Enterococcus species identified by MALDI-TOF MS in milk from dairy cow mastitis cases and herd surveys

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Reliable species information is required for correctly defining the regional epidemiology of enterococcal mastitis, establishing its relationships with management variables, and understanding its impact on udder health. We investigated the species distribution of enterococci in bovine milk from subclinical mastitis (SCM) and clinical mastitis (CM) cases and full herd surveys (HS) using MALDI-TOF MS as identification method. A total of 21,864 milk samples from 106 dairy herds were routinely collected and analyzed according to the National Mastitis Council (NMC) guidelines over one year. Enterococcus spp. were found in 4.86% of CM, 5.05% of SCM, and 1.37% of HS milk samples. Overall, E. saccharolyticus was the most prevalent species (55.65%), followed by E. faecium (27.25%), and E. cecorum (5.51%), which showed a significant association with SCM (p-value = 0.0128). This study provides novel data on the Enterococcus species distribution in full herds survey and mastitis cases and highlights the relevance of MALDI-TOF MS in refining enterococcal mastitis epidemiology, whose prevalence may have been underestimated by conventional biochemical tests. Enterococci species identification clinical mastitis subclinical mastitis full herd survey 1. Introduction Enterococcus species are Gram-positive, facultative anaerobic bacteria widely distributed as commensals in the gastrointestinal tracts of humans, livestock, and insects (Guzman Prieto et al. 2016 ). In dairy production, Enterococcus spp. is classified within environmental streptococci and streptococci-like organisms (SSLO), alongside Streptococcus spp. and Lactococcus spp., due to their ubiquity in farm environments (e.g., bedding, soil, and water) and their role as causative agents of intramammary infections (IMI)(Klaas and Zadoks 2018 ). Globally, enterococci contribute to both clinical mastitis (CM) and subclinical mastitis (SCM), with prevalence varying from 1.3% in Sweden (Duse et al. 2021 ) to 1.73% in China (Song et al. 2020 ), up to 2.4% in France (Botrel et al. 2010 ). Recent data have found Enterococcus spp. in 3.49% of SCM and 2.10% of CM samples in Italy (Addis et al. 2024 ), though these figures derive from genus-level identification. Species-specific differences are critical, as E. faecium is associated with multidrug resistance, while E. faecalis exhibits virulence traits such as biofilm formation and mammary epithelial cell invasion (Guzman Prieto et al. 2016 ; Rodrigues et al. 2022 ). Many species are non-pathogenic, but E. faecium and E. faecalis are commonly considered opportunistic pathogens implicated in severe infections, including bovine mastitis, and are notorious for their intrinsic and acquired antimicrobial resistance (AMR; (Guimarães et al. 2024 ). These bacteria exhibit reduced susceptibility to glycopeptides, β-lactams, and fluoroquinolones, and show high resistance levels to aminoglycosides driven by genetic mechanisms such as resistance genes (e.g., tet , erm ) often located on mobile genetic elements and class I integrons (Gao et al. 2019 ; Liu et al. 2024 ). Their resistance profiles not only complicate the effective treatment of the infections they cause but also position enterococci as key indicators of antimicrobial resistance dissemination dynamics within dairy farm ecosystems, with significant implications for both animal and public health (Różańska et al. 2019 ; Juliano et al. 2022 ). Accurate species identification with advanced diagnostic methods is crucial for correctly defining enterococcal mastitis epidemiology, its management and environmental drivers, and its impact on udder health. For instance, emerging evidence points to less-studied species as significant mastitis pathogens, such as E. saccharolyticus , when identified with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS; (Guimarães et al. 2024 ). Traditional biochemical methods, including API galleries and VITEK 2, have dominated historical studies but frequently misclassified Enterococcus spp. as Streptococcus or Lactococcus due to phenotypic overlap (Werner et al. 2014 ; Guimarães et al. 2024 ). Biochemical assays may underestimate species of veterinary interest, like E. saccharolyticus , while overreporting E. faecalis or E. faecium (Różańska et al. 2019 ; Gao et al. 2019 ). This diagnostic limitation has skewed epidemiological profiles and hindered targeted management strategies. In contrast, MALDI-TOF MS offers rapid, high-resolution species identification, achieving > 90% accuracy in mastitis pathogen speciation (Nonnemann et al. 2019 ). Recent applications of MALDI-TOF MS in dairy contexts have revealed E. saccharolyticus as the predominant species in CM milk (62.4%) and E. faecium in bulk tank milk (BTM; 67.8%) in Brazil, challenging prior assumptions(Guimarães et al. 2024 ). Mastitis control and treatment remain the primary reasons for antimicrobial use in dairy farming, highlighting the need for precise epidemiological data to support prudent antibiotic use (Ruegg 2022 ). In this context, misidentification of Enterococcus species may lead to ineffective management or inappropriate therapies, exacerbate resistance, and overlook emerging pathogens, compromising herd health and milk quality (Cameron et al. 2016 ). This study presents the data on Enterococcus spp. isolates identified by MALDI-TOF MS and obtained from CM, SCM, and HS bovine milk samples across 106 dairy farms in Northern Italy. By providing a detailed species-level profile, we aimed to refine regional epidemiology, enabling future elucidation of ecological associations and farm-specific interventions for enterococcal mastitis control. 2. Materials and methods 2.1. Milk samples and contributing herds Milk samples were shipped to the Animal Infectious Disease Laboratory (MiLab) at the University of Milan between December 2022 and November 2023 to be processed during the diagnostic routine. The sample set included 106 dairy farms located in Northern Italy, housing Holstein-Friesian cows in free-stall barns. Among these farms, 96 and 34 sent quarter milk samples with clinical mastitis (CM) and subclinical mastitis (SCM), respectively, while 78 sent full herd survey (HS) as composite milk samples. Throughout the collection process, all personnel followed National Mastitis Council (NMC) protocols (Adkins et al. 2017 ). For CM, trained personnel identified affected mammary quarters using established clinical criteria (Adkins et al. 2017 ). The evaluation involved visually inspecting the first milk streams for abnormalities such as flakes, clots, discoloration, or watery consistency, as well as checking for udder swelling, redness, or pain. SCM samples were collected from cows reported by high somatic cell counts (SCC) in recent Dairy Herd Improvement (DHI) records. These cows were further evaluated with the California Mastitis Test (CMT), and milk samples from quarters testing positive for CMT without visible clinical signs were classified as SCM following established guidelines (Adkins et al. 2017 ). HS milk, representing milk pooled, was collected during herd-wide sampling events to determine the prevalence of infectious pathogens at the farm level. 2.2. Bacteriological analysis of milk Milk samples were processed for bacterial culture in accordance with NMC standards (Adkins et al. 2017 ), as outlined previously (Addis et al. 2024 ). In summary, 10 µL of milk was plated on blood agar (Microbiol, Cagliari, Italy), and plates were aerobically incubated at 37°C for 24–48 hours. The plates were then evaluated and categorized as positive, contaminated, or negative based on NMC criteria. From each positive sample, representative colonies were identified using MALDI-TOF MS and the MBT Compass® Library Revision H (2022). Identification scores ≥ 2.0 were considered reliable for species-level identification, while scores ≥ 1.7 and < 2.0 indicated genus-level identification. Isolates scoring < 1.7 were re-analyzed in duplicate, and if subsequent scores remained < 1.7, they were classified as unidentified (Rosa et al. 2022 ). 2.3. Data analysis This study analyzed a total of 21,864 milk samples, including 6,278 from CM, 4,039 from SCM, and 11,547 from HS milk. Data regarding farm identification, sampling dates, sample classifications, microbiological results, and identification scores were recorded in a Microsoft Access database (Microsoft Office, version 16.82, 2024). Furthermore, information on subclinical and clinical status was recorded. Data extraction was performed using Microsoft Excel (version 16.82, 2024), utilizing pivot tables and built-in functions for descriptive statistical analysis. Statistical analysis was carried out with SPSS version 29.0 (IBM, SPSS, Armonk, USA). A multinomial logistic regression model was applied to assess the prevalence of Enterococcus species in SCM and CM cases, considering the subclinical condition as the reference. Wald statistics were used for parameter estimation, and statistical significance was determined at p-values < 0.05 and < 0.01. 3. Results The bacteriological culture results for the 21,864 milk samples are summarized in Table 1 according to the sample type. Out of 6,278 CM milk samples, 63.57% (3,991) were positive, of which 4.86% (194) for Enterococcus spp. Out of 4,039 SCM milk samples, positive samples were 69.03% (2,788), of which 4.05% (113) for Enterococcus spp. Out of 11,547 HS milk samples, 32.37% (3,738) tested positive, Enterococcus spp. was identified in 1.36% (51). Table 1 Bacteriological culture results obtained for all the milk samples considered in this study, according to their respective categories. Species CM quarter milk SCM quarter milk HS composite milk Total general Streptococcus spp. 1,259 (31.55%) 578 (20.73%) 526 (14.07%) 2363 (22.49%) NASM* 947 (23.73%) 1,162 (41.68%) 1,819 (48.66%) 3,928 (37.38%) E. coli 641 (16.06%) 82 (2.94%) 127 (3.4%) 850 (8.09%) Serratia spp. 230 (5.76%) 142 (5.09%) 42 (1.12%) 414 (3.94%) Enterococcus spp. 194 (4.86%) 113 (4.05%) 51 (1.36%) 358 (3.41%) Corynebacterium spp. 125 (3.13%) 229 (8.21%) 287 (7.68%) 641 (6.1%) S. aureus 109 (2.73%) 198 (7.1%) 160 (4.28%) 467 (4.44%) Klebsiella spp. 74 (1.85%) 23 (0.82%) 39 (1.04%) 136 (1.29%) Gram negative 70 (1.75%) 32 (1.15%) 184 (4.92%) 286 (2.72%) Bacilli 53 (1.33%) 8 (0.29%) 27 (0.72%) 88 (0.84%) Yeast 51 (1.28%) 98 (3.52%) 6 (0.16%) 155 (1.48%) Pasteurella multocida 46 (1.15%) 10 (0.36%) 5 (0.13%) 61 (0.58%) Pseudomonas spp. 44 (1.1%) 14 (0.5%) 21 (0.56%) 79 (0.75%) T. pyogenes 37 (0.93%) 11 (0.39%) 18 (0.48%) 66 (0.63%) Enterobacter spp. 29 (0.73%) 6 (0.22%) 23 (0.62%) 58 (0.55%) Prototheca spp. 23 (0.58%) 4 (0.14%) 202 (5.4%) 229 (2.18%) Lactococcus spp. 21 (0.53%) 39 (1.4%) 52 (1.39%) 112 (1.07%) Citrobacter spp. 14 (0.35%) 6 (0.22%) 7 (0.19%) 27 (0.26%) A. viridans 13 (0.33%) 17 (0.61%) 109 (2.92%) 139 (1.32%) Other 8 (0.2%) 1 (0.04%) 9 (0.24%) 9 (0.09%) Proteus spp. 3 (0.08%) 15 (0.54%) 24 (0.64%) 42 (0.4%) Total 3,991 (100%) 2,788 (100%) 3,738 (100%) 10,508 (100%) * Counts for NASM (non- aureus staphylococci and mammaliicocci) include both genus-only and genus-plus-species identifications. Table 2 Total number of isolates for each Enterococcus species with their respective average MALDI-TOF MS Log score. Species Isolates with log score > 2.0 (%) Average Log score Isolates with log score 1.7–1.99 (%) Average Log score E. saccharolyticus 112 (53.33%) 2.16 76 (68.47%) 1.86 E. faecium 62 (29.52%) 2.32 15 (13.51%) 1.83 E. cecorum 12 (5.71%) 2.17 6 (5.41%) 1.93 E. faecalis 6 (2.86%) 2.30 9 (8.11%) 1.82 E. casselliflavus 6 (2.86%) 2.16 - - E. hirae 3 (1.43%) 2.32 3 (2.7%) 1.84 E. devriesei 3 (1.43%) 2.21 - - E. canintestini 1 (0.48%) 2.33 1 (0.9%) 1.95 E. columbae - - 1 (0.9%) 1.88 E. durans 1 (0.48%) 2.39 - - E. glivus 1 (0.48%) 2.36 - - E. italicus 1 (0.48%) 2.08 - - E. malodoratus 1 (0.48%) 2.02 - - E. villorum 1 (0.48%) 2.36 - - Total 210 (100%) 2.22 111 (100%) 1,86 * For 24 isolates, log scores were not found in the database: E. cecorum (1), E. faecalis (2), E. faecium (17), and E. saccharolyticus (4). The species was not identified in 13 of 358 of the total enterococci isolates: three from MSC samples, eight from MC samples, and two from HS samples. Table 3 describes the Enterococcus species distribution across the different sample types. A total of 345 Enterococcus isolates were identified at the species level. Overall, E. saccharolyticus was the most prevalent (55.65%;192), followed by E. faecium (27.25%; 94) and E. cecorum (5.51%; 19). E. faecalis was identified in 4.93% (17) of the samples. Particularly, E. saccharolyticus was the most frequently isolated species from both CM (66.67%, 124 out of 186) and SCM (48.18%, 53 out of 110) milk, whereas E. faecium was the most frequently found in HS samples (44.9%, 22 out of 49). E. cecorum represented 10.91% (12 out of 110) of all isolates in SCM samples and was the only species with a significantly higher frequency in SCM compared to other sample types (p-value = 0.0128). Several other Enterococcus species, including E. casselliflavus , E. hirae , E. devriesei , E. canintestini , E. columbae , E. durans , E. gilvus , E. italicus , E. malodoratus , and E. villorum were collectively less than 7% of the total isolates. Table 3 Distribution of Enterococcus species across sample categories. Species CM quarter milk SCM quarter milk HS composite milk Total general E. saccharolyticus 124 (66,67%) 53 (48,18%) 15 (30,61%) 192 (55,65%) E. faecium 41 (22,04%) 31 (28,18%) 22 (44,9%) 94 (27,25%) E. cecorum 6 (3,23%) 12 (10,91%) 1 (2,04%) 19 (5,51%) E. faecalis 6 (3,23%) 5 (4,55%) 6 (12,24%) 17 (4,93%) E. casselliflavus 2 (1,08%) 4 (3,64%) - 6 (1,74%) E. hirae 2 (1,08%) 2 (1,82%) 2 (4,08%) 6 (1,74%) E. devriesei 2 (1,08%) - 1 (2,04%) 3 (0,87%) E. canintestini - 1 (0,91%) 1 (2,04%) 2 (0,58%) E. columbae - 1 (0,91%) - 1 (0,29%) E. durans 1 (0,54%) - - 1 (0,29%) E. glivus - 1 (0,91%) - 1 (0,29%) E. italicus - - 1 (2,04%) 1 (0,29%) E. malodoratus 1 (0,54%) - - 1 (0,29%) E. villorum 1 (0,54%) - - 1 (0,29%) TOTAL 186 (100%) 110 (100%) 49 (100%) 345 (100%) 4. Discussion This retrospective study on 21,864 milk samples from Northern Italy over one year provides the first description of Enterococcus spp. in bovine milk in Italy using MALDI-TOF MS. The identification of 14 distinct species, with E. saccharolyticus (55.65%), E. faecium (27.25%), and E. cecorum (5.51%) as the most prevalent ones, is in line with emerging data at worldwide level and highlights the role of advanced microbiological tools in providing further information on mastitis epidemiology. The predominance of E. saccharolyticus , particularly in CM samples (66.67%), is in line with a recent report from Brazil, where a prevalence of 62.4% was recorded for CM using MALDI-TOF MS (Guimarães et al. 2024 ). This contrasts with earlier studies relying on biochemical methods, which identified E. faecalis as a prominent species (Różańska et al. 2019 ; Gao et al. 2019 ). The discrepancy suggests that traditional approaches, such as API systems or VITEK 2, may have misclassified E. saccharolyticus as E. faecalis or other SSLO due to overlapping phenotypic traits (Werner et al. 2014 ; Cameron et al. 2016 ). The high frequency of E. saccharolyticus in CM samples prompts further research on its pathogenicity, potentially linked to environmental abundance, adaptability, resistance, and presence of virulence traits such as biofilm formation capabilities (Rodrigues et al. 2022 ). However, data on the specific role of E. saccharolyticus in bovine IMI remain scarce, and limited AMR has been reported for this species, suggesting distinct ecological or clinical profiles compared to other enterococci (Paschoalini et al. 2023 ). In contrast, E. faecium emerged as the leading species in HS milk (44.9%), consistent with its frequent isolation from BTM in Brazil (67.8%; (Guimarães et al. 2024 ). This pattern may reflect its ecological adaptability and persistence in farm environments, such as bedding and fecal matter. In-vitro studies from our group have shown the survival and multiplication ability of E. faecium in lime-conditioned bedding, with minimal growth inhibition compared to other udder pathogens (Fusar Poli et al. 2024 ). These recent findings are in line with its prominence in composite HS samples from healthier quarters, while its lower prevalence in CM (22.04%) and SCM (28.18%) may suggest a less important role in IMI. On the other hand, its well-documented multidrug resistance, exceeding 20% (Liu et al. 2024 ), which could represent a challenge for infection treatment. Overall, the lower Enterococcus spp. prevalence in HS (1.36%) compared to CM (4.86%) and SCM (4.05%) could be related to dilution in pooled samples or to the healthier status of udders sampled for herd survey compared to mastitis diagnostic purposes. The significant association of E. cecorum with SCM was a notable finding and represents its first reported connection to bovine mastitis in Europe. E. cecorum was previously identified as an emerging pathogen in poultry, causing septicemia and locomotor disorders (Laurentie et al. 2023 ), and from healthy calves (Devriese et al. 1992 ). Its isolation from SCM cases may suggest a potential subclinical persistence in dairy cows, while its lower frequency in CM (3.23%) and HS (2.04%) could imply adaptation to chronic, low-grade infections, possibly facilitated by virulence traits like epithelial adhesion, as seen in poultry isolates (Laurentie et al. 2023 ). However, its pathogenicity in bovine udders has not been investigated yet. On the other hand, this finding shows the value of MALDI-TOF MS in refining mastitis epidemiology, as highlighted by studies like Nonnemann et al. ( 2019 ). The technique's enhanced ability to accurately identify a diverse range of bacteria overcomes some limitations of traditional biochemical methods, potentially revealing the involvement of species previously neglected or misclassified. The role of E. cecorum in SCM will require further studies to confirm its relevance and understand its pathogenicity. The observed species distribution highlights the complexity of managing enterococcal mastitis, as different species may require different approaches. While this study did not assess AMR, the known species-dependent variability in resistance profiles reported elsewhere, such as higher resistance often found in E. faecium and E. faecalis compared to E. saccharolyticus (Gao et al. 2019 ; Paschoalini et al. 2023 ), shows the practical importance of the species-level identification achieved using MALDI-TOF MS. This detailed epidemiological information is important for guiding future research on species-specific pathogenicity and resistance patterns, ultimately leading to more specific control strategies and prudent antimicrobial use (Ruegg 2022 ). This study provides the first detailed report using MALDI-TOF MS to characterize Enterococcus species distribution within the context of bovine mastitis and herd health in Northern Italy. Our findings are representative of the region's prevalent dairy systems, primarily involving Holstein-Friesian cows housed in free-stall barns. Understanding the specific enterococcal species prevalent under these European management and environmental conditions is important, as factors such as local climate, bedding materials, and antibiotic stewardship practices can significantly shape microbial ecology compared to other regions globally. Potential study limitations should be acknowledged. Firstly, while MALDI-TOF MS is a powerful identification tool, the absence of routine molecular confirmation (e.g., PCR, sequencing) for all isolates carries a minor risk of misclassification, particularly concerning very closely related or rarer species like some identified here. Secondly, the retrospective analysis relied on samples submitted for routine diagnostics, which might not fully represent the complete epidemiological picture compared to a structured prospective sampling design 5. Conclusion This study demonstrates the ability of MALDI-TOF MS to provide a detailed and reliable picture of enterococcal mastitis epidemiology, suggesting that E. saccharolyticus might have a more relevant role in bovine mastitis than the so-far reported E. faecalis , and indicating the possibly neglected role of emerging species as E. cecorum . Implementing MALDI-TOF MS in mastitis research does, therefore, provide more reliable information, possibly enabling tailored management strategies, including enhancement of bedding treatment to reduce E. faecium or targeted monitoring for E. cecorum in herds with SCM issues by SSLO. Further investigations into pathogenicity, resistance patterns, and ecological drivers will be required to optimize enterococcal mastitis control. Declarations Ethics approval Not Applicable. CRediT authorship contribution statement Fernando Ulloa: Data curation, Formal analysis, Investigation, Writing – original draft. Martina Penati: Data curation, Formal analysis, Investigation, Writing – original draft. Valentina Monistero: Investigation, Writing – review and editing. Valerio Bronzo: Formal analysis, Software, Writing – review and editing. Paolo Moroni: Writing – review and editing. Miguel Salgado: Writing – review and editing. Maria Filippa Addis: Conceptualization, Methodology, Supervision, Writing – original draft. Declaration of competing interest The authors declare no competing interests. Funding This research was supported by internal funds from the Laboratory of Animal Infectious Diseases (MiLab) at the University of Milan. Author Contribution Fernando Ulloa: Data curation, Formal analysis, Investigation, Writing – original draft. Martina Penati: Data curation, Formal analysis, Investigation, Writing – original draft. Valentina Monistero: Investigation, Writing – review and editing. Valerio Bronzo: Formal analysis, Software, Writing – review and editing. Paolo Moroni: Writing – review and editing. Miguel Salgado: Writing – review and editing. Maria Filippa Addis: Conceptualization, Methodology, Supervision, Writing – original draft. Acknowledgement We thank the farmers and veterinarians for their collaboration. Data Availability Data will be made available upon reasonable request. References Addis MF, Locatelli C, Penati M, et al (2024) Non-aureus staphylococci and mammaliicocci isolated from bovine milk in Italian dairy farms: a retrospective investigation. Vet Res Commun 48:547–554. https://doi.org/10.1007/s11259-023-10187-x Adkins PRF, Middleton JR, Fox LK, et al (2017) Laboratory Handbook on Bovine Mastitis. 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Cite Share Download PDF Status: Published Journal Publication published 23 Jul, 2025 Read the published version in Veterinary Research Communications → Version 1 posted Editorial decision: Revision requested 25 May, 2025 Reviews received at journal 18 May, 2025 Reviews received at journal 13 May, 2025 Reviewers agreed at journal 09 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviews received at journal 05 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 30 Apr, 2025 Editor assigned by journal 30 Apr, 2025 Submission checks completed at journal 30 Apr, 2025 First submitted to journal 24 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6522723","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":452305189,"identity":"be57f2c7-3e59-4df5-89a8-fede8e12d78b","order_by":0,"name":"Fernando Ulloa","email":"","orcid":"","institution":"Universidad Austral de Chile","correspondingAuthor":false,"prefix":"","firstName":"Fernando","middleName":"","lastName":"Ulloa","suffix":""},{"id":452305190,"identity":"32fa9ccb-a3e9-466a-8e89-3579de1c6f75","order_by":1,"name":"Martina Penati","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Martina","middleName":"","lastName":"Penati","suffix":""},{"id":452305191,"identity":"cfbf5988-cdef-43ce-8feb-885893969880","order_by":2,"name":"Valentina Monistero","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Valentina","middleName":"","lastName":"Monistero","suffix":""},{"id":452305192,"identity":"a3794386-f6d6-4345-88dd-3ef14d1727b5","order_by":3,"name":"Valerio Bronzo","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Valerio","middleName":"","lastName":"Bronzo","suffix":""},{"id":452305194,"identity":"72dd3284-74c7-46e6-9fb9-9b16257a6fca","order_by":4,"name":"Paolo Moroni","email":"","orcid":"","institution":"University of Milan","correspondingAuthor":false,"prefix":"","firstName":"Paolo","middleName":"","lastName":"Moroni","suffix":""},{"id":452305195,"identity":"2c159ef8-ec04-4b96-bf13-b054eed322da","order_by":5,"name":"Miguel Salgado","email":"","orcid":"","institution":"Instituto de Medicina Preventiva Veterinaria, Universidad Austral de Chile","correspondingAuthor":false,"prefix":"","firstName":"Miguel","middleName":"","lastName":"Salgado","suffix":""},{"id":452305199,"identity":"2ccbe46d-1648-4c7e-9b17-d0adae21ed54","order_by":6,"name":"Maria Filippa Addis","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIie3PMQrCMBSA4RcKdUntJoKgV6izoldpKegiYnERFIwI9QxF0TOIUBwjD3TpAVy7uItL3WwtBR0aOzrkn8LjfSQBkMn+sNJSYTw9mgaHCYAKhIEpIBTJJwkyIjCUxwsZAeJmcxEpkQU6xw7oK3t/em5xWK4ggzASEIUw9AIbqsFthJqPY7VmiR/WTYjmKgDXgYHER8v9RWhK5tCIyem5KU4QjJhwjRUlXnChzeQv9Nx/E272BERHfDjHab1+sQ/3aNaydut+GEbtfJIUXwT0a8LFICUymUwmy+8F5INX3RdSYNAAAAAASUVORK5CYII=","orcid":"","institution":"University of Milan","correspondingAuthor":true,"prefix":"","firstName":"Maria","middleName":"Filippa","lastName":"Addis","suffix":""}],"badges":[],"createdAt":"2025-04-24 16:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6522723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6522723/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11259-025-10834-5","type":"published","date":"2025-07-23T15:58:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87757716,"identity":"f2c10778-36fd-4876-90fd-de27993240f1","added_by":"auto","created_at":"2025-07-28 16:11:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":828885,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6522723/v1/212031fb-2e23-4309-bc7d-da624643558c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enterococcus species identified by MALDI-TOF MS in milk from dairy cow mastitis cases and herd surveys","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cem\u003eEnterococcus\u003c/em\u003e species are Gram-positive, facultative anaerobic bacteria widely distributed as commensals in the gastrointestinal tracts of humans, livestock, and insects (Guzman Prieto et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In dairy production, \u003cem\u003eEnterococcus\u003c/em\u003e spp. is classified within environmental streptococci and streptococci-like organisms (SSLO), alongside \u003cem\u003eStreptococcus\u003c/em\u003e spp. and \u003cem\u003eLactococcus\u003c/em\u003e spp., due to their ubiquity in farm environments (e.g., bedding, soil, and water) and their role as causative agents of intramammary infections (IMI)(Klaas and Zadoks \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Globally, enterococci contribute to both clinical mastitis (CM) and subclinical mastitis (SCM), with prevalence varying from 1.3% in Sweden (Duse et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) to 1.73% in China (Song et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), up to 2.4% in France (Botrel et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Recent data have found \u003cem\u003eEnterococcus\u003c/em\u003e spp. in 3.49% of SCM and 2.10% of CM samples in Italy (Addis et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), though these figures derive from genus-level identification. Species-specific differences are critical, as \u003cem\u003eE. faecium\u003c/em\u003e is associated with multidrug resistance, while \u003cem\u003eE. faecalis\u003c/em\u003e exhibits virulence traits such as biofilm formation and mammary epithelial cell invasion (Guzman Prieto et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rodrigues et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Many species are non-pathogenic, but \u003cem\u003eE. faecium\u003c/em\u003e and \u003cem\u003eE. faecalis\u003c/em\u003e are commonly considered opportunistic pathogens implicated in severe infections, including bovine mastitis, and are notorious for their intrinsic and acquired antimicrobial resistance (AMR; (Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These bacteria exhibit reduced susceptibility to glycopeptides, β-lactams, and fluoroquinolones, and show high resistance levels to aminoglycosides driven by genetic mechanisms such as resistance genes (e.g., \u003cem\u003etet\u003c/em\u003e, \u003cem\u003eerm\u003c/em\u003e) often located on mobile genetic elements and class I integrons (Gao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Their resistance profiles not only complicate the effective treatment of the infections they cause but also position enterococci as key indicators of antimicrobial resistance dissemination dynamics within dairy farm ecosystems, with significant implications for both animal and public health (R\u0026oacute;żańska et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Juliano et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccurate species identification with advanced diagnostic methods is crucial for correctly defining enterococcal mastitis epidemiology, its management and environmental drivers, and its impact on udder health. For instance, emerging evidence points to less-studied species as significant mastitis pathogens, such as \u003cem\u003eE. saccharolyticus\u003c/em\u003e, when identified with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS; (Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Traditional biochemical methods, including API galleries and VITEK 2, have dominated historical studies but frequently misclassified \u003cem\u003eEnterococcus\u003c/em\u003e spp. as \u003cem\u003eStreptococcus\u003c/em\u003e or \u003cem\u003eLactococcus\u003c/em\u003e due to phenotypic overlap (Werner et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Biochemical assays may underestimate species of veterinary interest, like \u003cem\u003eE. saccharolyticus\u003c/em\u003e, while overreporting \u003cem\u003eE. faecalis\u003c/em\u003e or \u003cem\u003eE. faecium\u003c/em\u003e (R\u0026oacute;żańska et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This diagnostic limitation has skewed epidemiological profiles and hindered targeted management strategies. In contrast, MALDI-TOF MS offers rapid, high-resolution species identification, achieving\u0026thinsp;\u0026gt;\u0026thinsp;90% accuracy in mastitis pathogen speciation (Nonnemann et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Recent applications of MALDI-TOF MS in dairy contexts have revealed \u003cem\u003eE. saccharolyticus\u003c/em\u003e as the predominant species in CM milk (62.4%) and \u003cem\u003eE. faecium\u003c/em\u003e in bulk tank milk (BTM; 67.8%) in Brazil, challenging prior assumptions(Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMastitis control and treatment remain the primary reasons for antimicrobial use in dairy farming, highlighting the need for precise epidemiological data to support prudent antibiotic use (Ruegg \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In this context, misidentification of \u003cem\u003eEnterococcus\u003c/em\u003e species may lead to ineffective management or inappropriate therapies, exacerbate resistance, and overlook emerging pathogens, compromising herd health and milk quality (Cameron et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This study presents the data on \u003cem\u003eEnterococcus\u003c/em\u003e spp. isolates identified by MALDI-TOF MS and obtained from CM, SCM, and HS bovine milk samples across 106 dairy farms in Northern Italy. By providing a detailed species-level profile, we aimed to refine regional epidemiology, enabling future elucidation of ecological associations and farm-specific interventions for enterococcal mastitis control.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Milk samples and contributing herds\u003c/h2\u003e \u003cp\u003eMilk samples were shipped to the Animal Infectious Disease Laboratory (MiLab) at the University of Milan between December 2022 and November 2023 to be processed during the diagnostic routine. The sample set included 106 dairy farms located in Northern Italy, housing Holstein-Friesian cows in free-stall barns. Among these farms, 96 and 34 sent quarter milk samples with clinical mastitis (CM) and subclinical mastitis (SCM), respectively, while 78 sent full herd survey (HS) as composite milk samples. Throughout the collection process, all personnel followed National Mastitis Council (NMC) protocols (Adkins et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor CM, trained personnel identified affected mammary quarters using established clinical criteria (Adkins et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The evaluation involved visually inspecting the first milk streams for abnormalities such as flakes, clots, discoloration, or watery consistency, as well as checking for udder swelling, redness, or pain. SCM samples were collected from cows reported by high somatic cell counts (SCC) in recent Dairy Herd Improvement (DHI) records. These cows were further evaluated with the California Mastitis Test (CMT), and milk samples from quarters testing positive for CMT without visible clinical signs were classified as SCM following established guidelines (Adkins et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). HS milk, representing milk pooled, was collected during herd-wide sampling events to determine the prevalence of infectious pathogens at the farm level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Bacteriological analysis of milk\u003c/h2\u003e \u003cp\u003eMilk samples were processed for bacterial culture in accordance with NMC standards (Adkins et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), as outlined previously (Addis et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In summary, 10 \u0026micro;L of milk was plated on blood agar (Microbiol, Cagliari, Italy), and plates were aerobically incubated at 37\u0026deg;C for 24\u0026ndash;48 hours. The plates were then evaluated and categorized as positive, contaminated, or negative based on NMC criteria. From each positive sample, representative colonies were identified using MALDI-TOF MS and the MBT Compass\u0026reg; Library Revision H (2022). Identification scores\u0026thinsp;\u0026ge;\u0026thinsp;2.0 were considered reliable for species-level identification, while scores\u0026thinsp;\u0026ge;\u0026thinsp;1.7 and \u0026lt;\u0026thinsp;2.0 indicated genus-level identification. Isolates scoring\u0026thinsp;\u0026lt;\u0026thinsp;1.7 were re-analyzed in duplicate, and if subsequent scores remained\u0026thinsp;\u0026lt;\u0026thinsp;1.7, they were classified as unidentified (Rosa et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data analysis\u003c/h2\u003e \u003cp\u003eThis study analyzed a total of 21,864 milk samples, including 6,278 from CM, 4,039 from SCM, and 11,547 from HS milk. Data regarding farm identification, sampling dates, sample classifications, microbiological results, and identification scores were recorded in a Microsoft Access database (Microsoft Office, version 16.82, 2024). Furthermore, information on subclinical and clinical status was recorded. Data extraction was performed using Microsoft Excel (version 16.82, 2024), utilizing pivot tables and built-in functions for descriptive statistical analysis. Statistical analysis was carried out with SPSS version 29.0 (IBM, SPSS, Armonk, USA). A multinomial logistic regression model was applied to assess the prevalence of \u003cem\u003eEnterococcus\u003c/em\u003e species in SCM and CM cases, considering the subclinical condition as the reference. Wald statistics were used for parameter estimation, and statistical significance was determined at p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and \u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe bacteriological culture results for the 21,864 milk samples are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e according to the sample type. Out of 6,278 CM milk samples, 63.57% (3,991) were positive, of which 4.86% (194) for \u003cem\u003eEnterococcus\u003c/em\u003e spp. Out of 4,039 SCM milk samples, positive samples were 69.03% (2,788), of which 4.05% (113) for \u003cem\u003eEnterococcus\u003c/em\u003e spp. Out of 11,547 HS milk samples, 32.37% (3,738) tested positive, \u003cem\u003eEnterococcus\u003c/em\u003e spp. was identified in 1.36% (51).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBacteriological culture results obtained for all the milk samples considered in this study, according to their respective categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCM quarter milk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCM quarter milk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHS composite milk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal general\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,259 (31.55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e578 (20.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e526 (14.07%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2363 (22.49%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNASM*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e947 (23.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,162 (41.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,819 (48.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,928 (37.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e641 (16.06%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82 (2.94%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127 (3.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e850 (8.09%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSerratia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e230 (5.76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142 (5.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42 (1.12%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e414 (3.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 (4.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (4.05%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (1.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e358 (3.41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCorynebacterium\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e125 (3.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e229 (8.21%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e287 (7.68%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e641 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eS. aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (2.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e198 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e160 (4.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e467 (4.44%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74 (1.85%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (0.82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39 (1.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e136 (1.29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGram negative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70 (1.75%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (1.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e184 (4.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e286 (2.72%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacilli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53 (1.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (0.29%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27 (0.72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88 (0.84%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYeast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (1.28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (3.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (0.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155 (1.48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePasteurella multocida\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (1.15%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (0.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5 (0.13%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61 (0.58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (0.56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79 (0.75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eT. pyogenes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (0.93%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (0.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66 (0.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (0.73%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (0.62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e58 (0.55%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePrototheca\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (0.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (0.14%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e202 (5.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e229 (2.18%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLactococcus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (0.53%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52 (1.39%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112 (1.07%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCitrobacter\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (0.35%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (0.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (0.19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27 (0.26%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eA. viridans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (0.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (0.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (2.92%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e139 (1.32%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (0.24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9 (0.09%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProteus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (0.54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (0.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3,991 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2,788 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3,738 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10,508 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003e*\u003c/em\u003e Counts for NASM (non-\u003cem\u003eaureus\u003c/em\u003e staphylococci and mammaliicocci) include both genus-only and genus-plus-species identifications.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal number of isolates for each \u003cem\u003eEnterococcus\u003c/em\u003e species with their respective average MALDI-TOF MS Log score.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIsolates with log score\u0026thinsp;\u0026gt;\u0026thinsp;2.0 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Log score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIsolates with log score 1.7\u0026ndash;1.99 (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage Log score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. saccharolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112 (53.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (68.47%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. faecium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (29.52%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (13.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. cecorum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (5.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (5.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. faecalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (8.11%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. casselliflavus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (2.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. hirae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3 (2.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. devriesei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (1.43%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. canintestini\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. columbae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. durans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. glivus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. italicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. malodoratus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. villorum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e210 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e111 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e* For 24 isolates, log scores were not found in the database: \u003cem\u003eE. cecorum\u003c/em\u003e (1), \u003cem\u003eE. faecalis\u003c/em\u003e (2), \u003cem\u003eE. faecium\u003c/em\u003e (17), and \u003cem\u003eE. saccharolyticus\u003c/em\u003e (4).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe species was not identified in 13 of 358 of the total enterococci isolates: three from MSC samples, eight from MC samples, and two from HS samples. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the \u003cem\u003eEnterococcus\u003c/em\u003e species distribution across the different sample types. A total of 345 \u003cem\u003eEnterococcus\u003c/em\u003e isolates were identified at the species level. Overall, \u003cem\u003eE. saccharolyticus\u003c/em\u003e was the most prevalent (55.65%;192), followed by \u003cem\u003eE. faecium\u003c/em\u003e (27.25%; 94) and \u003cem\u003eE. cecorum\u003c/em\u003e (5.51%; 19). \u003cem\u003eE. faecalis\u003c/em\u003e was identified in 4.93% (17) of the samples. Particularly, \u003cem\u003eE. saccharolyticus\u003c/em\u003e was the most frequently isolated species from both CM (66.67%, 124 out of 186) and SCM (48.18%, 53 out of 110) milk, whereas \u003cem\u003eE. faecium\u003c/em\u003e was the most frequently found in HS samples (44.9%, 22 out of 49). \u003cem\u003eE. cecorum\u003c/em\u003e represented 10.91% (12 out of 110) of all isolates in SCM samples and was the only species with a significantly higher frequency in SCM compared to other sample types (p-value\u0026thinsp;=\u0026thinsp;0.0128). Several other \u003cem\u003eEnterococcus\u003c/em\u003e species, including \u003cem\u003eE. casselliflavus\u003c/em\u003e, \u003cem\u003eE. hirae\u003c/em\u003e, \u003cem\u003eE. devriesei\u003c/em\u003e, \u003cem\u003eE. canintestini\u003c/em\u003e, \u003cem\u003eE. columbae\u003c/em\u003e, \u003cem\u003eE. durans\u003c/em\u003e, \u003cem\u003eE. gilvus\u003c/em\u003e, \u003cem\u003eE. italicus\u003c/em\u003e, \u003cem\u003eE. malodoratus\u003c/em\u003e, and \u003cem\u003eE. villorum\u003c/em\u003e were collectively less than 7% of the total isolates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of \u003cem\u003eEnterococcus\u003c/em\u003e species across sample categories.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCM quarter milk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCM quarter milk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHS composite milk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal general\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. saccharolyticus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (66,67%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (48,18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15 (30,61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e192 (55,65%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. faecium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (22,04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31 (28,18%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (44,9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94 (27,25%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. cecorum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3,23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (10,91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2,04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19 (5,51%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. faecalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (3,23%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4,55%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6 (12,24%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17 (4,93%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. casselliflavus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (3,64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (1,74%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. hirae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1,82%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (4,08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6 (1,74%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. devriesei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1,08%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2,04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3 (0,87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. canintestini\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0,91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2,04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2 (0,58%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. columbae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0,91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. durans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0,54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. glivus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0,91%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. italicus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (2,04%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. malodoratus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0,54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eE. villorum\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0,54%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1 (0,29%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTOTAL\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e186 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e110 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e49 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e345 (100%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis retrospective study on 21,864 milk samples from Northern Italy over one year provides the first description of \u003cem\u003eEnterococcus\u003c/em\u003e spp. in bovine milk in Italy using MALDI-TOF MS. The identification of 14 distinct species, with \u003cem\u003eE. saccharolyticus\u003c/em\u003e (55.65%), \u003cem\u003eE. faecium\u003c/em\u003e (27.25%), and \u003cem\u003eE. cecorum\u003c/em\u003e (5.51%) as the most prevalent ones, is in line with emerging data at worldwide level and highlights the role of advanced microbiological tools in providing further information on mastitis epidemiology.\u003c/p\u003e \u003cp\u003eThe predominance of \u003cem\u003eE. saccharolyticus\u003c/em\u003e, particularly in CM samples (66.67%), is in line with a recent report from Brazil, where a prevalence of 62.4% was recorded for CM using MALDI-TOF MS (Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This contrasts with earlier studies relying on biochemical methods, which identified \u003cem\u003eE. faecalis\u003c/em\u003e as a prominent species (R\u0026oacute;żańska et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The discrepancy suggests that traditional approaches, such as API systems or VITEK 2, may have misclassified \u003cem\u003eE. saccharolyticus\u003c/em\u003e as \u003cem\u003eE. faecalis\u003c/em\u003e or other SSLO due to overlapping phenotypic traits (Werner et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cameron et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high frequency of \u003cem\u003eE. saccharolyticus\u003c/em\u003e in CM samples prompts further research on its pathogenicity, potentially linked to environmental abundance, adaptability, resistance, and presence of virulence traits such as biofilm formation capabilities (Rodrigues et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, data on the specific role of \u003cem\u003eE. saccharolyticus\u003c/em\u003e in bovine IMI remain scarce, and limited AMR has been reported for this species, suggesting distinct ecological or clinical profiles compared to other enterococci (Paschoalini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, \u003cem\u003eE. faecium\u003c/em\u003e emerged as the leading species in HS milk (44.9%), consistent with its frequent isolation from BTM in Brazil (67.8%; (Guimar\u0026atilde;es et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This pattern may reflect its ecological adaptability and persistence in farm environments, such as bedding and fecal matter. \u003cem\u003eIn-vitro\u003c/em\u003e studies from our group have shown the survival and multiplication ability of \u003cem\u003eE. faecium\u003c/em\u003e in lime-conditioned bedding, with minimal growth inhibition compared to other udder pathogens (Fusar Poli et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These recent findings are in line with its prominence in composite HS samples from healthier quarters, while its lower prevalence in CM (22.04%) and SCM (28.18%) may suggest a less important role in IMI. On the other hand, its well-documented multidrug resistance, exceeding 20% (Liu et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which could represent a challenge for infection treatment. Overall, the lower \u003cem\u003eEnterococcus\u003c/em\u003e spp. prevalence in HS (1.36%) compared to CM (4.86%) and SCM (4.05%) could be related to dilution in pooled samples or to the healthier status of udders sampled for herd survey compared to mastitis diagnostic purposes.\u003c/p\u003e \u003cp\u003eThe significant association of \u003cem\u003eE. cecorum\u003c/em\u003e with SCM was a notable finding and represents its first reported connection to bovine mastitis in Europe. \u003cem\u003eE. cecorum\u003c/em\u003e was previously identified as an emerging pathogen in poultry, causing septicemia and locomotor disorders (Laurentie et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and from healthy calves (Devriese et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Its isolation from SCM cases may suggest a potential subclinical persistence in dairy cows, while its lower frequency in CM (3.23%) and HS (2.04%) could imply adaptation to chronic, low-grade infections, possibly facilitated by virulence traits like epithelial adhesion, as seen in poultry isolates (Laurentie et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, its pathogenicity in bovine udders has not been investigated yet. On the other hand, this finding shows the value of MALDI-TOF MS in refining mastitis epidemiology, as highlighted by studies like Nonnemann et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The technique's enhanced ability to accurately identify a diverse range of bacteria overcomes some limitations of traditional biochemical methods, potentially revealing the involvement of species previously neglected or misclassified. The role of \u003cem\u003eE. cecorum\u003c/em\u003e in SCM will require further studies to confirm its relevance and understand its pathogenicity.\u003c/p\u003e \u003cp\u003eThe observed species distribution highlights the complexity of managing enterococcal mastitis, as different species may require different approaches. While this study did not assess AMR, the known species-dependent variability in resistance profiles reported elsewhere, such as higher resistance often found in \u003cem\u003eE. faecium\u003c/em\u003e and \u003cem\u003eE. faecalis\u003c/em\u003e compared to \u003cem\u003eE. saccharolyticus\u003c/em\u003e (Gao et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Paschoalini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), shows the practical importance of the species-level identification achieved using MALDI-TOF MS. This detailed epidemiological information is important for guiding future research on species-specific pathogenicity and resistance patterns, ultimately leading to more specific control strategies and prudent antimicrobial use (Ruegg \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study provides the first detailed report using MALDI-TOF MS to characterize \u003cem\u003eEnterococcus\u003c/em\u003e species distribution within the context of bovine mastitis and herd health in Northern Italy. Our findings are representative of the region's prevalent dairy systems, primarily involving Holstein-Friesian cows housed in free-stall barns. Understanding the specific enterococcal species prevalent under these European management and environmental conditions is important, as factors such as local climate, bedding materials, and antibiotic stewardship practices can significantly shape microbial ecology compared to other regions globally.\u003c/p\u003e \u003cp\u003ePotential study limitations should be acknowledged. Firstly, while MALDI-TOF MS is a powerful identification tool, the absence of routine molecular confirmation (e.g., PCR, sequencing) for all isolates carries a minor risk of misclassification, particularly concerning very closely related or rarer species like some identified here. Secondly, the retrospective analysis relied on samples submitted for routine diagnostics, which might not fully represent the complete epidemiological picture compared to a structured prospective sampling design\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates the ability of MALDI-TOF MS to provide a detailed and reliable picture of enterococcal mastitis epidemiology, suggesting that \u003cem\u003eE. saccharolyticus\u003c/em\u003e might have a more relevant role in bovine mastitis than the so-far reported \u003cem\u003eE. faecalis\u003c/em\u003e, and indicating the possibly neglected role of emerging species as \u003cem\u003eE. cecorum\u003c/em\u003e. Implementing MALDI-TOF MS in mastitis research does, therefore, provide more reliable information, possibly enabling tailored management strategies, including enhancement of bedding treatment to reduce \u003cem\u003eE. faecium\u003c/em\u003e or targeted monitoring for \u003cem\u003eE. cecorum\u003c/em\u003e in herds with SCM issues by SSLO. Further investigations into pathogenicity, resistance patterns, and ecological drivers will be required to optimize enterococcal mastitis control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCRediT authorship contribution statement\u003c/h2\u003e \u003cp\u003eFernando Ulloa: Data curation, Formal analysis, Investigation, Writing \u0026ndash; original draft. Martina Penati: Data curation, Formal analysis, Investigation, Writing \u0026ndash; original draft. Valentina Monistero: Investigation, Writing \u0026ndash; review and editing. Valerio Bronzo: Formal analysis, Software, Writing \u0026ndash; review and editing. Paolo Moroni: Writing \u0026ndash; review and editing. Miguel Salgado: Writing \u0026ndash; review and editing. Maria Filippa Addis: Conceptualization, Methodology, Supervision, Writing \u0026ndash; original draft.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eDeclaration of competing interest\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by internal funds from the Laboratory of Animal Infectious Diseases (MiLab) at the University of Milan.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFernando Ulloa: Data curation, Formal analysis, Investigation, Writing \u0026ndash; original draft. Martina Penati: Data curation, Formal analysis, Investigation, Writing \u0026ndash; original draft. Valentina Monistero: Investigation, Writing \u0026ndash; review and editing. Valerio Bronzo: Formal analysis, Software, Writing \u0026ndash; review and editing. Paolo Moroni: Writing \u0026ndash; review and editing. Miguel Salgado: Writing \u0026ndash; review and editing. Maria Filippa Addis: Conceptualization, Methodology, Supervision, Writing \u0026ndash; original draft.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the farmers and veterinarians for their collaboration.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData will be made available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAddis MF, Locatelli C, Penati M, et al (2024) Non-aureus staphylococci and mammaliicocci isolated from bovine milk in Italian dairy farms: a retrospective investigation. Vet Res Commun 48:547\u0026ndash;554. https://doi.org/10.1007/s11259-023-10187-x\u003c/li\u003e\n \u003cli\u003eAdkins PRF, Middleton JR, Fox LK, et al (2017) Laboratory Handbook on Bovine Mastitis. National Mastitis Council, New Prague\u003c/li\u003e\n \u003cli\u003eBotrel M-A, Haenni M, Morignat E, et al (2010) Distribution and Antimicrobial Resistance of Clinical and Subclinical Mastitis Pathogens in Dairy Cows in Rh\u0026ocirc;ne-Alpes, France. Foodborne Pathog Dis 7:479\u0026ndash;487. https://doi.org/10.1089/fpd.2009.0425\u003c/li\u003e\n \u003cli\u003eCameron M, Saab M, Heider L, et al (2016) Antimicrobial Susceptibility Patterns of Environmental Streptococci Recovered from Bovine Milk Samples in the Maritime Provinces of Canada. Front Vet Sci 3:. https://doi.org/10.3389/fvets.2016.00079\u003c/li\u003e\n \u003cli\u003eDevriese LA, Laurier L, De Herdt P, Haesebrouck F (1992) Enterococcal and streptococcal species isolated from faeces of calves, young cattle and dairy cows. \u003cstrong\u003eJ Appl Microbiol\u003c/strong\u003e 72:29\u0026ndash;31. https://doi.org/10.1111/j.1365-2672.1992.tb04877.x\u003c/li\u003e\n \u003cli\u003eDuse A, Persson-Waller K, Pedersen K (2021) Microbial Aetiology, Antibiotic Susceptibility and Pathogen-Specific Risk Factors for Udder Pathogens from Clinical Mastitis in Dairy Cows. Animals (Basel) 11:2113. https://doi.org/10.3390/ani11072113\u003c/li\u003e\n \u003cli\u003eFusar Poli S, Freu G, Lohana L, et al (2024) \u003cem\u003eIn Vitro\u003c/em\u003e Evaluation of a Lime-Based Conditioner on the Growth of Udder Pathogens in Different Dairy Bedding Materials. Preprint. Available at SSRN: http://dx.doi.org/10.2139/ssrn.5188563\u003c/li\u003e\n \u003cli\u003eGao X, Fan C, Zhang Z, et al (2019) Enterococcal isolates from bovine subclinical and clinical mastitis: Antimicrobial resistance and integron-gene cassette distribution. Microb Pathog 129:82\u0026ndash;87. https://doi.org/10.1016/j.micpath.2019.01.031\u003c/li\u003e\n \u003cli\u003eGuimar\u0026atilde;es FF, Moraes GN, Joaquim SF, et al (2024) Identification of \u003cem\u003eEnterococcus\u003c/em\u003e spp. by MALDI-TOF mass spectrometry isolated from clinical mastitis and bulk tank milk samples. BMC Vet Res 20:378. https://doi.org/10.1186/s12917-024-04217-2\u003c/li\u003e\n \u003cli\u003eGuzman Prieto AM, Van Schaik W, Rogers MRC, et al (2016) Global Emergence and Dissemination of Enterococci as Nosocomial Pathogens: Attack of the Clones? Front Microbiol 7:788. https://doi.org/10.3389/fmicb.2016.00788\u003c/li\u003e\n \u003cli\u003eJuliano LCB, Gouv\u0026ecirc;a FLR, Latosinski GS, et al (2022) Species diversity and antimicrobial resistance patterns of \u003cem\u003eEnterococcus\u003c/em\u003e spp. isolated from mastitis cases, milking machine and the environment of dairy cows. Lett Appl Microbiol 75:924\u0026ndash;932. https://doi.org/10.1111/lam.13768\u003c/li\u003e\n \u003cli\u003eKlaas IC, Zadoks RN (2018) An update on environmental mastitis: Challenging perceptions. Transbound Emerg Dis 65:166\u0026ndash;185. https://doi.org/10.1111/tbed.12704\u003c/li\u003e\n \u003cli\u003eLaurentie J, Loux V, Hennequet-Antier C, et al (2023) Comparative Genome Analysis of \u003cem\u003eEnterococcus\u003c/em\u003e \u003cem\u003ececorum\u003c/em\u003e Reveals Intercontinental Spread of a Lineage of Clinical Poultry Isolates. mSphere 8:e00495-22. https://doi.org/10.1128/msphere.00495-22\u003c/li\u003e\n \u003cli\u003eLiu J, Liang Z, Zhongla M, et al (2024) Prevalence and Molecular Characteristics of Enterococci Isolated from Clinical Bovine Mastitis Cases in Ningxia. Infect Drug Resist 17:2121\u0026ndash;2129. https://doi.org/10.2147/IDR.S461587\u003c/li\u003e\n \u003cli\u003eNonnemann B, Lyhs U, Svennesen L, et al (2019) Bovine mastitis bacteria resolved by MALDI-TOF mass spectrometry. J Dairy Sci\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e102:2515\u0026ndash;2524. https://doi.org/10.3168/jds.2018-15424\u003c/li\u003e\n \u003cli\u003ePaschoalini BR, Nu\u0026ntilde;ez KVM, Maffei JT, et al (2023) The Emergence of Antimicrobial Resistance and Virulence Characteristics in \u003cem\u003eEnterococcus\u003c/em\u003e Species Isolated from Bovine Milk. Antibiotics (Basel)12:1243. https://doi.org/10.3390/antibiotics12081243\u003c/li\u003e\n \u003cli\u003eRodrigues DS, Lannes-Costa PS, Santos GS, et al (2022) Antimicrobial resistance, biofilm production and invasion of mammary epithelial cells by \u003cem\u003eEnterococcus faecalis\u003c/em\u003e and \u003cem\u003eEnterococcus mundtii\u003c/em\u003e strains isolated from bovine subclinical mastitis in Brazil. Lett Appl Microbiol\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e75:184\u0026ndash;194. https://doi.org/10.1111/lam.13718\u003c/li\u003e\n \u003cli\u003eRosa NM, Penati M, Fusar-Poli S, et al (2022) Species identification by MALDI-TOF MS and gap PCR\u0026ndash;RFLP of non-aureus \u003cem\u003eStaphylococcus\u003c/em\u003e, \u003cem\u003eMammaliicoccus\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e spp. associated with sheep and goat mastitis. Vet Res 53:84. https://doi.org/10.1186/s13567-022-01102-4\u003c/li\u003e\n \u003cli\u003eR\u0026oacute;żańska H, Lewtak-Piłat A, Kubajka M, Weiner M (2019) Occurrence of enterococci in mastitic cow\u0026rsquo;s milk and their antimicrobial resistance. J Vet Res\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e63:93\u0026ndash;97. https://doi.org/10.2478/jvetres-2019-0014\u003c/li\u003e\n \u003cli\u003eRuegg PL (2022) Realities, Challenges and Benefits of Antimicrobial Stewardship in Dairy Practice in the United States. Microorganisms 10:1626. https://doi.org/10.3390/microorganisms10081626\u003c/li\u003e\n \u003cli\u003eSong X, Huang X, Xu H, et al (2020) The prevalence of pathogens causing bovine mastitis and their associated risk factors in 15 large dairy farms in China: An observational study. Vet Microbiol\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e247:108757. https://doi.org/10.1016/j.vetmic.2020.108757\u003c/li\u003e\n \u003cli\u003eWerner B, Moroni P, Gioia G, et al (2014) Short communication: Genotypic and phenotypic identification of environmental streptococci and association of \u003cem\u003eLactococcus lactis\u003c/em\u003e ssp. lactis with intramammary infections among different dairy farms. J Dairy Sci 97:6964\u0026ndash;6969. https://doi.org/10.3168/jds.2014-8314\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"veterinary-research-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"verc","sideBox":"Learn more about [Veterinary Research Communications](https://www.springer.com/journal/11259)","snPcode":"11259","submissionUrl":"https://submission.nature.com/new-submission/11259/3","title":"Veterinary Research Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Enterococci, species identification, clinical mastitis, subclinical mastitis, full herd survey","lastPublishedDoi":"10.21203/rs.3.rs-6522723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6522723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eEnterococcus\u003c/em\u003e species are increasingly recognized as mastitis pathogens in dairy cows. Reliable species information is required for correctly defining the regional epidemiology of enterococcal mastitis, establishing its relationships with management variables, and understanding its impact on udder health. We investigated the species distribution of enterococci in bovine milk from subclinical mastitis (SCM) and clinical mastitis (CM) cases and full herd surveys (HS) using MALDI-TOF MS as identification method. A total of 21,864 milk samples from 106 dairy herds were routinely collected and analyzed according to the National Mastitis Council (NMC) guidelines over one year. \u003cem\u003eEnterococcus\u003c/em\u003e spp. were found in 4.86% of CM, 5.05% of SCM, and 1.37% of HS milk samples. Overall, \u003cem\u003eE. saccharolyticus\u003c/em\u003e was the most prevalent species (55.65%), followed by \u003cem\u003eE. faecium\u003c/em\u003e (27.25%), and \u003cem\u003eE. cecorum\u003c/em\u003e (5.51%), which showed a significant association with SCM (p-value\u0026thinsp;=\u0026thinsp;0.0128). This study provides novel data on the \u003cem\u003eEnterococcus\u003c/em\u003e species distribution in full herds survey and mastitis cases and highlights the relevance of MALDI-TOF MS in refining enterococcal mastitis epidemiology, whose prevalence may have been underestimated by conventional biochemical tests.\u003c/p\u003e","manuscriptTitle":"Enterococcus species identified by MALDI-TOF MS in milk from dairy cow mastitis cases and herd surveys","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 10:57:43","doi":"10.21203/rs.3.rs-6522723/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-25T17:06:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-18T09:42:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-13T21:48:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78400790495016520855835060633430033349","date":"2025-05-09T09:37:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297353534967686224548432755081973428621","date":"2025-05-05T23:17:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-05T17:50:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"234526409511255465698141323003011078611","date":"2025-05-05T17:38:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35056725153636790782508953912414315025","date":"2025-05-05T07:12:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"81737599710261411831068764301984581649","date":"2025-05-03T01:45:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-30T21:49:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T13:49:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-30T13:44:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Veterinary Research Communications","date":"2025-04-24T16:48:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"veterinary-research-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"verc","sideBox":"Learn more about [Veterinary Research Communications](https://www.springer.com/journal/11259)","snPcode":"11259","submissionUrl":"https://submission.nature.com/new-submission/11259/3","title":"Veterinary Research Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"334952fb-41f8-472e-aa8b-377c3e836ac6","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-28T16:10:42+00:00","versionOfRecord":{"articleIdentity":"rs-6522723","link":"https://doi.org/10.1007/s11259-025-10834-5","journal":{"identity":"veterinary-research-communications","isVorOnly":false,"title":"Veterinary Research Communications"},"publishedOn":"2025-07-23 15:58:07","publishedOnDateReadable":"July 23rd, 2025"},"versionCreatedAt":"2025-05-06 10:57:43","video":"","vorDoi":"10.1007/s11259-025-10834-5","vorDoiUrl":"https://doi.org/10.1007/s11259-025-10834-5","workflowStages":[]},"version":"v1","identity":"rs-6522723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6522723","identity":"rs-6522723","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}