We first attempted to analyze the pathogen-specific kinetics of the mounting of the mammary immune defense in an animal model of clinical mastitis (35
). This model was based on sequential infections of three quarters of healthy first-lactation cows with pathogenic E. coli
strain 1303 or S. aureus
strain 1027. However, we were unable to define with statistical significance a clear time course in the modulated expression of infection-regulated genes in E. coli
-infected animals, due to variation between individuals. The individual-specific influence of immune reactivity was even more pronounced in the S. aureus
-infected animals. An obvious alternative is the use of primary cultures of mammary epithelial cells from cows (pbMEC) for monitoring the kinetics of the activation of immune mechanisms. Pilot experiments had shown that pbMEC cultures reflect in part the kinetic aspects of immune response regulation as they are observed in mammary tissue, eventually displaying less variance in different biological replicas than encountered in samplings with live cows after different times of infection (see Fig. S1 and Table S2 in the supplemental material). Hence, we used pbMEC cultures to globally profile pathogen-specific transcriptome alterations.
Gene expression profiling of the pbMEC response after challenge with E. coli or S. aureus.
We challenged pbMEC cultures with identical concentrations (107 particles/ml) of either heat-inactivated E. coli 1303 or S. aureus 1027 for 1, 3, 6, and 24 h and compared their transcriptomes to those of untreated control cells. The experiment included three biological replicas for each pathogen stimulus, each with pbMEC derived from a different cow and stimulating the cultures in parallel with both pathogens.
We used the Affymetrix bovine genome array for global transcription analysis. Transcripts were considered differentially expressed if their q value was ≤0.05 and their fold change compared to the control was ≥2. qRT-PCR was used to validate the quality of the data derived from microarray hybridizations. The mRNA concentrations were measured for 10 different genes in all three individual pbMEC culture experiments across all five time points (control and inductions for 1, 3, 6, and 24 h). A comparison of these data to the corresponding values determined from the microarrays revealed a high degree of correlation (r = 0.86 to 1.00) (see Table S3 in the supplemental material).
E. coli causes faster and stronger changes in mRNA abundance than does S. aureus.
A total of 104, 382, 325, and 306 genes were significantly regulated at 1, 3, 6, and 24 h after challenge with E. coli particles. In contrast, we found only 17, 27, 33, and 111 genes to be differentially expressed at those time points after challenge with S. aureus (Table ). For subsequent analyses, we will only consider genes with known human orthologs, and we use that time after challenge at which the mRNA concentration of the genes was maximally altered as a principal criterion to cluster genes according to their regulation kinetics (Table ). The complete lists of genes regulated by the E. coli and S. aureus challenges are compiled in Tables S4A and S4B, respectively, in the supplemental material. Genes are ranked in those lists according to the extents of their mRNA concentration alteration. The annotated gene functions are indicated.
Significantly differentially expressed genes at each time pointa
The global characteristics of pathogen-specific transcriptome profiling show that both the number and extent of differentially expressed genes were much higher after challenge with E. coli than after challenge with S. aureus (Table ). The E. coli challenge significantly altered the expression of 625 annotated genes, while the S. aureus challenge altered the expression of only 138 genes. Moreover, the E. coli challenge increased the mRNA concentration of 69 annotated genes to more than 10-fold compared to the unstimulated control, while those from only 11 genes were found to exceed this strong level of regulation after the S. aureus challenge. The E. coli challenge provoked a much faster response than did the S. aureus challenge, although those particles from both pathogen species had been applied at the same time and at identical concentrations. The E. coli challenge maximally altered the mRNA concentration of 243 genes as early as 3 h after the challenge, while it took 24 h after the S. aureus challenge to achieve the maximum impact on the mRNA levels of 101 genes altogether (Table ). We found only 34 genes to be exclusively but very weakly (2- to 3-fold) regulated by the S. aureus challenge, while the expressions of as many as 521 genes were regulated exclusively by the E. coli stimulus.
Time course of the regulation of inflammatory response genes in E. coli- and S. aureus-challenged pbMEC.
For the analysis of functional pathways governing the immune response in these cells, we first considered the time course of the expression of “inflammatory response” genes defined by use of IPA software and subdivided the time pattern according to the response elicited by E. coli.
Immediately early primary response (maximum alteration at 1 h postinduction [p.i.]).
The E. coli stimulation swiftly increased the mRNA concentrations of cytokine-encoding genes (such as CXCL1, CXCL2, CCL20, and IL-8) and transcription regulators (e.g., NFKBIZ and ZFP36). Also, the entire group of inflammatory genes (20 from a total of 61 genes) (see Table S4A in the supplemental material) contributes prominently to the very early and strongly regulated genes of this cluster (Fig. ). The mRNA concentrations of all these chemokines and of NFKBIZ were also significantly affected by S. aureus but at later times after the stimulus.
FIG. 1. Comparison of differentially expressed genes in response to challenges with E. coli or S. aureus particles. Considered are genes contributing to the group of “inflammatory response” genes, as defined by IPA software. Heat maps (red, increase; (more ...) Delayed early primary response (maximum alteration at 3 h p.i.).
Most of the genes that were significantly regulated by the challenge with E. coli particles belong to the group of delayed early primary response genes (n = 243) (Table and see Table S4A in the supplemental material), comprising transcripts of numerous cytokine receptors (e.g., FAS, IL1RAP, and IL6ST), pattern recognition receptors (TLR2 and TLR4), and transcriptional regulators. The latter include the NF-κB and C/EBP families of factors (e.g., RELB, NFKB1, BCL3, and CEBPD) (Fig. ). The regulation of this battery of transcription factors highlights that E. coli provoked significant changes in the signal transduction machinery of the host cell. In addition, the concentrations of mRNAs encoding factors important for host defense (e.g., NOS2, SOD2, and PDGS1/2) were also maximally increased at that time after the stimulus. Only few of those genes were significantly regulated by S. aureus, including NOS2, IL-6, TLR2, SOD, and CCL2, but they all reached their maximally altered mRNA concentration at later times and with lower levels of modulation.
Late (secondary) response (maximum alteration at 6 to 24 h p.i.).
Genes featuring maximal altered mRNA concentrations at 6 h and 24 h after E. coli challenge are considered together, since their separate analyses did not very obviously indicate functional differences.
The late inflammatory response after E. coli challenge regulated the transcript concentrations of many antigen-presenting receptors of the major histocompatibility complex (MHC) (e.g., HLA-DQA1, -DRA, -DQB1, -A, and -DRB1 and CD74), of enzymes known to be expressed in response to type I interferon (IFN) (e.g., MX1/2, GBP2, and OAS1), and of peptidases contributing to the acute-phase response (e.g., HP, LTF, the matrix metallopeptidases [MMPs] MMP9 and MMP13, and complement factors) (Fig. ). Some cytokines and transcription regulators are also part of the late response elicited by E. coli.
The secondary response of pbMEC caused by the S. aureus challenge was characterized by the expression of IFN-induced enzymes. Transcription factors regulated late after the S. aureus challenge (HIF1A, NFKB2, SP140, and IFN regulatory factor 7 [IRF7]) were also found to be regulated late after E. coli stimulation. However, the S. aureus challenge, unlike E. coli, did not cause any alteration in the levels of mRNAs encoding MHC receptors as well as of matrix metallopeptidases and complement peptidases (Fig. ).
Overall, we found that the mRNA abundance of many more genes was increased rather than decreased subsequent to challenge with both E. coli and S. aureus (Table ). Furthermore, the extent of the downregulation was relatively weak (see Tables S4A and S4B in the supplemental material). Only a few inflammatory response genes were significantly downregulated. Among them, the E. coli challenge significantly but weakly (−2-fold) decreased the level of expression of components from relevant signaling cascades, including MAPK1 (synonym, extracellular signal-regulated kinase 2 [ERK2]), MAPK14 (synonym, p38), v-akt murine thymoma viral oncogene homolog 1 (AKT1), and transforming growth factor beta receptor 1 (TGFBR1; this factor is not comprised in the IPA category “inflammatory response”) (Fig. and Table S4A). These genes were not regulated by the S. aureus challenge (Table S4B). Endothelin receptor type A (EDNRA) was 2-fold downregulated by both the E. coli and S. aureus challenges (Fig. ). This represents a rare example of a factor whose expression is similarly diminished by both pathogen species. Interestingly, two downstream targets of this receptor signaling, the histone deacetylase HDAC7 and the growth factor receptor tyrosine kinase ERBB2, were both found to be downregulated by E. coli only (both −2-fold). Thus, E. coli but not S. aureus significantly reduces the signaling of endothelin growth factor.
Aside from those genes relevant to inflammation, we note that E. coli but not S. aureus caused a quite strong downregulation of genes encoding structural molecules (e.g., keratin 15 was −7-fold and −4-fold regulated at 1 h and 3 h p.i., respectively) and members of the tight-junction protein complex (e.g., the claudins CLDN23 and CLDN3, vinculin, and tight-junction protein 3, with a range of changes of expressional regulation of −6-fold to −3-fold) (see Tables S4A and S4B in the supplemental material). Moreover, the E. coli challenge reduced the level of expression of the well-known tumor suppressor breast cancer 1, early onset (BRCA1) (−2 and −3-fold at 6 h and 24 h p.i., respectively).
Pathogen-specific cytokine expression profile.
The induction of cytokines/chemokines is crucial for establishing the cellular branch of immune defense in the udder. We directly compared the kinetics of their regulation after the E. coli and S. aureus challenges (Table ).
Alteration of mRNA levels of cytokine/chemokine-encoding genes after stimulation with E. coli or S. aureus
We found 19 cytokines/chemokines to be significantly regulated by E. coli. Ten of them were also significantly induced by S. aureus. However, E. coli always elicited a much stronger alteration of the mRNA levels quantitatively than did S. aureus.
E. coli significantly induced the expression of 10 chemokines. Eight of those were significantly regulated as early as 1 h poststimulation. The concentration of mRNAs encoding CXCL1, CXCL2, CXCL3, and IL-8 peaked at this early time point. Subsequently, they were quickly and strongly downregulated. These chemokines interact with the CXCR2 receptor, known to be predominantly expressed on neutrophils. Hence, the MEC signals to recruit those cells into the infected udder and to activate them.
The mRNAs encoding chemokines important for the recruitment of monocytes and lymphocytes are known to be induced later than those factors recruiting neutrophils. The mRNA concentrations of the monocyte-recruiting factor CCL2 and of the adhesion mediator CX3CL1 reached their maximum 3 h after E. coli stimulation, while the mRNAs encoding the predominantly T-cell-recruiting chemokines CCL5 and CCL28 reached their maximal concentrations 6 and 24 h after E. coli challenge (maximally induced 175- and 2-fold, respectively). The induction kinetics of CCL20 differ from those of the other lymphocyte-recruiting factors. This chemokine was massively (529-fold) and more swiftly (1 h p.i.) induced after E. coli stimulation.
S. aureus significantly regulated the expression of eight chemokines in pbMEC. Their induction was retarded and weak compared to those found with the E. coli challenge. Nevertheless, CXCL1, CXCL2, CXCL3, and IL-8 were again the earliest maximally induced chemokines after pathogen contact. CXCL2 and CXCL1 are among the immune factors most strongly induced by S. aureus (maximum inductions of 24- and 17-fold, respectively, at 3 h p.i.). CCL2 and CCL5 were again found to be induced later than the CXCL factors. The induction of CCL20 occurred later and was much weaker (only 3-fold at 24 h p.i.) than that of the E. coli challenge.
The expression of three key proinflammatory cytokines (IL-1A and IL-1B, TNF-α, and IL-6) was quickly and strongly induced by the E. coli challenge (Table ). S. aureus, in contrast, did not significantly induce the expression of IL-1 and TNF-α. We reevaluated these observations by qRT-PCR (Fig. ). These measurements revealed a statistically significant induction of TNF-α 24 h after S. aureus challenge. However, the maximum expression level after S. aureus challenge was only 1% of that recorded after the E. coli challenge. IL-1A was not found to be significantly regulated by the S. aureus challenge.
FIG. 2. Alteration in the mRNA concentrations of TNF-α, IL-1A, IL-6, and IFN-β (IFN-β1 and IFN-β2) in pbMEC after challenge with E. coli () and S. aureus (□) particles. Shown are values for mean fold induction (more ...)
The microarray analysis revealed IL-6 to be the only master cytokine to be significantly induced by S. aureus. Its mRNA concentration was increased by about 3-fold, as measured by qRT-PCR (Fig. ). While this was quantitatively less of an induction than that seen after the E. coli challenge (13-fold), it nevertheless amounted to approximately 20% of the E. coli-induced enhancement in concentrations.
We realized that, confusingly, two different genes are annotated for the bovine genome (Bovine Genome Assembly 4.2) as encoding the factor IL-6, while for humans, only one IL-6-encoding gene is known. This human gene is synonymous with IFN-β2. Its bovine ortholog is represented on the bovine Affymetrix array and corresponds to the gene localized on Bos taurus autosome 4 (BTA4) (IL-6 [IFN-β2]; Entrez GeneID 280826). The other bovine gene, IL-6 (IFN-β2), is localized on BTA8 within an IFN gene cluster (Entrez GeneID 517016). This gene is an ortholog of the human IFN-β1 gene and has no sequence similarity to the former bovine IL-6 gene but shares 84% DNA sequence similarity to the bovine IFN-β1-encoding gene. We refer throughout to the IL-6 gene (GeneID 280826) as encoding IL-6 and to that gene denoted by GeneID 517016 as encoding IFN-β2.
In the microarray experiment the mRNA concentration of IFN-β1 was below the level of detection, and the array contained no probe set for IFN-β2. Given that IFN-βs are known as relevant immune factors, we measured the alteration of their mRNA abundances in response to E. coli and S. aureus by qRT-PCR. We applied a pair of primers amplifying both IFN-β1- and IFN-β2-encoding messages. Surprisingly, we observed a significant and late induction of these factors only in S. aureus- and not in E. coli-stimulated cells (Fig. ). Thus, we identified the group of IFN-βs as immune factors exclusively regulated by S. aureus.
It was reported previously that increased mRNA concentrations of proinflammatory cytokines eventually poorly correlate with the respective protein synthesis or abundance (40
). Taking IL-1A as an example for one of our key factors, we therefore examined changes in the intracellular abundance of the 31-kDa IL-1A precursor protein subsequent to our pathogen challenges by Western blotting. We found that E. coli
stimulation strongly induced its synthesis, while the S. aureus
challenge barely changed the abundance (Fig. , insert in IL-1A graph). Thus, the protein abundance of this key factor reflects the respective mRNA concentration in pbMEC. It is known that the abundance of the IL-1A precursor exceeds that of the cleaved 17-kDa IL-1A factor itself and that both factors are bioactive (20
Differential regulation of functional networks.
We next examined gene interactions revealed by their known contribution to regulatory networks in order to identify key regulatory principles of the pathogen-specific immune response in the MEC.
The large number of E. coli-regulated genes necessitated the introduction of constraints in order to reduce the complexity of the network analysis. Therefore, we focused our analysis on the most strongly regulated genes. We selected the 20 most strongly induced genes from each of the four time groups (maximum at 1, 3, 6, and 24 h p.i.). Twenty-seven genes from among these 80 top regulated genes were found to be regulated exclusively by E. coli. The latter genes were subjected to an IPA-Network analysis. Nineteen of them were identified as part of the top-ranked network (network score of 49). This network features the key proinflammatory cytokines IL-1A and TNF-α in the central position (Fig. ).
FIG. 3. Network analysis of regulatory relationships. (A) E. coli network. IPA analysis reveals an interaction network dominated by IL-1A and TNF-α as having the highest statistical probability (network score of 49) of the 27 top-ranked genes regulated (more ...) S. aureus.
All 138 annotated genes regulated by S. aureus were included in this analysis. The top-ranked network (network score of 48) comprised 26 genes with IFN-β in a central position (Fig. ). The Ingenuity software subsumes under IFN-β both factors IFN-β1 and IL-6. Only three genes of this S. aureus-regulated network were induced relatively early, from 3 h on (Fig. and see Table S4B in the supplemental material). These genes are IL-6 and two inhibitors of NF-κB signaling (NFKBIZ and TNFAIP3). NFKBIZ is known as an essential inducer of IL-6 expression.
Considering the orchestration of the immune response in MEC, our data together indicate that the coordinated gene regulation governed by the activation of IL-1A and TNF-α signaling is an E. coli-specific response feature. The S. aureus response, in contrast, induces a functional network dominated by IFN-β/IL-6.
Aside from considering genes relevant to the immune response and inflammation, we note that S. aureus induced quickly and very strongly the expression of genes involved in the detoxification of xenobiotic substances (see Table S4B in the supplemental material). These genes include the monooxygenases CYP1B1 (25-fold induced 1 h after challenge) and CYP1A1 (maximum induction 3 h after challenge; 391-fold) as well as the gene encoding the 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)-inducible poly(ADP-ribose) polymerase (TIPARP) (4-fold induced 1 h after challenge). These three genes are key factors for the oxidative metabolism of various toxic substances and showed similar patterns of regulation after the E. coli challenge as well.
Functional knockdown of MyD88-dependent signaling reduces the induction of TNF-α and IL-1A but does not influence the induction of IL-6 expression in E. coli-stimulated MEC.
We wanted to examine if the blockage of TLR-mediated signaling in MEC would still allow an induction of IL-6 expression in this cell type upon stimulation with E. coli
particles. Central to TLR signaling are receptor-proximal adaptors. Among these molecules, myeloid differentiation primary response 88 (MyD88) is widely used from almost all TLRs, while TIR-domain-containing adapter-inducing IFN-β (TRIF) is specific for TLR4 and TLR3 signaling. The functional knockdown of TLR signaling can be achieved by stably overexpressing trans
-dominant negative (DN) mutants of the factors MyD88 and TRIF in these cells (34
). Therefore, we stably transfected vectors expressing these DN factors into cells of the transformed and permanent bovine MEC cell line MAC-T. Wild-type (WT) MAC-T cells and DN mutants expressing pools of clones were challenged with E. coli
for 1, 3, and 24 h. The change in the abundance of mRNAs encoding TNF-α, IL-1A, and IL-6 was measured over time (Fig. ). While the extent of TNF
-α and IL-1A
expression was significantly reduced in transfectants expressing DN-MyD88 and DN-MyD88-DN-TRIF, IL-6
expression was not affected by those DN factors. The efficacies of both mutations (DN-MyD88 and DN-MyD88-DN-TRIF) were quite similar. We validated with control experiments that pathogen-induced NF-κB induction was significantly reduced in both transfectants, down to 56% and 63% for DN-MyD88-TRIF and DN-MyD88, respectively.
FIG. 4. Impact of functional knockdown of TLR signaling on induced cytokine gene expression. Shown are data for the alteration of TNF-α, IL-1A, and IL-6 mRNA concentrations in WT MAC-T cells or those stably transfected with vectors expressing DN-MyD88 (more ...)
We did not conduct the same experiment using S. aureus as a stimulus since these cells show generally a much weaker response after stimulation with E. coli than that recorded for pbMEC (cf. Fig. and ). S. aureus generally fails to significantly stimulate the expression of any of the three master cytokines (e.g., IL-1A, TNF-α, and IL-6) in those cells.