Next Article in Journal
Integrating ATAC-Seq and RNA-Seq Reveals the Signal Regulation Involved in the Artemia Embryonic Reactivation Process
Previous Article in Journal
Structural Variations Identified in Patients with Autism Spectrum Disorder (ASD) in the Chinese Population: A Systematic Review of Case-Control Studies
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 15
Issue 8
10.3390/genes15081081
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Meta-Genomic Analysis of Different Bacteria and Their Genomes Found in Raw Buffalo Milk Obtained in Various Farms Using Different Milking Methods

by
Ling Li
1,2,†,
Wenhao Miao
1,†,
Zhipeng Li
1,2,
Li Huang
2,
Enghuan Hau
2,
Muhammad Farhan Khan
3,
Qingyou Liu
1,4,
Qingkun Zeng
2,* and
Kuiqing Cui
1,4,*
1
State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, Guangxi University, Nanning 530004, China
2
Guangxi Zhuang Autonomous Region Buffalo Milk Quality and Safety Control Technology Engineering Research Center, Guangxi Buffalo Research Institute, Chinese Academy of Agricultural Sciences, Nanning 530001, China
3
Department of Chemistry, Gomal University, Dera Ismail Khan 29050, Pakistan
4
Guangdong Provincial Key Laboratory of Animal Molecular Design and Precise Breeding, School of Life Science and Engineering, Foshan University, Foshan 528225, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Genes 2024, 15(8), 1081; https://doi.org/10.3390/genes15081081
Submission received: 12 July 2024 / Revised: 6 August 2024 / Accepted: 13 August 2024 / Published: 15 August 2024
(This article belongs to the Special Issue Buffalo Genetics and Genomics)
Browse Figures
Versions Notes

Abstract

:
Milking methods have significant impacts on the microbiological composition, which could affect the quality of raw buffalo milk. Hence, the current study was conducted on the impact of milking methods on microorganisms in buffalo tank raw milk from 15 farms in Guangxi, China. The farms were divided into two groups based on the milking method: mechanical milking (MM, n = 6) and hand milking (HM, n = 9). Somatic cell counts, bacterial cell counts and nutrients of the raw buffalo milk samples were analyzed. The comparison of raw buffalo milk samples was analyzed using metagenomic sequencing to detect any differences between the two groups. There was no significant difference in the basic nutritional compositions and somatic cell count of raw buffalo milk between the two milking methods. However, the HM samples had significantly higher bacterial counts and diversity compared to the MM samples. The results showed that Staphylococcus spp., Klebsiella spp., Streptococcus spp., and Pseudomonas spp. were the major microbes present in canned raw buffalo milk. However, the differences between the two milking methods were the relative abundance of core microorganisms and their potential mastitis-causing genera, including the content of antibiotic-resistance genes and virulence genes. Our study revealed that Staphylococcus spp. and Streptococcus spp. were significantly more abundant in the MM group, while Klebsiella spp. was more abundant in the HM group. Regardless of the milking method used, Pseudomonas spp. was identified as the primary genus contributing to antibiotic resistance and virulence genes in canned raw buffalo milk. These findings affirm that there are differences in the microbial and genomic levels in canned raw milk. To prove the functional roles of the discovered genes and how these genes affect milk quality, further research and experimental validation are necessary.

1. Introduction

Buffalo milk, with its intriguing nutritional profile, is attracting investments and research attention worldwide [1]. In southern China, Southeast Asia, and some European countries, buffalo milk and its products have a positive impact on the local population as a source of protein, vitamins, and minerals [2]. However, the high nutrient content in raw buffalo milk also creates an ideal environment for bacterial growth and development. Microorganisms have not only an important impact on the pricing and quality of dairy products but also on the financial performance of business corporates [3]. Microbial colonization in milk poses a major threat to both product quality and human health [4]. Currently, dairy buffalo farming in Guangxi exists in two modes: large-scale farms and small-scale free-range farming. Raw buffalo milk is purchased centrally by milk stations, and then transported to processing enterprises in a cold chain. Milking methods can have significant impacts on the microbiological compositions and qualities of raw buffalo milk.
In addition to its native microbiota, milk promotes the growth of microorganisms from animal surfaces and the surrounding environment [5]. Three primary sources of microbial contamination of bulk milk at the farm level are bacteria from the outer layer of the udder and teats, microorganisms from the surface of the milking apparatus, and organisms that cause mastitis from inside the udder [6]. Furthermore, mastitis in cows can be caused by environmental pathogens that enter through the teat, where the common bacteria are Escherichia coli, Klebsiella spp., Streptococcus lactis, and Streptococcus uberis [7], at which the presence of these bacteria in high yielding cows could result in significant economic losses [8]. Mastitis can be classified into two forms: clinical and subclinical. Previous literature reports that the global prevalence of clinical and subclinical mastitis in dairy buffaloes was 28% and 46%, respectively [9]. Moreover, mastitis reduces milk production and increases the risk of culling in buffalos [10].
After milking, raw buffalo milk is stored at a low temperature of 4 °C and transported to the processing plant via a cold chain. It has been demonstrated in the literature that low temperatures inhibit the growth of most bacteria, but cryophilic bacteria in milk continue to grow and produce heat-stable proteases, which are capable of hydrolyzing casein micelles, subsequently affecting the quality of the dairy product [11]. According to a previous study, Pseudomonas spp. and Acinetobacter spp. dominated raw buffalo milk with a combined relative abundance of over 60%, after 72 h of storage [12]. Therefore, monitoring Pseudomonas spp. in raw milk has become an essential aspect of product quality control.
Despite continuous updates on farming techniques and treatments, the treatment of mastitis in dairy cows depends heavily on antibiotic administration [10]. In May 2023, the World Organization for Animal Health (WOAH) revealed that global quantities of antimicrobials intended for use in animals were 114.5 million tonnes, 91.11 million tonnes, and 96.73 million tonnes in 2017, 2018, and 2019, respectively. In the past three years, the use of antibiotics in animals has increased by 45% in African countries and 5% in the whole of America [13]. However, the continuous use of antibiotics leads to mutations in the bacteria and gradual buildup of multidrug resistance genes in their genomes, which lead to the development of resistant phenotypes that have the capacity to survive under the higher pressure of antibiotics. Moreover, it is probable that the survival of bacteria resistant to different drugs may be significantly influenced by variations in the use of various clinical and nonclinical environments. Following the emergence of co-selection, this persistence causes co-resistance to proliferate in the surrounding environment [14]. Similarly, many genes associated with virulence factor genes (VFGs) can be transferred horizontally through mobile genetic elements such as plasmids, gene islands, and phages. The role of various VFGs targeted in bacterial mastitis pathogens has been extensively studied [15,16,17,18]. However, there is still a lack of research conducted on the buffalo milk microbiome. Raw buffalo milk, as an animal product, serves as the intersection for the horizontal and vertical transmission of antibiotic resistance genes and virulence genes in the environment and individual buffaloes. The enrichment of these genes in raw buffalo milk may pose a new risk. A systematic understanding of the structure of antibiotic resistance genes and virulence genes in raw buffalo milk, as well as their relationship with milking methods, can enhance our knowledge and education. This can increase awareness of antibiotic resistance and bacterial virulence, and also provide a guide on production practices.
The amount of microbe and its compositions in raw buffalo milk can affect the quality of the final dairy product and, subsequently, affect human and animal health. This study aims to evaluate the types and distribution of microorganisms, as well as their genome compositions, in raw buffalo milk harvested using different milking methods. High-throughput sequencing was used to analyze the number and distribution characteristics of microorganisms within the milk. This study aims to ensure better quality of dairy products by understanding the occurrence and spread of pathogens in raw buffalo milk. Furthermore, it can increase employees’ awareness of safety measures and decrease health hazards in work settings, such as animal housing.

2. Materials and Methods

2.1. Sample Collection

Fifteen raw buffalo milk samples were collected in 2022 from five major buffalo milk-producing areas in Guangxi (China), including Nanning, Lingshan, and Hengxian. There were six from mechanically milked (MM) large-scale farms and nine from hand-milked (HM) farms. The samples were collected from each tank and divided into sample sizes after collection. The samples for macro-genomic sequencing were immediately stored on dry ice and transported to Shanghai Biotechnology Co., Ltd. (Shanghai, China). Other milk samples were kept at low temperature and transported back to the laboratory for further analyses including milk compositions, somatic cell count (SCC) and total number of bacterial cells (analyzed using a BacSomatic™ instrument (FOSS Analytical, Hillerød, Denmark)). The compositions of milk including milk protein, fat, total milk solids, nonfat milk solids and lactose content, were analyzed using a MilkScan™ FT 3 Milk Composition Meter (FOSS Analytical, Hillerød, Denmark).

2.2. DNA Extraction

The milk sample is a low-biomass material. The potential contaminants in low-biomass samples during the experimental process of sequencing-based studies could produce biased results. To address these concerns, we implemented several measures to minimize contamination and ensure the accuracy of our results. All equipment and reagents were sterilized prior to use, and samples were handled in a laminar flow hood to reduce the risk of airborne contaminants. DNA from raw buffalo milk samples was extracted as previously described [19]. A 15 mL of the milk sample was centrifuged at 4500× g for 20 min at 4 °C, followed by 13,000× g for 1 min at 4 °C for 2 times (Eppendorf, Hamburg, Germany). After each centrifugation step, the supernatant was aseptically removed, and sterile phosphate-buffered saline (PBS) was used to wash the retained pellet. The pellet was used for DNA extraction using the E.Z.N.A. Soil DNA Kit (Omega Bio-Tek, Norcross, GA, USA), following the manufacturer’s guidelines. We measured the DNA concentration using a Qubit 4.0 (Thermo Fisher Scientific, Waltham, MA, USA) and constructed the sample library using VAHTS Universal Plus DNA Library Pren Kit for Illumina. The library underwent fragment quality inspection with Qsep-400, and its concentration was measured using Qubit 3.0. Finally, the constructed library was sequenced with the Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA).

2.3. Metagenomic Sequencing

The library’s fragment quality was assessed using the Qsep-400, and its concentration was determined via the Qubit 3.0. Sequencing of the constructed library was executed on the Illumina NovaSeq 6000 platform (Illumina, San Diego, CA, USA). MEGAHIT (Version 1.2.9) software is used for collecting clean reads [20]. Post-inspection of the library fragments by Qsep-400 and concentration measurement with Qubit 3.0, the sequencing process was completed using the Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA) [21]. The non-redundant gene sets were compiled using MMseqs2 [22], applying a sequence similarity threshold of 95% and an alignment coverage threshold of 90% [23].

2.4. Taxonomic Assignment, Functional Annotation and Gene Function Analysis

The Nr database is a comprehensive non-redundant protein database that contains detailed information about the corresponding species as well as substantial protein sequences and annotations. Genes were aligned using Blastp to the Nr database to identify the most similar sequences. Based on the matched Nr sequences, the species composition and relative abundance in the raw buffalo milk samples were determined. Public databases such as CARD [24] and VFDB [25] were employed to predict gene functions, using e-values less than 10−5 and scores exceeding 60.

2.5. Statistical Analysis

α diversity was calculated using QIIME 1.1, and β diversity was assessed using UniFrac distances. α diversity results were expressed as “mean ± standard deviation”, with p < 0.05 indicating significant differences. Statistical analyses were conducted using Microsoft Excel 2019 and IBM SPSS Statistics 25 software, and community structure analysis histograms in bacterial diversity were generated in GraphPad Prism 9.5.0. β diversity, which assessed microbiome differences between samples, was visualized using dimensionality reduction methods such as principal coordinate analysis (PCoA), non-metric multidimensional scaling (NMDS), or constrained principal component analysis (PCA). These analyses were visualized using the R vegan software package (version 2.5-6), and the distances between samples were displayed as scatter plots. Other figures were created using the R package ggplot2 v3.3.5.

3. Results and Discussion

3.1. Effect of Different Milking Methods on the Basal Nutrient Content, SCC and Number of Bacterial Cells of Raw Buffalo Milk

The results showed that there was no significant difference in milk composition and SCC between the two milking methods, although a few farms had a higher SCC in HM. In contrast, there was a significant increase in total number of bacterial cells in HM compared to MM (Table 1). The results were aligned with previous literature, where the MM appears to be highly effective in terms of milking and milk hygiene [26]. Literature suggests that the hygiene of the milker’s hands, parlor, and barn during the milking process influences the number and composition of microorganisms in the final raw milk [27]. Mechanical milking reduced the residence time of teat bacteria in raw buffalo milk through high milking frequency, which reduced the exposure time of raw buffalo milk to air. In conclusion, the differences in the effects of different milking methods on raw milk quality and SCC were not significant, but the effects on total bacterial cells in final buffalo raw milk were more significant.

3.2. Milking Methods Influence the Raw Milk Microbiota

An average of 62,336,920 high-quality paired-end reads were generated per sample. An average of 94.7% of metagenomic reads were host genome reads (range 81.9% to 98.7%). This resulted in an average of 3,403,722 reads that were assigned as microbial reads, which can be used for taxonomic and functional characterization (Supplementary Table S1). α diversity refers to the diversity within a specific environment or ecosystem, where it is mainly used to reflect the richness and evenness of species. The diversity is often reflected by Chao1, Observed species, Good’s coverage, Shannon, Simpson, and other indices (Table 2). The results showed that the observed species, Chao1, ACE, Shannon, and Good’s coverage indices reflected that the species richness in the milk of the HM group was significantly higher than that in the MM group. The Good’s coverage results show that uni-genes were abundant in all the samples. As shown in Figure 1a. PCoA plots based on the Bray–Curtis distance method at the genus level indicated that the samples in the MM group were more tightly clustered (yellow circles), while the samples in the HM group were more dispersed (red circles). The NMDS grading plots demonstrated a clear separation of HM and MM milk samples by the Bray–Curtis distance method on the read data at the genus level (Figure 1b). The microbiota associated with MM was tightly clustered on the left side of NMDS1, while the HM milk microbiota was dispersed towards the other side of NMDS1. In addition, the microbiome profiles at the upper genus level between HM and MM milk showed a clear separation (p = 0.023), when the two images were combined. There were significant differences in α diversity (chao1, ACE, and Shannon’s index) between HM and MM samples, indicating that the microbial ecosystems of MM milk were more diverse. Previous literature confirmed that raw milk with mastitis (either clinical or subclinical) exhibited a significant increase in species diversity (α diversity) compared to the healthy group, using 16S rRNA-based gene sequencing [28].

3.3. Structure and Composition of the Milk Microbiome of Buffaloes with Different Milking Methods

At the domain level, bacteria were the most abundant community in raw buffalo milk, accounting for approximately 99.85% of the total microorganisms. Fungi accounted for approximately 0.13%, viruses about 0.02%, and archaea were 0.00004% of the total microorganisms, respectively. Though the relative abundance of microbes was higher in HM milk, compared to MM milk, the abundance fluctuated more (CV = 49.9758 vs. 71.3008). Figure 2a shows Venn diagrams of microbial taxa found in HM and MM samples. The study detected 501 bacteria genera in HM and 262 bacterial genera in MM samples. A total of 255 genera (50.20%) were detected in both genomes (Figure 2a). The results showed that there was a significant difference in the microbial composition in raw buffalo milk from HM and MM. Moreover, the relative abundance analysis of the macro-genomes showed that the four most abundant phyla were Firmicutes, Proteobacteria, Actinobacteria and Bacteroidetes (about 99.4% of the total sequences). Furthermore, the genera with an average relative abundance greater than 1% in the final results were Staphylococcus spp., Klebsiella spp., Streptococcus spp., Pseudomonas spp., Maritimibacter spp., Clostridium spp., Mycobacterium spp., Nitratireductor spp., Arcobacter spp., Mycolicibacterium spp., Enterococcus spp., Anaplasma spp., Aliidongia spp. and Bacillus spp. (Figure 2b). The result showed that metagenomic increased the discriminatory power of this cutting-edge technology in identifying taxa in the milk microbiome [29,30], where the core bacteria Staphylococcus spp., Pseudomonas spp. and Bacillus spp. of raw buffalo milk have been discussed elsewhere in the literature, and the highest abundance of Staphylococcus spp. and Streptococcus spp. as the main bacterial genera in the milk trucks were similar to the results from the existing assay [31,32].
Reconstruction of the “core” milk microbiota, i.e., the bacterial taxonomic units shared by specific groups of samples, allows the identification of the most common bacterial species in cow milk. In this case, the “core” microbiota of milk samples was compared between different milking methods, in order to identify the effect of these methods on the most common bacterial species in milk. As mentioned above, only those genera that were more than 75% in the samples, with an average relative abundance of at least more than 0.1% were considered as a part of the “core” milk community [33]. The results showed that a total of 27 genera were found in HM and 25 genera in MM (Figure 3). Rhizobium spp. being the specific genus in MM. The genera Moraxella spp., Epilithonimonas spp. and Chryseobacterium spp. were the core bacterial genera specific to HM. Rhizopus spp. causes food contamination during storage, transport and storage, and the high temperatures and humidity required for Rhizopus spp. to grow are more difficult to achieve, but it is important to be aware of fungal contamination during subsequent handling [34]. Moraxella spp. is a genus of aerobic or partially anaerobic fermentative Gram-negative bacilli in the family Moraxellaceae of the order Pseudomonadales, and there is literature on Moraxella spp., Epilithonimonas spp. and Chryseobacterium spp. detected as part of the psychrophile from raw milk [35]. Therefore, the growth of the relevant psychrophilic bacteria should be closely monitored during refrigerated transport to avoid them becoming dominant species due to the low-temperature environment. The remaining 24 genera were prevalent in both sets of samples, suggesting that these microbial species are typical colonizers of buffalo’s milk, where to some extent, the presence of these species was not related to the milking method. To gain a deeper understanding of the differences in key microbial genera between milking methods, a differential analysis of relative abundance was conducted to determine the variability of bacterial genera between milking practices.

3.4. Differences in the Relative Abundance Composition of Core Microbiota and Bacteria Potentially Associated with Mastitis at the Genus Level between Milking Methods

The differences in the relative abundance of bacteria at the genus level between HM and MM samples were further investigated to understand the potential impact of the microbiota in raw buffalo milk on mastitis. The conventional causative organisms of bovine mastitis were summarized in Laboratory Handbook on Bovine Mastitis, 3rd Edition, published by the National Mastitis Council (NMC) [36], mainly as Streptococcus spp., Staphylococcus spp., Enterococcus spp., Lactococcus spp., Klebsiella spp., Enterobacter spp., Salmonella spp., Pseudomonas spp., Aspergillus spp., Pasteurella spp., Mycoplasma spp., Nocardia spp., Prochlorococcus spp., Corynebacterium spp., Tropezia spp., Mycobacterium spp. and Bacillus spp.. Our results showed that there was no significant difference in the total relative abundance of the main bacteria responsible for mastitis in HM and MM samples (Figure 4a). However, the relative abundance of individual bacteria including Staphylococcus spp. (p = 0.0000917), Streptococcus spp. (p = 0.000128), Mycobacterium spp. (p = 0.000139), Enterococcus spp. (p = 0.003456) and Corynebacterium spp. (p = 0.019608) were significantly higher in the mechanically milked samples than in the manually milked samples (Figure 4b). This is because the teat canal is opened after milking using high-frequency suction in mechanical milking. Moreover, a higher number of microbes in MM compared to HM could be due to stagnant fluids such as residual milk in the suction which may increase the bacterial counts in raw milk, and contribute to bacterial enrichment of the final tanks [37]. Our findings were aligned with the previous literature which proved that Staphylococcus spp. and Streptococcus spp. in breastmilk significantly increased with the usage of breast pumps [38].
On the other hand, Klebsiella spp. was significantly higher in HM (p = 0.006535) than in MM. Moreover, the mean of Pseudomonas spp. count was higher in HM than in MM, although the difference was not significant (p = 0.094561) (Figure 4b). This result is consistent with the trend of previous studies that Klebsiella spp. had a high distribution in HM samples, and Streptococcus spp. and Staphylococcus spp. were also widely distributed within MM samples [39,40]. The study found that Klebsiella spp. were mainly derived from faeces on bedding and associated equipment, particularly organic bedding such as sawdust [41]. The hygiene of the milking parlor as well as the cleaning and feeding facilities of the cows in MM greatly reduces the contact spread of this bacterium. In addition, hygiene conditions have a strong influence on the relative abundance of psychrophiles, which can reach up to 75% in unhygienic conditions [42]. A recent study applied 16S rDNA sequencing to raw milk samples collected over 12 consecutive months and identified Pseudomonas spp. as the most common genus [43]. Although Pseudomonas spp. as a psychrophile did not differ in t-test results between milking methods (p = 0.094561), the mean value for HM was almost four times higher than that for MM. Previous studies showed that identified bath cups, teat skin and milkers’ hands as the main sources of Pseudomonas spp. in raw milk [44]. Therefore, in HM environments, it is important to focus on controlling Pseudomonas spp. contamination. The types of flora that require attention vary depending on the milking method used. It is recommended to enhance the control of relevant flora based on the differences in milking methods.

3.5. Effect of Milking Methods on Antibiotic Resistance Genes

The relationship between drug resistance of microorganisms and different milking styles is illustrated in Figure 5. The level of bacterial antibiotic resistance genes in HM was much higher than that of MM, but the t-test difference was not significant (p = 0.0838). The most dominant carriers of antibiotic resistance genes were the genera Pseudomonas spp., Enterobacter spp., and Acinetobacter spp., as determined by corresponding the antibiotic resistance genes to the level of bacterial genera. The main types of antibiotics targeted by antibiotic-resistance genes were Fluoroquinolone, Tetracycline, Macrolide, Phenicol, and Diaminopyrimidine antibiotics. The comparison of antibiotic resistance in raw buffalo’s milk using different milking methods and the reads of their host bacteria were analyzed using statistical analysis of metagenomic profiles (STAMP). The results showed that the four β-lactam antibiotics, including penam, penem, monobactam and cephamycin, had significant differences between the different milking methods (Figure 5b). The genera Epilithonimonas spp. and Chryseobacterium spp. showed significant differences in antibiotic resistance between different milking methods, hence these genera should be given special attention during the HM process (Figure 5c). This is because the microbiota in animal products is influenced by human bacterial flora during processing or consumption. Antibiotics used for disease control in livestock often share active ingredients with human drugs. Therefore, antibiotic resistance genes accumulated from high levels of antibiotic usage in animal agriculture may be transferred to the human microbiota via animal products. The spread of antibiotic resistance genes may further reduce the efficacy of antibiotic therapy, at which new multi-drug resistant strains may be developed over time [45]. The widespread colonization of raw milk with Pseudomonas spp. and Enterobacter spp. and their strong correlation with antibiotic resistance genes requires us to pay more attention to the risks and their impact on humans.

3.6. Effect of Milking Methods on the Level of Virulence Factors of Pathogenic Bacteria

The results of the microbial virulence factors of the different milking methods and their correspondence to the bacterial genus level are shown in Figure 6. The amount of virulence genes was much higher in HM than in MM, but the difference was not significant (p = 0.1299). The correlation of virulence genes with bacterial genus level showed that Pseudomonas spp. and Aeromonas spp. were the most dominant carriers of virulence genes. Based on the differential analysis of virulence genes, ABK1_0086, mla, hsiF2, eno, BJAB07104_00098, M3Q_303, sypA and srt1 were the virulence factors that were significantly different between HM and MM. Irrespective of the milking method (Figure 6b), raw buffalo milk is ultimately transported to the processing plant under refrigerated conditions and the virulence factors are associated with the gradual production of extracellular enzymes and exotoxins as Pseudomonas spp. and Aeromonas spp. multiply. Greater attention should therefore be paid to the effects on milk quality during refrigerated transport.

4. Conclusions

Meta-genomic analysis is a complete way to learn about the different kinds of microbes that live in raw buffalo milk. The macro-genomic analysis of raw buffalo milk samples including chemical properties and microbial diversity revealed no significant difference between the samples from different milking methods concerning the major components of milk and somatic cell counts. However, the total number of bacterial cells and microbial diversity were significantly higher in HM than in MM. Moreover, the relative abundance analyses on the core microbiome showed significant differences in microflora between milking methods. Hence, it is important to highlight Staphylococcus spp. and Streptococcus spp. in MM. Additionally, the effect of Klebsiella spp. on buffalo milk should be noted in HM. On the other hand, Pseudomonas spp. is widely present in various milking methods; hence, it has significantly contributed to antibiotic resistance genes and virulence genes. Therefore, it is crucial to pay attention to their impact on the quality of raw milk during cold storage before processing. Our result will open the door for new research about this health concept.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/genes15081081/s1, Table S1: Evaluation statistics of sequencing data of 15 milk samples.

Author Contributions

Conceptualization, L.L.; methodology, L.L.; validation, Q.Z. and K.C.; formal analysis, L.L. and W.M.; investigation, L.L. and L.H.; data curation, L.L. and W.M.; writing—original draft preparation, L.L. and W.M.; writing—review and editing, Z.L., E.H., M.F.K. and K.C.; visualization, W.M.; supervision, Q.Z., Q.L. and K.C.; project administration, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was financed by the Guangxi Science and Technology Major Project (Project No. AA22068099); the Guangxi Innovation Team Construction of National Modern Agricultural Industrial Technology System (Project No. nycytxgxcxtd-2021-21-02).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files). The datasets generated and analyzed during the current study are available in the NCBI SRA repository submission number: PRJNA1084919. Data has not been released yet and will be available upon manuscript publication.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Nanda, A.S.; Nakao, T. Role of buffalo in the socioeconomic development of rural Asia: Current status and future prospectus. Anim. Sci. J. 2003, 74, 443–455. [Google Scholar] [CrossRef]
  2. Nie, P.; Pan, B.; Ahmad, M.J.; Zhang, X.; Chen, C.; Yao, Z.; Lv, H.; Wei, K.; Yang, L. Summer buffalo milk produced in China: A desirable diet enriched in polyunsaturated fatty acids and amino acids. Foods 2022, 11, 3475. [Google Scholar] [CrossRef]
  3. Quigley, L.; O’Sullivan, O.; Stanton, C.; Beresford, T.P.; Ross, R.P.; Fitzgerald, G.F.; Cotter, P.D. The complex microbiota of raw milk. FEMS Microbiol. Rev. 2013, 37, 664–698. [Google Scholar] [CrossRef] [PubMed]
  4. Addis, M.F.; Tanca, A.; Uzzau, S.; Oikonomou, G.; Bicalho, R.C.; Moroni, P. The bovine milk microbiota: Insights and perspectives from -omics studies. Mol. BioSyst. 2016, 12, 2359–2372. [Google Scholar] [CrossRef]
  5. Machado, S.G.; Baglinière, F.; Marchand, S.; Van Coillie, E.; Vanetti, M.C.D.; De Block, J.; Heyndrickx, M. The biodiversity of the microbiota producing heat-resistant enzymes responsible for spoilage in processed bovine milk and dairy products. Front. Microbiol. 2017, 8, 302. [Google Scholar] [CrossRef]
  6. Elmoslemany, A.M.; Keefe, G.; Dohoo, I.; Wichtel, J.; Stryhn, H.; Dingwell, R. The association between bulk tank milk analysis for raw milk quality and on-farm management practices. Prev. Vet. Med. 2010, 95, 32–40. [Google Scholar] [CrossRef]
  7. Abebe, R.; Hatiya, H.; Abera, M.; Megersa, B.; Asmare, K. Bovine mastitis: Prevalence, risk factors and isolation of Staphylococcus aureus in dairy herds at Hawassa milk shed, South Ethiopia. BMC Vet. Res. 2016, 12, 270. [Google Scholar] [CrossRef]
  8. Bhakat, C.; Mohammad, A.; Mandal, D.; Mandal, A.; Rai, S.; Chatterjee, A.; Ghosh, M.; Dutta, T. Readily usable strategies to control mastitis for production augmentation in dairy cattle: A review. Vet. World 2020, 13, 2364. [Google Scholar] [CrossRef]
  9. Krishnamoorthy, P.; Goudar, A.L.; Suresh, K.P.; Roy, P. Global and countrywide prevalence of subclinical and clinical mastitis in dairy cattle and buffaloes by systematic review and meta-analysis. Res. Vet. Sci. 2021, 136, 561–586. [Google Scholar] [CrossRef]
  10. Sharun, K.; Dhama, K.; Tiwari, R.; Gugjoo, M.B.; Iqbal Yatoo, M.; Patel, S.K.; Pathak, M.; Karthik, K.; Khurana, S.K.; Singh, R.; et al. Advances in therapeutic and managemental approaches of bovine mastitis: A comprehensive review. Vet. Q. 2021, 41, 107–136. [Google Scholar] [CrossRef] [PubMed]
  11. Yuan, L.; Sadiq, F.A.; Liu, T.J.; Li, Y.; Gu, J.S.; Yang, H.Y.; He, G.Q. Spoilage potential of psychrotrophic bacteria isolated from raw milk and the thermo-stability of their enzymes. J. Zhejiang Univ. Sci. B 2018, 19, 630–642. [Google Scholar] [CrossRef]
  12. Li, L.; Renye, J.A.; Feng, L.; Zeng, Q.; Tang, Y.; Huang, L.; Ren, D.; Yang, P. Characterization of the indigenous microflora in raw and pasteurized buffalo milk during storage at refrigeration temperature by high-throughput sequencing. J. Dairy Sci. 2016, 99, 7016–7024. [Google Scholar] [CrossRef] [PubMed]
  13. Éloit, M. Annual Report on Antimicrobial Agents Intended for Use in Animals, 7th ed.; World Organisation for Animals Health: Paris, France, 2023. [Google Scholar]
  14. Maurya, A.P.; Rajkumari, J.; Bhattacharjee, A.; Pandey, P. Development, spread and persistence of antibiotic resistance genes (ARGs) in the soil microbiomes through co-selection. Rev. Environ. Health 2020, 35, 371–378. [Google Scholar] [CrossRef]
  15. Thompson-Crispi, K.; Atalla, H. Bovine mastitis: Frontiers in immunogenetics. Front. Immunol. 2014, 5, 111762. [Google Scholar] [CrossRef]
  16. Sakwinska, O.; Bosco, N. Host microbe interactions in the lactating mammary gland. Front. Microbiol. 2019, 10, 448318. [Google Scholar] [CrossRef]
  17. Curone, G.; Filipe, J.; Cremonesi, P.; Trevisi, E.; Amadori, M.; Pollera, C.; Castiglioni, B.; Turin, L.; Tedde, V.; Vigo, D. What we have lost: Mastitis resistance in Holstein Friesians and in a local cattle breed. Res. Vet. Sci. 2018, 116, 88–98. [Google Scholar] [CrossRef] [PubMed]
  18. Bi, Y.; Wang, Y.J.; Qin, Y.; Guix Vallverdú, R.; Maldonado García, J.; Sun, W.; Li, S.; Cao, Z. Prevalence of bovine mastitis pathogens in bulk tank milk in China. PLoS ONE 2016, 11, e0155621. [Google Scholar] [CrossRef] [PubMed]
  19. Yap, M.; Feehily, C.; Walsh, C.J.; Fenelon, M.; Murphy, E.F.; McAuliffe, F.M.; van Sinderen, D.; O’Toole, P.W.; O’Sullivan, O.; Cotter, P.D. Evaluation of methods for the reduction of contaminating host reads when performing shotgun metagenomic sequencing of the milk microbiome. Sci. Rep. 2020, 10, 21665. [Google Scholar] [CrossRef]
  20. Li, D.; Liu, C.-M.; Luo, R.; Sadakane, K.; Lam, T.-W. MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 2015, 31, 1674–1676. [Google Scholar] [CrossRef]
  21. Zhu, W.; Lomsadze, A.; Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010, 38, e132. [Google Scholar] [CrossRef]
  22. Steinegger, M.; Söding, J. MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets. Nat. Biotechnol. 2017, 35, 1026–1028. [Google Scholar] [CrossRef] [PubMed]
  23. Li, W.; Godzik, A. Cd-hit: A fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 2006, 22, 1658–1659. [Google Scholar] [CrossRef] [PubMed]
  24. McArthur, A.G.; Waglechner, N.; Nizam, F.; Yan, A.; Azad, M.A.; Baylay, A.J.; Bhullar, K.; Canova, M.J.; Pascale, G.D.; Ejim, L.; et al. The comprehensive antibiotic resistance database. Antimicrob. Agents Chemother. 2013, 57, 3348–3357. [Google Scholar] [CrossRef] [PubMed]
  25. Chen, L.; Yang, J.; Yu, J.; Yao, Z.; Sun, L.; Shen, Y.; Jin, Q. VFDB: A reference database for bacterial virulence factors. Nucleic Acids Res. 2005, 33, D325–D328. [Google Scholar] [CrossRef] [PubMed]
  26. Janštová, B.; Dračková, M.; Dlesková, K.; Cupáková, Š.; Necidová, L.; Navrátilová, P.; Vorlová, L. Quality of raw milk from a farm with automatic milking system in the Czech Republic. Acta Vet. Brno 2011, 80, 207–214. [Google Scholar] [CrossRef]
  27. Ouamba, A.J.; Gagnon, M.; LaPointe, G.; Chouinard, P.Y.; Roy, D. Graduate Student Literature Review: Farm management practices: Potential microbial sources that determine the microbiota of raw bovine milk. J. Dairy Sci. 2022, 105, 7276–7287. [Google Scholar] [CrossRef]
  28. Hoque, M.N.; Istiaq, A.; Clement, R.A.; Sultana, M.; Crandall, K.A.; Siddiki, A.Z.; Hossain, M.A. Metagenomic deep sequencing reveals association of microbiome signature with functional biases in bovine mastitis. Sci. Rep. 2019, 9, 13536. [Google Scholar] [CrossRef] [PubMed]
  29. Jovel, J.; Patterson, J.; Wang, W.; Hotte, N.; O’Keefe, S.; Mitchel, T.; Perry, T.; Kao, D.; Mason, A.L.; Madsen, K.L. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front. Microbiol. 2016, 7, 459. [Google Scholar] [CrossRef]
  30. Oniciuc, E.A.; Likotrafiti, E.; Alvarez-Molina, A.; Prieto, M.; Santos, J.A.; Alvarez-Ordóñez, A. The present and future of whole genome sequencing (WGS) and whole metagenome sequencing (WMS) for surveillance of antimicrobial resistant microorganisms and antimicrobial resistance genes across the food chain. Genes 2018, 9, 268. [Google Scholar] [CrossRef] [PubMed]
  31. Salman, M.M.; Nawaz, M.; Yaqub, T.; Mushtaq, M.H. Exploring the milk microbiota of healthy and mastitic Nili Ravi buffalo using 16S rRNA gene base metagenomic analysis. Animals 2023, 13, 2298. [Google Scholar] [CrossRef] [PubMed]
  32. Kable, M.E.; Srisengfa, Y.; Laird, M.; Zaragoza, J.; McLeod, J.; Heidenreich, J.; Marco, M.L. The core and seasonal microbiota of raw bovine milk in tanker trucks and the impact of transfer to a milk processing facility. MBio 2016, 7, e00836-16. [Google Scholar] [CrossRef] [PubMed]
  33. Alessandri, G.; Sangalli, E.; Facchi, M.; Fontana, F.; Mancabelli, L.; Donofrio, G.; Ventura, M. Metataxonomic analysis of milk microbiota in the bovine subclinical mastitis. FEMS Microbiol. Ecol. 2023, 99, fiad136. [Google Scholar] [CrossRef] [PubMed]
  34. Zhang, H.; Wang, L.; Zheng, X.; Dong, Y. Effect of yeast antagonist in combination with heat treatment on postharvest blue mold decay and Rhizopus decay of peaches. Int. J. Food Microbiol. 2007, 115, 53–58. [Google Scholar] [CrossRef] [PubMed]
  35. Du, B.; Zheng, N.; Zhao, S.; Wang, J. Single molecule real-time sequencing and traditional cultivation techniques reveal complex community structures and regional variations of psychrotrophic bacteria in raw milk. Front. Microbiol. 2022, 13, 853263. [Google Scholar] [CrossRef] [PubMed]
  36. Middleton, J.R.; Fox, L.K.; Pighetti, G.; Petersson-Wolfe, C. Laboratory Handbook on Bovine Mastitis, 3rd ed.; National Mastitis Council: New Prague, MN, USA, 2017. [Google Scholar]
  37. Katsarou, E.I.; Katsafadou, A.I.; Karakasidis, T.; Chatzopoulos, D.C.; Vasileiou, N.G.; Lianou, D.T.; Mavrogianni, V.S.; Petinaki, E.; Fthenakis, G.C. Growth of Staphylococcus epidermidis on the surface of teatcups from milking parlours. Microorganisms 2021, 9, 852. [Google Scholar] [CrossRef]
  38. Sindi, A.S.; Cheema, A.S.; Trevenen, M.L.; Geddes, D.T.; Payne, M.S.; Stinson, L.F. Characterisation of human milk bacterial DNA profiles in a small cohort of Australian women in relation to infant and maternal factors. PLoS ONE 2023, 18, e0280960. [Google Scholar] [CrossRef] [PubMed]
  39. Mahanti, A.; Joardar, S.N.; Bandyopadhyay, S.; Banerjee, J.; Ghosh, S.; Dutta, T.K.; Samanta, I. Raw bovine milk as a reservoir of multi-drug resistant, β-lactamase-producing Klebsiella. J. Appl. Life Sci. Environ. 2024, 57, 197. [Google Scholar] [CrossRef]
  40. Woudstra, S.; Wente, N.; Zhang, Y.; Leimbach, S.; Kirkeby, C.; Gussmann, M.K.; Krömker, V. Reservoirs of Staphylococcus spp. and Streptococcus spp. associated with intramammary infections of dairy cows. Pathogens 2023, 12, 699. [Google Scholar] [CrossRef] [PubMed]
  41. Leso, L.; Barbari, M.; Lopes, M.; Damasceno, F.; Galama, P.; Taraba, J.; Kuipers, A. Invited review: Compost-bedded pack barns for dairy cows. J. Dairy Sci. 2020, 103, 1072–1099. [Google Scholar] [CrossRef]
  42. Hantsis-Zacharov, E.; Halpern, M. Culturable psychrotrophic bacterial communities in raw milk and their proteolytic and lipolytic traits. Appl. Environ. Microbiol. 2007, 73, 7162–7168. [Google Scholar] [CrossRef]
  43. Li, N.; Wang, Y.; You, C.; Ren, J.; Chen, W.; Zheng, H.; Liu, Z. Variation in raw milk microbiota throughout 12 months and the impact of weather conditions. Sci. Rep. 2018, 8, 2371. [Google Scholar] [CrossRef] [PubMed]
  44. Vidal, A.; Saran Netto, A.; Vaz, A.C.; Capodifóglio, E.; Gonçalves, A.; Rossi, G.A.; Figueiredo, A.S.; Ruiz, V.L. Pseudomonas spp.: Contamination sources in bulk tanks of dairy farms. Pesqui. Veterinária Bras. 2017, 37, 941–948. [Google Scholar] [CrossRef]
  45. Tóth, A.G.; Csabai, I.; Krikó, E.; Tőzsér, D.; Maróti, G.; Patai, Á.V.; Makrai, L.; Szita, G.; Solymosi, N. Antimicrobial resistance genes in raw milk for human consumption. Sci. Rep. 2020, 10, 7464. [Google Scholar] [CrossRef] [PubMed]
Genes 15 01081 g001
Figure 1. Principal component analysis (PCA) and principal coordinate analysis (PCA) of bacterial communities in raw milk. (a) Microbial communities in the HM and MM milk sample pools from β-diversity (principal component analysis; PCA), measured in the Bray–Curtis distance method (genus level). (b) Non-metric multidimensional scaling (NMDS) ordination plot with fitted macro-genomic variables (genus level).
Figure 1. Principal component analysis (PCA) and principal coordinate analysis (PCA) of bacterial communities in raw milk. (a) Microbial communities in the HM and MM milk sample pools from β-diversity (principal component analysis; PCA), measured in the Bray–Curtis distance method (genus level). (b) Non-metric multidimensional scaling (NMDS) ordination plot with fitted macro-genomic variables (genus level).
Genes 15 01081 g001
Genes 15 01081 g002
Figure 2. Taxonomic composition of the raw buffalo milk microbiome. (a) Venn diagram showing unique and shared bacteria at the genus level (b) Taxonomic signatures of relative abundance at the microbial genus level greater than 1% for samples based on macro-genomics.
Figure 2. Taxonomic composition of the raw buffalo milk microbiome. (a) Venn diagram showing unique and shared bacteria at the genus level (b) Taxonomic signatures of relative abundance at the microbial genus level greater than 1% for samples based on macro-genomics.
Genes 15 01081 g002
Genes 15 01081 g003
Figure 3. Composition of core microbiota of raw buffalo milk and chord diagrams (Red circles indicate unique micro-organisms at genus level with different milking methods).
Figure 3. Composition of core microbiota of raw buffalo milk and chord diagrams (Red circles indicate unique micro-organisms at genus level with different milking methods).
Genes 15 01081 g003
Genes 15 01081 g004
Figure 4. Differences in the relative abundance composition of core microbiota and bacteria potentially associated with mastitis at the genus level between milking methods. (a). Stacked histogram of bacterial core genera associated with mastitis in different milking practices; (b). Histogram of differences in bacterial core genera associated with mastitis in different milking methods.
Figure 4. Differences in the relative abundance composition of core microbiota and bacteria potentially associated with mastitis at the genus level between milking methods. (a). Stacked histogram of bacterial core genera associated with mastitis in different milking practices; (b). Histogram of differences in bacterial core genera associated with mastitis in different milking methods.
Genes 15 01081 g004
Genes 15 01081 g005
Figure 5. Effect of Milking Methods on Antibiotic resistance genes. (a). Alluvial plot of genus level of antibiotic resistance for different milking practices. (b). STAMP plot of antibiotic resistance for different milking practices. (c). STAMP plot of genus-level classification of antibiotic resistance genes for different.
Figure 5. Effect of Milking Methods on Antibiotic resistance genes. (a). Alluvial plot of genus level of antibiotic resistance for different milking practices. (b). STAMP plot of antibiotic resistance for different milking practices. (c). STAMP plot of genus-level classification of antibiotic resistance genes for different.
Genes 15 01081 g005
Genes 15 01081 g006
Figure 6. Effect of milking methods on the level of virulence factors of pathogenic bacteria. (a). Alluvial plots of virulence factors of pathogenic bacteria at the genus level for different milking styles (top 50 reads). (b). STAMP plots of virulence factors of pathogenic bacteria for different milking styles.
Figure 6. Effect of milking methods on the level of virulence factors of pathogenic bacteria. (a). Alluvial plots of virulence factors of pathogenic bacteria at the genus level for different milking styles (top 50 reads). (b). STAMP plots of virulence factors of pathogenic bacteria for different milking styles.
Genes 15 01081 g006
Table 1. Differences in basal nutrient composition, SCC and colony size of raw buffalo milk between two milking methods.
Table 1. Differences in basal nutrient composition, SCC and colony size of raw buffalo milk between two milking methods.
Milking
Method		Protein
(g/100 g)		Fat
(g/100 g)		Total Solid
(g/100 g)		Solids-Non-Fat
g/100 g)		Lactose
(g/100 g)		SCC
(×104/mL)		CFU
(×104 CFU/mL)
HM4.55 ± 0.39 a7.33 ± 0.46 a17.81 ± 1.34 a9.77 ± 0.88 a4.99 ± 0.39 a45.85 ± 31.33 a37.06 ± 17.62 a
MM4.50 ± 0.29 a7.03 ± 0.68 a17.61 ± 0.76 a9.99 ± 0.44 a5.18 ± 0.20 a29.58 ± 15.56 a8.37 ± 8.37 b
Note: All data shown were (mean ± standard deviation). The same lowercase letter in the shoulder scale represents a non-significant difference (p > 0.05), and different lowercase letters represent a significant difference (p < 0.05).
Table 2. Analysis of α diversity was conducted on raw buffalo milk samples from HM and MM groups.
Table 2. Analysis of α diversity was conducted on raw buffalo milk samples from HM and MM groups.
Milking
Method		Observed.SimpsonChao1ACEShannonGood’s
Coverage
HM830.4444 ± 49.9758 a0.8603 ± 0.0713 a852.363 ± 35.7754 a849.2941 ± 28.6194 a3.2567 ± 0.5710 a0.9998 ± 0.0003 a
MM665.1667 ± 71.3008 b0.7875 ± 0.0994 a777.734 ± 53.5937 b 780.1879 ± 45.1084 b2.5035 ± 0.6112 b0.9982 ± 0.0011 b
Note: All data shown were (mean ± standard deviation). The same lowercase letter in the shoulder scale represents a non-significant difference (p > 0.05), and different lowercase letters represent a significant difference (p < 0.05).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, L.; Miao, W.; Li, Z.; Huang, L.; Hau, E.; Khan, M.F.; Liu, Q.; Zeng, Q.; Cui, K. Meta-Genomic Analysis of Different Bacteria and Their Genomes Found in Raw Buffalo Milk Obtained in Various Farms Using Different Milking Methods. Genes 2024, 15, 1081. https://doi.org/10.3390/genes15081081

AMA Style

Li L, Miao W, Li Z, Huang L, Hau E, Khan MF, Liu Q, Zeng Q, Cui K. Meta-Genomic Analysis of Different Bacteria and Their Genomes Found in Raw Buffalo Milk Obtained in Various Farms Using Different Milking Methods. Genes. 2024; 15(8):1081. https://doi.org/10.3390/genes15081081

Chicago/Turabian Style

Li, Ling, Wenhao Miao, Zhipeng Li, Li Huang, Enghuan Hau, Muhammad Farhan Khan, Qingyou Liu, Qingkun Zeng, and Kuiqing Cui. 2024. "Meta-Genomic Analysis of Different Bacteria and Their Genomes Found in Raw Buffalo Milk Obtained in Various Farms Using Different Milking Methods" Genes 15, no. 8: 1081. https://doi.org/10.3390/genes15081081

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop