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Relationship between total bacteria counts and somatic cell counts from mammary quarters infected by mastitis pathogens/Relacao entre contagem total de bacterias e contagem de celulas somaticas de quartos mamarios infectados por patogenos da mastite.


Differences in somatic cell count (SCC) variations and pathological modifications of the mammary gland, according to mastitis pathogens, have been reported in previous studies. The major pathogens cause the greatest SCC increases (Staphylococcus aureus, Streptococcus agalactiae, coliforms, and Streptococcus spp. other than S. agalactiae), and the minor pathogens (Corynebacterium bovis and coagulase-negative staphylococci) usually cause only a moderate increase in SCC (SOUZA et al., 2009). Metaanalysis showed the effects associated with intramammary infection (IMI) at the quarter level by a bacterium or a group of bacteria on SCC. This study showed that the geometric mean (GM) of SCC in bacteriologically negative quarters was 68,000 cells [mL.sup.-1]. In cases of IMI, the SCC were 357,000, 138,000, 857,000, 1,024,000 and 547,000 cells [mL.sup.-1] in quarters infected by Staphylococcus aureus, coagulase-negative staphylococci (CNS), S. agalactiae, Streptococcus uberis and Streptococcus dysgalactiae, respectively (DJABRI et al., 2002).

Cows with udder infection have the potential to shed in excess of 107 bacteria per mL according to BRAMLEY et al. (1990). For instance, S. agalactiae is usually shed in high numbers from infected glands, with a cyclic shedding pattern being typical. Infected cows in the early stage of infection with S. agalactiae can shed up to [10.sup.2] to [10.sup.6] bacteria per mL (KEEFE, 1997). The number of S. agalactiae and S. aureus shed in chronic infections rises and falls in a cyclic pattern (SEARS et al., 1990).

Previous studies (SEARS et al., 1990; ZADOKS et al., 2004) showed that mastitis seldom contributed large numbers of bacteria to the raw milk bulk tank. Nevertheless, the relationship between mastitis pathogens number in bulk tank milk and the percentage of infected cows showed significant a positive correlation (ZADOKS et al., 2004; RYSANEK et al., 2005). However, there is a scarcity of studies designed to assess the bacteria-shedding pattern from infected mammary gland by different mastitis pathogens. The purpose of the present study was to evaluate the shedding of bacteria from mammary quarters according to mastitis pathogens and its relationship with SCC.


Sampling procedure

From June 2009 through December 2009, milk samples from 638 mammary quarters without clinical mastitis were collected. These mammary quarters were from 124 cows on five dairy farms located in Minas Gerais State, Brazil. From each mammary quarter, we collected three milk samples into three different vials. Teats were washed with water and then pre-dipped with a chlorine solution (150 ppm) and dried with individual paper towels after 20-30 seconds. Beginning with the teat farthest from the milker, all four teats were scrubbed with gauze moistened with 70% alcohol. Beginining with the teat closest to the milker, one or two streams of foremilk were discarded from each teat, and, for first quarter sampling, 5 to 10 mL of milk from each quarter was collected in a separate sterile vial for bacteriological examination. The second and third milk samples of each quarter, 30 to 40 mL, were used to determine total bacterial count (TBC) and SCC, respectively. The aseptic sampling procedures for bacteriological examination are described by HARMON et al. (1990). The sampling procedures for TBC and SCC were performed according to international standard (ISO 707, 2008).

Bacteriological examination

Bacteriological culturing of milk samples was performed according to the standards of the National Mastitis Council (HARMON et al., 1990).

Somatic cell count and total bacterial count

SCC were performed using a Bentley Somacount 300 (CHASKA, MN) and TBC determination was performed by direct counting of the bacterial cells using a Bentley IBC (CHASKA, MN).

Statistical analyses

Descriptive statistics and percentiles were performed, using the SCC and TBC values (Table 1) according to the bacteriological examination results. The normality of the raw data (SCC and TBC) distribution was tested. To be able to work with the normal distribution data, we carried out a logarithmic transformation (with logarithm base 10). The significance of the arithmetic mean differences between the [log.sub.10]SCC (Table 2) and [log.sub.10]TBC (Table 2) values according to the bacteriological examination results was determined by a two-tailed unpaired t-test. Linear regression analyses were done using the [log.sub.10]TBC of each mastitis pathogen isolated plus the [log.sub.10]TBC of the milk samples with no growth in the bacteriological examination as the dependent variable (y) and using the [log.sub.10]SCC of the same mammary quarters as the independent variable (x). Thus, four linear regression analyses were performed to compare the slope among mastitis pathogens. A comparison was made between the Pearson correlation coefficients and the Spearman rank correlation coefficients. The linear regression and correlation coefficients were evaluated for statistical significance (Table 3).


In table 1, the descriptive statistics of SCC and TBC according to bacteriological examination results are shown. Of the 638 total mammary quarter milk samples, 421 (66.0%) demonstrated no growth in the bacteriological examination. The SCC geometric mean and median were 52,000 and 70,000 cells [mL.sup.-1], respectively. Mastitis pathogens isolated from mammary quarter milk samples were S. aureus (69/ 10.8%), coagulase negative staphylococci CNS (64/ 10.0%), S. agalactiae (45/7.1%) and other streptococci (39/6.1%). The higher geometric mean and median SCC observed for S. agalactiae were 1,572,000 and 1,839,000 cells [mL.sup.-1], respectively. S. aureus (587,000 and 742,000 cells [mL.sup.-1]) and other streptococci (432,000 and 414,00 cells [mL.sup.-1]) demonstrated higher values. The lower values for the descriptive statistics of the SCC according to mastitis pathogens were for CNS, with values close to being bacteriologically negative. The arithmetic mean of the [log.sub.10]SCC (Table 3) according to the bacteriological examination results increased in this order: no growth, CNS, other streptococci, S. aureus and S. agalactiae. Analysis of the arithmetic mean differences between the [log.sub.10]SCC revealed a significant difference between bacteriological examination results (P<0.05). Bacteriologically negative quarters had the lower [log.sub.10]SCC arithmetic means (1.71) and were different from CNS (1.93). The S. aureus (2.77) and other streptococci (2.64) [log.sub.10] SCC arithmetic means were different from CNS and S. agalactiae (3.20) but both had equal [log.sub.10] SCC arithmetic means (P>0.05).

The TBC geometric mean and median in bacteriologically negative quarters were 12,000 and 10,000cfu [mL.sup.-1], respectively. The values of descriptive statistics for TBC according to mastitis pathogens did not have the same order as SCC. The higher values of the geometric mean and median were for S. agalactiae (333,000 and 590,000cfu [mL.sup.-1]). Other streptococci (108,000 and 100,00cfu [mL.sup.-1]) and S. aureus (77,000 and 66,000cfu [mL.sup.-1]) presented with higher TBC values. The TBC geometric mean (17,000cfu [mL.sup.-1]) and median (15,000cfu [mL.sup.-1]) for CNS were as for SCC, in other words, close to bacteriologically negative quarter values. Analysis of the arithmetic mean differences between the [log.sub.10] TCB revealed a significance between bacteriological examination results (P<0.05). The arithmetic mean of the [log.sub.10] TBC (Table 3) according to bacteriological examination results increased in a different order than the [log.sub.10] SCC. The order was as follows: no growth, CNS, S. aureus, other streptococci and S. agalactiae. In this situation, no difference (P>0.05) was observed between the [log.sub.10] TBC arithmetic mean for no growth (1.09) and CNS (1.22). S. aureus and other streptococci had no difference in the [log.sub.10] TBC arithmetic mean (P>0.05). However, these were different (P<0.05) from the no growth, CNS and S. agalactiae arithmetic means. The geometric mean and median, which are relatively non-affected by extreme high values, better indicated the central number of the SCC and TBC found in mammary quarter milk samples than did the arithmetic mean. Large standard deviations were found for each mastitis pathogen in SCC and TBC, indicating that the data set had a great deal of variation. Because of the wide range of SCC and TBC results, logarithmic transformation was performed for comparison between means and the association between SCC and TBC. The corresponding Spearman's rank correlations and Pearson correlations were in agreement (Table 2). Spearman and Pearson correlations used the logarithmic transformation of SCC and TBC. The correlation coefficients found between [log.sub.10] SCC and [log.sub.10] TBC for S. agalactiae (0.674, Spearman; 0.677, Pearson), S. aureus (0.677, Spearman; 0.659, Pearson), other streptococci (0.665, Spearman; 0.654, Pearson) and CNS (0.632, Spearman; 0.626, Pearson) were moderately high (P<0.01). The linear regression model significances were high (P<0.001) and showed different slopes for each mastitis pathogen (Table 3). In order, the higher slope was found for S. agalactiae (0.542), S. aureus (0.503), other streptococci (0.486) and CNS (0.440).


In mammary quarter milk samples that were not observed to have growth in the bacteriological examination, the geometric mean of the SCC was 52,000 cells [mL.sup.-1]. The 95% confidence interval of individual values based on standard deviation varied between 205,000 and 357,000 cells [mL.sup.-1]. A meta-analysis study on quarter milk SCC in dairy cows found 68,000 cells [mL.sup.-1] to be the geometric mean in the absence of a diagnosed infection (DJABRI et al., 2002). The summary SCC values in bacteriologically negative quarters were between 68,000 and 187,000 cells [mL.sup.-1]. The SCC geometric means of mammary quarters classified as not infected in this study and by DJABRI et al., (2002) were close and it could be considered equal in theoretical and practical approach. The SCC geometric mean found in the present study for S. agalactiae (1,572,000 cells [mL.sup.-1]) was also close to the value found in the meta-analysis study (1,129,000 cells [mL.sup.-1]). Among the causative pathogens of subclinical cases, such as S. aureus, S. agalactiae, CNS and other streptococci (HARMON, 1994), SCC values for S. agalactiae were higher than other mastitis pathogens in both studies. The highest SCC identified among mastitis pathogens found by DJABRI et al. (2002) was caused by Escherichia coli, but this pathogen usually promotes clinical mastitis due to virulence factors and consequently pathophysiological processes (HARMON, 1994; HOGAN & SMITH, 2003). The SCC geometric mean for S. aureus (587,000 cells [mL.sup.-1]) was twice as high as that found in the meta-analysis study (333,000 cells [mL.sup.-1]). The inverse situation was observed for CNS; in the meta-analysis study, the SCC geometric mean was 155,000 cells [mL.sup.-1], and in present study it was 85,000 cells [mL.sup.-1]. However, the 95% confidence intervals for the SCC according to these mastitis pathogens had a high concordance of values with those reported by DJABRI et al. (2002). The SCC geometric mean for other streptococci could not be compared with the results found because the present study did not identify other streptococci than S. agalactiae. The studies cited in the meta-analysis evaluated SCC from mammary quarters infected by Streptococcus uberis and Streptococcus dysgalactiae. Although the present study did not identify S. uberis and S. dysgalactiae, the SCC geometric mean value for other streptococci was 432,000 cells [mL.sup.-1]. Data reported by DJABRI et al (2002) showed that SCC geometric means for S. uberis and S. dysgalactiae were 1,024,000 and 547,000 cells [mL.sup.-1], respectively. The SCC geometric mean for other streptococci was closer to that of S. dysgalactiae than S. uberis, but the values contained in the confidence interval overlapped in both studies. Thus, it cannot be suggested, based on these results that most of the other streptococci were S. dysgalactiae or S. uberis.

Considering the threshold of70,000 cells [mL.sup.-1] to sort infected mammary quarters, the results of this study are consistent with previous studies (SCHEPERS et al., 1997; DJABRI et al., 2002). The descriptive statistics showed wide variation in the results of SCC and TBC according to bacteriological examination results. This variation suggests that bacteriological examination may have provided false positive and false negative results due to the use of a single quarter milk sample to determine the infection status and mastitis pathogens, such as S. agalactiae and S. aureus, in chronic infections that were shedding bacteria in a cyclic pattern (DINSMORE et al., 1991; HARMON, 1994; SEARS et al., 1990). Moreover, milk samples from non-infected mammary quarters obtained without washing or with improper washing of teats had higher TBC (7,000cfu [mL.sup.-1]) than cows with teats dipped and dried with paper towels properly (1,500cfu [mL.sup.-1]) (MCKINNON et al., 1988). Thus, bacteria, such as CNS, other streptococci and Corynebacterium spp., may have contributed to higher TBC values in the milk samples with no growth of mastitis pathogens due to the contamination of these pathogens from a part of the resident bacterial flora on the teat skin (HARMON, 1994).

Results on the shedding of bacteria by the mammary quarters according to bacteriological examination results showed similarity with the results of SCC. The main difference in the results of TBC in relation to SCC was for S. aureus and other streptococci. In this case, the shedding of bacteria from infected mammary quarters by other streptococci was higher when compared with those infected with S. aureus. The other pathogens isolated (S. agalactiae and CNS) presented the higher and lower shedding of bacteria and SCC, respectively. The geometric mean found for milk samples with the isolation of mastitis pathogens ranged from 17,000 to 333,000cfu [mL.sup.-1]. The variation in the TBC results are in agreement with KEEFE (1997) and BRAMLEY et al. (1990), who indicated that the shedding of bacteria by infected cows ranges from 102 to 107 per mL. GONZALEZ et al. (1986) found a moderately high correlation obtained for S. agalactiae, indicating that values greater than 4,000cfu [mL.sup.-1] in bulk milk tank indicates that at least 7% of cows were shedding this bacteria. This correlation was fewer compared to S. aureus, other streptococci, CNS and coliform bacteria. However, S. uberis was identified as a mastitis pathogen causing TBC peaks in bulk tank milk, ranging from 14,000 to 600,000cfu [mL.sup.-1] (HAYES et al., 2001; ZADOKS et al., 2004). Thus, the impact of mastitis pathogens on bulk tank TBC depends on the percentage of infected mammary quarters and type of bacterial mastitis pathogen involved. The stage of infection can also contribute, with an increased shedding of bacteria from the mammary gland (SEARS et al., 1990; KEEFE, 1997) causing an increase in the bulk tank TBC (RYSANEK et al., 2005).

Correlation results showed that when SCC were higher, the number of mastitis pathogens per milliliter of milk sample was also expected to be high. Thus, SCC was a good indicator of shedding bacteria from mammary glands, but these correlations were not sufficient to assess differences between mastitis pathogen with the same increasing SCC. However, linear regression slopes predicted that the same increasing SCC was associated with different numbers of bacteria being shed from the mammary gland according to mastitis pathogen. These results showed that S. agalactiae and other streptococci can cause TBC peaks in bulk tank milk (HAYES et al., 2001; ZADOKS et al., 2004). As mentioned previously, the magnitude of the peak of bulk tank milk TBC will depend on the percentage of infected mammary quarters, the strain of bacteria and stage of infection.


In conclusion, the greater the intensity of the inflammatory process, the greater was the shedding of bacterial mastitis pathogens from the mammary gland. However, the magnitude of shedding of mastitis pathogens from the mammary gland was different according to mastitis pathogens. The combination of SCC and TBC from mammary quarters may be useful as screnning test for streptococci mastitis. More studies are necessary to better understand the variation in the shedding of mastitis pathogens according to the age, parity, lactation stage and infection stage of the cow.


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Joao Emidio Ferreira Lopes Junior (I) Carla Cristine Lange (II) Maria Aparecida Vasconcelos Paiva Briton (II) Fabiana Ribeiro Santos (III) Marco Aurelio Souto Silva (II) Luciano Castro Dutra de Moraes (II) Guilherme Nunes de Souza (II) *

(I) Mestrado Profissional em Ciencia e Tecnologia do Leite e Derivados, Faculdade de Farmacia e Bioquimica, Campus da Universidade Federal de Juiz de Fora (UFJF), Juiz de Fora, MG, Brasil.

(II) Embrapa Gado de Leite, Rua Eugenio do Nascimento, 610, Dom Bosco, 36038-330, Juiz de Fora, MG, Brasil. E-mail: * Autor para correspondencia.

(III) Fundacao de Apoio e Desenvolvimento ao Ensino, Pesquisa e Extensao (FADEPE), Centro Regional de Inovacao e Transferencia de Tecnologia (CRITT), Juiz de Fora, MG, Brasil.

Received 08.18.11 Approved 11.15.11 Returned by the author 04.09.12 CR-5856
Table 1--Descriptive statistics for somatic cell counts (x 1,000
cells [mL.sup.-1]) and total bacterial counts (x 1,000cfu
[mL.sup.-1]) from mammary quarters according to bacteriological
examination results.

                   NG              CNS              STA

Statistics    SCC     TBC     SCC     TBC      SCC     TBC

N            421     421     64      64       69      69
GM           52      12      85      17       587     77
AM           281     10      258     31       1,041   260
SD           793     256     492     59       1,138   460
P10          3       2       3       4        172     14
P25          10      6       62      8        273     23
ME           70      10      133     15       742     66
P75          227     22      215     35       1,342   269
P90          610     79      595     56       2,175   772

                   STR             SAG

Statistics    SCC     TBC     SCC     TBC

N            39      39      45      45
GM           432     108     1,572   333
AM           1,504   239     2,508   759
SD           2,468   452     1,792   749
P10          67      21      390     18
P25          118     48      1,151   174
ME           414     100     1,839   590
P75          996     189     4,411   1,028
P90          7,426   758     5,248   2,078

N--number of samples; GM--geometric mean; AM--aritmethic mean;
SD--Standard deviation ; P10--10th percentile; P25--25th percentile;
ME--median; P75--75th percentile; P90--90th percentile; NG--no growth;
CNS--coagulase negative staphylococci; STA--Staphylococcus aureus;
STR--other streptococci; SAG--Streptococcus agalactiae;
SCC--somatic cell count; TBC--total bacterial count.

Table 2--Significance of differences between the means of mammary
quarter somatic cell count ([Log.sub.10]SCC) and total bacterial
count ([Log.sub.10]TBC) according to bacteriological examination

BER   Number           [Log.sub.10]SCC             [Log.sub.10]TBC
      samples   Mean     SD        CI       Mean     SD       CI

NC    421       1.71a   0.88   1.63-1.80    1.09a   0.63   1.03-1.15
CNS   64        1.93b   0.80   1.73-2.13    1.22a   0.46   1.10-1.33
STA   69        2.77c   0.57   2.63-2.90    1.88b   0.72   1.71-2.06
STR   39        2.64c   0.72   2.40-2.87    2.03b   0.50   1.87-2.20
SAG   45        3.20d   0.60   3.03-3.39    2.52c   0.77   2.29-2.76

BER--bacteriological examination results; NG--no growth;
CNS--coagulase negative staphylococci; STA--Staphylococcus aureus;
STR--other streptococci; SAG--Streptococcus agalactiae; SD--standard
deviation; CI--confidence interval (95%). Different letters between
rows indicate statistic significance by two-tailed unpaired t-test

Table 3--Correlation and simple regression analysis for somatic cell
count ([log.sub.10]SCC) and total bacterial count ([log.sub.10]TBC)
according to bacteriological examination results.

                                     Parametric analysis

           Non parametric    Pearson
BER         analysis (1)    Correlation    Regression equation

NC + CNS      0.632 **       0.626 **     y = 0.338 + 0.440x ***
NC + STA      0.677 **       0.659 **     y = 0.263 + 0.503x ***
NC + STR      0.665 **       0.654 **     y = 0.295 + 0.486x ***
NC + SAG      0.674 **       0.677 **     y = 0.217 + 0.542x ***

BER--bacteriological examination results; NG--no growth;
CNS--coagulase negative staphylococci; STA--Staphylococcus aureus;
STR--other streptococci; SAG--Streptococcus agalactiae; (1) Spearman's
rank correlation; ** (P<0.01); *** (P<0.001).
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Author:Lopes, Joao Emidio Ferreira, Jr.; Lange, Carla Cristine; Briton, Maria Aparecida Vasconcelos Paiva;
Publication:Ciencia Rural
Date:Apr 1, 2012
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