The effect of the main factors related to milk production performance on microflora varies in Holstein raw milk

The effect of the main factors related to milk production performance on microflora varies in Holstein raw milk | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effect of the main factors related to milk production performance on microflora varies in Holstein raw milk xiulan xie, Mei Cao, Shi-ying Yan, Shu Li, Hai-hui Gao, Gang Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3282014/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Milk microflora is closely associated with the physiology and pathology in the mammary gland, and plays an important role in offspring development. The objective of the study was to illustrate the variation of milk microflora accompanied by the main factors related to milk performance. Results Milk samples were collected from 285 cows in Ningxia, China, and then microflora was explored using 16S rRNA pyrosequencing. All samples were grouped with the season (summer and winter), cow status (healthy and subclinical mastitis), farms (6 commercial dairy farms), and parity (primiparity and multiparity). The bacterial diversity, community composition, and abundance were analyzed among different groups. Also, the milk microflora among samples from summer, winter, and colostrum was compared. The results showed that the bacterial diversity of the milk varied significantly between samples from summer and winter. Higher bacterial richness was observed from summer samples than from winter samples. The gut-related genera, Parabacteroides , Staphylococcus , Corynebacterium _1, Sphingomonas , and Lactobacillus , were prevalent in summer milk samples. Although Escherichia_Shigella , Pseudomonas , Streptococcus , Psychrobacter , Rhizobium , Bifidobacterium , and Clostridium_sensu_stricto _1 were common in winter samples. In addition, different farms exhibited differences in bacterial diversity. Subclinical mastitis increased alpha diversity and decreased the enrichment of KEGG pathways in summer. Moreover, significant differences of milk microflora were observed from summer, winter and colostrum samples. Conclusions The study revealed that the milk microflora varied companies with seasons, farms, health status, and parities. Also, milk from summer, winter, and colostrum showed their unique microflora characteristics. Milk microflora 16S rRNA pyrosequencing Season Farm Subclinical mastitis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Background Microflora in milk is important for the growth and development of offspring and is also closely related to the health of the maternal mammary gland [ 1 – 4 ]. Meanwhile, some microbes in milk shorten the storage period of raw milk and even spoil the milk. After 20 years of research by cultured and uncultured methods, although with a low quantity of microflora, milk harbors rich microbial diversity. Based on a meta-study of 3105 breast milk samples from 2655 women, the microorganisms in breast milk included 58 phyla, 133 classes, 263 orders, 596 families, 590 genera, 1300 species, and 3563 OTUs [ 5 ]. Since mastitis is the most common disease in the dairy industry, which affects the mammary gland's health and productivity performance, the analysis of bovine milk microflora was mainly focused on mastitis episodes [ 6 ]. According to a survey, Faecalibacterium spp., unclassified Lachnospiraceae , Propionibacterium spp., and Aeribacillus spp., were present in healthy quarters; Sphingobacterium and Streptococcus associated with increased somatic cell counts (SCC) [ 7 ]. Another study reported that abundant Sphingomonas , Stenotrophomonas , Burkholderia , and Brevundimonas in clinical mastitis milk, while Pseudomonas and Psychrobcter , and Ralstonia in healthy milk [ 2 ]. In addition, most microbes in milk were irrelevant to mastitis. For example, Staphylococcus. aureus and Streptococcus are commonly existed in healthy cows and seemed as core microbiota in milk. Thus, recent studies provided new insights into the ecology of the microflora of the mammary gland [ 8 , 9 ]. Moreover, milk microbiota was constantly changing since the milk fluid washes out and the mammary gland immunity effects. Also, milk microbiota can be easily affected by many factors, such as host, environment, and sampling methods [ 10 ]. The mediterranean diet increased lactic acid bacteria and bile acid metabolites in milk than western diets [ 11 ]. The abundance of Staphylococcaceae , Bacillaceae , Streptococcaceae , Microbacteriaceae , and Micrococcaceae in milk was influenced by season [ 12 ]. Klebsiella , Escherichia_Shigella , and Streptococcus were the pathogens in Farm A (lower incidence rate of subclinical mastitis), while Streptococcus and Corynebacterium were pathogens in farm B (higher incidence rate) [ 13 ]. Those results indicated that milk microflora is highly diverse and affected by multiple factors, which contributes to a better understanding of milk composition and its complex relationship with the host and environment. However, till now, no study combined these elements to explore the milk microbiota. Although the physiology or pathology implications of the milk microflora are still in research, the existing studies showed that it has vital importance in maintaining the mammary gland health to fight against mastitis both in women and animals, and the underlying role in the performance of milking in dairy cows. Therefore, the present study uses 16S rRNA sequencing method to explore the raw milk microflora and analyze the main factors related to milk production performance, such as season, farms, health and subclinical mastitis, and parity in Ningxia, China. Our study aims to illuminate the patterns of milk microflora changes. Results A significant difference in OTUs was observed with summer and winter samples ( p < 0.01, Fig 1A). Also, the OTUs in Farms A and B were significantly lower than that in Farms C, D, E, and F (Fig 1B, 1E). In summer, the OTUs were significantly higher in the subclinical mastitis group than in the health group ( p < 0.01, Fig 1C). The same pattern was also observed in multiparity and primiparity cows (Fig 1D). But the same case was not observed in samples collected in winter (Fig 1F, 1G). Alpha diversity evaluates the richness and uniformity of species in samples. The chao1 index was used to indicate community richness, and the shannon index was used to indicate community uniformity. In our study, both chao1 and shannon indices were significantly higher in summer than in winter ( p < 0.01, Fig 2A and B). Farm A and B had a lower chao1 value than Farm C, D, and E (in summer)/F (in winter) ( p < 0.01, Fig 2C, I). Subclinical mastitis groups had a higher chao1 index than the health group in summer, but it was not the case in winter (Fig 2D, J). The multiparity groups had a higher chao1 index than the primiparity group in summer, but there was no significant difference observed in winter (Fig 2E, K). In summer, the shannon index was significantly higher in Farm C and D than in Farm B (Fig 2F). Significant differences were also observed in the health and subclinical mastitis group ( p < 0.01, Fig 2G). But there was no significant difference between the multiparity and primiparity groups (Fig2H). In winter, differences among farms were observed. In general, Farm D and F had a higher shannon index value than Farm A, B, and C (Fig 2L), but there were no significant changes between the health and subclinical mastitis group, and primiparity and multiparity groups (Fig 2M, N). To investigate the similarities and differences in microbial communities, beta diversity analysis was performed using unweighted uniFrac PCoA. The microbiota boundaries of raw milk samples in summer and winter groups were clear, indicating that season was the main factor affecting the composition of raw milk microflora (Fig 2A). Farms from group1 (farmA, B) and group2 (farmC, D and E) had distant bacterial compositions in summer and from group1 (farmA, B), group2 (farmC, D) and farmF had distant distances in winter (Fig 2B). In addition, there was no difference in the composition of the microflora between health and subclinical mastitis groups, and primiparity and multiparity groups (Fig 2C, 2D). In the summer group, the main phyla were Bacteroidetes (41.72%), Firmicutes (37.53%), Proteobacteria (11.57%), Actinobacteria (3.13%), Gemmatimonadetes (1.41%) and Acidobacteria (0.76%), which were accounted for 96.13% of total sequences. In winter, Firmicutes (40.01%), Proteobacteria (22.69%), Bacteroidetes (21.87%) and Actinobacteria (11.46%) accounted for 96.03% of total sequences (Fig 4A). Compared with samples collected in summer, samples in winter had abundant phyla of Proteobacteria (22.69% vs. 11.57%) and Actinobacteria (11.46% vs. 3.13%), and fewer Bacteroidetes (21.87% vs. 41.72%) (Fig 4A). There were significant differences in the relative abundance of predominant phyla among farms, especially in winter (Fig 4B, E). But no changes were observed between the health and subclinical mastitis group, or primiparity and multiparity groups, both in summer and winter (Fig 4C, D, F, and G). Gemmatimonadetes was the only phylum that was higher in primiparity than multiparity in summer (Fig 4D). In summer, the predominant genera were 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 compared the top 10 genera changes in different groups. There were differences among farms, especially in winter, but no significant changes were found between health and subclinical mastitis groups, as well as primiparity and multiparity groups (Fig 5A-D; Fig 6A-D). In summer, the abundance of Alloprevotella , Bacteroides , and Ruminiclostridium_9 was higher in Farm B than in the other four farms; Desulfovibrio was higher in Farm C than the other farms; Helicobacter in Farm A was lower than other farms; In winter, Farm A and B had higher genera of Bacteroides , Faecalibacterium , Escherichia_shigella , and Blautia than Farm C, D, and F. Farm C had the highest relative abundance of Corynebacterium_1 , Psychrobacter , and Bifidobacterium , while the lowest Pseudomonas , Streptococcus , and Rhizobium . The primiparity group had a higher relative abundance of Bacteroides than the multiparity group in summer ( p 0.05). In summer, o_ Rhizobiales , o_ Acidobacteriales , o_ Acidimiorobiaceae were biomarkers in health groups. While, g_ Bacteroidales_S24_7_group , o_ Fibrobacterles , o_ Nitrospiraceae , g_ Sporolactobacillus , f_ Mycobacteriaceae and g_ Mycobacterium were biomarkers in subclinical mastitis groups (Fig 7A). In winter, k_ Actinobacter , such as o_ Pseudomondales , o_ Micrococcales , f_ Nocardioidaceae , f_ Rhodospirillaceae , and o_ Acidimicrobiales were biomarkers in health groups. While, g_ Escherichia_Shigella , f_ Lactobacillaceae , g_ Lactobacillus , g_ Prevotella_9 , f_ Actionmycetaceae , o_ Actionmycetales , f_ TRA3_20 were biomarkers in subclinical mastitis groups (Fig 7B). The enrichment of the KEGG pathways were higher in summer than in winter. In summer, 17 pathways were significantly higher in healthy cows than in the subclinical mastitis group ( p < 0.05), and 3 pathways (cell motility, membrane transport, and signal transduction) were extremely significantly different ( p < 0.01) (Fig 8A). The same trend was observed (Fig 8B) in winter. In addition, Farm A was somewhat different from other farms with 5/20 KEGG pathways in summer; while in winter, the main changes with 15 pathways were observed between Farm A and F, as well as Farm C and F. The top 20 KEGG pathways had no change between primiparity, and multiparity, both in summer and winter (data not shown). As shown in Fig 9, the top 30 genera were correlated with each other, especially Lactobacillus and Bifidobacterium . While, Staphylococcus and Roseburia, Parasutterella , and Parabacteroides were with the least interaction with other genera (Fig 9). The OTUs in summer, winter and colostrum were 21976, 13293, and 8506 respectively. 8182 OTUs were shared in Summer and Winter samples, but only 3134 OTUs were shared among the colostrum, Summer, and Winter samples (Fig 10A). The alpha diversity (accessed by chao1 and shannon indices) was significantly different among the three groups, with the highest values observed in the summer group, following the colostrum group and winter group (Fig 10B, 10C). The beta diversity of raw milk and colostrum was compared by PCoA and UPGMA analysis. As it showed in Fig 10D, there were distinct microflora composition among raw milk in summer, winter, and colostrum groups, indicating that the species composition of samples from groups varied greatly. In summary, from the OTUs counts, alpha diversity and beta diversity, we were able to conclude that the milk samples from summer, winter and colostrum are unique. We further compare the bacterial composition at the phylum and genus levels. As shown in Fig 10A, Firmicutes enriched in colostrum groups, Proteobacteria and Actinobacteriota enriched in winter groups, and the other seven top15 phyla enriched in summer groups (Fig 11A). At the genus levels, there were 851 different bacterial genera among the summer, winter and colostrum sample groups. In the top30 genera, Escherichia-shigella and Lactobacillus enriched in colostrum, Bacteroides , Faecalibacterium , Corynebacterium , [Eubacterium]_eligens_group and Blautia enriched in the winter, while Muribaculaceae , Lachnospiraceae_NK4A136_group , Clostridia_UCG-014 enriched in summer (Fig 11B). As shown in Fig 11C, the Lefse analysis revealed that Bacteroides , Faecalibacterium , Eubacterium_eligens_group , Corynebacterium , Blautia , Pseudomonas were biomarkers genera in winter group, Muribaculaceae , Lachnospiraceae_NK4A136_group , Clostridia_UCG_014 , Alloprevotella were biomarkers in summer group, and Lactobacillus , Escherichia_Shigella , Collinsella , Prevotella , Clostridium_sensu_stricto_1 , Serratia , Klebsiella , Alistipes , and Bifidobacterium were biomarkers in Colostrum. Discussion To our knowledge, this is the first comprehensive analysis of the cows’ milk microflora associated with the season, farm, health status, and parity simultaneously using pyrosequencing and on a large scale. The present study provided a better understanding of milk microflora varies associated with the main factors related to milk production performance. The results confirmed that milk harbors a rich and diverse microbial community and different factors had an important role in milk microflora. Also, this study revealed the unique microbiome characteristics of summer, winter and colostrum milk. The variation between the two seasons The effects of season on microorganisms are usually referred to as temperature and humidity, which are the determining factors for most variations of bacterial taxonomic structure [14, 15]. Coinciding with the temperature, it was shown that bacterial richness was generally lower in winter than that in summer [14]. Similarly, in the present study, significantly higher bacterial richness and diversity were observed in milk samples collected in summer than that in winter. Milk samples from summer and winter were readily distinguished based on bacterial profiles (Fig 3A). The microbiota composition was also altered seasonally. Metzger reported 11 of the top 20 OTUs varied seasonally [15]. Nalepa showed the bacterial species composition in the raw bovine milk varied significantly within 22 months and some species/groups occurred seasonally, e.g., Lactobacillus helveticus (summer), Lactobacillus casei (winter) [16]. Nguyen found the relative abundance of genera Staphylococcaceae , Bacillaceae , Ruminococcaceae , Veillonellaceae , Methylobacteriaceae , and Moraxellaceae were different between the two seasons [17]. In the present study, the gut-associated genera were prevalent in the summer milk samples, such as Lachnospiraceae_NK4A136_group , Ruminococcaceae_UCG_014 , and Alloprevotella . While in winter, genera of Escherichia_Shigella , Corynbacterium_1 , Pseudomonas , Streptococcus , Psychrobacter , Rhizobium , and Bifidobacterium were common. Gut bacteria, such as the genera of Prevotella , Ruminococcus , Bacteroides , Rikenella , and Alistipes are prevalent in milk as previous studies reported [18-21]. Corynebacterium , Streptococcus , Pseudomonas , and Psychrobacter were the more common genera detected in the colder season than the warmer season, partly because of their psychotropic features [20, 22]. Besides, Bifidobacterium was commonly detected in winter, while Lactobacillus was commonly detected in summer. Both of them are regarded as potential probiotics and are common in milk samples. The reasons for their distribution differences remain unknown. Moreover, some studies reported Rhizobium or Bradyrhizobium were detected in human milk [23, 24], goat milk [25], and cow milk [2, 26, 27]. Rhizobium and Bradyrhizobium are members of the bacterial order Rhizobiales . Rhizobia are a soil bacterium that aer a symbiont of the legumes, and not a documented gut microbe [15]. The plausible explanation of them presented in milk might be through an endogenous entero-mammary pathway [4]. Besides the above genera discussed here, Parabacteroides were also common in our samples. Drago also found that Parabacteroides have a pivotal role in the bacterial network in the mature milk from Italian mothers [24]. Our results suggested that the individual difference was more common in winter than in summer (Fig 3). This might be due to the higher gut-associated microbiota presented in bovine milk in summer, and that microbiota was relatively stable in individuals. The KEGG pathway was used to analyze the known bacterial gene function. Data from our study suggested the higher abundance of metabolism in summer than that in winter (Fig 8). Our results were similar to Li's [14]. Owing to the higher bacterial communities in summer than in winter, the bacterial metabolism consequently increased. The mammary gland health status affected milk microbiota To compare the main differences in milk microflora between healthy and suffering subclinical mastitis cows was the original objective of our study. Greater richness and diversity are generally associated with better health outcomes in systems with a well-characterized dense microflora, such as the gut or skin [15]. The milk from healthy cows generally harbors greater richness and diversity than mastitis cows, even in different mammary glands from the same cow [28]. Some researchers observed differences in the milk microbiota between healthy and clinical mastitis quarters [15, 19]. In the present study, overall, we didn't observe significant differences in microbiota between healthy and subclinical groups. Only in summer, a significantly higher alpha diversity (chao1 and Shannon index) was found in subclinical mastitis groups (Fig 2D, G). Hoque also reported that there was 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]. Therefore, we could speculate that milk microbiota composition might not contribute to subclinical mastitis. In the current work, we showed all the top 20 KEGG pathways were significantly lower in the subclinical mastitis group compared with the health group, especially the pathways of membrane transport, cell motility, and signal transduction in summer [Fig 8A]. The higher microbial KEGG pathways represent active metabolism activity. These results suggested that the subclinical mastitis status be characterized by decreasing bacterial pathways. But these results need to be further confirmed. The interfarms difference of the milk microbiota Compared with the factors of health status, little research studied the interfarms difference of the microbiota. Pang observed the microbial diversity from two farms could be separated [13]. Nguyen found that the relative abundance of Pseudomonadaceae , Enterobacteriaceae , and Streptococcaceae , Lactobacillaceae , Bifidobacteriaceae , and Cellulomonadaceae in milk was different in the two farms [17]. Besides the milk, Weese also reported that the fecal microbiota of calves was highly variable between farms [30]. In our study, the four dominant phyla were similar to Farm A and B, which enriched with phyla of Bacteroidetes , but decreased Actinobacteria compared with other farms; Farm C had the highest phyla of Firmicutes , Actinobacteriota , but the lowest Bacteroidetes (Fig 4E). In summer, slight variations were observed among farms compared with samples from winter. Briefly, Farm D and E had similar phyla, but Farm B had higher Bacteroidetes , Firmicutes , and lower Proteobacteriota than Farm A. Farm C had higher Firmicutes than Farm A. Besides, significant differences were also found in the phyla of Gemmatimonadetes and Acidobacteriota (Fig 4B). From PCoA plots, we observed that Farm A and B were more similar in milk microbiota, while Farm D and F were also similar. Farm C was different from other farms (Fig 3). In our study, all farms are well-managed, productive operations and take part in the DHI schedule. The interfarm differences are for multiple reasons. Unidentified management practices may significantly influence the microbiota. Unlike samples in summer, in winter, Farm A and B enriched with genera of Bacteroides , Faecalibacterium , Escherichia_Shigella , Blautia , but lower Corynebacterium_1 and Pseudomonas ; Farm C enriched with Corynebacterium_1 , Psychrobacter , Bifidobacterium , and lower Rhizobium ; Farm D and F enriched with Pseudomonas and lower Escherichia_Shigella (Fig 6B). Considering that Pseudomonas was detected more in milk from healthy cows, Escherichia_Shigella from mastitis cows, we spectated that Farm D and F harbored a higher balanced microbiota of milk than Farm A, B, and C. In addition, data presented in our study indicated that Farm A and B were more accessible with mammary gland problems with Escherichia_Shigella , Farm C with Corynebacterium_1 . In most cases, management practices have a significant influence on milk quality. The parity difference of the milk microbiota Multiparous cows are at greater risk of mastitis than primiparous cows [31]. Thus, we speculated that the parity number was an important factor that affect the milk microbiota. Lima reported the differences in the bacterial composition of colostrum between primiparous and multiparous cows [32]. Staphylococcus , Fusobacterium , Acinetobacter , and Bacteroides were more abundant in the colostrum of multiparous cows than in primiparous cows. The colostrum microbiota of primiparous cows was richer than that of multiparous cows. On the contrary, we observed a higher bacterial richness in multiparous cows than in primiparous cows (Fig 2E). The plausible reason might be the increasing exposure of the intramammary ecosystem to environmental sources of microbes in multiparous cows than in primiparous cows. Except for the factors discussed above, many other factors also affected the milk microbiota. Toscano reported differences between Cesarean section and vaginal delivery [33]. Derakhshani observed the role of BoLA-gene polymorphism in modulating the composition of colostrum microbiota in dairy cows [34]. Metzger found the overall bacterial community composition differed among bedding types in dairy farms [35]. Cabrera-Rubio found that the weight and mode of delivery affected the women's milk microbiota [36]. All in all, the research on milk microbiota was still in the early stage. In addition, the comparative analysis of summer, winter and colostrum samples showed that the alpha diversity of summer samples was significantly higher than that of winter and colostrum samples, and colostrum was significantly higher than that of winter samples (Chao1 index). Based on the beta diversity of PCoA and UPGMA, we found that the samples between groups had clear boundaries, and the samples within groups were better clustered together. The Top10 phyla and genera of each group were also significantly different. The results of the above studies fully indicated that the samples of each group had a unique diversity of bacterial flora. Therefore, our study clarified significant differences in microbiota diversity among raw milk in summer, winter, and colostrum. Future analyses of milk microbiota in dairy cows should take full account of the sampling season. Moreover, the bacteria in milk and its metabolites in the production of high-quality fresh milk and dairy cattle have an important function in the protection of the mammary gland. However, the current understanding of the field is just beginning, the bacteria in milk and its function correlation research literature have not been reported. Hence, the microflora analysis of this paper is descriptive content, which lacked transverse, longitudinal association analysis and flora and function of the correlation analysis. Conclusion In the present study, we showed cows’ milk microflora varied associating to several main factors related to milk production performance. Firstly, a distinct difference was observed between summer and winter raw milk samples. In summer, the 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, healthy status, and parity also affected the raw milk microflora. Finally, the unique characteristics of summer, winter raw milk and colostrum were observed. This study provided a better understanding of cow milk microflora varies accompanied by the main factors related to milk production performance. Methods Animals and milk sample collection The large commercial dairy farms in the study milked 1000-3000 Holstein cows thrice daily in a double 52-stall (Farm A, B, D, E, F) and 26-stall (Farm C) parallel milking parlor. Farms are situated in north (Farm A), mid (Farm B and C), and south (Farm D, E, and F) areas of Ningxia, PR 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. In summer, raw milk was collected in farms A, B, C, D, and E, the total samples were 135; milk samples from 150 cows were collected in farms A, B, C, D, and F in winter. Sampling methods followed the 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 using an individual towel by farm personnel. Then, 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. After sampling, milk samples were thawed on ice and centrifuged at 12,000 rpm for 10 min at 4 °C to separate fat and cells from the whey. The pellets were collected in a 1.8 mL sterile freezing tube and stored at -80 °C for further analysis. Cows that have given birth to one calf were defined as the primiparous cow, and two or above calves were the multiparous cows. All samples were collected from the cows that did not have visible signs of clinical mastitis, such as swelling or redness of breasts. Lanzhou mastitis test (LMT) reagent was used to diagnose subclinical mastitis by experiencing veterinarians on farms. In brief, about 2 mL milk was sterile collected with a detection disk, and then mixed with 2 mL LMT regent. The mixture was observed within 1 min. The diagnosed standard was the mixture has evident flocculent or gelatinous diagnosed as subclinical mastitis milk; No above phenomenon and with good fluidity was defined as healthy milk. The total sampling numbers were presented in table 1. Table 1 The sampling counts summary Season Farm A Farm B Farm C Farm D Farm E Farm F Healthy Subclinical mastitis Primiparity Multiparity Summer 30 33 25 24 23 / 67 68 35 100 Winter 21 27 38 28 / 36 73 77 58 73 Total 52 60 63 52 23 36 140 145 93 173 “/” means not collected. DNA extraction and library construction DNA extraction and library construction from milk samples were performed according to the procedures described [37]. Briefly: genomic DNA was diluted to a concentration of 1 ng µL-1. The V3-V4 variable regions of the 16S rRNA gene were amplified with the 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 described [37]. Briefly: 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 compared the milk microbiota differences among different groups, such as summer and winter, farms, health and subclinical mastitis, and parities. The OTUs were compared among the different groups. The Shannon and chao1 indices diversity were calculated to evaluate the alpha diversity among the groups. The unweighted unifrac distance-based principal coordinate analysis (PCoA) was used to assess beta diversity. The top 10 predominant phyla and top 30 genera among groups were compared both in summer and winter. The Lefse analysis was performed to observe the biomarkers from health and subclinical mastitis groups. PICRUSt functional prediction analysis of the 16S sequencing data was performed based on Greengenes database annotation. Using PICRUSt software, the components of known microbial gene functions were analyzed to calculate the functional differences between health and subclinical mastitis groups. Corrplot analysis was performed by calculating the correlations (spearman coefficients) among the top 30 genera in all summer and winter milk samples. Besides, the milk microbiota from summer and winter milk and colostrum groups was also compared. Statistical analysis The diversity of the two groups was analyzed by a two-paired t-test using GraphPad Prism 8.0, and an ANOVA test was used among the groups. All p -values were calculated with a 95% confidence level. Differences were considered significant when p < 0.05, while p < 0.01 was considered to indicate an extremely significant difference. Abbreviations SCC: somatic cell counts; PCoA: principal coordinate analysis; OTUs: Operational taxonomic units; LMT: Lanzhou mastitis test Declarations Acknowledgements Not applicable. Authors' contributions Xiu-lan Xie conceived and performed the experiments, analyzed the data, and wrote the paper. Jian Zhao conceived the experiments and revised the manuscript. Mei Cao, Shi-ying Yan, Shu Li, Hai-hui Gao, Gang Zhang, and Jia-yi Zeng contributed to sampling and performed the experiments. All authors read and approved the final manuscript. Funding This work was supported by 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 that support 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 other relevant laws and regulations. The animal experimental ethics committee of Ningxia Academy of Agriculture and Forestry Sciences approved this study. All experiments with animals were performed in accordance with the ARRIVE guidelines. Consent for publication Not applicable. Competing interests All authors declared that there was no interest conflict. Author details 1 Institute of Animal Science, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan75002, PR. China 2 Key Laboratory of Biological Resource and Ecological Environment of Chinese Education Ministry, College of Life Sciences, Sichuan University, Chengdu 610064, PR. China 3 Core Laboratory, School of Medicine, Sichuan Provincial People's Hospital Affiliated to University of Electronic Science and Technology of China, Chengdu 610072, PR China 4 Key Laboratory of Ministry of Education for Protection and Utilization of Special Biological Resources in Western China, Department of Biochemistry and Molecular Biology, College of Life Science, Ningxia University, Yinchuan 750021, PR. China References Jeurink PV, van Bergenhenegouwen J, Jimenez E, Knippels LM, Fernandez L, Garssen J, et al. Human milk: a source of more life than we imagine. Benef Microbes. 2013;4(1):17-30. 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An investigation of seasonal variations in the microbiota of milk, feces, bedding, and airborne dust. Asian-Australas J Anim Sci. 2020;33(11):1858-65. Pang M, Xie X, Bao H, Sun L, He T, Zhao H, et al. Insights Into the Bovine Milk Microbiota in Dairy Farms With Different Incidence Rates of Subclinical Mastitis. Frontiers in Microbiology. 2018;9. Li N, Wang Y, You C, Ren J, Chen W, Zheng H, et al. Variation in Raw Milk Microbiota Throughout 12 Months and the Impact of Weather Conditions. Sci Rep. 2018;8(1):2371. Metzger SA, Hernandez LL, Skarlupka JH, Walker TM, Suen G, Ruegg PL. A Cohort Study of the Milk Microbiota of Healthy and Inflamed Bovine Mammary Glands From Dryoff Through 150 Days in Milk. Front Vet Sci. 2018;5:247. Nalepa B, Olszewska MA, Markiewicz LH. Seasonal variances in bacterial microbiota and volatile organic compounds in raw milk. Int J Food Microbiol. 2018;267:70-6. Nguyen QD, Tsuruta T, Nishino N. 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A Metataxonomic Approach Could Be Considered for Cattle Clinical Mastitis Diagnostics. Front Vet Sci. 2017;4:36. Marchand S, Heylen K, Messens W, Coudijzer K, De Vos P, Dewettinck K, et al. Seasonal influence on heat-resistant proteolytic capacity of Pseudomonas lundensis and Pseudomonas fragi, predominant milk spoilers isolated from Belgian raw milk samples. Environ Microbiol. 2009;11(2):467-82. Hunt KM, Foster JA, Forney LJ, Schutte UM, Beck DL, Abdo Z, et al. Characterization of the diversity and temporal stability of bacterial communities in human milk. PLoS One. 2011;6(6):e21313. Drago L, Toscano M, De Grandi R, Grossi E, Padovani EM, Peroni DG. Microbiota network and mathematic microbe mutualism in colostrum and mature milk collected in two different geographic areas: Italy versus Burundi. ISME J. 2017;11(4):875-84. McInnis EA, Kalanetra KM, Mills DA, Maga EA. Analysis of raw goat milk microbiota: impact of stage of lactation and lysozyme on microbial diversity. Food Microbiol. 2015;46:121-31. Cremonesi P, Ceccarani C, Curone G, Severgnini M, Pollera C, Bronzo V, et al. Milk microbiome diversity and bacterial group prevalence in a comparison between healthy Holstein Friesian and Rendena cows. PLoS One. 2018;13(10):e0205054. Curone G, Filipe J, Cremonesi P, Trevisi E, Amadori M, Pollera C, et al. What we have lost: Mastitis resistance in Holstein Friesians and in a local cattle breed. Res Vet Sci. 2018;116:88-98. Metzger SA, Hernandez LL, Suen G, Ruegg PL. Understanding the Milk Microbiota. Vet Clin North Am Food Anim Pract. 2018;34(3):427-38. Hoque MN, Istiaq A, Rahman MS, Islam MR, Anwar A, Siddiki A, et al. Microbiome dynamics and genomic determinants of bovine mastitis. Genomics. 2020;112(6):5188-203. Weese JS, Jelinski M. Assessment of the Fecal Microbiota in Beef Calves. J Vet Intern Med. 2017;31(1):176-85. Jamali H, Barkema HW, Jacques M, Lavallee-Bourget EM, Malouin F, Saini V, et al. Invited review: Incidence, risk factors, and effects of clinical mastitis recurrence in dairy cows. J Dairy Sci. 2018;101(6):4729-46. Lima SF, Teixeira AGV, Lima FS, Ganda EK, Higgins CH, Oikonomou G, et al. The bovine colostrum microbiome and its association with clinical mastitis. J Dairy Sci. 2017;100(4):3031-42. Toscano M, De Grandi R, Peroni DG, Grossi E, Facchin V, Comberiati P, et al. Impact of delivery mode on the colostrum microbiota composition. BMC Microbiol. 2017;17(1):205. Derakhshani H, Plaizier JC, De Buck J, Barkema HW, Khafipour E. Association of bovine major histocompatibility complex (BoLA) gene polymorphism with colostrum and milk microbiota of dairy cows during the first week of lactation. Microbiome. 2018;6(1):203. Metzger SA, Hernandez LL, Skarlupka JH, Suen G, Walker TM, Ruegg PL. Influence of sampling technique and bedding type on the milk microbiota: Results of a pilot study. J Dairy Sci. 2018;101(7):6346-56. Cabrera-Rubio R, Collado MC, Laitinen K, Salminen S, Isolauri E, Mira A. The human milk microbiome changes over lactation and is shaped by maternal weight and mode of delivery. Am J Clin Nutr. 2012;96(3):544-51. Xie X-l, Zhang G, Gao H-h, Deng K-x, Chu Y-f, Wu D-y, et al. Analysis of bovine colostrum microbiota at a dairy farm in Ningxia, China. International Dairy Journal. 2021;119:104984. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3282014","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":230340345,"identity":"e9df1231-aba4-44f5-afc2-f8c5bc5bec7e","order_by":0,"name":"xiulan xie","email":"","orcid":"","institution":"Ningxia Academy of Agriculture and Forestry Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"xiulan","middleName":"","lastName":"xie","suffix":""},{"id":230340346,"identity":"3052efc7-f6d0-45f6-8db4-9b5b7685d57d","order_by":1,"name":"Mei Cao","email":"","orcid":"","institution":"Sichuan Provincial People's Hospital Affiliated to University of Electronic Science and Technology of China","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mei","middleName":"","lastName":"Cao","suffix":""},{"id":230340348,"identity":"2ae880ad-0a51-42ca-b606-a908dfe2312d","order_by":2,"name":"Shi-ying Yan","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shi-ying","middleName":"","lastName":"Yan","suffix":""},{"id":230340349,"identity":"d6c9b62d-2da3-48a3-8704-96342b2fade2","order_by":3,"name":"Shu Li","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Li","suffix":""},{"id":230340350,"identity":"25cf2859-1b1f-4919-8c59-c11f939b9876","order_by":4,"name":"Hai-hui Gao","email":"","orcid":"","institution":"Ningxia Academy of Agriculture and Forestry Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hai-hui","middleName":"","lastName":"Gao","suffix":""},{"id":230340352,"identity":"cab9cd49-8c27-4043-98b4-4c4e47178b76","order_by":5,"name":"Gang Zhang","email":"","orcid":"","institution":"Ningxia University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Zhang","suffix":""},{"id":230340353,"identity":"abd39320-dd49-48f8-8f7f-977a39f54be2","order_by":6,"name":"Jia-yi Zeng","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jia-yi","middleName":"","lastName":"Zeng","suffix":""},{"id":230340355,"identity":"d99600c3-cebc-4938-89e0-eb0ada12104f","order_by":7,"name":"Jian Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYFCCwwcOSFRIyMmToOVY4gOLMxbGhg3Ea+ExNqhsq0hkOECsBvnGY2kSN+dJJDA2MD98dIMYLYwNh49JztwmkcfOwGZsnEOMFmaGY2nSktskihkbeNikidLCxnDGTPrvHInEhgPEauFhOGNsINlAihYJUCBLHJMwNmwm1i/yM0BRWVMnJ8/e/PAxUVoYJA5AGcxEKQcB/gailY6CUTAKRsFIBQDlPTBTLIXU0QAAAABJRU5ErkJggg==","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2023-08-21 09:59:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3282014/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3282014/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42671235,"identity":"01d622f3-dfe2-4960-a9f5-bfcdac4d1b08","added_by":"auto","created_at":"2023-09-05 23:31:05","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147844,"visible":true,"origin":"","legend":"\u003cp\u003eThe OTUs among groups. A. OTUs in summer and winter. B-D. OTUs compared with farms, health and subclinical mastitis, multiparity and primiparity in summer. E-G. OTUs compared with farms, health and subclinical mastitis, multiparity and primiparity in winter. Data showed as Mean ± SD, \u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05; ns, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; *, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, **.\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/24be6a39bd2a342678e6cede.jpg"},{"id":42672516,"identity":"ae1e2655-2ea6-4e51-99ef-af25b7f50bc0","added_by":"auto","created_at":"2023-09-05 23:39:05","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":218274,"visible":true,"origin":"","legend":"\u003cp\u003eThe alpha diversity among groups. A, B. Chao1 and Shannon indices compared between summer and winter. C-E. The chao1 index was compared among farms, health and subclinical mastitis, multiparity and primiparity in summer, respectively. F-H. The shannon index was compared among farms, health and subclinical mastitis, multiparity and primiparity in summer. I-K. The chao1 index was compared among farms, health and subclinical mastitis, multiparity and primiparity in winter, respectively. L-N. The shannon index was compared among farms, health and subclinical mastitis, multiparity and primiparity in winter, respectively. Data showed as Mean ± SD, \u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05, ns; \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, *; \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.01, **.\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/b9e87aa3e3fbd3cd2613716c.jpg"},{"id":42672515,"identity":"acbde32c-8cd9-4bdd-adca-f1cd378df078","added_by":"auto","created_at":"2023-09-05 23:39:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":208411,"visible":true,"origin":"","legend":"\u003cp\u003eThe PCoA plot (unweighted unifrac distance) among groups. A. The PCoA plot between summer and winter groups. B. The PCoA plot among Farms. Group1 represents FarmA and FarmB, group2 represents Farm C and FarmD. C. The PCoA plot between Health and subclinical mastitis. D. The PCoA plot between Primiparity and Multiparity.\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/6000e7c52a81282aa7f6fb39.jpg"},{"id":42671232,"identity":"849d6202-3240-479e-ad59-4f1567d821ad","added_by":"auto","created_at":"2023-09-05 23:31:05","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":237614,"visible":true,"origin":"","legend":"\u003cp\u003eThe predominant phyla among groups. A. The phyla were observed in summer and winter groups. B-D. The predominant phyla compared with farms, health and subclinical mastitis, primiparity and multiparity in summer, respectively. E-G. The predominant phyla compared with farms, health and subclinical mastitis, primiparity and multiparity in winter, respectively. Data showed as mean ± SD, \u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05, ns; \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05, *; p\u0026lt;0.01, **.\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/0d245a865b3a72b19be90a93.jpg"},{"id":42671234,"identity":"f291f2ff-2826-48c3-a43f-18a81ae8ee28","added_by":"auto","created_at":"2023-09-05 23:31:05","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":218653,"visible":true,"origin":"","legend":"\u003cp\u003eThe dominant genera among groups in summer samples. A. The profile of genera in groups. B. The top 10 genera were compared among farms. C. The top 10 genera were compared between health and subclinical mastitis. D. The top 10 genera were compared between multiparity and primiparity. Data showed as Mean±SD, \u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05; ns, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.05; *,\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.01, **.\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/9dd4e64544ca0fc6a9303a4d.jpg"},{"id":42671241,"identity":"ad9357cb-5f9d-4640-a3a7-c17b55c3e467","added_by":"auto","created_at":"2023-09-05 23:31:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":200886,"visible":true,"origin":"","legend":"\u003cp\u003eThe dominant genera among groups in winter samples. A. The profile of genera in groups. B. The top 10 genera were compared among farms. C. The top 10 genera were compared between health and subclinical mastitis. D. The top 10 genera were compared between multiparity and primiparity. Data showed as Mean ± SD, \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05, ns; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, *;\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.01, **.\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/dda04f457bd64428f4a62a69.jpg"},{"id":42671243,"identity":"df3a4574-0c55-4e20-981e-e22afbf1bc39","added_by":"auto","created_at":"2023-09-05 23:31:05","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":237644,"visible":true,"origin":"","legend":"\u003cp\u003eLefse analyze health and subclinical mastitis groups. A. The different taxa between health and subclinical mastitis in summer. B. The different taxa between health and subclinical mastitis in winter\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/48baa2272f6fa1d88c52c874.jpg"},{"id":42671237,"identity":"dc5c7686-153c-416b-9236-4febc407af87","added_by":"auto","created_at":"2023-09-05 23:31:05","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":88726,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG pathways between health and subclinical samples. A. KEGG pathways were compared in summer. B. KEGG pathways were compared in winter.\u003c/p\u003e","description":"","filename":"floatimage8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/38069ad8eb95763d08914ca7.jpg"},{"id":42671239,"identity":"fc782154-3468-4e73-8807-8cd6d82ca03f","added_by":"auto","created_at":"2023-09-05 23:31:05","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":375306,"visible":true,"origin":"","legend":"\u003cp\u003eCo-occurrence network of Top 30 genera. Positive and negative correlations between genera are shown in red and blue, respectively. The values in the circles represent spearman's correlation, with higher values indicating stronger correlations.\u003c/p\u003e","description":"","filename":"floatimage9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/8dfac95727900b8b2cbbb8a4.jpg"},{"id":42672517,"identity":"11ff5d05-664f-434e-8af1-2671e7455f54","added_by":"auto","created_at":"2023-09-05 23:39:05","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":147609,"visible":true,"origin":"","legend":"\u003cp\u003eThe variation analysis of OTU, alpha and beta diversity among groups. A. Venn diagram at OTUs level. B. Chao1 value was compared among summer, winter and colostrum groups. C. Shannon value was compared among summer, winter and colostrum groups. D. Based unweighted unifrac distance of PCoA among summer, winter and colostrum groups.\u003c/p\u003e","description":"","filename":"floatimage10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/2a64c17f38a94f78f88c97b2.jpg"},{"id":42672518,"identity":"4cd5f44d-3f08-42eb-a861-bd52bf651ff6","added_by":"auto","created_at":"2023-09-05 23:39:05","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":344270,"visible":true,"origin":"","legend":"\u003cp\u003eThe bacterial composition among summer, winter and colostrum.\u003cstrong\u003e \u003c/strong\u003eA. TOP15 phyla were compared among groups. B. TOP30 genera were compared among groups. C. Lefse analysis among groups.\u003c/p\u003e","description":"","filename":"floatimage11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/10faec5eb231fce10c85ad75.jpg"},{"id":57435622,"identity":"f5d17a3c-b631-4339-9bbf-cb91cdf5b857","added_by":"auto","created_at":"2024-05-30 16:00:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3036198,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3282014/v1/b4d4745e-4983-4844-bb35-0c889a809f99.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of the main factors related to milk production performance on microflora varies in Holstein raw milk","fulltext":[{"header":"Background","content":"\u003cp\u003eMicroflora in milk is important for the growth and development of offspring and is also closely related to the health of the maternal mammary gland [\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Meanwhile, some microbes in milk shorten the storage period of raw milk and even spoil the milk. After 20 years of research by cultured and uncultured methods, although with a low quantity of microflora, milk harbors rich microbial diversity. Based on a meta-study of 3105 breast milk samples from 2655 women, the microorganisms in breast milk included 58 phyla, 133 classes, 263 orders, 596 families, 590 genera, 1300 species, and 3563 OTUs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Since mastitis is the most common disease in the dairy industry, which affects the mammary gland's health and productivity performance, the analysis of bovine milk microflora was mainly focused on mastitis episodes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. According to a survey, \u003cem\u003eFaecalibacterium\u003c/em\u003e spp., \u003cem\u003eunclassified Lachnospiraceae\u003c/em\u003e, \u003cem\u003ePropionibacterium\u003c/em\u003e spp., and \u003cem\u003eAeribacillus\u003c/em\u003e spp., were present in healthy quarters; \u003cem\u003eSphingobacterium\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e associated with increased somatic cell counts (SCC) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Another study reported that abundant \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eStenotrophomonas\u003c/em\u003e, \u003cem\u003eBurkholderia\u003c/em\u003e, and \u003cem\u003eBrevundimonas\u003c/em\u003e in clinical mastitis milk, while \u003cem\u003ePseudomonas\u003c/em\u003e and \u003cem\u003ePsychrobcter\u003c/em\u003e, and \u003cem\u003eRalstonia\u003c/em\u003e in healthy milk [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, most microbes in milk were irrelevant to mastitis. For example, \u003cem\u003eStaphylococcus. aureus\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e are commonly existed in healthy cows and seemed as core microbiota in milk. Thus, recent studies provided new insights into the ecology of the microflora of the mammary gland [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, milk microbiota was constantly changing since the milk fluid washes out and the mammary gland immunity effects. Also, milk microbiota can be easily affected by many factors, such as host, environment, and sampling methods [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The mediterranean diet increased lactic acid bacteria and bile acid metabolites in milk than western diets [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The abundance of \u003cem\u003eStaphylococcaceae\u003c/em\u003e, \u003cem\u003eBacillaceae\u003c/em\u003e, \u003cem\u003eStreptococcaceae\u003c/em\u003e, \u003cem\u003eMicrobacteriaceae\u003c/em\u003e, and \u003cem\u003eMicrococcaceae\u003c/em\u003e in milk was influenced by season [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e were the pathogens in Farm A (lower incidence rate of subclinical mastitis), while \u003cem\u003eStreptococcus\u003c/em\u003e and \u003cem\u003eCorynebacterium\u003c/em\u003e were pathogens in farm B (higher incidence rate) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Those results indicated that milk microflora is highly diverse and affected by multiple factors, which contributes to a better understanding of milk composition and its complex relationship with the host and environment. However, till now, no study combined these elements to explore the milk microbiota.\u003c/p\u003e \u003cp\u003eAlthough the physiology or pathology implications of the milk microflora are still in research, the existing studies showed that it has vital importance in maintaining the mammary gland health to fight against mastitis both in women and animals, and the underlying role in the performance of milking in dairy cows. Therefore, the present study uses 16S rRNA sequencing method to explore the raw milk microflora and analyze the main factors related to milk production performance, such as season, farms, health and subclinical mastitis, and parity in Ningxia, China. Our study aims to illuminate the patterns of milk microflora changes.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA significant difference in OTUs was observed with summer and winter samples (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Fig 1A). Also, the OTUs in Farms A and B were significantly lower than that in Farms C, D, E, and F (Fig 1B, 1E). In summer, the OTUs were significantly higher in the subclinical mastitis group than in the health group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Fig 1C). The same pattern was also observed in multiparity and primiparity cows (Fig 1D). But the same case was not observed in samples collected in winter (Fig 1F, 1G).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlpha diversity evaluates the richness and uniformity of species in samples. The chao1 index was used to indicate community richness, and the shannon index was used to indicate community uniformity. In our study, both chao1 and shannon indices were significantly higher in summer than in winter (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Fig 2A and B). Farm A and B had a lower chao1 value than Farm C, D, and E (in summer)/F (in winter) (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Fig 2C, I). Subclinical mastitis groups had a higher chao1 index than the health group in summer, but it was not the case in winter (Fig 2D, J). The multiparity groups had a higher chao1 index than the primiparity group in summer, but there was no significant difference observed in winter (Fig 2E, K). In summer, the shannon index was significantly higher in Farm C and D than in Farm B (Fig 2F). Significant differences were also observed in the health and subclinical mastitis group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, Fig 2G). But there was no significant difference between the multiparity and primiparity groups (Fig2H). In winter, differences among farms were observed. In general, Farm D and F had a higher shannon index value than Farm A, B, and C (Fig 2L), but there were no significant changes between the health and subclinical mastitis group, and primiparity and multiparity groups (Fig 2M, N).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the similarities and differences in microbial communities, beta diversity analysis was performed using unweighted uniFrac PCoA. The microbiota boundaries of raw milk samples in summer and winter groups were clear, indicating that season was the main factor affecting the composition of raw milk microflora (Fig 2A). Farms from group1 (farmA, B) and group2 (farmC, D and E) had distant bacterial compositions in summer and from group1 (farmA, B), group2 (farmC, D) and farmF had distant distances in winter (Fig 2B). In addition, there was no difference in the composition of the microflora between health and subclinical mastitis groups, and primiparity and multiparity groups (Fig 2C, 2D).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the summer group, the main phyla were \u003cem\u003eBacteroidetes\u003c/em\u003e (41.72%), \u003cem\u003eFirmicutes\u003c/em\u003e (37.53%), \u003cem\u003eProteobacteria\u003c/em\u003e (11.57%), \u003cem\u003eActinobacteria\u003c/em\u003e (3.13%), \u003cem\u003eGemmatimonadetes\u003c/em\u003e (1.41%) and \u003cem\u003eAcidobacteria\u003c/em\u003e (0.76%), which were accounted for 96.13% of total sequences. In winter, \u003cem\u003eFirmicutes\u003c/em\u003e (40.01%), \u003cem\u003eProteobacteria\u003c/em\u003e (22.69%), \u003cem\u003eBacteroidetes\u003c/em\u003e (21.87%) and \u003cem\u003eActinobacteria\u003c/em\u003e (11.46%) accounted for 96.03% of total sequences (Fig 4A). Compared with samples collected in summer, samples in winter had abundant phyla of \u003cem\u003eProteobacteria\u003c/em\u003e (22.69% vs. 11.57%) and \u003cem\u003eActinobacteria\u003c/em\u003e (11.46% vs. 3.13%), and fewer \u003cem\u003eBacteroidetes\u003c/em\u003e (21.87% vs. 41.72%) (Fig 4A). There were significant differences in the relative abundance of predominant phyla among farms, especially in winter (Fig 4B, E). But no changes were observed between the health and subclinical mastitis group, or primiparity and multiparity groups, both in summer and winter (Fig 4C, D, F, and G). \u003cem\u003eGemmatimonadetes\u003c/em\u003e was the only phylum that was higher in primiparity than multiparity in summer (Fig 4D).\u003c/p\u003e\n\u003cp\u003eIn summer, the predominant genera were \u003cem\u003eLachnospiraceae_NK4A136_group\u003c/em\u003e, \u003cem\u003eRuminococcaceae_UCG_014\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003e, \u003cem\u003eDesulfovibrio\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eLachnospiraceae_UCG_001\u003c/em\u003e, \u003cem\u003eHelicobacter\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eOdoribacter\u003c/em\u003e, \u003cem\u003eRuminiclostridium_9\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eParasutterella\u003c/em\u003e, \u003cem\u003eAllobaculum\u003c/em\u003e, \u003cem\u003eLachnoclostridium\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eRuminiclostridium_5\u003c/em\u003e, \u003cem\u003eLachnospiraceae_UCG_006\u003c/em\u003e, \u003cem\u003ePrevotellaceae_UCG_001\u003c/em\u003e, \u003cem\u003eAnaeroplasma\u003c/em\u003e, \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eLachnospiraceae_FCS020_group\u003c/em\u003e, \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e, \u003cem\u003eCorynebacterium_1\u003c/em\u003e, \u003cem\u003eSphingomonas\u003c/em\u003e, \u003cem\u003eCoprococcus\u003c/em\u003e\u003cem\u003e_1\u003c/em\u003e, \u003cem\u003eIncertae_Sedis\u003c/em\u003e, \u003cem\u003eRikenella\u003c/em\u003e and \u003cem\u003eMucispirillum\u003c/em\u003e (Fig 5A). In winter, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, \u003cem\u003eCorynebacterium_1\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, \u003cem\u003eParasutterella\u003c/em\u003e, \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e, \u003cem\u003eTuricibacter\u003c/em\u003e, \u003cem\u003ePlanococcus\u003c/em\u003e, \u003cem\u003eRuminococcaceae_UCG_005\u003c/em\u003e, \u003cem\u003eAtopostipes\u003c/em\u003e, \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eRalstonia\u003c/em\u003e, \u003cem\u003eJeotgalicoccus\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eRikenellaceae_RC9_gut_group\u003c/em\u003e, \u003cem\u003eLachnospiraceae_UCG_004\u003c/em\u003e, \u003cem\u003eRuminococcaceae_UCG_014,\u003c/em\u003e \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eSutterella\u003c/em\u003e, \u003cem\u003ePantoea\u003c/em\u003e, \u003cem\u003e[Eubacterium]_coprostanoligenes_group\u003c/em\u003e and \u003cem\u003eSphingomonas\u003c/em\u003e were the prevalent genera (Fig 6A). Four predominant genera (\u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eCorynebacterium_1\u003c/em\u003e, \u003cem\u003eParasutterella\u003c/em\u003e, and \u003cem\u003eParabacteroides\u003c/em\u003e) were shared in two seasons.\u003c/p\u003e\n\u003cp\u003eWe further compared the top 10 genera changes in different groups. There were differences among farms, especially in winter, but no significant changes were found between health and subclinical mastitis groups, as well as primiparity and multiparity groups (Fig 5A-D; Fig 6A-D). In summer, the abundance of \u003cem\u003eAlloprevotella\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, and \u003cem\u003eRuminiclostridium_9\u003c/em\u003e was higher in Farm B than in the other four farms; \u003cem\u003eDesulfovibrio\u003c/em\u003e was higher in Farm C than the other farms; \u003cem\u003eHelicobacter\u003c/em\u003e in Farm A was lower than other farms; In winter, Farm A and B had higher genera of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEscherichia_shigella\u003c/em\u003e, and \u003cem\u003eBlautia\u003c/em\u003e than Farm C, D, and F. Farm C had the highest relative abundance of \u003cem\u003eCorynebacterium_1\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e, while the lowest \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eRhizobium\u003c/em\u003e. The primiparity group had a higher relative abundance of Bacteroides than the multiparity group in summer (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). While, no significant differences were observed between health and subclinical mastitis, as well as primiparity and multiparity in winter (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eIn summer, o_\u003cem\u003eRhizobiales\u003c/em\u003e, o_\u003cem\u003eAcidobacteriales\u003c/em\u003e, o_\u003cem\u003eAcidimiorobiaceae\u003c/em\u003e were biomarkers in health groups. While, g_\u003cem\u003eBacteroidales_S24_7_group\u003c/em\u003e, o_\u003cem\u003eFibrobacterles\u003c/em\u003e, o_\u003cem\u003eNitrospiraceae\u003c/em\u003e, g_\u003cem\u003eSporolactobacillus\u003c/em\u003e, f_\u003cem\u003eMycobacteriaceae\u003c/em\u003e and g_\u003cem\u003eMycobacterium\u003c/em\u003e were biomarkers in subclinical mastitis groups (Fig 7A). In winter, k_\u003cem\u003eActinobacter\u003c/em\u003e, such as o_\u003cem\u003ePseudomondales\u003c/em\u003e, o_\u003cem\u003eMicrococcales\u003c/em\u003e, f_\u003cem\u003eNocardioidaceae\u003c/em\u003e, f_\u003cem\u003eRhodospirillaceae\u003c/em\u003e, and o_\u003cem\u003eAcidimicrobiales\u0026nbsp;\u003c/em\u003ewere biomarkers in health groups. While, g_\u003cem\u003eEscherichia_Shigella\u003c/em\u003e, f_\u003cem\u003eLactobacillaceae\u003c/em\u003e, g_\u003cem\u003eLactobacillus\u003c/em\u003e, g_\u003cem\u003ePrevotella_9\u003c/em\u003e, f_\u003cem\u003eActionmycetaceae\u003c/em\u003e, o_\u003cem\u003eActionmycetales\u003c/em\u003e, f_\u003cem\u003eTRA3_20\u003c/em\u003e were biomarkers in subclinical mastitis groups (Fig 7B). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe enrichment of the KEGG pathways were higher in summer than in winter. In summer, 17 pathways were significantly higher in healthy cows than in the subclinical mastitis group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and 3 pathways (cell motility, membrane transport, and signal transduction) were extremely significantly different (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) (Fig 8A). The same trend was observed (Fig 8B) in winter. In addition, Farm A was somewhat different from other farms with 5/20 KEGG pathways in summer; while in winter, the main changes with 15 pathways were observed between Farm A and F, as well as Farm C and F. The top 20 KEGG pathways had no change between primiparity, and multiparity, both in summer and winter (data not shown).\u003c/p\u003e\n\u003cp\u003eAs shown in Fig 9, the top 30 genera were correlated with each other, especially \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e. While, \u003cem\u003eStaphylococcus\u0026nbsp;\u003c/em\u003eand \u003cem\u003eRoseburia, Parasutterella\u003c/em\u003e, and \u003cem\u003eParabacteroides\u0026nbsp;\u003c/em\u003ewere with the least interaction with other genera (Fig 9).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe OTUs in summer, winter and colostrum were 21976, 13293, and 8506 respectively. 8182 OTUs were shared in Summer and Winter samples, but only 3134 OTUs were shared among the colostrum, Summer, and Winter samples (Fig 10A). The alpha diversity (accessed by chao1 and shannon indices) was significantly different among\u0026nbsp;the three groups, with the highest values observed in the summer group, following the colostrum group and winter group (Fig 10B, 10C). The beta diversity of raw milk and colostrum was compared by PCoA and UPGMA analysis. As it showed in Fig 10D, there were distinct microflora composition among raw milk in summer, winter, and colostrum groups, indicating that the species composition of samples from groups varied greatly. In summary, from the OTUs counts, alpha diversity and beta diversity, we were able to conclude that the milk samples from summer, winter and colostrum are unique.\u003c/p\u003e\n\u003cp\u003eWe further compare the bacterial composition at the phylum and genus levels. As shown in Fig 10A, \u003cem\u003eFirmicutes\u003c/em\u003e enriched in colostrum groups, \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteriota\u003c/em\u003e enriched in winter groups, and the other seven top15 phyla enriched in summer groups (Fig 11A). At the genus levels, there were 851 different bacterial genera among the summer, winter and colostrum sample groups. In the top30 genera, \u003cem\u003eEscherichia-shigella\u003c/em\u003e and \u003cem\u003eLactobacillus\u003c/em\u003e enriched in colostrum, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003e[Eubacterium]_eligens_group\u003c/em\u003e and \u003cem\u003eBlautia\u003c/em\u003e enriched in the winter, while \u003cem\u003eMuribaculaceae\u003c/em\u003e, \u003cem\u003eLachnospiraceae_NK4A136_group\u003c/em\u003e, \u003cem\u003eClostridia_UCG-014\u003c/em\u003e enriched in summer (Fig 11B). As shown in Fig 11C, the Lefse analysis revealed that \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEubacterium_eligens_group\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e were biomarkers genera in winter group, \u003cem\u003eMuribaculaceae\u003c/em\u003e, \u003cem\u003eLachnospiraceae_NK4A136_group\u003c/em\u003e, \u003cem\u003eClostridia_UCG_014\u003c/em\u003e, \u003cem\u003eAlloprevotella\u003c/em\u003e were biomarkers in summer group, and \u003cem\u003eLactobacillus\u003c/em\u003e, \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, \u003cem\u003eCollinsella\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eClostridium_sensu_stricto_1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Serratia\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e were biomarkers in Colostrum.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first comprehensive analysis of the cows\u0026rsquo; milk microflora associated with the season, farm, health status, and parity simultaneously using pyrosequencing and on a large scale. The present study provided a better understanding of milk microflora varies associated with the main factors related to milk production performance. The results confirmed that milk harbors a rich and diverse microbial community and different factors had an important role in milk microflora. Also, this study revealed the unique microbiome characteristics of summer, winter and colostrum milk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe variation between the two seasons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe effects of season on microorganisms are usually referred to as temperature and humidity, which are the determining factors for most variations of bacterial taxonomic structure\u0026nbsp;[14, 15]. Coinciding with the temperature, it was shown that bacterial richness was generally lower in winter than that in summer\u0026nbsp;[14]. Similarly, in the present study, significantly higher bacterial richness and diversity were observed in milk samples collected in summer than that in winter. Milk samples from summer and winter were readily distinguished based on bacterial profiles (Fig 3A). The microbiota composition was also altered seasonally. Metzger reported 11 of the top 20 OTUs varied seasonally\u0026nbsp;[15]. Nalepa showed the bacterial species composition in the raw bovine milk varied significantly within 22 months and some species/groups occurred seasonally, e.g., \u003cem\u003eLactobacillus helveticus\u003c/em\u003e (summer), \u003cem\u003eLactobacillus casei\u003c/em\u003e (winter)\u0026nbsp;[16]. Nguyen found the relative abundance of genera \u003cem\u003eStaphylococcaceae\u003c/em\u003e, \u003cem\u003eBacillaceae\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u003c/em\u003e, \u003cem\u003eVeillonellaceae\u003c/em\u003e, \u003cem\u003eMethylobacteriaceae\u003c/em\u003e, and \u003cem\u003eMoraxellaceae\u003c/em\u003e were different between the two seasons\u0026nbsp;[17]. In the present study, the gut-associated genera were prevalent in the summer milk samples, such as \u003cem\u003eLachnospiraceae_NK4A136_group\u003c/em\u003e, \u003cem\u003eRuminococcaceae_UCG_014\u003c/em\u003e, and \u003cem\u003eAlloprevotella\u003c/em\u003e. While in winter, genera of \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, \u003cem\u003eCorynbacterium_1\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u0026nbsp;\u003c/em\u003ewere common.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGut bacteria, such as the genera of \u003cem\u003ePrevotella\u003c/em\u003e, \u003cem\u003eRuminococcus\u003c/em\u003e, \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eRikenella\u003c/em\u003e, and \u003cem\u003eAlistipes\u003c/em\u003e are prevalent in milk as previous studies reported\u0026nbsp;[18-21]. \u003cem\u003eCorynebacterium\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, and \u003cem\u003ePsychrobacter\u003c/em\u003e were the more common genera detected in the colder season than the warmer season, partly because of their psychotropic features\u0026nbsp;[20, 22]. Besides, \u003cem\u003eBifidobacterium\u003c/em\u003e was commonly detected in winter, while \u003cem\u003eLactobacillus\u003c/em\u003e was commonly detected in summer. Both of them are regarded as potential probiotics and are common in milk samples. The reasons for their distribution differences remain unknown. Moreover, some studies reported \u003cem\u003eRhizobium\u003c/em\u003e or \u003cem\u003eBradyrhizobium\u003c/em\u003e were detected in human milk\u0026nbsp;[23, 24], goat milk\u0026nbsp;[25], and cow milk\u0026nbsp;[2, 26, 27]. \u003cem\u003eRhizobium\u003c/em\u003e and \u003cem\u003eBradyrhizobium\u003c/em\u003e are members of the bacterial order \u003cem\u003eRhizobiales\u003c/em\u003e. \u003cem\u003eRhizobia\u003c/em\u003e are a soil bacterium that aer a symbiont of the legumes, and not a documented gut microbe\u0026nbsp;[15]. The plausible explanation of them presented in milk might be through an endogenous entero-mammary pathway\u0026nbsp;[4]. Besides the above genera discussed here, \u003cem\u003eParabacteroides\u003c/em\u003e were also common in our samples.\u0026nbsp;Drago also found that Parabacteroides have a pivotal role in the bacterial network in the mature milk from Italian mothers\u0026nbsp;[24]. Our results suggested that the individual difference was more common in winter than in summer (Fig 3). This might be due to the higher gut-associated microbiota presented in bovine milk in summer, and that microbiota was relatively stable in individuals. The\u0026nbsp;KEGG pathway was used to analyze the known bacterial gene function. Data from our study suggested the higher abundance of metabolism in summer than that in winter\u0026nbsp;(Fig 8). Our results were similar to\u0026nbsp;Li\u0026apos;s\u0026nbsp;[14]. Owing to the higher bacterial communities in summer than in winter, the bacterial metabolism consequently\u0026nbsp;increased.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe mammary gland health status affected milk microbiota\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo compare the main differences in milk microflora between healthy and suffering subclinical mastitis cows was the original objective of our study. Greater richness and diversity are generally associated with better health outcomes in systems with a well-characterized dense microflora, such as the gut or skin\u0026nbsp;[15]. The milk from healthy cows generally harbors greater richness and diversity than mastitis cows, even in different mammary glands from the same cow\u0026nbsp;[28]. Some researchers observed differences in the milk microbiota between healthy and clinical mastitis quarters\u0026nbsp;[15, 19]. In the present study, overall, we didn\u0026apos;t observe significant differences in microbiota between healthy and subclinical groups. Only in summer, a significantly higher alpha diversity (chao1 and Shannon index) was found in subclinical mastitis groups (Fig 2D, G). Hoque also reported that there was higher microbiota diversity and species richness in milk with mastitis conditions (including clinical mastitis, recurrent mastitis, and subclinical mastitis) than in healthy milk samples\u0026nbsp;[29]. Therefore, we could speculate that milk microbiota composition might not contribute to subclinical mastitis.\u0026nbsp;In the current work, we showed all the top 20 KEGG pathways were significantly lower in the subclinical mastitis group compared with the health group, especially the pathways of membrane transport, cell motility, and signal transduction in summer [Fig 8A]. The higher microbial KEGG pathways represent active metabolism activity. These results suggested that the subclinical mastitis status\u0026nbsp;\u003ca href=\"javascript%3A;\"\u003ebe\u003c/a\u003e \u003ca href=\"javascript%3A;\"\u003echaracterized\u003c/a\u003e \u003ca href=\"javascript%3A;\"\u003eby\u003c/a\u003e decreasing bacterial pathways. But these results need to be further confirmed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe interfarms difference of the milk microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared with the factors of health status, little research studied the interfarms difference of the microbiota. Pang observed the microbial diversity\u0026nbsp;from two farms could be separated\u0026nbsp;[13]. Nguyen found that the relative abundance of \u003cem\u003ePseudomonadaceae\u003c/em\u003e, \u003cem\u003eEnterobacteriaceae\u003c/em\u003e, and \u003cem\u003eStreptococcaceae\u003c/em\u003e, \u003cem\u003eLactobacillaceae\u003c/em\u003e, \u003cem\u003eBifidobacteriaceae\u003c/em\u003e, and \u003cem\u003eCellulomonadaceae\u003c/em\u003e in milk was different in the two farms\u0026nbsp;[17]. Besides the milk, Weese also reported that the fecal microbiota of calves was highly variable between farms\u0026nbsp;[30]. In our study, the four dominant phyla were similar to Farm A and B, which enriched with phyla of \u003cem\u003eBacteroidetes\u003c/em\u003e, but decreased \u003cem\u003eActinobacteria\u003c/em\u003e compared with other farms; Farm C had the highest phyla of \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eActinobacteriota\u003c/em\u003e, but the lowest \u003cem\u003eBacteroidetes\u003c/em\u003e (Fig 4E). In summer, slight variations were observed among farms compared with samples from winter. Briefly, Farm D and E had similar phyla, but Farm B had higher \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eFirmicutes\u003c/em\u003e, and lower \u003cem\u003eProteobacteriota\u003c/em\u003e than Farm A. Farm C had higher \u003cem\u003eFirmicutes\u003c/em\u003e than Farm A. Besides, significant differences were also found in the phyla of \u003cem\u003eGemmatimonadetes\u003c/em\u003e and \u003cem\u003eAcidobacteriota\u003c/em\u003e (Fig 4B). From PCoA plots, we observed that Farm A and B were more similar in milk microbiota, while Farm D and F were also similar. Farm C was different from other farms (Fig 3). In our study, all farms are well-managed, productive operations and take part in the DHI schedule. The interfarm differences are for multiple reasons. Unidentified management practices may significantly influence the microbiota. Unlike samples in summer, in winter, Farm A and B enriched with genera of \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, but lower \u003cem\u003eCorynebacterium_1\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e; Farm C enriched with \u003cem\u003eCorynebacterium_1\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and lower \u003cem\u003eRhizobium\u003c/em\u003e; Farm D and F enriched with \u003cem\u003ePseudomonas\u003c/em\u003e and lower \u003cem\u003eEscherichia_Shigella\u003c/em\u003e (Fig 6B). Considering that \u003cem\u003ePseudomonas\u003c/em\u003e was detected more in milk from healthy cows, \u003cem\u003eEscherichia_Shigella\u003c/em\u003e from mastitis cows, we spectated that Farm D and F harbored a higher balanced microbiota of milk than Farm A, B, and C. In addition, data presented in our study indicated that Farm A and B were more accessible with mammary gland problems with \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, Farm C with \u003cem\u003eCorynebacterium_1\u003c/em\u003e. In most cases, management practices have a significant influence on milk quality.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe parity difference of the milk microbiota\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultiparous cows are at greater risk of mastitis than primiparous cows\u0026nbsp;[31]. Thus, we speculated that the parity number was an important factor that affect the milk microbiota. Lima reported the differences in the bacterial composition of colostrum between primiparous and multiparous cows\u0026nbsp;[32]. \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eFusobacterium\u003c/em\u003e, \u003cem\u003eAcinetobacter\u003c/em\u003e, and \u003cem\u003eBacteroides\u003c/em\u003e were more abundant in the colostrum of multiparous cows than in primiparous cows. The colostrum microbiota of primiparous cows was richer than that of multiparous cows. On the contrary, we observed a higher bacterial richness in multiparous cows than in primiparous cows (Fig 2E). The plausible reason might be the increasing exposure of the intramammary ecosystem to environmental sources of microbes in multiparous cows than in primiparous cows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExcept for the factors discussed above, many other factors also affected the milk microbiota. Toscano reported differences between Cesarean section and vaginal delivery\u0026nbsp;[33]. Derakhshani observed the role of BoLA-gene polymorphism in modulating the composition of colostrum microbiota in dairy cows\u0026nbsp;[34]. Metzger \u0026nbsp;found the overall bacterial community composition differed among bedding types in dairy farms\u0026nbsp;[35]. Cabrera-Rubio found that the weight and mode of delivery affected the women\u0026apos;s milk microbiota\u0026nbsp;[36]. All in all, the research on milk microbiota was still in the early stage.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the comparative analysis of summer, winter and colostrum samples showed that the alpha diversity of summer samples was significantly higher than that of winter and colostrum samples, and colostrum was significantly higher than that of winter samples (Chao1 index). Based on the beta diversity of PCoA and UPGMA, we found that the samples between groups had clear boundaries, and the samples within groups were better clustered together. The Top10 phyla and genera of each group were also significantly different. The results of the above studies fully indicated that the samples of each group had a unique diversity of bacterial flora. Therefore, our study clarified significant differences in microbiota diversity among raw milk in summer, winter, and colostrum. Future analyses of milk microbiota in dairy cows should take full account of the sampling season. Moreover, the bacteria in milk and its metabolites in the production of high-quality fresh milk and dairy cattle have an important function in the protection of the mammary gland. However, the current understanding of the field is just beginning, the bacteria in milk and its function correlation research literature have not been reported. Hence, the microflora analysis of this paper is descriptive content, which lacked transverse, longitudinal association analysis and flora and function of the correlation analysis.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, we showed cows\u0026rsquo; milk microflora varied associating to several main factors related to milk production performance. Firstly, a distinct difference was observed between summer and winter raw milk samples. In summer, the gut-related genera were predominant, while \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, \u003cem\u003eCorynebacterium_1\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e were prevalent in winter milk. Secondly, our study confirmed that farm, healthy status, and parity also affected the raw milk microflora. Finally, the unique characteristics of summer, winter raw milk and colostrum were observed. This study provided a better understanding of cow milk microflora varies accompanied by the main factors related to milk production performance.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimals and milk sample collection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe large commercial dairy farms in the study milked 1000-3000 Holstein cows thrice daily in a double 52-stall (Farm A, B, D, E, F) and 26-stall (Farm C) parallel milking parlor. Farms are situated in north (Farm A), mid (Farm B and C), and south (Farm D, E, and F) areas of Ningxia, PR 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. In summer, raw milk was collected in farms A, B, C, D, and E, the total samples were 135; milk samples from 150 cows were collected in farms A, B, C, D, and F in winter. Sampling methods followed the 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 using an individual towel by farm personnel. Then, 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 \u0026deg;C. After sampling, milk samples were thawed on ice and centrifuged at 12,000 rpm for 10 min at 4 \u0026deg;C to separate fat and cells from the whey. The pellets were collected in a 1.8 mL sterile freezing tube and stored at -80 \u0026deg;C for further analysis. Cows that have given birth to one calf were defined as the primiparous cow, and two or above calves were the multiparous cows.\u003c/p\u003e\n\u003cp\u003eAll samples were collected from the cows that did not have visible signs of clinical mastitis, such as swelling or redness of breasts. Lanzhou mastitis test (LMT) reagent was used to diagnose subclinical mastitis by experiencing veterinarians on farms. In brief, about 2 mL milk was sterile collected with a detection disk, and then mixed with 2 mL LMT regent.\u0026nbsp;The mixture was observed within 1 min. The diagnosed standard was the mixture has evident flocculent or gelatinous diagnosed as subclinical mastitis milk; No above phenomenon and with good fluidity was defined as healthy milk.\u0026nbsp;The total sampling numbers were presented in table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e The sampling counts summary\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eFarm A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eFarm B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eFarm C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eFarm D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eFarm E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003eFarm F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003eHealthy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003eSubclinical\u003c/p\u003e\n \u003cp\u003emastitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003ePrimiparity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003eMultiparity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"top\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"top\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"top\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026ldquo;/\u0026rdquo; means not collected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA extraction and library construction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDNA extraction and library construction from milk samples were performed according to the procedures described [37]. Briefly: genomic DNA was diluted to a concentration of 1 ng \u0026micro;L-1. The V3-V4 variable regions of the 16S rRNA gene were amplified with the universal primers 343F and 798R (5\u0026apos;- TACGGRAGGCAGCAG-3\u0026apos;; 5\u0026apos;-AGGGTATCTAATCCT-3\u0026apos;). 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequence library analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequencing data were analyzed as described [37]. Briefly: 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.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe compared the milk microbiota differences among different groups, such as summer and winter, farms, health and subclinical mastitis, and parities. The OTUs were compared among the different groups. The Shannon and chao1 indices diversity were calculated to evaluate the alpha diversity among the groups. The unweighted unifrac distance-based principal coordinate analysis (PCoA) was used to assess beta diversity. The top 10 predominant phyla and top 30 genera among groups were compared both in summer and winter. The Lefse analysis was performed to observe the biomarkers from health and subclinical mastitis groups. PICRUSt functional prediction analysis of the 16S sequencing data was performed based on Greengenes database annotation. Using PICRUSt software, the components of known microbial gene functions were analyzed to calculate the functional differences between health and subclinical mastitis groups. Corrplot analysis was performed by calculating the correlations (spearman coefficients) among the top 30 genera in all summer and winter milk samples. Besides, the milk microbiota from summer and winter milk and colostrum groups was also compared.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe diversity of the two groups was analyzed by a two-paired t-test using GraphPad Prism 8.0, and an ANOVA test was used among the groups. All\u003cem\u003e\u0026nbsp;p\u003c/em\u003e-values were calculated with a 95% confidence level. Differences were considered significant when \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, while \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01 was considered to indicate an extremely significant difference.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eSCC: somatic cell counts; PCoA: principal coordinate analysis; OTUs: Operational taxonomic units; LMT: Lanzhou mastitis test\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiu-lan Xie conceived and performed the experiments, analyzed the data, and wrote the paper. Jian Zhao conceived the experiments and revised the manuscript. Mei Cao, Shi-ying Yan, Shu Li, Hai-hui Gao, Gang Zhang, and Jia-yi Zeng contributed to sampling and performed the experiments. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by 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).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support 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).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study followed the international standards of the \u0026ldquo;Guide to the feeding, management and use of experimental animals\u0026rdquo; (8th edition), the \u0026ldquo;Regulations on the management of experimental animals\u0026rdquo; and other relevant laws and regulations. The animal experimental ethics committee of Ningxia Academy of Agriculture and Forestry Sciences approved this study. All experiments with animals were performed in accordance with the ARRIVE guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declared that there was no interest conflict.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e Institute of Animal Science, Ningxia Academy of Agriculture and Forestry Sciences, Yinchuan75002, PR. China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003e Key Laboratory of Biological Resource and Ecological Environment of Chinese Education Ministry, College of Life Sciences, Sichuan University, Chengdu 610064, PR. China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003e Core Laboratory, School of Medicine, Sichuan Provincial People\u0026apos;s Hospital Affiliated to University of Electronic Science and Technology of China, Chengdu 610072, PR China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003e Key Laboratory of Ministry of Education for Protection and Utilization of Special Biological Resources in Western China, Department of Biochemistry and Molecular Biology, College of Life Science, Ningxia University, Yinchuan 750021, PR. China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJeurink PV, van Bergenhenegouwen J, Jimenez E, Knippels LM, Fernandez L, Garssen J, et al. Human milk: a source of more life than we imagine. Benef Microbes. 2013;4(1):17-30.\u003c/li\u003e\n\u003cli\u003eKuehn JS, Gorden PJ, Munro D, Rong R, Dong Q, Plummer PJ, et al. 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PLoS One. 2012;7(10):e47671.\u003c/li\u003e\n\u003cli\u003eKable ME, Srisengfa Y, Laird M, Zaragoza J, McLeod J, Heidenreich J, et al. 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(4).\u003c/li\u003e\n\u003cli\u003eOultram JW, Ganda EK, Boulding SC, Bicalho RC, Oikonomou G. A Metataxonomic Approach Could Be Considered for Cattle Clinical Mastitis Diagnostics. Front Vet Sci. 2017;4:36.\u003c/li\u003e\n\u003cli\u003eMarchand S, Heylen K, Messens W, Coudijzer K, De Vos P, Dewettinck K, et al. Seasonal influence on heat-resistant proteolytic capacity of Pseudomonas lundensis and Pseudomonas fragi, predominant milk spoilers isolated from Belgian raw milk samples. Environ Microbiol. 2009;11(2):467-82.\u003c/li\u003e\n\u003cli\u003eHunt KM, Foster JA, Forney LJ, Schutte UM, Beck DL, Abdo Z, et al. Characterization of the diversity and temporal stability of bacterial communities in human milk. PLoS One. 2011;6(6):e21313.\u003c/li\u003e\n\u003cli\u003eDrago L, Toscano M, De Grandi R, Grossi E, Padovani EM, Peroni DG. Microbiota network and mathematic microbe mutualism in colostrum and mature milk collected in two different geographic areas: Italy versus Burundi. ISME J. 2017;11(4):875-84.\u003c/li\u003e\n\u003cli\u003eMcInnis EA, Kalanetra KM, Mills DA, Maga EA. Analysis of raw goat milk microbiota: impact of stage of lactation and lysozyme on microbial diversity. Food Microbiol. 2015;46:121-31.\u003c/li\u003e\n\u003cli\u003eCremonesi P, Ceccarani C, Curone G, Severgnini M, Pollera C, Bronzo V, et al. Milk microbiome diversity and bacterial group prevalence in a comparison between healthy Holstein Friesian and Rendena cows. PLoS One. 2018;13(10):e0205054.\u003c/li\u003e\n\u003cli\u003eCurone G, Filipe J, Cremonesi P, Trevisi E, Amadori M, Pollera C, et al. What we have lost: Mastitis resistance in Holstein Friesians and in a local cattle breed. Res Vet Sci. 2018;116:88-98.\u003c/li\u003e\n\u003cli\u003eMetzger SA, Hernandez LL, Suen G, Ruegg PL. Understanding the Milk Microbiota. Vet Clin North Am Food Anim Pract. 2018;34(3):427-38.\u003c/li\u003e\n\u003cli\u003eHoque MN, Istiaq A, Rahman MS, Islam MR, Anwar A, Siddiki A, et al. Microbiome dynamics and genomic determinants of bovine mastitis. Genomics. 2020;112(6):5188-203.\u003c/li\u003e\n\u003cli\u003eWeese JS, Jelinski M. Assessment of the Fecal Microbiota in Beef Calves. J Vet Intern Med. 2017;31(1):176-85.\u003c/li\u003e\n\u003cli\u003eJamali H, Barkema HW, Jacques M, Lavallee-Bourget EM, Malouin F, Saini V, et al. Invited review: Incidence, risk factors, and effects of clinical mastitis recurrence in dairy cows. J Dairy Sci. 2018;101(6):4729-46.\u003c/li\u003e\n\u003cli\u003eLima SF, Teixeira AGV, Lima FS, Ganda EK, Higgins CH, Oikonomou G, et al. The bovine colostrum microbiome and its association with clinical mastitis. J Dairy Sci. 2017;100(4):3031-42.\u003c/li\u003e\n\u003cli\u003eToscano M, De Grandi R, Peroni DG, Grossi E, Facchin V, Comberiati P, et al. Impact of delivery mode on the colostrum microbiota composition. BMC Microbiol. 2017;17(1):205.\u003c/li\u003e\n\u003cli\u003eDerakhshani H, Plaizier JC, De Buck J, Barkema HW, Khafipour E. Association of bovine major histocompatibility complex (BoLA) gene polymorphism with colostrum and milk microbiota of dairy cows during the first week of lactation. Microbiome. 2018;6(1):203.\u003c/li\u003e\n\u003cli\u003eMetzger SA, Hernandez LL, Skarlupka JH, Suen G, Walker TM, Ruegg PL. Influence of sampling technique and bedding type on the milk microbiota: Results of a pilot study. J Dairy Sci. 2018;101(7):6346-56.\u003c/li\u003e\n\u003cli\u003eCabrera-Rubio R, Collado MC, Laitinen K, Salminen S, Isolauri E, Mira A. The human milk microbiome changes over lactation and is shaped by maternal weight and mode of delivery. Am J Clin Nutr. 2012;96(3):544-51.\u003c/li\u003e\n\u003cli\u003eXie X-l, Zhang G, Gao H-h, Deng K-x, Chu Y-f, Wu D-y, et al. Analysis of bovine colostrum microbiota at a dairy farm in Ningxia, China. International Dairy Journal. 2021;119:104984. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Milk microflora, 16S rRNA pyrosequencing, Season, Farm, Subclinical mastitis","lastPublishedDoi":"10.21203/rs.3.rs-3282014/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3282014/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMilk microflora is closely associated with the physiology and pathology in the mammary gland, and plays an important role in offspring development. The objective of the study was to illustrate the variation of milk microflora accompanied by the main factors related to milk performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eMilk samples were collected from 285 cows in Ningxia, China, and then microflora was explored using 16S rRNA pyrosequencing. All samples were grouped with the season (summer and winter), cow status (healthy and subclinical mastitis), farms (6 commercial dairy farms), and parity (primiparity and multiparity). The bacterial diversity, community composition, and abundance were analyzed among different groups. Also, the milk microflora among samples from summer, winter, and colostrum was compared. The results showed that the bacterial diversity of the milk varied significantly between samples from summer and winter. Higher bacterial richness was observed from summer samples than from winter samples. The gut-related genera, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eCorynebacterium\u003c/em\u003e_1, \u003cem\u003eSphingomonas\u003c/em\u003e, and \u003cem\u003eLactobacillus\u003c/em\u003e, were prevalent in summer milk samples. Although \u003cem\u003eEscherichia_Shigella\u003c/em\u003e, \u003cem\u003ePseudomonas\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, \u003cem\u003ePsychrobacter\u003c/em\u003e, \u003cem\u003eRhizobium\u003c/em\u003e, \u003cem\u003eBifidobacterium\u003c/em\u003e, and \u003cem\u003eClostridium_sensu_stricto\u003c/em\u003e_1 were common in winter samples. In addition, different farms exhibited differences in bacterial diversity. Subclinical mastitis increased alpha diversity and decreased the enrichment of KEGG pathways in summer. Moreover, significant differences of milk microflora were observed from summer, winter and colostrum samples.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe study revealed that the milk microflora varied companies with seasons, farms, health status, and parities. Also, milk from summer, winter, and colostrum showed their unique microflora characteristics.\u003c/p\u003e","manuscriptTitle":"The effect of the main factors related to milk production performance on microflora varies in Holstein raw milk","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-09-05 23:31:00","doi":"10.21203/rs.3.rs-3282014/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"664a0630-2f54-4d69-9e72-6fad07ebf552","owner":[],"postedDate":"September 5th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-30T15:52:46+00:00","versionOfRecord":[],"versionCreatedAt":"2023-09-05 23:31:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3282014","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3282014","identity":"rs-3282014","version":["v1"]},"buildId":"369fNeqWncA4NS6XSWjrt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}