• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of iaiPermissionsJournals.ASM.orgJournalIAI ArticleJournal InfoAuthorsReviewers
Infect Immun. Mar 2006; 74(3): 1907–1915.
PMCID: PMC1418644

Genome-Wide Expression Analysis of Lipopolysaccharide-Induced Mastitis in a Mouse Model


To better understand the acute host response to Escherichia coli mastitis, we analyzed gene expression patterns of approximately 23,000 transcripts 4 h after an intramammary infusion of lipopolysaccharide (LPS) in a mouse model. A total of 489 genes were significantly affected, of which 391 were induced and 98 were repressed. Gene ontology analysis demonstrated that most of the induced genes were associated with the innate immune response, apoptosis, and cell proliferation. Substantial induction of the chemokines CXCL1, CXCL2, and S100A8; the acute-phase protein SAA3; and the LPS binding protein CD14 were confirmed by Northern blot analysis. A subsequent time course experiment revealed CXCL1 induction prior to that of CD14 and SAA3. Mammary epithelial cell cultures also showed marked expression of these factors in response to LPS. The expression of immune-related genes in mammary epithelial cells indicates the importance of this cell type in initiating the inflammatory responses. Repressed genes include several carbohydrate and fatty acid metabolic enzymes and potassium transporters, which may contribute to milk composition changes during mastitis. Therefore, the overall transcription profile, in conjunction with gene ontology analysis, provides a detailed picture of the molecular mechanisms underlying the complex biological processes that occur during LPS-induced mastitis.

Mastitis is an inflammation of the mammary gland that is usually due to bacterial infection. It is relatively common in lactating women, and incidence rates of 10% to 20% are typically reported, although it has not been extensively studied (5). In contrast, the disease receives considerable attention in the dairy industry because it is the most costly infectious disease of dairy cattle worldwide. In the U.S. dairy industry alone, it causes $2 billion in annual losses due to discarded milk, decreased milk yield and quality, and increased costs for veterinary care and herd management labor (29).

Mastitis caused by Escherichia coli is relatively common in high-producing cows, particularly around parturition or during early lactation (8). Though most cows that have E. coli mastitis undergo self-cure, this disease is very acute, and in some cases it can cause severe tissue damage and even death of the animals due to endotoxin (lipopolysaccharide [LPS]) shock. Previous studies of dairy animals have analyzed changes in specific protein production and/or gene expression during experimental challenge or naturally occurring mastitis (3, 14, 27), but the information provided by these studies is limited to a relatively small number of genes encoding cytokines and chemokines; the expression of other genes involved in mastitis, as well as the signal transduction cascades underlying these changes, is not well defined.

Microarray technology has become an important tool to study the complex interactions of host and bacterial pathogens in various infectious-disease settings (6, 9, 26). The major advantage of this technology is the ability to simultaneously monitor differential gene expression for thousands of genes. The finding of known and unknown genes whose expression is modulated by bacterial pathogens, as well as the elucidation of intracellular signaling cascades underlying these changes, has greatly improved the understanding of the host response to infection. Therefore, the goal of the present study was to analyze the global transcriptional responses to an intramammary infusion of LPS in a mouse model to better understand the complex physiological and cellular processes of mastitis. Large, well-annotated bovine microarray chips are on the near horizon; in the meantime, the well-annotated murine microarray chip and genome allowed detailed analysis of the mammary gland response to LPS.



Seven-week-old C57BL/6J mice were purchased from Jackson Laboratory (Bar Harbor, Maine) and housed in the animal facility at the University of Vermont. All animal experiments were conducted in accordance with a protocol approved by the University of Vermont Institutional Animal Care and Use Committee. Females were mated at 8 weeks of age. Following parturition, lactating females were selected for further experiments when they were raising at least five pups.

Intramammary challenge with E. coli O111:B4 lipopolysaccharide.

On days 7 to 11 of lactation, two abdominal mammary glands (L4 and R4) of avertin-anesthetized mice were infused with 50 μl of either LPS (L3024; Sigma) solution (20 ng/μl; n = 3) or saline solution (n = 3) through a 34-gauge needle (MF34G-5; World Precision Instruments, Inc.) affixed to a Hamilton syringe. In order to confirm the success of infusion, LPS solution (20 μg/ml) was made in a trypan blue solution (prepared in 0.81% sodium chloride and 0.06% potassium phosphate; T-8154; Sigma), which was also used as the saline control. For infusion, the anesthetized mice were placed on the platform of the dissecting microscope, and the abdominal surface was sanitized with 70% ethyl alcohol. The distal ends of the teats (1 to 2 mm) were aseptically removed with fine scissors in order to precisely inject the solution into the lactophore. Immediately after the injection, the mice were given buprenorphine (0.05 mg/kg) as an analgesic through intraperitoneal injection. The mice were disinfected again and allowed to recover under a warming lamp.

Mammary tissue collection.

The mice were euthanized 4 h postinjection, and the entire infused mammary glands were removed. One-half of each mammary gland (~200 mg) was immediately homogenized in 2 ml of Trizol (Invitrogen), frozen in liquid nitrogen, and then stored at −80°C until further RNA extraction was performed. A portion of the remaining tissue was fixed in 10% neutral buffered formalin for subsequent histological analysis (~100 mg), and the remainder was frozen in liquid nitrogen and kept at −80°C prior to protein isolation (~100 mg).

Total RNA was extracted from the homogenized tissues according to the manufacturer's procedure, eluted in nuclease-free water, and then stored in aliquots at −80°C until it was used. In order to eliminate contaminating genomic DNA, total RNA was treated with DNA-Free DNase (Ambion) according to the manufacturer's instructions. The quality of total-RNA samples was analyzed by measurement of the optical density at 260/280 nm using a NanoDrop ND-1000 (NanoDrop Technologies) and by inspection of 18S and 28S rRNA bands after agarose gel electrophoresis. The integrity of total-RNA samples was further verified with a Bioanalyzer (Agilent Technologies) before they were used in microarray analysis.

Target preparation, hybridization, and probe array processing.

Target preparation, hybridization, and probe array processing (washing, staining, and scan) were performed according to the protocols in the manufacturer’s instructions (Affymetrix Inc.) by the University of Vermont's DNA analysis facility. Briefly, 5 μg of total RNA was first reverse transcribed to the single-stranded cDNA using a T7 oligo(dT) primer, followed by RNase H-mediated second-stranded cDNA synthesis using T4 DNA polymerase. The resulting double-stranded cDNA was used as a template to produce the biotinylated cRNA targets in the presence of T7 RNA polymerase and a biotinylated nucleotide analog-ribonucleotide mixture. The full-length biotinylated cRNA (15 μg) was then fragmented into 35- to 200-base fragments by metal-induced hydrolysis. This fragmented cRNA was then hybridized to Moe430a arrays for 16 h at 45°C in a rotating Affymetrix GeneChip Hybridization Oven 320. Immediately following hybridization, the arrays were washed and stained with streptavidin-phycoerythrin on an automated Affymetrix GeneChip fluidics 450 station, followed by scanning on an Affymetrix GeneChip scanner 2700.

Microarray expression analysis.

The quality of microarray data was assessed by monitoring a series of quality control parameters as suggested by Affymetrix, including visually inspecting the array images to confirm scanner alignment and the absence of any image artifacts; determining that Affymetrix MAS 5.0 scaling factors, as well as background, Q values, and mean intensities, for all arrays were within acceptable limits; and determining that the 3′/5′ ratios for the probe sets for β-actin and GAPDH (glyceraldehyde-3-phosphate dehydrogenase) were within acceptable limits.

The initial data analysis, including normalization, background correction, expression index calculation, and visualization of chip-to-chip variation, was performed using the affy package of Bioconductor version 1.7.1 (13). The default settings used for our analysis were qspline (33) for array normalization; Li-Wong model-based expression indexing (20) for gene expression calculations; and bg.adjust, found in the affy package of Bioconductor, to perform global background correction. We employed a t test on each gene in each of the two categories (control and LPS treated) using the R statistics programming language (http://www.r-project.org) and subsequently deemed genes with a P value of <0.05 and an absolute change of more than or less than 1.7-fold to be significantly differentially expressed. Change values (n-fold) for significant genes were calculated as ratios of mean signal values (saline control arrays served as the baseline for the LPS treatment comparison). For downregulated genes, the ratio was further transformed by dividing into 1 and designation as a negative change value (n-fold).

Gene ontology analysis using GenMAPP.

In order to better understand the biological significance of gene expression data, GenMAPP (Gene Microarray Pathway Profiler) 2.0 was used for graphically viewing and analyzing microarray data in the context of biological pathways, such as gene ontology (GO) terms (http://www.genmapp.org/) (10, 11). Briefly, a file containing the complete set of gene expression data was imported into GenMAPP 2.0 to produce a GenMAPP Expression Data set file according to the instructions. In the GenMAPP Expression Data set file, several user-defined criteria for biological-pathway analysis were established: (i) red color indicated that gene expression was significantly increased (P < 0.05 and changes ≥ 1.7-fold), (ii) blue color indicated that gene expression was significantly decreased (P < 0.05 and changes ≥ −1.7-fold), (iii) gray color indicated that gene expression was not significantly changed, and (iv) white color indicated that the gene did not exist in the array. After this step, MAPPFinder 2.0, a component of GenMAPP 2.0, could dynamically assign these gene expression data to the GO biological-process, cellular-component, and molecular-function terms. A statistically ranked list of GO terms was then generated in terms of the z score, which is a statistical measure of the relative amounts of gene expression changes in a given GO term (11). The z score was calculated by “subtracting the expected number of genes in a GO term meeting the criterion from the observed number of genes, and dividing by the standard deviation of the observed number of genes” (11). A z score of more than 2 is considered a statistically significant association between the differentially regulated genes and their corresponding GO terms. The GO list was further manually filtered in terms of the following criteria: (i) nonredundancy, (ii) at least three significantly regulated genes in the selected GO terms, and (iii) biological relevance to LPS-induced mastitis. The induced or repressed genes were extracted from their corresponding GO categories.

Northern blot analysis.

Total RNA was extracted from the homogenized tissues according to the manufacturer's procedure. Sample RNA (10 μg) was denatured, separated on a 1.1% agarose gel containing 0.66 M formaldehyde, and then transferred to Gene Screen hybridization membranes (NEN/Perkin-Elmer) by capillary blotting. Murine probes were prepared by reverse transcription (RT)-PCR from total RNA from LPS-treated mouse mammary tissues using the oligonucleotides listed in Table Table1.1. The mouse GAPDH probe was used as a loading control. The probes were labeled with [32P]dCTP using a Prime-IT Random Primer Labeling Kit (Stratagene) and purified using ProbeQuant G-50 Micro Columns (Amersham). For hybridization, the membranes were placed in hybridization bottles containing 2× SSPE (1× SSPE is 0.18 M NaCl, 10 mM NaH2PO4, and 1 mM EDTA [pH 7.7]) at room temperature for 30 min and at 42°C for 30 min. The prewarmed membrane was prehybridized at 42°C for 1 h with 10 ml of prewarmed hybridization buffer containing 5× SSPE, 5× Denhardt's solution, 50% formamide, 5% dextran, 1% sodium dodecyl sulfate (SDS), and 100 μg/ml salmon sperm DNA. Each probe was added to the hybridization buffer and mixed immediately, and then the mixture was incubated overnight at 42°C. The membranes were washed twice with 2× SSPE-1% SDS at room temperature and twice with 0.2× SSPE-0.1% SDS at 60°C. Radioactive signals were detected with a phosphorimager (Storm; Molecular Dynamics) and quantified and analyzed using the accompanying software (ImageQuant).

Gene names and primer sequences for RT-PCR

Western blot analysis.

Total protein was extracted from frozen mammary gland tissue as described previously (30). Briefly, the tissue was homogenized in lysis buffer, incubated for 10 min at 4°C, and cleared by centrifugation at 20,000 × g at 4°C for 10 min. The protein concentration of the supernatant was determined by Bio-Rad protein assay according to the instructions, and the protein extract was stored in aliquots at −80°C until further protein assays were performed. For Western blot analyses, proteins (80 μg) were fractionated on SDS-12% polyacrylamide gels with the Mini-Protein II Electrophoresis Cell System (Bio-Rad) and transferred to nitrocellulose membranes (Millipore Corporation) using a Semi-Dry Transfer Cell (Bio-Rad). The membranes were blocked with 5% skim milk (Difco) in phosphate-buffered saline with 0.05% Tween 20 (PBS-T) at 4°C overnight with gentle agitation, followed by a 2-hour incubation at room temperature with a 1:200 (vol/vol) dilution of primary antibody in PBS-T. The blots were washed in PBS-T three times for 5 min each time with gentle agitation and then incubated with a 1:20,000 dilution (vol/vol) of secondary antibody for 2 h at room temperature with gentle agitation. The blots were washed three times for 5 min each time with Tris buffer (0.1 M Tris, 0.5 mM MgCl2, pH 9.5), and target proteins were visualized using a BCIP (5-bromo-4-chloro-3-indolylphosphate)/nitroblue tetrazolium system (Bio-Rad). The antibodies were purchased from Santa Cruz Biotechnology (CD14, M-20; calgranulin A [S100A8], M-19).

Histological analysis.

Fresh mammary tissue was fixed in 10% neutral buffered formalin (Fisher) overnight at room temperature and then stored in 70% ethyl alcohol until it was processed. Tissue samples were embedded in paraffin, sectioned at ~4-μm thicknesses, and mounted onto silanized slides, with two sections mounted per slide. For hemotoxylin and eosin (HE) staining, after deparaffinization and hydration, sections were stained with hemotoxylin and eosin. For immunohistochemical staining, a SuperPicTure Polymer Detection kit (Zymed Laboratories Inc.) was used, following the manufacturer's protocol. The dilution of CD14 antibody (M-20) was 1:100.

Mammary epithelial cell culture.

HC11 cells (a murine mammary epithelial cell line) were maintained in RPMI 1640 medium supplemented with 10% fetal bovine serum, 5 μg/ml of insulin, 10 ng/ml epidermal growth factor, and 50 μg/ml gentamicin at 37°C in a humidified atmosphere containing 5% CO2. HC11 cells were plated in T-25 flasks at 1 × 105 cells per flask. After 1 day, the HC11 cells were stimulated with various doses of LPS as described previously (35). The experiment was repeated three times on separate days, and three different blots were made using sample RNA (5 μg) from three independent experiments. The blots were randomly hybridized to one of three probes (CD14, CXCL1, or SAA3) as described above. Mouse β-actin was used as a loading control for each blot.

Time course assay of LPS-induced mastitis.

Intramammary infusion, tissue collection, and Northern blot analysis were performed as described above with the following changes: saline and LPS were infused into the L4 and R4 abdominal glands, respectively, of each mouse; the mice were killed at 1, 2, and 6 h postinfusion; and only mice who had both glands successfully infused were included for subsequent assay (as a result, the 1-h time point had two mice, and both the 2-h and 6-h time points had three mice).


Microarray expression analysis of LPS-induced mastitis.

Microarray analysis of mammary RNA obtained 4 h post-LPS or -saline infusion revealed a total of 489 differentially expressed transcripts (2.16% of the 22,690 probe sets represented on the array) using the following cutoff criteria: P < 0.05 and change ≥1.7-fold (for a complete list of significantly regulated genes, see the supplemental material). Eight representative genes were selected for validation using Northern blot analysis (Fig.(Fig.1A),1A), including cytokines (interleukin 1β [IL-1β], IL-6, and colony-stimulating factor 3 [CSF3]), chemokines (CXCL1 and CXCL2), an LPS signaling receptor (CD14), an acute-phase protein (SAA3), and a chemotactant (S100A8). GAPDH was analyzed as an internal control. While GAPDH remained unchanged by both microarray and Northern blot analyses, the levels of induction (n-fold) in expression of the selected genes were of similar magnitudes in both techniques. In order to verify that changes at the mRNA level reflected changes at the protein level, Western blot analysis was used to validate CD14 and S100A8 expression. Western blot analysis indicated that CD14 and S100A8 proteins from the LPS-treated tissue lysates were induced compared to those from the saline controls (Fig. (Fig.1B1B).

FIG. 1.
Microarray analysis of selected mammary gland gene expression changes 4 h after intramammary infusion of saline or LPS was verified by Northern blot and Western blot analyses. (A) Northern blot analysis. A total of nine representative genes were selected ...

GO analysis using MAPPFinder.

In order to gain biological insights from the enormous amount of microarray data, gene expression results were analyzed in the context of biological processes. MAPPFinder 2.0, a part of GenMAPP 2.0, was used to dynamically assign gene expression data to the GO biological-process categories. The program calculates z scores, which are then used to rank GO categories. Lists of nonredundant GO biological-process categories that are significantly (z score > 2) associated with differentially regulated genes are shown in Table Table22 (induced genes) and Table Table33 (repressed genes).

List of GO biological processes associated with significantly upregulated genes
GO biological processes associated with significantly downregulated genes

The GO biological-process categories “cytokine biosynthesis,” “inflammatory response,” “chemotaxis,” and “acute-phase response” were significantly associated with the induced inflammatory genes, such as those for cytokines (IL-1β and IL-6), chemokines (CXCL1 and CXCL2), acute-phase proteins (SAA3 and Hp), the LPS signaling receptors (TLR-4 and CD14), and adhesion molecules (SELP), as well as the induced genes encoding several stress-responsive transcription factors (STAT3 and NF-κB) (Table (Table2).2). These results indicate that the immune response is the predominant biological process influenced by LPS-induced mastitis. In addition, the GO categories “cell proliferation” and “apoptosis” were also significantly associated with induced genes (Table (Table2).2). The proapoptotic induced genes are those for IL-6, CASP4, and BID, while antiapoptotic induced genes include those for BIRC2, BIRC3, BCL2ALA, TNFALP3, and MCL1. Most of the genes involved in “cell proliferation” encode protein kinases and transcription factors that are associated with the regulation of the cell cycle, such as FYN, RELA, FOS, and JUN. GO analysis of the repressed genes revealed several clear biological processes, including “potassium transport,” “main pathways of carbohydrate metabolism,” and “fatty acid metabolism” (Table (Table3).3). Three members of potassium channels were repressed, including KCNK1, KCNK4, and KCNK5. Five repressed fatty acid metabolic enzymes (UCP3, CTE1, EHHADH, FACL2, and HADHB) were found, as were three repressed enzymes involved in carbohydrate metabolism (GPD1, OXCT, and ACLY).

The infiltration of inflammatory cells into LPS-treated mammary glands.

The influx of inflammatory cells, particularly neutrophils, into the mammary gland is a characteristic feature of mammary gland infection. In order to confirm this phenomenon, we performed HE staining of formalin-fixed lactating mammary tissues obtained 4 h after the intramammary infusion of LPS. As shown in Fig. Fig.2A,2A, a moderate infiltration of inflammatory cells was evident at this fairly early time point. A group of induced genes that may be involved in this chemotactic process were also identified (Fig. (Fig.2B).2B). These include chemokines (CXCL1, CXCL2, CXCL10, CXCL16, CCL2, CCL3, CCL7, and CCL20), a chemokine receptor (CCRL2), adhesion molecules (ICAM1, ICAM2, VCAM1, ITGA8, SELP, CD44, TNFAIP6, and EVA1), and other chemotactants (S100A8 and S100A9).

FIG. 2.
Infiltration of immune cells into the mammary gland tissues in response to LPS infection. (A) HE staining of mouse mammary tissues at 4 h after intramammary infusion of saline and LPS (1 μg). The infiltration of inflammatory cells (dark dots) ...

Gene expression analysis in murine mammary epithelial cells.

Immunohistochemistry analysis demonstrated that CD14 was strongly induced in mammary luminal epithelial cells at 4 h after LPS infusion (Fig. (Fig.3A),3A), thus confirming that mammary epithelial cells are a potential source of the induced proinflammatory mediators. The staining appeared to detect membrane-bound CD14 and was located almost exclusively on the epithelial cells.

FIG. 3.
Expression of immune-related genes in mammary epithelial cells. (A) Immunohistochemistry analysis of CD14 protein in mouse mammary tissues at 4 h after intramammary infusion of saline and LPS (1 μg). One saline control tissue section and one LPS-treated ...

Additionally, as shown in Fig. Fig.3B,3B, CD14, CXCL1, and SAA3 mRNAs were dramatically induced by LPS in a dose-dependent manner in HC11 cells. Expression of other genes, including those for CXCL2, S100A8, CSF3, IL-6, and IL-1β, were also detected in LPS (1 μg)-treated HC11 cells using standard RT-PCR (data not shown). Thus, these data suggest that, in addition to immune cells, mammary epithelial cells also play an important role in the modulation of LPS-induced mastitis.

Time course assay of gene expression during LPS-induced mastitis.

In order to analyze the kinetic pattern of gene expression, a time course experiment was performed. Total RNA (20 μg) was extracted from mammary glands of mice euthanized at 1, 2, and 6 h after intramammary infusion of either saline or LPS and subjected to Northern blot analysis for expression of CD14, CXCL1, SAA3. As shown in Fig. Fig.4,4, CD14 and SAA3 were induced in a time-dependent manner, whereas CXCL1 expression was rapidly induced at 1 h and then remained elevated through 6 h. The very early and potent induction of CXCL1 undoubtedly reflects epithelial signaling leading to infiltration of immune cells during LPS-induced mastitis. The induction of CD14 at 6 h, also observed at 4 h (Fig. (Fig.1A),1A), is likely an early epithelial response supplemented over time by macrophage activation and infiltration. Furthermore, in this experiment, the saline- and LPS-infused glands were from the same animal. The lack of gene induction in the saline-infused glands indicates the local nature of the LPS response and also that any inflammation due to removal of the distal-teat tissue was minimal.

FIG. 4.
Time course of CD14, CXCL1, and SAA3 gene expression during LPS-induced mastitis. Mice were killed at 1, 2, and 6 h after intramammary infusion of either saline (−) or LPS (+). Total RNA (20 μg) was isolated from mammary glands ...


In this work, the genomic response of the mammary gland to an intramammary infusion of LPS was captured 4 hours following an LPS challenge. At this early time point, there was minimal infiltration of immune cells. The differentially regulated genes were analyzed in the context of biological pathways to facilitate a better understanding of previously known biological observations and to generate new hypotheses. The GO biological processes associated with significantly induced or repressed genes during LPS-induced mastitis are shown in Table Table22 and Table Table3.3. In light of recent rapid progress in the understanding of the molecular mechanisms of the TLR4 signaling pathway (1), the gene expression patterns revealed by the array data clearly illustrate the innate immune response of the mammary gland, which is characterized by production of a plethora of important mediators of innate immunity following activation of the TLR4. TLR4 has evolved with its accessory proteins (LBP, CD14, and MD-2/Ly96) to detect the presence of bacterial LPS. The recognition of the LPS molecule by TLR4 results in the activation of intracellular signaling molecules (TIRAP, MyD88, IRAK4, IRAK1, TRAF6, and TAK complex) and then initiates two divergent signaling cascades, the I-κB/NF-κB cascade and the MAPK cascade. The I-κB/NF-κB cascade activates the NF-κB transcription factor family, while the MAPK cascade involves the activation of MAP kinase (p38 and JNK), which leads to the activation of the AP1 transcription factor family. Activated NF-κB and AP1 then induce target genes associated with cytokine biosynthesis, inflammatory response, and chemotaxis. The induction of these biological processes was clearly observed in the current study (Table (Table2),2), as was the induction of TLR4 itself and its accessory proteins, CD14 and MD-2/Ly96. Of note are the static expression levels of many intracellular signaling molecules, suggesting that events such as phosphorylation are of key importance in the activation of intracellular signals of infection. The most common dimer forms of NF-κB and AP1, p50 (a proteolytic form of NFKB1)/p65 (RELA) and FOS/JUN, respectively, were induced, as was RELB.

The induced cytokines (IL-1β and IL-6) are known to be major and potent inducers for the production of acute-phase proteins in the liver (12); however, marked induction of genes encoding acute-phase proteins (SAA1, SAA2, SAA3, and haptoglobin) was observed within the mammary gland (Table (Table2).2). This suggests that acute-phase proteins in the milk (15) are not exclusively derived from blood during mastitis. It has been suggested that the induced transcription factors (NF-κB, STAT3, CEBPB, and/or CEBPD) are responsible for the induction of acute-phase protein genes through IL-1β- and IL-6-mediated signaling pathways (25). Thus, whether the induction of acute-phase proteins in the mammary gland is an autocrine response of TLR4-mediated cytokine induction or a direct result of a TLR4-mediated signaling pathway remains unknown. The functions of the acute-phase proteins are not well described, but recent reports indicate roles in leukocyte attraction (2).

Cytokines, such as IL-1β, can also induce vascular endothelial adhesion molecule expression, thus promoting leukocyte adhesion and diapedesis. Our array data indicated that several adhesion molecules (SELP, ICAM1, ICAM2, VCAM1, CD44, TNFAIP6, EVA1, and ITGA8) were induced. These factors, in conjunction with induced chemokines, chemokine receptor, and other chemotactants (Fig. (Fig.2B),2B), likely cause the infiltration of immune cells that is characteristic of mastitis. The early stages of this infiltration were observed in the LPS-treated mammary gland (Fig. (Fig.2A2A).

The rapid influx of inflammatory cells, particularly neutrophils, into the mammary gland and the effective elimination of the pathogen at the earliest stage of infection are key factors in the host defense against invading pathogens (24). On the other hand, a robust but ineffective immune response is harmful to the host and can cause death due to hypovolemic shock. Therefore, the immune response must be tightly controlled to maintain the most appropriate immune response. Several induced genes that negatively regulate TLR signaling pathways were identified in the microarray data. Suppressors of cytokine signaling (SOCS) are known to be involved in the negative-feedback regulation of several intracellular signaling pathways, including the JAK/STAT signaling pathway and the TLR4 signaling pathway (16, 19). It has been demonstrated that overexpression of SOCS1 in macrophages can inhibit LPS-induced NF-κB activation (18). While SOCS1 was not induced in the current experiment, genes encoding other SOCS family members (SOCS2 and SOCS3) were found to be induced in LPS-treated mammary glands. The observed induction of IκBα (NFKBIA) might be also be involved with negative feedback of the TLR signaling pathway.

Given that the mammary gland is a mixed-cell model, including epithelial and myoepithelial cells, macrophages, neutrophils, and other cell types, the cellular source of the differentially expressed genes requires further analysis. Generally speaking, the immune cells, such as macrophages and neutrophils, are known to synthesize cytokines, chemokines, and other inflammatory mediators. The influx of these inflammatory cells and their increased transcription as a result of activation may contribute, at least in part, to the increased abundance of the mRNAs of these molecules. However, the importance of epithelial cells in the host defense against invading pathogens has been recognized by the wide expression of immune-related genes in mammary epithelial cells. Our group and other groups have demonstrated that mammary epithelial cells in vitro are able to produce cytokines, chemokines, innate signaling receptors, and acute-phase proteins in various culture systems, including a primary bovine mammary cell culture system (23, 32), an established bovine mammary epithelial cell line (7), and a murine mammary epithelial cell line, as shown in Fig. Fig.3B.3B. We have also shown that CD14 was strongly induced in mammary epithelial cells of LPS-treated glands (Fig. (Fig.3A),3A), further demonstrating the important role of the epithelial cells in the host defense against invading pathogens. Finally, the marked induction of CXCL1 at 1 and 2 hours post-LPS treatment identifies this chemokine as an important early-response gene.

The induction of apoptosis by bacterial pathogens is a well-established cellular process (31). It has been reported that E. coli can induce programmed cell death in mammary cells in vivo (21), and this increased cell death may contribute to milk loss during mastitis. Interestingly, GO analysis indicates that mammary cell proliferation may also be modulated by LPS, given the induction of a number of cell proliferation-related genes, including protein kinases and transcription factors (Fig. (Fig.2A).2A). For example, it is well documented that NF-κB acts as a prosurvival transcription factor by inducing apoptotic inhibition (17). The induced cell proliferation appears paradoxical to the induced apoptosis, but it has been suggested that an increase in mammary cell proliferation during E. coli mastitis might be one of the mechanisms to minimize tissue damage (21). Induction of both proapoptotic (BID and CASP11) and antiapoptotic (BIRC2, BIRC3, BCL2ALA, TNFALP3, and MCL1) genes (Table (Table2)2) may coordinately modulate cell apoptosis. In addition, it is important to note that some gene products that have been assigned to various GO categories have dual or multiple functions. For example, Gadd45b induces apoptosis in kidney cells exposed to hyperosmotic stress for up to 12 h but promotes cell survival at a later stage (22). In another study, this molecule was induced by CD40 and was shown to play an important role in the inhibition of Fas-mediated apoptosis (34). Therefore, caution is advised in interpreting the functions of genes obtained by GO analysis. The exact roles of these genes during LPS-induced mastitis require further study.

In contrast to gene induction, examination of repressed genes also reveals some downregulated GO biological pathways, such as carbohydrate and fatty acid metabolism and potassium transport (Table (Table3).3). The decreased potassium transport activity may affect the apical membrane electrical potential, which results in changes of ion concentration in milk (28). In addition, downregulation of several metabolic enzyme transcripts related to carbohydrate and fatty acid metabolism may contribute to the decreased fat in milk during mastitis (4).

In summary, we have taken advantage of microarray technology to analyze transcriptional profiles in a mouse model of mastitis. This has provided a global view of the host response to LPS exposure in the context of biological pathways and sheds light on the complex physiological and cellular processes that occur during the early stages of infection. The local, acute nature of changes in gene expression suggests an important role of the epithelial cell in initiating the response. Further studies will be required to determine the mammary-specific nature of the responses, but it appears that further manipulation of this model system has great potential to contribute to the understanding of the innate immune system during mastitis.

Supplementary Material

[Supplemental material]


This work was supported by a grant from USDA/NRICGP.

We are grateful to F. Q. Zhao, K. I. Plaut, and T. A. Lewis for critically reading the manuscript.


Editor: V. J. DiRita


Supplemental material for this article may be found at http://iai.asm.org/.


1. Akira, S., and K. Takeda. 2004. Toll-like receptor signalling. Nat. Rev. Immunol. 4:499-511. [PubMed]
2. Badolato, R., J. M. Wang, W. J. Murphy, A. R. Lloyd, D. F. Michiel, L. L. Bausserman, D. J. Kelvin, and J. J. Oppenheim. 1994. Serum amyloid A is a chemoattractant: induction of migration, adhesion, and tissue infiltration of monocytes and polymorphonuclear leukocytes. J. Exp. Med. 180:203-209. [PMC free article] [PubMed]
3. Bannerman, D. D., M. J. Paape, J. W. Lee, X. Zhao, J. C. Hope, and P. Rainard. 2004. Escherichia coli and Staphylococcus aureus elicit differential innate immune responses following intramammary infection. Clin. Diagn. Lab. Immunol. 11:463-472. [PMC free article] [PubMed]
4. Bansal, B. K., J. Hamann, N. T. Grabowskit, and K. B. Singh. 2005. Variation in the composition of selected milk fraction samples from healthy and mastitic quarters, and its significance for mastitis diagnosis. J. Dairy Res. 72:144-152. [PubMed]
5. Barbosa-Cesnik, C., K. Schwartz, and B. Foxman. 2003. Lactation mastitis. JAMA 289:1609-1612. [PubMed]
6. Belcher, C. E., J. Drenkow, B. Kehoe, T. R. Gingeras, N. McNamara, H. Lemjabbar, C. Basbaum, and D. A. Relman. 2000. The transcriptional responses of respiratory epithelial cells to Bordetella pertussis reveal host defensive and pathogen counter-defensive strategies. Proc. Natl. Acad. Sci. USA 97:13847-13852. [PMC free article] [PubMed]
7. Boudjellab, N., H. S. Chan-Tang, and X. Zhao. 2000. Bovine interleukin-1 expression by cultured mammary epithelial cells (MAC-T) and its involvement in the release of MAC-T derived interleukin-8. Comp. Biochem. Physiol. A 27:191-199. [PubMed]
8. Burvenich, C., V. Van Merris, J. Mehrzad, A. Diez-Fraile, and L. Duchateau. 2003. Severity of E. coli mastitis is mainly determined by cow factors. Vet. Res. 34:521-564. [PubMed]
9. Cohen, P., M. Bouaboula, M. Bellis, V. Baron, O. Jbilo, C. Poinot-Chazel, S. Galiegue, E. H. Hadibi, and P. Casellas. 2000. Monitoring cellular responses to Listeria monocytogenes with oligonucleotide arrays. J. Biol. Chem. 275:11181-11190. [PubMed]
10. Dahlquist, K. D., N. Salomonis, K. Vranizan, S. C. Lawlor, and B. R. Conklin. 2002. GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet. 31:19-20. [PubMed]
11. Doniger, S. W., N. Salomonis, K. D. Dahlquist, K. Vranizan, S. C. Lawlor, and B. R. Conklin. 2003. MAPPFinder: using Gene Ontology and GenMAPP to create a global gene-expression profile from microarray data. Genome Biol. 4:R7. [PMC free article] [PubMed]
12. Ganter, U., R. Arcone, C. Toniatti, G. Morrone, and G. Ciliberto. 1989. Dual control of C-reactive protein gene expression by interleukin-1 and interleukin-6. EMBO J. 8:3773-3779. [PMC free article] [PubMed]
13. Gautier, L., L. Cope, B. M. Bolstad, and R. A. Irizarry. 2004. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:307-315. [PubMed]
14. Goldammer, T., H. Zerbe, A. Molenaar, H. J. Schuberth, R. M. Brunner, S. R. Kata, and H. M. Seyfert. 2004. Mastitis increases mammary mRNA abundance of beta-defensin 5, toll-like-receptor 2 (TLR2), and TLR4 but not TLR9 in cattle. Clin. Diagn. Lab. Immunol. 11:174-185. [PMC free article] [PubMed]
15. Gronlund, U., C. Hallen Sandgren, and K. Persson Waller. 2005. Haptoglobin and serum amyloid A in milk from dairy cows with chronic sub-clinical mastitis. Vet. Res. 36:191-198. [PubMed]
16. Heeg, K., and A. Dalpke. 2003. TLR-induced negative regulatory circuits: role of suppressor of cytokine signaling (SOCS) proteins in innate immunity. Vaccine 21(Suppl. 2):S61-S67. [PubMed]
17. Karin, M., and A. Lin. 2002. NF-κB at the crossroads of life and death. Nat. Immunol. 3:221-227. [PubMed]
18. Kinjyo, I., T. Hanada, K. Inagaki-Ohara, H. Mori, D. Aki, M. Ohishi, H. Yoshida, M. Kubo, and A. Yoshimura. 2002. SOCS1/JAB is a negative regulator of LPS-induced macrophage activation. Immunity 17:583-591. [PubMed]
19. Larsen, L., and C. Ropke. 2002. Suppressors of cytokine signalling: SOCS. APMIS 110:833-844. [PubMed]
20. Li, C., and W. H. Wong. 2001. Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. Proc. Natl. Acad. Sci. USA 98:31-36. [PMC free article] [PubMed]
21. Long, E., A. V. Capuco, D. L. Wood, T. Sonstegard, G. Tomita, M. J. Paape, and X. Zhao. 2001. Escherichia coli induces apoptosis and proliferation of mammary cells. Cell Death Differ. 8:808-816. [PubMed]
22. Mak, S. K., and D. Kultz. 2004. Gadd45 proteins induce G2/M arrest and modulate apoptosis in kidney cells exposed to hyperosmotic stress. J. Biol. Chem. 279:39075-39084. [PubMed]
23. Okada, H., T. Ito, H. Ohtsuka, R. Kirisawa, H. Iwai, K. Yamashita, T. Yoshino, and T. J. Rosol. 1997. Detection of interleukin-1 and interleukin-6 on cryopreserved bovine mammary epithelial cells in vitro. J. Vet. Med. Sci. 59:503-507. [PubMed]
24. Paape, M., J. Mehrzad, X. Zhao, J. Detilleux, and C. Burvenich. 2002. Defense of the bovine mammary gland by polymorphonuclear neutrophil leukocytes. J. Mammary Gland Biol. Neoplasia 7:109-121. [PubMed]
25. Poli, V. 1998. The role of C/EBP isoforms in the control of inflammatory and native immunity functions. J. Biol. Chem. 273:29279-29282. [PubMed]
26. Rosenberger, C. M., M. G. Scott, M. R. Gold, R. E. Hancock, and B. B. Finlay. 2000. Salmonella typhimurium infection and lipopolysaccharide stimulation induce similar changes in macrophage gene expression. J. Immunol. 164:5894-5904. [PubMed]
27. Schmitz, S., M. W. Pfaffl, H. H. Meyer, and R. M. Bruckmaier. 2004. Short-term changes of mRNA expression of various inflammatory factors and milk proteins in mammary tissue during LPS-induced mastitis. Domest. Anim. Endocrinol. 26:111-126. [PubMed]
28. Shennan, D. B. 1992. K+ and Cl− transport by mammary secretory cell apical membrane vesicles isolated from milk. J. Dairy Res. 59:339-348. [PubMed]
29. Sordillo, L. M., and K. L. Streicher. 2002. Mammary gland immunity and mastitis susceptibility. J. Mammary Gland Biol. Neoplasia 7:135-146. [PubMed]
30. Stein, T., J. S. Morris, C. R. Davies, S. J. Weber-Hall, M. A. Duffy, V. J. Heath, A. K. Bell, R. K. Ferrier, G. P. Sandilands, and B. A. Gusterson. 2004. Involution of the mouse mammary gland is associated with an immune cascade and an acute-phase response, involving LBP, CD14 and STAT3. Breast Cancer Res. 6:R75-R91. [PMC free article] [PubMed]
31. Weinrauch, Y., and A. Zychlinsky. 1999. The induction of apoptosis by bacterial pathogens. Annu. Rev. Microbiol. 53:155-187. [PubMed]
32. Wellnitz, O., and D. E. Kerr. 2004. Cryopreserved bovine mammary cells to model epithelial response to infection. Vet. Immunol. Immunopathol. 101:191-202. [PubMed]
33. Workman, C., L. J. Jensen, H. Jarmer, R. Berka, L. Gautier, H. B. Nielser, H. H. Saxild, C. Nielsen, S. Brunak, and S. Knudsen. 2002. A new nonlinear normalization method for reducing variability in DNA microarray experiments. Genome Biol. 3:research0048.1-0048.16. [PMC free article] [PubMed]
34. Zazzeroni, F., S. Papa, A. Algeciras-Schimnich, K. Alvarez, T. Melis, C. Bubici, N. Majewski, N. Hay, E. De Smaele, M. E. Peter, and G. Franzoso. 2003. Gadd45 beta mediates the protective effects of CD40 costimulation against Fas-induced apoptosis. Blood 102:3270-3279. [PubMed]
35. Zheng, J., J. L. Ather, T. S. Sonstegard, and D. E. Kerr. 2005. Characterization of the infection-responsive bovine lactoferrin promoter. Gene 353:107-117. [PubMed]

Articles from Infection and Immunity are provided here courtesy of American Society for Microbiology (ASM)
PubReader format: click here to try


Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...