An investigation into the seasonal, herd-specific, parity, and subclinical mastitis-related fluctuations in the microflora of raw milk from dairy cows in Ningxia, China

doi:10.21203/rs.3.rs-4871575/v1

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Research Article

An investigation into the seasonal, herd-specific, parity, and subclinical mastitis-related fluctuations in the microflora of raw milk from dairy cows in Ningxia, China

https://doi.org/10.21203/rs.3.rs-4871575/v1

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Background

The milk microflora is closely associated with the physiology and pathology of the mammary gland, and plays a pivotal role in the development of offspring. The aim of this study was to examine the variability of milk microflora diversity in relation to seasonal, herd-specific, subclinical mastitis status, and parity-related effects.

Results

Milk samples were collected from 285 cows in Ningxia, China, followed by the exploration of microflora using 16S rRNA pyrosequencing. Subsequently, all samples were categorized based on seasonal variations (summer and winter), cow health status (healthy and subclinical mastitis cows), farm origins (6 commercial dairy farms), and parity levels (primiparity and multiparity). The bacterial diversity, community composition, and abundance were assessed in distinct cohorts. Additionally, the milk microflora from samples collected during summer, winter, and colostrum was compared. The findings indicated a significant variation in the bacterial diversity of milk between samples collected from summer and winter. It was observed that the summer samples exhibited higher bacterial richness compared to the winter samples. The gut-related genera, including Parabacteroides, Staphylococcus, Corynebacterium_1, Sphingomonas, and Lactobacillus, were found to be prevalent in milk samples collected in summer. Conversely, Escherichia_Shigella, Pseudomonas, Streptococcus, Psychrobacter, Rhizobium, Bifidobacterium, and Clostridium_sensu_stricto_1 were commonly observed in winter samples. The subclinical mastitis cows showed a significantly higher alpha diversity value (chao1 and Shannon index) compared to the healthy cows (summer). Sporolactobacillus, Mycobacterium, Escherichia_Shigella, and Actionmycetaceae were identified as biomarkers in the subclinical mastitis cows. In addition, the top 20 KEGG pathways were significantly reduced in the subclinical mastitis group compared to the healthy cows in summer, particularly in membrane transport, cell motility, and signal transduction pathways. The bacterial diversity varied across different farms, with Farms A and B demonstrating a higher degree of similarity of milk bacterial composition, while Farms D and F also displayed similarities. Farm C appeared distinct from other farms. Furthermore, there was observed a greater bacterial richness in multiparous cows compared to primiparous cows. Additionally, the comparative analysis revealed a significant difference in alpha and beta diversity, bacterial profiles among summer, winter and colostrum samples.

Conclusions

The study indicated that the composition of milk microflora exhibited variability across different seasons, farms, healthy status, and parities. Furthermore, distinct microflora characteristics were observed in milk samples obtained during summer, winter, and colostrum stages.

Milk microflora

16S rRNA pyrosequencing

Season

Farm

Subclinical mastitis

The presence of microflora in milk plays a crucial role in the growth and development of offspring, as well as the overall health of the maternal mammary gland [1–4]. However, certain microbes found in milk can also contribute to a shortened storage period for raw milk and potential spoilage. Through 20 years of research utilizing both cultured and uncultured methods, it had been determined that despite being present in low quantities, milk contains a diverse range of microbial species. Based on a comprehensive analysis of 3105 breast milk samples collected from 2655 women, it was found that the microbial composition of breast milk encompassed 58 phyla, 133 classes, 263 orders, 596 families, 590 genera, 1300 species, and 3563 OTUs [5]. Due to its prevalence and impact on mammary gland health and productivity, the analysis of bovine milk microflora had primarily centered on episodes of mastitis, the most common disease in the dairy industry [6]. According to a survey, Faecalibacterium spp., unclassified Lachnospiraceae, Propionibacterium spp., and Aeribacillus spp., were found to be present in healthy quarters, while Sphingobacterium and Streptococcus were associated with elevated somatic cell counts (SCC) [7]. Another investigation elucidated the compositional differences within the milk microbiota, identifying Sphingomonas, Stenotrophomonas, Burkholderia, and Brevundimonas as prevalently present in clinical mastitis, whereas healthy milk samples exhibited a dominance of Pseudomonas and Psychrobcter, and Ralstonia [2]. Furthermore, it was established that the majority of milk microbiota exhibited no correlation with mastitis pathogenesis. Notably, Staphylococcus. aureus and Streptococcus spp. populations, commonly encountered in the microbiota of healthy cattle, were found to constitute a core component of the milk microbiota. These findings offer novel perspectives into the ecological dynamics of the mammary gland microbiota [8, 9].

Moreover, the composition of milk microbiota undergoes constant changes as a result of the flushing out of milk fluid and the effects of mammary gland immunity. Additionally, milk microbiota is susceptible to various influencing factors including host characteristics, environmental conditions, and sampling methods [10]. It had been observed that the Mediterranean diet led to an increase in lactic acid bacteria and bile acid metabolites in milk compared to Western diets [11]. The abundance of Staphylococcaceae, Bacillaceae, Streptococcaceae, Microbacteriaceae, and Micrococcaceae in milk was been found to be influenced by seasonal variations [12]. In Farm A with a low incidence rate of subclinical mastitis, Klebsiella, Escherichia_Shigella, and Streptococcus were identified as primary; whereas in Farm B with a high incidence rate of subclinical mastitis, Streptococcus and Corynebacterium were determined as the main pathogens [13]. Those findings suggest that milk microflora exhibits a high level of diverse and is influenced by various factors, thereby enhancing our comprehension of milk composition and its intricate interplay with the host and environment. Nevertheless, to date, no study had integrated these components to investigate the milk microbiota.

Although the physiology and pathology implications of the milk microflora are still under research, existing studies had demonstrated its crucial role in maintaining mammary gland health to combat mastitis in both women and animals, as well as its underlying impact on dairy cow milking performance. Therefore, this current study employs 16S rRNA sequencing to explore raw milk microflora and analyze key factors related to milk production performance, including seasons, farms, healthy and subclinical mastitis cows, and parity in Ningxia, China. The aim of our study is to elucidate the patterns of changes in milk microflora.

A significant difference in OTUs was observed between summer and winter samples (p < 0.01, Fig. 1A). Additionally, the OTUs in Farms A and B exhibited a significantly lower abundance compared to Farms C, D, E, and F (Fig. 1B and E). The OTUs showed a notably higher prevalence in the subclinical mastitis group than in the healthy cows in summer (p < 0.01, Fig. 1C). The trend was also evident among multiparity and primiparity cows (Fig. 1D). However, this pattern was not observed in samples collected in winter (Fig. 1F and G).

Alpha diversity evaluates the species richness and uniformity. The chao1 index was utilized to indicate community richness, while the Shannon index was used to represent community evenness. In our study, both chao1 and Shannon indices exhibited significantly higher values in summer compared to winter (p < 0.01, Fig. 2A and B). Farm A and B showed lower chao1 values than Farm C, D, and E (in summer)/F (in winter) (p < 0.01, Fig. 2C, and I). The subclinical mastitis cows displayed a higher chao1 index than the healthy cows in summer but not in winter (Fig. 2D and J). Additionally, the multiparous cows had a higher chao1 index than the primiparous group in summer but not in winter (Fig. 2E and K). In summer, the Shannon index was significantly higher in Farm C and D compared to Farm B (Fig. 2F) with significant differences also observed between the healthy and subclinical mastitis cows (p < 0.01, Fig. 2G). However, there were no significant difference between the multiparity and primiparity cows (Fig. 2H). In winter, variations among farms were noted; generally speaking, Farm D and F exhibited higher Shannon index values than Farm A, B, and C (Fig. 2L), but no significant changes were found between healthy and subclinical mastitis group, and primiparity and multiparity cows (Fig. 2M and N).

Beta diversity analysis was conducted using unweighted uniFrac PCoA to explore the similarities and differences in microbial communities. The results revealed distinct microbiota boundaries between raw milk samples in summer and winter cows, indicating that seasonality was the primary factor influencing the composition of raw milk microflora (Fig. 3A). Furthermore, farms from group1 (FarmA, B) and group2 (Farm C, D and E) exhibited significant differences in bacterial compositions in summer, while farms from group1 (Farm A, B), group2 (Farm C, D) and Farm F showed distant distances in winter (Fig. 3B). Notably, no variations were observed in the microflora composition between healthy and subclinical mastitis cows, and primiparity and multiparity cows (Fig. 3C and D).

In the summer group, the predominant phyla were Bacteroidetes (41.72%), Firmicutes (37.53%), Proteobacteria (11.57%), Actinobacteria (3.13%), Gemmatimonadetes (1.41%) and Acidobacteria (0.76%), collectively accounting for 96.13% of total sequences. In winter, Firmicutes (40.01%), Proteobacteria (22.69%), Bacteroidetes (21.87%) and Actinobacteria (11.46%) comprised 96.03% of total sequences (Fig. 4A). In comparison to samples collected in summer, winter samples exhibited higher abundance Proteobacteria (22.69% vs. 11.57%) and Actinobacteria (11.46% vs. 3.13%), while showing lower levels of Bacteroidetes (21.87% vs. 41.72%) (Fig. 4A). Significant variations in the relative abundance of dominant phyla were observed among farms, particularly in winter (Fig. 4B and E). However, no differences were detected between the healthy and subclinical mastitis cows or between primiparity and multiparity cows, both in summer and winter (Fig. 4C, D, F, and G). Notably, Gemmatimonadetes was the only phylum that showed higher levels in primiparity compared to multiparity in summer (Fig. 4D).

In summer, the dominant genera included Lachnospiraceae_NK4A136_group, Ruminococcaceae_UCG_014, Alloprevotella, Desulfovibrio, Bacteroides, Lachnospiraceae_UCG_001, Helicobacter, Roseburia, Odoribacter, Ruminiclostridium_9, Parabacteroides, Staphylococcus, Parasutterella, Allobaculum, Lachnoclostridium, Alistipes, Anaerotruncus, Ruminiclostridium_5, Lachnospiraceae_UCG_006, Prevotellaceae_UCG_001, Anaeroplasma, Lactobacillus, Lachnospiraceae_FCS020_group, Rikenellaceae_RC9_gut_group, Corynebacterium_1, Sphingomonas, Coprococcus_1, Incertae_Sedis, Rikenella and Mucispirillum (Fig. 5A). In winter, Bacteroides, Faecalibacterium, Escherichia_Shigella, Corynebacterium_1, Blautia, Pseudomonas, Streptococcus, Psychrobacter, Rhizobium, Bifidobacterium, Parasutterella, Clostridium_sensu_stricto_1, Turicibacter, Planococcus, Ruminococcaceae_UCG_005, Atopostipes, Anaerostipes, Rothia, Ralstonia, Jeotgalicoccus, Staphylococcus, Parabacteroides, Rikenellaceae_RC9_gut_group, Lachnospiraceae_UCG_004, Ruminococcaceae_UCG_014, Alistipes, Sutterella, Pantoea, [Eubacterium]_coprostanoligenes_group and Sphingomonas were the prevalent genera (Fig. 6A). Four predominant genera (Bacteroides, Corynebacterium_1, Parasutterella, and Parabacteroides) were shared in two seasons.

We further conducted a comparative analysis of the top 10 genera changes across different cows. Variations were observed among farms, particularly in winter; however, no significant differences were found between the healthy and subclinical mastitis cows, as well as the primiparity and multiparity cows (Fig. 5A-D; Fig. 6A-D). In summer, Farm B exhibited higher abundance of Alloprevotella, Bacteroides, and Ruminiclostridium_9 compared to the other four farms; Farm C showed higher levels of Desulfovibrio than the other farms; while Farm A had lower levels of Helicobacter compared to other farms. In winter, Farm A and B displayed higher levels of genera of Bacteroides, Faecalibacterium, Escherichia_shigella, and Blautia compared to Farm C, D, and F. Additionally in winter, Farm C had the highest relative abundance of Corynebacterium_1, Psychrobacter, and Bifidobacterium, and the lowest Pseudomonas, Streptococcus, and Rhizobium. In summer, the primiparity group exhibited a significantly higher relative abundance of Bacteroides compared to the multiparity group (p < 0.01). Conversely, no significant differences were observed between healthy and subclinical mastitis cows, as well as primiparity and multiparity in winter (p > 0.05).

During the summer, o_Rhizobiales, o_Acidobacteriales, and o_Acidimiorobiaceae were identified as biomarkers in healthy cows, while g_Bacteroidales_S24_7_group, o_Fibrobacterles, o_Nitrospiraceae, g_Sporolactobacillus, f_Mycobacteriaceae and g_Mycobacterium were identified as biomarkers in subclinical mastitis cows (Fig. 7A). In winter, k_Actinobacter, such as o_Pseudomondales, o_Micrococcales, f_Nocardioidaceae, f_Rhodospirillaceae, and o_Acidimicrobiales were found to be biomarkers in the healthy cows. Meanwhile, g_Escherichia_Shigella, f_Lactobacillaceae, g_Lactobacillus, g_Prevotella_9, f_Actionmycetaceae, o_Actionmycetales, and f_TRA3_20 was identified as biomarkers in the subclinical mastitis cows (Fig. 7B).

The enrichment of KEGG pathways were found to be higher in summer compared to winter. Specifically, 17 pathways exhibited significantly higher enrichment in healthy cows compared to those with subclinical mastitis group in summer (p < 0.05), with three pathways (cell motility, membrane transport, and signal transduction) showing extremely significantly differences (p < 0.01) (Fig. 8A). This trend was also observed in winter (Fig. 8B). Furthermore, Farm A showed some distinct differences from other farms with regards to 5 out of 20 KEGG pathways in summer; while in winter, significant changes were observed between Farm A and F, as well as Farm C and F involving a total of 15 pathways. Notably, there were no changes among the top 20 KEGG pathways between primiparity, and multiparity cows both in summer and winter (data not shown).

The number of OTUs in summer, winter and colostrum samples were 21976, 13293, and 8506 respectively. There were 8182 shared OTUs between Summer and Winter samples, while only 3134 OTUs were shared among the colostrum, Summer, and Winter samples (Fig. 9A). The alpha diversity (accessed by chao1 and Shannon indices) differed significantly among the three cows, with the highest values observed in the summer group, following by the colostrum group and winter group (Fig. 9B and C). The beta diversity of raw milk and colostrum was compared using PCoA and UPGMA analysis. As depicted in Fig. 9D, there were distinct differences in microflora composition among raw milk samples collected in summer, winter, and colostrum cows, suggesting significant variations in species composition. In conclusion, based on the OTUs counts, alpha diversity and beta diversity analyses, it can be inferred that the milk samples obtained from summer, winter, and colostrum stages exhibited distinct characteristics.

We further compared the bacterial composition at the phylum and genus levels. As shown in Fig. 10A, Firmicutes is significantly enriched within the colostrum cows, whereas the Proteobacteria and Actinobacteriota were more abundant in winter cows. Conversely, the remaining seven top15 phyla demonstrated a higher prevalence in summer cows (Fig. 10A). At the genus levels, a total of 851 distinct bacterial genera were identified across the summer, winter and colostrum sample cows. Among the top 30 genera, the colostrum sample group was characterized by a higher abundance of Escherichia-shigella and Lactobacillus, whereas Bacteroides, Faecalibacterium, Corynebacterium, [Eubacterium]_eligens_group and Blautia were more prevalent in the winter cows. Conversely, the summer cows showed enrichment of Muribaculaceae, Lachnospiraceae_NK4A136_group, Clostridia_UCG-014(Fig. 10B). The Lefse analysis depicted in Fig. 10C identified Bacteroides, Faecalibacterium, Eubacterium_eligens_group, Corynebacterium, Blautia, Pseudomonas were biomarkers genera in winter group. Muribaculaceae, Lachnospiraceae_NK4A136_group, Clostridia_UCG_014, Alloprevotella were identified as biomarkers in summer group. Additionally, Lactobacillus, Escherichia_Shigella, Collinsella, Prevotella, Clostridium_sensu_stricto_1, Serratia, Klebsiella, Alistipes, and Bifidobacterium were found to be biomarkers in Colostrum.

To our knowledge, this study represents the first comprehensive analysis of the microflora in cows’ milk, taking into account seasonal variations, farm, health status (healthy cows or subclinical mastitis), and parity simultaneously using pyrosequencing on a large scale. The findings from this study contributed to a deeper understanding of how milk microflora varies in relation to key factors influencing milk production performance. Our results confirmed that milk hosts a diverse and abundant microbial community influenced by various factors. Furthermore, our investigation reveals distinct microflora characteristics specific to summer, winter, and colostrum milk.

The discrepancy between the two seasons

The influence of seasonal variations on microorganisms is commonly attributed to temperature and humidity, which serve as the primary determinants for the majority of bacterial taxonomic fluctuation [14, 15]. Consistent with temperature patterns, it had been demonstrated that bacterial richness tends to be lower in winter compared to summer [14]. Similarly, our study revealed a significantly higher bacterial richness and diversity in milk samples collected in summer compared to winter. Furthermore, distinct differences in bacterial profiles allowed for clear differentiation between milk samples from summer and winter (Fig. 3A). The seasonal variation also extended to changes in microbiota composition. Metzger reported 11 out of the top 20 OTUs exhibited seasonal variability [15]. Nalepa’s findings indicated significant fluctuations in the species composition of raw bovine milk over a period of 22 months, with certain species/groups appearing seasonally; for example, Lactobacillus helveticus was more prevalent during summer, while Lactobacillus casei was more common during winter [16]. Nguyen observed differences in the relative abundance of genera Staphylococcaceae, Bacillaceae, Ruminococcaceae, Veillonellaceae, Methylobacteriaceae, and Moraxellaceae between the two seasons [17]. In our study, gut-associated genera such as Lachnospiraceae_NK4A136_group, Ruminococcaceae_UCG_014, and Alloprevotella were predominant in summer milk samples. Conversely, Escherichia_Shigella, Corynbacterium_1, Pseudomonas, Streptococcus, Psychrobacter, Rhizobium, and Bifidobacterium were more prevalent in winter.

Gut bacteria, including Prevotella, Ruminococcus, Bacteroides, Rikenella, and Alistipes had been prevalent in milk in previous studies [18–21]. Corynebacterium, Streptococcus, Pseudomonas, and Psychrobacter were found to be more commonly detected in the colder season compared to the warmer season due to their psychrophilic features [20, 22]. Additionally, Bifidobacterium was frequently detected in winter while Lactobacillus was commonly found in summer. Both of these genera are regarded as potential probiotics and are commonly present in milk samples. The reasons for the differences in their distribution remain elusive. Furthermore, several studies had documented the presence of Rhizobium or Bradyrhizobium in human milk [23, 24], goat milk [25], and cow milk [2, 26, 27]. Rhizobium and Bradyrhizobium belong to the bacterial order Rhizobiales. While Rhizobia are typically soil bacteria that form symbiotic relationships with legumes, they are not commonly recognized as gut microbes [15]. The potential explanation for their presence in milk may involve an endogenous entero-mammary pathway [4]. In addition to the aforementioned genera, Parabacteroides was also prevalent in our samples. Drago also discovered that Parabacteroides play a pivotal role in the bacterial network within mature milk from Italian mothers [24]. Our findings suggested indicate that individual variations were more prevalent in winter compared to summer (Fig. 3). This may be attributed to the higher gut-associated microbiota in bovine milk during summer, with relatively stable microbiota in individuals. The KEGG pathway was utilized for analyzing known bacterial gene functions. Data from our study suggests a greater abundance of metabolism during summer compared to winter (Fig. 8), which aligns with the results reported by Li [14]. As a result of the higher bacterial communities present during summer as opposed to winter, there is t consequently an increase in bacterial metabolism.

The health status the mammary gland had an impact on milk microbiota

The original objective of our study was to compare the predominant variances in milk microflora between healthy and subclinical mastitis cows. Enhanced richness and diversity are typically associated with improved health outcomes in systems characterized by a dense microflora, such as the gut or skin [15]. Milk from healthy cows generally exhibits greater richness and diversity of microbiota compared to that from mastitis-infected cows, even within different mammary glands of the same cow [28]. Some researchers had observed disparities in milk microbiota between healthy quarters and those affected by clinical mastitis quarters [15, 19]. Hoque also reported higher microbiota diversity and species richness in milk with mastitis conditions (including clinical mastitis, recurrent mastitis, and subclinical mastitis) than in healthy milk samples [29]. The present study revealed a significantly higher alpha diversity value (chao1 and Shannon index) in the subclinical mastitis cows compared to the healthy cows in the summer samples (Fig. 2D and G). Furthermore, Sporolactobacillus, Mycobacterium, Escherichia_Shigella, and Actionmycetaceae, common pathogens associated with bovine mastitis, were identified as biomarkers in the subclinical mastitis cows in this study (Fig. 7A and B). In addition, our research indicated that all the top 20 KEGG pathways were significantly reduced in the subclinical mastitis group compared to the healthy group in summer, particularly in membrane transport, cell motility, and signal transduction pathways [Fig. 8A]. The elevated microbial KEGG pathways indicate heightened host metabolic activity. These findings suggest that subclinical mastitis is characterized by a weakened host metabolic. However, further validation of these results is necessary.

The inter-farm variation in milk microbiota

Compared to the factors influencing health status, there was limited research on the inter-farm variation of the microbiota. Pang observed that microbial diversity between two farms could be distinguished[13]]. Nguyen identified differences in the relative abundance of Pseudomonadaceae, Enterobacteriaceae, and Streptococcaceae, Lactobacillaceae, Bifidobacteriaceae, and Cellulomonadaceae in milk from the two farms [17]]. In addition to milk, Weese also reported high variability in fecal microbiota of calves between farms [30]. In our study, the dominant phyla were found to be similar between Farm A and B, both enriched with Bacteroidetes, but showing a decrease in Actinobacteria compared to other farms. Conversely, Farm C exhibited the highest levels of Firmicutes and Actinobacteriota, but the lowest levels of Bacteroidetes in winter (Fig. 4E). During summer, slight variations were observed among farms (Fig. 4B). Specifically, Farms D and E displayed similar phyla compositions, while Farm B showed higher levels of Bacteroidetes and Firmicutes as well as lower levels of Proteobacteriota compared to Farm A. Additionally, Farm C exhibited higher levels of Firmicutes than Farm A. Furthermore, significant differences were also noted in the phyla of Gemmatimonadetes and Acidobacteriota (Fig. 4B). From PCoA plots analysis it was observed that Farms A and B exhibited more similarity in milk microbiota composition whereas Farms D and F also showed similarities. In contrast, Farm C appeared distinct from other farms (Fig. 3). In our study, all farms are well-managed and productive operations that participate in the DHI schedule. Farms A, B and C are located at the foot of Helan mountain, providing a relatively suitable natural environment. Farms B and C are geographical closer in distance. Farms D, E and F are situated in an open area with close proximity to each other. Farm A exhibits higher SCC, while Farm B had lower yields of milk, milk fat, protein, herd size, and outdated facilities. Conversely, Farms C, E and F demonstrated higher yield with lower milk SCC due to strict feeding management practices. Farm D showed higher levels of milk fat and protein but a lower yield of milk overall. Notably among those farms is Farm F which possessed the largest herd size. The differences between farms can be attributed to multiple factors. The unique characteristics of each farm may impact the microbial diversity in cow’s milk. Unidentified management practices could have a significant influence on the microbiota composition. For instance, during winter, Farms A and B showed enrichment of genera, such as Bacteroides, Faecalibacterium, Escherichia_Shigella, Blautia, but lower levels of Corynebacterium_1 and Pseudomonas; Farm C exhibited enrichment of Corynebacterium_1, Psychrobacter, Bifidobacterium, and lower levels of Rhizobium; while Farms D and F were enriched with Pseudomonas and lower levels of Escherichia_Shigella (Fig. 6B). Giving that Pseudomonas was prevalent in milk from healthy cows and Escherichia_Shigella was associated with mastitis cows, it is spectated that Farms D and F harbored a more balanced microbiota in their milk compared to Farms A, B, and C. Furthermore, our data suggested that Farms A and B were more prone to mammary gland issues related to Escherichia_Shigella while Farm C had higher occurrences of Corynebacterium_1. Overall, it is evident from our study that management practices play a crucial role in determining milk quality.

The parity difference of the milk microbiota

Multiparous cows are at a higher risk of mastitis compared to primiparous cows [31]. Therefore, we hypothesized that parity number was an important factor influencing the milk microbiota. Lima documented variations in the bacterial composition of colostrum between primiparous and multiparous cows, with Staphylococcus, Fusobacterium, Acinetobacter, and Bacteroides being more prevalent in the colostrum of multiparous cows than in primiparous cows[32]. The colostrum microbiota of primiparous cows exhibited greater richness compared to that of multiparous cows. In contrast, our observations revealed higher bacterial richness in multiparous cows as opposed to primiparous cows (Fig. 2E). This phenomenon may be ascribed to increased exposure of the intramammary ecosystem to environmental sources of microbes in multiparous cow’s relative to primiparous ones.

In addition to the aforementioned factors discussed, numerous other variables had been shown to impact milk microbiota. Toscano reported disparities in Cesarean section and vaginal delivery [33]. Derakhshani observed the influence of BoLA-gene polymorphism on colostrum microbiota in dairy cows [34]. Metzger found variations in bacterial community composition among different bedding types in dairy farms [35]. Furthermore, Cabrera-Rubio demonstrated that maternal weight and mode of delivery can also affect the women's milk microbiota [36]. In summary, research on milk microbiota is still at an early stage.

Additionally, the comparative analysis revealed a significant difference in alpha diversity among summer, winter and colostrum samples exhibiting higher diversity than both winter and colostrum samples. Furthermore, colostrum samples showed significantly higher diversity compared to winter samples (Chao1 index). The beta diversity analysis using PCoA and UPGMA indicated distinct boundaries between sample groups, while samples within each group were more closely clustered together. Moreover, there were significant differences in the Top10 phyla and genera of each group. The findings collectively demonstrated the unique bacterial flora diversity presented in each sample group. Therefore, our study revealed significant differences in microbiota diversity among raw milk samples collected during summer, winter, and colostrum. Future analyses of milk microbiota in dairy cows should carefully consider the seasonal variations in sampling. Additionally, it is important to recognize the crucial role of bacteria and their metabolites in the production of high-quality fresh milk and protection of mammary gland health in dairy cattle. However, it is worth noting that current knowledge in this field is still at an early stage, with limited literature on the correlation between milk bacteria and their functions. Therefore, the microflora analysis presented in this paper was primarily descriptive and lacked comprehensive cross-sectional or longitudinal association analysis as well as correlation studies between flora and function.

In the current investigation, we demonstrated that the microflora of cows’ milk varied in association with several key factors related to milk production performance. Firstly, a clear distinction was observed between raw milk samples collected from summer and winter. During the summer, gut-related genera were predominant, while Bacteroides, Faecalibacterium, Escherichia_Shigella, Corynebacterium_1, Blautia, Pseudomonas, Streptococcus, Psychrobacter, Rhizobium, and Bifidobacterium were prevalent in winter milk. Secondly, our study confirmed that farm location, health status of the cows, and parity also influenced the raw milk microflora. Finally, the unique characteristics of summer and winter as well as colostrum were observed. This study contributed to a better understanding of how cow milk microflora varies in relation to factors associated with milk production performance.

Animals and milk sample collection

The large commercial dairy farms included in the study operated double 52-stall (Farms A, B, D, E, and F) and 26-stall (Farm C) parallel milking parlor to milk 1000–3000 Holstein cows thrice daily. These farms are located in the north (Farm A, N106.69, E39.12), mid (Farm B, N106.04, E38.42 and Farm C, N106.07, E38.67), and south (Farm D, N106.29, E37.75; Farm E, N106.30, E37.58; and Farm F, N106.24, E37.59) regions of Ningxia Province in China. The summer milk samples were collected from August 8 to 19, 2018, and the winter samples were collected from January 14 to 23, 2019, separately. Milk samples were collected from one teat for each cow. During the summer, raw milk was collected from farms A, B, C, D, and E, totaling 135 samples; while in winter, milk samples were obtained from 150 cows in farms A, B, C, D, and F. The sampling methods adhered to standard recommendations. In brief, the first streams of milk were discarded, and the teats were subsequently exposed to iodine tincture for 30 s and dried with individual towels by farm veterinarians. Subsequently, the first streams of milk were discarded and the milk samples were collected. Approximately 30 mL of milk was collected into a 50 mL sterile centrifuge tube and stored at -20°C. Following collection, the milk samples were thawed on ice and subsequently centrifuged at 12,000 rpm for 10 min at 4°C to separate fat and cells from the whey. The pellets were then carefully collected in a 1.8 mL sterile freezing tube and stored at -80°C for further analysis. Cows with a lactation number of 1 were defined as primiparous cows, while those with a lactation number greater than 1 (ranging from 2–8) were defined as multiparous cows.

All samples were collected from cows without visible signs of clinical mastitis, such as udder swelling or redness. Subclinical mastitis was diagnosed using the Lanzhou mastitis test (LMT) reagent by experiencing veterinarians on farms. Briefly, approximately 2 mL of milk was aseptically collected with a detection disk and then mixed with 2 mL LMT regent. The mixture was observed within 1 min and subclinical mastitis milk was diagnosed by the presence of obvious flocculence or gelation in the mixture; healthy milk was defined as milk without these phenomena and with good fluidity. The total sample numbers were presented in Table 1.

Table 1

List of sample collections
Season	Farm A	Farm B	Farm C	Farm D	Farm E	Farm F	Health	Sub clinical mastitis	primiparity	multiparity
Summer	30	33	25	24	23	/	67	68	35	100
Winer	21	27	38	28	/	36	73	77	58	73
Total	52	60	63	52	23	36	140	145	93	173

The symbol "/" in the table indicates non-collection of the sample, while the numerical values represent the quantity of raw milk collected from cow.

DNA extraction and library construction

The DNA extraction and library construction from milk samples followed the methods described in the references [37]. Briefly, total DNA was extracted using a DNeasy PowerSoil Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol. The quality and quantity of DNA were assessed using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and agarose gel electrophoresis. The extracted DNA was diluted to a concentration of 1 ng µL^− 1 and stored at -20°C for subsequent processing. This diluted DNA served as the template for PCR amplification of bacterial 16S rRNA genes with barcoded primers and Ex Taq DNA polymerase kit (Takara, Dalian, China). The V3-V4 variable regions of the 16S rRNA gene were amplified with universal primers 343F and 798R (5'- TACGGRAGGCAGCAG-3'; 5'-AGGGTATCTAATCCT-3'). After two rounds of amplified PCR amplicons and purification, the final amplicons were pooled for subsequent sequencing. Paired-end sequences were obtained with the Illumina MiSeq platform at the OE Technology Company of Shanghai.

Sequence library analysis

The raw sequencing data were analyzed as previously described [37]. In brief, high-quality paired-end reads were assembled using FLASH software (version 1.2.11). Operational taxonomic units (OTUs) were generated using VSEARCH software (version 2.4.2). Finally, the representative reads of each OTU were selected by the QIIME package, and representative reads were annotated and blasted against the Silva database (version 123) using the RDP classifier.

We conducted a comparative analysis of milk microbiota differences across various groups, including summer and winter seasons, different farms, health status and subclinical mastitis conditions, as well as parities. The OTUs were compared among these groups. Alpha diversity was evaluated by calculating the Shannon and chao1 indices, while beta diversity was assessed using unweighted unifrac distance-based principal coordinate analysis (PCoA). The top 10 predominant phyla and top 30 genera among groups were compared both in summer and winter. Lefse analysis was performed to identify biomarkers in the healthy and subclinical mastitis cows. Functional prediction analysis of the 16S sequencing data was carried out using PICRUSt based on Greengenes database annotation to assess microbial gene functions in these groups. Correlations among the top 30 genera in all summer and winter milk samples were analyzed using Corrplot analysis (spearman coefficients). Furthermore, we compared the milk microbiota between summer and winter milk as well as colostrum groups.

Statistical analysis

The diversity of the two groups was assessed using a two-paired t-test in GraphPad Prism 8.0, and an ANOVA test was conducted to compare between the groups. All p-values were calculated at a 95% confidence level. Significance was determined at p < 0.05, with p < 0.01 indicating extremely significant difference.

SCC

somatic cell counts

PCoA

principal coordinate analysis

OTUs

Operational taxonomic units

LMT

Lanzhou mastitis test

Acknowledgements

Not applicable.

Authors' contributions

Xiu-lan Xie conceived and conducted the experiments, analyzed the data, and drafted the paper. Jian Zhao conceived the experiments and revised the manuscript. Mei Cao, Shi-ying Yan, Shu Li, Hai-hui Gao, Gang Zhang, Ke-xin Deng and Jia-yi Zeng contributed to sample collection and experimental procedures. All authors reviewed and approved the final version of the manuscript.

Funding

This research received support from the foreign cooperation project of Ningxia Academy of Agricultural and Forest Sciences (grant no. DW-X-2018022), the Natural Science Foundation of Ningxia Hui Autonomous (grant no. 2022AAC02051).

Availability of data and materials

The data supporting the findings of this study are openly available in the National center for biotechnology information (NCBI) sequence read archive (SRA) (accession numbers PRJNA680351 and PRJNA612492).

Ethics approval and consent to participate

This study followed the international standards of the “Guide to the feeding, management and use of experimental animals” (8th edition), the “Regulations on the management of experimental animals” and the “Guidelines for Ethical Review of laboratory animal welfare (GB/T 35892-2018)”. Sample collection on farms was approved by the animal experimental ethics committee of Ningxia Academy of Agriculture and Forestry Sciences (Yinchuan, China) and was under the requirements of the regulation for the collection of genetic resources (HJ 628-2011). Verbal informed consent for milk sample collection was obtained by researchers visiting farms and speaking directly with owners. Informed consent was obtained at each farm without any objections noted.

Consent for publication

Not applicable.

Competing interests

All authors declared that there was no interest conflict.

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No competing interests reported.

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An investigation into the seasonal, herd-specific, parity, and subclinical mastitis-related fluctuations in the microflora of raw milk from dairy cows in Ningxia, China

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Version 1

Abstract

Background

Results

Conclusions

Figures

Introduction

Results

Discussion

The discrepancy between the two seasons

The health status the mammary gland had an impact on milk microbiota

The inter-farm variation in milk microbiota

The parity difference of the milk microbiota

Conclusion

Methods

Animals and milk sample collection

DNA extraction and library construction

Sequence library analysis

Statistical analysis

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1