# Longitudinal analysis reveals transitions in pathogens profiles associated with mastitis in dairy cows

Hélène Lirot, Laurent Crespin, Patrick Gasqui, Séverine Barry, Sébastien

Masseglia, Valerie Poux, Xavier Bailly, Anaïs Bompard

► To cite this version:

Hélène Lirot, Laurent Crespin, Patrick Gasqui, Séverine Barry, Sébastien Masseglia, et al.. Longitudinal analysis reveals transitions in pathogens profiles associated with mastitis in dairy cows. 2025. [https://hal-inrae.fr/hal-05044456v1](https://hal-inrae.fr/hal-05044456v1)

HAL Id: hal-05044456

https://hal.inrae.fr/hal-05044456v1

Preprint submitted on 23 Apr 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Longitudinal analysis reveals transitions in pathogens profiles associated with mastitis in dairy COWS

Hélène Lirot

helene.lirot@gmail.com

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques https://orcid.org/0009-0005-8691-427X

Laurent Crespin

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques

Patrick Gasqui

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques

Séverine Barry

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques

Sébastien Masseglia

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques

Valérie Poux

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques

Xavier Bailly

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques

Anaïs Bompard

INRAE UMR 0346: Epidémiologie des maladies Animales et zoonotiques

## Research Article

**Keywords:** Mastitis, Pathobiome, Dynamics, Pathogens, Corynebacterium bovis, Streptococcus uberis

**Posted Date:** April 14th, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-6393953/v1

**License:** © This work is licensed under a Creative Commons Attribution 4.0 International License.
Read Full License
**Abstract**

Mastitis is a multifactorial infection of the udder potentially caused by many pathogens of varying severity and prevalence. They are one of the most common diseases on dairy farms. Limited information exists about the interactions between pathogens in mastitis infections. The first aim of this study was to investigate the dynamics of pathogens and their associations in the udder microbiota. We then explored the impact of these dynamics on mastitis risk, in order to gain a better understanding of this disease. We finally looked for factors influencing these dynamics and the potential sources of the studied pathogens at farm level. To address these objectives, two four-month longitudinal studies were conducted on cows of six dairy farms in the Auvergne region of France. Milk and faeces were collected from 33 cows, along with environmental samples (bedding and milk filter). The 16S DNA in these samples was analysed, using a commercial qPCR kit (PathoProof™) to detect 15 mastitis-causing pathogens. The data were then processed using principal component analysis, the Ward clustering method and discrete-time Markov chain models. We classified pathogens according to their distribution in the different compartments. Clustering analyses of milk quarter samples revealed distinct profiles of pathogen distribution associated with different somatic cell counts and cow recovery dynamics. According to these dynamics, *C. bovis*, though considered a minor pathogen, and *S. uberis* were pivotal in the definition of milk pathogen profiles, with the possible involvement of non-*aureus* staphylococci.

**Introduction**

In dairy cattle farming, mastitis is considered to be the most frequent infectious disease [1]. These infections of the udder lead to significant economic losses due to a reduction in the quantity and quality of milk production, and an increase in antibiotic treatments, resulting in significant economic losses for farmers [1, 2]. Because mastitis can become chronic and the causing pathogens can be transmitted to other cows [3], the management of mastitis often involves early culling of infected cows [4].

Mastitis is a multifactorial disease influenced by cow-level factors and environmental factors. At the individual level, cattle genotype, lactation stage and number of lactations, udder characteristics and immune response all have an impact on mastitis risk [5]. There are also factors related to the farming environment that include breeding practices, such as milking equipment and hygiene, bedding, feed and litter [6]. Litter is the main source of environmental pathogens responsible for mastitis. The number and composition of bacteria in litter varies according to the type of material used [7, 8]. In addition, one study has highlighted the potential impact of bedding type on the bacterial composition of milk [9].

Mastitis are categorized in two different forms: (1) clinical mastitis, identifiable by visible symptoms such as redness and swelling of the udder, presence of clots in the milk, and signs of fatigue and fever of the cow; and (2) subclinical mastitis, which do not manifest in obvious signs of infection by a pathogen, either in terms of clinical udder presentation or in milk consistency, but can be detected through high milk somatic cell counts [10]. A somatic cell count (SCC) below 200,000 cells/mL of milk is commonly used to define healthy udder quarters, while higher counts indicate mastitis [11, 12]
Somatic cells are made up of epithelial cells and leukocytes (white blood cells) usually present in milk in low quantities and their number increases in the milk in case of infection by a pathogen [12, 13]. The count of somatic cells is influenced by various factors, for example lactation stage, parity, and milk production [13]. This somatic cell count is further used in the dairy industry to modulate the purchase price of milk to farmers [2]. It is therefore important to describe the dynamics of somatic cell count induced by different pathogens, as well as the impact of this immune response on pathogen development.

Mastitis-causing pathogens can be classified into two categories according to pathogen origin and infection pathway: environmental, originating from the farm environment, and contagious, which can spread between cows [14]. Mastitis is caused by a diverse range of pathogens of varying pathogenicity and prevalence. *Staphylococcus aureus* is one of the main causative agents in France [15]. This contagious pathogen can result in chronic infections and form biofilms, reducing its sensitivity to antibiotic treatment and complicating control measures [16]. *S. aureus* could promote later infections by other pathogens [17, 18]. *Streptococcus uberis*, which is considered as a less severe mastitis-causing pathogen than *S. aureus*, is increasingly detected in France [19]. This environmental pathogen can also behave as a contagious pathogen, it can be found in healthy udders as well as in symptomatic udders [20]. Alongside with *S. aureus* and *S. uberis*, *Escherichia coli* is considered as another major mastitis associated pathogen [21, 22]. Additionally, several other pathogens, including *Corynebacterium bovis* and coagulase-negative staphylococci, are considered to contribute to a lower extent to mastitis [23, 24].

In addition, microbial interactions may play a role in the triggering and development of mastitis. Antagonistic relationships were described between various mastitis-causing pathogens. Cross-inhibitory relationships have been detected in milk microbiota between *S. aureus* and *Staphylococcus* spp. [25]. *Staphylococcus* spp. secrete antimicrobial substances that inhibit the development of some pathogens [26]. Experimental infections have shown that *Corynebacterium bovis* significantly reduces the proliferation of *S. aureus* [27]. The study of interactions between pathogens would thus be relevant for understanding mastitis occurrence and fate. However, few studies took into account several mastitis-causing pathogens: most focus on one or two specific pathogens. Examination of a wide range of mastitis-causing pathogens as well as the mammary microbial diversity would make it possible to identify interaction mechanisms.

While studies have been carried out to identify either mastitis-causing pathogens, or factors influencing mastitis risk, few examined both together and used a longitudinal framework to understand how they impact mastitis dynamics through time. The udder of cows is a complex and multifactorial environment [20], which makes it difficult to control all parameters when conducting cross-sectional studies. By contrast, a longitudinal follow-up approach facilitates comparison of samples over time for each individual cow and the detection of interactions between pathogens based on co-fluctuation of their abundances, giving more clues on causal links between pathogen dynamics and discrete or continuous factors (e.g. somatic cell counts, temperature, etc.). Identification of the mechanisms that influence
these temporal dynamics is yet key to understanding mastitis development and to propose management methods.

The objectives of this study were (1) to identify the potential sources of mastitis-causing pathogens at the farm level, (2) to analyse the associations and dynamics of these pathogens over time in the udder microbiota, (3) to assess their impact on mastitis risk and somatic cell count fluctuation and (4) to identify factors influencing these dynamics.

Two twice-weekly longitudinal follow-ups for 16 weeks were carried out in cows of several dairy farms in Auvergne (France). Milk and faeces from the cows and environmental samples (bedding and milk filter) were analysed using a commercial kit to detect the presence and quantity of 15 mastitis pathogens.

Analysis of the results of this study, using PCA, enabled us to identify the pathogens responsible for mastitis in the cow's environment. In addition, cluster analysis of milk quarter samples revealed pathogen profiles and cow recovery dynamics.

# Material and methods

## Sample collection

Two independent 16-week longitudinal studies were carried out between November and March, one in 2021–2022 and the second one in 2022–2023 (Fig. 1).

Each follow-up included three commercial dairy farms of Auvergne, France. On each farm, two or three multiparous cows with a low somatic cell count (SCC) and two or three multiparous cows with a high SCC were included in the study. Cows were selected according to their average cell count, based on the farmer's reports. In addition, the cows were selected to ensure they will remain in the lactation period throughout the entire sampling period, with no calving anticipated during this time frame. The cows belonged to three different breeds: Holstein (n = 21), Montbeliarde (n = 5) and Jersey (n = 7). A total of 33 lactating cows were followed twice a week during this period. On each sampling day, milk and faeces were collected from each cow, as well as milk filter and bedding from the farm (Table 1 & additional file 1).
<table><caption>Table 1
- Characteristics of cows between sampling 1 and 2</caption><thead><tr><th>Variable</th><th>Sampling 1</th><th>Sampling 2</th></tr></thead><tbody><tr><td>Farms number</td><td>3</td><td>3</td></tr><tr><td>Cow number</td><td>15</td><td>18</td></tr><tr><td>Parity</td><td>Min: 2; Max: 9; Mean: 5.2; Median: 5.5</td><td>Min: 2; Max: 7; Mean: 3.77; Median: 3.5</td></tr><tr><td>Breeds</td><td>5 Holstein, 5 Jersey, 5 Montbeliarde</td><td>16 Holstein, 2 Jersey</td></tr><tr><td colspan="3">Parity values (the number of lactations per cow) are reported as minimum, maximum, mean, and median for each year. Breed composition is detailed.</td></tr></tbody></table>

<caption>Table 2 - Characteristics of cows between sampling 1 and 2</caption>

<table><caption>Table 2
- Definition of states for the Markov model.</caption><thead><tr><th>States</th><th>Definition</th><th>R pseudo code#</th><th>Sample size</th></tr></thead><tbody><tr><td>A*</td><td>Absence of the 15 pathogens</td><td>C_bovis == 0 &amp; E_coli == 0 &amp; Entero_sp == 0 &amp; Klebsie_sp == 0 &amp; M_bovis == 0 &amp; M_sp == 0 &amp; NAS == 0 &amp; Proto_sp == 0 &amp; S_aureus == 0 &amp; S_agala == 0 &amp; S_dysgala == 0 &amp; S_uberis == 0 &amp; Yeasts == 0 &amp; T_pyo_P_indo == 0</td><td>252</td></tr><tr><td>B*</td><td>Presence of NAS and absence of C. bovis, S. uberis, yeasts and T. pyogenes/P. indolicus</td><td>NAS > 0 &amp; C_bovis == 0 &amp; Str_uberis == 0 &amp; Yeasts == 0 &amp; T_pyo_P_indo == 0</td><td>238</td></tr><tr><td>C*</td><td>Presence of C. bovis and S. uberis</td><td>C_bovis > 0 &amp; Str_uberis > 0</td><td>55</td></tr><tr><td>D*</td><td>Presence of C. bovis and absence of S. uberis</td><td>C_bovis > 0 &amp; Str_uberis == 0</td><td>184</td></tr><tr><td>E*</td><td>Presence of S. uberis and absence of C. bovis</td><td>Str_uberis > 0 &amp; C_bovis == 0</td><td>95</td></tr><tr><td>F*</td><td>Presence of T. pyogenes/P. indolicus and/or yeasts, and absence of C. bovis and S. uberis</td><td>(Yeasts > 0 | T_pyo_P_indo > 0) &amp; C_bovis == 0 &amp; Str_uberis == 0</td><td>145</td></tr></tbody></table>

Milk sampling was performed during morning milking. The first milk sprays (approximately 4 mL) from a single teat were collected in a sterile 5 mL tube by hand milking, after the usual cleaning protocol of the
farmer and ethanol disinfection of the targeted teat. The same teat was used for sampling throughout the study period. The appearance of milk and the udder was recorded at each sampling. In addition, once a week, a second milk sample from the same teat was collected for somatic cell count analysis.

Cow faeces were collected non-invasively, i.e. the sample was taken immediately after the cow defecation. The faeces sample was collected with a sterile single-use spoon and transferred in a sterile 120 mL container. The appearance and consistency of the faeces were recorded for each sample.

One bedding sample was collected per farm on each sampling day, using a sterile sampling sock (StériSox® (SodiBox, Névez, France)) fitted onto a weighted boot covered with a sterile over-boot. The boot and Stérisox was applied at ten points on the cow bedding area, then placed in a sterile pouch supplied with the kit.

At the end of the morning milking, the milk filter from the milking machine, through which all the herd milk has been filtered for a given milking, was supplied by the farmer, and recovered in a sterile pouch. In Farm no. 5, where a milk filter was not available, 300 mL of bulk tank milk was sampled instead, using a disinfected measuring cup, and stored in sterile tubes.

All samples were transported in a cool box to the laboratory, then placed at 4°C within two hours of collection.

On each sampling day, ambient temperature and humidity conditions are measured in the air where the cows were lying down.

## Farm management

The farm's general husbandry practices were identified prior to the sampling period. In addition, on each sampling day, a questionnaire was completed on the farm's breeding practices. The farmer was asked questions about the various changes that have taken place on the farm (i.e. changes in bedding, feed, treatment) and on the health status of the cows (i.e. symptoms, treatment). Additional general information on selected cows was also collected, such as breed, date of last calving and parity.

## Laboratory analysis

Somatic cell counts in milk samples were determined by the specialised service provider Agrolabs (Clermont-Ferrand, France). The number of somatic cells was measured weekly throughout the sampling period.

## Sample preparation
The milk samples were placed at -20°C on the day of sampling, for direct DNA extraction. Solid samples (faeces, milk filter) were suspended in PBS 1X (1 mL of PBS 1X for 1 g of sample), then homogenised by grounding in a BAGMIXER 400 S® for 30 seconds at speed 3 (8 strokes/s) on the day of sampling. For the litter bedding samples, the bedding collection over-sock were placed in 40 mL of PBS 1X and homogenised by grounding with a BAGMIXER 400 S® for 30 seconds at speed 3 (8 strokes/s) on the day of sampling. The resulting solutions were then stored at -20°C until DNA extraction.

Tank milk was stored at -20°C after collection. Four 50-mL tubes of tank milk were aliquoted into Falcon tubes for each sample. Tubes were centrifuged for 30 minutes at 5,250 g at 4°C. The supernatant was discarded, and the pellet recovered in 1 mL PBS 1X for all four tubes. To do this, the first pellet was resuspended, then this liquid was used to resuspend the second, and so on. This solution was centrifuged for 5 minutes at 13,000 g at 4°C, and the resulting pellet was resuspended in 500 µL of PBS 1X. The resulting solution was used for DNA extraction on the same day.

## DNA extraction

DNA extractions were performed from ZymoBIOMICS™ DNA Miniprep Kit. For each set of 24 extractions, a negative control (PBS 1X) and a positive control was used.

In the case of faeces and bedding extractions, the positive control consisted of an aliquot of faeces collected from different cows and farms on the first day of sampling. Similarly, for milk and filter samples, the positive control consisted of an aliquot of milk. Positive controls were aliquoted to ensure one tube per extraction run. These positive controls were repeated between the two sampling years.

For milk and filter samples, 250 µL of the sample was added to a ZR Bashing Bead Lysis Tube, followed by 750 µL of ZymoBIOMICS Lysis Solution and 20 µL of Proteinase K (20 µg/µL). The mixture was vortexed for 5 seconds and incubated overnight at room temperature. After incubation, the solution was ground using a Precellys® at 9,000 rpm for 1 minute, repeated four times with a 2-minute break between each.

For faecal samples (faeces and bedding), 150 µL was mixed with 120 µL of PBS 1X in a ZR Bashing Bead Lysis Tube. A lysis solution of 750 µL ZymoBIOMICS Lysis Solution was added, followed by homogenization using a Precellys at 9,000 rpm for 1 minute, repeated four times with a 2-minute break between each.

The lysate for all samples (milk, filter, faeces, and bedding) was centrifuged at 8,000 g for 1 minute. For filtration of the lysate, 400 µL of the supernatant was filtered using a Zymo-Spin™ III-F Filter and centrifuged for 1 minute at 8,000 g.

DNA purification was carried out according to the extraction kit protocol.
To eliminate any potential inhibitors in the eluted DNA of faecal samples (faeces and bedding), a Zymo-Spin™ III-HRC Filters column was used with 600 µL of ZymoBIOMICS™ HRC Prep, followed by centrifugation (3 min at 8,000 g). The eluate DNA was then transferred to a Zymo-Spin™ II-µHRC Filters and centrifuged at 16,000 g for 3 minutes. The ready-to-use DNA is stored at 4°C, while long-term storage occurs at -20°C.

## DNA quantification

The quantification of 16S rRNA genes was performed using qPCR with the SsoAdvanced Universal SYBR® Green Supermix kit (BioRad, Ref: 1725272) on a CFX96 from Biorad. The qPCR mixture for a single sample consisted of the following components: 10 µL of SYBR Green Mix, 0.75 µL of primer 515F (20 µM), 0.75 µL of primer 928R (20 µM), 3.5 µL of ultrapure water, and 5 µL of DNA extract solution. The qPCR program for amplifying the 16S rRNA genes included an initial denaturation step at 95°C for 30 seconds, followed by 40 cycles of denaturation at 95°C for 30 seconds and a hybridization step at 60°C for 3 minutes. A melting curve analysis was also conducted to confirm the specificity of the amplification.

## Pathogens detection

DNA extracts were analysed using ThermoFisher's "PathoProof_Complete-16" PCR kit (Thermo Fisher Scientific, 2015) on an applied Biosystems™ QuantStudio device. This PCR semi-quantitatively detects the presence of the staphylococcus β-lactamase gene (penicillin resistance gene) and 15 mastitis pathogens:

- Corynebacterium bovis

- Escherichia coli

- Enterococcus spp. (Including faecalis and faecium)

- Klebsiella spp. (Including oxytoca and pneumonia)

- Mycoplasma bovis

- Mycoplasma spp.

- Prototheca spp.

- Serratia marcescens

- Staphylococcus aureus
- *Staphylococcus* spp.

- *Streptococcus agalactiae*

- *Streptococcus dysgalactiae*

- *Streptococcus uberis*

- *Trueperella* (Arcanobacter) *pyogenes* and/or *Peptostreptococcus indolicus* (these pathogens cannot be distinguished and are therefore noted *T. pyogenes/P. indolicus*)

- Yeasts

The Thermo Scientific™ PathoProof™ Norden Lab Studio™ software provides semi-quantitative data categorised as +++, ++, +, +/-, and 0, along with CT values. To obtain quantitative data, two scaling approaches were used. The first approach was based on positive controls from the kit: dilution series were prepared using these controls to establish a CT range from 15 to 38. The second approach involved dilution series of DNA extracts from *Staphylococcus aureus* and *Staphylococcus xylosus* strains (ranging from 10 genome copies/µL to 10^6 copies/µL) to differentiate between *Staphylococcus aureus* and non-*aureus* staphylococci species (hereafter NAS). The *Staphylococcus* spp. DNA was provided by Dr. Sabine Leroy, who performs research on these bacteria at the Microbiology, Digestive Environment, Health (MEDIS) University Clermont Auvergne - INRAE research unit, France.

## Statistical analysis

All analyses were performed with R version 4.2.2.

## Pathogen copy number approximation transformation

Based on the calibration ranges, the slope and intercept of the relationship between DNA amounts and CT values were calculated for each pathogen, using an arbitrary value of 10 copies for the last point in the range and the copy number determined from the concentrations for the S. aureus and S. xylosus samples. The CT values were then transformed using the following formula.

$$ \textit{Number of copies} / rxn = 10^{\left(\frac{CT - \text{intercept}}{\text{Slope}}\right)} $$

(1)

To separate the data for non-*aureus* staphylococci from that of *Staphylococcus aureus* in the results of *Staphylococcus* spp. quantification, we assumed that $\Delta CT = CT_{SP} - CT_{SA}$, $X_{SP} = 2^{-\Delta CT} * X_{SA}$ and $X_{SP,SA} = X_{SA}$. This allowed us to calculate the CT values corresponding only to non-*aureus* staphylococci when the CT values for *Staphylococcus* spp. were lower than those for *S. aureus*:
$$ (2) $$

where SP refers to *Staphylococcus* spp., SA to *Staphylococcus aureus*, SP,SA to *Staphylococcus aureus* detected by qPCR for *Staphylococcus* spp., and SP,NA to the detection for *Staphylococcus* spp. corresponding only to non-*aureus* staphylococci. The variable α represents the slope for the detection of the *S. aureus* strain. The CT values were then processed in the same manner as those directly provided by the kit.

The number of copies per reaction was then adjusted to take account of the sample volume used for DNA extraction (250 µL for milk and milk filter samples, and 150 µL for faeces and bedding samples). Thus, the results obtained represent an estimate of the number of copies per millilitre of sample.

## Somatic cells

Linear temporal interpolation was used to estimate missing data in somatic cell count, using the approx() function in the stat package ("linear" method).

## Principal component analysis

A principal component analysis (PCA) was conducted on the quantities of 14 pathogens detected in milk (n = 1008), milk filters (n = 171), faeces (n = 917), and bedding samples (n = 189), after transforming the values using the natural logarithm + 1. *M. bovis* had been removed from the analysis as it was absent from all the samples. The R package FactoMineR was used with the function PCA() to perform principal component analysis, and graphical representations were created using the plot.PCA() function

## Clustering milk samples

Clustering was performed on the quantities of pathogens detected in the milk samples (n = 1008), after transforming the values into natural logarithm + 1. To do this, a matrix of Euclidean distances was calculated using the dist() function in the stats package ("euclidean" method). Several approaches were used to determine the optimal number of clusters: the sum-of-squares curve within assumed clusters, the silhouette index and the gap statistics using the fviz function in the factoextra software package (see additional file 2). Hierarchical agglomerative clustering was then performed using the Ward method [28] implemented in the hclust() function with "ward.D2" option in the same package following [29]. The results were visualized using a multidimensional ladder representation, obtained with the cmdscale() function (stats package). The clusters were characterized by calculating the mean, median, minimum, maximum, and standard deviation of the quantity of each pathogen (see additional file 3). Clustering was performed several times, randomly removing 10, 15 and 20% of the samples, in order to assess the robustness of the clusters to sampling variation (see additional file 4).

## Markov Chain modelling
The profiles obtained from the clustering were used to define states for Markov Chain modelling (either based on raw clusters or on more stringent homogenous infection profiles as described in additional file 5). The transitions between these different Markov chain states were identified by a discrete-time Markov chain-based fit, using the markovchainFit() function from the markovchain package ("mle" method) [30] (see additional file 6). The time step of the Markov chain corresponds to the sampling interval, which is approximately 3 to 4 days. The transition matrix was graphically represented using Python (version 3.11.0) and the pandas, networkx, and matplotlib libraries. To assess the effect of somatic cells on the transition between states, t-tests were performed. Somatic cell growth rates were calculated between the transition day and the previous sampling, as well as between the two sampling points preceding the transitions.

# Results

## Bacterial composition and comparison of different farm matrices

The most frequently detected pathogen in milk samples were non-*aureus* staphylococci (NAS) (in 55.6% of samples) and *Corynebacterium bovis* (23.7%). In contrast, NAS (90.3%) and yeasts (93.5%) were predominant in milk filter samples, and both taxa were consistently present in all tank milk samples. Regarding bedding samples, the most prevalent pathogens were yeasts, NAS and *T. pyogenes/P. indolicus* detected in over 95% of samples. Similarly, faecal samples exhibited high prevalence rates for yeasts, *T. pyogenes/P. indolicus*, and *Escherichia coli* (above 80%), while NAS was present in 79% of the samples.

The distribution of pathogens varied across different sample matrices, revealing distinct patterns (Fig. 2). Some pathogens, such as *Staphylococcus aureus*, *Streptococcus dysgalactiae*, and *Streptococcus uberis*, were mainly found in milk filter and bedding samples, but rarely detected in milk and faecal samples (< 16%). In contrast, *E. coli*, *T. pyogenes/P. indolicus*, NAS, and yeasts were frequently identified in bedding and faeces (≥ 75% of samples) and were also present in over 50% of milk filter samples. Moreover, *C. bovis* was present only in milk filters (89.6%) and in milk samples (23.7%). This pathogen was not detected in tank milk samples but was present in only one milk sample from farm 5. Only 17 samples of tank milk were analysed, as farm 5 did not have a milk filter. The pathogen composition of the milk filters varied considerably from farm to farm. For example, *S. aureus* was detected in only one sample from farm 1, whereas it was found in 100% of the samples from farm 4.

In addition, 25% of milk samples were pathogen-free, compared with 4.5% for milk filter samples and less than 3% for tank milk, bedding and faeces samples. Milk samples contained fewer pathogens per sample, with 66% containing between one and three pathogens, whereas 83.2% of milk filter samples contained between six and eleven pathogens, and 89% of tank milk samples had between three and seven. Similarly, 74% of faecal samples contained between four and six pathogens, while 82% of bedding samples contained between six and nine.
Principal Component Analysis (PCA) of pathogen quantity data across all samples revealed differences between microbial communities associated with sampled compartments on the farms (Fig. 3). The first axis differentiated milk samples from other matrices (bedding, faeces, and milk filters), with milk samples negatively correlated along this axis, in contrast to the positive correlations observed for the other matrices (Fig. 3a). This axis was primarily associated with the presence of yeasts (Pearson's coefficient of correlation: r = 0.84, p-value < 0.001), *T. pyogenes/P. indolicus* (r = 0.83, p-value < 0.001), *E. coli* (r = 0.73, p-value < 0.001), and NAS (0.68, p-value < 0.001). The second axis distinguished faecal samples from other sampling matrices, with faeces showing a negative correlation with this dimension. This axis was mainly structured by the presence of *C. bovis* (r = 0.71, p-value < 0.001) and *S. uberis* (r = 0.62, p-value < 0.001) (Fig. 3b).

The circle of variable contributions identified different groups of pathogens: firstly, *C. bovis* appeared in isolation. Next, *S. uberis*, *S. aureus* and *S. marcescens* clustered together, with *S. dysgalactiae* positioned nearby. NAS and *Enterococcus* spp. grouped together. *T. pyogenes/P. indolicus*, yeasts, *E. coli* and *Prototheca* spp formed another group. *Klebsiella* spp., *Mycoplasma* spp. and *Streptococcus agalactiae* were poorly represented in the first two dimensions of the factorial plane as indicated by the short length of their arrows. However, they played a structuring role in the following dimensions, 3 (7.44% of the total variance), 4 (7.19% of the total variance) and 5 (7.06% of the total variance) respectively.

The effect of the sampling session, represented by the farms (1 to 3 for the first session and 4 to 6 for the second session), was far less important in this analysis than the effect of the sampling matrices. The milk filter samples were very isolated though scattered in the first factorial plane. The tank milk samples from farm 5 were located at the intersection of the milk filter, bedding and faeces samples.

## Pathogen profiles in milk samples

To identify the main associations of pathogens in the milk samples, a clustering analysis was carried out. Several approaches were used to determine the optimal number of clusters. Based on statistical methods and taking into account biological relevance, we selected to partition the samples into 6 clusters for further analysis (see additional file 2).

Hierarchical clustering was performed on pathogen data from milk samples. After calculating a Euclidean distance matrix based on pathogen quantities, hierarchical agglomerative clustering was performed using Ward's method [28]. This approach grouped the samples according to the chosen number of clusters, as illustrated by the dendrogram and multidimensional representation (Fig. 4).

These six clusters were characterized by specific pathogen compositions and associated cellular responses (Fig. 5). Profiles A, B, E and F included samples from all the farms. Farm 5 had no samples corresponding to profile C and only one corresponding to profile D, because this farm had only one sample containing the specific and structuring pathogen for these profiles (see below).

A full description of the pathogens present in each profile can be found in additional file 3.
Profile A included samples with few pathogens (between 1 and 3 pathogens, n = 43, i.e. 15%), or no pathogens (n = 252, i.e. 85%). This profile includes all samples in which no pathogens were detected. These samples had a median cellular response of 15 (Interquartile range (IQR): 8, 56) x 1000 cells/ml for the analysed quarter of the udder. Some samples exhibited very high somatic cell counts (SCC) within this profile. A total of 15.3% (n = 45) of the samples had SCC exceeding 200 x 1000 cells/ml. Among these, 19 samples contained *S. aureus*, 25 were pathogen-free, and one sample was associated with *E. coli*. Notably, 82.2% (37 samples) of profile A samples with over 200 x 1000 cells/mL originated from Farm 2.

Profile B grouped 250 samples, of which 202 (81%) contained at least one pathogen. NAS is the main pathogen structuring this group (n = 230, i.e. 92%). This profile also included 26 samples with *S. dysgalactiae* and 22 with *Enterococcus* spp.. The median cellular response associated with these samples was 28 (IQR: 12, 305) x 1000 cells/ml for the quarter analysed.

Profile C contained 47 samples showing both *C. bovis* and *S. uberis*. A majority of the samples in this profile contained NAS (n = 36, i.e. 76.6%). *S. aureus* was present in nine samples and yeasts in nine samples. SCC in this group was very high, with a median cell response of 931 (IQR: 257, 1200) x 1000 cells/ml.

Profile D included 192 samples, all of which contained *C. bovis*. Among these, some also contain NAS (n = 112, 58%). Additional microorganisms were detected at a lower prevalence in this group: yeasts (25%), *S. aureus* (22.4%), *S. dysgalactiae* (16.7%), *T. pyogenes/P. indolicus* (14.1%). These samples showed a median cellular response of 97 (IQR: 27, 768) x 1,000 cells/mL, with 40% (n = 78) exhibiting somatic cell counts exceeding 200,000 cells/mL. Among these 78 samples with elevated somatic cell counts, *S. aureus* was present in 30 samples, *S. dysgalactiae* in 20, and both pathogens in nine.

Profile E comprises 82 samples, all carrying *S. uberis*, with 55, (67%) also containing NAS. The median cellular response associated with these samples was 28 (IQR: 14, 463) x 1000 cells/ml, with 37.8% of samples in this group showing a somatic cell count greater than 200,000 cells/mL.

Finally, profile F was characterized by the presence of NAS, *T. pyogenes/P. indolicus* and/or yeasts. 76% (n = 108) of the samples in this profile contained NAS. Among these samples, 30.3% contained only *T. pyogenes/P. indolicus*, 47.9% contained only yeasts and 21.8% contained both pathogens. When we partition the data into 7 clusters, this pathogen profile splits into two depending on the presence of yeast or *T. pyogenes/P. indolicus*. These samples showed a median cellular response of 23 (IQR: 12, 204) x 1000 cells/ml.

*S. aureus* was found in 110 milk samples, with an average somatic cell count of 860 (351, 1500) x 1000 cells/mL. It appeared in all the profiles identified by clustering. The pathogen was absent in 11 cows and found in isolation samples in 18 cows. One cow had two consecutive positive samples. In cow 2C, it was detected in 16 out of 30 samples, with up to 8 consecutive positive samples. In cow 2E, it was present in
24/30 samples, with a maximum of 12 consecutive samples. Finally, in cow 4D, it was detected in 30/32 samples, persisting for up to 21 consecutive samples.

In order to assess the robustness of our clustering analysis, we repeated it after randomly removing 10%, 15%, and 20% of the samples (10 analysis at each level) (see additional file 4). Of the 30-clustering analysis with partial data, 21 (i.e., 70%) led to clusters with the same characteristics, and an average of only 4.9% of samples were classified differently (with proportions of 2.6%, 5.1% and 6.7%, for the elimination of 10%, 15% and 20% of the data respectively). When clusters differed, the profile composed of NAS, yeasts, *T. pyogenes/P. indolicus* (profile F) was split into two clusters according to the presence of *T. pyogenes/P. indolicus* on the one hand and yeasts on the other. The profiles separated by *S. uberis* presence were sometimes merged (profiles C and D, in 5 cases; profiles B and E, in 4 cases).

Comparison of the total 16S rRNA genes copies with the sum of the estimated amounts of pathogens detected by the commercial PathoProof™ kit illustrates the occurrence of bacteria non-targeted by the qPCR detection test in our samples (Fig. 6).

The data from the faeces, milk filter and bedding samples showed similar quantities of pathogens, but faeces and bedding revealed a greater total bacteria abundance (16S), suggesting a larger amount of non-pathogenic bacteria.

In milk samples, the relationship between pathogen quantities and total bacteria abundances varied between groups. In profiles C and D, (*C. bovis* and NAS, with or without *S. uberis*) pathogen abundance is closely associated with the amount of 16S DNA. On the other hand, profiles A and B, which were characterized by low somatic cell count and fewer pathogens, showed larger quantities of 16S DNA than explained by pathogen abundance, suggesting the presence of substantial amounts of non-pathogenic bacteria.

# Patterns in pathogen dynamics

In order to study pathogen dynamics, we used a discrete-time Markov chain model [31] that allowed samples to transition at each time step between, either initial pathogen profiles or more stringent homogeneous states. States were defined at the sample level based on the pathogen profiles from the clustering, using the main characteristics of each profile (presence/absence of the structuring pathogen (Table 2).

## Table 2 - Definition of states for the Markov model.

Using this stringent homogeneous state definition, 84 samples (8.33%) were classified differently relatively to the initial clustering (see additional file 5). 39 samples were unclassified and considered as missing values in the Markov model which means that all transitions from and to these samples were ignored (see additional file 5). Using the discrete-time Markov chain on the homogeneous state or on the
initial clusters resulted in similar transition matrices (see additional file 6). The following results are
based on the homogeneous states definition.

For most of the states, the probability of remaining in the same state (hereafter persistence probability)
was greater than the probability of transitioning to another state. The highest persistence probabilities
were observed for the states characterized by a high *C. bovis* frequency, with a probability of 0.69 for the
C* state and 0.79 for the D* state, with and without *S. uberis* respectively (see additional file 5). For *C.
bovis* and *S. uberis* (C*) infections, samples came from 11 different cows, but 49% (n = 27) of these
samples came from the same cow. All milk samples from this cow were associated with C* status, with
the exception of two samples corresponding to E* and D* status. Thus, the probability of persistence of
this state should be interpreted with caution. The persistence probability of state A* was 0.48, while the
persistence probability of state B* was 0.47. In contrast, states E* and F* had the lowest persistence
probabilities, with values of 0.33 and 0.34, respectively. Udders were equally likely to transition from
state E* (with NAS and *S. uberis*) to state B* (with only NAS) than they were to remain in state E*.

Transitions between states were heterogeneous and structured. The states with the lowest persistent
probabilities, E* (NAS and *S. uberis*) and F* (NAS, *T. pyogenes*/*P. indolicus* and yeasts), frequently
transitioned towards the pathogen-free state A* or the NAS-only state B*, with probabilities greater than
0.20 (Fig. 7). When NAS were present in samples associated with the E* state, transitions were
preferentially towards the B state (24 transitions out of 38 total transitions between E* and other states).
On the other hand, in the absence of NAS, transitions were mainly between E* and A* (12 transitions out
of 22 total transitions). It was more likely to lose *T. pyogenes*/*P. indolicus* and/or yeasts in the presence
of NAS than to acquire these pathogens, the transition from state F* to B* being more frequent than
transition from state B* to F*. The probability of losing NAS and moving to a pathogen-free state
(transition from state B* to state A*) was also large (0.22). The probability of acquiring *C. bovis* (mostly
transition from state B* to state D* and from state E* to state C*) was very low (less than 0.03). The
probability of losing *S. uberis* and moving from state C* to state D* was large (0.25).

Pathogen-free states (A*) and those with only NAS (B*) were the most frequent over time, with
probabilities from the stationary distribution of 0.25 for state A* and 0.25 for state B* (see additional file
5). State D* was also frequent, with a probability from the stationary distribution of 0.20. State F* had a
lower probability from the stationary distribution of 0.14. Finally, states C* and E*, the two states
containing *S. uberis*, were the least frequent, with probability from the stationary distribution of 0.07 and
0.10 respectively.

During the sampling period, 14 antibiotic treatments were administered to 9 cows, 7 of which were
intramammary treatments for mastitis. Out of 439 state transitions, 6 occurred during sampling
following antibiotic treatment. Seven antibiotic treatments did not induce any change in state (Fig. 8).
Antibiotic treatments were used when the cows were in different pathogenic states. Four treatments
were administered when the cows were in state C*, and in one case, the cow transitioned to profile A*.
When a treatment was applied to a cow in state A*, in 2 out of 3 cases, the cow acquired pathogens following the treatment.

To test if the growth rate of somatic cells would be higher just before a transition to state A*, t-tests were conducted on somatic cell growth rates. When comparing the averages between the day of transition to state A* (n = 128) and the previous day, no significant difference was found compared to other observed transitions (n = 374) (t-test, t = -0.48, df = 389.31, p-value = 0.63). Additionally, somatic cell growth between the two sampling points before the transition to state A* (samples - 1 and - 2) was not significantly different (t = 0.94, df = 160.37, p-value = 0.35). No consistent pattern in somatic cell growth rates was observed across the different transitions between states (t test: p-value > 0.05).

# Discussion

## Sources of exposure to mastitis pathogens?

A study of the distribution of pathogens between the different compartments of the farm helped identify different possible sources of contamination for cows, depending on the pathogen.

Firstly, some pathogens are found in the environment and may originate from cattle feces. *Trueperella pyogenes* and/or *Peptostreptococcus indolicus* (these pathogens could not be distinguished using the Pathoproof kit and are therefore noted *T. pyogenes/P. indolicus*), *Escherichia coli*, *Prototheca* spp. and yeasts were present in large proportions in faeces and bedding samples. The similar distribution of these pathogens in the farm compartments is also illustrated by the principal component analysis. *Enterococcus* spp. are also pathogens mainly present in faeces, and their detection in 20% of bedding samples can therefore be explained by faecal contamination [32]. Their presence in many milk filter and tank milk samples could indicate contamination of these samples by faecal matter [33]. Groups of non-*aureus* staphylococci (NAS) are present in all the compartments analysed. In fact, several studies have identified certain NAS species in bedding, on cow coats, nostrils, and teats [34, 35]. NAS are a rather ubiquitous group of pathogens. These pathogens, which are abundant in faeces and bedding, could therefore constitute a potential source of contamination for the udder [36].

Secondly, some mastitis pathogens can be contagious and transmitted between cows. *Corynebacterium bovis* was present in the majority of milk and milk filter samples but absent from faeces and bedding. Therefore, it can be assumed to be transmitted from one cow to another through direct contact between cows or via milking equipment, as supported by [37]. This can be explained by its lipophilic [38] and microaerophilic nature [39] suggesting its presence in the mammary microbiota but scarcity in the environment [40]. In our study, this pathogen was absent from samples from one farm. This absence could be explained by the replacement of animals solely by internal births, thus limiting imports and perhaps preventing the introduction of *C. bovis*. It could also be due to the presence of other *Corynebacterium* species, not detected by the kit used, occupying its ecological niche, as shown by Kittl and Lücken [41, 42]. *Streptococcus uberis*, *Staphylococcus aureus* and *Streptococcus dysgalactiae* have
similar distributions in the farm compartments, as shown by the principal component analysis. These pathogens were found in large quantities in the milk filter samples, in smaller proportions in the milk samples and occasionally in bedding, which could be explained by the contamination of bedding by milk. *S. aureus* is a contagious pathogen [37, 43, 44], while *S. uberis* and *S. dysgalactiae*, although mainly associated with environmental infections, can also be considered contagious pathogens [37, 45]. These pathogens, which are abundant in milk filter samples, seem to be transmitted between cows during the milking process.

Milk filter is a tool that allows to identify the presence of contagious pathogens on the farm because it can potentially gather pathogens present in the milk from all cows in the herd. Indeed, in our study, pathogens were found in large quantities in the filtered milk samples, suggesting that some cows in the herd (potentially unsampled) were infected with these pathogens. Yet, the presence of pathogens in milk filters can also result from faecal contamination or the release of bacteria from biofilms in the milking system [33, 46]. For example, *S. aureus*, detected in over 60% of filters in our study, can originate from cows with mastitis or biofilms. The omnipresence of *S. aureus* on farm 4, with the absence of cows with *S. aureus* mastitis (according to the farmer's statements), suggests thus biofilm contamination. Bulk tank milk was collected to obtain an estimate of herd health despite the fact that no milk filters were used on farm 5. Nevertheless, bacteriological analysis of filtered milk samples is a more sensitive method than analysis of tank milk for assessing bacteriological contaminants in the herd [47, 48]. Despite this, milk filters and bulk tank milk can be a valuable tool for assessing the health of dairy farms.

A greater diversity of pathogens is present in the milk filters than in the individual milk samples, suggesting that the pathogens are not homogeneously distributed in the herd and cows may present specific pathogen profiles. We investigated these pathogen profiles to gain insight on pathogen ecology, host-pathogen interactions, and mastitis epidemiology.

# Infection dynamics and classification of pathogen profiles

By clustering the milk samples by proximity of their pathogen composition we were able to identify 6 distinct pathogen profiles that were ubiquitous to most farms. States were defined based on the pathogen composition of the profiles to analyse transitions over time. The probabilities of remaining in the current state (persistence probability) are correlated with the length of time a sample remains in that state. A probability of 1 corresponds to an absorbing state and low probabilities correspond to transient states [30]. Depending on the time spent by the cows in each profile and the transitions between states, we classified them into three types of states that can be associated with the corresponding pathogen profiles:

1. States C* and D* exhibit weak transition rates to other states, potentially corresponding to persistent *C. bovis* infection?

2. States E* and F* with high exit and low entry transitions rates, which could be defined as temporary states i.e. states in which cows stayed a small amount of time?
3. States A* and B* are characterized by no pathogens or only NAS, with multiple incoming transitions and few outgoing transitions, potentially indicating persistent healthy states?

i) Persistent *C. bovis* infection?

Firstly, two pathogen states, C* and D*, have the highest probability of staying in the current state (persistence probability), and can thus be defined as persistent. The pathogen profiles samples associated with these states are structured by the presence of *C. bovis*. This minor pathogen was the second most common pathogen in the milk samples in this study, with a prevalence of 23.7%. The difference in composition of the samples in these two pathogen profiles is due to the presence of *S. uberis* (C) or its absence (D). *S. uberis* presence also distinguishes profile E from B, in absence of *C. bovis*.

Infections with *C. bovis* and *S. uberis* (C*) have a higher probability of remaining in the same state than infections with *S. uberis* without *C. bovis* (E*). In addition, the probability of remaining in the state E* is the same as that of moving to the state structured by the presence of NAS, without *S. uberis* (B*). These results suggest that cows stay longer in state C* than in state E*. *S. uberis* infections can be short-termed, and naturally controlled by the immune system, or persistent over a long period, and there is still debate concerning the host and pathogen factors that may drive this dichotomy [45, 49]. Most of our samples presented short-termed *S. uberis* infections, but one cow in state C* presented a chronic infection across the sampling period, driving up the probability of staying in this state. Thus, the persistence of the C* state must be interpreted with caution.

The combined presence of *C. bovis* and *S. uberis* (pathogen profile C) led to higher somatic cell counts than when *S. uberis* was without *C. bovis* (pathogen profile E) exceeding 200,000 cells/mL, characteristic of subclinical mastitis. *S. uberis* appears to have a different virulence in samples from profiles E and C. We can hypothesise that co-infection with *C. bovis* and *S. uberis* increases virulence, or that these are distinct strains of *S. uberis* with varying levels of virulence [45, 50]. The majority of samples came from cows on farm 1, which have a high number of lactations (> 5). This could be explained by the higher prevalence of *S. uberis* in cows with at least 4 lactations [51]. In addition, the absence of *C. bovis* on farm 5 could also contribute to this structuring. During clustering robustness tests, profiles C and D and E and B could be grouped together when 10% or more of samples were removed, probably due to the low number of samples containing *S. uberis* (14.9%).

Transitions corresponding to the disappearance of *S. uberis* are frequent (C* to D* and E* to B*), while reverse transitions are rare. This could reflect a decrease in *S. uberis* infections over time, consistent with studies showing an increased prevalence of *S. uberis* in early lactation [45, 51–53].

When *C. bovis* is present without *S. uberis* (pathogen profile D), the median immune response was below the subclinical mastitis detection threshold of 200,000 cells/mL [12], suggesting *C. bovis* may not induce a large immune response by itself. However, 40% of the samples in this profile had somatic cell levels above the threshold used in this study (200,000 cells/mL), which could be attributed to the presence of
other major pathogens, notably *S. aureus* or *S. dysgalactiae*, detected in 76% of samples with high cell loads. *C. bovis* might favour coinfections, particularly with *S. uberis*, *S. aureus* or *S. dysgalactiae*, leading to high somatic cell counts.

Transitions from states containing *C. bovis* (C* and D*) to states without *C. bovis* were rare and the persistence probability are high, suggesting *C. bovis* infections tend to be chronic or permanent in cows. These states can therefore be considered as persistent states in this dataset. Samples from these states show that the total amount of bacteria is similar to that or pathogens, suggesting that *C. bovis* and/or the associated NAS constitute a large part of the mammary microbiota. This observation is consistent with a study of human mastitis, which found that infections disrupted the microbiota, allowing opportunistic pathogens to become dominant [54].

## ii) **Temporary states?**

States E* and F* are "temporary" states: their persistence probability is the lowest of all groups, and they are characterised by high exit and low entry transitions rates. State E* corresponds to samples all containing *S. uberis* and no *C. bovis*. Direct transitions from state E* to state A* (pathogen-free) or to state B* (NAS) have been observed in longitudinal follow-up. When NAS are present, there is a tendency to move preferentially towards state B*, which implies a loss of *S. uberis*, but with the NAS remaining. In contrast, few transitions show a loss of *S. uberis* accompanied by a gain of NAS. One hypothesis is that these transitions (E to A or B) are linked to an effective immune response against *S. uberis* specifically in the cows. The mammary microbiota is in fact capable of spontaneously recovering after an infection, as shown by Ganda et al. [55]. When *S. uberis* is present without *C. bovis* (profile E), the median somatic cell count was below the threshold for subclinical mastitis, with 37% of samples exceeding this threshold, and few major pathogens other than *S. uberis* were detected.

Pathogen profile F, identified during clustering, is characterised by a low number of somatic cells (75% of samples contain less than 200,000 cells per mL). Although no pathogen strictly structures this profile, a high prevalence of non-*aureus* staphylococci (NAS) is observed. Samples in this profile contain either yeasts or *T. pyogenes*/*P. indolicus*, or all simultaneously, suggesting that the immune response to these pathogens is low. The total amount of bacteria is much higher than the quantity of pathogens in this pathogen profile, suggesting that non-pathogenic bacteria are also present in large numbers. Yeasts were found in 17% of the milk sample studied; in milk, these likely belong to the *Candida* genus [56–58]. Higher proportions were observed in samples from udders with mastitis [56–58]. Their impact on somatic cells depends on their co-occurrence with other pathogens [59, 60]. *T. pyogenes* and *P. indolicus* are jointly detected using the PathoProof™ kit, with a prevalence of 21.8% in samples from profile F. These anaerobic bacteria are recognised as major agents of summer mastitis [61, 62]. *T. pyogenes*, formerly known as *Arcanobacterium pyogenes*, is an opportunistic pathogen implicated in various infections: mastitis, abscesses, pneumonia, and metritis [63, 64]. In contrast, *P. indolicus* has been little studied in isolation in the context of mastitis. Interpretation of this profile remains limited, as the kit does not differentiate between yeast species or between *T. pyogenes* and *P. indolicus*.
Samples may evolve from the F* state towards B* state, dominated by NAS, or towards A* state, devoid of pathogens. However, reverse transitions (from B* or A* to F*) are rare. These observations raise the question of the potential influence of the stage of lactation on these transitions. The literature suggests that *T. pyogenes* is more frequently found in milk in the postpartum period [64], which could explain some of these dynamics.

### iii) Persistent healthy states?

Finally, the study of the dynamics of the pathogen states enabled us to highlight two states A* and B* with multiple incoming transitions and few outgoing transitions. State A* is characterised by the absence of pathogens. The absence of pathogens and the presence of a somatic cell count of less than 200,000 cells/ml mean that the udder quarters from which these samples were taken can be defined as healthy [12]. In this profile, some samples show high somatic cell counts despite low abundances of pathogens. Some of these samples contain *S. aureus*, a major mastitis pathogen, which could explain the high somatic cell counts [65–67]. However, half of the samples with somatic cells above 200,000 cells/mL contained no pathogens. Such samples were also identified in a UK study [21]. A high somatic cell count with no pathogen detected can be explained by an unstudied pathogen, a general infection, mammary trauma or a late phase of mastitis with a persistent cellular response.

Approximately 25% of the samples analysed in this study were grouped into pathogen profile B following clustering. NAS are the structuring pathogens in this profile. The prevalence of NAS, minor mastitis pathogens, in the milk samples from this study exceeded 55%. NAS are not structuring in the other profiles identified (C, D, E and F). The lack of a structuring effect of NAS in these profiles can be explained by the fact that the NAS correspond to different species, which may have different impacts. Samples associated with profile B had a median somatic cell count of 28, which correspond to healthy udder quarters when using cell count criteria alone [12]. NAS include several bacterial species, including coagulase-negative and coagulase-positive staphylococci, but excluding *S. aureus* [34]. Several studies have shown that NAS can cause mastitis, with varying impacts on somatic cells depending on the species [40, 68], or with no significant difference between species [69].

Pathogen-free samples (pathogen profile A) show a significant amount of total 16s DNA. Studies have shown that mammary microbiota exist even in healthy quarters [55, 70, 71]. The total bacterial composition observed in profile B samples exceeds the sum of identified pathogens, suggesting the presence of bacterial diversity not restricted to the NAS. Future microbiota analysis will be needed to better characterize this microbial diversity, to determine whether it is comparable to that of profile A samples.

The transition of a cow from state B* to state A* is associated with the loss of NAS. We hypothesise that this loss of NAS results from interactions with commensal udder bacteria rather than immune response, as somatic cell levels remain low.
The most significant transitions observed in this study were from states E* and F* (characterised by the presence of multiple pathogens) to state B*, and from B* to A*. This pattern could indicate the existence of an intermediate state of health (state B*), which may or may not lead to a healthy state (state A*). Furthermore, states A* and B* are the most stable over time, reinforcing the hypothesis that, after an infection, the microbiota can either recover completely (state A*) or reach an intermediate state that will be stable (state B*). This model of partial or complete recovery is consistent with the observations of Derakhshani et al. in their review [20]. The states without pathogens (A*) and with the presence of NAS (B*) have relatively similar probabilities from the stationary distribution, higher than probability from the stationary distribution of all the other states. NAS induce little immune response and structure a stable state, we question whether NAS should be systematically considered as pathogens or if some species may be commensal in our study. The lack of NAS species specificity prevents us from drawing conclusions on this aspect. This study quantified pathogen abundance from PathoProof™ PCR data instead of using a categorical approach, allowing detection of NAS, although limitations include lack of species differentiation and challenges of detecting low concentrations affecting pathogen interaction analyses.

*S. aureus*, detected in 10.9% of milk samples, was a non-structuring pathogen in this study. Samples containing *S. aureus* were found in all pathogen profiles, this pathogen not being correlated with the presence of the other specific pathogens in the milk. The cellular response associated with samples containing this pathogen is high, with an average of 860,000 cells/mL. *S. aureus* is a major pathogen in mastitis that induces a high immune response [65, 72]. This pathogen was found in multiple samples from four cows in the study, with a large number of consecutive samples where it was present. This suggests that *S. aureus* may be responsible for chronic infections in these four cows. Indeed, *S. aureus* can cause chronic infections, particularly in its subclinical form and lead to a large increase in somatic cell counts [73, 74].

The clustering results suggest that samples associated with state C* correspond to subclinical mastitis. In addition, several samples showing *S. aureus* are associated with a significant cellular response. All these samples are problematic. However, antibiotic treatments did not significantly influence pathogen transitions, likely due to variation in efficacy [75], low sample size, lack of information on the analysed versus studied quarter and limited analysis by molecule type. Whereas somatic cell count changes also had no detectable effect, possibly because the sampling interval was too long to capture rapid immune responses [65, 76]. Transitions may instead be driven by bacterial interactions within the udder. Underlying bacterial dynamics could have contributed to the changes in state. Only a small fraction of the bacteria present, 15 pathogens, are being examined, while there are likely significant structures within the commensal microbiota as well [55, 70, 71]. Studying the microbiota in these samples will provide a clearer understanding of the bacterial composition within the profiles and help identify any non-pathogenic bacteria.

Conclusion
Analysis of the distribution of pathogens on the farm helps identify potential sources of contamination. Some, abundant in the environment (*E. coli*, yeasts, *T. pyogenes*/*P. indolicus*, NAS), can infect the udder, while others, mainly detected in milk filters (*S. aureus*, *S. dysgalactiae*, *S. uberis*), suggest cow-to-cow transmission.

Clustering analyses of milk quarter samples revealed distinct pathogen profiles, linked to somatic cell levels and cow recovery. Although *C. bovis* is a minor pathogen, it plays a key role alongside *S. uberis* and NAS and might indirectly drive high somatic cell count. Longitudinal milk monitoring identified three types of states: chronic *C. bovis* infection, temporary states (*S. uberis* and yeasts, *T. pyogenes*/*P. indolicus*) and two potential healthy states (pathogen absence or NAS). Analysis of the microbiota in these samples will enable us to better characterize the bacterial composition of the profiles and identify any non-pathogenic bacteria.

# Declarations

## Acknowledgements

We sincerely thank the farmers for welcoming us to their farms and allowing us to conduct the sampling. We also extend our gratitude to the team at Thermo Fisher Scientific for providing the thermocycler and for their support in data acquisition. Additionally, we would like to thank Dr. Sabine Leroy from the Microbiology, Digestive Environment and Health (MEDIS) unit of the French National Research Institute for Agriculture, Food and the Environment (INRAE) for providing the *Staphylococcus* strains. We would like to express our gratitude to Christelle Besqueut, Léa Lesserteur, Léa Mouty, and Mathilde Dutuel who contributed to the sampling and laboratory analysis. Thanks to the French National Research Agency for funding the project that made it possible to carry out these analyses.

## Authors' contributions

AB, LC, PG, SB, SM, VP, and XB contributed to the conceptualization and experimental design. AB obtained funding for the study. SB, SM, VP, AB, and HL conducted the experiments, while SB, SM, VP, and HL performed the laboratory analyses. HL was responsible for writing the first draft. HL, LC, PG, AB, and XB carried out the data analysis, visualization, and interpretation. All authors reviewed and approved the final manuscript.

## Availability of data and materials

The dataset and analysis scripts supporting the conclusions of this article are available in the Data INRAE repository: Bompard, Anaïs, 2025, "Mastitis pathogen and 16S qPCR results and associated metadata collected in Auvergne farms (France)", https://doi.org/10.57745/M7M95.

## Ethics approval and consent to participate
The animal samples were collected non-invasively; consequently, ethical committee approval was not required in accordance with French law. Informed consent was obtained from the farmers prior to their participation in the study, and the data were pseudonymized to ensure confidentiality.

**Consent for publication**

Not applicable.

**Funding**

This study was funded by the French National Research Agency: ANR-21-CE32-0005: Ecosystem-based control of *Staphylococcus aureus* mastitis in dairy farms – EcoSA.

**Competing interests**

The authors declare that they have no conflict of interest.

**References**

1. Halasa T, Huijps K, Østerås O, Hogeveen H (2007) Economic effects of bovine mastitis and mastitis management: A review. Veterinary Q 29:18-31. https://doi.org/10.1080/01652176.2007.9695224

2. Hogeveen H, Huijps K, Lam T (2011) Economic aspects of mastitis: New developments. N Z Vet J 59:16-23. https://doi.org/10.1080/00480169.2011.547165

3. Merl K, Abdulmawjood A, Lämmler C, Zschöck M (2003) Determination of epidemiological relationships of Streptococcus agalactiae isolated from bovine mastitis. FEMS Microbiol Lett 226:87-92. https://doi.org/10.1016/S0378-1097(03)00564-0

4. Heikkilä A-M, Nousiainen JI, Pyörälä S (2012) Costs of clinical mastitis with special reference to premature culling. J Dairy Sci 95:139-150. https://doi.org/10.3168/jds.2011-4321

5. Oliveira CSF, Hogeveen H, Botelho AM et al (2015) Cow-specific risk factors for clinical mastitis in Brazilian dairy cattle. Prev Vet Med 121:297-305. https://doi.org/10.1016/j.prevetmed.2015.08.001

6. De Vliegher S, Ohnstad I, Piepers S (2018) Management and prevention of mastitis: A multifactorial approach with a focus on milking, bedding and data-management. J Integr Agric 17:1214-1233. https://doi.org/10.1016/S2095-3119(17)61893-8

7. Hogan JS, Smith KL, Hoblet KH et al (1989) Bacterial Counts in Bedding Materials Used on Nine Commercial Dairies. J Dairy Sci 72:250-258. https://doi.org/10.3168/jds.S0022-0302(89)79103-7

8. Li H, Wang X, Wu Y et al (2021) Relationships among bedding materials, bedding bacterial composition and lameness in dairy cows. Anim Biosci 34:1559-1568. https://doi.org/10.5713/ajas.20.0565

9. Metzger SA, Hernandez LL, Skarlupka JH et al (2018) Influence of sampling technique and bedding type on the milk microbiota: Results of a pilot study. J Dairy Sci 101:6346-6356. https://doi.org/10.3168/jds.2017-14212
10. Cobirka M, Tancin V, Slama P (2020) Epidemiology and Classification of Mastitis. Anim (Basel) 10:2212. https://doi.org/10.3390/ani10122212

11. Schepers AJ, Lam TJGM, Schukken YH et al (1997) Estimation of Variance Components for Somatic Cell Counts to Determine Thresholds for Uninfected Quarters. J Dairy Sci 80:1833-1840.
https://doi.org/10.3168/jds.S0022-0302(97)76118-6

12. Schukken YH, Wilson DJ, Welcome F et al (2003) Monitoring udder health and milk quality using somatic cell counts. Vet Res 34:579-596. https://doi.org/10.1051/vetres:2003028

13. Alhussien MN, Dang AK (2018) Milk somatic cells, factors influencing their release, future prospects, and practical utility in dairy animals: An overview. Vet World 11:562-577.
https://doi.org/10.14202/vetworld.2018.562-577

14. Sharun K, Dhama K, Tiwari R et al (2021) Advances in therapeutic and managerial approaches of bovine mastitis: a comprehensive review. Veterinary Q 41:107-136.
https://doi.org/10.1080/01652176.2021.1882713

15. Botrel M-A, Haenni M, Morignat E et al (2010) Distribution and Antimicrobial Resistance of Clinical and Subclinical Mastitis Pathogens in Dairy Cows in Rhône-Alpes, France. Foodborne Pathog Dis 7:479-487. https://doi.org/10.1089/fpd.2009.0425

16. Melchior MB, Vaarkamp H, Fink-Gremmels J (2006) Biofilms: A role in recurrent mastitis infections? Vet J 171:398-407. https://doi.org/10.1016/j.tvjl.2005.01.006

17. Barkema HW, Schukken YH, Zadoks RN (2006) Invited Review: The role of cow, pathogen, and treatment regimen in the therapeutic success of bovine Staphylococcus aureus mastitis. J Dairy Sci 89:1877-1895. https://doi.org/10.3168/jds.S0022-0302(06)72256-1

18. Zadoks RN, Allore HG, Barkema HW et al (2001) Cow- and Quarter-Level Risk Factors for Streptococcus uberis and Staphylococcus aureus Mastitis. J Dairy Sci 84:2649-2663.
https://doi.org/10.3168/jds.S0022-0302(01)74719-4

19. Poutrel B, Bareille S, Lequeux G, Leboeuf F (2018) Prevalence of Mastitis Pathogens in France: Antimicrobial Susceptibility of Staphylococcus aureus, Streptococcus uberis and Escherichia coli. J Veterinary Sci Technol 09. https://doi.org/10.4172/2157-7579.1000522

20. Derakhshani H, Fehr KB, Sepehri S et al (2018) Invited review: Microbiota of the bovine udder: Contributing factors and potential implications for udder health and mastitis susceptibility. J Dairy Sci 101:10605-10625. https://doi.org/10.3168/jds.2018-14860

21. Bradley AJ, Leach KA, Breen JE et al (2007) Survey of the incidence and aetiology of mastitis on dairy farms in England and Wales. Vet Rec 160:253-257. https://doi.org/10.1136/vr.160.8.253

22. Keane OM (2019) Symposium review: Intramammary infections—Major pathogens and strain-associated complexity. Journal of Dairy Science 102:4713-4726. https://doi.org/10.3168/jds.2018-15326

23. Taponen S, Pyörälä S (2009) Coagulase-negative staphylococci as cause of bovine mastitis—Not so different from Staphylococcus. aureus? Veterinary Microbiol 134:29-36.
https://doi.org/10.1016/j.vetmic.2008.09.011
24. Watts JL, Lowery DE, Teel JF, Rossbach S (2000) Identification of Corynebacterium bovis and other Coryneforms Isolated from Bovine Mammary Glands. J Dairy Sci 83:2373-2379.
https://doi.org/10.3168/jds.S0022-0302(00)75126-5

25. Matthews KR, Harmon RJ, Smith BA (1990) Protective Effect of Staphylococcus chromogenes Infection Against Staphylococcus aureus Infection in the Lactating Bovine Mammary Gland1. J Dairy Sci 73:3457-3462.
https://doi.org/10.3168/jds.S0022-0302(90)79044-3

26. Nascimento Jdos, Fagundes S, de Brito PC P, et al (2005) Production of bacteriocins by coagulase-negative staphylococci involved in bovine mastitis. Vet Microbiol 106:61-71.
https://doi.org/10.1016/j.vetmic.2004.10.014

27. Brooks BW, Barnum DA (1984) The susceptibility of bovine udder quarters colonized with Corynebacterium bovis to experimental infection with Staphylococcus aureus or Streptococcus agalactiae. Can J Comp Med 48:146-150

28. Ward JH Jr (1963) Hierarchical Grouping to Optimize an Objective Function. J Am Stat Assoc 58:236-244.
https://doi.org/10.1080/01621459.1963.10500845

29. Murtagh F, Legendre P (2014) Ward's Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward's Criterion? J Classif 31:274-295.
https://doi.org/10.1007/s00357-014-9161-z

30. Spedicato GA (2017) Discrete Time Markov Chains with R. R J 9:84-104.
https://doi.org/10.32614/RJ-2017-036

31. Strelioff CC, Crutchfield JP, Hubler AW (2007) Inferring Markov Chains: Bayesian Estimation, Model Comparison, Entropy Rate, and Out-of-class Modeling. Phys Rev E 76:011106.
https://doi.org/10.1103/PhysRevE.76.011106

32. Kagkli DM, Vancanneyt M, Hill C et al (2007) Enterococcus and Lactobacillus contamination of raw milk in a farm dairy environment. Int J Food Microbiol 114:243-251.
https://doi.org/10.1016/j.ijfoodmicro.2006.09.016

33. Van Kessel JS, Karns JS, Gorski L et al (2004) Prevalence of Salmonellae, Listeria monocytogenes, and Fecal Coliforms in Bulk Tank Milk on US Dairies. J Dairy Sci 87:2822-2830.
https://doi.org/10.3168/jds.S0022-0302(04)73410-4

34. Buck JD, Ha V, Naushad S et al (2021) Non-aureus Staphylococci and Bovine Udder Health: Current Understanding and Knowledge Gaps. Front Veterinary Sci 8:658031.
https://doi.org/10.3389/fvets.2021.658031

35. White DG, Harmon RJ, Matos JES, Langlois BE (1989) Isolation and Identification of Coagulase-Negative Staphylococcus Species from Bovine Body Sites and Streak Canals of Nulliparous Heifers. J Dairy Sci 72:1886-1892.
https://doi.org/10.3168/jds.S0022-0302(89)79307-3

36. Hogan J, Smith KL (2012) Managing Environmental Mastitis. Veterinary Clinics of North America. Food Anim Pract 28:217-224.
https://doi.org/10.1016/j.cvfa.2012.03.009

37. Fox LK, Gay JM (1993) Contagious Mastitis. Veterinary Clinics of North America. Food Anim Pract 9:475-487.
https://doi.org/10.1016/S0749-0720(15)30615-0
38. Fernandez-Garayzabal JF, Collins MD, Hutson RA et al (1997) Corynebacterium mastitidis sp. nov., isolated from milk of sheep with subclinical mastitis. Int J Syst Bacteriol 47:1082-1085.
https://doi.org/10.1099/00207713-47-4-1082

39. Harrigan WF (1966) The Nutritional Requirements and Biochemical Reactions of Corynebacterium bovis. J Appl Bacteriol 29:380-394. https://doi.org/10.1111/j.1365-2672.1966.tb03487.x

40. Woudstra S, Lücken A, Wente N et al (2023) Reservoirs of Corynebacterium spp. in the Environment of Dairy Cows. Pathogens 12:139. https://doi.org/10.3390/pathogens12010139

41. Kittl S, Studer E, Brodard I et al (2022) Corynebacterium uberis sp. nov. frequently isolated from bovine mastitis. Syst Appl Microbiol 45:126325. https://doi.org/10.1016/j.syapm.2022.126325

42. Lücken A, Woudstra S, Wente N et al (2022) Intramammary infections with Corynebacterium spp. in bovine lactating udder quarters. PLoS ONE 17:e0270867.
https://doi.org/10.1371/journal.pone.0270867

43. Gomes F, Saavedra MJ, Henriques M (2016) Bovine mastitis disease/pathogenicity: evidence of the potential role of microbial biofilms. Pathogens Disease 74:ftw006.
https://doi.org/10.1093/femspd/ftw006

44. Petersson-Wolfe CS, Mullarky IK, Jones GM (2010) Staphylococcus aureus Mastitis. Cause, Detection, and Control

45. Zadoks RN, Gillespie BE, Barkema HW et al (2003) Clinical, epidemiological and molecular characteristics of Streptococcus uberis infections in dairy herds. Epidemiol Infect 130:335-349.
https://doi.org/10.1017/S0950268802008221

46. Latorre AA, Van Kessel JS, Karns JS et al (2010) Biofilm in milking equipment on a dairy farm as a potential source of bulk tank milk contamination with Listeria monocytogenes. J Dairy Sci 93:2792-2802. https://doi.org/10.3168/jds.2009-2717

47. Latorre AA, Van Kessel JAS, Karns JS et al (2009) Molecular Ecology of Listeria monocytogenes: Evidence for a Reservoir in Milking Equipment on a Dairy Farm. Appl Environ Microbiol 75:1315-1323. https://doi.org/10.1128/AEM.01826-08

48. Van Kessel JS, Karns JS, Wolfgang DR et al (2008) Environmental Sampling To Predict Fecal Prevalence of Salmonella in an Intensively Monitored Dairy Herd. J Food Prot 71:1967-1973.
https://doi.org/10.4315/0362-028X-71.10.1967

49. Leelahapongsathon K, Schukken YH, Srithanasuwan A, Suriyasathaporn W (2020) Molecular epidemiology of Streptococcus uberis intramammary infections: Persistent and transient patterns of infection in a dairy herd. J Dairy Sci 103:3565-3576. https://doi.org/10.3168/jds.2019-17281

50. Phuektes P, Mansell PD, Dyson RS et al (2001) Molecular Epidemiology of Streptococcus uberis Isolates from Dairy Cows with Mastitis. J Clin Microbiol 39:1460-1466.
https://doi.org/10.1128/JCM.39.4.1460-1466.2001

51. Jayarao BM, Gillespie BE, Lewis MJ et al (1999) Epidemiology of Streptococcus uberis Intramammary Infections in a Dairy Herd. J Veterinary Med Ser B 46:433-442.
https://doi.org/10.1046/j.1439-0450.1999.00254.x
52. Green MJ, Green LE, Schukken YH et al (2004) Somatic Cell Count Distributions During Lactation Predict Clinical Mastitis. J Dairy Sci 87:1256-1264. https://doi.org/10.3168/jds.S0022-0302(04)73276-2

53. Todhunter DA, Smith KL, Hogan JS (1995) Environmental Streptococcal Intramammary Infections of the Bovine Mammary Gland1. J Dairy Sci 78:2366-2374. https://doi.org/10.3168/jds.S0022-0302(95)76864-3

54. Patel SH, Vaidya YH, Patel RJ et al (2017) Culture independent assessment of human milk microbial community in lactational mastitis. Sci Rep 7:7804. https://doi.org/10.1038/s41598-017-08451-7

55. Ganda EK, Gaeta N, Sipka A et al (2017) Normal milk microbiome is reestablished following experimental infection with Escherichia coli independent of intramammary antibiotic treatment with a third-generation cephalosporin in bovines. Microbiome 5:74. https://doi.org/10.1186/s40168-017-0291-5

56. de Casia dos Santos R, Marin JM (2005) Isolation of Candida spp. from mastitic bovine milk in Brazil. Mycopathologia 159:251-253. https://doi.org/10.1007/s11046-004-2229-2

57. Du J, Wang X, Luo H et al (2018) Epidemiological investigation of non-albicans Candida species recovered from mycotic mastitis of cows in Yinchuan, Ningxia of China. BMC Vet Res 14:251. https://doi.org/10.1186/s12917-018-1564-3

58. Erbaş G, Parin U, Kirkan Ş et al (2017) Identification of Candida strains with nested PCR in bovine mastitis and determination of antifungal susceptibilities. Turk J Vet Anim Sci 41:757-763. https://doi.org/10.3906/vet-1704-39

59. Moretti A, Pasquali P, Mencaroni G et al (1998) Relationship between cell counts in bovine milk and the presence of mastitis pathogens (yeasts and bacteria). J Vet Med Ser B-Infect Dis Vet Public Health 45:129-132. https://doi.org/10.1111/j.1439-0450.1998.tb00775.x

60. Tomanić D, Samardžija M, Stančić I et al (2024) Mastitis challenges in Serbian dairy farming: a study on somatic cell counts and pathogen distribution. Mljekarstvo: časopis za unaprjeđenje proizvodnje i prerade mlijeka 74:239-248. https://doi.org/10.15567/mljekarstvo.2024.0307

61. Madsen M, Sørensen GH, Aalbaek B et al (1992) Summer mastitis in heifers: Studies on the seasonal occurrence of Actinomyces pyogenes, Peptostreptococcus indolicus and Bacteroidaceae in clinically healthy cattle in Denmark. Vet Microbiol 30:243-255. https://doi.org/10.1016/0378-1135(92)90118-D

62. Pyörälä S, Jousimies-Somer H, Mero M (1992) Clinical, bacteriological and therapeutic aspects of bovine mastitis caused by aerobic and anaerobic pathogens. Br Vet J 148:54-62. https://doi.org/10.1016/0007-1935(92)90067-B

63. Ribeiro MG, Risseti RM, Bolaños CAD et al (2015) Trueperella pyogenes multispecies infections in domestic animals: a retrospective study of 144 cases (2002 to 2012). Veterinary Q 35:82-87. https://doi.org/10.1080/01652176.2015.1022667

64. Rzewuska M, Kwiecień E, Chrobak-Chmiel D et al (2019) Pathogenicity and virulence of trueperella pyogenes: A review. Int J Mol Sci 20. https://doi.org/10.3390/ijms20112737
65. Bannerman D, Paape MJ, Lee J-W et al (2004) Escherichia coli and Staphylococcus aureus Elicit Differential Innate Immune Responses following Intramammary Infection. Clin Vaccine Immunol 11:463-472. https://doi.org/10.1128/CDLI.11.3.463-472.2004

66. Djabri B, Bareille N, Beauveau F, Seegers H (2002) Quarter milk somatic cell count in infected dairy cows: a meta-analysis. Vet Res 33:335-357. https://doi.org/10.1051/vetres:2002021

67. Schwarz D, Diesterbeck US, Failing K et al (2010) Somatic cell counts and bacteriological status in quarter foremilk samples of cows in Hesse, Germany—A longitudinal study. J Dairy Sci 93:5716-5728. https://doi.org/10.3168/jds.2010-3223

68. Supré K, Haesebrouck F, Zadoks RN et al (2011) Some coagulase-negative Staphylococcus species affect udder health more than others. J Dairy Sci 94:2329-2340. https://doi.org/10.3168/jds.2010-3741

69. Hogan JS, Pankey JW, Duthie AH (1987) Growth Responses of Staphylococcus aureus and Streptococcus agalactiae to Corynebacterium bovis Metabolites. J Dairy Sci 70:1294-1301.
https://doi.org/10.3168/jds.S0022-0302(87)80144-3

70. Kizil S, Aydin FE, Önlen Günerİ C et al (2024) Comparison of milk microbiota between healthy and mastitic cows. Turkish J Veterinary Anim Sci 48:17-32. https://doi.org/10.55730/1300-0128.4333

71. Oikonomou G, Bicalho ML, Meira E et al (2014) Microbiota of Cow's Milk; Distinguishing Healthy, Sub-Clinically and Clinically Diseased Quarters. PLoS ONE 9:e85904.
https://doi.org/10.1371/journal.pone.0085904

72. Sharma N, Singh NK, Bhadwal MS (2011) Relationship of Somatic Cell Count and Mastitis: An Overview. Asian Australas J Anim Sci 24:429-438. https://doi.org/10.5713/ajas.2011.10233

73. Kerro Dego O, Vidlund J (2024) Staphylococcal mastitis in dairy cows. Front Vet Sci 11.
https://doi.org/10.3389/fvets.2024.1356259

74. Park S, Ronholm J (2021) Staphylococcus aureus in Agriculture: Lessons in Evolution from a Multispecies Pathogen. Clin Microbiol Rev 34:e00182-e00120.
https://doi.org/10.1128/CMR.00182-20

75. Wilson DJ, Gonzalez RN, Case KL et al (1999) Comparison of Seven Antibiotic Treatments with No Treatment for Bacteriological Efficacy Against Bovine Mastitis Pathogens. J Dairy Sci 82:1664-1670. https://doi.org/10.3168/jds.S0022-0302(99)75395-6

76. Blum SE, Heller DE, Jacoby S et al (2020) Physiological response of mammary glands to Escherichia coli infection: A conflict between glucose need for milk production and immune response. Sci Rep 10:9602. https://doi.org/10.1038/s41598-020-66612-7

Figures
<table><thead><tr><th rowspan="2"></th><th colspan="12">2021</th></tr><tr><th colspan="3">November</th><th colspan="3">December</th><th colspan="3">January</th><th colspan="3">February</th><th colspan="3">March</th></tr></thead><tbody><tr><th scope="row">Farm 1</th><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><th scope="row">Farm 2</th><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><th scope="row">Farm 3</th><td></td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr></tbody></table>

<table><caption>Figure 1</caption><thead><tr><th rowspan="2"></th><th colspan="12">2022</th></tr><tr><th colspan="3">November</th><th colspan="3">December</th><th colspan="3">January</th><th colspan="3">February</th><th colspan="3">March</th></tr></thead><tbody><tr><th scope="row">Farm 4</th><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><th scope="row">Farm 5</th><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr><tr><th scope="row">Farm 6</th><td></td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td><td>•</td></tr></tbody></table>

**Design of longitudinal monitoring in this study** - Each point represents a farm visit for sampling cow's milk, faeces, milk filter, and bedding.
<figure><img src="image_2.png" /><figcaption>Figure 2</figcaption></figure>

Prevalence of mastitis-causing pathogens in different matrices: milk (n=1008), milk filter (n=154), tank milk (n=17), bedding (n=189) and faeces (n=917). The colours represent the different matrices, with each plot corresponding to a specific pathogen. NAS stands for non-*aureus* staphylococci.
<figure><img src="image_3.png" /><figcaption>Figure 3</figcaption></figure>

a) Projection of samples on the first factorial plane and b) circle of variables contributions for the first two dimensions of principal component analysis (PCA) of pathogen quantities in all samples. The colours represent the different matrices. The labels indicate the matrix: B for bedding, F for faeces, M for milk, I for milk filters and T for tank milk), followed by the farm number (1 to 6) (e.g., M1 designates milk samples from farm n°1). Farms 1 to 3 correspond to the first sampling period, while farms 4 to 6 correspond to the second period.
<figure><img src="image_4.png" /><figcaption>Figure 4</figcaption></figure>

The dendrogram (a) shows the result of hierarchical clustering with 6 distinct profiles (k = 6), followed by the representation of milk sample clustering results in multidimensional space for dimensions 1 and 2 (b), and for dimensions 3 and 4 (c). The ellipses correspond to a 95% confidence interval for each cluster. Colours indicate the profiles identified by clustering.
<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Figure 5**</figcaption></figure>

(a) Distribution of pathogen quantities for each pathogen profile. The x-axis (log-transformed) indicates the quantity of pathogens detected, and the y-axis shows the individual pathogens. Results are separated by profiles identified by clustering. N corresponds to the number of samples per profile. (b) Number of samples per farm according to the different pathogen profiles. (c) Number of somatic cells associated with samples from different profiles. The red line represents the threshold of 200,000 cells, commonly used to detect mastitis. The colours correspond to the profiles identified by clustering.
<figure><img src="image_10.png" /><figcaption>Figure 6</figcaption></figure>

**Bacterial abundance (16S DNA) depending on pathogen abundance (total quantity detected by Pathoproof kit) for each sample.** The shapes of the dots distinguish the matrices, while the colours of the dots reflect the profiles identified in the milk samples. The 95% confidence ellipses are drawn around the milk profiles, and the dotted line represents the y=x relationship.
<figure><img src="image_11.png" /><figcaption>Figure 7</figcaption></figure>

**Graph of state transitions estimated by the Markov chain.** The nodes (circles) represent the states, while the arcs between the nodes illustrate the transition probabilities between these profiles. The width and colour of the arcs are proportional to the transition probabilities between states, with the colour scale ranging from cool (representing low probability) to warm (representing high probability).
<figure><img src="image_12.png" /><figcaption>Figure 8</figcaption></figure>

**Transitions between states following an antibiotic treatment.** The nodes (circles) represent the states, each arrow represents a change in state (n=13). The colours and thickness of the lines correspond to the associated transition numbers.

**Supplementary Files**

This is a list of supplementary files associated with this preprint. Click to download.

- Additionalfile1.xlsx

- Additionalfile2.docx

- Additionalfile3.xlsx

- Additionalfile4.xlsx

- Additionalfile5.docx

- Additionalfile6.docx