–8

The essence of antibiotic treatment failure lies in bacterial adaptive countermeasures against chemical intervention, where adaptive changes propagate vertically or horizontally among bacteria.^9,10 HGT enables resistance genes like bla_NDM₋₁ to jump between species via plasmids at frequencies much higher than those of vertical inheritance, directly fueling the global spread of multidrug-resistant (MDR) superbugs.¹¹ Carbapenem-resistant Acinetobacter baumannii (CRAB) disseminates through conjugative plasmids in intensive care units (ICUs), elevating sepsis mortality to 74%, while vancomycin-resistant enterococci (VRE) contaminate medical devices, triggering hospital outbreaks.^12,13 Modeling projections indicate that without effective intervention, antimicrobial resistance (AMR)-related annual deaths will exceed 10 million by 2050, surpassing current cancer mortality.^14,15

HGT manifests through diverse mechanisms: classical pathways include transformation, conjugation, and transduction, while non-classical routes encompass gene transfer agents (GTAs), outer membrane vesicles (OMVs), and nanotubes.^16–24 Transformation efficiency is governed by the expression of comEC, conjugation involves nanoscale channel assembly via type IV secretion systems (TraA protein), with the oriT site of plasmids initiating single-stranded DNA transfer under the catalysis of relaxase (NikB).^25–27 Transduction requires the integration of phage genes into the host genome.²⁸ GTAs can modify the functions of the host through genomic insertion, while OMVs and nanotubes mediate the entry of resistance gene-containing DNA and plasmids into recipient bacteria, enhancing horizontal transfer efficiency.^29–32 Throughout this process, key genetic elements—plasmids, integrons, transposons, ICEs, IMEs, and prophages—can be detected in environmental samples via high-throughput sequencing coupled with bioinformatic analysis.^33–39 The efficiency of HGT is modulated by host states and environmental factors.^40,41 Targeted interventions at genetic and protein levels can mitigate clinical resistance events, exemplified by CRISPR-Cas9 systems that cleave resistance plasmids and chemical inhibitors that disrupt HGT-associated proteins to block cellular uptake and chromosomal integration.^42–44

Consequently, this review systematically delineates the mechanisms of HGT and associated genetic elements, investigates factors influencing HGT efficiency, summarizes genomic and bioinformatic applications in resistance gene identification and outbreak prediction, and explores high-efficacy human interventions against HGT—ultimately charting a strategic roadmap for humanity’s scientific campaign against drug-resistant bacteria.

The Mechanisms of HGT

Horizontal transfer of antibiotic resistance genes (ARGs) plays a pivotal role in bacterial evolution under antibiotic selection pressure, driving the emergence of untreatable “superbugs” by concentrating multiple ARGs within single cells. Understanding HGT mechanisms is therefore crucial for combating resistant bacteria. Classical and non-classical pathways facilitate HGT, disseminating ARGs between bacteria.

The Classical Mechanisms of HGT

The classical mechanisms of HGT comprise transformation, conjugation, and transduction.^45–47 Transformation represents the oldest HGT mechanism, wherein donor bacteria release 7–50 kb DNA fragments during cell death, active secretion, or specific growth phases.⁴⁸ The recipient uptakes extracellular DNA (eDNA), which may integrate into chromosomes via homologous recombination—a process demanding stringent sequence complementarity. Only DNA fragments with allelic counterparts in the recipient’s genome can integrate, while completely heterologous sequences are degraded by nucleases after uptake, positioning the recipient bacterium as the key actor in this process.^49,50 The Type VI Secretion System (T6SS) critically enhances HGT efficiency by enabling donor cells to secrete DNA, deliver virulence factors, and lyse neighboring cells.^51–53 In naturally transformable bacteria, DNA uptake involves proteins encoded by the com regulon: exogenous double-stranded DNA (dsDNA) enters through ComEC/ComA transmembrane channels as single-stranded DNA (ssDNA) in a 3′-5′ direction. Internalized ssDNA binds DNA processing protein A (DprA), which recruits RecA recombinase to facilitate homology search and strand invasion. RecA then mediates homologous recombination between the ssDNA and recipient DNA, forming hybrid duplexes.^25,54–56 Environmental ARGs primarily originate from actively secreted or cell death-derived eDNA. Although 99% of eDNA degrades within 7 days in natural settings, 11 of 12 priority antibiotic-resistant pathogens acquire eARGs via natural transformation in clinical environments. Moreover, soil eARG concentrations increase 20-fold with 5-fold higher resistant bacterial counts, underscoring transformation’s significant role in environmental resistance evolution.^57,58

Conjugation facilitates ARG transfer through direct cell contact mediated by large (>30 kb) dsDNA plasmids. This mechanism was first demonstrated in 1946 by Joshua Lederberg and Edward Tatum using bacterial auxotrophs to prevent revertant interference.⁵⁹ Classical F-plasmid-driven conjugation occurs intra- and interspecifically via three modes: F⁺×F⁻, Hfr×F⁻, and F′×F⁻. F⁺ denotes bacteria with cytoplasmic F-plasmids, F⁻ lacks F-factors, Hfr results from F-factor chromosomal integration, and F′ carries F-factors recombined with host genes. The latter two modes transfer partial chromosomal DNA to recipients.^60–62 The F-plasmid structure includes oriT (transfer origin), tra (transfer gene cluster encoding conjugation proteins, constituting 1/3 of plasmid length), par (partition genes), ssb (single-strand binding protein), RepFIC/RepFIB (replication origins), Tn1000 (transposon), and finO/sraB (regulatory genes).^63,64 Conjugative ARG transfer predominates in Enterobacteriaceae like Escherichia coli (E. coli), Klebsiella, Salmonella, Shigella, and Citrobacter.^65–69 Broad-host-range plasmids (eg., IncP-1) enable cross-genera transfer between Gram-negative proteobacteria and Gram-positive Bacillus species,^70–72 making conjugation the most extensively studied HGT type.

Transduction introduces >100 kb DNA fragments into bacterial chromosomes via viruses.^73,74 Bacteriophages serve as primary vectors, operating through lytic or lysogenic cycles. Lytic phages replicate within hosts, lysing cells to release progeny virions that infect new bacteria.^75,76 Lysogenic phages integrate into specific host genomic sites, remaining dormant until induced to enter lytic cycles. Upon host lysis, phage particles may package bacterial DNA fragments, transferring these genes to subsequent hosts during infection.⁷⁷ Transduction occurs via generalized (any host gene packaged post-lysis) or specialized (only host genes adjacent to integrated phage DNA transferred) mechanisms.⁷⁸

The Nonclassical Mechanisms of HGT

Beyond classical mechanisms, bacterial DNA transfer also occurs through gene transfer agents (GTAs), outer membrane vesicles (OMVs), and pilus-like nanotubular structures.^79,80 GTAs are hypothesized to originate from ancestral phages, sharing structural features such as capsids and tails, yet predominantly carrying non-self DNA for horizontal transfer rather than for self-replication—thus representing a “domesticated” viral vector. Upon genomic insertion, GTAs can modulate host bacterial motility, quorum sensing, exopolysaccharide synthesis, and biofilm formation, playing pivotal roles in bacterial and archaeal evolution.^81–85 GTAs differ from viruses in four key aspects: they package random host DNA fragments for delivery to compatible recipients, exhibit fragmented genomes at multiple chromosomal loci, cannot fully package their own encoding genes, and replicate vertically with the host genome.⁸² Three distinct GTA lineages are recognized: Type I (α-proteobacteria; model host: Rhodobacter spp)., Type II (α-proteobacteria; model host: Bartonella spp)., and Type III (spirochetes; model host: Brachyspira hyodysenteriae).⁸⁶ The GTA of Rhodobacter capsulatus (RcGTA) exemplifies a hybrid mechanism where donor DNA is packaged in transducing phage-like particles while recipient cells employ natural transformation machinery for DNA uptake, facilitating environmental adaptation and biofilm maintenance.^87,88

Outer membrane vesicles, spherical lipid nanostructures released by bacteria, are ubiquitous in both Gram-positive and Gram-negative species.⁸⁹ They were first observed in E. coli (1965), later documented in Brucella through electron microscopy and SDS-PAGE analysis (1987), and confirmed in Staphylococcus aureus (S. aureus) (2009).^31,90,91 OMVs carry virulence factors, plasmids, adhesins, toxins, and nucleic acids, enabling functions like intercellular communication, immune modulation, and virulence dissemination. Their role in HGT—termed vesiduction—involves transferring DNA fragments >30 kbp (single-copy prokaryotic sequences).^{31,79,92–94} OMV-encapsulated DNA exhibits enhanced stability and transfer efficiency due to protection from exonucleases and resistance to environmental dilution.⁹⁵ Marchant et al demonstrated OMV-mediated transfer of ΔtolR (bacterial envelope maintenance) and ΔdegS (σ^E activation for misfolded proteins) genes from Salmonella Typhi to polymyxin B-susceptible bacteria.⁹⁶ Qiao et al achieved functional transfer of the nirS gene via OMVs, demonstrating that E. coli DH5α released OMVs containing recombinant pET28a-nirS-EGFP plasmids. This yielded a transformation frequency of 2.76×10⁶ CFU/g and active nitrite reductase expression (18.16 U/mL) in recipient E. coli BL21 cells at 200 μg OMV concentrations.⁹⁷ Federica et al further established OMVs as key vectors for Klebsiella pneumoniae to disseminate β-lactam resistance plasmids across species including E. coli, Salmonella enterica, Pseudomonas aeruginosa, and Burkholderia cepacia.⁹⁸

Nanotube-mediated HGT, discovered by Dubey et al in adjacent Bacillus subtilis (B. subtilis) cells, facilitates intercellular transfer of nutrients, electrons, toxins, and genetic material.^32,99,100 These structures exhibit species-specific dimensions: Thermococcus species produce micron-length nanotubes often filled with extracellular vesicles, while the hyperthermophilic crenarchaeon Pyrodictium forms nanotubes with only 25 nm.¹⁰¹ Antibiotic stress significantly increases nanotube production in pathogens like Salmonella Typhi, Enterococcus faecalis, Proteus spp., and Klebsiella spp.¹⁰² Crucially, Dubey demonstrated the co-transfer of chloramphenicol acetyltransferase (cat) and erythromycin resistance methylase (erm) genes between B. subtilis strains via nanotubes, generating dual-resistant bacteria, with cross-genera transfer observed in E. coli and S. aureus.⁹⁹

Mobile Genetic Elements (MGEs) Associated with HGT

Plasmids

Plasmids, as mobile genetic elements, participate in genetic material transfer across multiple horizontal gene transfer (HGT) forms and represent the most crucial mobile elements. Unique to prokaryotes, these extrachromosomal DNA molecules replicate independently within cells or integrate into bacterial chromosomes for replication. Their classification into incompatibility (Inc) groups stems from the inability of plasmids from the same group to coexist stably within a single bacterial cell. Plasmids sharing an Inc group typically possess identical or similar replication/partitioning systems; however, when two such plasmids coexist, interference in replication occurs, leading to the loss of one plasmid during cell division. This results in uneven plasmid distribution within bacterial populations, where strains carrying environmentally advantageous plasmids gain survival benefits. Plasmids serve as reservoirs for ARGs, with 5% of neonatal gut bacteria carrying ARG-harboring plasmids.¹⁰³ They carry key resistance genes, exemplified by plasmid-borne colistin resistance genes (mcr), which accelerate the emergence of colistin resistance in multidrug-resistant Gram-negative bacteria, posing a significant global health threat. In late 2015, Chinese researchers identified the first plasmid-mediated colistin resistance gene, mcr-1, in E. coli. This gene encodes a PmrC-like pEtN transferase that modifies lipid A by transferring pEtN from the cell membrane, conferring colistin resistance. Subsequently, nine mcr-1 homologs (mcr-2 to mcr-10) were discovered worldwide.^104–114 Current research indicates that nearly all colistin resistance in E. coli is mediated by plasmid-borne mcr-1, while mcr genes can be carried by diverse plasmids. For instance, mcr-1-bearing IncI2-like and IncX4 plasmids have disseminated globally, and mcr-3 has been identified on various plasmid types including IncHI2, IncA/C, and IncFII/FIB, with IncHI2 being predominant.^115,116 The lipid A modification mediated by these genes reduces the negative charge of the bacterial outer membrane, consequently enhancing resistance to antimicrobial peptides (AMPs) produced by intestinal epithelial cells (β-defensins 1–3 and LL37) and Paneth cells (α-defensins 5 and 6).^106,117,118

Transposons and Integrons

Transposons, as mobile DNA sequences within genomes, comprise two categories: Class I retrotransposons replicate via RNA intermediates and integrate into genomes using reverse transcriptase, while Class II DNA transposons employ a “cut-and-paste” mechanism catalyzed by transposase and feature terminal inverted repeats (IRs). These elements stably insert into host genomes, carrying foreign genes ranging from several kb to tens of kb, enabling large resistance gene fragments from external DNA to integrate into host chromosomes for stable expression. For instance, the chromosomally integrated Tn1549 transposon in vancomycin-resistant Enterococcus faecium ST117 harbors a highly mutated vanB2 operon.¹¹⁹ Integrons capture and express exogenous genes, particularly accelerating resistance gene integration and dissemination among bacteria under antibiotic pressure. Classified into five types based on IntI integrase amino acid sequences, Class 1 integrons are most prevalent and notorious, distributed widely across Gram-negative and Gram-positive bacteria including E. coli, Pseudomonas, Salmonella, Corynebacterium, Streptococcus, Enterococcus, and Staphylococcus. They associate with diverse resistance gene cassettes such as aadA (streptomycin resistance), bla (β-lactam resistance), dfrA (trimethoprim resistance), and sul1 (sulfonamide resistance). This integron family achieves mobility through association with Tn402-like transposons and Tn3 family elements (eg., Tn21, Tn1696).^120,121 Class 1 integrons were detected in 84/91 multidrug-resistant (MDR) and extensively drug-resistant (XDR) Pseudomonas aeruginosa isolates from burn wound infections in Bangladesh.¹²² Classes 2 and 3 integrons exhibit lower clinical detection rates in Gram-negative bacteria, mediating resistance to trimethoprim, streptothricin, streptomycin, chloramphenicol, erythromycin, and β-lactams.^123–125 Class 4 and 5 integrons are specifically associated with Vibrio cholerae and Vibrio salmonicida, respectively.^126,127

ICEs and IMEs

Integrative and conjugative elements (ICEs) and integrative and mobilizable elements (IMEs) are composite genetic units that carry multiple functional modules enabling autonomous excision and integration within bacterial genomes, serving as critical vectors for disseminating acquired antibiotic resistance genes. ICEs contain core genes including int (encoding an integrase, typically a tyrosine recombinase that recognizes chromosomal target sites for site-specific recombination), xis (encoding an excisionase that directs recombination orientation), a type IV secretion system (T4SS, facilitating ICE transfer between bacteria), and nic/oriT (the relaxase recognition site initiating ICE replication and conjugation). Normally integrated into the host chromosome with conjugation genes silenced, ICEs excise to form double-stranded DNA circles upon induction. The T4SS then assembles a transfer channel, where relaxase nicks oriT to generate single-stranded T-DNA. This T-DNA translocates through the channel into recipient cells, reforms a double-stranded circular structure, and integrates into the recipient genome via integrase activity. In contrast, IMEs require auxiliary conjugation machinery for interbacterial resistance gene transfer. In seafood sold in China, tigecycline-resistant bacteria were identified with Shewanella spp. predominating (33/76 isolates, 43.4%). Genomic sequencing revealed that tet(X4) and tmexCD2-toprJ2 resistance genes in resistant Shewanella reside on SXT/R391-family ICEs. Thus, SXT/R391 ICEs mediate tigecycline resistance genes among aquatic bacteria, warranting further investigation into their contribution to clinical tigecycline resistance.¹²⁸ Similarly, the ICE designated ICECiPOL15 was identified in the β-lactam-resistant Chryseobacterium indologenes strain POL15, harboring a class C β-lactamase gene (bla_AQU) alongside genes for integrase, excisionase, relaxase, T4SS coupling proteins, and conjugative transposon proteins.¹²⁹ Additionally, Guillem López de Egea demonstrated that the tetracycline resistance gene tet(M) in Streptococcus dysgalactiae subsp. equisimilis (SDSE) can reside within Tn916-family ICEs and a novel IME designated IME_SDSE_HUB-4529.¹³⁰

Prophage

A prophage refers to the genome of a temperate bacteriophage that has integrated into the host bacterial chromosome. Typically dormant, it does not immediately lyse the host cell but replicates passively with the host during cell division. Prophages can persist long-term in bacterial genomes and may reactivate under specific conditions, entering the lytic cycle to produce new phage particles that rupture the host cell. The prophage genome contains integration and excision genes (mediating chromosomal integration and excision to initiate lysis), replication genes (encoding enzymes for accurate genome replication), transcription and translation genes (including promoters, operators, and regulatory proteins for gene expression), structural protein genes (encoding capsid and tail components for virion assembly), auxiliary metabolic genes (involved in bacterial metabolism and environmental adaptation), toxin/virulence factor genes (conferring pathogenicity), and antibiotic resistance genes (providing antibiotic resistance). These prophages play crucial roles in disseminating resistance genes within host bacteria. Tony et al observed that higher prophage abundance in synovial fluid samples from periprosthetic joint infection patients correlates with elevated antibiotic resistance gene counts.¹³¹ Furthermore, complex interactions exist between phages and plasmids in spreading ARGs.¹³²

Niche-Specific Facilitators of HGT

Niche-specific facilitators of HGT encompass both host-related and environmental factors, where the genetic compatibility and ecological coexistence between recipient and donor bacteria critically determine the success of resistance gene transfer, while environmental elements such as antibiotic usage, biofilm formation, and plastic presence significantly influence the probability of horizontal resistance gene dissemination across bacterial populations. Delving into these mechanisms is essential for developing effective containment strategies.

Host Factors

Bacterial genetic compatibility refers to the ability of different bacteria to achieve compatible integration and stable transmission of genetic material during transfer processes.¹³³ This characteristic not only determines the efficiency of antibiotic resistance gene transfer—for instance, conjugation efficiency between highly compatible E. coli and Salmonella strains can increase by 3- to 5-fold—but also governs post-transfer stability, where codon usage compatibility between donor and recipient bacteria serves as the critical determinant for successful integration.^134,135 Ecological coexistence, manifested through physical proximity and metabolic interdependence among microorganisms in specific environments, significantly facilitates resistance gene exchange: mutualistic symbiosis between Enterococcus faecalis and Bacteroides in the human gut accelerates horizontal transfer of macrolide resistance gene ermB and tetracycline resistance gene tetM; biofilm alliances formed by Staphylococcus and Corynebacterium in skin microenvironments drive dissemination of β-lactamase gene bla_CTX-M; whereas metabolic cooperation between Aeromonas and Chloroflexi in wastewater systems promotes prevalence of multidrug efflux pump gene mexAB-oprM.^136–144 Concurrently, host-intrinsic factors regulate resistance gene acquisition through multiple mechanisms: carbon starvation activates the ComK pathway in B. subtilis to enhance transformation competence, while mitomycin C-induced DNA damage in Pseudomonas aeruginosa triggers SOS response (recA upregulation) increasing conjugation frequency 10-fold.^145–148 Host restriction-modification systems constitute key barriers—E. coli Type I restriction enzymes reduce conjugative efficiency by >80% when methylation patterns are incompatible, and the CRISPR-Cas9 system in Streptococcus pneumoniae blocks stable integration of macrolide resistance gene mefA by PAM-guided DNA cleavage.^149–151 Genomic architecture equally influences resistance maintenance, exemplified by Salmonella pathogenicity island SGI1 capturing cephalosporin resistance gene bla_CTX-M through site-specific recombination.¹⁵² These bacterial factors, shown in Figure 1, synergize with environmental pressures to shape HGT “hotspots”, whose mechanistic elucidation provides critical theoretical foundations for targeted containment strategies.

Figure 1 Bacterial Factors Contributing to Enhanced HGT. Genetic compatibility: the capacity for compatible integration between bacterial endogenous DNA and exogenous DNA (eDNA) during transfer processes; Ecological coexistence: mutualistic symbiosis achieved through physical proximity and biological communication between two or more bacterial species within specific environments; Host factors: 1) carbon starvation activates ComK on the cell membrane, enhancing membrane permeability to eDNA, 2) mitomycin C-induced host DNA damage upregulates the SOS response, facilitating integration of endogenous DNA with eDNA, 3) specialized genomic structures such as SGI1 enable eDNA capture.

Environmental Factors

Environmental factors significantly accelerate the horizontal transfer and cross-media dissemination of antibiotic resistance genes via driving mechanisms such as antibiotic selection pressure, biofilm microenvironments, and plastics (shown in Figure 2). The widespread use of antibiotics directly drives horizontal resistance gene transfer, where environmental antibiotic residues (eg., subinhibitory concentrations of tetracycline) activate bacterial SOS stress responses (recA upregulation), increasing conjugative efficiency by up to 3.19-fold while promoting class 1 integrons to capture exogenous resistance genes.^153,154 In aquaculture and medical wastewater, high-concentration antibiotics further select for strains carrying multidrug-resistant genes, accelerating resistance gene diffusion within environmental microbiota.¹⁵⁵ Biofilms serve as “hotspot microenvironments”, where their three-dimensional structures enhance HGT through multiple mechanisms: extracellular polymeric substance (EPS) matrices encase bacteria to form physical barriers that block antibiotic penetration while prolonging plasmid conjugation duration; quorum sensing (QS) systems (eg., lasI/R in Pseudomonas aeruginosa) upregulate tra gene expression, elevating conjugation frequency within biofilms compared to free states; additionally, metabolic gradients in biofilms induce bacterial competence, augmenting uptake of free resistance genes such as ermB.^156–160 For instance, S. aureus biofilms on hospital catheters exhibit transposon Tn916-mediated tetracycline resistance gene (tetM) transfer rates reaching 10⁻³ per cell.¹⁶¹ Plastic pollution provides persistent vectors for resistance genes: microplastics (<5 mm) adsorb pathogens and free DNA via high surface area, forming “plastic-biofilm” complexes that protect ARG (eg., mcr-1) from environmental degradation.^162–166 Crucially, plastic surface hydrophobicity promotes tight adhesion between diverse bacterial species, elevating cross-species plasmid conjugative efficiency.^167,168 Studies confirm that polyethylene microparticles in wastewater treatment plant effluents concentrate ARGs abundance at 10²-10³ times higher levels than ambient water, entering agricultural ecosystems through irrigation and forming invisible chains of resistance transmission.^169,170

Figure 2 Environmental Factors Contributing to Enhanced HGT. Antibiotic selection pressure: the presence of antibiotics in the environment selects for the survival of antibiotic-resistant bacteria while eliminating non-resistant bacteria; Biofilm: biofilms in the environment enrich bacteria and exogenous DNA (eDNA), increasing the local concentration of eDNA, and additionally form physical barriers that impede antibiotic penetration; Plastics: the hydrophobic properties of plastic surfaces facilitate tight adhesion of bacteria and eDNA.

Applications of Genomics in HGT

The advancement of high-throughput sequencing technologies and bioinformatics has enabled comprehensive genomic studies of bacteria. Current research utilizes these tools to investigate HGT.

High-Throughput Sequencing Technologies

Whole Genome Sequencing (WGS) precisely tracks horizontal transfer of resistance genes by resolving genome-wide variation sites. In clinical outbreak tracing, WGS identifies single-nucleotide polymorphism (SNP) differences between strains (threshold of ≤5 SNPs) and reconstructs mobile genetic elements carrying resistance genes using plasmid assembly tools (eg., PlasmidSPAdes). Applying this approach, Shravani et al revealed diverse resistance plasmids in carbapenemase-producing E. coli and K. pneumoniae isolated from Indian ICUs: bla_NDM₋₁, bla_NDM₋₅, bla_NDM₋₇ (21/31), bla_KPC₋₂ (1/31), and bla_OXA_−181, bla_OXA₋₂₃₂ (8/31).¹⁷¹ Compared to individual-level WGS, metagenomic sequencing (mNGS) uncovers dynamic resistance gene exchange networks at the community scale. Through unbiased sequencing of total DNA combined with assembly and binning techniques, it reconstructs microbe-function correlation maps. Li et al employed population metagenomics in bovine fecal E. coli communities, detecting co-localization of multiple ARGs on the same MGE, which was linked to prevalent resistance phenotypes in isolates. Population metagenomics identified significantly distinct resistomes with higher ARG relative abundance and HGT risk compared to direct mNGS.¹⁷² Meanwhile, technical innovations enhance HGT monitoring resolution. Oxford Nanopore long-read sequencing (>10 kb) resolves repetitive regions, directly characterizing mobile genetic elements flanking resistance genes while avoiding short-read assembly errors. Francesca et al identified a 237-kb conjugative plasmid carrying acquired bla_TEM₋₁ in a hypervirulent E. coli ST73 strain isolated from a clozapine-treated schizophrenia patient.¹⁷³ EpicPCR (emulsion, paired isolation, and concatenation PCR)—a single-cell technology—links target genes to 16S fragments (spanning V4-V9, ~1000 bp) via optimized primer design, advancing host-specific ARG detection in microbial communities.¹⁷⁴ These technologies demonstrate critical value in clinical surveillance. Kim et al analyzed carbapenemase-producing Enterobacteriaceae (CPE) and vancomycin-resistant Enterococcus (VRE) using WGS to investigate hospital-acquired transmission. Infectious disease inpatients showed higher CPE/VRE acquisition rates, with the first hospitalization week being the critical window. Dementia, central/urinary catheters, and carbapenem use were significant risk factors. Hospital-acquired CPE/VRE shared resistance genes with circulating strains, while community-acquired cases carried novel genes (eg., bla_OXY_−4-1, optrA). Microbiome analysis revealed significantly altered species abundance and reduced diversity at discharge.¹⁷⁵

Bioinformatics Tools

The maturation of sequencing technologies has catalyzed explosive growth in bioinformatics, with extensive applications in clinically relevant contexts. Numerous bacterial genome databases—such as CARD and HGTree—have been established, providing researchers with rich genomic data resources (Table 1). Bioinformatics tools are being developed to efficiently process genomic data, construct resistance gene transmission networks, predict resistance trends, support precision medical decisions, optimize treatment strategies, and accelerate novel drug development. For instance, the Comprehensive Antibiotic Resistance Database (CARD) integrates multi-source data encompassing resistance genes, mutations, and resistance mechanisms, while also offering analytical tools and algorithms for antibiotic resistance research and surveillance.¹⁷⁶ The HGTree database serves as a comprehensive HGT repository containing 2,472 prokaryotic genome HGT events,¹⁷⁷ with its latest version HGTree v2.0 expanding to 20,536 non-redundant prokaryotic genomes and rigorously filtered HGT data. Additionally, HGTree v2.0 provides extended datasets including 6,361,199 putative horizontally transferred genes annotated with KEGG pathways, virulence factors, and antibiotic resistance features.¹⁷⁸ The development of key bioinformatic tools focuses on mobile element identification, resistance gene detection, resistance phenotype prediction, and HGT network modeling (Table 2). PlasFlow distinguishes chromosomal from plasmid sequences in metagenomic data,¹⁷⁹ while Plascad classifies bacterial plasmids, co-locates AMR genes, and visualizes plasmid structures.¹⁸⁰ Prophage Hunter employs machine learning to assess prophage activity states and dissemination potential.¹⁸¹ ARG identification leverages tools like ResFinder and PointFinder (from the ResFinder database) alongside CARD’s Resistance Gene Identifier (RGI), both capable of detecting acquired resistance genes and point mutations.¹⁸² Breakthroughs in resistance transmission prediction include Zhou et al’s algorithm for constructing HGT networks in pathogens like Acinetobacter baumannii and E. coli to forecast antibiotic resistance emergence, achieving AUROC = 0.990 predictive performance.¹⁸³ Pioneering live-tracing technologies integrate cross-disciplinary approaches, exemplified by Gong et al’s CRISPR-AMRtracker—a fluorescent tracing tool combining 16S rRNA sequencing, CRISPR/Cas9 tagging, fluorescence-activated cell sorting, and microbiome analysis. This system integrates fluorescent markers downstream of ARGs without compromising host antibiotic susceptibility, fitness, conjugation, or transposition, offering novel perspectives for studying ARG dissemination in complex microbiomes and elucidating resistance evolution mechanisms.¹⁸⁴

Table 1 The Classic Bacterial Genome Databases

Table 2 The Bioinformatic Tools for HGT Researches

Strategies for Curbing HGT

Multi-omics strategies offer novel approaches to inhibit antibiotic resistance evolution by targeting multiple stages of HGT, restoring microbial ecological balance.

Genomic Strategies

Genomic strategies primarily rely on targeting MGEs like plasmids and transposons to combat HGT. The CRISPR-Cas system has emerged as a key tool to block this process through guide RNA (gRNA)-mediated precise cleavage. Within the same bacterial species, strains carrying the CRISPR-Cas system contain significantly fewer plasmids than those without it (0.86 vs. 1.86). Furthermore, CRISPR-Cas-positive strains show a 31% reduction in intact prophages (1.17 vs. 1.75). These data support the role of CRISPR-Cas in reducing the incidence of HGT.²⁰⁸ Using PCR technology, Tao et al detected the CRISPR-Cas system, antibiotic resistance genes, and virulence factors in clinical Enterococcus isolates. They found that 46 strains (46.0%) carried at least one CRISPR-Cas locus. Compared to CRISPR-Cas-negative isolates, CRISPR-Cas-positive isolates exhibited significantly lower resistance rates to ampicillin, erythromycin, levofloxacin, tetracycline, vancomycin, gentamicin, streptomycin, and rifampicin. Fewer CRISPR loci were identified in isolates carrying resistance genes such as vanA, tetM, ermB, aac6′-aph(2″), aadE, and ant(6). Additionally, the CRISPR-Cas locus was significantly negatively correlated with the enterococcal virulence factor enterococcal surface protein gene (esp).²⁰⁹ To target carbapenem-resistant E. coli carrying the bla_KPC-₂ gene, the same team constructed a specific prokaryotic CRISPR-Cas9 system plasmid designed to cleave bla_KPC₋₂. Within 8 hours, this system achieved over 98.8% clearance of the resistance plasmid carrying bla_KPC-₂, restoring bacterial susceptibility to carbapenems.²¹⁰ In Streptococcus pyogenes (S. pyogenes), two distinct CRISPR loci were identified. Strains lacking CRISPR contained significantly more prophages than those carrying CRISPR. Although S. pyogenes CRISPR arrays contained fewer spacers than those in other streptococci, a significant negative correlation was confirmed between validated spacer content and prophage abundance. Thus, the CRISPR system in S. pyogenes can restrict the acquisition of prophage-derived resistance determinants.²¹¹ Moreover, engineered phage technology can block transduction through a dual mechanism: first, by screening naturally lytic phages (such as JG004 targeting Pseudomonas aeruginosa) that specifically lyse strains carrying certain resistance genes (eg., aac(6′)-Ib-cr);^212,213 second, by genetically modifying phages—enhancing their recognition and targeting capabilities—to achieve precise elimination of resistant bacteria.^214–216 In patients with uncomplicated urinary tract infections caused by E. coli, a CRISPR-Cas3-enhanced phage cocktail, LBP-EC01, was evaluated in a Phase II clinical trial. When used in combination with trimethoprim-sulfamethoxazole (TMP-SMX), it resulted in rapid and sustained reduction of E. coli load in urine and corresponding resolution of clinical symptoms in evaluable patients.²¹⁷

Proteomic and Metabolomic Strategies

The homeostasis of microbial communities serves as a natural barrier against the invasion of antibiotic resistance genes. Restriction-modification (R-M) systems, composed of DNA methyltransferases and restriction endonucleases, function as a form of “genomic immune defense” in bacteria by recognizing and degrading foreign unmethylated DNA, such as invading resistance plasmids, and are regarded as a barrier to horizontal gene exchange.^218–220 Park et al performed comparative genomic analysis of S. aureus isolates associated with bovine mastitis and human infections, and found that the distribution of strain-specific genes, virulence genes, and AMR genes was closely linked to the presence of R-M systems. This suggests that R-M systems may promote clonal diversification by providing a genetic barrier against HGT. Moreover, lineage-specific R-M systems may restrict genetic exchange between strains from different lineages.²²¹ Xu et al analyzed the distribution of AMR genes associated with MGEs in the context of a novel DNA phosphorothioate (PT) modification-based R-M system. They found that the presence of PT R-M effectively reduced the genomic distribution of horizontally acquired AMR genes. Furthermore, pangenome analysis and assessment of unique gene variants through MGE prediction demonstrated that PT R-M presence suppresses the frequency of HGT.²²² Djordjevic et al developed a mathematical model reflecting the dynamics of R-M systems, linking changes in the methyltransferase-to-restriction enzyme (M/R) ratio to factors such as cell division time and plasmid replication rates, to predict the emergence of bacterial population heterogeneity resulting from horizontal gene transfer.²²³ QS is an interbacterial communication process that regulates numerous phenotypes and gene expression patterns, including ARGs and genes involved in AMR development. Quorum sensing inhibitors (QSIs) suppress conjugative plasmid transfer between bacteria by interfering with this process. For example, in wastewater, vanillin at a sub-minimum inhibitory concentration (sub-MIC, <0.1 g/L) reduced the conjugation transfer frequency of the resistance plasmid RP4 between E. coli and Pseudomonas aeruginosa by inhibiting biofilm formation, extracellular polymeric substance synthesis, and virulence factor secretion. This finding has important implications for controlling the environmental spread of bacterial resistance.²²⁴ Metabolic reprogramming strategies exploit nutrient competition to diminish the fitness advantage of resistant bacteria. Conjugation efficiency is influenced by the metabolic state of bacteria; high carbon concentration and an active physiological status can significantly enhance plasmid transfer kinetics. After transfer, energy demands related to plasmid recycling and host establishment—such as the metabolic burden associated with increased protein production—can limit the spread and trajectory of plasmids.²²⁵ However, in a ventricular assist device driveline infection model, Sun et al observed a strong negative correlation between bacterial metabolic activity and AMR when comparing different stages of biofilm development. The application of gentamicin—a weakly metabolism-dependent antibiotic—successfully killed staphylococcal biofilms, whereas vancomycin, which is strongly metabolism-dependent, did not. Thus, suppressed bacterial metabolism is a core mechanism of biofilm-associated AMR and a promising therapeutic target for biofilm-related driveline infections.²²⁶

Conclusion

Antibiotic resistance represents a critical global public health challenge, primarily driven by the dissemination of ARGs among bacteria via horizontal gene transfer mechanisms such as conjugation, transformation, and transduction. This review systematically elaborates on the key mechanisms of HGT and associated genetic elements, discusses environmental factors in different ecological niches that facilitate HGT, summarizes the applications of genomics and bioinformatics for monitoring HGT and predicting resistance phenotypes, and synthesizes the role of multi-omics strategies in restricting HGT. Conjugation and transduction are the most prominent pathways for HGT dissemination; among these, genomics-based targeting approaches—such as CRISPR-Cas systems—and phage therapy show promise as effective tools to suppress HGT. In the field of bacterial resistance prevention and control, the application of synthetic biology remains in its early stages. A future direction may involve the design of an engineered “nucleic acid sponge” probiotic capable of sequestering and degrading free plasmids and nucleic acid fragments in the environment, thereby reducing the incidence of HGT. Furthermore, artificial intelligence models powered by machine learning can be employed for early identification of hospitalized patients at high risk of resistant bacterial infections, monitoring the abundance dynamics of ARGs in the environment, and optimizing antibiotic use strategies in animal husbandry to prevent the spread of resistant bacteria. Despite these promising avenues, significant challenges remain in translating these strategies from bench to bedside. The precision of CRISPR-based editing is often compromised by off-target effects and the emergence of anti-CRISPR proteins; the narrow host range of phages limits their broad-spectrum applicability; and the high cost and lack of standardized protocols hinder the routine clinical implementation of metagenomic sequencing. Furthermore, interventions such as quorum sensing inhibitors and engineered probiotics have predominantly been validated in simplified systems, with their efficacy and safety in complex human microbiota or environmental ecosystems remaining largely unexplored. In conclusion, addressing antibiotic resistance, a pervasive global health threat, requires the integration of advanced computational models, mutation databases, and stringent regulatory frameworks. This interdisciplinary and international effort urgently demands close collaboration and concerted contributions from scientists worldwide.

Abbreviations

HGT, Horizontal Gene Transfer; ICEs, Integrative and Conjugative Elements; IMEs, Integrative and Mobilizable Elements; MDR, Multidrug-Resistant; CRAB, Carbapenem-resistant Acinetobacter baumannii; ICUs, Intensive Care Units; VRE, Vancomycin-resistant enterococci; AMR, Antimicrobial Resistance; GTAs, Gene Transfer Agents; OMVs, Outer Membrane Vesicles; ARGs, Antibiotic Resistance Genes; eDNA, Extracellular DNA; T6SS, Type VI Secretion System; dsDNA, Double-stranded DNA; ssDNA, Single-stranded DNA; DprA, DNA processing protein A; S. aureus, Staphylococcus aureus; B. subtilis, Bacillus subtilis; E. coli, Escherichia coli; cat, chloramphenicol acetyltransferase; erm, erythromycin resistance methylase; Inc group, Incompatibilitygroups; AMPs, Antimicrobial Peptides; IRs, Inverted Repeats; XDR, extensively drug-resistant; QS, Quorum Sensing; CPE, Carbapenemase-producing Enterobacteriaceae; VRE, Vancomycin-resistant Enterococcus; MGEs, Mobile Genetic Elements; CARD, Comprehensive Antibiotic Resistance Database; AMR, Antimicrobial Resistance; QSIs, Quorum Sensing Inhibitors; R-M system, Restriction-modification systems.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was funded by the National Key Clinical Specialties Construction Program.

Disclosure

The authors report no conflicts of interest in this work.

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