Age-specific composition of milk microbiota in Tibetan sheep and goats.

This study investigates the dynamic changes in milk nutritional composition and microbial communities in Tibetan sheep and goats during the first 56 days of lactation. Milk samples were systematically collected at five time points (D0, D7, D14, D28, D56) post-delivery. In Tibetan sheep, milk fat, protein, and casein contents were highest on D0, gradually decreased, and stabilized after D14, while lactose and galactose levels showed the opposite trend. Goat milk exhibited similar initial peaks, with significant changes particularly between D0, D7, D14, and D56. 16S rRNA gene sequencing revealed increasing microbial diversity in both species over the lactation period. Principal coordinates analysis identified distinct microbial clusters corresponding to early (D0-D7), transitional (D14-D28), and mature (D56) stages. Core phyla, including Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, dominated the milk microbiota, with significant temporal shifts. Core microbes like Lactobacillus, Leuconostoc, and Streptococcus were common in both species, with species-specific taxa observed (e.g., Pediococcus in sheep, Shewanella in goats). Furthermore, we observed a highly shared core microbiota in sheep and goat milk, including Lactobacillus, Leuconostoc, and Streptococcus. Spearman correlation analysis highlighted significant relationships between specific microbial genera and milk nutrients. For instance, Lactobacillus positively correlated with total solids, non-fat milk solids, protein, and casein, while Mannheimia negatively correlated with protein content. This study underscores the complex interplay between milk composition and microbial dynamics in Tibetan sheep and goats, informing strategies for livestock management and nutritional enhancement. KEY POINTS: • The milk can be classified into three types based on the microbiota composition • The changes of milk microbiota are closely related to the variations in nutrition • Filter out microbiota with species specificity and age specificity in the milk.

Microbial communities in feed, bedding material, and bulk milk - experiences from a feeding trial.

There is an increasing interest in the microbiota of the dairy value chain, from field to fork. Studies to understand the effects of environmental, feed and management factors on the raw milk microbiota have been performed to elucidate the origin of the bacteria and find ways to control the presence or absence of specific bacteria. In this study, we explored the microbiota in feedstuff, bedding material and milk on a Swedish dairy farm to investigate the effects of feeding different silages on the bacterial compositions throughout the dairy value chain. Three ensiling treatments were evaluated: without additive, with acid treatment, and with inoculation of starter culture. The silage treatments were fed as partial mixed rations to 67 dairy cows for 3 weeks each, with one treatment fed twice to evaluate if a potential change in milk microbiota could be repeated. The highest average total bacteria counts were found in the used bedding material (9.6 log10 cfu/g), while milk showed the lowest (3.5 log10 cfu/g). Principal coordinate analysis of the weighted UniFrac distance matrix showed clear separation between 3 clusters of materials: 1) herbage, 2) silage and partial mixed ration, and 3) used bedding material and milk. Surprisingly, the expected effect of the ensiling treatments on silage microbiota was not clear. Transfer of major bacteria from the silages and resulting partial mixed rations to the used bedding material was observed, but rarely to milk. The milk microbiota showed most resemblance to that of the used bedding material. Lactobacillus was a major genus in both feed and milk, but investigations at amplicon sequence variant level showed that in most cases the sequences differed between materials. However, low total bacteria count in the milk in combination with a high diversity suggests that results may be biased due to environmental contamination of the milk samples. Considering that the study was performed on a research farm, strict hygienic measures during the feeding experiment may have contributed to the low transfer of bacteria from feed to milk.

Alpine grazing management, breed, and diet effects on coagulation properties, composition, and microbiota of dairy cow milk by commercial mountain-based herds.

Cow milk microbiota has received increased attention in recent years, not only because of its importance for human health but also because of its effect on the quality and technological properties of milk. Several studies, therefore, have investigated the effect of various production factors on the microbial composition of milk. However, most of the previous studies considered a limited number of animals from experimental or single farm, which could have biased the results. Therefore, this study aimed to understand the effect of different alpine production systems in Italy on the compositional and microbiological quality of milk, considering commercial herds with different feeding intensities and cattle breeds. The results obtained in this work indicated that the month and season of sampling (July for summer or February for winter) more than farm, breed, and cow diet exerted significant effects on cow milk parameters and microbiota. In particular, significant differences were observed for urea content in milk between sampling seasons. Differences in milk fat were mainly related to breed specific effects. From a microbiological point of view, statistically significant differences were found in presumptive lactic acid bacteria counts. Based on a culture-independent method, milk obtained in February harbored the highest number of Firmicutes (e.g., Lactobacillus) and the lowest number of Actinobacteria (e.g., Corynebacterium). Moreover, bacterial richness and diversity were higher in July during alpine pasture season indicating a significant effect of pasture feed on the growth of bacterial communities. The results of this study highlighted the effect of month or season mainly related to differences in feeding management (e.g., access to pasture during vegetation period, concentrates supplementation) on composition and microbiota in milk.

The microbiota of ensiled forages and of bulk tank milk on dairy cattle farms in northern Sweden - a case study.

Factors contributing to variations in the quality and microbiota of ensiled forages and in bulk tank microbiota in milk from cows fed different forages were investigated. Nutritional quality, fermentation parameters and hygiene quality of forage samples and corresponding bulk tank milk samples collected in 3 periods from 18 commercial farms located in northern Sweden were compared. Principal coordinates analysis revealed that the microbiota in forage and bulk milk, analyzed using 16S rRNA gene-based amplicon sequencing, were significantly different. The genera Lactobacillus, Weissella and Leuconostoc dominated in forage samples, whereas Pseudomonas, Staphylococcus and Streptococcus dominated in bulk milk samples. Forage quality and forage-associated microbiota were affected by ensiling method and by use of silage additive. Forages stored in bunker and tower silos (confounded with use of additive) were associated with higher levels of acetic and lactic acid and Lactobacillus. Forage ensiled as bales (confounded with no use of additive) was associated with higher dry matter content, water-soluble carbohydrate content, pH, yeast count and the genera Weissella, Leuconostoc and Enterococcus. For bulk tank milk samples, milking system was identified as the major factor affecting the microbiota and type of forage preservation had little impact. Analysis of common amplicon sequence variants (ASVs) suggested that forage was not the major source of Lactobacillus found in bulk tank milk.

Investigation of milk microbiota of healthy and mastitic Sahiwal cattle.

BACKGROUND: Sahiwal cattle is an indigenous cattle breed of Pakistan and mastitis is one of the major problems faced by Sahiwal cattle which hinders its production potential. The study was designed to investigate the milk microbiota of healthy and mastitic Sahiwal cattle as part of a multistep project to develop probiotics for the mitigation and control of mastitis. Milk samples of Sahiwal cattle (healthy clinical mastitis and subclinical mastitis) reared under similar husbandry and management practices were processed for 16S rRNA gene base metagenomics analysis. RESULTS: Results revealed that Proteobacteria were dominant in the healthy group and subclinical mastitis group (56.48% and 48.77%, respectively) as compared to the clinical mastitis group (2.68%). In contrast, Firmicutes were abundant in the clinical mastitis group (64%) as compared to the healthy and subclinical mastitis groups (15.87% and 38.98%, respectively). Dominant species assigned in the healthy group were Ignavibacterium album, Novosphingobium capsulatum, Akkermansia muciniphila and Lactobacillus fermentum.The clinical mastitis group was dominated by Streptococcus dysgalactiae and Corynebacterium bovis, while subclinical mastitis group included Lactobacillus fermentum and uncultured acidobacteriales and Akkermansia muciniphila as dominant species. Alpha diversity indices showed higher microbial diversity in the healthy group compared to the clinical and sub-clinical mastitis groups. CONCLUSION: It is concluded that the milk microbiota of healthy sahiwal cattle has higher diversity and dominant taxa in the different groups may be used as signature microbes for mastitis susceptibility. Akkermansia muciniphila is one of candidate specie that was identified and may be used for development of probiotics.

Absence of changes in the milk microbiota during Escherichia coli endotoxin induced experimental bovine mastitis.

Changes in the milk microbiota during the course of mastitis are due to the nature of a sporadic occurring disease difficult to study. In this study we experimentally induced mastitis by infusion of Escherichia coli endotoxins in one udder quarter each of nine healthy lactating dairy cows and assessed the bacteriological dynamics and the milk microbiota at four time points before and eight time points after infusion. As control, saline was infused in one udder quarter each of additionally nine healthy cows that followed the same sampling protocol. The milk microbiota was assessed by sequencing of the 16 S rRNA gene and a range of positive and negative controls were included for methodological evaluation. Two different data filtration models were used to identify and cure data from contaminating taxa. Endotoxin infused quarters responded with transient clinical signs of inflammation and increased SCC while no response was observed in the control cows. In the milk microbiota data no response to inflammation was identified. The data analysis of the milk microbiota was largely hampered by laboratory and reagent contamination. Application of the filtration models caused a marked reduction in data but did not reveal any associations with the inflammatory reaction. Our results indicate that the microbiota in milk from healthy cows is unaffected by inflammation.

Milk metagenomics and cheese-making properties as affected by indoor farming and summer highland grazing.

The study of the complex relationships between milk metagenomics and milk composition and cheese-making efficiency as affected by indoor farming and summer highland grazing was the aim of the present work. The experimental design considered monthly sampling (over 5 mo) of the milk produced by 12 Brown Swiss cows divided into 2 groups: the first remained on a lowland indoor farm from June to October, and the second was moved to highland pastures in July and then returned to the lowland farm in September. The resulting 60 milk samples (2 kg each) were used to analyze milk composition, milk coagulation, curd firming, and syneresis processes, and to make individual model cheeses to measure cheese yields and nutrient recoveries in the cheese. After DNA extraction and Illumina Miseq sequencing, milk microbiota amplicons were also processed by means of an open-source pipeline called Quantitative Insights Into Microbial Ecology (Qiime2, version 2018.2; https://qiime2.org). Out of a total of 44 taxa analyzed, 13 bacterial taxa were considered important for the dairy industry (lactic acid bacteria, LAB, 5 taxa; and spoilage bacteria, 4) and for human (other probiotics, 2) and animal health (pathogenic bacteria, 2). The results revealed the transhumant group of cows transferred to summer highland pastures showed an increase in almost all the LAB taxa, bifidobacteria, and propionibacteria, and a reduction in spoilage taxa. All the metagenomic changes disappeared when the transhumant cows were moved back to the permanent indoor farm. The relationships between 17 microbial traits and 30 compositional and technological milk traits were investigated through analysis of correlation and latent explanatory factor analysis. Eight latent factors were identified, explaining 75.3% of the total variance, 2 of which were mainly based on microbial traits: pro-dairy bacteria (14% of total variance, improving during summer pasturing) and pathogenic bacteria (6.0% of total variance). Some bacterial traits contributed to other compositional-technological latent factors (gelation, udder health, and caseins).

Changes in bovine milk bacterial microbiome from healthy and subclinical mastitis affected animals of the Girolando, Gyr, Guzera, and Holstein breeds.

Raw milk samples were collected from 200 dairy cows belonging to Girolando 1/2, Gyr, Guzera, and Holstein breeds, and the bacterial diversity was explored using 16S rRNA amplicon sequencing. SCC analysis showed that 69 animals were classified as affected with subclinical mastitis. The milk bacterial microbiome was dominated by Firmicutes, Proteobacteria, and Actinobacteria, with an increase of Firmicutes in animals with subclinical mastitis and Proteobacteria in healthy animals. At the family and genus level, the milk bacterial microbiome was dominated by Staphylococcus, Acinetobacter, Pseudomonas, members of the family Enterobacteriaceae, Lactococcus, Aerococcus, members of the family Rhizobiaceae, Anaerobacillus, Streptococcus, members of the family Intrasporangiaceae, members of the family Planococcaceae, Corynebacterium, Nocardioides, and Chryseobacterium. Significant differences in alpha and beta diversity analysis suggest an effect of udder health status and breed on the composition of raw bovine milk microbiota. LEfSe analysis showed 45 and 51 discriminative taxonomic biomarkers associated with udder health status and with one of the four breeds respectively, suggesting an effect of subclinical mastitis and breed on the microbiota of milk in cattle.

Milking system and premilking routines have a strong effect on the microbial community in bulk tank milk.

In this study, we investigated the variation in the microbial community present in bulk tank milk samples and the potential effect of different farm management factors. Bulk tank milk samples were collected repeatedly over one year from 42 farms located in northern Sweden. Total and thermoresistant bacteria counts and 16S rRNA gene-based amplicon sequencing were used to characterize microbial community composition. The microbial community was in general heterogeneous both within and between different farms and the community composition in the bulk tank milk was commonly dominated by Pseudomonas, Acinetobacter, Streptococcus, unclassified Peptostreptococcaceae, and Staphylococcus. Principal component analysis including farm factor variables and microbial taxa data revealed that the microbial community in milk was affected by type of milking system. Milk from farms using an automatic (robot) milking system (AMS) and loose housing showed different microbial community composition compared with milk from tiestall farms. A discriminant analysis model revealed that this difference was dependent on several microbial taxa. Among farms using an automatic milking system, there were further differences in the microbial community composition depending on the brand of the milking robot used. On tiestall farms, routines for teat preparation and cleaning of the milking equipment affected the microbial community composition in milk. Total bacteria count (TBC) in milk differed between the farm types, and TBC were higher on AMS than tiestall farms (log 4.05 vs. log 3.79 TBC/mL for AMS and tiestalls, respectively). Among tiestall farms, milk from farms using a chemical agent in connection to teat preparation and a more frequent use of acid to clean the milking equipment had lower TBC in milk, than milk from farms using water for teat preparation and a less frequent use of acid to clean the milking equipment (log 3.68 vs. 4.02 TBC/mL). There were no significant differences in the number of thermoresistant bacteria between farm types. The evaluated factors explained only a small proportion of total variation in the microbiota data, however, despite this, the study highlights the effect of routines associated with teat preparation and cleaning of the milking equipment on raw milk microbiota, irrespective of type of milking system used.

Single-molecule real-time sequencing reveals differences in bacterial diversity in raw milk in different regions and seasons in China.

The quality of raw milk is a key factor influencing the whole dairy processing chain. The richness and diversity of bacteria in raw milk affect its quality and safety. However, traditional microbial detection methods mainly depend on the known microbe culture and are often time consuming. Thus, the development of efficient ways for supervising any possible microbiological contamination is desiderated. In the current work, single-molecule real-time (SMRT) sequencing, developed by Pacific Biosciences (PacBio), was applied to acquire long reads and applied for discrimination of bacteria at species level. Forty samples of raw milk obtained from Beijing, Hebei, Inner Mongolia, Shanghai, and Guangdong in China during summer, autumn, and winter were investigated. Among 35 bacteria species identified in these samples, Acinetobacter albensis, Pseudomonas gessardii, Pseudomonas weihenstephanensis, and Rahnella inusitata were the bacteria with the highest relative abundance in the overall sample, whereas the bacteria with the highest relative abundance in raw milk samples of different origins and seasons are different. Significant differences in bacterial richness and bacterial community diversity in raw milk grouped according to different production areas and different sampling seasons were confirmed by Welch's t-test. Interestingly, the transport distance and transport time positively correlated with the relative abundance of Pseudomonas weihenstephanensis, suggesting that the content of this bacteria was expected to be a standard for evaluating the freshness of raw milk. Pathogens Bacillus cereus and Klebsiella pneumoniae were detected in most samples, indicating that the raw milk was at risk of contamination by pathogenic bacteria. Moreover, the findings of this study provide important evidence for quality and safety monitoring and biological control of raw milk.

[Breast milk microbiota of mothers with different glucose tolerance at 42 days postpartum].

OBJECTIVE: To compare the differences in microbial community between lactating mothers with gestational diabetes mellitus(GDM) and normal glucose tolerance lactating mothers(control) at 42 days postpartum, and to explore the effect of GDM on the microbial composition and structure of breast milk. METHODS: A total of 21 mothers with GDM and 25 healthy mothers in Fuqing Maternity and Child Health Care Hospital at 42 days postpartum from May 2019 to September 2020 were enrolled. A questionnaire was used to collect the basic information and dietary intakes of mothers. The mother's milk was collected by using a sterile electric breast pump. Breast milk microbiota profiles were assessed by 16 S rDNA gene amplicon based sequencing of the V3-V4 region and the sequencing platform was Illumina Miseq PE3000, bioinformatics analysis was performed on the sequencing result. RESULTS: Compared with the control group, the mothers in the GDM group consumed more vegetables(222.7(190.6, 333.1)g/d vs.176.4(49.5, 247.0)g/d, P=0.042). There was no significant difference in Alpha diversity between the two groups(P>0.05). Beta diversity analysis showed that there were significant differences in the microbial composition between the two groups(P<0.01). Breast milk microbiota species difference analysis showed that there were differences in several species between GDM group and NGT group at the levels from phylum to genus. Compared with control group, the relative abundance of Bacteroidota and Cyanobacteria in GDM group decreased significantly [(3.41±2.59)% vs. (1.23±0.82)%, (1.08±3.02)% vs. (0.10±0.11)%, P<0.05]. In the GDM group, Ralstonia, Rhodococcus, Burkholderia-Caballeronia-Paraburkholderia, Acinetobacter and Fluviicola were decreased((38.93±28.85)% vs. (26.70±28.37)%, (9.23±6.87)% vs. (4.88±6.03)%, (7.66±4.80)% vs. (2.77±1.33)%, (6.18±11.90)% vs. (2.76±6.10)%, (1.21±1.31)% vs. (0.33±0.62)%, P<0.05). The unclassified＿f＿＿xanthobacteraceae was increased, and the difference was statistically significant((0.85±3.15)% vs. (23.64±23.63)%, P<0.05). CONCLUSION: There are differences in breast milk microbial community structure in women with different glucose tolerance at 42 days postpartum, Ralstonia, Rhodococcus, Burkholderia-Caballeronia-Paraburkholderia, Acinetobacter and Fluviicola were significantly lower comparing to the normal glucose tolerance mothers.

PacBio sequencing revealed variation in the microbiota diversity, species richness and composition between milk collected from healthy and mastitis cows.

Mastitis is the economically most important disease of dairy cows. This study used PacBio single-molecule real-time sequencing technology to sequence the full-length 16S rRNAs from 27 milk samples (18 from mastitis and nine from healthy cows; the cows were at different stages of lactation). We observed that healthy or late stage milk microbiota had significantly higher microbial diversity and richness. The community composition of the microbiota of different groups also varied greatly. The healthy cow milk microbiota was predominantly comprised of Lactococcus lactis, Acinetobacter johnsonii, and Bacteroides dorei, while the milk from mastitis cows was predominantly comprised of Bacillus cereus. The prevalence of L. lactis and B. cereus in the milk samples was confirmed by digital droplets PCR. Differences in the milk microbiota diversity and composition could suggest an important role for some these microbes in protecting the host from mastitis while others associated with mastitis. The results of our research serve as useful references for designing strategies to prevent and treat mastitis.

Maternal Diet and Infant Feeding Practices Are Associated with Variation in the Human Milk Microbiota at 3 Months Postpartum in a Cohort of Women with High Rates of Gestational Glucose Intolerance.

BACKGROUND: Human milk contains a diverse community of bacteria believed to play a role in breast health and inoculation of the infant's gastrointestinal tract. The role of maternal nutrition and infant feeding practices on the human milk microbiota remains poorly understood. OBJECTIVE: Our aim was to explore the associations between maternal diet (delivery to 3 mo postpartum), infant feeding practices, and the microbial composition and predicted function in milk from women with varied metabolic status. METHODS: This was an exploratory analysis of a previously completed prospective cohort study of women with varying degrees of gestational glucose intolerance (NCT01405547). Milk samples (n = 93 mothers) were collected at 3 mo postpartum. Maternal dietary information (validated food-frequency questionnaire) and infant feeding practices (human milk exclusivity, frequency of direct breastfeeding per day) were collected. V4-16S ribosomal RNA gene sequencing (Illumina MiSeq) was conducted to determine microbiota composition. RESULTS: Intake of polyunsaturated fat [ß estimate (SE): 0.036 (0.018), P = 0.047] and fiber from grains [0.027 (0.013), P = 0.048] were positively associated with ɑ-diversity (Shannon index) of human milk. Overall microbial composition of human milk clustered based on human milk exclusivity (weighted UniFrac R2 = 0.034, P = 0.015; Bray-Curtis R2 = 0.041, P = 0.007), frequency of direct breastfeeding per day (Bray-Curtis R2 = 0.057, P = 0.026), and maternal fiber intake from grains (Bray-Curtis R2 = 0.055, P = 0.040). Total fiber, fiber from grains, dietary fat, and infant feeding practices were also associated with a number of differentially abundant taxa. The overall composition of predicted microbial functions was associated with total fiber consumption (Bray-Curtis R2 = 0.067, P = 0.036) and human milk exclusivity (Bray-Curtis R2 = 0.041, P = 0.013). CONCLUSIONS: Maternal consumption of fiber and fat, as well as mother's infant feeding practices, are important determinants of the human milk microbiota. Understanding whether these microbial changes impact an infant's overall health and development requires future study.

Oligosaccharides and Microbiota in Human Milk Are Interrelated at 3 Months Postpartum in a Cohort of Women with a High Prevalence of Gestational Impaired Glucose Tolerance.

BACKGROUND: Human milk is a rich source of human milk oligosaccharides (HMOs) and bacteria. It is unclear how these components interact within the breast microenvironment. OBJECTIVES: The objectives were first, to investigate the association between maternal characteristics and HMOs, and second, to assess the association between HMOs and microbial community composition and predicted function in milk from women with high rates of gestational glucose intolerance. METHODS: This was an exploratory analysis of a previously completed prospective cohort study (NCT01405547) where milk samples (n = 107) were collected at 3 mo postpartum. Milk microbiota composition was analyzed by V4-16S ribosomal RNA gene sequencing and HMOs by rapid high-throughput HPLC. Data were stratified and analyzed by maternal secretor status phenotype and associations between HMOs and microbiota were determined using linear regression models (ɑ-diversity), Adonis (B-diversity), Poisson regression models (differential abundance), and general linear models (predicted microbial function). RESULTS: Prepregnancy BMI, race, and frequency of direct breastfeeding, but not gestational glucose intolerance, were found to be significantly associated with a number of HMOs among secretors and non-secretors. Fucosyllacto-N-hexaose was negatively associated with microbial richness (Chao1) among secretors [B-estimate (SE): -9.3 × 102 (3.4 × 102); P = 0.0082] and difucosyllacto-N-hexaose was negatively associated with microbiota diversity (Shannon index) [-1.7 (0.78); P = 0.029] among secretors. Lacto-N-neotetraose (LNnT) was associated with both microbial B-diversity (weighted UniFrac R2 = 0.040, P = 0.036) and KEGG ortholog B-diversity (Bray-Curtis R2 = 0.039, P = 0.043) in secretors. Additionally, difucosyllactose in secretors and disialyllacto-N-hexaose and LNnT in non-secretors were associated with enrichment of predicted microbial genes encoding for metabolism- and infection-related pathways (P-false discovery rate < 0.1). CONCLUSIONS: HMOs are associated with the microbial composition and predicted microbial functions in human milk at 3 mo postpartum. Further research is needed to investigate the role these relations play in maternal and infant health.

Effect of therapeutic administration of ß-lactam antibiotics on the bacterial community and antibiotic resistance patterns in milk.

Dairy cows with mastitis are frequently treated with antibiotics. The potential effect of antibiotics on the milk microbiome is still not clear. Therefore, the objective of this research was to investigate the effect of 2 commonly used cephalosporins on the milk microbiota of dairy cows and the antibiotic resistance genes in the milk. The milk samples were collected from 7 dairy cows at the period before medication (d 0), medication (d 1, 2, 3), withdrawal period (d 4, 6, 8), and the period after withdrawal (d 9, 11, 13, 15). We applied 16S rRNA sequencing to explore the microbiota changes, and antibiotic resistance patterns were investigated by quantitative PCR. The microbiota richness and diversity in each sample were calculated using the Chao 1 (richness), Shannon (diversity), and Simpson (diversity) indices. The cephalosporins treatment lowered the Simpson diversity value at the period of withdrawal. Members of the Enterobacter genera were the most affected bacteria associated with mastitis. Meanwhile, antibiotic resistance genes in the milk were also influenced by antibiotic treatment. The cephalosporins treatment raised the proportion of blaTEM in milk samples at the period of withdrawal. Therefore, the treatment of cephalosporins led to change in the milk microbiota and increase of ß-lactam resistance gene in the milk at the time of withdrawal period.

A review of microbes and chemical contaminants in dairy products in sub-Saharan Africa.

Animal milk types in sub-Saharan Africa (SSA) are processed into varieties of products using different traditional methods and are widely consumed by households to support nutritional intake and diet. Dairy products contain several microorganisms, their metabolites, and other chemical compounds, some with health benefits and many others considered as potential health hazards. Consumption of contaminated milk products could have serious health implications for consumers. To access the safety of milk products across SSA, studies in the region investigating the occurrences of pathogens as well as chemical compounds such as heat stable toxins and veterinary drug residues in animal milk and its products were reviewed. This is done with a holistic view in light of the emerging exposome paradigm for improving food safety and consumer health in the region. Herein, we showed that several published studies in SSA applied conventional and/or less sensitive methods in detecting microbial species and chemical contaminants. This has serious implications in food safety because the correct identity of a microbial species and accurate screening for chemical contaminants is crucial for predicting the potential human health effects that undermine the benefits from consumption of these foods. Furthermore, we highlighted gaps in determining the extent of viral and parasitic contamination of milk products across SSA as well as investigating multiple classes of chemical contaminants. Consequently, robust studies should be conducted in this regard. Also, efforts such as development cooperation projects should be initiated by all stakeholders including scientists, regulatory agencies, and policy makers to improve the dairy product chain in SSA in view of safeguarding consumer health.

Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota.

BACKGROUND: In recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed. Although these methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear. RESULTS: Here, we re-processed our recently published milk microbiota data using Qiime1 to identify OTUs, and Qiime2 to identify ASVs, with or without contaminant removal using decontam. Qiime2 resolved the mock community more accurately, primarily because Qiime1 failed to detect Lactobacillus. Qiime2 also considerably reduced the average number of ASVs detected in human milk samples (364 ± 145 OTUs vs. 170 ± 73 ASVs, p < 0.001). Compared to the richness, the estimated diversity measures had a similar range using both methods albeit statistically different (inverse Simpson index: 14.3 ± 8.5 vs. 15.6 ± 8.7, p = 0.031) and there was strong consistency and agreement for the relative abundances of the most abundant bacterial taxa, including Staphylococcaceae and Streptococcaceae. One notable exception was Oxalobacteriaceae, which was overrepresented using Qiime1 regardless of contaminant removal. Downstream statistical analyses were not impacted by the choice of algorithm in terms of the direction, strength, and significance of associations of host factors with bacterial diversity and overall community composition. CONCLUSION: Overall, the biological observations and conclusions were robust to the choice of the sequencing processing methods and contaminant removal.

Short communication: Milk microbiota profiling on water buffalo with full-length 16S rRNA using nanopore sequencing.

The identification of milk microbial communities in ruminants is relevant for understanding the association between milk microbiota and health status. The most common approach for studying the microbiota is amplifying and sequencing specific hypervariable regions of the 16S rRNA gene using massive sequencing techniques. However, the taxonomic resolution is limited to family and, in some cases, genus level. We aimed to improve taxonomic classification of the water buffalo milk microbiota by amplifying and sequencing the full-length 16S rRNA gene (1,500 bp) using Nanopore sequencing (single-molecule sequencing). When comparing with short-read results, we improved the taxonomic classification, reaching species level. We identified the main microbial agents of subclinical mastitis at the species level that were in accordance with the microbiological culture results. These results confirm the potential of single-molecule sequencing for in-depth analysis of microbial populations in dairy animals.

Microbiota of bovine milk, teat skin, and teat canal: Similarity and variation due to sampling technique and milk fraction.

The aim of this study was to evaluate the effect of sampling technique and milk fraction on bovine milk microbiota data and to compare the microbiota in milk to microbiota on the teat end and in the teat canal. Representative milk samples are highly important for assessment of bacteriological findings and microbiota in milk. Samples were obtained from 5 healthy lactating dairy cows at udder quarter level during 1 milking. Swab samples from the teat end and teat canal, and milk samples collected using different techniques and in different milk fractions were included. Milk was collected by hand stripping and through a teat canal cannula before and after machine milking, through a trans-teat wall needle aspirate after milking, and from udder quarter composite milk. The microbiota of the samples was analyzed with sequencing of the V1-V3 region of the 16S rRNA gene. In addition, somatic cell counts and bacterial cultivability were analyzed in the milk samples. Microbiota data were analyzed using multivariate methods, and differences between samples were tested using analysis of similarity (ANOSIM). Differences between samples were further explored via individual studies of the 10 most abundant genera. The microbiota on the teat end, in the teat canal, and in udder quarter composite milk, collected using a milking machine, differed in composition from the microbiota in milk collected directly from the udder quarter. No differences in milk microbiota composition were detected between hand-stripped milk samples, milk samples taken through a teat canal cannula, or milk samples taken as a trans-teat wall needle aspirate before or after milking. We conclude that for aseptic milk samples collected directly from the lactating udder quarter, sampling technique or milk fraction has minor effect on the microbiota composition.

Microbiota characterization of sheep milk and its association with somatic cell count using 16s rRNA gene sequencing.

This work aimed to use 16S ribosomal RNA sequencing with the Illumina MiSeq platform to describe the milk microbiota from 50 healthy Assaf ewes. The global observed microbial community for clinically healthy milk samples analysed was complex and showed a vast diversity. The core microbiota of the sheep milk includes five genera: Staphylococcus, Lactobacillus, Corynebacterium, Streptococcus and Escherichia/Shigella. Although there are some differences, some of these genera are common with the microbiota core pattern of milk from other species, especially with dairy cows. The microbial composition of the studied samples, based on the definition of amplicon sequence variants, was analysed through a correlation network. A preliminary analysis by grouping the milk samples based on their somatic cell count (SCC), which is considered an indicator of subclinical mastitis (SM), showed certain differences for the core of the samples identified as SM. The differences in the microbiota diversity pattern among samples might also suggest that subclinical mastitis would be associated with the significant increase in some genera that are inhabitants of the mammary gland and a remarkable concomitant reduction in the microbial diversity. Additionally, we have also presented here a preliminary analysis to assess the impact of the sheep milk microbiome on SCC, as an indicator of subclinical mastitis. The results here reported provide a first characterization of the sheep milk microbiota and settle the basis for future studies in this field.