Microbiome-based environmental monitoring of a dairy processing facility highlights the challenges associated with low microbial-load samples

Microbiome mapping in dairy industry reveals new species and genes for probiotic and bioprotective activities

The resident microbiome in food industries may impact on food quality and safety. In particular, microbes residing on surfaces in dairy industries may actively participate in cheese fermentation and ripening and contribute to the typical flavor and texture. In this work, we carried out an extensive microbiome mapping in 73 cheese-making industries producing different types of cheeses (fresh, medium and long ripened) and located in 4 European countries. We sequenced and analyzed metagenomes from cheese samples, raw materials and environmental swabs collected from both food contact and non-food contact surfaces, as well as operators' hands and aprons. Dairy plants were shown to harbor a very complex microbiome, characterized by high prevalence of genes potentially involved in flavor development, probiotic activities, and resistance to gastro-intestinal transit, suggesting that these microbes may potentially be transferred to the human gut microbiome. More than 6100 high-quality Metagenome Assembled Genomes (MAGs) were reconstructed, including MAGs from several Lactic Acid Bacteria species and putative new species. Although microbial pathogens were not prevalent, we found several MAGs harboring genes related to antibiotic resistance, highlighting that dairy industry surfaces represent a potential hotspot for antimicrobial resistance (AR) spreading along the food chain. Finally, we identified facility-specific strains that can represent clear microbial signatures of different cheesemaking facilities, suggesting an interesting potential of microbiome tracking for the traceability of cheese origin.

Metagenomic analysis of the bacterial microbiome, resistome and virulome distinguishes Portuguese Serra da Estrela PDO cheeses from similar non-PDO cheeses: An exploratory approach

This study aimed to evaluate the microbiome, resistome and virulome of two types of Portuguese cheese using high throughput sequencing (HTS). Culture-dependent chromogenic methods were also used for certain groups/microorganisms. Eight samples of raw ewe's milk cheese were obtained from four producers: two producers with cheeses with a PDO (Protected Designation of Origin) label and the other two producers with cheeses without a PDO label. Agar-based culture methods were used to quantify total mesophiles, Enterobacteriaceae, Escherichia coli, Staphylococcus, Enterococcus and lactic acid bacteria. The presence of Listeria monocytogenes and Salmonella was also investigated. The selected isolates were identified by 16S rRNA gene sequencing and evaluated to determine antibiotic resistance and the presence of virulence genes. The eight cheese samples analyzed broadly complied with EC regulations in terms of the microbiological safety criteria. The HTS results demonstrated that Leuconostoc mesenteroides, Lactococcus lactis, Lactobacillus plantarum, Lacticaseibacillus rhamnosus, Enterococcus durans and Lactobacillus coryniformis were the most prevalent bacterial species in cheeses. The composition of the bacterial community varied, not only between PDO and non-PDO cheeses, but also between producers, particularly between the two non-PDO cheeses. Alpha-diversity analyses showed that PDO cheeses had greater bacterial diversity than non-PDO cheeses, demonstrating that the diversity of spontaneously fermented foods is significantly higher in cheeses produced without the addition of food preservatives and dairy ferments. Despite complying with microbiological regulations, both PDO and non-PDO cheeses harbored potential virulence genes as well as antibiotic resistance genes. However, PDO cheeses exhibited fewer of these virulence and antibiotic resistance genes compared to non-PDO cheeses. Therefore, the combination of conventional microbiological methods and the metagenomic approach could contribute to improving the attribution of the PDO label to this type of cheese.

Longitudinal Analysis of Urban Stormwater Microbiome and Resistome from Watersheds with and without Green Infrastructure using Long-Read Sequencing

Since stormwater conveys a variety of contaminants into water bodies, green infrastructure (GI) is increasingly being adopted as an on-site treatment solution in addition to controlling peak flows. The purpose of this study was to identify differences in microbial water quality of stormwater in watersheds retrofitted with GI vs. those without GI. Considering stormwater is recently recognized as a contributor to the antibiotic resistance (AR) threat, another goal of this study was to characterize changes in the microbiome and collection of AR genes (resistome) of urban stormwater with season, rainfall characteristics, and fecal contamination. MinION long-read sequencing was used to analyze stormwater microbiome and resistome from watersheds with and without GI in Columbus, Ohio, United States, over 18 months. We characterized fecal contamination in stormwater via culturing Escherichia coli and with molecular microbial source tracking (MST) to identify sources of fecal contamination. Overall, season and storm event (rainfall) characteristics had the strongest relationships with changes in the stormwater microbiome and resistome. We found no significant differences in microbial water quality or the microbiome of stormwater in watersheds with and without GI implemented. However, there were differences between the communities of microorganisms hosting antibiotic resistance genes (ARGs) in stormwater from watersheds with and without GI, indicating the potential sensitivity of AR bacteria to treatment. Stormwater was contaminated with high concentrations of human-associated fecal bacterial genes, and the ARG host bacterial community had considerable similarities to human feces/wastewater. We also identified 15 potential pathogens hosting ARGs in these stormwater resistome, including vancomycin-resistant Enterococcus faecium (VRE) and multidrug-resistant Pseudomonas aeruginosa. In summary, urban stormwater is highly contaminated and has a great potential to spread AR and microbial hazards to nearby environments. This study presents the most comprehensive analysis of stormwater microbiome and resistome to date, which is crucial to understanding the potential microbial risk from this matrix. This information can be used to guide future public health policy, stormwater reuse programs, and urban runoff treatment initiatives.

Time-series metagenomics reveals changing protistan ecology of a temperate dimictic lake

BACKGROUND

Protists, single-celled eukaryotic organisms, are critical to food web ecology, contributing to primary productivity and connecting small bacteria and archaea to higher trophic levels. Lake Mendota is a large, eutrophic natural lake that is a Long-Term Ecological Research site and among the world's best-studied freshwater systems. Metagenomic samples have been collected and shotgun sequenced from Lake Mendota for the last 20 years. Here, we analyze this comprehensive time series to infer changes to the structure and function of the protistan community and to hypothesize about their interactions with bacteria.

RESULTS

Based on small subunit rRNA genes extracted from the metagenomes and metagenome-assembled genomes of microeukaryotes, we identify shifts in the eukaryotic phytoplankton community over time, which we predict to be a consequence of reduced zooplankton grazing pressures after the invasion of a invasive predator (the spiny water flea) to the lake. The metagenomic data also reveal the presence of the spiny water flea and the zebra mussel, a second invasive species to Lake Mendota, prior to their visual identification during routine monitoring. Furthermore, we use species co-occurrence and co-abundance analysis to connect the protistan community with bacterial taxa. Correlation analysis suggests that protists and bacteria may interact or respond similarly to environmental conditions. Cryptophytes declined in the second decade of the timeseries, while many alveolate groups (e.g., ciliates and dinoflagellates) and diatoms increased in abundance, changes that have implications for food web efficiency in Lake Mendota.

CONCLUSIONS

We demonstrate that metagenomic sequence-based community analysis can complement existing efforts to monitor protists in Lake Mendota based on microscopy-based count surveys. We observed patterns of seasonal abundance in microeukaryotes in Lake Mendota that corroborated expectations from other systems, including high abundance of cryptophytes in winter and diatoms in fall and spring, but with much higher resolution than previous surveys. Our study identified long-term changes in the abundance of eukaryotic microbes and provided context for the known establishment of an invasive species that catalyzes a trophic cascade involving protists. Our findings are important for decoding potential long-term consequences of human interventions, including invasive species introduction. Video Abstract.

Recent advances in smart wearable sensors for continuous human health monitoring

In recent years, the biochemical and biological research areas have shown great interest in a smart wearable sensor because of its increasing prevalence and high potential to monitor human health in a non-invasive manner by continuous screening of biomarkers dispersed throughout the biological analytes, as well as real-time diagnostic tools and time-sensitive information compared to conventional hospital-centered system. These smart wearable sensors offer an innovative option for evaluating and investigating human health by incorporating a portion of recent advances in technology and engineering that can enhance real-time point-of-care-testing capabilities. Smart wearable sensors have emerged progressively with a mixture of multiplexed biosensing, microfluidic sampling, and data acquisition systems incorporated with flexible substrate and bodily attachments for enhanced wearability, portability, and reliability. There is a good chance that smart wearable sensors will be relevant to the early detection and diagnosis of disease management and control. Therefore, pioneering smart wearable sensors into reality seems extremely promising despite possible challenges in this cutting-edge technology for a better future in the healthcare domain. This review presents critical viewpoints on recent developments in wearable sensors in the upcoming smart digital health monitoring in real-time scenarios. In addition, there have been proactive discussions in recent years on materials selection, design optimization, efficient fabrication tools, and data processing units, as well as their continuous monitoring and tracking strategy with system-level integration such as internet-of-things, cyber-physical systems, and machine learning algorithms.

Improved sampling and DNA extraction procedures for microbiome analysis in food-processing environments

Deep investigation of the microbiome of food-production and food-processing environments through whole-metagenome sequencing (WMS) can provide detailed information on the taxonomic composition and functional potential of the microbial communities that inhabit them, with huge potential benefits for environmental monitoring programs. However, certain technical challenges jeopardize the application of WMS technologies with this aim, with the most relevant one being the recovery of a sufficient amount of DNA from the frequently low-biomass samples collected from the equipment, tools and surfaces of food-processing plants. Here, we present the first complete workflow, with optimized DNA-purification methodology, to obtain high-quality WMS sequencing results from samples taken from food-production and food-processing environments and reconstruct metagenome assembled genomes (MAGs). The protocol can yield DNA loads >10 ng in >98% of samples and >500 ng in 57.1% of samples and allows the collection of, on average, 12.2 MAGs per sample (with up to 62 MAGs in a single sample) in ~1 week, including both laboratory and computational work. This markedly improves on results previously obtained in studies performing WMS of processing environments and using other protocols not specifically developed to sequence these types of sample, in which <2 MAGs per sample were obtained. The full protocol has been developed and applied in the framework of the European Union project MASTER (Microbiome applications for sustainable food systems through technologies and enterprise) in 114 food-processing facilities from different production sectors.

Host DNA depletion methods and genome-centric metagenomics of bovine hindmilk microbiome

Bovine mastitis is a multi-etiological and complex disease, resulting in serious economic consequences for dairy farmers and industry. In recent years, the microbiological evaluation of raw milk has been investigated in-depth using next-generation sequencing approaches such as metataxonomic analysis. Despite this, host DNA is a major concern in the shotgun metagenomic sequencing of microbial communities in milk samples, and it represents a big challenge. In this study, we aimed to evaluate different methods for host DNA depletion and/or microbial DNA enrichment and assess the use of PCR-based whole genome amplification in milk samples with high somatic cell count (SCC) by using short- and long-read sequencing technologies. Our results evidenced that DNA extraction performed differently in terms of host DNA removal, impacting metagenome composition and functional profiles.. Moreover, the ratio of SCC/bacteria ultimately impacts microbial DNA yield, and samples with low SCC (SCC below 100,000 cells/mL) are the most problematic. When milk samples with high SCC (SCC above 200,000 cells/mL) underwent multiple-displacement amplification (MDA), we successfully recovered high-quality metagenome-assembled genomes (MAGs), and long-read sequencing was feasible even for samples with low DNA concentration. By associating MDA and short-read sequencing, we recovered two times more MAGs than in untreated samples, and an ongoing co-infection not reported by traditional methods was detected for mastitis pathogen. Overall, this new approach will improve the detection of mastitis-associated microorganisms and make it possible to examine host-microbiome interactions in bovine mastitis.IMPORTANCENext-generation sequencing technologies have been widely used to gain new insights into the diversity of the microbial community of milk samples and dairy products for different purposes such as microbial safety, profiling of starter cultures, and host-microbiome interactions. Milk is a complex food matrix, and additionally, the presence of host nucleic acid sequences is considered a contaminant in untargeted high-throughput sequencing studies. Therefore, genomic-centric metagenomic studies of milk samples focusing on the health-disease status in dairy cattle are still scarce, which makes it difficult to evaluate the microbial ecophysiology of bovine hindmilk. This study provides an alternative method for genome-centric metagenome studies applied to hindmilk samples with high somatic cell content, which is indispensable to examining host-microbiome interactions in bovine mastitis.

Psychrotrophic Bacteria Equipped with Virulence and Colonization Traits Populate the Ice Cream Manufacturing Environment

Several microbial taxa have been associated with food processing facilities, and they might resist by attaching on tools and equipment even after sanitation procedures, producing biofilms that adhere to the surfaces and might embed other microorganisms, including spoilers and pathogens. There is increasing evidence that these communities can be transferred to the final product. To explore the microbial contamination routes in a facility producing ice creams, we collected foods and environmental swabs from industrial surfaces of equipment and tools and performed taxonomic and functional analyses of the microbial DNA extracted from the environmental samples. Our results suggest that complex communities dominated by psychrotrophic bacteria (e.g., Pseudomonas and Acinetobacter spp.) inhabit the food processing environment, and we demonstrate that these communities might be transferred from the surfaces to the products. Functional analysis performed on environmental samples highlighted the presence of several genes linked to antimicrobial resistance and adherence on abiotic surfaces; such genes were more abundant on food contact (FC) than on other surfaces. Metagenome-assembled genomes (MAGs) of Pseudomonas stutzeri showed genes linked with biofilm formation and motility, which are surely linked to colonizing capabilities in the processing lines. The study highlights clear potential advantages of applying microbiome mapping in the food industry for source tracking of microbial contamination and for planning appropriate ad hoc sanitization strategies. IMPORTANCE Several microbial species might permanently establish in food processing facilities, thus contributing to food loss. In fact, food contact surfaces might transfer microorganisms to intermediates and products, potentially representing a hazard to human health. In this work, we provide evidence of the existence of complex microbial communities overcoming sanitation in an ice cream-producing facility. These communities harbored several genes that could potentially lead to attachment to surfaces and antimicrobial resistance. Also, prediction of routes of contamination showed that several potential spoilage taxa might end up in the final product. Importantly, in this work, we show that mapping the environmental microbiome is a high-resolution technique that might help food business operators ensure food quality and safety through detection of potentially hazardous microorganisms.

Microbial Dynamics during labneh Ambaris Production in Earthenware Jars

The responses of various microbial populations to modifications in the physicochemical properties of a food matrix, as well as interactions between these populations already present, are the main factors that shape microbial dynamics in that matrix. This work focused on the study of microbial dynamics during labneh Ambaris production, a traditional Lebanese concentrated fermented goat milk made in jars during 3 months. This was assessed in two earthenware jars at a production facility. DNA metabarcoding of the ITS2 region as well as the V3-V4 region of the 16S rRNA gene was used to characterize the fungal and bacterial communities, respectively. Viable bacterial isolates were also identified by Sanger sequencing of the V1-V4 region of the 16S rRNA gene. Our results showed that the dominant microorganisms identified within labneh Ambaris (Lactobacillus kefiranofaciens, Lentilactobacillus kefiri, Lactococcus lactis, Geotrichum candidum, Pichia kudriavzevii and Starmerella sp.) settle early in the product and remain until the end of maturation with varying abundances throughout fermentation. Microbial counts increased during early fermentation stage, and remained stable during mid-fermentation, then declined during maturation. While microbial compositions were globally comparable between the two jars during mid-fermentation and maturation stages, differences between the two jars were mainly detected during early fermentation stage (D0 until D10). No significant sensorial differences were observed between the final products made in the two jars. Neither coliforms nor Enterobacteriaceae were detected in their viable state, starting D7 in both jars, suggesting the antimicrobial properties of the product.

Artisanal Household Milk Pasteurization Is Not a Determining Factor in Structuring the Microbial Communities of Labneh Ambaris: A Pilot Study

Labneh Ambaris is a traditional Lebanese dairy product traditionally made using raw goat's milk in earthenware jars, but recently the use of artisanally pasteurized milk was introduced for safety reasons. In this study, 12 samples of labneh Ambaris were studied, six made using raw goat's milk and six others using artisanally pasteurized goat's milk. These samples were collected during fermentation and their microbial compositions were analyzed. The 16S V3-V4 and the ITS2 regions of the rDNA were sequenced by DNA metabarcoding analyses for the identification and comparison of bacterial and fungal communities, respectively. The samples had high microbial diversity but differences in samples microbiota were unrelated to whether or not milk was pasteurized. The samples were consequently clustered on the basis of their dominant bacterial or fungal species, regardless of the milk used. Concerning bacterial communities, samples were clustered into 3 groups, one with a higher abundance of Lactobacillus helveticus, another with Lactobacillus kefiranofaciens as the dominant bacterial species, and the third with Lentilactobacillus sp. as the most abundant species. Species belonging to the Enterobacteriaceae family were detected in higher abundance in all raw milk samples than in artisanally pasteurized milk samples. As for fungal communities, the samples were clustered into two groups, one dominated by Geotrichum candidum and the other by Pichia kudriavzevii.

Development of sequencing-based methodologies to distinguish viable from non-viable cells in a bovine milk matrix: A pilot study

Although high-throughput DNA sequencing-based methods have been of great value for determining the composition of microbial communities in various environments, there is the potential for inaccuracies arising from the sequencing of DNA from dead microorganisms. In this pilot study, we compared different sequencing-based methods to assess their relative accuracy with respect to distinguishing between viable and non-viable cells, using a live and heat-inactivated model community spiked into bovine milk. The methods used were shotgun metagenomics with and without propidium monoazide (PMA) treatment, RNA-based 16S rRNA sequencing and metatranscriptomics. The results showed that methods were generally accurate, though significant differences were found depending on the library types and sequencing technologies. Different molecular targets were the basis for variations in the results generated using different library types, while differences in the derived composition data from Oxford Nanopore Technologies-and Illumina-based sequencing likely reflect a combination of different sequencing depths, error rates and bioinformatics pipelines. Although PMA was successfully applied in this study, further optimisation is required before it can be applied in a more universal context for complex microbiomes. Overall, these methods show promise and represent another important step towards the ultimate establishment of approaches that can be applied to accurately identify live microorganisms in milk and other food niches.

Biosensors and Microfluidic Biosensors: From Fabrication to Application

Biosensors are ubiquitous in a variety of disciplines, such as biochemical, electrochemical, agricultural, and biomedical areas. They can integrate various point-of-care applications, such as in the food, healthcare, environmental monitoring, water quality, forensics, drug development, and biological domains. Multiple strategies have been employed to develop and fabricate miniaturized biosensors, including design, optimization, characterization, and testing. In view of their interactions with high-affinity biomolecules, they find application in the sensitive detection of analytes, even in small sample volumes. Among the many developed techniques, microfluidics have been widely explored; these use fluid mechanics to operate miniaturized biosensors. The currently used commercial devices are bulky, slow in operation, expensive, and require human intervention; thus, it is difficult to automate, integrate, and miniaturize the existing conventional devices for multi-faceted applications. Microfluidic biosensors have the advantages of mobility, operational transparency, controllability, and stability with a small reaction volume for sensing. This review addresses biosensor technologies, including the design, classification, advances, and challenges in microfluidic-based biosensors. The value chain for developing miniaturized microfluidic-based biosensor devices is critically discussed, including fabrication and other associated protocols for application in various point-of-care testing applications.

Metagenomics Approaches for Improving Food Safety: A Review

ABSTRACT

Advancements in next-generation sequencing technology have dramatically reduced the cost and increased the ease of microbial whole genome sequencing. This approach is revolutionizing the identification and analysis of foodborne microbial pathogens, facilitating expedited detection and mitigation of foodborne outbreaks, improving public health outcomes, and limiting costly recalls. However, next-generation sequencing is still anchored in the traditional laboratory practice of the selection and culture of a single isolate. Metagenomic-based approaches, including metabarcoding and shotgun and long-read metagenomics, are part of the next disruptive revolution in food safety diagnostics and offer the potential to directly identify entire microbial communities in a single food, ingredient, or environmental sample. In this review, metagenomic-based approaches are introduced and placed within the context of conventional detection and diagnostic techniques, and essential considerations for undertaking metagenomic assays and data analysis are described. Recent applications of the use of metagenomics for food safety are discussed alongside current limitations and knowledge gaps and new opportunities arising from the use of this technology.

HIGHLIGHTS

A Round Trip to the Desert: In situ Nanopore Sequencing Informs Targeted Bioprospecting

Bioprospecting expeditions are often performed in remote locations, in order to access previously unexplored samples. Nevertheless, the actual potential of those samples is only assessed once scientists are back in the laboratory, where a time-consuming screening must take place. This work evaluates the suitability of using Nanopore sequencing during a journey to the Tabernas Desert (Spain) for forecasting the potential of specific samples in terms of bacterial diversity and prevalence of radiation- and desiccation-resistant taxa, which were the target of the bioprospecting activities. Samples collected during the first day were analyzed through 16S rRNA gene sequencing using a mobile laboratory. Results enabled the identification of locations showing the greatest and the least potential, and a second, informed sampling was performed focusing on those sites. After finishing the expedition, a culture collection of 166 strains belonging to 50 different genera was established. Overall, Nanopore and culturing data correlated well, since samples holding a greater potential at the microbiome level also yielded a more interesting set of microbial isolates, whereas samples showing less biodiversity resulted in a reduced (and redundant) set of culturable bacteria. Thus, we anticipate that portable sequencers hold potential as key, easy-to-use tools for in situ-informed bioprospecting strategies.

Antimicrobial resistance prediction in Neisseria gonorrhoeae: Current status and future prospects

INTRODUCTION

Several nucleic acid amplification tests (NAATs), mostly real-time PCRs, to detect antimicrobial resistance (AMR) determinants and predict AMR in Neisseria gonorrhoeae are promising, and some may be ready to apply at the point-of-care (POC), but important limitations remain with most NAATs. Next-generation sequencing (NGS) can overcome many of these limitations.Areas covered: Recent advances, with main focus on publications since 2017, in the development and use of NAATs and NGS to predict gonococcal AMR for surveillance and clinical use, and pros and cons of these tests as well as future perspectives for appropriate use of molecular AMR prediction for N. gonorrhoeae.Expert Commentary: NAATs and/or NGS for AMR prediction should supplement culture-based AMR surveillance, which will remain because it detects also AMR due to unknown AMR determinants, and translation into POC tests is imperative for the end-goal of individualized treatment, sparing ceftriaxone±azithromycin. Several challenges for direct testing of clinical, especially pharyngeal, specimens and for accurate prediction of cephalosporins and azithromycin resistance, especially using NAATs, remain. The choice of AMR prediction assay needs to carefully consider the intended use of the assay; limitations intrinsic to the AMR prediction technology, algorithms and specific to chosen methodology; specimen types analyzed; and cost-effectiveness.

Novel methods of microbiome analysis in the food industry

The study of the food microbiome has gained considerable interest in recent years, mainly due to the wide range of applications that can be derived from the analysis of metagenomes. Among these applications, it is worth mentioning the possibility of using metagenomic analyses to determine food authenticity, to assess the microbiological safety of foods thanks to the detection and tracking of pathogens, antibiotic resistance genes and other undesirable traits, as well to identify the microorganisms responsible for food processing defects. Metataxonomics and metagenomics are currently the gold standard methodologies to explore the full potential of metagenomes in the food industry. However, there are still a number of challenges that must be solved in order to implement these methods routinely in food chain monitoring, and for the regulatory agencies to take them into account in their opinions. These challenges include the difficulties of analysing foods and food-related environments with a low microbial load, the lack of validated bioinformatics pipelines adapted to food microbiomes and the difficulty of assessing the viability of the detected microorganisms. This review summarizes the methods of microbiome analysis that have been used, so far, in foods and food-related environments, with a specific focus on those involving Next-Generation Sequencing technologies.

Next-Generation Food Research: Use of Meta-Omic Approaches for Characterizing Microbial Communities Along the Food Chain

Microorganisms exist along the food chain and impact the quality and safety of foods in both positive and negative ways. Identifying and understanding the behavior of these microbial communities enable the implementation of preventative or corrective measures in public health and food industry settings. Current culture-dependent microbial analyses are time-consuming and target only specific subsets of microbes. However, the greater use of culture-independent meta-omic approaches has the potential to facilitate a thorough characterization of the microbial communities along the food chain. Indeed, these methods have shown potential in contributing to outbreak investigation, ensuring food authenticity, assessing the spread of antimicrobial resistance, tracking microbial dynamics during fermentation and processing, and uncovering the factors along the food chain that impact food quality and safety. This review examines the community-based approaches, and particularly the application of sequencing-based meta-omics strategies, for characterizing microbial communities along the food chain. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 13 is March 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Full-Length SSU rRNA Gene Sequencing Allows Species-Level Detection of Bacteria, Archaea, and Yeasts Present in Milk

Full-length SSU rRNA gene sequencing allows species-level identification of the microorganisms present in milk samples. Here, we used bulk-tank raw milk samples of two German dairies and detected, using this method, a great diversity of bacteria, archaea, and yeasts within the samples. Moreover, the species-level classification was improved in comparison to short amplicon sequencing. Therefore, we anticipate that this approach might be useful for the detection of possible mastitis-causing species, as well as for the control of spoilage-associated microorganisms. In a proof of concept, we showed that we were able to identify several putative mastitis-causing or mastitis-associated species such as Streptococcusuberis, Streptococcusagalactiae, Streptococcusdysgalactiae, Escherichiacoli and Staphylococcusaureus, as well as several Candida species. Overall, the presented full-length approach for the sequencing of SSU rRNA is easy to conduct, able to be standardized, and allows the screening of microorganisms in labs with Illumina sequencing machines.