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. 2025 Jul 11:16:1595051.
doi: 10.3389/fmicb.2025.1595051. eCollection 2025.

Prevalence of antibiotic resistance genes its association with microbiota in raw milk of northwest Xinjiang

Yan Zhao #  1   2 , Yifan Niu #  1   2 , Min Zhao  2 , Wanting Huang  2 , Yanan Qin  1   2
Affiliations

Prevalence of antibiotic resistance genes its association with microbiota in raw milk of northwest Xinjiang

Yan Zhao et al. Front Microbiol. .

Abstract

The issue of antibiotic resistance caused by antibiotic resistance genes (ARGs) has become a significant concern in environmental research in recent years, while raw milk is an important link in the food chain and has become one of the carriers and reservoirs of ARGs, which has not been taken seriously. This research employed high-throughput quantitative PCR and Illumina sequencing techniques targeting the 16S rRNA gene. These methods were used to examine the bacterial community composition and genes associated with antibiotic resistance in raw milk samples collected from the northwestern area of Xinjiang. An aggregate of 31 distinct resistance alleles were identified, with their abundance reaching as high as 3.70 × 105 copies per gram in the analyzed raw milk samples. Microorganisms harboring ARGs that confer resistance to beta-lactams, tetracyclines, aminoglycosides, and chloramphenicol derivatives were prevalent in raw milk. Procrustes analysis revealed a certain degree of correlation between the microbial community and the antibiotic resistance gene (ARG) profiles. Furthermore, network analysis demonstrated that Actinobacteria and Firmicutes were the predominant phyla exhibiting co-occurrence relationships with specific ARGs. Combining the findings from Variance Partitioning Analysis (VPA), the distribution of ARGs was mainly driven by three factors: the combined effect of physicochemical properties and mobile genetic elements (MGEs) (33.5%), the interplay between physicochemical parameters and microbial communities (31.8%), and the independent contribution of physicochemical factors (20.7%). The study demonstrates that the overall abundance of ARGs correlates with physicochemical parameters, bacterial community composition, and the presence of MGEs. Furthermore, understanding these associations facilitates the evaluation of antibiotic resistance risks, thereby contributing to enhanced farm management practices and the assurance of food safety.

Keywords: High throughput qPCR; antibiotic resistance genes; antibiotic-resistant bacteria; host; raw milk.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Stacked bar chart showing detected ARGs numbers for groups M1, M2, M3, and M4. Each bar is divided into categories: Others, MGEs, Multidrug, Chloramphenicol, MLSB, Vancomycin, Sulfonamide, Tetracycline, Beta_Lactam, and Aminoglycoside. M4 has the highest ARGs count, with various categories represented.
Figure 1
Number of ARGs and MGEs in raw milk.
Two bar charts labeled A and B compare the abundance and normalized copy number of different antibiotic resistance genes (ARGs) across four groups (M1, M2, M3, M4). Each chart includes stacked bars representing various ARGs, such as Aminoglycoside, Beta_Lactam, Tetracycline, among others, with a color-coded legend. Chart A shows abundance in copies per gram, while Chart B shows normalized copy number. Each bar has error bars for variability. The MGEs (Mobile Genetic Elements) category is prominent in green.
Figure 2
ARGs in raw milk form four farms. (A) Absolute copy numbers of ARGs. (B) The normalized copy numbers of ARGs presented as total ARG copies per bacterial cell.
Two plots illustrating principal component analysis results. Plot A shows data points for four groups (M1, M2, M3, M4) in different colors on axes PC1 (47.45%) and PC2 (31.17%). Plot B overlays elliptical confidence intervals for the same groups on axes PC1 (55.15%) and PC2 (17.24%). Each group is represented by distinct markers: cyan circles for M1, red circles for M2, blue circles for M3, and purple circles for M4.
Figure 3
Principal coordinate analysis (PCoA) based on the Bray–Curtis distance showing the overall distribution pattern of (A) ARGs in raw milk; (B) bacterial community in raw milk.
Two stacked bar charts labeled A and B show the relative abundance of different bacterial groups for categories M1, M2, M3, and M4. Chart A includes bacterial groups like Proteobacteria, Firmicutes, and Actinobacteria, with a legend displaying varied colors. Chart B includes groups like Pseudomonas, Ralstonia, and Streptococcus. Each chart indicates abundance by stacking colored segments representing different bacterial groups, with a scale from 0 to 1. Both have a similar structure and are labeled with group names and a color-coded legend on the right side.
Figure 4
Histogram of relative species abundance, (A) phylum level; (B) genus level.
Panel A shows a redundancy analysis (RDA) biplot illustrating the relationships between bacterial communities and variables such as protein, fat, and non-fat milk solid, with arrows representing vectors for different bacterial groups. Panel B displays a Venn diagram showing percentage overlaps among physicochemical factors, bacterial communities, and mobile genetic elements (MGEs).
Figure 5
(A) Redundancy analysis diagram; (B) VPA analysis diagram.
Four circular network diagrams labeled M1, M2, M3, and M4 illustrate microbial interactions. Nodes are sized by degree and colored to show directionality, with red indicating higher degrees. Solid lines represent positive relations; dashed lines, negative. M4 shows the most complex network. Legends specify degree, relation, and node size.
Figure 6
Network analysis diagram.
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