Bovine mastitis is one of the most important world-wide infectious diseases in dairy cattle that affects both the quality and quantity of milk (Seegers et al., Reference Seegers, Fourichon and Beaudeau2003), and subsequently, leads to an immeasurable economic loss to farmers (Hertl et al., Reference Hertl, Schukken, Welcome, Tauer and Gröhn2014; Gomes and Henriques, Reference Gomes and Henriques2016). This disease is characterized by an inflammatory response of the mammary tissue caused by bacterial and fungal infections. Approximately 30% of cows carry mastitis pathogens, and one third of all cows suffer from mastitis each year (Gussmann et al., Reference Gussmann, Steeneveld, Kirkeby, Hogeveen, Farre and Halasa2019). Meanwhile, it is of a particular concern that the inevitable indiscriminate use of antibiotics in tackling cattle mastitis may worsen the antibiotic-resistance issues associated with antimicrobial therapy in humans (Im et al., Reference Im, Lee, Jeon, Baik, Kim, Kang, Yun, Kim and Han2014). Therefore, the development of novel therapies for mastitis is urgently needed (Fu et al., Reference Fu, Gao, Cao, Guo, Wei, Zhou, Li, Yao, Yang and Zhang2014). While vaccines are in development, the wide range of species and strains of micro-organisms causing mastitis makes this approach challenging. One alternative strategy is to improve an individual animal's ability to rid itself of the infective agents by either genetically selecting for or inducing enhanced innate immune capabilities within the population of dairy cattle. However, this strategy is critically dependent on better understanding of the innate host immune responses during mastitis (Philpott et al., Reference Philpott, Girardin and Sansonetti2001).
Bovine mammary epithelial cells (BMECs) are the main sites for milk protein synthesis and secretion. They are also a major line of defense against pathogenic bacteria and contribute significantly to the immunity of the mammary gland (Gutsmann et al., Reference Gutsmann, Müller, Carroll, Mackenzie, Wiese and Seydel2001). Once the pathogenic bacteria enter the udder, pro-inflammatory cytokines such as IL-1β, IL-6, and TNF-α are produced (Corl et al., Reference Corl, Robinson, Contreras, Holcombe, Cook and Sordillo2010; Kang et al., Reference Kang, Lee, Lee, Petriello, Kim, Do, Lim, Lee and Han2016) and local mammary inflammation events are induced (Strandberg et al., Reference Strandberg, Gray, Vuocolo, Donaldson, Broadway and Tellam2005; Im et al., Reference Im, Lee, Jeon, Baik, Kim, Kang, Yun, Kim and Han2014). A number of studies have shown that BMECs in culture are able to sense bacteria or bacterial products and respond by upregulating several sets of genes involved in the innate immune response (Griesbeckzilch et al., Reference Griesbeckzilch, Meyer, Kühn, Schwerin and Wellnitz2008; Ibeaghaawemu et al., Reference Ibeaghaawemu, Lee, Ibeagha, Bannerman, Paape and Zhao2008). Although a large amount of information has been gathered, the innate immune mechanisms of sensing E. coli in the mammary gland and subsequently inducing the immune responses are not completely understood. There were some differences in the mammary immune response to distinct mammary pathogenic E. coli strains (Blum et al., Reference Blum, Heller, Jacoby, Krifucks and Leitner2017). Lipopolysaccharide (LPS), a structural component of the outer membrane of Gram-negative bacteria, has been identified as an important risk factor for mastitis and is often used to simulate Gram negative mastitis (Kang et al., Reference Kang, Lee, Lee, Petriello, Kim, Do, Lim, Lee and Han2016). Furthermore, LPS provokes the secretion of pro-inflammatory cytokines (TNF-α, IL-1β, IL-6), which in turn activate or suppress the expression of acute phase genes in hepatocytes, vascular endothelium and other target cells (Jiang et al., Reference Jiang, Sørensen, Røntved, Vels and Ingvartsen2008). Although some studies have investigated the activation of LPS-induced Toll-like receptor 4 (TLR4) and NF-κB and the production of secreted cytokines IL-1β and IL-6 in BMECs (Günther et al., Reference Günther, Petzl, Zerbe, Schuberth and Seyfert2016; Wang et al., Reference Wang, Feng, Ding, He, Li, Li, Ding, Ding, Li, Wu and Li2018), the NOD-like receptor (NLR) pathways involved in regulating the secretion of mature IL-1β in E. coli mastitis has not yet been characterized. The procession and secretion of mature IL-1β is required for the activation of NACHT, LRR and PYD domain-containing protein 3 (NLRP3) inflammasome (Wang et al., Reference Wang, Gong, Chen, Xiong, Zhou, Huang and Kong2014; Lemarchand et al., Reference Lemarchand, Barrington, Chenery, Haley, Coutts, Allen, Allan and Brough2019), and this NLRP3 inflammasome plays an important role in the development of LPS-induced mastitis (Li et al., Reference Li, Wang, Kuang, Li, Wang, Yang and Wang2019).
Recently, integrating transcriptomic and quantitative proteomic analyses has been widely used to promote a better understanding of the molecular mechanisms driving biological process in cells and tissues (Wenting et al., Reference Wenting, Quanjuan, Fengqi, Jianxin and Hongyun2018). The aim of this study was (1) to elucidate a more complete understanding of molecular mechanisms underlying mammary epithelial cell inflammatory response to LPS; and (2) to identify the role of NLRP3 in LPS-induced inflammatory response. Our data may provide valuable information for uncovering the mechanism of mastitis resistance in dairy cows and assist the treatment of mastitis.
Materials and methods
The study was approved by the Institutional Animal Care and Use Committee of Nanjing Agricultural University and performed in accordance with the ‘Guidelines for Experimental Animals’ of the Ministry of Science and Technology (Beijing, China).
Cell preparation and treatment
Mammary tissues were collected from three Holstein dairy cows that have similar age, parity, and lactation stage at a local slaughterhouse. All were at the end of lactation, two of them were 5 years old and one was 6 years old (parity = 4). They were culled due to low milk production. The overall study design is summarized in online Supplementary Fig. S1. BMECs were cultured in accordance with the established method of the Nanjing Agricultural University of Dairy Cow Science Institute (Sun et al., Reference Sun, Li, Wu, Yu, Li, Tang, Li, Huang and Wang2015). Cells were incubated at 37 °C in 5% CO2. Cells cultured in medium consisting of Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (Gibco, Grand Island, NY, U.S.A.), 100 U/ml of antibiotic (penicillin and streptomycin; Sigma-Aldrich, St. Louis, MO, U.S.A.) were used at passage 3 or 4 for further research. The epithelial origin and purity of the BMEC were assessed by immunofluorescence for cytokeratin 18, an epithelial cell-specific marker (online Supplementary Fig. S2).
After cells were grown to 70–80% confluence, all cells were washed twice with PBS, and then DMEM (serum free) was added to each well of culture plates. The culture plates were then divided into two groups of three plates: CON1–3 (control) and LPS1–3 (lipopolysaccharide), O55:B5, 10 µg/ml in culture medium, based on our previous study and others studies (Sun et al., Reference Sun, Li, Wu, Yu, Li, Tang, Li, Huang and Wang2015; Qu et al., Reference Qu, Wang, Li, Li, Zhang, Fu and Zhang2017). After 12 h of LPS treatment, the cells were harvested and used in experiments as described in figure legends. Then, the cell culture supernatant of BMECs was collected for ELISA analysis.
RNA isolation and microarray assay
The detail steps were performed as described in the online Supplementary File Materials and methods. Total RNA was extracted from BMECs using Trizol reagent (Hoffmann-La Roche Ltd., Shanghai, China) according to the manufacturer instructions. The gene chip used in the Bovine Genome Array was generated by a service provider (SBC Co., Ltd., Shanghai). The total RNAs of the NT and LPS-treated BMECs were individually hybridized with gene chips. Affymetrix Micro Array Suite 5.0-Specific Terms GCOS v1.4 was used for quantitative analysis of the hybridization gene expression levels; those with ≥ 2-fold (P-value < 0.05) difference between the groups were checked and further analyzed (Affymetrix microarray analysis followed by RMA normalization method). The Molecule Annotation System (http://david.abcc.ncifcrf.gov/) was used to analyze the differentially expressed genes, using the Kyoto encyclopedia of genes and genomes (KEGG) public pathway resource and the gene ontology (GO) consortium.
Protein extraction, quantification and digestion
The detail steps were performed as described in the online Supplementary Materials and Methods. After protein extraction and protein digestion, equal volumes of 0.1% formamide (FA) was added to acidify the solution. Peptides were purified on a Strata-X C18 pillar three times, washed with 0.1% FA + 5% acetonitrile (ACN) twice, and eluted with 1 ml 0.1% FA + 80% ACN. Eluted peptides were dried with a vacuum concentration meter. The dried peptide powder was re-dissolved in 20 µl of 0.5 mol/l triethylamine borane (TEAB) for peptide labeling.
iTRAQ labeling and fractionation
Samples were labeled with iTRAQ Reagent-8 plex Multiplex Kit (AB Sciex UK Ltd., Warrington, UK). Control groups were labeled as iTRAQ channels 113–115 and LPS-treated BMECs were labeled as iTRAQ channels 116–118. All of the labeled samples were mixed in equal amounts. Next, the labeled samples were fractionated using high performance liquid chromatography (HPLC) (Thermo DINOEX Ultimate 3000 BioRS, THERMO FISHER, Berlin, Germany) using a Durashell C18 (5um, 100 Å, 4.6 × 250 mm). Finally, 12 fractions were collected.
LC-MS/MS and data analysis
Liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) analysis was performed on an AB Sciex nano LC-MS/MS (Triple TOF 5600 plus) system. The detailed steps were performed as described in the online Supplementary Materials and Methods
The original MS/MS file data were submitted to Protein Pilot Software v4.5 (Applied Biosystems, Foster City, CA, USA) for data analysis. For protein identification, the Paragon algorithm integrated into Protein Pilot was employed against the SwissProt bovine database from Uniprot website (http://www.uniprot.org) using Mascot software version 2.3.02 (Matrix Science, London, UK). The following filter was used in this study: 1% false positive rate at the protein level and two unique peptides for each protein. After filtering the results, the peptide abundances in the different reporter ion channels of MS/MS scan were normalized. For DEPs determination, fold changes were calculated as the average comparison pairs among biological replicates. Proteins with a fold change larger than 1.3 and P-value < 0.05 were considered to be significantly differentially expressed. The Molecule Annotation System (http://david.abcc.ncifcrf.gov/) was used to analyze the differentially expressed proteins. The GO terms of biological process in DAVID (http://david.abcc.ncifcrf.gov/) were employed to categorize enriched biological themes in differentially expressed protein lists.
Cytokine and apoptosis assays
The cell supernatant was used to evaluate the levels of cytokines of TNF-α (the intra- and inter- assay CV were 3.64 and 4.39%), IL-1β (the intra- and inter- assay CV were 2.45 and 2.66%) and IL-6 (the intra- and inter- assay CV were 4.21 and 3.86%) with the ELISA kits under the instructions of the manufacturer (BioLegend, Inc., Camino Santa Fe, Suite E, San Diego, CA, USA).The level of apoptosis was detected using the Annexin V-FITC kit (MACS, Miltenyi Biotec, Bergisch Gladbach, Germany). Briefly, after the indicated treatments, cells were collected and stained with Annexin V-FITC following the manufacturer's instructions. The stained cells were then counted by using a FACS Calibur flow cytometer (BD Biosciences, San Jose, CA, USA). The equation (number of cells in the upper right quadrant + cells in the lower right quadrant)/(total cell number) was used to calculate the rate of apoptosis.
Real-time reverse transcription-polymerase chain reaction (RT-PCR)
Real-time RT-PCR was performed to confirm the microarray results. Total RNA was extracted from mixed BMECs as described above and was reverse transcribed using a reverse transcription level kit (TaKaRa, Dalian, China) according to the manufacturer's protocol. The GAPDH rRNA gene was used as an invariant control. Primers were designed using Primer Premier 5.0 and are shown in online Supplementary Table S1. RT-PCR was performed with SYBR®Premix Ex Taq™ (Takara). The reaction solution was prepared on ice and comprised 10 µl of 2 × SYBR®Premix Ex Taq™, 0.8 µl PCR forward primer (10 µm), 0.8 µl PCR reverse primer (10 µm), 0.4 ml 50 ROX reference dye, 2 µl cDNA (100 ng/μl), and distilled H2O to a final volume of 20 µl. The reaction mixtures were incubated in a 96-well plate at 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s, 60 °C for 30 s, and 72 °C for 30 s. All target genes were normalized to the endogenous reference gene GAPDH by employing an optimized comparative Ct (2−ΔΔCt) value method, where ΔΔCt = ΔCt target – ΔCt GAPDH.
Protein isolation and western blotting
Total protein from BMECs (all three cows) was isolated using a tissue protein extraction kit (Bio-Rad, Shanghai) and quantified using a Bradford Assay Kit (Sangon Biotech Co., Ltd, Shanghai, China). For western blotting, the extracted materials (30–50 µg/lane) were subjected to gel electrophoresis (8% SDS-PAGE) followed by electrotransfer to a polyvinylidene fluoride membrane. The membrane was then blocked with 5% freshly-prepared milk/Tris-buffered saline containing 20% Tween (TBST) for 2 h at RT and then incubated overnight at 4 °C with primary antibodies {rabbit anti-rat NLRP3 (1:1000, Novus), Pro-caspase-1 (1:1000, Novus), IL-1β (1:1000, Abcom, Cambridge, USA), or COX-2 (1:1000, Abcom, Cambridge, USA)}. After three washes in TBST, the membrane was incubated with peroxidase-conjugated secondary antibody (GGHL-15P anti-IgG, 1:1000 dilution; Santa Cruz Biotechnology, CA, USA) in blocking solution for 2 h at RT. After a final wash in TBST, the presence of the secondary antibodies was visualized using ECL reagent (Amersham Life Science, Arlington Heights, MA, USA). In all analyses, probing with anti-β-actin was used to monitor sample loading.
siRNA interference
The BMECs were seeded in a six-well-plate and cultured for 24 h until they reached 50–60% confluency. The cells were transfected with 20 µm of either experimental siRNA oligos or a non-targeting control with Lipofectamine®2000 (Invitrogen, Carlsbad, CA, USA). The knockdown efficiency was determined using qRT-PCR and western blotting. The NLRP3 and negative control (NC) siRNA sequences were synthesized by Shanghai GenePharma (Shanghai, People's Republic of China). The sequences of siRNA and knockdown efficiency are shown in online Supplementary Table S2 and Fig. S3.
Statistical analysis
All data were obtained from each single independent experiment carried out in triplicate. Main and interactive effects were analyzed by one-way analysis of variance (ANOVA) using SPSS16.0 software (Chicago, IL). P-value < 0.05 were considered statistically significant.
Results
Differentially-expressed genes and proteins in LPS-induced BMECs
Using transcriptomics and proteomics analysis, we identified 1019 DEGs (fold change ≥2 and P-value < 0.05) and 340 DEPs (fold change ≥1.3 and P-value < 0.05), of which 536 genes and 162 proteins were upregulated and 483 genes and 178 proteins were downregulated following exposure to LPS (online Supplementary Full Data Set).
GO analyses of DEGs and DEPs
To clarify the different biological patterns of the two groups, GO analysis of DEGs and DEPs were conducted. The GO results showed that the DEGs were associated with 172 biological processes, which consisted of 15 GO terms associated with response to stimulus, 4 associated with defense processes, and 3 associated with inflammatory process. The DEPs were associated with 51 biological processes, which consisted of 2 GO terms associated with response to stimulus, 1 associated with defense processes and 2 associated with inflammatory processes. The full list is provided in Supplementary Table S3.
Validation of differentially expressed genes and proteins related to stimulus, defense, and inflammatory responses
To characterize DEGs and DEPs related to stimulus, defense, and inflammatory responses in BMEC that were affected by LPS, the upregulated expressions of PTGS2 and NLRP3 genes at both transcript and protein levels were screened. GO analysis showed that PTGS2 and NLRP3 were involved in acute inflammatory responses (online Supplementary Full Data Set). To confirm the microarray results for the DEGs, real-time RT-PCR was performed for 7 genes, including NLRP3, IL-1, CXCL10, PTGS2(COX-2), COL4A5, CATHL5, and LTF (Fig. 1). As shown in Fig. 1, NLRP3, IL-1, PTGS2(COX-2), and CXCL10 were upregulated, while COL4A5, CATHL5, and LTF were downregulated, as determined by both the microarray assays and real-time RT-PCR. To confirm the iTRAQ results for the DEPs, western blotting was performed for inflammation-associated protein-NLRP3 and PTGS2 (COX-2) (Fig. 2). The expression of NLRP3 and PTGS2 (COX-2) were upregulated.
Comparison of RT-PCR findings to microarray results by fold-change of 7 select genes. The fold-changes upon LPS stimulation for 7 representative BMEC DEGs are shown, as determined by microarray analysis (blue) and verified by RT-PCR (red). The real-time data represent averages of triplicates and were normalized to GAPDH.
Comparison of western blotting findings to iTRAQ results by fold-change of 2 select proteins. (a) The protein expression of NLRP3 and PTSG2 (COX-2) were analyzed by western blotting with specific antibodies. β-actin was used as a control. The values presented are the means ± sem of three independent experiments. (b) The fold-changes upon LPS stimulation for 2 representative BMEC DEPs are shown, as determined by iTRAQ analysis (blue) and verified by western blotting (red). *P < 0.05 and **P < 0.01 are significantly different from the control group.
KEGG pathway analyses of DEGs and DEPs
KEGG analysis showed that the differentially expressed mRNAs were mainly involved in pathways associated with Chemokine, TNF, TLR, NF-κB, and NLR signaling pathways (Fig. 3a). Protein expression changes that reached statistical significance were analyzed in KEGG. Differentially expressed proteins were associated with the following signaling pathways: ECM-receptor interaction, focal adhesion, ribosome, and the PI3K-Akt signaling pathway (Fig. 3b).
KEGG analysis of the differentially expressed genes (P < 0.01, gene number >10) and proteins (P < 0.05, gene number >5). (a) The KEGG analysis results showed that the most enriched pathways included chemokine signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, NF-κB signaling pathway, Inflammatory mediator regulation of TRP channels, etc. (b) The KEGG analysis results showed that the most enriched pathways included ECM-receptor interaction, Focal adhesion, Ribosome, PI3K-Akt signaling pathway, etc.
LPS induced the expression of inflammatory cytokines and apoptosis of BMECs
To investigate the inflammatory effects of LPS, LPS-induced inflammatory cytokine production and apoptosis were measured in this study. As shown in Fig. 4a, compared with the control group, levels of TNF-α, IL-1β, and IL-6 markedly increased after LPS stimulation. As shown in Fig. 4b, compared with the control group, apoptosis rate of BMECs markedly increased after LPS stimulation (P < 0.05).
Effects of LPS on proinflammatory cytokines production and apoptosis. (a)The levels of TNF-α, IL-1β and IL-6 markedly increased. (b) After BMEC were treated with 10 µg/ml of LPS for 12 h, the relative number of cells undergoing apoptosis was determined by flow cytometry after annexin V/propidium iodide (PI) staining. *P < 0.05 and **P < 0.01 are significantly different from the control group.
Confirmation of the important role of NLRP3 in inflammatory responses
To investigate the important role of NLRP3 in inflammatory responses, we used three pairs of NLRP3 siRNAs to knock down gene expression. The mRNA expression level of NLRP3 was examined by qRT-PCR after the transfection of BMECs with NLRP3 siRNA. Hence, we chose siRNA-3 (siNLRP3) for the subsequent experiment (online Supplementary Table S2 lists all the siRNA sequences and screening results). The siControl cells and siNLRP3 cells were simultaneously treated with LPS. The expression of NLRP3, pro-caspase-1, IL-1β, and PTGS2 was analyzed by western blotting. As shown, siRNA against NLRP3 successfully down-regulated the protein levels of NLRP3 in LPS-treated BMECs (Fig. 5). Also, the siRNA of NLRP3 diminished the expression of pro-caspase-1, IL-1β and PTG2 in LPS-stimulated BMECs, indicating that NLRP3 may play an important role in the inflammatory response of BMECs.
Effects of silencing NLRP3 on proinflammatory cytokines expression in BMECs. Cells with stable silencing of NLRP3 with siRNA (siControl and siNLRP3) and treatment with LPS. *P < 0.05 and **P < 0.01 are significantly different from the control group.
Discussion
In this study, we performed a comprehensive evaluation of expression profile in the LPS-induced BMECs by microarray and iTRAQ analysis, providing new data on the in vitro events occurring in the mammary epithelium during persistent infection. Subsequently, GO enrichment and pathway analyses of DEGs and DEPs in combination with RT-PCR, western blotting and ELISA detection for genes focused on the consistent results involved in the inflammatory response for mastitis infection. To date, the mechanism of LPS-induced epithelial injury has mainly concentrated on TLR-4 pathway (Yang et al., Reference Yang, Zerbe, Petzl, Brunner, Günther, Draing, von Aulock, Schuberth and Seyfert2008, Reference Yang, Zhou, Li and Niu2016; Porcherie et al., Reference Porcherie, Cunha, Trotereau, Roussel, Gilbert, Rainard, Gilbert, Rainard and Germon2012), while the crosstalk between TLRs and NLRs is rarely mentioned. As many studies on LPS-induced inflammatory responses have focused on TLR-4/NF-κB pathways, we verified the NLRs signaling pathways involved in the LPS-induced inflammatory response and clarified the role of the NLRP3 inflammasome in BMECs.
Our GO analysis showed that the DEGs and DEPs are involved in many inflammatory responses processes (online Supplementary Table S3). Compared to the KEGG analysis of DEGs, which revealed the involvement of many inflammatory pathways, the analysis of DEPs were mainly related to ECM-receptor interaction and the PI3K-Akt signaling pathway, which plays an important role in cell metabolism, growth, proliferation, and survival, as well as synthesis and metabolic pathways (Fig. 3). These differences between DEGs and DEPs may be due to inconsistent expression level at the mRNA and protein levels, but the exact mechanism still needs further research.
In the present study, we found many DEGs involved in multiple inflammatory response signaling pathways, such as toll-like receptors (TLRs), NF-κB, and NLRs signaling pathways. During microbial infection, crosstalk between TLRs and NLRs has been reported (Becker and O'Neill, Reference Becker and O'Neill2007). This regulates inflammation processes and establishes clear interplay, which leads to the secretion of mature interleukin-1β (IL-1β) during microbial infection or injury (Yang et al., Reference Yang, Zhou, Li and Niu2016). The NLRs family contains more than 20 members in mammals, and the activated member can form multiprotein complexes (called inflammasomes). These inflammasomes activate caspase-1 and induce the secretion of pro-inflammatory IL-1β and IL-18. One of the best-described is NLRP3, which acts as a sensor of metabolic stress and plays a key role in inflammation (Franchi et al., Reference Franchi, Muñoz-Planillo and Núñez2012). NLRP3 inflammasome plays an important role in the development of LPS-induced mastitis (Qu et al., Reference Qu, Wang, Li, Li, Zhang, Fu and Zhang2017). The procession and secretion of IL-1β is required for the activation of NLRP3 inflammasome (Wang, et al., Reference Wang, Gong, Chen, Xiong, Zhou, Huang and Kong2014; Lemarchand et al., Reference Lemarchand, Barrington, Chenery, Haley, Coutts, Allen, Allan and Brough2019). Once activated, NLRP3 interacts with apoptosis-associated speck-like protein containing a CARD (ASC), then leads to mutual recognition of NLRP3 and pro-caspase-1, followed by activating caspase-1 (Mariathasan and Monack, Reference Mariathasan and Monack2007). The activation of caspase-1 is responsible for the secretion of the mature IL-1β (Guo et al., Reference Guo, Callaway and Ting2015; Ozaki, et al., Reference Ozaki, Campbell and Doyle2015; Jo et al., Reference Jo, Kim, Shin and Sasakawa2016). NLRP3 was not only important for the assembly of the inflammasome and maturation of IL-1β, but also caused cellular changes that affected host-bacteria interactions in Bladder Epithelial Cells (Demirel et al., Reference Demirel, Persson, Brauner, Särndahl, Kruse and Persson2018) and BMECs (Li et al., Reference Li, Wang, Kuang, Li, Wang, Yang and Wang2019). In the present study, we found that NLRP3 gene was upregulated at both transcript and protein levels, and knockdown of NLRP3 expression inhibited the expression of pro-caspase-1, IL-1β, and PTGS (COX-2). This finding is consistent with previous studies, in which the authors found that both Caspase-1 and IL-1β production was decreased after knocking down NLRP3 (Liu et al., Reference Liu, Chen, Zhang, Ma, Chen, Du, Liu, Zong and Lu2017). Furthermore, the expression of IL-1β was subject to partial inhibition by the knockdown of NLRP3 gene, which means that IL-1β may derive from both of two distinct inflammatory pathways, TLR/NF-κB and NOD-like/NLRP3. This possibility is shown schematically in Fig. 6.
Proposed mechanisms of LPS-induced inflammatory response in bovine mammary epithelial cells. Differently expression genes and proteins were involved in several signaling pathways associated with inflammatory signaling pathways. Depletion of NLRP3 partially inhibited inflammatory cytokine secretion. Consistent with previous studies, we found that NLRP3 depletion-inhibited inflammatory cytokine secretion was mediated by Pro-caspase-1. Meanwhile, the increased expression of IL-1β inhibited the PTGS3(COX-2) expression activity. These results suggested that NLRs signaling pathways works in coordination with TLR4/NF-κB signaling pathways via NLRP3-inflammasome activation and pro-inflammatory cytokine secretion in LPS-induced mastitis.
Interleukin-1β is also involved in the synthesis of prostaglandins (PGs) and lipid eicosanoids through cyclooxygenase enzyme (COX) activity (Shaftel et al., Reference Shaftel, Carlson, Olschowka, Kyrkanides, Matousek and O'Banion2007). COX is a key enzyme that presents two isoforms: the constitutive enzyme COX-1, which is present in most cells, and COX-2, which is induced in response to proinflammatory stimuli, growth factors, infections, and other harmful stimuli (Abdallah et al., Reference Abdallah, Crj, Stephan, Khalil, Mroueh, Eid and Faour2017). COX-2 is an inflammatory mediator that converts arachidonic acid into prostaglandin E, a protein involved in inflammatory responses. COX-2 activation is a reliable end-point in cells suffering from inflammation. Previous studies have shown that there is increased expression of COX-2 in bovine mastitis (Corl et al., Reference Corl, Robinson, Contreras, Holcombe, Cook and Sordillo2010; Kang et al., Reference Kang, Lee, Lee, Petriello, Kim, Do, Lim, Lee and Han2016). In the present study, LPS induced the expression of COX-2 at both transcript and protein levels. However, the molecular mechanisms involved in these processes need to be further elucidated.
In conclusion, our results demonstrate that LPS-induced mammary inflammation involves several signaling pathways including the TLR4, NF-κB, and NLRs signaling pathways. NLRs signaling pathways works in coordination with TLR4/NF-κB signaling pathways via NLRP3-inflammasome activation and proinflammatory cytokine secretion in LPS-induced mastitis. The findings indicate the essential role of NLRP3 in bovine mastitis and may provide an effective therapeutic approach for E-coli mastitis.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0022029919000761
Acknowledgements
This research was supported by the National Key Research and Development Program of China (Grant No. 2018YFD0501604), the National Natural Science Foundation of China (Grant No. 31772567).
Conflict of interest
The authors declare that they have no competing interests.
