Global landscape of antibiotic resistance genes in the human gut microbiome MAG

Global landscape of antibiotic resistance genes in the human gut microbiome MAG | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Global landscape of antibiotic resistance genes in the human gut microbiome MAG Chenjie Wang, Cancan Wang, Si Chen, Kai Shi, Juanjuan Yu, Yiping Ding, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7434467/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in BMC Microbiology → Version 1 posted 11 You are reading this latest preprint version Abstract Antibiotic resistance poses a significant threat to human health, and the human gut microbiota serves as a major reservoir of antibiotic resistance genes (ARGs). In this study, we analyzed 149,515 metagenome-assembled genomes (MAGs) from human gut microbiomes and revealed marked geographic variations in the global distribution of gut-associated ARGs. Compared with South America, Africa, and Oceania, Europe, Asia, and North America exhibited significantly higher ARG richness. At the phylum level, Pseudomonadota was identified as the predominant ARG host among pathogenic bacteria, with its pathogenic strains frequently exhibiting high levels of multidrug resistant strains harboring ≥5 ARGs accounting for up to 88.5% and 79.1% in Africa and South America, respectively. Campylobacterota was also recognized as a potential high-risk ARG host phylum. Horizontal gene transfer (HGT) analysis revealed that ARG transmission predominantly occurred within the same phylum, with Bacillota being the most active donor, which was likely influenced by antibiotic selection pressure. Actinomycetota and Bacteroidota were identified as major recipients of interphylum HGT, indicating their greater capacity to acquire exogenous ARGs. Through the integration of deep learning and structural calculation, we also identified a potentially novel class of β-lactam resistance genes. This study provides a comprehensive global landscape of gut-associated resistomes, underscores the critical roles of public health infrastructure, antibiotic misuse, and HGT in shaping antimicrobial resistance (AMR), and offers methodological insights for the discovery of novel ARGs. Our findings highlight urgent challenges and provide a scientific basis for developing global AMR mitigation strategies. Human gut microbiome MAG ARG HPB HGT Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Antimicrobial resistance (AMR) is one of the most pressing threats to global public health and sustainable development. It is estimated that in 2019 alone, bacterial AMR directly caused approximately 1.27 million deaths and contributed to as many as 4.95 million deaths worldwide [ 1 ]. Over the past two decades, AMR has evolved into a critical global health concern, particularly in low- and middle-income countries where medical resources are limited and public health policies are often underdeveloped. In such settings, the misuse of antibiotics has further accelerated the emergence and spread of resistant bacterial strains. Moreover, in an increasingly interconnected world, the movement of people and goods facilitates the rapid cross-border dissemination of regionally emerging AMR threats, increasing the risk of global outbreaks. Infectious diseases are projected to become the leading cause of death globally by 2050 [ 2 , 3 ]. The human gut harbors a vast and diverse microbial community that plays a critical role in host physiology and nutrient metabolism. It is estimated that approximately 2,000 microbial genera reside in the gastrointestinal tract of a healthy individual [ 4 ], with the total bacterial population reaching approximately 3.8 × 10¹³ [ 5 ], which is roughly equivalent to the number of human cells. Collectively, the gut microbiome encodes more than 5 million genes, vastly outnumbering the human genome, and is therefore often referred to as the human “second genome” [ 6 ]. Accumulating evidence has demonstrated strong associations between the gut microbiota and a wide range of human diseases, including cancer [ 7 ], neurodegenerative disorders [ 8 ], and cardiovascular conditions [ 9 ]. Moreover, the gut microbiome constitutes one of the largest reservoirs of antibiotic resistance genes (ARGs), making its resistome potential a critical concern in the context of antimicrobial resistance [ 10 ]. Against the backdrop of the global spread of antimicrobial resistance, the "One Health" framework has been proposed to promote an integrated assessment of health risks across humans, animals, and the environment [ 11 ]. Approximately 85% of ARGs across diverse habitats can be traced back to the human fecal resistome, suggesting that human feces have become a central reservoir of the global resistome [ 12 ]. This phenomenon is not coincidental; modern human activities have significantly accelerated microbial exchange across ecosystems [ 13 ], thereby facilitating the dissemination of ARGs between human-associated and environmental microbiomes. Understanding antibiotic resistance genes in the human gut microbiome is critical not only for elucidating transmission dynamics but also for informing risk assessment and intervention strategies under the One Health framework. The rapid advancement of high-throughput sequencing technologies and bioinformatics has created unprecedented opportunities to investigate the global antibiotic resistome [ 14 ]. In 2013, Hu et al. analyzed human gut metagenomic data and identified 149 ARGs, reporting that individuals from China harbored a greater number and abundance of ARGs than did those from Denmark and Spain [ 15 ]. However, their study was limited by a relatively small sample size and restricted geographic scope, covering only three countries. Moreover, numerous novel ARGs have been discovered in the past decade, making earlier findings insufficient to meet the needs of current large-scale resistome research. In this study, we analyzed 149,515 high-quality metagenome-assembled genomes (MAGs) from human gut microbiomes, collected from 97 countries across six continents. This dataset offers broader geographic representation and significantly greater data depth compared with previous studies. We identified a total of 428 ARGs and constructed a global distribution network, systematically characterizing their host pathogenicity and ARG burden across microbial taxa. We investigated horizontal gene transfer (HGT) events on a global scale and explored the underlying mechanisms driving ARG dissemination. Furthermore, by integrating deep learning with structural calculations, we identified a potentially novel class of antibiotic resistance genes. This study provides a comprehensive and current framework for understanding the global dissemination of antimicrobial resistance and offers essential scientific insights for guiding the surveillance and control of antibiotic resistance. Materials and Methods Data collection A total of 149,515 MAGs were obtained from the human gut microbiome reference catalog established by Almeida et al. MAGs with ≥ 90% completeness and ≤ 5% contamination were retained as high-quality genomes for downstream analysis [ 16 ]. These high-quality MAGs were further classified by country and continent on the basis of sample geographic metadata. The geographic distribution of the samples was visualized using Flourish Studio ( https://flourish.studio ), which illustrates their spatial coverage. ARG annotation and classification ARGs were annotated using DeepARG v2 [ 17 ] with default parameters. A total of 23 ARG types and 428 ARG subtypes were identified and used for subsequent analysis. The distribution of ARG subtypes across continents was visualized as a hierarchical clustering tree using the df2tree function from the R package pctax [ 18 ], followed by visual enhancement using the Chiplot tool [ 19 ]. To compare the composition of ARGs across continents, visualizations were generated using OriginPro 2025 (OriginLab Corporation, Northampton, MA, USA). For ARG subtypes, only the top 10 most relatively abundant categories on each continent are shown to highlight representative geographic trends. ARG host annotation and pathogen identification Taxonomic classification of ARG-carrying MAGs was performed using the classify_wf subcommand of GTDB-Tk v2.3.2 [ 20 ] with the GTDB reference database. Potential pathogenic MAGs were identified by aligning the ARG-hosting MAGs against the VFDB [ 21 ] using BLASTn v2.16.0+ [ 22 ] with the following parameters: -outfmt 6, -max_target_seqs 1, -perc_identity 90. Only the best matches with ≥ 90% identity were retained. MAGs containing virulence factors were defined as HPBs. The phylogenetic relationships of the annotated results were visualized using GraPhlAn v1.1.3 [ 23 ] to display the HPB distribution across the phylogenetic tree. In addition, the distribution of HPBs at the phylum and genus levels across different continents was visualized using OriginPro 2025 (OriginLab Corporation, Northampton, MA, USA). The number of ARGs per MAG was further analyzed by continent and by phylum to assess differences in resistance burden. Identification of HGT events associated with ARGs To avoid redundancy in the downstream analysis, MAGs from each continent were dereplicated using dRep v3.4.5 [ 24 ] with the parameters: --ignoreGenomeQuality, --multiround_primary_clustering. HGT events at the genus level were then detected using MetaCHIP [ 25 ]. The protein-coding sequences of genes identified by MetaCHIP [ 25 ] as involved in HGT were further annotated using DeepARG v2 [ 17 ] to identify ARGs and their associated host MAGs. Cytoscape v3.10.3 [ 26 ] was used to visualize the transfer network of HGT-related ARGs, illustrating potential intergenus transfer pathways and dissemination patterns. Identification of novel ARGs Protein-coding genes from all African MAGs were predicted using Prodigal v2.6.3 [ 27 ] under default settings to detect open reading frames (ORFs) and corresponding amino acid sequences. These sequences were annotated using DeepARG v2 [ 17 ] to identify potential ARG-related proteins. Potential novel ARG protein sequences were clustered on the basis of sequence similarity using EFI-EST [ 28 , 29 ]. Sequences with ≥ 95% identity were treated as the same gene, and a 70% identity threshold was applied for clustering, resulting in a network of putative ARGs with functional or evolutionary similarity. Representative sequences from each cluster were structurally modeled using AlphaFold2 [ 30 ] to obtain their 3D conformations. Molecular docking was performed between these predicted structures and candidate antibiotic molecules using CB-Dock2 [ 31 , 32 ] to assess the binding potential and interaction patterns. The resulting docking structures were visualized and refined using PyMOL [ 33 ] to clearly present the protein‒ligand interfaces and key binding residues. Results Global distribution of ARGs We obtained 149,515 MAGs from the human gut genome catalog constructed by Almeida et al. using quality filtering criteria of ≥ 90% completeness and ≤ 5% contamination [ 16 ]. These samples originated from across the globe and were categorized by continent, with the majority coming from Europe, Asia, and North America (Fig. 1 A). Using DeepARG[ 17 ], we identified ARGs, detecting 23 ARG types and 428 ARG subtypes (Fig. 1 B). We further analyzed the geographic distribution of all the ARG subtypes by continent and found that, in terms of diversity, Europe, Asia, and North America harbored a greater variety of ARGs than South America, Africa, and Oceania did. Analysis of the relative abundances of major ARG types across continents (Fig. 1 C) revealed that multidrug ARGs were the predominant type worldwide, accounting for more than 35% of the ARGs from each continent. In addition to multidrug ARGs, resistance genes against macrolide-lincosamid-streptogramin (MLS), β-lactam, glycopeptide, and peptide antibiotics also featured prominently across continents. For example, MLS ARGs constituted 16.9%, 17.9%, 16.7%, and 13.1% of ARGs in Asia, Europe, Oceania, and North America, respectively, whereas glycopeptide and peptide resistance genes each accounted for more tahn 6% in these regions. In South America and Africa, β-lactam resistance genes represented more than 12% of the detected ARGs. The distribution of ARG subtypes varied across continents (Fig. 1 D). In Asia, Europe, Oceania, and North America, the most prevalent ARG subtypes were generally associated with specific antibiotic classes, such as LlmA_23s_CLI (MLS), rpoB2 (rifamycin), and ugd (peptide resistance), each accounting for more than 6% of the total ARGs in these regions. In contrast, multidrug resistance genes were not predominant in these regions, with subtypes such as arlR and efrB accounting for less than 6% of the total. In Africa, however, several multidrug ARG subtypes, including tolC , emrD , and mdtO , ranked among the top 10 most abundant, with relatively high prevalence rates observed only on this continent. Similarly, South America presented distinct multidrug ARG signatures, with high proportions of H-NS , penA , and PBP1A . These regional differences likely reflect disparities in antibiotic usage intensity and socioeconomic development. In economically developed regions—Asia, Europe, Oceania, and North America—extensive antibiotic use in clinical and agricultural settings has created strong selection pressure, driving both the accumulation and dissemination of ARGs targeting specific antibiotic classes [ 34 , 35 ]. In addition, centralized healthcare systems may facilitate ARG transmission [ 36 ]. In contrast, Africa and South America, with lower overall antibiotic consumption due to limited healthcare access and economic constraints, exhibit reduced ARG diversity and abundance, particularly for class-specific resistance genes. The abundance of multidrug resistance genes was relatively higher in Africa and South America. This paradox may reflect their ecological origin: genes such as tolC and emrD encode efflux pumps that respond to broad environmental stresses [ 37 ], not solely antibiotics. Their widespread presence may reflect microbial adaptation to natural selective pressures rather than direct antibiotic exposure. Identification of ARG hosts and assessment of their curative potential In this study, we identified a total of 120,466 MAGs carrying ARGs, representing the core ARG hosts. Taxonomic classification of these MAGs using GTDB-Tk revealed 1,978 species across 11 bacterial phyla. At the phylum level, Bacillota emerged as the predominant ARG host in the human gut across all continents, accounting for 67.4% in Europe, 67.0% in Asia, 62.6% in North America, 33.1% in Africa, 44.7% in South America, and 71.1% in Oceania. Other major phyla contributing to ARG carriage included Pseudomonadota, Actinomycetota, and Bacteroidota. To further assess the pathogenic potential of these ARG hosts, we annotated virulence factors (VFs) using the Virulence Factor Database (VFDB) [ 21 ]. MAGs carrying VFs were defined as human pathogen bacteria (HPB). In total, 749 species from 9 phyla were identified as HPB. Intercontinental comparisons revealed regional differences in the proportion of HPB within each phylum. In Europe (Fig. 2 A), Campylobacterota (94.1%), Fusobacteriota (55.6%), and Pseudomonadota (64.0%) had the highest HPB proportions. In Asia (Fig. 2 B), Campylobacterota (86.2%) and Pseudomonadota (61.9%) were the most prominent, whereas in North America (Fig. S1 ), Campylobacterota (71.0%) and Pseudomonadota (81.6%) presented the highest pathogenic potential. In South America (Fig. S2), Campylobacterota (78.9%) and Pseudomonadota (93.6%) dominated. In Africa (Fig. S3), only Pseudomonadota (89.5%) presented a notably high proportion of HPB, whereas in Oceania (Fig. S4), HPB proportions across all phyla remained below 50%, with no clearly dominant high-risk phylum. Campylobacterota demonstrated consistently high pathogenicity in Asia, Europe, North America, and South America, with over 70% of its species identified as HPB. This trend suggested that Campylobacterota in the human gut microbiota of these regions was predominantly composed of HPB; however, its pathogenic potential was substantially lower in Africa and Oceania. In contrast, Pseudomonadota was a widely distributed pathogenic phylum across all continents, except Oceania, where its HPB proportion was relatively low. Given that this phylum includes many well-known pathogens, such as Escherichia and Klebsiella , this finding is consistent with expectations. The low pathogenicity of Pseudomonadota observed in Oceania may reflect technical limitations rather than biological differences. Virulence factor annotation in this study relied solely on sequence homology with VFDB entries. Novel or region-specific virulence genes, particularly those unique to Oceania, may have been missed. Owing to its geographic isolation, Oceania may harbor distinct microbial communities with uncharacterized virulence traits. Further investigations involving experimental validation and advanced computational approaches are needed to identify these potentially novel virulence genes [ 38 ]. The strong regional pattern of Campylobacterota pathogenicity may be linked to increased poultry consumption in Asia, Europe, North America, and South America. Members of the Campylobacter genus are commonly associated with poultry and can colonize the human gut via foodborne transmission [ 39 ]. The enrichment of Campylobacterota pathogens in these regions may thus be driven by dietary habits. Given its dual role as a high-risk ARG host and prevalent gut pathogen, Campylobacterota warrants greater attention in future AMR surveillance and risk assessment frameworks( http://www.ygsite.cn/show.asp?trcms=1&id=86732&pageno=1 ). HPB distribution and ARG burden We identified a wide range of HPB distributions across continents on the basis of their taxonomic classification (Fig. 3 A). In Europe, the majority of HPB belonged to Bacillota (34.0%) and Pseudomonadota (33.6%), with Actinomycetota (14.3%) and Bacteroidota (11.1%) as additional contributors. A similar pattern was observed in Asia, where Pseudomonadota (43.0%) and Bacillota (37.0%) dominated, and Actinomycetota (8.8%) and Bacteroidota (8.3%) were less represented than in Europe. In North America, Pseudomonadota accounted for 50.7%, with Bacillota accounting for 36.5%, whereas in Oceania, these phyla accounted for 42.9% and 49.4%, respectively. Pseudomonadota overwhelmingly dominated South America (77.5%) and Africa (89.8%), with Bacillota contributing only 12.0% and 7.6%, respectively. While Bacillota and Pseudomonadota were consistently the two main HPB phyla globally, their relative proportions varied considerably across regions. At the genus level, Escherichia was the most prevalent ARG harboring HPB across all continents. Several genera were uniquely enriched in Oceania, including Lactococcus , CAG-882 , CAG-710 , PeH17 , Parafannyhessea , and Mycobacterium . Notably, Catenibacterium and Klebsiella accounted for 15.6% and 16.1%, respectively, of pathogenic ARG hosts in Oceania, values that were higher than those reported for continents. These findings align with earlier observations, as Catenibacterium belongs to Bacillota and Klebsiella to Pseudomonadota. In North America, unique HPB genera included Staphylococcus , Sarcina , Clostridioides , and Acinetobacter . Enterococcus also had a relatively high abundance (10.6%). Except for Acinetobacter (Pseudomonadota), these genera belong to Bacillota, suggesting that although Pseudomonadota is more abundant, Bacillota associated with HPB may represent emerging threats in this region and warrant closer surveillance (Fig. 3 B). In Africa, Escherichia was the most abundant genus, followed by Vibrio , which accounted for 16.0% of the identified taxa. The high prevalence of Vibrio may be associated with inadequate sanitation and dietary habits that increase the risk of colonization by waterborne pathogens [ 40 ]. In Asia, while Escherichia was dominant, no other genus showed striking overrepresentation, possibly due to high population mobility and microbial exchange between regions. In Europe, Prevotella (11.7%) and Bifidobacterium (17.3%) were more abundant than they were in other continents, which may reflect dietary habits such as higher protein and dairy intake, as these genera are involved in the metabolism of such nutrients [ 41 ]. In South America, Salmonella (22.6%) had a high prevalence, likely driven by the tropical climate across the continent, which favors its growth and transmission through food [ 42 ]. AMR has become a global concern. In all continents, more than 40% of HPB carried more than five ARGs (Fig. 3 C), with the highest proportions detected in South America (79.1%) and Africa (88.5%). This trend indicates an increasing difficulty in treating infections with conventional antibiotics. Pathogenic Pseudomonadota were particularly ARG-rich, with most carrying more than five ARGs. This feature is likely due to their high capacity for HGT, exemplified by Escherichia coli , which exhibits strong gene acquisition and recombination abilities in both natural and clinical environments [ 43 ]. This observation aligns with the regional data: both Africa and South America presented a high prevalence of Pseudomonadota and high-ARG-load strains, likely reflecting frequent antibiotic exposure and a poor public health infrastructure. HPB belonging to Cyanobacteriota , Desulfobacterota , Verrucomicrobiota , and Campylobacterota , however, generally carried three or fewer ARGs (Fig. 3 D). This finding may be due to their limited colonization ability in the human gut, lower antibiotic selection pressure, more conserved genomes, and reduced HGT potential due to the absence of mobile genetic elements such as plasmids and transposons. HGT detection The horizontal transfer of ARGs within the same phylum is particularly important, especially when ARGs are transmitted from commensal microbes to pathogenic bacteria, potentially endowing the latter with new resistance traits. As shown in Fig. 4 , most ARG transfer events occured within the same phylum. Among all the phyla, Bacillota presented the highest frequency of intra-phylum HGT events, which is consistent with its status as the dominant ARG host in the human gut. The relatively even ARG distribution among Bacillota strains further suggests that resistance genes in this group are acquired primarily via horizontal, rather than vertical, transmission. Pseudomonadota and Bacteroidota presented high levels of intra-phylum HGT. Pseudomonadota showed broad ARG type diversity, reflecting a strong capacity to acquire resistance genes, which is consistent with the prevalence of strains carrying more than five ARGs. Despite frequent HGT events, Bacteroidota typically carry only 2–3 ARGs, possibly due to lower pathogenicity and reduced antibiotic selection pressure. “Long-distance” HGT events between distantly related organisms are rare, except among species in certain extreme environments, such as halophiles, thermophiles, and saccharolytic or fermentative organisms inhabiting termite or ruminant guts rich in organic matter [ 44 – 46 ]. Extreme habitats show cross-phyla gene exchange, and our findings indicate that similar HGT occurs in the human gut microbiome. Bacillota acts as a major donor, likely due to its wide distribution and frequent internal HGT. The main pathogens, Actinomycetota and Bacteroidota, include clinically relevant pathogens. Antibiotic pressure overcomes genetic barriers [ 47 ], facilitating the spread of ARGs to these high-risk groups. Although Bacteroidota currently shows modest ARG abundance, its frequent involvement as an HGT recipient highlights its potential as a future ARG reservoir. In Actinomycetota, the widespread presence of multiple ARGs—especially in pathogenic strains—suggests active intra-phylum transfer among pathogens, positioning this group as a potential incubator for emerging multidrug-resistant "superbugs". Frequently transferred ARGs in Bacillota are enriched in genes conferring resistance to MLS, tetracyclines, and glycopeptides. This pattern aligns with that of dominant ARG classes and highlights the central role of antibiotic selection pressure in driving resistance emergence and dissemination. Identification of novel potential ARGs Current ARG databases provide only a partial view of resistance gene diversity. The discovery of novel ARGs is vital for understanding resistance evolution and for supporting the development of improved detection methods and therapeutic strategies. Antibiotic misuse in Africa has become increasingly severe, creating sustained selection pressure that may drive the emergence of novel resistance mechanisms. To explore this possibility, we focused on MAGs from this region to identify representative candidate ARGs. All African MAGs were analyzed, and their protein-coding genes were predicted. Potential ARGs were annotated using DeepARG [ 17 ] on the basis of the predicted proteomes. All the predicted ARG sequences were then subjected to clustering analysis using the EFI-EST platform [ 28 , 29 ]. Genes sharing ≥ 95% sequence identity were considered the same, and a 70% threshold was used to define gene clusters, allowing us to construct a similarity-based ARG network (Fig. S5). Within this network, we identified a distinct cluster of genes that were initially annotated by DeepARG [ 17 ] as multidrug resistance genes but presented high sequence similarity to classical PBP1A . Since PBP1A is a well-characterized β-lactam resistance gene, we hypothesized that this cluster might represent a group of mutated PBP1A variants. One representative gene from this cluster was selected for further investigation and provisionally named Ppbp. We used AlphaFold2 [ 30 ] to predict the 3D structure of the Ppbp protein (Fig. 5 A), and the protein sequence was simultaneously submitted to the InterPro database [ 48 ] for domain annotation. Ppbp was predicted to contain both a glycosyltransferase domain and a penicillin-binding protein domain—an architecture consistent with its involvement in cell wall biosynthesis and β-lactam interactions. This dual-domain structure strongly suggests that Ppbp may retain a functional role related to antibiotic binding, albeit potentially modified from canonical PBP1A. We assessed the novelty of this gene by performing a BLASTp [ 22 ] search against the Comprehensive Antibiotic Resistance Database (CARD) [ 49 ]. The closest homolog was a PBP1A gene from Streptococcus pneumoniae , which shares only 62% sequence identity. This moderate similarity implies that Ppbp is functionally related yet structurally distinct from classical PBP1A , potentially conferring resistance through a different mechanism. To explore structural differences, we retrieved the closest PBP1A sequence from National Center for Biotechnology Information (NCBI) ( https://www.ncbi.nlm.nih.gov/ ) and predicted its 3D structure using AlphaFold2 (Fig. 5 B). Structural alignment with Ppbp using PyMOL [ 33 ] (Fig. 5 C) revealed a root-mean-square deviation (RMSD) of 5.195 Å, indicating significant spatial differences. Despite the overall fold similarity, the deviation suggests that Ppbp is not a direct homolog but a structurally distinct analog, potentially with altered active site conformation or substrate binding properties. Resistance mediated by PBP1A is primarily attributed to mutations that reduce their binding affinity to β-lactam antibiotics, thereby impairing antibiotic efficacy [ 50 ]. We further investigated the drug-binding capacity of Ppbp. Using CB-Dock2 [ 31 , 32 ], we performed molecular docking of both wild-type PBP1A and Ppbp with cefotaxime, a representative β-lactam. The lowest binding energy for PBP1A was − 7.2 kcal/mol, with key hydrogen-bonding residues, including TYR, ARG, VAL, and ASN (Fig. 5 D). In contrast, Ppbp exhibited a binding energy of − 6.9 kcal/mol at the same binding pocket as PBP1A, involving distinct key residues: ASN, THR, TYR, and ARG (Fig. 5 D). This slightly higher binding energy indicates reduced affinity, which is consistent with a resistance-associated phenotype. Compared with PBP1A, Ppbp has altered residue positioning in the binding pocket, increasing the binding energy and reducing affinity while preserving enzymatic activity. As a novel PBP1A-type β-lactam resistance gene with distinct structural features, Ppbp highlights ongoing resistance evolution and demonstrates the value of integrating deep learning with structural modeling to uncover hidden elements of the global resistome. Discussion This study, which is based on the global human gut genome catalog constructed by Almeida et al., provides a comprehensive analysis of the distribution and host characteristics of ARGs across diverse geographic regions [ 16 ]. Our findings revealed that ARGs in Asia, Europe, Oceania, and North America are more often associated with resistance to specific antibiotic classes, whereas multidrug ARGs are more prevalent in South America and Africa. Notably, the elevated multidrug ARG burden in these latter regions may reflect delayed but intensified antibiotic usage, suggesting that ARG accumulation is still ongoing. These geographic differences underscore that antibiotic consumption levels are a key driver of ARG dissemination, particularly for single-drug resistance. In contrast, the emergence and spread of multidrug ARGs appear to be shaped by more complex ecological and evolutionary dynamics, including long-term selection pressure and microbial adaptation to diverse environmental conditions [ 37 ]. At the host level, Pseudomonadota has emerged as the central carrier of multidrug ARGs, playing a key role in their accumulation and dissemination across global gut microbiomes. Campylobacterota has also demonstrated high pathogenic potential across several regions. Although currently underappreciated in resistance surveillance, Campylobacterota may become a critical target for future AMR control efforts. Bacillota and Pseudomonadota dominate the global distribution of ARG hosts but exhibit distinct regional patterns: Europe, North America, Asia, and Oceania show a balanced presence of both phyla, whereas South America and Africa are predominantly occupied by Pseudomonadota. In Africa and South America, inadequate sanitation infrastructure may contribute to elevated bacterial infection rates, driving excessive antibiotic use that selects for predominant intestinal colonization by gram-negative bacteria in the human gut microbiome. At the genus level, Escherichia was the most prevalent ARG host globally. Region-specific dominant genera included Catenibacterium and Klebsiella in Oceania, Enterococcus in North America, Vibrio in Africa, Prevotella and Bifidobacterium in Europe, and Salmonella in South America. Asia showed a more balanced genus distribution. These patterns likely result from the interplay of healthcare conditions, antibiotic regulation, dietary habits, climate, and human mobility [ 51 – 53 ]. Over 40% of the ARGs harboring HPB across all continents carried more than five resistance genes, with the percentages in South America and Africa reaching 79.1% and 88.5%, respectively. Pseudomonadota was consistently associated with high ARG loads, likely due to its exceptional HGT capacity, enabling rapid ARG acquisition and dissemination [ 43 ]. Conversely, phyla such as Cyanobacteriota and Desulfobacterota carried fewer ARGs, which is consistent with their limited colonization potential, conserved genome architecture, and lower HGT activity. HGT analysis revealed that intra-phylum transfers were predominant, especially within Bacillota, which resulted in the frequent transfer of MLS, tetracycline, and glycopeptide resistance genes, highlighting the selective pressure from commonly used antibiotics [ 54 ]. Pseudomonadota exhibited high ARG receptor capacity, supporting its role as an MDR hotspot. Actinomycetota and Bacteroidota were major recipients of cross-phyla transfers, with the former contributing to the amplification of resistance in pathogenic strains and the latter—despite lower current resistance—representing a potential reservoir owing to its permissiveness to ARG acquisition. By integrating deep learning-based prediction with structure-informed analysis, we identified a novel PBP1A-like ARG candidate from African human gut MAGs. This gene, provisionally named Ppbp, differs from classical PBP1A in its β-lactam binding affinity. The combined approach enables the sensitive detection of ARG-like sequences beyond conventional homology thresholds and provides structural insights, particularly for genes with moderate sequence similarity but potentially divergent functions. This integrative framework highlights the presence of previously unrecognized resistance elements in the human gut microbiome and offers a broadly applicable strategy for ARG discovery beyond existing databases. The present findings advance our understanding of ARG diversity and evolution while providing theoretical support for future antibiotic development. However, the functional properties and clinical relevance of Ppbp require further experimental validation. The integration of genomic technologies will be essential for the high-throughput, real-time surveillance of ARGs, enabling precise tracking of their dissemination across environments and hosts. These strategies can greatly enhance early warning systems for antimicrobial resistance. Moreover, the development of novel antibiotics must be accelerated, with an emphasis on overcoming resistance mechanisms and exploring alternative therapies such as antivirulence agents, phage therapy, and microbiome-based interventions. A dual approach that combines effective surveillance with therapeutic innovation is critical to limiting the global spread of AMR. This study provides the first comprehensive overview of the global distribution, host range, and transfer potential of ARGs in the human gut microbiome. The results reveal both widespread and emerging resistance threats, offering valuable insights for antimicrobial stewardship and highlighting the need for sustained monitoring and targeted intervention. Abbreviations AMR Antimicrobial resistance ARGs Antibiotic resistance genes MAGs Metagenome-assembled genomes HGT Horizontal gene transfer ORFs Open reading frames MLS Macrolide-Lincosamid-Streptogramin HPB Human pathogenic bacteria VFs Virulence factors VFDB Virulence Factor Database CARD Comprehensive Antibiotic Resistance Database NCBI National Center for Biotechnology Information RMSD Revealed a root-mean-square deviation Declarations Ethics approval and consent to participate Not applicable. Clinical trial number Not applicable. Consent for publication Not applicable. Availability of data and materials Not applicable. Competing interests The authors declare that they have no competing interests. Funding The Project of International Cooperation and Exchanges NSFC-ASRT (No. W2412100), National Natural Science Foundation of China (No. 42276137), the National Key Research and Development Program of China (No. 2022YFC2804104, No. 2022YFC2804205), the Scientific Research Program of the Bozhou University (No.202524). Authors' contributions Chenjie Wang : Conceptualization, Methodology, Data curation, Investigation, Validation, Writing - Original draft, Review & Editing. Cancan Wang : Methodology, Data curation, Investigation. Si Chen , Kai Shi , Juanjuan Yu, Yiping Ding : Methodology, Investigation, Validation. Yujie Yue ， Yi Hua : Investigation, Data curation. Hong Wang : Conceptualization, Project administration, Writing - Original draft, Funding acquisition. Jianwei Chen : Conceptualization, Project administration, Writing - Original draft, Review & Editing, Supervision, Resources, Funding acquisition. Acknowledgments This work was supported by the Project of International Cooperation and Exchanges NSFC-ASRT (No. W2412100), National Natural Science Foundation of China (No. 42276137), the National Key Research and Development Program of China (No. 2022YFC2804104, No. 2022YFC2804205), the Scientific Research Program of the Bozhou University (No.202524). We appreciate Almeida et al (2021) for their work on the human gut genome and its publication as a public resource, and gratefully acknowledge platform support from Zhejiang International Sci-Tech Cooperation Base for the Exploitation and Utilization of Nature Product, Zhejiang Provincial Key Laboratory of TCM for Innovative R & D and Digital Intelligent Manufacturing of TCM Great Health Products, Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources. References Murray CJL, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet. 2022;399:629–55. De Kraker MEA, Stewardson AJ, Harbarth S. Will 10 Million People Die a Year due to Antimicrobial Resistance by 2050? PLoS Med. 2016;13:e1002184. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020;180:688-702.e13. Leviatan S, Shoer S, Rothschild D, Gorodetski M, Segal E. An expanded reference map of the human gut microbiome reveals hundreds of previously unknown species. Nat Commun. 2022;13:3863. Sender R, Fuchs S, Milo R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol. 2016;14:e1002533. Sommer F, Bäckhed F. The gut microbiota — masters of host development and physiology. Nature Reviews Microbiology. 2013;11:227–38. Tilg H, Adolph TE, Gerner RR, Moschen AR. The Intestinal Microbiota in Colorectal Cancer. Cancer Cell. 2018;33:954–64. Sampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, et al. Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson’s Disease. Cell. 2016;167:1469-1480.e12. De Vos WM, Tilg H, Van Hul M, Cani PD. Gut microbiome and health: mechanistic insights. Gut. 2022;71:1020–32. Sommer MOA, Dantas G, Church GM. Functional Characterization of the Antibiotic Resistance Reservoir in the Human Microflora. Science. 2009;325:1128–31. Kim D-W, Cha C-J. Antibiotic resistome from the One-Health perspective: understanding and controlling antimicrobial resistance transmission. Experimental & Molecular Medicine. 2021;53:301–9. Zhang Q, Xu N, Lei C, Chen B, Wang T, Ma Y, et al. Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome. Advanced Science. 2023;10:2303925. Zhu Y-G, Gillings M, Simonet P, Stekel D, Banwart S, Penuelas J. Microbial mass movements. Science. 2017;357:1099–100. Boolchandani M, Blake KS, Tilley DH, Cabada MM, Schwartz DJ, Patel S, et al. Impact of international travel and diarrhea on gut microbiome and resistome dynamics. Nature Communications. 2022;13:7485. Hu Y, Yang X, Qin J, Lu N, Cheng G, Wu N, et al. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nature Communications. 2013;4:2151. Almeida A, Nayfach S, Boland M, Strozzi F, Beracochea M, Shi ZJ, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nature Biotechnology. 2021;39:105–14. Arango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 2018;6:23. Peng C, Jiang C. pctax: Professional Comprehensive Omics Data Analysis Pipeline. NA. 2023. Xie J, Chen Y, Cai G, Cai R, Hu Z, Wang H. Tree Visualization By One Table (tvBOT): a web application for visualizing, modifying and annotating phylogenetic trees. Nucleic Acids Research. 2023;51:W587–92. Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics. 2022;38:5315–6. Zhou S, Liu B, Zheng D, Chen L, Yang J. VFDB 2025: an integrated resource for exploring anti-virulence compounds. Nucleic Acids Research. 2025;53:D871–7. Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421. Asnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ. 2015;3:e1029. Olm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. The ISME Journal. 2017;11:2864–8. Song W, Wemheuer B, Zhang S, Steensen K, Thomas T. MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches. Microbiome. 2019;7:36. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003;13:2498–504. Hyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119. Oberg N, Zallot R, Gerlt JA. EFI-EST, EFI-GNT, and EFI-CGFP: Enzyme Function Initiative (EFI) Web Resource for Genomic Enzymology Tools. Journal of Molecular Biology. 2023;435:168018. Zallot R, Oberg N, Gerlt JA. The EFI Web Resource for Genomic Enzymology Tools: Leveraging Protein, Genome, and Metagenome Databases to Discover Novel Enzymes and Metabolic Pathways. Biochemistry. 2019;58:4169–82. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583–9. Liu Y, Yang X, Gan J, Chen S, Xiao Z-X, Cao Y. CB-Dock2: improved protein–ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Research. 2022;50:W159–64. Yang X, Liu Y, Gan J, Xiao Z-X, Cao Y. FitDock: protein–ligand docking by template fitting. Briefings in Bioinformatics. 2022;23:bbac087. Schrödinger, LLC. The PyMOL Molecular Graphics System, Version 1.8. 2015. Diebold PJ, Rhee MW, Shi Q, Trung NV, Umrani F, Ahmed S, et al. Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide. Nature Communications. 2023;14:7366. Van Boeckel TP, Pires J, Silvester R, Zhao C, Song J, Criscuolo NG, et al. Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science. 2019;365:eaaw1944. Zhu C, Wu L, Ning D, Tian R, Gao S, Zhang B, et al. Global diversity and distribution of antibiotic resistance genes in human wastewater treatment systems. Nature Communications. 2025;16:4006. Gao M, Zhang Q, Lei C, Lu T, Qian H. Atmospheric antibiotic resistome driven by air pollutants. Science of The Total Environment. 2023;902:165942. Wang W, Song W, Majzoub ME, Feng X, Xu B, Tao J, et al. Decoupling of strain- and intrastrain-level interactions of microbiomes in a sponge holobiont. Nature Communications. 2024;15:8205. Kelbert L, Barmettler K, Horlbog JA, Stevens MJA, Cernela N, Nüesch-Inderbinen M, et al. Campylobacter in Raw Chicken Meat at Retail Level: Quantitative and Qualitative Assessment, Genomic Profiling, and Comparison With Isolates From Human Infections. Journal of Food Protection. 2025;88:100540. Baker-Austin C, Oliver JD, Alam M, Ali A, Waldor MK, Qadri F, et al. Vibrio spp. infections. Nature Reviews Disease Primers. 2018;4:1–19. Sánchez B, Ruiz L, Gueimonde M, Ruas-Madiedo P, Margolles A. Adaptation of bifidobacteria to the gastrointestinal tract and functional consequences. Pharmacological Research. 2013;69:127–36. Chen Zhao, Moreno-Switt Andrea I., Reyes-Jara Angelica, Delgado Suarez Enrique, Adell Aiko D., Oliveira Celso José Bruno, et al. A multicenter genomic epidemiological investigation in Brazil, Chile, and Mexico reveals the diversity and persistence of Salmonella populations in surface waters. mBio. 2024;15:e00777-24. Qiao W, Wang L, Luo Y, Miao J. Outer membrane vesicles mediated horizontal transfer of an aerobic denitrification gene between Escherichia coli. Biodegradation. 2021;32:435–48. Caro-Quintero A, Konstantinidis KT. Inter-phylum HGT has shaped the metabolism of many mesophilic and anaerobic bacteria. The ISME Journal. 2015;9:958–67. Fuchsman CA, Collins RE, Rocap G, Brazelton WJ. Effect of the environment on horizontal gene transfer between bacteria and archaea. PeerJ. 2017;5:e3865. Rhodes ME, Spear JR, Oren A, House CH. Differences in lateral gene transfer in hypersaline versus thermal environments. BMC Evolutionary Biology. 2011;11:199. Parras-Moltó M, Lund D, Ebmeyer S, Larsson DGJ, Johnning A, Kristiansson E. The transfer of antibiotic resistance genes between evolutionarily distant bacteria. mSphere. 2025;10. Blum M, Andreeva A, Florentino LC, Chuguransky SR, Grego T, Hobbs E, et al. InterPro: the protein sequence classification resource in 2025. Nucleic Acids Research. 2025;53:D444–56. Alcock BP, Huynh W, Chalil R, Smith KW, Raphenya AR, Wlodarski MA, et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research. 2023;51:D690–9. Contreras-Martel C, Job V, Di Guilmi AM, Vernet T, Dideberg O, Dessen A. Crystal Structure of Penicillin-binding Protein 1a (PBP1a) Reveals a Mutational Hotspot Implicated in β-Lactam Resistance in Streptococcus pneumoniae. Journal of Molecular Biology. 2006;355:684–96. Alhassan MY, Ahmad AA. Antimicrobial resistance in a changing climate: a One Health approach for adaptation and mitigation. Bulletin of the National Research Centre. 2025;49:26. Feng Y, Lu X, Zhao J, Li H, Xu J, Li Z, et al. Regional antimicrobial resistance gene flow among the One Health sectors in China. Microbiome. 2025;13:3. Forslund K, Sunagawa S, Kultima JR, Mende DR, Arumugam M, Typas A, et al. Country-specific antibiotic use practices impact the human gut resistome. Lund D, Parras-Moltó M, Inda-Díaz JS, Ebmeyer S, Larsson DGJ, Johnning A, et al. Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes. Nature Communications. 2025;16:2595. Additional Declarations No competing interests reported. Supplementary Files supplementary1.docx Supplementary data Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in BMC Microbiology → Version 1 posted Editorial decision: Revision requested 15 Oct, 2025 Reviews received at journal 30 Sep, 2025 Reviews received at journal 28 Sep, 2025 Reviewers agreed at journal 25 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor invited by journal 08 Sep, 2025 Editor assigned by journal 03 Sep, 2025 Submission checks completed at journal 03 Sep, 2025 First submitted to journal 22 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Each point represents a country; the point size indicates the number of samples, and the point color reflects the range of MAG counts. Samples without specific country information are grouped under “not provide.” \u003cstrong\u003e(B)\u003c/strong\u003e Distribution of identified antibiotic resistance genes(ARGs) across continents. The colors of the central sectors represent different ARG types. The presence of an outer ring indicates the detection of corresponding ARG subtypes, and the color of the ring denotes the continent. \u003cstrong\u003e(C)\u003c/strong\u003eDistribution of ARG types across continents, showing only the top 10 most prevalent ARG types within each continent. \u003cstrong\u003e(D)\u003c/strong\u003e Distribution of ARG subtypes across continents, showing only the top 10 most prevalent ARG subtypes within each continent.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7434467/v1/3e52b9b72e394e23afca1f76.png"},{"id":92164574,"identity":"85eaf18c-97eb-47f5-ad2a-281c2835d00d","added_by":"auto","created_at":"2025-09-25 10:42:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":333321,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic and taxonomic distribution of ARG-hosting species in Europe and Asia. \u003cstrong\u003e(A) \u003c/strong\u003eCircular phylogenetic tree showing the taxonomic classification of ARG-hosting species in Europe. From the innermost to the outermost rings, the layers represent phylum, class, order, family, genus, and species. The colors of the lines and nodes indicate different phyla. The outer symbols denote human pathogenic bacteria (HPB): circles represent non-HPB, and triangles represent HPB. The accompanying bar chart indicates the proportion of pathogenic species within each phylum in Europe. \u003cstrong\u003e(B)\u003c/strong\u003e The same representations as in panel A but for ARG-hosting species identified in Asia.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7434467/v1/b7d8e05a6c87da21e9b9a3c0.png"},{"id":92164897,"identity":"9ca50e8b-a6b2-4a9b-9cec-02cd92a128d9","added_by":"auto","created_at":"2025-09-25 10:50:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":402406,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal distribution and ARG burden of HPB. \u003cstrong\u003e(A)\u003c/strong\u003e Phylum-level composition of HPB across different continents. \u003cstrong\u003e(B)\u003c/strong\u003e Genus-level composition of HPB across continents. \u003cstrong\u003e(C) \u003c/strong\u003eDistribution of HPB according to the number of ARG subtypes they carry on each continent.\u003cstrong\u003e(D) \u003c/strong\u003ePhylum-level distribution of ARG subtype combinations carried by HPB. The labels “2-ARGs,” “3-ARGs,” “4-ARGs,” and “\u0026gt;5-ARGs” represent combinations involving 2, 3, 4, and more than 5 ARG subtypes, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7434467/v1/fd4d6b1ed3a5de4384ab59a2.png"},{"id":92164575,"identity":"21e3cb87-ff36-4775-a9e9-bab5c7c40977","added_by":"auto","created_at":"2025-09-25 10:42:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":178267,"visible":true,"origin":"","legend":"\u003cp\u003eIntergenus horizontal transfer of ARGs. Each connected pair of nodes represents two different bacterial genera. The shape of each node indicates the bacterial phylum, whereas the color of the arrows represents the type of ARG involved in the transfer.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7434467/v1/a91f4681103ed8f5c22b73a9.png"},{"id":92164579,"identity":"11ca6819-5036-4ea7-9464-a1c3c2fffa6d","added_by":"auto","created_at":"2025-09-25 10:42:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":302023,"visible":true,"origin":"","legend":"\u003cp\u003eStructural comparison and docking analysis of Ppbp and PBP1A Proteins.\u003cstrong\u003e (A)\u003c/strong\u003e Predicted 3D structure of the candidate Ppbp protein generated by AlphaFold2. The predicted domain architecture is shown below the structure.\u003cstrong\u003e (B)\u003c/strong\u003e Predicted 3D structure of a known PBP1A protein generated by AlphaFold2. \u003cstrong\u003e(C)\u003c/strong\u003e Structural alignment of the predicted models of Ppbp and PBP1A. \u003cstrong\u003e(D)\u003c/strong\u003e Molecular docking model of PBP1A with cefotaxime.\u003cstrong\u003e (E) \u003c/strong\u003eMolecular docking model of Ppbp with cefotaxime.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7434467/v1/06c8c4f2139a537dfd7ad72f.png"},{"id":98243675,"identity":"90e8a9d7-7b24-48ad-82cf-86abdf45c26f","added_by":"auto","created_at":"2025-12-15 16:10:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2149006,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7434467/v1/9762ec6c-91a3-4235-be0e-9f1de5d12de6.pdf"},{"id":92165845,"identity":"ae959691-7bc8-41ac-ae33-b12bbabc3497","added_by":"auto","created_at":"2025-09-25 10:58:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2010728,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary data\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"supplementary1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7434467/v1/43cd4ba33ead2308853a4190.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global landscape of antibiotic resistance genes in the human gut microbiome MAG","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAntimicrobial resistance (AMR) is one of the most pressing threats to global public health and sustainable development. It is estimated that in 2019 alone, bacterial AMR directly caused approximately 1.27\u0026nbsp;million deaths and contributed to as many as 4.95\u0026nbsp;million deaths worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over the past two decades, AMR has evolved into a critical global health concern, particularly in low- and middle-income countries where medical resources are limited and public health policies are often underdeveloped. In such settings, the misuse of antibiotics has further accelerated the emergence and spread of resistant bacterial strains. Moreover, in an increasingly interconnected world, the movement of people and goods facilitates the rapid cross-border dissemination of regionally emerging AMR threats, increasing the risk of global outbreaks. Infectious diseases are projected to become the leading cause of death globally by 2050 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe human gut harbors a vast and diverse microbial community that plays a critical role in host\u003c/p\u003e\u003cp\u003ephysiology and nutrient metabolism. It is estimated that approximately 2,000 microbial genera reside in the gastrointestinal tract of a healthy individual [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with the total bacterial population reaching approximately 3.8 \u0026times; 10\u0026sup1;\u0026sup3; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], which is roughly equivalent to the number of human cells. Collectively, the gut microbiome encodes more than 5\u0026nbsp;million genes, vastly outnumbering the human genome, and is therefore often referred to as the human \u0026ldquo;second genome\u0026rdquo; [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Accumulating evidence has demonstrated strong associations between the gut microbiota and a wide range of human diseases, including cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], neurodegenerative disorders [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and cardiovascular conditions [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, the gut microbiome constitutes one of the largest reservoirs of antibiotic resistance genes (ARGs), making its resistome potential a critical concern in the context of antimicrobial resistance [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Against the backdrop of the global spread of antimicrobial resistance, the \"One Health\" framework has been proposed to promote an integrated assessment of health risks across humans, animals, and the environment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Approximately 85% of ARGs across diverse habitats can be traced back to the human fecal resistome, suggesting that human feces have become a central reservoir of the global resistome [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This phenomenon is not coincidental; modern human activities have significantly accelerated microbial exchange across ecosystems [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], thereby facilitating the dissemination of ARGs between human-associated and environmental microbiomes. Understanding antibiotic resistance genes in the human gut microbiome is critical not only for elucidating transmission dynamics but also for informing risk assessment and intervention strategies under the One Health framework.\u003c/p\u003e\u003cp\u003eThe rapid advancement of high-throughput sequencing technologies and bioinformatics has created unprecedented opportunities to investigate the global antibiotic resistome [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In 2013, Hu et al. analyzed human gut metagenomic data and identified 149 ARGs, reporting that individuals from China harbored a greater number and abundance of ARGs than did those from Denmark and Spain [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, their study was limited by a relatively small sample size and restricted geographic scope, covering only three countries. Moreover, numerous novel ARGs have been discovered in the past decade, making earlier findings insufficient to meet the needs of current large-scale resistome research.\u003c/p\u003e\u003cp\u003eIn this study, we analyzed 149,515 high-quality metagenome-assembled genomes (MAGs) from human gut microbiomes, collected from 97 countries across six continents. This dataset offers broader geographic representation and significantly greater data depth compared with previous studies. We identified a total of 428 ARGs and constructed a global distribution network, systematically characterizing their host pathogenicity and ARG burden across microbial taxa. We investigated horizontal gene transfer (HGT) events on a global scale and explored the underlying mechanisms driving ARG dissemination. Furthermore, by integrating deep learning with structural calculations, we identified a potentially novel class of antibiotic resistance genes. This study provides a comprehensive and current framework for understanding the global dissemination of antimicrobial resistance and offers essential scientific insights for guiding the surveillance and control of antibiotic resistance.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eA total of 149,515 MAGs were obtained from the human gut microbiome reference catalog established by Almeida et al. MAGs with \u0026ge;\u0026thinsp;90% completeness and \u0026le;\u0026thinsp;5% contamination were retained as high-quality genomes for downstream analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These high-quality MAGs were further classified by country and continent on the basis of sample geographic metadata. The geographic distribution of the samples was visualized using Flourish Studio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://flourish.studio\u003c/span\u003e\u003cspan address=\"https://flourish.studio\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which illustrates their spatial coverage.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eARG annotation and classification\u003c/h3\u003e\n\u003cp\u003eARGs were annotated using DeepARG v2 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] with default parameters. A total of 23 ARG types and 428 ARG subtypes were identified and used for subsequent analysis. The distribution of ARG subtypes across continents was visualized as a hierarchical clustering tree using the df2tree function from the R package pctax [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], followed by visual enhancement using the Chiplot tool [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. To compare the composition of ARGs across continents, visualizations were generated using OriginPro 2025 (OriginLab Corporation, Northampton, MA, USA). For ARG subtypes, only the top 10 most relatively abundant categories on each continent are shown to highlight representative geographic trends.\u003c/p\u003e\n\u003ch3\u003eARG host annotation and pathogen identification\u003c/h3\u003e\n\u003cp\u003eTaxonomic classification of ARG-carrying MAGs was performed using the classify_wf subcommand of GTDB-Tk v2.3.2 [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] with the GTDB reference database. Potential pathogenic MAGs were identified by aligning the ARG-hosting MAGs against the VFDB [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] using BLASTn v2.16.0+ [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] with the following parameters: -outfmt 6, -max_target_seqs 1, -perc_identity 90. Only the best matches with \u0026ge;\u0026thinsp;90% identity were retained. MAGs containing virulence factors were defined as HPBs.\u003c/p\u003e\u003cp\u003eThe phylogenetic relationships of the annotated results were visualized using GraPhlAn v1.1.3 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] to display the HPB distribution across the phylogenetic tree. In addition, the distribution of HPBs at the phylum and genus levels across different continents was visualized using OriginPro 2025 (OriginLab Corporation, Northampton, MA, USA). The number of ARGs per MAG was further analyzed by continent and by phylum to assess differences in resistance burden.\u003c/p\u003e\n\u003ch3\u003eIdentification of HGT events associated with ARGs\u003c/h3\u003e\n\u003cp\u003eTo avoid redundancy in the downstream analysis, MAGs from each continent were dereplicated using dRep v3.4.5 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] with the parameters: --ignoreGenomeQuality, --multiround_primary_clustering. HGT events at the genus level were then detected using MetaCHIP [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The protein-coding sequences of genes identified by MetaCHIP [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] as involved in HGT were further annotated using DeepARG v2 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] to identify ARGs and their associated host MAGs. Cytoscape v3.10.3 [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] was used to visualize the transfer network of HGT-related ARGs, illustrating potential intergenus transfer pathways and dissemination patterns.\u003c/p\u003e\n\u003ch3\u003eIdentification of novel ARGs\u003c/h3\u003e\n\u003cp\u003eProtein-coding genes from all African MAGs were predicted using Prodigal v2.6.3 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] under default settings to detect open reading frames (ORFs) and corresponding amino acid sequences. These sequences were annotated using DeepARG v2 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] to identify potential ARG-related proteins. Potential novel ARG protein sequences were clustered on the basis of sequence similarity using EFI-EST [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Sequences with \u0026ge;\u0026thinsp;95% identity were treated as the same gene, and a 70% identity threshold was applied for clustering, resulting in a network of putative ARGs with functional or evolutionary similarity. Representative sequences from each cluster were structurally modeled using AlphaFold2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to obtain their 3D conformations. Molecular docking was performed between these predicted structures and candidate antibiotic molecules using CB-Dock2 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] to assess the binding potential and interaction patterns. The resulting docking structures were visualized and refined using PyMOL [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] to clearly present the protein‒ligand interfaces and key binding residues.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eGlobal distribution of ARGs\u003c/h2\u003e\u003cp\u003eWe obtained 149,515 MAGs from the human gut genome catalog constructed by Almeida et al. using quality filtering criteria of \u0026ge;\u0026thinsp;90% completeness and \u0026le;\u0026thinsp;5% contamination [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These samples originated from across the globe and were categorized by continent, with the majority coming from Europe, Asia, and North America (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Using DeepARG[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], we identified ARGs, detecting 23 ARG types and 428 ARG subtypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). We further analyzed the geographic distribution of all the ARG subtypes by continent and found that, in terms of diversity, Europe, Asia, and North America harbored a greater variety of ARGs than South America, Africa, and Oceania did. Analysis of the relative abundances of major ARG types across continents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) revealed that multidrug ARGs were the predominant type worldwide, accounting for more than 35% of the ARGs from each continent. In addition to multidrug ARGs, resistance genes against macrolide-lincosamid-streptogramin (MLS), β-lactam, glycopeptide, and peptide antibiotics also featured prominently across continents. For example, MLS ARGs constituted 16.9%, 17.9%, 16.7%, and 13.1% of ARGs in Asia, Europe, Oceania, and North America, respectively, whereas glycopeptide and peptide resistance genes each accounted for more tahn 6% in these regions. In South America and Africa, β-lactam resistance genes represented more than 12% of the detected ARGs.\u003c/p\u003e\u003cp\u003eThe distribution of ARG subtypes varied across continents (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). In Asia, Europe, Oceania, and North America, the most prevalent ARG subtypes were generally associated with specific antibiotic classes, such as LlmA_23s_CLI (MLS), rpoB2 (rifamycin), and \u003cem\u003eugd\u003c/em\u003e (peptide resistance), each accounting for more than 6% of the total ARGs in these regions. In contrast, multidrug resistance genes were not predominant in these regions, with subtypes such as \u003cem\u003earlR\u003c/em\u003e and \u003cem\u003eefrB\u003c/em\u003e accounting for less than 6% of the total. In Africa, however, several multidrug ARG subtypes, including \u003cem\u003etolC\u003c/em\u003e, \u003cem\u003eemrD\u003c/em\u003e, and \u003cem\u003emdtO\u003c/em\u003e, ranked among the top 10 most abundant, with relatively high prevalence rates observed only on this continent. Similarly, South America presented distinct multidrug ARG signatures, with high proportions of \u003cem\u003eH-NS\u003c/em\u003e, \u003cem\u003epenA\u003c/em\u003e, and \u003cem\u003ePBP1A\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese regional differences likely reflect disparities in antibiotic usage intensity and socioeconomic development. In economically developed regions\u0026mdash;Asia, Europe, Oceania, and North America\u0026mdash;extensive antibiotic use in clinical and agricultural settings has created strong selection pressure, driving both the accumulation and dissemination of ARGs targeting specific antibiotic classes [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, centralized healthcare systems may facilitate ARG transmission [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrast, Africa and South America, with lower overall antibiotic consumption due to limited healthcare access and economic constraints, exhibit reduced ARG diversity and abundance, particularly for class-specific resistance genes. The abundance of multidrug resistance genes was relatively higher in Africa and South America. This paradox may reflect their ecological origin: genes such as \u003cem\u003etolC\u003c/em\u003e and \u003cem\u003eemrD\u003c/em\u003e encode efflux pumps that respond to broad environmental stresses [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], not solely antibiotics. Their widespread presence may reflect microbial adaptation to natural selective pressures rather than direct antibiotic exposure.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of ARG hosts and assessment of their curative potential\u003c/h3\u003e\n\u003cp\u003eIn this study, we identified a total of 120,466 MAGs carrying ARGs, representing the core ARG hosts. Taxonomic classification of these MAGs using GTDB-Tk revealed 1,978 species across 11 bacterial phyla. At the phylum level, Bacillota emerged as the predominant ARG host in the human gut across all continents, accounting for 67.4% in Europe, 67.0% in Asia, 62.6% in North America, 33.1% in Africa, 44.7% in South America, and 71.1% in Oceania. Other major phyla contributing to ARG carriage included Pseudomonadota, Actinomycetota, and Bacteroidota.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further assess the pathogenic potential of these ARG hosts, we annotated virulence factors (VFs) using the Virulence Factor Database (VFDB) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. MAGs carrying VFs were defined as human pathogen bacteria (HPB). In total, 749 species from 9 phyla were identified as HPB. Intercontinental comparisons revealed regional differences in the proportion of HPB within each phylum. In Europe (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), Campylobacterota (94.1%), Fusobacteriota (55.6%), and Pseudomonadota (64.0%) had the highest HPB proportions. In Asia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), Campylobacterota (86.2%) and Pseudomonadota (61.9%) were the most prominent, whereas in North America (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), Campylobacterota (71.0%) and Pseudomonadota (81.6%) presented the highest pathogenic potential. In South America (Fig. S2), Campylobacterota (78.9%) and Pseudomonadota (93.6%) dominated. In Africa (Fig. S3), only Pseudomonadota (89.5%) presented a notably high proportion of HPB, whereas in Oceania (Fig. S4), HPB proportions across all phyla remained below 50%, with no clearly dominant high-risk phylum. Campylobacterota demonstrated consistently high pathogenicity in Asia, Europe, North America, and South America, with over 70% of its species identified as HPB. This trend suggested that Campylobacterota in the human gut microbiota of these regions was predominantly composed of HPB; however, its pathogenic potential was substantially lower in Africa and Oceania. In contrast, Pseudomonadota was a widely distributed pathogenic phylum across all continents, except Oceania, where its HPB proportion was relatively low. Given that this phylum includes many well-known pathogens, such as \u003cem\u003eEscherichia\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e, this finding is consistent with expectations. The low pathogenicity of Pseudomonadota observed in Oceania may reflect technical limitations rather than biological differences. Virulence factor annotation in this study relied solely on sequence homology with VFDB entries. Novel or region-specific virulence genes, particularly those unique to Oceania, may have been missed. Owing to its geographic isolation, Oceania may harbor distinct microbial communities with uncharacterized virulence traits. Further investigations involving experimental validation and advanced computational approaches are needed to identify these potentially novel virulence genes [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe strong regional pattern of Campylobacterota pathogenicity may be linked to increased poultry consumption in Asia, Europe, North America, and South America. Members of the Campylobacter genus are commonly associated with poultry and can colonize the human gut via foodborne transmission [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The enrichment of Campylobacterota pathogens in these regions may thus be driven by dietary habits. Given its dual role as a high-risk ARG host and prevalent gut pathogen, Campylobacterota warrants greater attention in future AMR surveillance and risk assessment frameworks(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ygsite.cn/show.asp?trcms=1\u0026amp;id=86732\u0026amp;pageno=1\u003c/span\u003e\u003cspan address=\"http://www.ygsite.cn/show.asp?trcms=1\u0026amp;id=86732\u0026amp;pageno=1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eHPB distribution and ARG burden\u003c/h2\u003e\u003cp\u003eWe identified a wide range of HPB distributions across continents on the basis of their taxonomic classification (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In Europe, the majority of HPB belonged to Bacillota (34.0%) and Pseudomonadota (33.6%), with Actinomycetota (14.3%) and Bacteroidota (11.1%) as additional contributors. A similar pattern was observed in Asia, where Pseudomonadota (43.0%) and Bacillota (37.0%) dominated, and Actinomycetota (8.8%) and Bacteroidota (8.3%) were less represented than in Europe. In North America, Pseudomonadota accounted for 50.7%, with Bacillota accounting for 36.5%, whereas in Oceania, these phyla accounted for 42.9% and 49.4%, respectively. Pseudomonadota overwhelmingly dominated South America (77.5%) and Africa (89.8%), with Bacillota contributing only 12.0% and 7.6%, respectively. While Bacillota and Pseudomonadota were consistently the two main HPB phyla globally, their relative proportions varied considerably across regions. At the genus level, \u003cem\u003eEscherichia\u003c/em\u003e was the most prevalent ARG harboring HPB across all continents. Several genera were uniquely enriched in Oceania, including \u003cem\u003eLactococcus\u003c/em\u003e, \u003cem\u003eCAG-882\u003c/em\u003e, \u003cem\u003eCAG-710\u003c/em\u003e, \u003cem\u003ePeH17\u003c/em\u003e, \u003cem\u003eParafannyhessea\u003c/em\u003e, and \u003cem\u003eMycobacterium\u003c/em\u003e. Notably, \u003cem\u003eCatenibacterium\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e accounted for 15.6% and 16.1%, respectively, of pathogenic ARG hosts in Oceania, values that were higher than those reported for continents. These findings align with earlier observations, as \u003cem\u003eCatenibacterium\u003c/em\u003e belongs to Bacillota and \u003cem\u003eKlebsiella\u003c/em\u003e to Pseudomonadota.\u003c/p\u003e\u003cp\u003eIn North America, unique HPB genera included \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eSarcina\u003c/em\u003e, \u003cem\u003eClostridioides\u003c/em\u003e, and \u003cem\u003eAcinetobacter\u003c/em\u003e. \u003cem\u003eEnterococcus\u003c/em\u003e also had a relatively high abundance (10.6%). Except for \u003cem\u003eAcinetobacter\u003c/em\u003e (Pseudomonadota), these genera belong to Bacillota, suggesting that although Pseudomonadota is more abundant, Bacillota associated with HPB may represent emerging threats in this region and warrant closer surveillance (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). In Africa, \u003cem\u003eEscherichia\u003c/em\u003e was the most abundant genus, followed by \u003cem\u003eVibrio\u003c/em\u003e, which accounted for 16.0% of the identified taxa. The high prevalence of \u003cem\u003eVibrio\u003c/em\u003e may be associated with inadequate sanitation and dietary habits that increase the risk of colonization by waterborne pathogens [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In Asia, while \u003cem\u003eEscherichia\u003c/em\u003e was dominant, no other genus showed striking overrepresentation, possibly due to high population mobility and microbial exchange between regions. In Europe, \u003cem\u003ePrevotella\u003c/em\u003e (11.7%) and \u003cem\u003eBifidobacterium\u003c/em\u003e (17.3%) were more abundant than they were in other continents, which may reflect dietary habits such as higher protein and dairy intake, as these genera are involved in the metabolism of such nutrients [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In South America, \u003cem\u003eSalmonella\u003c/em\u003e (22.6%) had a high prevalence, likely driven by the tropical climate across the continent, which favors its growth and transmission through food [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. AMR has become a global concern. In all continents, more than 40% of HPB carried more than five ARGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), with the highest proportions detected in South America (79.1%) and Africa (88.5%). This trend indicates an increasing difficulty in treating infections with conventional antibiotics. Pathogenic Pseudomonadota were particularly ARG-rich, with most carrying more than five ARGs. This feature is likely due to their high capacity for HGT, exemplified by \u003cem\u003eEscherichia coli\u003c/em\u003e, which exhibits strong gene acquisition and recombination abilities in both natural and clinical environments [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This observation aligns with the regional data: both Africa and South America presented a high prevalence of Pseudomonadota and high-ARG-load strains, likely reflecting frequent antibiotic exposure and a poor public health infrastructure.\u003c/p\u003e\u003cp\u003eHPB belonging to \u003cem\u003eCyanobacteriota\u003c/em\u003e, \u003cem\u003eDesulfobacterota\u003c/em\u003e, \u003cem\u003eVerrucomicrobiota\u003c/em\u003e, and \u003cem\u003eCampylobacterota\u003c/em\u003e, however, generally carried three or fewer ARGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). This finding may be due to their limited colonization ability in the human gut, lower antibiotic selection pressure, more conserved genomes, and reduced HGT potential due to the absence of mobile genetic elements such as plasmids and transposons.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eHGT detection\u003c/h2\u003e\u003cp\u003eThe horizontal transfer of ARGs within the same phylum is particularly important, especially when ARGs are transmitted from commensal microbes to pathogenic bacteria, potentially endowing the latter with new resistance traits. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, most ARG transfer events occured within the same phylum. Among all the phyla, Bacillota presented the highest frequency of intra-phylum HGT events, which is consistent with its status as the dominant ARG host in the human gut. The relatively even ARG distribution among Bacillota strains further suggests that resistance genes in this group are acquired primarily via horizontal, rather than vertical, transmission.\u003c/p\u003e\u003cp\u003ePseudomonadota and Bacteroidota presented high levels of intra-phylum HGT. Pseudomonadota showed broad ARG type diversity, reflecting a strong capacity to acquire resistance genes, which is consistent with the prevalence of strains carrying more than five ARGs. Despite frequent HGT events, Bacteroidota typically carry only 2\u0026ndash;3 ARGs, possibly due to lower pathogenicity and reduced antibiotic selection pressure. \u0026ldquo;Long-distance\u0026rdquo; HGT events between distantly related organisms are rare, except among species in certain extreme environments, such as halophiles, thermophiles, and saccharolytic or fermentative organisms inhabiting termite or ruminant guts rich in organic matter [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eExtreme habitats show cross-phyla gene exchange, and our findings indicate that similar HGT occurs in the human gut microbiome. Bacillota acts as a major donor, likely due to its wide distribution and frequent internal HGT. The main pathogens, Actinomycetota and Bacteroidota, include clinically relevant pathogens. Antibiotic pressure overcomes genetic barriers [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], facilitating the spread of ARGs to these high-risk groups. Although Bacteroidota currently shows modest ARG abundance, its frequent involvement as an HGT recipient highlights its potential as a future ARG reservoir. In Actinomycetota, the widespread presence of multiple ARGs\u0026mdash;especially in pathogenic strains\u0026mdash;suggests active intra-phylum transfer among pathogens, positioning this group as a potential incubator for emerging multidrug-resistant \"superbugs\". Frequently transferred ARGs in Bacillota are enriched in genes conferring resistance to MLS, tetracyclines, and glycopeptides. This pattern aligns with that of dominant ARG classes and highlights the central role of antibiotic selection pressure in driving resistance emergence and dissemination.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of novel potential ARGs\u003c/h2\u003e\u003cp\u003eCurrent ARG databases provide only a partial view of resistance gene diversity. The discovery of novel ARGs is vital for understanding resistance evolution and for supporting the development of improved detection methods and therapeutic strategies. Antibiotic misuse in Africa has become increasingly severe, creating sustained selection pressure that may drive the emergence of novel resistance mechanisms. To explore this possibility, we focused on MAGs from this region to identify representative candidate ARGs. All African MAGs were analyzed, and their protein-coding genes were predicted. Potential ARGs were annotated using DeepARG [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] on the basis of the predicted proteomes. All the predicted ARG sequences were then subjected to clustering analysis using the EFI-EST platform [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Genes sharing\u0026thinsp;\u0026ge;\u0026thinsp;95% sequence identity were considered the same, and a 70% threshold was used to define gene clusters, allowing us to construct a similarity-based ARG network (Fig. S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eWithin this network, we identified a distinct cluster of genes that were initially annotated by DeepARG [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] as multidrug resistance genes but presented high sequence similarity to classical \u003cem\u003ePBP1A\u003c/em\u003e. Since \u003cem\u003ePBP1A\u003c/em\u003e is a well-characterized β-lactam resistance gene, we hypothesized that this cluster might represent a group of mutated \u003cem\u003ePBP1A\u003c/em\u003e variants. One representative gene from this cluster was selected for further investigation and provisionally named \u003cem\u003ePpbp.\u003c/em\u003e We used AlphaFold2 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] to predict the 3D structure of the Ppbp protein (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), and the protein sequence was simultaneously submitted to the InterPro database [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] for domain annotation. Ppbp was predicted to contain both a glycosyltransferase domain and a penicillin-binding protein domain\u0026mdash;an architecture consistent with its involvement in cell wall biosynthesis and β-lactam interactions. This dual-domain structure strongly suggests that Ppbp may retain a functional role related to antibiotic binding, albeit potentially modified from canonical PBP1A. We assessed the novelty of this gene by performing a BLASTp [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] search against the Comprehensive Antibiotic Resistance Database (CARD) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The closest homolog was a \u003cem\u003ePBP1A\u003c/em\u003e gene from \u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e, which shares only 62% sequence identity. This moderate similarity implies that \u003cem\u003ePpbp\u003c/em\u003e is functionally related yet structurally distinct from classical \u003cem\u003ePBP1A\u003c/em\u003e, potentially conferring resistance through a different mechanism. To explore structural differences, we retrieved the closest PBP1A sequence from National Center for Biotechnology Information (NCBI) ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and predicted its 3D structure using AlphaFold2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Structural alignment with Ppbp using PyMOL [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) revealed a root-mean-square deviation (RMSD) of 5.195 \u0026Aring;, indicating significant spatial differences. Despite the overall fold similarity, the deviation suggests that Ppbp is not a direct homolog but a structurally distinct analog, potentially with altered active site conformation or substrate binding properties. Resistance mediated by PBP1A is primarily attributed to mutations that reduce their binding affinity to β-lactam antibiotics, thereby impairing antibiotic efficacy [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. We further investigated the drug-binding capacity of Ppbp. Using CB-Dock2 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], we performed molecular docking of both wild-type PBP1A and Ppbp with cefotaxime, a representative β-lactam. The lowest binding energy for PBP1A was \u0026minus;\u0026thinsp;7.2 kcal/mol, with key hydrogen-bonding residues, including TYR, ARG, VAL, and ASN (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). In contrast, Ppbp exhibited a binding energy of \u0026minus;\u0026thinsp;6.9 kcal/mol at the same binding pocket as PBP1A, involving distinct key residues: ASN, THR, TYR, and ARG (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). This slightly higher binding energy indicates reduced affinity, which is consistent with a resistance-associated phenotype.\u003c/p\u003e\u003cp\u003eCompared with PBP1A, Ppbp has altered residue positioning in the binding pocket, increasing the binding energy and reducing affinity while preserving enzymatic activity. As a novel PBP1A-type β-lactam resistance gene with distinct structural features, Ppbp highlights ongoing resistance evolution and demonstrates the value of integrating deep learning with structural modeling to uncover hidden elements of the global resistome.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study, which is based on the global human gut genome catalog constructed by Almeida et al., provides a comprehensive analysis of the distribution and host characteristics of ARGs across diverse geographic regions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our findings revealed that ARGs in Asia, Europe, Oceania, and North America are more often associated with resistance to specific antibiotic classes, whereas multidrug ARGs are more prevalent in South America and Africa. Notably, the elevated multidrug ARG burden in these latter regions may reflect delayed but intensified antibiotic usage, suggesting that ARG accumulation is still ongoing. These geographic differences underscore that antibiotic consumption levels are a key driver of ARG dissemination, particularly for single-drug resistance. In contrast, the emergence and spread of multidrug ARGs appear to be shaped by more complex ecological and evolutionary dynamics, including long-term selection pressure and microbial adaptation to diverse environmental conditions [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAt the host level, Pseudomonadota has emerged as the central carrier of multidrug ARGs, playing a key role in their accumulation and dissemination across global gut microbiomes. Campylobacterota has also demonstrated high pathogenic potential across several regions. Although currently underappreciated in resistance surveillance, Campylobacterota may become a critical target for future AMR control efforts. Bacillota and Pseudomonadota dominate the global distribution of ARG hosts but exhibit distinct regional patterns: Europe, North America, Asia, and Oceania show a balanced presence of both phyla, whereas South America and Africa are predominantly occupied by Pseudomonadota. In Africa and South America, inadequate sanitation infrastructure may contribute to elevated bacterial infection rates, driving excessive antibiotic use that selects for predominant intestinal colonization by gram-negative bacteria in the human gut microbiome. At the genus level, Escherichia was the most prevalent ARG host globally. Region-specific dominant genera included \u003cem\u003eCatenibacterium\u003c/em\u003e and \u003cem\u003eKlebsiella\u003c/em\u003e in Oceania, \u003cem\u003eEnterococcus\u003c/em\u003e in North America, \u003cem\u003eVibrio\u003c/em\u003e in Africa, \u003cem\u003ePrevotella\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e in Europe, and \u003cem\u003eSalmonella\u003c/em\u003e in South America. Asia showed a more balanced genus distribution. These patterns likely result from the interplay of healthcare conditions, antibiotic regulation, dietary habits, climate, and human mobility [\u003cspan additionalcitationids=\"CR52\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOver 40% of the ARGs harboring HPB across all continents carried more than five resistance genes, with the percentages in South America and Africa reaching 79.1% and 88.5%, respectively. Pseudomonadota was consistently associated with high ARG loads, likely due to its exceptional HGT capacity, enabling rapid ARG acquisition and dissemination [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Conversely, phyla such as Cyanobacteriota and Desulfobacterota carried fewer ARGs, which is consistent with their limited colonization potential, conserved genome architecture, and lower HGT activity. HGT analysis revealed that intra-phylum transfers were predominant, especially within Bacillota, which resulted in the frequent transfer of MLS, tetracycline, and glycopeptide resistance genes, highlighting the selective pressure from commonly used antibiotics [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Pseudomonadota exhibited high ARG receptor capacity, supporting its role as an MDR hotspot. Actinomycetota and Bacteroidota were major recipients of cross-phyla transfers, with the former contributing to the amplification of resistance in pathogenic strains and the latter\u0026mdash;despite lower current resistance\u0026mdash;representing a potential reservoir owing to its permissiveness to ARG acquisition. By integrating deep learning-based prediction with structure-informed analysis, we identified a novel PBP1A-like ARG candidate from African human gut MAGs. This gene, provisionally named Ppbp, differs from classical PBP1A in its β-lactam binding affinity. The combined approach enables the sensitive detection of ARG-like sequences beyond conventional homology thresholds and provides structural insights, particularly for genes with moderate sequence similarity but potentially divergent functions. This integrative framework highlights the presence of previously unrecognized resistance elements in the human gut microbiome and offers a broadly applicable strategy for ARG discovery beyond existing databases. The present findings advance our understanding of ARG diversity and evolution while providing theoretical support for future antibiotic development. However, the functional properties and clinical relevance of Ppbp require further experimental validation.\u003c/p\u003e\u003cp\u003eThe integration of genomic technologies will be essential for the high-throughput, real-time surveillance of ARGs, enabling precise tracking of their dissemination across environments and hosts. These strategies can greatly enhance early warning systems for antimicrobial resistance. Moreover, the development of novel antibiotics must be accelerated, with an emphasis on overcoming resistance mechanisms and exploring alternative therapies such as antivirulence agents, phage therapy, and microbiome-based interventions. A dual approach that combines effective surveillance with therapeutic innovation is critical to limiting the global spread of AMR. This study provides the first comprehensive overview of the global distribution, host range, and transfer potential of ARGs in the human gut microbiome. The results reveal both widespread and emerging resistance threats, offering valuable insights for antimicrobial stewardship and highlighting the need for sustained monitoring and targeted intervention.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAMR\u0026nbsp; \u0026nbsp;\u0026nbsp;Antimicrobial resistance\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;ARGs\u0026nbsp; \u0026nbsp;\u0026nbsp;Antibiotic resistance genes\u003c/p\u003e\n\u003cp\u003eMAGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Metagenome-assembled genomes\u003c/p\u003e\n\u003cp\u003eHGT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Horizontal gene transfer\u003c/p\u003e\n\u003cp\u003eORFs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Open reading frames\u003c/p\u003e\n\u003cp\u003eMLS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Macrolide-Lincosamid-Streptogramin\u003c/p\u003e\n\u003cp\u003eHPB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Human pathogenic bacteria\u003c/p\u003e\n\u003cp\u003eVFs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Virulence factors\u003c/p\u003e\n\u003cp\u003eVFDB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Virulence Factor Database\u003c/p\u003e\n\u003cp\u003eCARD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Comprehensive Antibiotic Resistance Database\u003c/p\u003e\n\u003cp\u003eNCBI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;National Center for Biotechnology Information\u003c/p\u003e\n\u003cp\u003eRMSD \u0026nbsp; \u0026nbsp; \u0026nbsp; Revealed a root-mean-square deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Project of International Cooperation and Exchanges NSFC-ASRT (No. W2412100), National Natural Science Foundation of China (No. 42276137), the National Key Research and Development Program of China (No. 2022YFC2804104, No. 2022YFC2804205), the Scientific Research Program of the Bozhou University (No.202524).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eChenjie Wang\u003c/strong\u003e: Conceptualization, Methodology, Data curation, Investigation, Validation, Writing - Original draft, Review \u0026amp; Editing. \u003cstrong\u003eCancan Wang\u003c/strong\u003e: Methodology, Data curation, Investigation. \u003cstrong\u003eSi Chen\u003c/strong\u003e, \u003cstrong\u003eKai Shi\u003c/strong\u003e, \u003cstrong\u003eJuanjuan Yu, Yiping Ding\u003c/strong\u003e: Methodology, Investigation, Validation. \u003cstrong\u003eYujie Yue\u003c/strong\u003e\u003cstrong\u003e，\u003c/strong\u003e\u003cstrong\u003eYi Hua\u003c/strong\u003e: Investigation, Data curation. \u003cstrong\u003eHong Wang\u003c/strong\u003e: Conceptualization, Project administration, Writing - Original draft, Funding acquisition. \u003cstrong\u003eJianwei Chen\u003c/strong\u003e: Conceptualization, Project administration, Writing - Original draft, Review \u0026amp; Editing, Supervision, Resources, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Project of International Cooperation and Exchanges NSFC-ASRT (No. W2412100), National Natural Science Foundation of China (No. 42276137), the National Key Research and Development Program of China (No. 2022YFC2804104, No. 2022YFC2804205), the Scientific Research Program of the Bozhou University (No.202524). We appreciate Almeida et al (2021) for their work on the human gut genome and its publication as a public resource, and gratefully acknowledge platform support from Zhejiang International Sci-Tech Cooperation Base for the Exploitation and Utilization of Nature Product, Zhejiang Provincial Key Laboratory of TCM for Innovative R \u0026amp; D and Digital Intelligent Manufacturing of TCM Great Health Products, Zhejiang Key Laboratory of Green, Low-carbon and Efficient Development of Marine Fishery Resources.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMurray CJL, Ikuta KS, Sharara F, Swetschinski L, Robles Aguilar G, Gray A, et al. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The Lancet. 2022;399:629\u0026ndash;55.\u003c/li\u003e\n\u003cli\u003eDe Kraker MEA, Stewardson AJ, Harbarth S. Will 10 Million People Die a Year due to Antimicrobial Resistance by 2050? PLoS Med. 2016;13:e1002184.\u003c/li\u003e\n\u003cli\u003eStokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, et al. A Deep Learning Approach to Antibiotic Discovery. Cell. 2020;180:688-702.e13.\u003c/li\u003e\n\u003cli\u003eLeviatan S, Shoer S, Rothschild D, Gorodetski M, Segal E. An expanded reference map of the human gut microbiome reveals hundreds of previously unknown species. Nat Commun. 2022;13:3863.\u003c/li\u003e\n\u003cli\u003eSender R, Fuchs S, Milo R. Revised Estimates for the Number of Human and Bacteria Cells in the Body. PLoS Biol. 2016;14:e1002533.\u003c/li\u003e\n\u003cli\u003eSommer F, B\u0026auml;ckhed F. The gut microbiota \u0026mdash; masters of host development and physiology. Nature Reviews Microbiology. 2013;11:227\u0026ndash;38.\u003c/li\u003e\n\u003cli\u003eTilg H, Adolph TE, Gerner RR, Moschen AR. The Intestinal Microbiota in Colorectal Cancer. Cancer Cell. 2018;33:954\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eSampson TR, Debelius JW, Thron T, Janssen S, Shastri GG, Ilhan ZE, et al. Gut Microbiota Regulate Motor Deficits and Neuroinflammation in a Model of Parkinson\u0026rsquo;s Disease. Cell. 2016;167:1469-1480.e12.\u003c/li\u003e\n\u003cli\u003eDe Vos WM, Tilg H, Van Hul M, Cani PD. Gut microbiome and health: mechanistic insights. Gut. 2022;71:1020\u0026ndash;32.\u003c/li\u003e\n\u003cli\u003eSommer MOA, Dantas G, Church GM. Functional Characterization of the Antibiotic Resistance Reservoir in the Human Microflora. Science. 2009;325:1128\u0026ndash;31.\u003c/li\u003e\n\u003cli\u003eKim D-W, Cha C-J. Antibiotic resistome from the One-Health perspective: understanding and controlling antimicrobial resistance transmission. Experimental \u0026amp; Molecular Medicine. 2021;53:301\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eZhang Q, Xu N, Lei C, Chen B, Wang T, Ma Y, et al. Metagenomic Insight into The Global Dissemination of The Antibiotic Resistome. Advanced Science. 2023;10:2303925.\u003c/li\u003e\n\u003cli\u003eZhu Y-G, Gillings M, Simonet P, Stekel D, Banwart S, Penuelas J. Microbial mass movements. Science. 2017;357:1099\u0026ndash;100.\u003c/li\u003e\n\u003cli\u003eBoolchandani M, Blake KS, Tilley DH, Cabada MM, Schwartz DJ, Patel S, et al. Impact of international travel and diarrhea on gut microbiome and resistome dynamics. Nature Communications. 2022;13:7485.\u003c/li\u003e\n\u003cli\u003eHu Y, Yang X, Qin J, Lu N, Cheng G, Wu N, et al. Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nature Communications. 2013;4:2151.\u003c/li\u003e\n\u003cli\u003eAlmeida A, Nayfach S, Boland M, Strozzi F, Beracochea M, Shi ZJ, et al. A unified catalog of 204,938 reference genomes from the human gut microbiome. Nature Biotechnology. 2021;39:105\u0026ndash;14.\u003c/li\u003e\n\u003cli\u003eArango-Argoty G, Garner E, Pruden A, Heath LS, Vikesland P, Zhang L. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 2018;6:23.\u003c/li\u003e\n\u003cli\u003ePeng C, Jiang C. pctax: Professional Comprehensive Omics Data Analysis Pipeline. NA. 2023.\u003c/li\u003e\n\u003cli\u003eXie J, Chen Y, Cai G, Cai R, Hu Z, Wang H. Tree Visualization By One Table (tvBOT): a web application for visualizing, modifying and annotating phylogenetic trees. Nucleic Acids Research. 2023;51:W587\u0026ndash;92.\u003c/li\u003e\n\u003cli\u003eChaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk v2: memory friendly classification with the genome taxonomy database. Bioinformatics. 2022;38:5315\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eZhou S, Liu B, Zheng D, Chen L, Yang J. VFDB 2025: an integrated resource for exploring anti-virulence compounds. Nucleic Acids Research. 2025;53:D871\u0026ndash;7.\u003c/li\u003e\n\u003cli\u003eCamacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10:421.\u003c/li\u003e\n\u003cli\u003eAsnicar F, Weingart G, Tickle TL, Huttenhower C, Segata N. Compact graphical representation of phylogenetic data and metadata with GraPhlAn. PeerJ. 2015;3:e1029.\u003c/li\u003e\n\u003cli\u003eOlm MR, Brown CT, Brooks B, Banfield JF. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. The ISME Journal. 2017;11:2864\u0026ndash;8.\u003c/li\u003e\n\u003cli\u003eSong W, Wemheuer B, Zhang S, Steensen K, Thomas T. MetaCHIP: community-level horizontal gene transfer identification through the combination of best-match and phylogenetic approaches. Microbiome. 2019;7:36.\u003c/li\u003e\n\u003cli\u003eShannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks. Genome Res. 2003;13:2498\u0026ndash;504.\u003c/li\u003e\n\u003cli\u003eHyatt D, Chen G-L, LoCascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics. 2010;11:119.\u003c/li\u003e\n\u003cli\u003eOberg N, Zallot R, Gerlt JA. EFI-EST, EFI-GNT, and EFI-CGFP: Enzyme Function Initiative (EFI) Web Resource for Genomic Enzymology Tools. Journal of Molecular Biology. 2023;435:168018.\u003c/li\u003e\n\u003cli\u003eZallot R, Oberg N, Gerlt JA. The EFI Web Resource for Genomic Enzymology Tools: Leveraging Protein, Genome, and Metagenome Databases to Discover Novel Enzymes and Metabolic Pathways. Biochemistry. 2019;58:4169\u0026ndash;82.\u003c/li\u003e\n\u003cli\u003eJumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596:583\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eLiu Y, Yang X, Gan J, Chen S, Xiao Z-X, Cao Y. CB-Dock2: improved protein\u0026ndash;ligand blind docking by integrating cavity detection, docking and homologous template fitting. Nucleic Acids Research. 2022;50:W159\u0026ndash;64.\u003c/li\u003e\n\u003cli\u003eYang X, Liu Y, Gan J, Xiao Z-X, Cao Y. FitDock: protein\u0026ndash;ligand docking by template fitting. Briefings in Bioinformatics. 2022;23:bbac087.\u003c/li\u003e\n\u003cli\u003eSchr\u0026ouml;dinger, LLC. The PyMOL Molecular Graphics System, Version 1.8. 2015.\u003c/li\u003e\n\u003cli\u003eDiebold PJ, Rhee MW, Shi Q, Trung NV, Umrani F, Ahmed S, et al. Clinically relevant antibiotic resistance genes are linked to a limited set of taxa within gut microbiome worldwide. Nature Communications. 2023;14:7366.\u003c/li\u003e\n\u003cli\u003eVan Boeckel TP, Pires J, Silvester R, Zhao C, Song J, Criscuolo NG, et al. Global trends in antimicrobial resistance in animals in low- and middle-income countries. Science. 2019;365:eaaw1944.\u003c/li\u003e\n\u003cli\u003eZhu C, Wu L, Ning D, Tian R, Gao S, Zhang B, et al. Global diversity and distribution of antibiotic resistance genes in human wastewater treatment systems. Nature Communications. 2025;16:4006.\u003c/li\u003e\n\u003cli\u003eGao M, Zhang Q, Lei C, Lu T, Qian H. Atmospheric antibiotic resistome driven by air pollutants. Science of The Total Environment. 2023;902:165942.\u003c/li\u003e\n\u003cli\u003eWang W, Song W, Majzoub ME, Feng X, Xu B, Tao J, et al. Decoupling of strain- and intrastrain-level interactions of microbiomes in a sponge holobiont. Nature Communications. 2024;15:8205.\u003c/li\u003e\n\u003cli\u003eKelbert L, Barmettler K, Horlbog JA, Stevens MJA, Cernela N, N\u0026uuml;esch-Inderbinen M, et al. Campylobacter in Raw Chicken Meat at Retail Level: Quantitative and Qualitative Assessment, Genomic Profiling, and Comparison With Isolates From Human Infections. Journal of Food Protection. 2025;88:100540.\u003c/li\u003e\n\u003cli\u003eBaker-Austin C, Oliver JD, Alam M, Ali A, Waldor MK, Qadri F, et al. Vibrio spp. infections. Nature Reviews Disease Primers. 2018;4:1\u0026ndash;19.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez B, Ruiz L, Gueimonde M, Ruas-Madiedo P, Margolles A. Adaptation of bifidobacteria to the gastrointestinal tract and functional consequences. Pharmacological Research. 2013;69:127\u0026ndash;36.\u003c/li\u003e\n\u003cli\u003eChen Zhao, Moreno-Switt Andrea I., Reyes-Jara Angelica, Delgado Suarez Enrique, Adell Aiko D., Oliveira Celso Jos\u0026eacute; Bruno, et al. A multicenter genomic epidemiological investigation in Brazil, Chile, and Mexico reveals the diversity and persistence of Salmonella populations in surface waters. mBio. 2024;15:e00777-24.\u003c/li\u003e\n\u003cli\u003eQiao W, Wang L, Luo Y, Miao J. Outer membrane vesicles mediated horizontal transfer of an aerobic denitrification gene between Escherichia coli. Biodegradation. 2021;32:435\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eCaro-Quintero A, Konstantinidis KT. Inter-phylum HGT has shaped the metabolism of many mesophilic and anaerobic bacteria. The ISME Journal. 2015;9:958\u0026ndash;67.\u003c/li\u003e\n\u003cli\u003eFuchsman CA, Collins RE, Rocap G, Brazelton WJ. Effect of the environment on horizontal gene transfer between bacteria and archaea. PeerJ. 2017;5:e3865.\u003c/li\u003e\n\u003cli\u003eRhodes ME, Spear JR, Oren A, House CH. Differences in lateral gene transfer in hypersaline versus thermal environments. BMC Evolutionary Biology. 2011;11:199.\u003c/li\u003e\n\u003cli\u003eParras-Molt\u0026oacute; M, Lund D, Ebmeyer S, Larsson DGJ, Johnning A, Kristiansson E. The transfer of antibiotic resistance genes between evolutionarily distant bacteria. mSphere. 2025;10.\u003c/li\u003e\n\u003cli\u003eBlum M, Andreeva A, Florentino LC, Chuguransky SR, Grego T, Hobbs E, et al. InterPro: the protein sequence classification resource in 2025. Nucleic Acids Research. 2025;53:D444\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003eAlcock BP, Huynh W, Chalil R, Smith KW, Raphenya AR, Wlodarski MA, et al. CARD 2023: expanded curation, support for machine learning, and resistome prediction at the Comprehensive Antibiotic Resistance Database. Nucleic Acids Research. 2023;51:D690\u0026ndash;9.\u003c/li\u003e\n\u003cli\u003eContreras-Martel C, Job V, Di Guilmi AM, Vernet T, Dideberg O, Dessen A. Crystal Structure of Penicillin-binding Protein 1a (PBP1a) Reveals a Mutational Hotspot Implicated in \u0026beta;-Lactam Resistance in Streptococcus pneumoniae. Journal of Molecular Biology. 2006;355:684\u0026ndash;96.\u003c/li\u003e\n\u003cli\u003eAlhassan MY, Ahmad AA. Antimicrobial resistance in a changing climate: a One Health approach for adaptation and mitigation. Bulletin of the National Research Centre. 2025;49:26.\u003c/li\u003e\n\u003cli\u003eFeng Y, Lu X, Zhao J, Li H, Xu J, Li Z, et al. Regional antimicrobial resistance gene flow among the One Health sectors in China. Microbiome. 2025;13:3.\u003c/li\u003e\n\u003cli\u003eForslund K, Sunagawa S, Kultima JR, Mende DR, Arumugam M, Typas A, et al. Country-specific antibiotic use practices impact the human gut resistome.\u003c/li\u003e\n\u003cli\u003eLund D, Parras-Molt\u0026oacute; M, Inda-D\u0026iacute;az JS, Ebmeyer S, Larsson DGJ, Johnning A, et al. Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes. Nature Communications. 2025;16:2595.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Human gut microbiome, MAG, ARG, HPB, HGT","lastPublishedDoi":"10.21203/rs.3.rs-7434467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7434467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAntibiotic resistance poses a significant threat to human health, and the human gut microbiota serves as a major reservoir of antibiotic resistance genes (ARGs). In this study, we analyzed 149,515 metagenome-assembled genomes (MAGs) from human gut microbiomes and revealed marked geographic variations in the global distribution of gut-associated ARGs. Compared with South America, Africa, and Oceania, Europe, Asia, and North America exhibited significantly higher ARG richness. At the phylum level, Pseudomonadota was identified as the predominant ARG host among pathogenic bacteria, with its pathogenic strains frequently exhibiting high levels of multidrug resistant strains harboring ≥5 ARGs accounting for up to 88.5% and 79.1% in Africa and South America, respectively. Campylobacterota was also recognized as a potential high-risk ARG host phylum. Horizontal gene transfer (HGT) analysis revealed that ARG transmission predominantly occurred within the same phylum, with Bacillota being the most active donor, which was likely influenced by antibiotic selection pressure. Actinomycetota and Bacteroidota were identified as major recipients of interphylum HGT, indicating their greater capacity to acquire exogenous ARGs. Through the integration of deep learning and structural calculation, we also identified a potentially novel class of β-lactam resistance genes. This study provides a comprehensive global landscape of gut-associated resistomes, underscores the critical roles of public health infrastructure, antibiotic misuse, and HGT in shaping antimicrobial resistance (AMR), and offers methodological insights for the discovery of novel ARGs. Our findings highlight urgent challenges and provide a scientific basis for developing global AMR mitigation strategies.\u003c/p\u003e","manuscriptTitle":"Global landscape of antibiotic resistance genes in the human gut microbiome MAG","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-25 10:42:28","doi":"10.21203/rs.3.rs-7434467/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-15T14:40:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T11:42:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T00:14:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252485142192624624768664478192790376089","date":"2025-09-25T12:51:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"190743097290298510524659394885591041911","date":"2025-09-16T17:27:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"163771795539729621438079147238965690040","date":"2025-09-16T15:20:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-16T15:09:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T09:45:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-03T07:20:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-03T07:20:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2025-08-22T12:11:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f87be177-f9e1-40e9-9a79-b80f98dad8f8","owner":[],"postedDate":"September 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:02:24+00:00","versionOfRecord":{"articleIdentity":"rs-7434467","link":"https://doi.org/10.1186/s12866-025-04586-0","journal":{"identity":"bmc-microbiology","isVorOnly":false,"title":"BMC Microbiology"},"publishedOn":"2025-12-09 15:57:57","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-09-25 10:42:28","video":"","vorDoi":"10.1186/s12866-025-04586-0","vorDoiUrl":"https://doi.org/10.1186/s12866-025-04586-0","workflowStages":[]},"version":"v1","identity":"rs-7434467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7434467","identity":"rs-7434467","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}