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Article

The Global Antimicrobial Resistance Trends of Staphylococcus aureus and Influencing Factors

by
Haitao Yuan
1,†,
Jie Xu
1,†,
Ying Wang
1,
Yuan Li
1,
Yuqing Hao
1,
Jinzhao Long
1,
Fang Liu
1,
Jingyuan Zhu
2 and
Haiyan Yang
1,*
1
Department of Epidemiology, School of Public Health, Zhengzhou University, Zhengzhou 450001, China
2
Department of Environmental Health, School of Public Health, Zhengzhou University, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Microbiol. Res. 2025, 16(6), 118; https://doi.org/10.3390/microbiolres16060118
Submission received: 10 April 2025 / Revised: 1 June 2025 / Accepted: 3 June 2025 / Published: 4 June 2025
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Abstract

:
The increase in the antimicrobial resistance (AMR) of Staphylococcus aureus (S. aureus) has become a global public health concern. This study globally monitored the large-scale longitudinal trend of AMR in S. aureus and examined the various human and environmental climate factors that influence the occurrence and spread of AMR in S. aureus, which might provide valuable data to support the development of a global surveillance system for S. aureus AMR and provide a theoretical basis for coordinated actions to control the emergence and development of AMR from multiple perspectives. There was a significantly positive correlation between the number of antibiotic resistance genes (ARGs) in S. aureus and the collection year, with a sharp increase in ARGs over time. The number of ARGs in S. aureus genomes significantly increased each decade, with the average number of ARGs per genome rising from 10.37 ± 3.55 before 1990 to 12.75 ± 4.04 after 2010, suggesting a growing problem of S. aureus AMR. The Spearman correlation results indicated that the human development index (HDI), antibiotic consumption, and mobile genetic elements (MGEs) were significantly associated with the AMR of S. aureus, and these factors played a crucial role in the emergence and development of S. aureus AMR. The results of structural equation modeling showed that the HDI significantly promoted an increase in antibiotic consumption, thereby indirectly enhancing the AMR of S. aureus. Antibiotic consumption also indirectly facilitated the progression of AMR in S. aureus through its impact on MGEs. The results of restricted cubic spline and generalized linear models showed that climate change also played a significant role in the progression of S. aureus AMR. In summary, this study provides a theoretical framework for monitoring the longitudinal trend of ARGs in S. aureus isolates and examining the possible influencing variables of ARGs in these isolates.

1. Introduction

Staphylococcus aureus (S. aureus), a representative of Gram-positive bacteria belonging to the genus Staphylococcus, is a common pathogenic microorganism that can grow and reproduce in both aerobic and anaerobic environments [1]. Its optimal growth temperature is 37 °C, and although it has some tolerance to high temperatures, it can be entirely killed in environments above 80 °C after 30 min [2]. It is capable of thriving in high-salinity environments, withstanding sodium chloride concentrations of up to 15% [3]. These properties allow it to endure various harsh environments and establish its widespread presence in nature. S. aureus typically resides on the skin surface of humans and animals, as well as in the respiratory and digestive tracts, air, sewage, and other habitats [4]. It is the leading cause of both community-acquired and healthcare-associated bacterial infections, posing a significant threat to human health and resulting in substantial economic losses [5]. A previous study showed that there were an estimated 119,247 bloodstream infections and 19,832 associated deaths caused by S. aureus in the USA in 2017 [6]. The China Antimicrobial Resistance Surveillance System (CARSS, https://www.carss.cn/Report/Details?aId=978, accessed on 5 December 2024) showed that S. aureus ranked first in isolation rate (accounting for 33%) among all the Gram-positive bacteria isolated. The national average detection rate of methicillin-resistant S. aureus (MRSA) was 29.1% in 2023, reflecting an increase of 0.2 percentage points compared to 2022. Additionally, the sensitivity of S. aureus to some commonly used antibiotics was gradually declining, and resistance to penicillin G was as high as 92% [7]. According to a 2022 report by the Global Antimicrobial Resistance Surveillance System (GLASS, https://www.who.int/initiatives/glass, accessed on 5 December 2024) of the World Health Organization (WHO), S. aureus infections have been reported in 82 countries and regions worldwide, making it the third-most-prevalent bacterium overall. It ranks second among all bacteria in terms of total bloodstream infections and the prevalence of drug resistance, with 135,631 reported cases. Under suitable conditions, S. aureus can secrete various toxins and virulence factors, such as enterotoxins, which can lead to severe bacteremia and other serious infections in humans [8,9,10]. Food poisoning caused by S. aureus has also been frequently reported, accounting for approximately 25% of all foodborne microbial poisoning incidents [11]. As a result, S. aureus has emerged as the third-largest microbial pathogen after Salmonella and Vibrio parahaemolyticus. There is no doubt that S. aureus is highly pathogenic, with a high infection rate and rapid spread, making treatment challenging when infections occur [12]. At the same time, its adaptability and resistance to antimicrobials further contribute to its danger as a microorganism [13]. In fact, the USA Centers for Disease Control and Prevention (CDC) has categorized S. aureus as a serious threat to public health, while the WHO has prioritized it on its list of pathogens for which new antibiotics need to be developed [14,15].
Antibiotics have been the primary treatment for bacterial infections since their inception and remain so. Their use as a proven therapeutic has undoubtedly saved the lives of a large number of patients suffering from bacterial infections in the early stages, and they have demonstrated encouraging signs in terms of improving human life expectancy [16]. However, the widespread utilization of antibiotics and the indiscriminate abuse of antimicrobials in agriculture and animal husbandry have contributed to the emergence of various pathogenic microorganisms that exhibit antibiotic resistance [17,18,19]. During the 15 years from 2000 to 2015, global antibiotic consumption was reported to have risen by 65% overall, while per capita consumption increased by 39% [20]. This increase is largely attributed to low- and middle-income countries [21]. Reports also indicate that global antibiotic consumption rose from 9.8 defined daily doses (DDD) in 2000 to 14.3 DDD in 2018 [22]. As antibiotic consumption has increased, the issue of bacterial antimicrobial resistance (AMR) has intensified, posing a significant threat to global public health. AMR is projected to become the leading cause of death worldwide by 2050, resulting in an estimated 10 million fatalities annually [23]. The consumption of antibiotics is increasingly recognized as the primary factor driving pathogenic AMR [24,25]. The acquisition of resistance in S. aureus strains began with the extensive use of penicillin in the 1940s, followed by the introduction of methicillin, which ultimately led to the emergence of MRSA [26,27]. It is for this reason that S. aureus is listed as a member of ESKAPE (Enterococcus faecium, S. aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), a group of multi-drug-resistant (MDR) microorganisms of major concern to humans [28,29].
The emergence and proliferation of antibiotic resistance genes (ARGs) in bacteria are considered the foundation for the occurrence and transmission of bacterial AMR [30]. Mobile genetic elements (MGEs) act as vehicles for carrying ARGs, which can be transferred between bacterial species through horizontal gene transfer (HGT), thus facilitating the development of bacterial AMR [31,32,33]. Additionally, MGEs that carry DNA sequences encoding ARGs, such as insertion sequences (ISs), transposons, phages, etc., are also frequently transferred between microorganisms, contributing to the emergence of MDR and extensively drug-resistant (XDR) strains [34,35]. Due to the potential spread of superbugs with MDR and XDR mechanisms, AMR is gradually becoming a serious concern in both clinical treatment and food production settings [30]. MRSA, of course, bears the brunt of responsibility for the problem [36]. Furthermore, various human and environmental factors, such as income level and temperature, as well as PM2.5 (particulate matter < 2.5 μm) levels, can influence antibiotic consumption and bacterial growth and reproduction, thereby increasing the risk of AMR [37,38]. PM2.5 is commonly used to assess atmospheric environmental quality. The higher its concentration in the air, the more severe the air pollution. Due to its small particle size, large surface area, and high reactivity, PM2.5 can adsorb toxic and harmful substances as well as microorganisms (including S. aureus). Additionally, its long residence time in the atmosphere and ability to be transported over long distances make it a significant concern for human health. Therefore, exploring the contribution of these factors to AMR in S. aureus holds substantial significance and practical value. Although numerous studies have examined the AMR of S. aureus, there remains a lack of large-scale global studies and assessments focusing on AMR risks across different regions. Current studies primarily concentrate on the effects of antibiotics on S. aureus but seldom take into account the potential driving effects of human development and natural environmental changes. This study adopted a global perspective, analyzing S. aureus strains from 58 countries and regions worldwide, dating back to 1884. It extensively delineated the long-term evolutionary trends of AMR in S. aureus and explored the influence of various anthropogenic and environmental climate factors on its emergence and dissemination. This research fills a critical gap, providing valuable data to support the development of a global S. aureus resistance monitoring system and offering a theoretical foundation for implementing multifaceted, coordinated strategies to control the emergence and spread of AMR.

2. Materials and Methods

2.1. Data Collection

To ensure the rigor and broad representativeness of our analysis, a total of 1710 complete genomes of S. aureus and the corresponding background information (including collection year, isolation country, host common name, latitude and longitude) were obtained from the BV-BRC and NCBI databases. During the data collection and collation process, we strictly applied the inclusion and exclusion criteria to retain sequences that met the following conditions: (1) genome completeness annotated as “complete”; (2) available metadata specifying either the collection date or the geographic origin (country); and (3) the removal of duplicate strains or sequences from the same laboratory based on biosample information. After systematic screening, a total of 1710 complete genomes suitable for spatiotemporal analysis were retained. Considering the significant impact of human factors such as social development levels and policy planning in different countries on antibiotic consumption [39,40,41], human development index (HDI) data from 1990 to 2022 promulgated by the United Nations Development Programme (UNDP) were downloaded (https://hdr.undp.org/data-center/human-development-index#/indicies/HDI, accessed on 5 December 2024). The HDI is an important indicator that combines economic and social metrics to assess a country’s overall national strength. In addition, global and national antibiotic consumption estimate data from 2000 to 2018 were collected from GRAM (https://www.tropicalmedicine.ox.ac.uk/gram/research/visualisation-app-antibiotic-usage-and-consumption, accessed on 5 December 2024) and ResistanceMap (https://resistancemap.onehealthtrust.org/AntibioticUse.php, accessed on 5 December 2024). Finally, we also collected data on bioclimatic factors and PM2.5 from WorldClim (https://worldclim.org/data/monthlywth.html, accessed on 5 December 2024) and WorldBank (https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3, accessed on 5 December 2024).

2.2. Phylogenetic and Comparative Genomic Analysis

Prokka v1.13 was used to annotate S. aureus genomes with default settings [42]. Roary v3.13.0 was applied to perform the pan-genome analysis based on the GFF files generated by Prokka [43]. Sequences with over 95% similarity are considered the same gene, while genes present in at least 95% of the genome are classified as core genes. The phylogenetic tree was constructed in FastTree v2.1.11 based on the core-genome single-nucleotide polymorphism (SNP) using the core genes aligned in Roary. iTOL v5 (https://itol.embl.de/, accessed on 25 December 2024) was selected to conduct the visualization and retouching of the tree [44]. The pan- and core-genome profiles were visualized in R v4.4.1.

2.3. Genome Annotation

The CARD [45] and VFDB [46] libraries were used to detect the ARGs and virulence factors (VFs) of all complete S. aureus genomes in ABRicate v1.0.1 with general parameters: —mincov 90 and —minid 80 [47]. A widely recognized database of MGEs established by a previous study was employed for MGE identification in BLAST v2.8.1 [48]. An element was designated as an MGE only if both elements demonstrated at least 85% alignment coverage and more than 90% nucleotide identity [48]. Based on 7 housekeeping genes (arcC, aroE, glpF, gmK, pta, tpi, yqiL), mlst v2.23.0 was applied to analyze the multi-locus sequence typing (MLST) of S. aureus genomes. GrapeTree was used to construct the minimum spanning tree of MLST allele profiles [49].

2.4. Statistical Analysis and Visualization

All statistical analyses and visualizations were performed using Python v3.11 and R v4.4.1. Spearman correlation analysis was adopted to explore the linear relationship between ARG number and collection year, HDI, antibiotic consumption, PM2.5 level, and MGEs (p < 0.05 indicated a correlation between two variables). Prior to formal testing, scatter plots were generated for visual evaluation of monotonic trends. The Mann–Whitney U test was used to compare the number of ARGs in different time periods. A two-tailed p < 0.05 was considered statistically significant. To evaluate temporal differences in ARG abundance, genomes were divided into four groups by collection year. A Kruskal–Wallis test was first applied to assess overall differences, followed by pairwise Mann–Whitney U tests if significance was observed. ST composition across time periods, countries, and host types was visualized. Correlation networks between ARGs and MGEs were constructed to explore potential co-occurrence patterns. To further investigate interdependent effects among HDI, antibiotic use, MGEs, and ARGs, structural equation modeling (SEM) was employed. Samples with missing data for any of these variables were excluded. Model fit was evaluated using the chi-square test, RMSEA, and p values; good fit was indicated by p > 0.05, lower chi-square values, and RMSEA values close to zero. SEM is a method used to construct, estimate, and test causal relationship models. It can serve as an alternative to methods such as multiple regression, path analysis, factor analysis, and covariance analysis. SEM integrates confirmatory factor analysis and multivariate path analysis, allowing for the simultaneous examination of complex causal relationships among multiple independent and dependent variables. This approach provides a clear understanding of how individual indicators influence the overall system, as well as the relationships between various indicators. Climatic data—including monthly temperature and precipitation from 1960 to 2021—were obtained for each genome based on geographic coordinates. Nineteen bioclimatic variables were then derived following standard formulas. Restricted cubic splines were used to examine potential nonlinear relationships between ARGs and each bioclimatic factor. Variables showing significant univariate associations were entered into generalized linear models (GLMs) for multivariable analysis. Model selection was based on the Akaike Information Criterion (AIC), with the lowest AIC indicating optimal fit. Assumptions of GLMs were carefully checked: linearity between predictors and outcomes was assessed via scatter plots of predicted versus observed values; homoscedasticity was examined through residual plots and the Levene test; and multicollinearity was evaluated using variance inflation factors (VIF), with values above 5 indicating concern. This analytic pipeline ensured robust inference while minimizing overfitting.

3. Results and Discussion

3.1. Global Distribution of Collected S. aureus Strains

A total of 1710 S. aureus genomes from 58 countries and regions were included in our study (Table S1). Among them, 1186 strains originated from developed countries, 477 strains originated from developing countries, and 47 strains had no geographic origin information (Figure 1). Of these, the top five in terms of the number of collected strains were as follows: the USA (513), Australia (204), Germany (160), China (153), and Kenya (80). Regarding the temporal distribution of these 1710 genomes, 107 strains were identified before the year 2000, 208 strains were isolated between 2000 and 2010, 1318 strains were collected after 2010, and 77 strains had missing isolation times. Outbreaks of S. aureus infections have been reported in various areas around the world, and the threat of infections is becoming increasingly widespread, causing serious diseases in humans and animals.

3.2. Phylogenetic Tree and Genomic Characterization of S. aureus

The phylogenetic tree was constructed to explain the evolutionary relationships among genomes from different countries, hosts, and time periods. As shown in Figure 2, ST8 and ST105 were predominantly prevalent in the USA, with ST8 primarily infecting humans and cattle. ST5 mainly circulated in the USA, Australia, and Korea, with humans as the primary host. ST398 was chiefly found in China and Germany, primarily infecting humans and pigs. ST30 was mainly distributed in Australia and Kenya, with humans as the main host. The results revealed that there were obvious clusters among different hosts and countries. However, the composition of ST and the number of ARGs, MGEs, and VFs varied across these groups. This suggested that S. aureus strains from different habitats might undergo selective adaptation during cross-host and cross-regional transmission, leading to the accumulation of genetic variations advantageous for survival and ultimately driving the evolution of diverse phenotypic traits [50]. The number of ARGs, MGEs, and VFs detected in each of the S. aureus genomes was annotated on the tree, and we observed that their counts were closely related to the phylogeny of the strains (Figure 2 and Figure S1). There was an even more pronounced association between ARGs and MGEs, with their fluctuations exhibiting remarkable consistency. This suggested that MGEs might act as a bridge or vehicle to facilitate gene exchange between bacteria in the genetic evolution of S. aureus [51].
The results of a pan-genomic analysis of all S. aureus strains showed that the number of various genes increased significantly as the number of genomes analyzed increased (Figure S2). This revealed that S. aureus might have open and diverse pan-genomic characteristics, which promote gene exchange between different habitats and species, and might have adapted and evolved in response to changing environments [52,53]. The results of MLST analysis showed that a total of 203 different STs were widely distributed in the genomes of 1597 strains among the 1710 strains we collected (Figure S3). As can be seen, ST8 (336/1597, accounting for 21%) was the most prevalent ST detected in the top two hosts and 11 countries among the top 15 countries. Moreover, from before 2000 to after 2020, ST8 has almost never been absent and has taken up a significant proportion each year. The second to fifth places are occupied by ST5 (226/1597, 14.15%), ST105 (98/1597, 6.14%), ST398 (74/1597, 4.63%), and ST30 (58/1597, 3.63%), all of which also account for a portion of the ST composition across multiple countries, hosts, and time periods. In addition, the diversity of ST has generally shown an increasing trend over time. In terms of countries, Australia has the highest ST diversity and Suriname has the lowest. In terms of hosts, ST diversity was highest in humans and lowest in monkeys. This also indicated that differences in the genomic composition and external characteristics of S. aureus might be attributed to geographic location, natural environmental climate, human activities, and other factors across different countries and regions. These influences likely contribute to the observed variations in the distribution and prevalence of different STs.

3.3. Monitoring the Antimicrobial Resistance Trends of S. aureus

We identified the types and quantities of ARGs and VFs in each S. aureus genome to analyze the lineage in order to explore the resistance and virulence trends of S. aureus. A total of 68 species with 21,497 ARGs and 82 species with 101,194 VFs were detected in all complete S. aureus genomes. The results of the correlation analysis between the number of ARGs and the collection year revealed a significantly positive correlation (Figure 3A), and the ARG number significantly increased over the observed period (r = 0.515, p < 0.0001). However, there was no association between the number of VFs and the collection year (r = 0.081, p = 0.5460) (Figure 3A). The virulence of S. aureus remained at a relatively stable level over time, with no obvious trend of enhancement, which is why this study primarily focuses on exploring the AMR of S. aureus. Virulence is a complex trait that can evolve through multiple mechanisms beyond changes in virulence gene counts. The stability of virulence factor counts over time suggested that S. aureus might employ alternative mechanisms—such as regulatory mutations, phenotypic plasticity, or niche-driven adaptations—to enhance or modulate its pathogenicity. Regulatory modifications can alter gene expression levels, leading to variations in pathogenicity and invasiveness. Mutations in key regulatory genes can significantly impact virulence phenotypes by modulating toxin production, adhesion factors, and immune evasion strategies. Niche adaptation also plays a crucial role in the evolution of virulence. As S. aureus persists in different host tissues or environments, selective pressures may favor strains that optimize resource utilization or evade host defenses through mechanisms not necessarily reflected in gene counts. For example, epigenetic modifications or post-transcriptional regulation can dynamically influence virulence factor expression in response to environmental cues. Future research exploring gene expression profiles, regulatory pathways, and epigenetic modifications would provide deeper insights into the nuanced evolution of virulence in this pathogen. To avoid bias in ARGs due to the impact of the number of strains per year, we introduced the total ARG avg (total ARG avg = the total ARG number in a particular year/the number of S. aureus strains that year). To further summarize the trends of ARGs across different countries and regions, we selected the six countries with the largest number of collected isolates to examine the temporal changes in ARGs within S. aureus genomes of various national origins (Figure S4). The results indicated that there was a significant positive correlation between the ARG number and the collection year in S. aureus genomes from the USA and Kenya, showing an increasing trend in ARGs over time (USA: r = 0.683, p < 0.0001; Kenya: r = 0.834, p = 0.0052). However, a negative correlation was observed between the number of ARGs and the collection year in S. aureus genomes from Australia (r = −0.481, p = 0.0235), indicating a decline in ARGs over time. Additionally, there was no correlation between ARG number and the collection year in S. aureus genomes from Germany, China, and South Korea (Germany: r = −0.210, p = 0.3620; China: r = 0.142, p = 0.5631; South Korea: r = 0.009, p = 0.9748). These results indicated that the differences in sample sizes across countries appeared to have a limited or even negligible impact on the findings of our study regarding the trends in S. aureus AMR. However, this warrants further in-depth investigation in future research. Although each country showed varying trends in ARGs due to geographic, social, and cultural factors, the overall global trend of S. aureus AMR continued to increase. Then, we divided the collected genomes of the S. aureus strains into four groups according to different time periods and compared the differences in the number of ARGs between the groups. The results showed that there were significant differences in ARGs within the genomes of S. aureus in different time periods. The number of ARGs in S. aureus genomes significantly increased each decade, with the average number of ARGs per genome rising from 10.37 ± 3.55 before 1990 to 12.75 ± 4.04 after 2010 (Figure 3B). Next, the 68 detected ARGs were classified into 13 categories according to their specific antibiotics. Subsequently, we explored the temporal trends in the abundance of these ARGs targeting the 13 antibiotic types. The results showed that, except for ARGs targeting fosfomycin and glycopeptide, which showed no correlation with collection years (fosfomycin: r = −0.144, p = 0.2814; glycopeptides: r = 0.152, p = 0.2560), the number of ARGs targeting the other 11 antibiotic classes exhibited significant positive correlations with the passage of time (aminoglycoside: r = 0.518, p < 0.0001; chloramphenicol: r = 0.552, p < 0.0001; lincosamide: r = 0.426, p = 0.0009; penam: r = 0.593, p < 0.0001; nucleoside: r = 0.580, p < 0.0001; fluoroquinolone: r = 0.419, p = 0.0011; diaminopyrimidine: r = 0.690, p < 0.0001; fusidic_acid: r = 0.440, p = 0.0005; macrolide: r = 0.652, p < 0.0001; tetracycline: r = 0.512, p < 0.0001; mupirocin: r = 0.571, p < 0.0001). The number of ARGs in S. aureus genomes targeting these 11 antibiotics increased significantly as the year increased (Figure S5). This is consistent with the previous correlation analysis results. The problem of S. aureus AMR is worsening year by year, and the susceptibility of this bacterium to some common antibiotics is decreasing with the widespread use of antibiotics. Additionally, integrating available phenotypic susceptibility data for S. aureus would provide functional validation for ARG predictions and further strengthen the conclusions of our study. However, due to limitations in data availability from public databases, we are currently unable to achieve this. Once more publicly accessible data become available, incorporating phenotypic susceptibility information to predict ARGs in S. aureus will be a promising direction for our future research.
Antibiotics have been used as a routine and preferred treatment for bacterial infections since their invention. Their use undoubtedly curbed the development of bacterial AMR to a certain extent in the early stages, saving countless patients suffering from bacterial infections. However, since the widespread use of antibiotics in the 1970s, the problem of bacterial AMR has come into people’s focus and is gradually becoming more and more serious [54]. We explored the relationship between antibiotic consumption and the AMR of S. aureus from different perspectives (Figure 3C). Overall, there was a significant positive correlation between antibiotic consumption and the number of ARGs (r = 0.176, p < 0.0001), and the increase in antibiotic consumption significantly increased the number of ARGs in S. aureus. Moreover, the analysis of developing and developed countries separately yielded similar results (developing countries: r = 0.135, p = 0.0197; developed countries: r = 0.205, p < 0.0001). Although the correlation coefficients were relatively small, indicating a weak association, all of their p values demonstrated statistical significance. This clearly supported the widespread role of antibiotic consumption in promoting S. aureus AMR. This further illustrated that the overuse of antibiotics led to the evolution of AMR in pathogenic bacteria, and antibiotics were increasingly recognized as the main selective pressure for bacterial AMR. Therefore, segmented regression modeling was employed to analyze the longitudinal trends of ARGs within S. aureus genomes, aiming to identify breakpoints potentially associated with policy changes or shifts in antibiotic consumption. The results indicated that 2016 serves as a significant “turning point”, marking a notable shift in the trend of S. aureus AMR (Figure S6). Between 2000 and 2014, issues related to antibiotic overuse were prominent, but management measures at the time were limited. The period of 2015–2016 was a critical juncture during which antibiotic regulation gradually intensified, with initial restrictions on medical use. In 2016, large-scale antimicrobial stewardship programs were officially implemented, initiating a nationwide effort to tighten control. Internationally, the USA formulated the “Antimicrobial Use Monitoring and Management Policy” in 2014, while the WHO released the “Global Action Plan on Antimicrobial Resistance” in 2015. In China, the 2016 Thirteenth Five-Year Plan explicitly emphasized strengthening nationwide antibiotic management, restricting the use of antibiotics in animal husbandry, and promoting the digitization of prescription management. These developments clearly illustrate that from 2015 to 2016 onward, antibiotic use in both clinical and agricultural settings was progressively restricted and regulated, becoming a key focus of policy initiatives.
As reported in previous studies, antibiotic market dynamics have mainly been influenced by the regional environment and gross domestic product (GDP) [55]. In light of this, we investigated the relationship between the HDI, a composite measure of a country’s socioeconomic and environmental development, and S. aureus AMR (Figure 3D). The results showed that there was a positive correlation between the HDI and the number of ARGs (r = 0.087, p = 0.0009). However, when we separated the study by developing and developed countries, we found that only developing countries exhibited a positive correlation between the HDI and ARG number (r = 0.159, p = 0.0015), while the number of ARGs in developed countries showed a negative correlation with the HDI (r = −0.295, p < 0.0001). To further confirm the relationship between the HDI and S. aureus AMR in developed countries, the top four countries with the highest numbers of collected S. aureus strains were selected for more pronounced correlation analysis (Figure S7). The results were generally consistent in that there was a negative correlation between the HDI and ARG number in S. aureus isolated from Germany (r = −0.386, p < 0.0001), but no correlation in the USA, Australia, and Korea (USA: r = −0.042, p = 0.3612; Australia: r = −0.077, p = 0.2780; Korea: r = 0.097, p = 0.5135). The correlation coefficients (total: r = 0.087; developing countries: 0.159; developed countries: −0.295) indicated weak or even negative associations, which might seem perplexing at first glance. However, all their p values were less than 0.001, demonstrating statistical significance. We hypothesize that this may be due to the fact that most developed countries have excellent and robust antimicrobial stewardship programs, even though their economies and antibiotic consumption are in a higher position [56]. Developed countries tend to allocate considerable resources to drug development, vaccination, antimicrobial resistance surveillance, and health education. They have strict regulations on the use of antibiotics and are effective in the treatment of antibiotic waste. On the contrary, developing countries face serious challenges in terms of antibiotic misuse and inadequate medical facilities. Therefore, an observed trend was that antimicrobial resistance tended to decrease as the HDI increased among S. aureus strains collected from developed countries. Antibiotic consumption may act as a potential variable influencing the relationship between the HDI and antimicrobial resistance. Given the numerous differences between developed and developing countries, further research on the role of the HDI in driving S. aureus AMR is warranted as more data become available in the future.

3.4. The Intrinsic Driving Effect of MGEs on ARGs

Based on the results of phylogenetic analysis, we believed that MGEs played a crucial role in the propagation of ARGs in S. aureus. MGEs acted as vectors carrying gene fragments to spread between bacteria and even species through HGT, which greatly promoted the dissemination of ARGs, the development of AMR, and occurrence of MDR [31,57]. The relevant results for AMR and MGEs indicated that the number of ARGs and MGEs in the S. aureus genomes exhibited a strong positive correlation (r = 0.851, p < 0.0001) (Figure 4A). The results from both developed and developing countries also indicated that the number of ARGs in S. aureus significantly increased with the rise in the MGE number (developing countries: r = 0.827, p < 0.0001; developed countries: r = 0.857, p < 0.0001). To explore the interconnectedness between MGEs and ARGs, we specifically examined MGEs that were around 5 kilobases (kb) above and below each ARG and considered this ARG to be mobile. A total of 110 types of MGEs were detected in the vicinity of ARGs in all S. aureus genomes. The largest number of these were tnpA, which accounted for 58.8% of all MGEs, followed by delta (2.2%) (Figure 4B). TnpA is a gene associated with transposable elements, typically encoding a transposase enzyme that is essential for the mobility of the transposon itself. Similarly to tnpA, delta typically refers to a specific sequence or structure associated with transposons or insertion sequences. It plays a role in the recognition and binding of transposases, mediating the cutting, movement, or integration of these genetic elements. As carriers of genetic material, tnpA and delta facilitate the spread of ARGs and VFs among bacteria, thereby enhancing bacterial resistance and pathogenicity. We figuratively referred to these adjacent structures as MGE_ARG pairs (Figure 4C) and visualized all detected MGE_ARG pairs (Figure 4D). A variety of types of 1388 MGE_ARG pairs were detected in all genomes of S. aureus (Table S2). Of these, 11 ARGs were detected in only one type of MGE_ARG pair, while 49 ARGs were detected in two or more types of MGE_ARG pairs. This illustrated that the combination of MGEs and movable ARGs tended to be more complex and diverse. The fusion and transfer capabilities between ARGs and MGEs form the foundation for the development and dissemination of AMR in bacteria, as MGEs can carry multiple ARGs and facilitate their exchange among hosts [57]. The AMR of S. aureus is linked to specific genomic structures, such as MGE_ARG pairs. The presence of these structures not only enables AMR but also reflects S. aureus’s ability to adapt to diverse ecological niches. The greater the diversity and complexity of these structures are, the more robust the spread of S. aureus AMR appears to be. In addition, it can be seen from Figure 4D that the same MGE (tnpA) appeared among the top 10 MGE_ARG pair types with the highest counts. This indicated that tnpA was always an ARG-carrying MGE and made a significant contribution to the transmission of ARGs [58]. We also explored the differences in MGE_ARG pairs between developed and developing countries. Total numbers of types of 1099 and 663 MGE_ARG pairs were detected in developed and developing countries, respectively (Tables S3 and S4). The results showed that the diversity and mobility of ARGs were higher in developed countries.
We also built the co-occurrence networks of MGEs and ARGs in different time periods (Figure 4E). Before 1990, the network contained 25 ARG nodes and 56 MGE nodes. From 1990 to 2000, the network included 31 ARG nodes and 86 MGE nodes. In the period from 2000 to 2010, there were 44 ARG nodes and 89 MGE nodes. After 2010, the network expanded to 66 ARG nodes and 109 MGE nodes. The results showed that the number of MGEs and ARGs gradually increased over time, and the correlation network between them became more and more complex. This indicated that with the widespread use of antibiotics, the structure of the related MGE_ARG pairs within the genome of S. aureus became more and more diverse and intricate. MGEs accelerated the continuous acquisition of ARGs by S. aureus through HGT. Additionally, prior to 1990, the largest node in the network was AAC(6′)-Ie-APH(2″)-Ia, with a betweenness centrality of 0.0322. AAC(6′)-Ie-APH(2″)-Ia is a gene that encodes an aminoglycoside-modifying enzyme, which enables bacteria carrying this gene to resist a broad spectrum of aminoglycoside antibiotics. This indicated that the use of aminoglycoside antibiotics increased significantly during this period, which promoted the emergence and spread of aminoglycoside resistance genes that occupied a dominant position. From 1990 to 2000, the largest node became tetM, exhibiting a betweenness centrality of 0.1890. During the 2000–2010 period, tetM remained the largest node with a betweenness centrality of 0.1504. TetM is a gene that encodes a tetracycline resistance protein. It belongs to the ribosomal protection protein family, which confers resistance to tetracycline antibiotics—a class of broad-spectrum antibiotics that inhibit bacterial protein synthesis. This also indicated that during this period, tetracycline antibiotics were extensively used to treat S. aureus infections, leading to the rapid development of resistance. However, after 2010, the largest node became tnpA, which had a betweenness centrality of 0.0163. The number of nodes in the network was continuously increasing, and the largest node was also changing accordingly. This indicated that the contribution of MGEs to ARGs was becoming more pronounced. Furthermore, with the development and utilization of new antibiotics, the dominant MGEs and ARGs were constantly evolving, reflecting ongoing shifts in their prevalence and interactions.

3.5. Interpreting the Mediation Effects of Various Factors on S. aureus AMR

Our results in the previous sections indicated that the HDI, antibiotic consumption, and MGEs all contributed to the occurrence and progression of S. aureus AMR. Given that there was a mutual influence between these factors, we conducted SEM for developed and developing countries to explore the interactions and their direct and indirect effects on S. aureus AMR, respectively (Figure 5). Unfortunately, SEM for developed countries has not been successful (model overall: p < 0.001), but there are traces of reasons for this. As early as when we separately verified the effect of the HDI on S. aureus AMR in developed and developing countries, we found that although the HDI and ARGs were positively correlated as a whole, the number of ARGs in developed countries decreased with the increase in the HDI. This variation may be attributed to significant differences among countries in terms of socioeconomic development, average income, infrastructure, and population density. Additionally, numerous factors such as antibiotic usage, travel frequency, healthcare standards, sanitation conditions, and education levels could influence the resistance patterns of S. aureus [39]. Therefore, we believe that there must be certain factors in developed countries which may involve social, economic, political, and cultural aspects. These aspects warrant further in-depth investigation in our future studies, particularly once more comprehensive variable data with detailed information become available. It is likely due to these reasons that the construction of the SEM using S. aureus strains from developed countries was not successful in this study. The SEM results in developing countries showed that the HDI, antibiotic consumption, and MGEs could directly and significantly contribute to the aggravation of S. aureus AMR (HDI: estimate = 0.835, p < 0.001; antibiotic consumption: estimate = 0.863, p < 0.001; MGEs: estimate = 0.828, p < 0.001). Moreover, the HDI could significantly promote antibiotic consumption, thereby indirectly enhancing S. aureus AMR (estimate = 0.987, p < 0.001). In other words, socioeconomic development has shaped the antibiotic consumption market and promoted the selective evolution of S. aureus AMR. At the same time, antibiotic consumption could promote the frequent exchange of ARG-carrying MGEs between bacteria through HGT, leading to the emergence and rapid spread of S. aureus AMR (estimate = 0.182, p = 0.001).

3.6. Global Risk Assessment of S. aureus AMR

As the problem of global warming becomes more prominent, people pay more and more attention to the non-negligible role of environmental climate factors in the distribution and propagation of AMR [59,60,61]. Global warming is conducive to the potential exchange of MGEs and ARGs through HGT, and the increase in temperature will create favorable conditions for bacterial infections and the emergence of AMR [62,63,64]. A previous study showed that for every 1 °C increase in the average ambient temperature, the risk of bacterial resistance increases by 1.06–1.14 times [65]. In addition to temperature, other climatic factors such as PM2.5 [37], precipitation [66], etc., can have an impact on bacterial AMR by a variety of means [67,68,69]. We used the three basic climate data points obtained from WorldClim and combined R and Python to calculate the nineteen bioclimate factor data points specified by it (Table S5). These 19 bioclimate variables are derived from climatic data through statistical calculations, aimed at capturing various climate features and their impacts on ecosystems and biodiversity. They not only represent average conditions but also include seasonal and extreme climate characteristics. Widely used in ecology, geography, and climate research, these variables help elucidate the relationship between climate and ecosystems and provide a scientific basis for predicting climate change and assessing associated risks. The 19 bioclimate factors cover a variety of extreme weather events and seasonal environments, which are more significant for our research. The RCS was then adopted to explore the relationship between individual bioclimatic factors and the number of ARGs in the S. aureus genomes (Figure S8). The results showed that BIO3 (Isothermality), BIO4 (Temperature Seasonality), BIO5 (Max Temperature of Warmest Month), BIO6 (Min Temperature of Coldest Month), BIO7 (Temperature Annual Range), BIO10 (Mean Temperature of Warmest Quarter), BIO11 (Mean Temperature of Coldest Quarter), BIO12 (Annual Precipitation), BIO13 (Precipitation of Wettest Month), BIO14 (Precipitation of Driest Month), and BIO16 (Precipitation of Wettest Quarter) had a statistically significant nonlinear correlation with the AMR of S. aureus. Of the 19 bioclimatic factors related to temperature and precipitation, 11 exhibited nonlinear correlations with S. aureus AMR, highlighting the complex relationship between climate variables and AMR dynamics. Rising temperatures and changing precipitation patterns can significantly impact bacterial survival, reproduction, and the dissemination of ARGs. Higher temperatures can promote bacterial growth, leading to increased infections and creating opportunities for AMR to spread [63]. Additionally, temperature influences gene transfer mechanisms within bacteria, including the movement of ARGs responsible for AMR. Elevated temperatures often intensify competition among bacteria, enabling strains harboring ARGs to survive and propagate more easily under antibiotic pressure [64]. Moreover, increased precipitation has been positively correlated with the abundance of ARGs; epidemiological studies have observed a notable rise in S. aureus AMR cases following heavy precipitation events in water bodies [66]. The Spearman linear correlation results between PM2.5 and the ARG number showed that PM2.5 was also significantly positively correlated with S. aureus AMR (Figure S9). PM2.5 in the air not only contains harmful chemicals and heavy metals but may also carry various bacteria and ARGs. These tiny particles can enter the human respiratory system, posing potential infection risks. Furthermore, PM2.5 can serve as a vector for bacteria, facilitating the airborne spread of S. aureus. When bacteria are attached to or transported by PM2.5, they can survive for extended periods in the environment and exchange genetic materials (including ARGs) with other bacteria under certain conditions [70]. The bioclimatic factors that exhibited nonlinear correlation were included in the form of the RCS. The HDI, antibiotic consumption, MGEs, and PM2.5 were included in a linear form. The GLM was established for multivariate analysis. The model with the minimum AIC value (1371.574) was selected as the optimal model. The initial null deviance of this optimal model was 3192.8, and when the variables were included in the model one by one, the residual deviance of the final model reduced to 652.52. BIO3, BIO4, BIO5, BIO6, BIO10, BIO11, BIO12, BIO13, BIO14, PM2.5, MGEs, and antibiotic consumption were incorporated into the final model, each playing a crucial role in the global AMR risk of S. aureus. Among these, BIO10, BIO11, and BIO14 acted as negative selective pressures influencing AMR development, while BIO3, BIO4, BIO5, BIO6, BIO12, and BIO13, along with PM2.5, MGEs, and antibiotic consumption, served as positive selective pressures contributing to the rise in AMR (Table 1). It is noteworthy that BIO10, BIO11, and BIO14 negatively influenced S. aureus AMR. This is likely because, although increased temperature and precipitation can enhance the survival advantage of ARG-carrying resistant strains to some extent, there is an optimal range for these factors. When values fall outside this range (either higher or lower) they may exert an opposite, suppressive effect on AMR development.

4. Conclusions

In summary, our study conducted a comprehensive analysis to monitor the changing trends in the AMR of S. aureus on a large scale and explore various anthropogenic and environmental climatic factors that influence the occurrence and spread of AMR. The global problem of S. aureus AMR has become increasingly severe over time, with its invasion extending to a wider range of regions. MGEs play a crucial role in the transmission of AMR in S. aureus, and the specific combinations of MGEs and ARGs within its genome tend to be diverse and complex. In addition, the results of multi-dimensional analysis showed that the presence of various human factors (including HDI and antibiotic consumption) and natural environmental climate factors (including temperature, precipitation, and PM2.5 level) could significantly increase the risk of S. aureus AMR. It is time for a more comprehensive surveillance approach and multifaceted joint action to prevent and control S. aureus AMR.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/microbiolres16060118/s1, Figure S1: The phylogenetic tree of 1710 S. aureus genomes. From inside to outside, the colored rings represent the isolation country, the host, the ST type, the number of MGEs, and the number of VFs. Figure S2: The changing trend of various types of genes with the increase in the number of analyzed genomes in pan-genome analysis. Figure S3: (A) Minimum spanning tree of S. aureus isolates based on MLST. The different numbers and colors represent different ST types, and the size of the circle represents the number of STs. (B) Composition and distribution of ST types in different countries, different hosts, and different collection years. The different colors represent different ST types. Figure S4: Trends in ARGs within the genomes of S. aureus from different countries over time. Figure S5: Trends in the number of ARGs targeting 13 antibiotics within the S. aureus genomes over time. Figure S6: Segmented regression modeling to analyze the longitudinal trends of ARGs within S. aureus genomes. Figure S7: The relationship between the HDI and ARG number in the top four developed countries with the largest number of collected S. aureus genomes. Figure S8: The association between 19 bioclimatic factors and the number of ARGs in S. aureus genomes based on the RCS. Figure S9: The linear correlations between PM2.5 and the ARG number based on the Spearman correlation method. Table S1: Basic information of the 1710 strains of S. aureus included in this study. Table S2: The distribution of ARG_MGE pair counts in all complete S. aureus genomes. Table S3: The distribution of ARG_MGE pair counts in S. aureus genomes from developed countries. Table S4: The distribution of ARG_MGE pair counts in S. aureus genomes from developing countries. Table S5: Historical monthly climate data extracted from the Geotiff file given by WorldClim using latitude and longitude information, and 19 bioclimatic factors calculated using R and Python according to the formulas specified by WorldClim.

Author Contributions

H.Y. (Haitao Yuan): Methodology, Writing—original draft, Writing—review and editing, Data curation, Software, Visualization. J.X.: Methodology, Writing—original draft, Writing—review and editing, Data curation, Software, Visualization. Y.W.: Data curation, Writing—review. Y.L.: Writing—review. Y.H.: Data curation. J.L.: Methodology, Data curation. F.L.: Writing—review, Visualization. J.Z.: Writing—review and editing. H.Y. (Haiyan Yang): Conceptualization, Supervision, Writing—review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by grants from the National Natural Science Foundation of China (No. 82273696 and No. 81973105). The funder has no role in the data collection, data analysis, the preparation of the manuscript, and decision to submit it.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data generated in the present study may be requested from the corresponding author.

Acknowledgments

We would like to thank the National Supercomputing Center in Zhengzhou for their support.

Conflicts of Interest

All authors report that they have no potential conflicts of interest.

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Figure 1. The geographic distribution of 1710 complete S. aureus genomes collected from around the world.
Figure 1. The geographic distribution of 1710 complete S. aureus genomes collected from around the world.
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Figure 2. Phylogenetic tree using maximum likelihood analysis based on core-genome SNPs of 1710 S. aureus genomes. From inside to outside, the colored rings represent the isolation country, the host, the ST type, and the number of ARGs.
Figure 2. Phylogenetic tree using maximum likelihood analysis based on core-genome SNPs of 1710 S. aureus genomes. From inside to outside, the colored rings represent the isolation country, the host, the ST type, and the number of ARGs.
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Figure 3. (A) Trends in the number of ARGs and VFs in the genomes of S. aureus over time. (B) Distribution and comparison of the number of ARGs in each S. aureus genome in different time periods based on the Mann–Whitney U test; “*”: p < 0.05; “**”: p < 0.01; “***”: p < 0.001; “****”: p < 0.0001. (C) Linear correlations between antibiotic consumption and number of ARGs based on Spearman correlation method. (D) Linear correlations between HDI and ARG number based on Spearman correlation method.
Figure 3. (A) Trends in the number of ARGs and VFs in the genomes of S. aureus over time. (B) Distribution and comparison of the number of ARGs in each S. aureus genome in different time periods based on the Mann–Whitney U test; “*”: p < 0.05; “**”: p < 0.01; “***”: p < 0.001; “****”: p < 0.0001. (C) Linear correlations between antibiotic consumption and number of ARGs based on Spearman correlation method. (D) Linear correlations between HDI and ARG number based on Spearman correlation method.
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Figure 4. (A) The linear correlations between the number of MGEs and the ARG number in S. aureus genomes based on the Spearman correlation method; (B) composition ratios of MGE species detected in all S. aureus gene sequences; (C) 5 kb upstream and downstream of the movable ARGs for annotating the MGEs; (D) percentages of the types of MGE_ARG pairs detected in all complete S. aureus genomes; (E) co-occurrence network analysis of MGEs and ARGs in S. aureus strains isolated in different time periods.
Figure 4. (A) The linear correlations between the number of MGEs and the ARG number in S. aureus genomes based on the Spearman correlation method; (B) composition ratios of MGE species detected in all S. aureus gene sequences; (C) 5 kb upstream and downstream of the movable ARGs for annotating the MGEs; (D) percentages of the types of MGE_ARG pairs detected in all complete S. aureus genomes; (E) co-occurrence network analysis of MGEs and ARGs in S. aureus strains isolated in different time periods.
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Figure 5. The direct and indirect effects between the HDI, antibiotic consumption, MGEs, and S. aureus AMR analyzed by SEM for developing countries and developed countries, respectively. Solid lines represent correlation, dotted lines represent no correlation, red numbers represent positive correlation, and blue represents negative correlation.
Figure 5. The direct and indirect effects between the HDI, antibiotic consumption, MGEs, and S. aureus AMR analyzed by SEM for developing countries and developed countries, respectively. Solid lines represent correlation, dotted lines represent no correlation, red numbers represent positive correlation, and blue represents negative correlation.
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Table 1. Basic information about the variables included in the final optimal GLM.
Table 1. Basic information about the variables included in the final optimal GLM.
Results of Generalized Linear Model a
VariablesEstimatesSEt Valuep Value
rcs(BIO3,3)1.0820.4692.3090.021
rcs(BIO4,3)0.0150.0062.5620.011
rcs(BIO5,3)0.3540.1222.8940.004
rcs(BIO6,3)0.5270.1882.8100.005
rcs(BIO10,3)−0.2600.129−2.0160.044
rcs(BIO11,3)−0.5010.251−2.0010.046
rcs(BIO12,3)0.0080.0023.616<0.001
rcs(BIO13,3)0.0350.0113.0250.003
rcs(BIO14,3)−0.0620.020−3.0150.003
PM2.50.0330.0093.737<0.001
MGEs0.1380.00623.339<0.001
Antibiotic consumption0.0630.0203.1770.002
a BIO1 = Annual Mean Temperature; BIO2 = Mean Diurnal Range; BIO3 = Isothermality; BIO4 = Temperature Seasonality; BIO5 = Max Temperature of Warmest Month; BIO6 = Min Temperature of Coldest Month; BIO7 = Temperature Annual Range; BIO8 = Mean Temperature of Wettest Quarter; BIO9 = Mean Temperature of Driest Quarter; BIO10 = Mean Temperature of Warmest Quarter; BIO11 = Mean Temperature of Coldest Quarter; BIO12 = Annual Precipitation; BIO13 = Precipitation of Wettest Month; BIO14 = Precipitation of Driest Month; BIO15 = Precipitation Seasonality; BIO16 = Precipitation of Wettest Quarter; BIO17 = Precipitation of Driest Quarter; BIO18 = Precipitation of Warmest Quarter; BIO19 = Precipitation of Coldest Quarter.
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Yuan, H.; Xu, J.; Wang, Y.; Li, Y.; Hao, Y.; Long, J.; Liu, F.; Zhu, J.; Yang, H. The Global Antimicrobial Resistance Trends of Staphylococcus aureus and Influencing Factors. Microbiol. Res. 2025, 16, 118. https://doi.org/10.3390/microbiolres16060118

AMA Style

Yuan H, Xu J, Wang Y, Li Y, Hao Y, Long J, Liu F, Zhu J, Yang H. The Global Antimicrobial Resistance Trends of Staphylococcus aureus and Influencing Factors. Microbiology Research. 2025; 16(6):118. https://doi.org/10.3390/microbiolres16060118

Chicago/Turabian Style

Yuan, Haitao, Jie Xu, Ying Wang, Yuan Li, Yuqing Hao, Jinzhao Long, Fang Liu, Jingyuan Zhu, and Haiyan Yang. 2025. "The Global Antimicrobial Resistance Trends of Staphylococcus aureus and Influencing Factors" Microbiology Research 16, no. 6: 118. https://doi.org/10.3390/microbiolres16060118

APA Style

Yuan, H., Xu, J., Wang, Y., Li, Y., Hao, Y., Long, J., Liu, F., Zhu, J., & Yang, H. (2025). The Global Antimicrobial Resistance Trends of Staphylococcus aureus and Influencing Factors. Microbiology Research, 16(6), 118. https://doi.org/10.3390/microbiolres16060118

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