Evaluation of the matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS) system in the detection of mastitis pathogens from bovine milk samples

Point-of-need mastitis pathogen biosensing in bovine milk: From academic sample preparation novelty to industry prototype field testing

Bovine mastitis is an inflammation of the mammary gland, and it is the most common infectious disease in dairy cattle. Mastitis reduces milk yield and quality, costing dairy farmers millions of dollars each year. The aim of this study was to develop a point-of-need test for identifying mastitis pathogens that is field portable, cost-effective and can be used with minimal training. Using a proprietary polymer-based milk sample preparation method to rapidly extract pathogen DNA in milk samples, we demonstrated quantitative Polymerase Chain Reaction (qPCR) assays for six common bovine bacterial mastitis pathogens: Staphylococcus aureus, Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Mycoplasma bovis and Escherichia coli. We also implemented this sample preparation method on a prototype point-of-need system in a proof-of-concept field trial to evaluate user experience. Importantly, the protype system enabled a sample-to-result turnaround time of within 70 min to quantitatively detect all six target pathogens. The key advantage of our point-of-need prototype system is being culture-independent yet providing automated milk sample preparation for molecular identification of key mastitis pathogens by non-expert users. Our point-of-need prototype system showed a good correlation to laboratory-based qPCR for target pathogen detection outcomes, thus potentially removing the need for milk samples to be transported off-site for laboratory testing. Above all, we successfully achieved our objective of developing a point-of-need biosensor technology for mastitis and increased its readiness level with industry partners towards technology commercialization.

The impact of kit, environment, and sampling contamination on the observed microbiome of bovine milk

In low-microbial biomass samples such as bovine milk, contaminants can outnumber endogenous bacteria. Because of this, milk microbiome research suffers from a critical knowledge gap, namely, does non-mastitis bovine milk contain a native microbiome? In this study, we sampled external and internal mammary epithelia and stripped and cisternal milk and used numerous negative controls, including air and sampling controls and extraction and library preparation blanks, to identify the potential sources of contamination. Two algorithms were used to mathematically remove contaminants and track the potential movement of microbes among samples. Results suggest that the majority (i.e., >75%) of sequence data generated from bovine milk and mammary epithelium samples represents contaminating DNA. Contaminants in milk samples were primarily sourced from DNA extraction kits and the internal and external skin of the teat, while teat canal and apex samples were mainly contaminated during the sampling process. After decontamination, the milk microbiome displayed a more dispersed, less diverse, and compositionally distinct bacterial profile compared with epithelial samples. Similar microbial compositions were observed between cisternal and stripped milk samples, as well as between teat apex and canal samples. Staphylococcus and Acinetobacter were the predominant genera detected in milk sample sequences, and bacterial culture showed growth of Staphylococcus and Corynebacterium spp. in 50% (7/14) of stripped milk samples and growth of Staphylococcus spp. in 7% (1/14) of cisternal milk samples. Our study suggests that microbiome data generated from milk samples obtained from clinically healthy bovine udders may be heavily biased by contaminants that enter the sample during sample collection and processing workflows.IMPORTANCEObtaining a non-contaminated sample of bovine milk is challenging due to the nature of the sampling environment and the route by which milk is typically extracted from the mammary gland. Furthermore, the very low bacterial biomass of bovine milk exacerbates the impacts of contaminant sequences in downstream analyses, which can lead to severe biases. Our finding showed that bovine milk contains very low bacterial biomass and each contamination event (including sampling procedure and DNA extraction process) introduces bacteria and/or DNA fragments that easily outnumber the native bacterial cells. This finding has important implications for our ability to draw robust conclusions from milk microbiome data, especially if the data have not been subjected to rigorous decontamination procedures. Based on these findings, we strongly urge researchers to include numerous negative controls into their sampling and sample processing workflows and to utilize several complementary methods for identifying potential contaminants within the resulting sequence data. These measures will improve the accuracy, reliability, reproducibility, and interpretability of milk microbiome data and research.

Randomized clinical trial evaluating the effects of administering acidogenic boluses at dry-off on udder health, milk yield, and herd removal

Acidogenic boluses can mitigate potential negative effects of high milk yield at dry-off on udder health. This randomized controlled trial aimed to investigate the effect of administering acidogenic boluses at dry-off on dry period intramammary infection (IMI) dynamics and on milk production parameters, somatic cell count linear score (LSCC), clinical mastitis (CM), and herd removal in the next lactation. A total of 901 cows from 3 dairy farms were randomly allocated to a control (CON, n = 458; no administration of acidogenic boluses at dry-off) or treatment group (TRT, n = 443; administration of 2 acidogenic boluses at dry-off). Quarter milk samples were collected at dry-off and after calving and submitted for bacteriological milk culture. The effects of treatment on the presence of quarter-level postpartum IMI, cure of existing IMI, and acquisition of new IMI, and on the prevalence of cow-level high LSCC (LSCC ≥4) in the first 30 days in milk (DIM) were analyzed using mixed effects logistic regression. Mixed linear regression was used to analyze cow-level milk production parameters (i.e., milk yield, fat corrected milk, fat and protein yield, and LSCC) in the first 90 DIM and until 300 DIM. For CM and herd removal, Cox proportional hazard regression models were used. In addition to treatment group, lactation group at dry-off, presence of high LSCC in the last test-day, average milk yield in the week before dry-off, presence of CM in the lactation of enrollment, and biologically relevant interactions were offered in all models. There was no evidence of a difference in IMI dynamics or in milk, fat corrected milk, protein or fat yields in the subsequent lactation between groups. The TRT group had a lower LSCC in the first 2 mo postpartum compared with the CON group (2.58 ± 0.3 vs. 2.92 ± 0.3 and 2.42 ± 0.3 vs. 2.81 ± 0.3, for first and second month postpartum). The prevalence of high LSCC in the first 30 DIM was 9.1% lower in the TRT compared with the CON group (16.3% vs. 25.5%; risk difference: -9.2; 95% confidence interval [CI]: -15.8, -2.5). Cows in the TRT group exhibited reduced hazards of CM in the subsequent lactation compared with cows in the CON group (hazard ratio: 0.75; 95% CI: 0.63, 0.89) as well as a reduced hazard of herd removal (hazard ratio: 0.82, 95% CI: 0.77, 0.88). The administration of acidogenic boluses as a component of dry-off management is a promising approach to maintain good udder health and reduce the hazard of CM and herd removal during the subsequent lactation.

Troubleshooting high laboratory pasteurization counts in organic raw milk requires characterization of dominant thermoduric bacteria, which includes nonsporeformers as well as sporeformers

Laboratory pasteurization count (LPC) enumerates thermoduric bacteria and is one parameter used to assess raw milk quality. No regulatory limit has presently been set for LPC, but LPC data are used by some dairy processors and cooperatives to designate raw milk quality premiums paid to farmers and may also be used for troubleshooting bacterial contamination issues. Although it is occasionally used as a proxy for levels of bacterial spores in raw milk, limited knowledge is available on the types of organisms that are enumerated by LPC in contemporary raw milk supplies. Although historical studies have reported that thermoduric bacteria quantified by LPC may predominantly represent gram-positive cocci, updated knowledge on microbial populations enumerated by LPC in contemporary organic raw milk supplies is needed. To address this gap, organic raw milk samples from across the United States (n = 94) were assessed using LPC, and bacterial isolates were characterized. LPC ranged from below detection (<0.70 log cfu/mL) to 4.07 log cfu/mL, with a geometric mean of 1.48 log cfu/mL. Among 380 isolates characterized by 16S rDNA sequencing, 52.6%, 44.5%, and 2.4% were identified as gram-positive sporeformers, gram-positive nonsporeformers, and gram-negatives, respectively; 0.5% could not be categorized into those groups because they could only be assigned a higher level of taxonomy. Isolates identified as gram-positive sporeformers were predominantly Bacillus (168/200), and gram-positive nonsporeformers were predominantly Brachybacterium (56/169) and Kocuria (47/169). To elucidate if the LPC level can be an indicator of the type of thermoduric (e.g., sporeforming bacteria) present in raw milk, we evaluated the proportion of sporeformers in raw milk samples with LPC of ≤100 cfu/mL, 100 to 200 cfu/mL, and ≥200 cfu/mL (51%, 67%, and 35%), showing a trend for sporeformers to represent a smaller proportion of the total thermoduric population when LPC increases, although overall linear regression showed no significant association between the proportion of sporeformers and the LPC concentration. Hence, LPC level alone provides no insight into the makeup of the thermoduric population in raw milk, and further characterization is needed to elucidate the bacterial drivers of elevated LPC in raw milk. We therefore further characterized the isolates from this study using MALDI-TOF mass spectrometry (MALDI-TOF MS), a rapid microbial identification tool that is more readily available to dairy producers than 16S rDNA PCR and sequencing. Although our data indicated agreement between 16S rDNA sequencing and MALDI-TOF MS for 66.6% of isolates at the genus level, 24.2% and 9.2% could not be reliably identified or were mischaracterized using MALDI-TOF MS, respectively. This suggests that further optimization of this method is needed to allow for accurate characterization of thermoduric organisms commonly found in raw milk. Ultimately, our study provides a contemporary perspective on thermoduric bacteria selected by the LPC method and establishes that the LPC alone is not sufficient for identifying the bacterial drivers of LPC levels. Further development of rapid characterization methods that are accessible to producers, cooperatives, and processors will support milk quality troubleshooting efforts and ultimately improve outcomes for dairy industry community members.

AM, Doster E, Fernandes L, Calles VF, Bauman C, Godden S, Heins B, Pinedo P, Machado VS, Caixeta LS, Noyes NR. Exploring associations between the teat apex metagenome and Staphylococcus aureus intramammary infections in primiparous cows under organic directives

The primary objective of this study was to identify associations between the prepartum teat apex microbiome and the presence of Staphylococcus aureus intramammary infections (IMI) in primiparous cows during the first 5 weeks after calving. We performed a case-control study using shotgun metagenomics of the teat apex and culture-based milk data collected longitudinally from 710 primiparous cows on five organic dairy farms. Cases had higher odds of having S. aureus metagenomic DNA on the teat apex prior to parturition compared to controls (OR = 38.9, 95% CI: 14.84-102.21). Differential abundance analysis confirmed this association, with cases having a 23.8 higher log fold change (LFC) in the abundance of S. aureus in their samples compared to controls. Of the most prevalent microorganisms in controls, those associated with a lower risk of post-calving S. aureus IMI included Microbacterium phage Min 1 (OR = 0.37, 95% CI: 0.25-0.53), Corynebacterium efficiens (OR = 0.53, 95% CI: 0.30-0.94), Kocuria polaris (OR = 0.54, 95% CI: 0.35-0.82), Micrococcus terreus (OR = 0.64, 95% CI: 0.44-0.93), and Dietzia alimentaria (OR = 0.45, 95% CI: 0.26-0.75). Genes encoding for Microcin B17 AMPs were the most prevalent on the teat apex of cases and controls (99.7% in both groups). The predicted abundance of genes encoding for Microcin B17 was also higher in cases compared to controls (LFC 0.26).

IMPORTANCE

Intramammary infections (IMI) caused by Staphylococcus aureus remain an important problem for the dairy industry. The microbiome on the external skin of the teat apex may play a role in mitigating S. aureus IMI risk, in particular the production of antimicrobial peptides (AMPs) by commensal microbes. However, current studies of the teat apex microbiome utilize a 16S approach, which precludes the detection of genomic features such as genes that encode for AMPs. Therefore, further research using a shotgun metagenomic approach is needed to understand what role prepartum teat apex microbiome dynamics play in IMI risk.

Associations between early lactation intramammary infections and udder health and performance during the first 180 days in milk in first-lactation organic dairy cows

Prior data from our group showed that first-lactation cows under organic management in United States have a high prevalence of Staphylococcus aureus, Streptococcus spp., and Staphylococcus chromogenes intramammary infections (IMI) in early lactation. Nonetheless, the relationship between IMI, udder health, and milk production in organically reared primiparous cows remains elusive. The objectives of this observational study were to investigate the relationship between presence and persistence of IMI in the first 35 d in milk (DIM) and somatic cell count (SCC) and milk production during the first 6 mo of lactation on first-lactation organic dairy cows. The analysis included a total of 1,348 composite milk samples collected during the first 35 DIM that were submitted for milk culture and 1,674 Dairy Herd Improvement Association (DHIA) tests during the first 180 DIM from 333 heifers in 4 organic dairy farms, enrolled between February 2019 and January 2020. The association between IMI in the first 35 DIM and new high SCC (SCC > 200,000 cells/mL) and milk production during the first 6 mo of lactation was investigated using Cox proportional hazards regression and mixed linear regression, respectively. The association between IMI persistence (harboring the same microorganism as reported by the laboratory for 2 or more samples) in the first 35 DIM and number of DHIA tests with high SCC during the first 6 mo of lactation was modeled using negative binomial regression. The presence of IMI by Staph. aureus (hazard ratio [HR] [95% confidence interval {CI}]: 3.35 [2.64, 4.25]) or Streptococcus spp. (HR [95% CI]: 2.25 [2.12, 2.39]) during the first 35 DIM was associated with an increased risk of new high SCC during the first 6 mo of lactation. Milk production was reduced when Streptococcus spp. were identified in milk samples. However, there was no evidence of a difference in milk production in Staph. aureus IMI. Isolation of non-aureus staphylococci and mammaliicocci was related to a mild increase in the hazards of high SCC (HR [95% CI]: 1.34 [0.97, 1.85]) and a decrease in milk production during one or more postpartum tests. Presence of gram-negative or Streptococcus-like organisms IMI was not associated with either high SCC or milk production. Presence of Bacillus IMI was associated with a lower hazard of new high SCC (HR [95% CI]: 0.45 [0.30, 0.68]), and higher milk production during the first 180 d of lactation (overall estimate [95% CI]: 1.7 kg/d [0.3, 3.0]). The persistence of IMI in the first 35 DIM was associated with the number of tests with high SCC during the lactation for all microorganisms except for Staphylococcus chromogenes. Therefore, our results suggest that the persistence of IMI in the first 35 DIM could be an important factor to understand the association between IMI detected in early lactation and lactational SCC and milk production in organic dairy heifers. Our study described associations between IMI, udder health, and milk production in first-lactation organic dairy cows that are consistent with findings from conventional dairy farms.

Milk as diagnostic fluid for udder health management

BACKGROUND

Mastitis is the major disease affecting milk production of dairy cattle, and milk is an obvious substrate for the detection of both the inflammation and its causative infectious agents at quarter, cow, or herd levels. In this review, we examine the use of milk to detect inflammation based on somatic cell count (SCC) and other biomarkers, and for the detection of mastitis pathogens through culture-based and culture-free methods.

FINDINGS

The use of SCC at a cow or bulk milk level to guide udder health management in lactation is well-established, and SCC is increasingly used to guide selective dry cow treatment. Other markers of inflammation include electrical conductivity, which is used commercially, and markers of disease severity such as acute phase proteins but are not pathogen-specific. Some pathogen-specific markers based on humoral immune responses are available, but their value in udder health management is largely untested. Commercial pathogen detection is based on culture or polymerase chain reaction, with other tests, for example, loop-mediated isothermal amplification or 16S microbiome analysis still at the research or development stage. Matrix-assisted laser desorption ionisation time of flight (MALDI-ToF) is increasingly used for the identification of cultured organisms whilst application directly to milk needs further development. Details of test sensitivity, specificity, and use of the various technologies may differ between quarter, cow, and bulk milk applications.

CONCLUSIONS

There is a growing array of diagnostic assays that can be used to detect markers of inflammation or infection in milk. The value of some of these methods in on-farm udder health improvement programs is yet to be demonstrated whilst methods with proven value may be underutilised.

Rapid discrimination of Bifidobacterium longum subspecies based on MALDI-TOF MS and machine learning

Although MALDI-TOF mass spectrometry (MS) is widely known as a rapid and cost-effective reference method for identifying microorganisms, its commercial databases face limitations in accurately distinguishing specific subspecies of Bifidobacterium. This study aimed to explore the potential of MALDI-TOF MS protein profiles, coupled with prediction methods, to differentiate between Bifidobacterium longum subsp. infantis (B. infantis) and Bifidobacterium longum subsp. longum (B. longum). The investigation involved the analysis of mass spectra of 59 B. longum strains and 41 B. infantis strains, leading to the identification of five distinct biomarker peaks, specifically at m/z 2,929, 4,408, 5,381, 5,394, and 8,817, using Recurrent Feature Elimination (RFE). To facilate classification between B. longum and B. infantis based on the mass spectra, machine learning models were developed, employing algorithms such as logistic regression (LR), random forest (RF), and support vector machine (SVM). The evaluation of the mass spectrometry data showed that the RF model exhibited the highest performace, boasting an impressive AUC of 0.984. This model outperformed other algorithms in terms of accuracy and sensitivity. Furthermore, when employing a voting mechanism on multi-mass spectrometry data for strain identificaton, the RF model achieved the highest accuracy of 96.67%. The outcomes of this research hold the significant potential for commercial applications, enabling the rapid and precise discrimination of B. longum and B. infantis using MALDI-TOF MS in conjunction with machine learning. Additionally, the approach proposed in this study carries substantial implications across various industries, such as probiotics and pharmaceuticals, where the precise differentiation of specific subspecies is essential for product development and quality control.

Investigation of intramammary infections in primiparous cows during early lactation on organic dairy farms

Previous studies have shown that organically raised dairy cows have an increased prevalence of Staphylococcus aureus compared with conventionally raised dairy cows. However, little information exists about the dynamics of intramammary infection (IMI) in primiparous cows during early lactation on organic dairy farms. The objective of this study was to describe the IMI dynamics of primiparous cows on certified organic farms during early lactation. This longitudinal study enrolled 503 primiparous cows from 5 organic dairy farms from February 2019 to January 2020. Quarter-level milk samples were collected aseptically on a weekly basis during the first 5 wk of lactation. Samples were pooled by cow and time point into composite samples inside a sterilized laminar hood and submitted for microbiological culture. For each of the different microorganisms identified, we estimated the prevalence in each postpartum sample, period prevalence (PP), cumulative incidence, and persistence of IMI. Logistic regression models were used to investigate whether the prevalence of IMI differed by farm or sampling time points and whether IMI persistence differed between detected microorganisms. Our findings revealed a high prevalence of Staphylococcus aureus (PP = 18.9%), non-aureus staphylococci and closely related mammaliicoccal species (PP = 52.1%), and Streptococcus spp. and Streptococcus-like organisms (PP = 32.1%) within the study population. The prevalence of these microorganisms varied significantly between farms. Staphylococcus aureus and Staphylococcus chromogenes exhibited significantly higher IMI persistence compared with other detected bacterial taxa, confirming the divergent epidemiological behavior in terms of IMI chronicity across different microorganisms. This study improves our understanding of the epidemiology of mastitis-causing pathogens in organically raised primiparous cows, which can be used to tailor mastitis control plans for this unique yet growing subpopulation of dairy cows.

Advances in Diagnostic Approaches and Therapeutic Management in Bovine Mastitis

Mastitis causes huge economic losses to dairy farmers worldwide, which largely negatively affects the quality and quantity of milk. Mastitis decreases overall milk production, degrades milk quality, increases milk losses because of milk being discarded, and increases overall production costs due to higher treatment and labour costs and premature culling. This review article discusses mastitis with respect to its clinical epidemiology, the pathogens involved, economic losses, and basic and advanced diagnostic tools that have been used in recent times to diagnose mastitis effectively. There is an increasing focus on the application of novel therapeutic approaches as an alternative to conventional antibiotic therapy because of the decreasing effectiveness of antibiotics, emergence of antibiotic-resistant bacteria, issue of antibiotic residues in the food chain, food safety issues, and environmental impacts. This article also discussed nanoparticles'/chitosan's roles in antibiotic-resistant strains and ethno-veterinary practices for mastitis treatment in dairy cattle.

Differentiation between Weissella cibaria and Weissella confusa Using Machine-Learning-Combined MALDI-TOF MS

Although Weissella cibaria and W. confusa are essential food-fermenting bacteria, they are also opportunistic pathogens. Despite these species being commercially crucial, their taxonomy is still based on inaccurate identification methods. In this study, we present a novel approach for identifying two important Weissella species, W. cibaria and W. confusa, by combining matrix-assisted laser desorption/ionization and time-of-flight mass spectrometer (MALDI-TOF MS) data using machine-learning techniques. After on- and off-plate protein extraction, we observed that the BioTyper database misidentified or could not differentiate Weissella species. Although Weissella species exhibited very similar protein profiles, these species can be differentiated on the basis of the results of a statistical analysis. To classify W. cibaria, W. confusa, and non-target Weissella species, machine learning was used for 167 spectra, which led to the listing of potential species-specific mass-to-charge (m/z) loci. Machine-learning techniques including artificial neural networks, principal component analysis combined with the K-nearest neighbor, support vector machine (SVM), and random forest were used. The model that applied the Radial Basis Function kernel algorithm in SVM achieved classification accuracy of 1.0 for training and test sets. The combination of MALDI-TOF MS and machine learning can efficiently classify closely-related species, enabling accurate microbial identification.

Rapid identification of methicillin-resistant Staphylococcus aureus by MALDI-TOF MS: A meta-analysis

Invasive infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are associated with high mortality and morbidity. The sooner the pathogen is determined, the better it is beneficial to patient. However, routine laboratory inspections are time-consuming and laborious. A thorough research was conducted in PubMed and Web of Science (until June 2021) to identify studies evaluating the accuracy of MRSA identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). STATA 15.0 software was used to analyze the pooled results of sensitivity, specificity, and 95% confidence intervals (CI). The summary receiver operating characteristic curves (SROC) and area under the curve (AUC) were utilized to show the overall performance of MALDI-TOF MS. Fifteen studies involving 2471 isolates were included in this study after the final selection in this meta-analysis. Using the random effects model forest plot to summarize the overall statistics, the sensitivity of MALDI-TOF MS for identifying MRSA was 92% (95% CI: 81%-97%), and the specificity was 97% (95% CI: 89%-99%). In the SROC curve, the AUC reached 0.99 (95% CI: 97%-99%). Deeks' test showed no significant publication bias in this meta-analysis. Compared with clinical reference methods, MALDI-TOF MS identification of MRSA shows a higher degree of sensitivity and specificity.

Diagnosing Intramammary Infection: Meta-Analysis and Mapping Review on Frequency and Udder Health Relevance of Microorganism Species Isolated from Bovine Milk Samples

Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry provides accurate species-level identification of many, microorganisms retrieved from bovine milk samples. However, not all those microorganisms are pathogenic. Our study aimed to: (1) determine the species-specific prevalence of microorganisms identified in bovine milk of apparently healthy lactating quarters vs. quarters with clinical mastitis (CM); and (2) map current information and knowledge gaps on udder health relevance of microorganisms retrieved from bovine milk samples. A mixed study design (meta-analysis and mapping review) was chosen. We gathered several large Canadian, US and Brazilian data sets of MALDI-TOF results for organisms cultured from quarter milk samples. For meta-analysis, two datasets (apparently healthy quarters vs. CM samples) were organized. A series of meta-analyses was conducted to determine microorganisms' prevalence. Then, each species reported was searched through PubMed to investigate whether inflammation (increased somatic cell count (SCC) or signs of CM) was associated with microorganism's recovery from milk. A total of 294 different species of microorganisms recovered from milk samples were identified. Among 50,429 quarter-milk samples from apparently healthy quarters, the 5 most frequent species were Staphylococcus chromogenes (6.7%, 95% CI 4.5-9.2%), Aerococcus viridans (1.6%, 95% CI 0.4-3.5%), Staphylococcus aureus (1.5%, 95% CI 0.5-2.8%), Staphylococcus haemolyticus (0.9%, 95% CI 0.4-1.5%), and Staphylococcus epidermidis (0.7%, 95% CI 0.2-1.6%). Among the 43,924 quarter-milk CM samples, the 5 most frequent species were Escherichia coli (11%, 95% CI 8.1-14.3%), Streptococcus uberis (8.5%, 95% CI 5.3-12.2%), Streptococcus dysgalactiae (7.8%, 95% CI 4.9-11.5%), Staphylococcus aureus (7.8%, 95% CI 4.4-11.9%), and Klebsiella pneumoniae (5.6%, 95% CI 3.4-8.2%). When conducting the PubMed literature search, there were 206 species identified by MALDI-TOF for which we were not able to find any information regarding their association with CM or SCC. Some of them, however, were frequently isolated in our multi-country dataset from the milk of quarters with CM (e.g., Citrobacter koseri, Enterococcus saccharolyticus, Streptococcus gallolyticus). Our study provides guidance to veterinarians for interpretation of milk bacteriology results obtained using MALDI-TOF and identifies knowledge gaps for future research.

Nanopore-Based Surveillance of Zoonotic Bacterial Pathogens in Farm-Dwelling Peridomestic Rodents

The effective control of rodent populations on farms is crucial for food safety, as rodents are reservoirs and vectors for several zoonotic pathogens. Clear links have been identified between rodents and farm-level outbreaks of pathogens throughout Europe and Asia; however, comparatively little research has been devoted to studying the rodent–agricultural interface in the USA. Here, we address this knowledge gap by metabarcoding bacterial communities of rodent pests collected from Minnesota and Wisconsin food animal farms. We leveraged the Oxford Nanopore MinION sequencer to provide a rapid real-time survey of putative zoonotic foodborne pathogens, among others. Rodents were live trapped (n = 90) from three dairy and mixed animal farms. DNA extraction was performed on 63 rodent colons along with 2 shrew colons included as outgroups in the study. Full-length 16S amplicon sequencing was performed. Our farm-level rodent-metabarcoding data indicate the presence of multiple foodborne pathogens, including Salmonella spp., Campylobacter spp., Staphylococcus aureus, and Clostridium spp., along with many mastitis pathogens circulating within five rodent species (Microtus pennsylvanicus, Mus musculus, Peromyscus leucopus, Peromyscus maniculatus, and Rattus norvegicus) and a shrew (Blarina brevicauda). Interestingly, we observed a higher abundance of enteric pathogens (e.g., Salmonella) in shrew feces compared to the rodents analyzed in our study. Knowledge gained from our research efforts will directly inform and improve farm-level biosecurity efforts and public health interventions to reduce future outbreaks of foodborne and zoonotic disease.