Evaluation and cluster analysis of inflammatory reactions of dairy cattle mastitis pathogens in milk samples submitted for microbiological examination

Jose M. Díaz-Cao (Díaz-Cao, JM)

Universidade de Santiago de Compostela, Faculty of Veterinary Sciences, Dept. Animal Pathology. 27002 Lugo, Spain.

María L. Barreal (Barreal, ML)

Laboratorio Interprofesional Gallego de Análisis de la Leche (LIGAL), Edificio de Laboratorios Agrarios. 15318 Abegondo, A Coruña, Spain.

Belén Pombo (Pombo, B)

Laboratorio Interprofesional Gallego de Análisis de la Leche (LIGAL), Edificio de Laboratorios Agrarios. 15318 Abegondo, A Coruña, Spain.

Alberto Prieto (Prieto, A)

Universidade de Santiago de Compostela, Faculty of Veterinary Sciences, Dept. Animal Pathology. 27002 Lugo, Spain.

Jose M. Alonso (Alonso, JM)

Universidade de Santiago de Compostela, Faculty of Sciences, Dept. Statistics, Mathematical Analysis and Optimization. 27002 Lugo, Spain.

Antonio Iglesias (Iglesias, A)

Universidade de Santiago de Compostela, Faculty of Veterinary Sciences, Dept. Anatomy, Animal Production and Clinical Veterinary Sciences. 27002 Lugo, Spain.

Roberto Lorenzana (Lorenzana, R)

Laboratorio Interprofesional Gallego de Análisis de la Leche (LIGAL), Edificio de Laboratorios Agrarios. 15318 Abegondo, A Coruña, Spain.

Cynthia López-Novo (López-Novo, C)

Universidade de Santiago de Compostela, Faculty of Veterinary Sciences, Dept. Animal Pathology. 27002 Lugo, Spain.

Pablo Díez-Baños (Díez-Baños, P)

Universidade de Santiago de Compostela, Faculty of Veterinary Sciences, Dept. Animal Pathology. 27002 Lugo, Spain.

Gonzalo Fernández (Fernández, G)

Universidade de Santiago de Compostela, Faculty of Veterinary Sciences, Dept. Animal Pathology. 27002 Lugo, Spain.



Aim of study: We have classified into homogenous groups a wide spectrum of mammary pathogens according to their frequency of isolation in clinical mastitis and their somatic cell counts in mastitis.

Area of study: The study was conducted in Galicia (NW Spain).

Material and methods: 163,741 dairy cattle quarter milk samples were analyzed. We identified mastitis pathogens to the species level and performed a cluster analysis to classify these microorganisms by their median of Linear Score (mLS), percentage of isolation in clinical mastitis samples (%ICS) and percentage of isolation in samples with somatic cell counts under 100,000 cells/mL (%ISU100).

Main results: Forty-three different species were isolated. Cluster analysis identified 4 groups of pathogens; mLS and %ICS progressively increased from Group I to Group IV and %ISU100 decreased. However, several pathogens included in groups II and III showed %ISU100 values higher than 2% and 3%. Minor pathogens were mainly clustered in Group I (e.g., Corynebacterium spp. and most of Staphylococcus spp.), while known major pathogens were included in Groups II, III y IV. Species of the same family, genus or microbiological group like Enterobacteriaceae or Enterococcus spp. were frequently separated into different groups, thus showing heterogeneity among the members of these groups.

Research highlights: Results obtained here may aid in assessing the pathogenicity of sporadic pathogens in relation to more well-known pathogens and suggest that the traditional classification between minor and major pathogens is an oversimplification of the reality, especially for the latter category.

Additional keywords: subclinical mastitis; clinical mastitis; major pathogens; minor pathogens; somatic cell counts.

Abbreviations used: %ICS (percentage of isolation in clinical mastitis samples); %ISU100 (percentage of isolation in samples with somatic cell counts under 100,000 cells/mL); IMI (intramammary infection); LS (linear score); mLS (median of linear score); NAS (non-aureus staphylococci); PCA (principal components analysis); SCC (somatic cell counts); SP (similarity profile).

Authors' contributions: Conceived and designed the work: GF. Performed the experiments and acquisition of data: MLB, BP and RL. Statistical analysis: JMD. Interpretation of data JMD, JMA and GF. Critical revision of the manuscript for important intellectual content: CLN, AP, AI and PDB. Wrote the paper: JMD and GF.

Citation: Díaz-Cao, JM; Barreal, ML; Pombo, B; Prieto, A; Alonso, JM; Iglesias, A; Lorenzana, R; López-Novo, C; Díez-Baños, P; Fernández, G (2019). Evaluation and cluster analysis of inflammatory reactions of dairy cattle mastitis pathogens in milk samples submitted for microbiological examination. Spanish Journal of Agricultural Research, Volume 17, Issue 4, e0505.

Received: 19 Jun 2019. Accepted: 08 Jan 2020.

Copyright © 2019 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC-by 4.0) License.

Funding agencies/Institutions Project / Grant
Xunta de Galicia, Program for consolidating and structuring competitive research groups GRC2015/003

Competing interests: The authors have declared that no competing interests exist.

Correspondence should be addressed to José Manuel Díaz:





Material and methods





Bovine mastitis is a multietiologic disease that can be induced by more than 130 different species of microorganisms. Mastitis pathogens have traditionally been classified as “minor” or “major” pathogens in order to assess the prognosis of the infection and to establish priorities in the adoption of preventive and control measures. Thus, major pathogens are related to a higher impact on cattle udder health, as well as on milk quality and productivity (Zadoks & Fitzpatrick, 2009).

This classification has generally been based on the frequency of presentation in clinical mastitis as well as on the somatic cell counts (SCC) in subclinical mastitis, since it is broadly considered that the existence of an intramammary infection (IMI) is the most important cause of an SCC increase (Dohoo & Meek, 1982). Different thresholds have been set to SCC to consider the existence of IMI in subclinical mastitis (100,000–500,000 cells/mL), but 200,000 cells/mL is the most widely used (Schwarz et al., 2010; IDF, 2013). Ho­wever, since SCC also vary with the lactation stage, age, stress of the animals, season and time and frequency of milking (Dohoo & Meek, 1982; Harmon, 1994), this threshold may inaccurately classify some mastitis (Schwarz et al., 2010).

Some microorganisms like Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Sta­phy­lococcus aureus, Escherichia coli or Klebsiella pneumoniae are generally considered in literature as major pathogens (Bradley, 2002; Zadoks & Fitzpatrick, 2009), whereas coagulase-negative staphylococci and Corynebacterium spp. are considered minor pathogens (Bradley, 2002; Pyörälä & Taponen, 2009). However, there are a number of pathogens which regularly present low rates of isolation (Pitkälä et al., 2004; Tenhagen et al., 2006) and have not been reported in any of those groups (e.g., Streptococcus spp. or Enterococcus spp., as well as yeasts, coliforms or other Gram-negatives, etc.) (Bradley, 2002). Consequently, when these pa­thogens are studied, they are frequently merged in groups with other bacteria on the basis of genus, family or microbiological group. Thus, their effects on cattle mastitis are considered as a group or assumed to be similar to other more well-known pathogens of the same genus or microbiological group (Djabri et al., 2002; de Haas et al., 2004; Malinowski et al., 2006; Schwarz et al., 2010). Nevertheless, these groups may not be so homogenous to make such assumptions. For example, non-aureus staphylococci (NAS) are historically studied as a group in cattle mastitis and, while different authors agree that SCC in IMI caused by NAS as a whole are lower than that when caused by Staph. aureus (Schukken et al., 2009; Supré et al., 2011; Taponen et al., 2007), results are contradictory when different NAS species are individually compared. Therefore, several authors did not find any difference among NAS regarding the severity of clinical signs (Jarp, 1991; Taponen et al., 2006) or SCC (Thorberg et al., 2009), while others did find statistically significant differences (Sampimon et al., 2009; Fry et al., 2014; Condas et al., 2017), and some NAS species (e,g. Staphylococcus chromogenes, Staphylococcus si­mu­lans and Staphylococcus xylosus) even presented similar SCC to Staph. aureus (Supré et al., 2011).

Pathogens with low rates of isolation in mastitis gain importance by summing all the cases in which they all are involved (Makovec & Ruegg, 2003; Piepers et al., 2007; Schwarz et al., 2010; Kalmus et al., 2011). Besides, mastitis control programs have decreased the prevalence of some pathogens, such as Staph. aureus or Strep. agalactiae, but also increased the relative rate of isolation of others, which were less frequent in the past (Zadoks & Fitzpatrick, 2009; Fernández et al., 2013). Consequently, it is necessary to gain solid knowledge about the relative behavior of these pathogens in cattle mastitis.

Thus, the aim of this study was to analyze the behavior of a wide spectrum of mammary pathogens regarding different variables: percentage of isolation in clinical mastitis, SCC in non-clinical mastitis and percentage of isolation in samples with SCC ≤ 100, 000 cells/mL. These results were subsequently used to classify the different mammary pathogens into homogenous groups.

Material and methodsTop

Study area, collection and processing of samples

Galicia (NW Spain) is the largest cattle-rearing region in Spain, accounting for 53% of Spain’s dairy farms and approximately 350,000 dairy cows older than two years of age. This region accounts for 35% of the milk production in Spain, that is around 1.7% of the milk produced in the European Union.

The study included all quarter milk samples sub­mitted to the Laboratorio Interprofesional Gallego de Análisis de Leche (LIGAL) for microbiological examination from June 2005 to September 2011. Milk samples were collected by farm personnel and veterinarians, following the instructions of the Galician mastitis control program. They were submitted when farmers detected clinical mastitis or when practitioners detected a SCC increase in bulk-tank.

Milk samples were analyzed by spreading 10 µL of milk on a 5% sheep blood agar plate. Plates were incubated aerobically at 37 ºC and read at 24 h and 48 h. Plates which showed no growth at 48 h, were kept in incubation for 3 more days in which daily readings were performed.

An IMI was stated for Staph. aureus or Strep. agalactiae when at least 100 cfu/mL were isolated (Dohoo et al., 2011). For the rest of the microorganisms, a minimum of 500 cfu/mL were required to consider IMI (Taponen et al., 2006; Thorberg et al., 2009; Nam et al., 2010; Dohoo et al., 2011). Plates with no growth and primary culture plates containing more than one colony type were removed from the study.

Isolates were preliminary identified by colony mor­phology, hemolytic patterns, Gram-staining and catalase-test. Subsequently, identification was completed using the VITEK 2 system (bioMérieux, Hazelwood, MO, USA) with the VITEK 2 cards GP-test kit (for Gram-positive cocci), GN-test kit (for Gram-negative) and YST test kit (for yeasts and Prothoteca spp.). The procedures recommended by the manufacturer were followed and bacterial identification was regarded as acceptable only when the confidence level of the analysis was > 85%. However, three exceptions were considered: Corynebacterium spp., Nocardia spp and Trueperella pyogenes. In these cases, the identification was based on characteristics of isolation on blood-agar plates: form, presence of hemolysis and time of growth; Gram-staining and the result of the catalase test, following the characteristics defined for the identification of these genera and species (Brown et al., 1981; Quinn et al., 2011).

The isolates included in the analysis were identified to the species level, except in the case of Lactoco­ccus spp., Corynebacterium spp., Nocardia spp. and Prototheca spp.

Definitions used in this study

Milk samples were classified as clinical mastitis when a visible alteration in the milk or udder was observed in the animal by farm veterinarians; otherwise, they were considered non-clinical mastitis. Somatic cell counts were performed in all the non-clinical mastitis sam­ples using a Fossomatic-FC somatic cell counter (Foss, Denmark) and following the normalized protocol ISO 13366-2/IDF 148-2 accredited by the National Entity of Accreditation (ENAC). Non-clinical mastitis samples were additionally divided into two groups: SCC ≤ 100,000 cells/mL and SCC > 100,000 cells/mL, thus defining this value as a cut-off, because the inflammatory reaction is thought to start at this level (Schwarz et al., 2010).

In some cases, SCC could not be performed due to the scarce quantity of milk of the sample. Conse­quently, these samples were classified as insufficient and removed from the study.

Statistical analysis

The statistical analysis was performed in sam­ples which met the following criteria: a unique pathogen was isolated and the proportion of samples in which this pathogen was isolated was over 0.1%. Somatic cell count values in samples with SCC >

100,000 cells/ mL were transformed to Linear Score (LS) using the recommended formula by Shook (1982):

LS = log2 (SCC/100) + 3

The statistical analysis was performed with the statistical software R (R Core Team, 2014). The values of the median of LS (mLS), percentage of isolations in clinical mastitis (%ICS) and percentage of isolations in samples with SCC ≤ 100,000 cell/mL (%ISU100) regarding the total samples in which each microorganism was isolated were obtained for each microorganism and used to perform a cluster analysis. Firstly, values of these variables were z-standar­di­zed and a principal component analysis (PCA) was performed to explore the linear relationship between the variables. Subsequently, a hierarchical cluster analysis was performed according to the Ward’s method (Ward, 1963) from the three principal components obtained. PCA and cluster analysis were performed using the function “HCPC” in the package “FactoMineR” (et al., 2008; Husson et al., 2010). This function also performs the V-test which allows to estimate the presence of a significant overexpression (if > 2) or infraexpression (if < -2) of the variables in the resulting clusters (et al., 2008; Husson et al., 2010). The significance of the obtained clusters was assessed by a Similarity Profile (SP) analysis with the assumption of no “a priori” groups using the function “simprof” from the “clustsig” package (Clarke et al., 2008). The analysis was performed setting 10,000 similarity profiles for the expected distribution of the data and 9,999 for the comparison with its null distribution.


A total of 240,232 samples were submitted to the laboratory during the period of study; 32,852 (13.7%) were contaminated, 30,561 (12.7%) did not show any growth, and more than one species were isolated in 1,854 (0.8%) samples. Consequently, these samples were removed from the study. Additionally, 1,620 were also removed because the quantity of the sample was insufficient. As a result, 173,345 samples were finally included in this study; 50,389 (29.1%) were classified as clinical mastitis and 122,956 (70.9%) as non-clinical. Among the samples categorized as non-clinical mastitis, 8,036 (6.6%) presented SCC ≤ 100,000 cells/mL.

Forty-three different species were identified. Ho­wever, 5.4% of Gram-positive catalase-positive cocci could not be identified to the species level, as well as 1.9% of Streptococcus spp. different from Strep. agalactiae, 2.2% of Enterococcus spp. and 1.3% of Gram-negative. In 20% of yeasts, the identification to, at least the genus level, could not be achieved either.

Pathogens whose isolation frequency was over 0.1% accounted for a total of 163,741 (94.5%) samples. They represented 94.2% of the non-clinical samples with SCC > 100,000 cells/mL (n = 108,226/114,889), 95% of the non-clinical samples with SCC ≤ 100,000 cells/mL (n = 7,714/8,120), and 94.8% of the clinical samples (n = 47,719/50,336). The SCC and LS values of these pathogens are presented in Table 1, which shows data from the SCC > 100,000 threshold. Table 2 shows the %ICS and %ISU100 for each microorganism identified.

Table 1. Mean and median of the Linear Score (LS) and somatic cell counts (× 1,000 cells/mL) of each microorganism identified in dairy cattle milk samples from Galicia (NW Spain) with a percentage of isolation over 0.1% in samples with somatic cell counts (SCC) > 100,000 cells/mL.

Table 2. Percentage of isolation in clinical samples (%ICS) and percentage of isolation in samples with SCC under 100,000 cells/mL (%ISU100) for each microorganism identified in in dairy cattle milk samples from Galicia (NW Spain) in relation to the total number of samples in which the microorganism was isolated.

Principal component analysis prior to clustering provided one first component (Dim 1) which accoun­ted for 87.46% of the variance. The observed varia­bles contributed to this component similarly (30.84%-36.15%), being positively correlated in the case of mLS and %ICS and negatively in %ISU100. The second component (Dim 2) accounted for a 10.09% of the variability, highlighting the contribution of %ICS (61.05%) and, to a lesser extent, %ISU100 (36.97%), although the correlations were low (Table 3).

Table 3. Contribution (Contr) and correlation (Corr) of each variable [median of the Linear Score (mLS), percentage of isolation in clinical samples (%ICS) and percentage of isolation in samples with SCC under 100,000 cells/mL (%ISU100)] with each of the three dimensions (Dim 1-3) resulting from the principal components analysis.

Figure 1 shows the dendrogram from the cluster analysis. Looking at the results in the dendrogram, the optimum solution was considered when a cut-off threshold at a height of 3.88 was selected, resulting in the existence of four groups. The distribution of these clusters according to the coordinates of the microorganisms in the two main components is shown in Fig. 2a. Pairwise plots between the observed values of the three variables (mLS, %ICS and %ISU100) can be observed in Figs. 2b, 2c and 2d. In general, there is a good agreement between the distribution of the microorganisms and the assigned clusters, especially, between %ICS and %ISU100 (Fig. 2d).

Figure 1. Dendrogram with the groups of pathogens resulting from the cluster analysis. Numbers indicate the maximum α error for each branch.

Figure 2. Individual factor map of the principal component analysis (a) and pairwise plots for median of lineal score (mLS), % of isolation in clinical samples and % of isolations in samples with SCC under 100,000 cell/mL values of each pathogen isolated in in dairy cattle milk samples from Galicia (NW Spain) (b, c, d). Avi: Aerococcus viridans, Cfa: Candida famata, Ckr: Candida krusei, Cru: Candida rugosa, Ctr: Candida tropicalis, Cko: Citrobacter koseri, Cor: Corynebacterium spp., Efs: Enterococcus faecalis, Efm: Enterococcus faecium, Esa: Enterococcus saccharolyticus, Ecl: Enterobacter cloacae, Eco: Escherichia coli, Kos: Klebsiella oxytoca, Kpn: Klebsiella pneumoniae, Lac: Lactococcus spp., Noc: Nocardia spp., Pmu: Pasteurella multocida, Pro: Prototheca spp., Pae: Pseudomonas aeruginosa, Ror: Raoultella ornithinolytica, Sma: Serratia marcescens, Sli: Serratia liquefaciens, Shy: Staphylococcus hycus, Sau: Staphylococcus aureus, Sch: Staphylococcus chromogenes, Sep: Staphylococcus epidermidis, Sgm: Staphylococcus gallinarum, Sha: Staphylococcus haemolyticus, Sho: Staphylococcus hominis, Sin: Staphylococcus intermedius, Sle: Staphylococcus lentus, Ssa: Staphylococcus saprophyticus, Ssc: Staphylococcus sciuri, Ssi: Staphylococcus simulans, Swa: Staphylococcus warneri, Sxy: Staphylococcus xylosus, Sag: Streptococcus agalactiae, Sca: Streptococcus canis, Sdy: Streptococcus dysgalactiae, Shs: Streptococcus hyointestinalis, Smo: Staphylococcus mitis/oralis, Spo: Streptococcus porcinus, Ssu: Streptococcus suis, Sth: Streptococcus thoraltensis, Sub: Streptococcus uberis, Sgs: Streptococcus gallolyticus, Tpy: Trueperella pyogenes.

Table 4 shows the significance of the variables in each group in regard to the overall mean. Group I was characterized by low values for mLS and %ICS and high values of %ISU100. Group III presented high values of mLS and low values of %ISU100. Group IV presented the highest values of mLS and %ICS and lower values for %ISU100. Group II did not show any differences regarding the overall mean. Therefore, considering the results of the other groups, it can be placed between Group I and III.

Table 4. Results of the V-test and means of the median of linear score (mLS), percentage of isolations in clinical samples (%ICS) and percentage of isolations in samples with somatic cell counts under 100,000 cells/mL (%ISU100) for each group resulting from cluster analysis.

The maximum α error associated with these groups can also be observed in Fig. 1 and, overall, it matches rather well with the results of the SP analysis.


In general, the groups obtained here were quite consistent with previous literature (Djabri et al., 2002; Taponen et al., 2007; Schukken et al., 2009; Kalmus et al., 2011; Supré et al., 2011) and they may provide useful insight about pathogens with low isolation ra­tes regarding other well-known microorganisms. This classification aims to reflect the global trends in the population of the pathogens regarding mLS, %ICS and %ISU100, which is useful to assess the diagnostic significance of an isolation. We expected high mLS and %ICS values in pathogens related to severe masti­tis, whilst these values should decrease in pathogens associated to less severe mastitis, presenting high %ISU100 in this case. Pathogens were mainly ordered in that way and, according to PCA, the resulting groups were principally formed based on the values in Dim 1 (Table 3), as it can also be interpreted from the V-test results (Table 4). However, some of them punctually diverged from this order, principally due to the expression of different mLS values from those that could be expected from their %ICS values (Fig. 2b). This shows that some variations with regard to this general tendency are also possible, suggesting a likely more complex epidemiology underlying these results.

Group I contained most of minor pathogens, such as Corynebacterium spp. and most of NAS. Pathogens in this group showed the highest %ISU100 and the lowest mLS. Actually, isolations of some species in this group, like Corynebacterium bovis, have proved to mostly reflect a colonization of the teat channel rather than a real IMI (Bexiga et al., 2011). However, major pathogens were divided in three groups (II-IV), which seem to indicate certain heterogeneity among them. Group II presented the least virulent one, and it included pathogens like Staph. aureus and Staph. intermedius, whereas Strep. agalactiae, Strep. uberis, Strep. dysgalactiae were classified in Group III. Group IV, the most virulent one, included pathogens like E. coli and Klebsiella pneumoniae.

Staph. intermedius was the only NAS in the same group as Staph. aureus (Group II), suggesting a higher pathogenicity of Staph. intermedius in comparison to the rest of NAS. As aforementioned, contradictory findings are reported in NAS mastitis (Jarp, 1991; Taponen et al., 2006; Sampimon et al., 2009; Thorberg et al., 2009; Supré et al., 2011; Fry et al., 2014; Condas et al., 2017). These may reflect differences in NAS isolation frequencies or in the pathogenicity of the strains among the different studied populations, which include, in some cases, a limited number of infections.

Group III included pathogens known to be associated to severe mastitis, but also microorganisms with a discussed role in cattle mastitis, like yeasts or Prototheca spp. Yeasts are generally assumed to provoke weak infections rarely associated with clinical mastitis and likely to self-cure (Richard et al., 1980). However, outbreaks with high SCC have also been reported (Malinowsky et al., 2006) and our results seem to be more consistent with this finding. Likewise, mastitis caused by Prototheca spp. is also considered to be rarely associated with clinical signs (Scaccabarozzi et al., 2008), but we also found this genus in Group III. This could indicate that these kinds of infections are responsible for mastitis of relative importance. It is also noteworthy that Pseudomonas aeruginosa, described as a cause of chronic mastitis which can also provoke either acute or subacute processes or subclinical disease (Hogan et al., 1989), was classified in Group III in this study along with other pathogens responsible of severe mastitis.

Group IV was formed by mammary pathogens rarely isolated in mastitis samples such as T. pyogenes and Nocardia spp. This was not an unexpected finding since both pathogens had already been associated with severe mastitis (Packer, 1977; Tarabla et al., 1993). However, according to our results, Pasteurella multocida and Strep. hyointestinalis could also be associated with processes of similar severity.

It is interesting that despite pathogens with %ISU100 >5% were included in Group I, several pathogens included in Groups II and III presented %ISU100 over 2%. Ranges and means of SCC in IMI vary among different pathogens (Djabri et al., 2002), so their isolation frequency is also expected to vary in samples with low SCC. The diagnostic importance of isolations in samples with low SCC may vary depending on the pathogen. Thus, pathogens of Group I in samples with low SCC may indicate mild disease or colonization without IMI, but major pathogens isolated in this type of samples may be interpreted as an early indication of IMI (Schwarz et al., 2010) and their detection would be of importance in cattle mastitis programs. Moreover, higher %ISU100 were found in pathogens usually transmitted from other infected animals (e.g., Staph. aureus and Strep. agalactiae) than in pathogens with an environmental origin (e.g., E. coli or Strep. uberis) (Table 2). This fact has a great importance for control, thus calling for the implementation of new biological markers in order to get a more accurate information of the actual udder health status of dairy cows (Damm et al., 2017).

It is also noteworthy that pathogens traditionally merged in microbiological groups were separated in different clusters in our study, showing potential variability in their pathogenic role (Fig. 1). This could explain some of the divergences found in the literature for species of the genera Enterococcus, Aerococcus or Lactococcus. Species within these genera still have a discussed role in cattle mastitis (Devriese et al., 1999; Zadoks et al., 2011) and while some authors consider them to be minor pathogens (Guélat-Brechbuehl et al., 2010), outbreaks of clinical mastitis involving some of them have also been reported (Todhunter et al., 1995). This kind of differences had already been described for some pathogens among the Enterobacteriaceae family. For example, E. coli and Klebsiella spp. had been attributed to the most severe cases, whereas less clinical severity had been observed in infections caused by Serratia spp. and Enterobacter spp., which were more frequently related to chronic mastitis (Schukken et al., 2012). Since frequencies of isolation of the species that compose these groups may vary among studies and these pathogens may be implicated in mastitis of different severity, our results suggest that merging these species may lead to incorrect inferences of their role in cattle mastitis.

It has to be noted that it is impossible to evaluate all the factors related to the severity of mastitis in this kind of study (such as the duration of the infection or the severity of the exhibited signs in clinical mastitis). Cattle mastitis is dependent on different factors that may affect to the presentation and severity of clinical signs and SCC. Differences in pathogenicity have been observed among strains in both SCC (Zadoks et al., 2002) and clinical outcomes (Middleton & Fox, 2002). In addition, there are also cow-specific characteristics that may lead to wide variations in the course of IMI such as parity, month of lactation or SCC in previous lactation (Steeneweld et al., 2008; Oliveira et al., 2015). Including cow and pathogen factors in the cluster analysis would have provide a more robust result and it would be necessary to a more complete and accurate determination of the pathogenicity of these pathogens, but unfortunately, we could not register animal or herd level information in this study and so, a certain degree of bias is expected. However, since we have analyzed a large number of isolations by pathogen we aimed to be representative of the global trends of each pathogen in a population. Nevertheless, it must be noted that a different cluster solution may be expected in other populations due to differences of cow-speci­fic factors or in the circulating strains. Despite that, our classification is pretty consistent with previous literature (Djabri et al., 2002) and it allows the relati­ve comparison among pathogens. This is particularly interesting for microorganisms with little isolations since their available epidemiological information is limited and it is frequently derived from few herds, which may be influenced by particular characteristics of the animals and herds analyzed. An overrepresentation of severe mastitis in samples submitted for diagnosis is also expected in comparison to a random sampling, so it is possible a trend to over-associate microorga­nisms to more severe cases. Actually, and probably as a consequence of the design of our study, our SCC values were higher than those reported by Djabri et al. (2002) for the pathogens they included in a meta-analysis to evaluate the associated effects of bacteria or group of bacteria with IMI. However, in our opinion, this does not affect our aims, since this study does not intend to establish concrete values for each pathogen, but to perform a relative comparison between the analyzed pathogens.

This study provides a picture of some of the outcomes related to the pathogenicity of several pa­thogens involved in cattle mastitis. Our results are also helpful for the understanding of the importance and frequency of isolation of the different pathogens in samples with low SCC. The groups obtained in this study can aid, together with other factors, in the classification of mammary pathogens. The classi­fication of microorganisms is more complex than the traditional differentiation between minor and major pathogens, especially in the latter case. Our results provide a starting point for the interpretation of the information of pathogens with low prevalence, and highlight the differences among pathogens which are frequently merged in microbiological groups for their study. Further studies, in which other epidemiologic characteristics are added, could aid in understanding the differences in the epidemiology of mastitis pathogens.


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