Abstract
Objective
To establish a model of Escherichia coli–induced bovine mammary gland inflammation and investigate alterations in protein expression in bovine mammary glands under different health conditions.
Methods
6 Chinese Holstein cows were selected and randomly assigned to 2 groups. Bovine mammary glands were induced with either 105 CFU/mL E coli (n = 3) or 1 X PBS (n = 3), and label-free quantitative proteomics was applied to explore the effect of E coli on the expression of proteins in bovine mammary glands. Then, these data were combined with transcriptome sequencing data for integrative analysis.
Results
A total of 944 differentially expressed proteins (DEPs) were identified; 588 proteins were upregulated and 356 were downregulated in mastitis tissues compared to healthy tissues. Gene Ontology functional annotations unveiled that the identified DEPs were predominantly enriched in processes, such as the single-organism metabolic process, extracellular region, and catalytic activity. According to Kyoto Encyclopedia of Genes and Genomes enrichment analysis, DEPs were prominently enriched in pathways related to type I diabetes mellitus, graft-versus-host disease, and peroxisome signaling pathways. InterPro analysis identified that the most significantly enriched functional structural domains were the epidermal growth factor–like calcium-binding domains, C-type lectin folds, and C-type lectins. By combining transcriptomic with proteomics data, 7 (cytochrome P450 family 51 subfamily A member 1, ethanolamine kinase 1, carbonic anhydrase 2, complement factor H, indoleamine 2,3-dioxygenase 1, CD5, and IL-33) hub genes (proteins) closely associated with mastitis development were identified.
Conclusions
These findings offer a theoretical basis for understanding the molecular regulatory mechanisms underlying bovine mastitis.
Clinical Relevance
Our study presents a promising approach to understanding the mechanism of E coli infection, with a particular focus on the induction of bovine mastitis.
Mastitis remains one of the most prevalent and costly diseases in the dairy cattle industry worldwide.1 Escherichia coli is recognized as the primary causative agent of mastitis in dairy cows worldwide, leading to acute and clinical symptoms, such as systemic inflammation, milk abnormalities, and severe udder tissue damage.2,3 While other bacteria, such as Staphylococcus aureus and Streptococcus sp, are also associated with mastitis, E coli is known to cause more severe symptoms of mastitis and higher rates of animal mortality.4 Despite widespread implementation of mastitis control strategies globally, the incidence of E coli–induced mastitis has always been high.5 Antibiotics are the mainstay of treatment for mastitis, but their prolonged use contributes to antibiotic resistance and poses significant risks to public health.6 Escherichia coli is also a highly adaptable pathogen capable of long-term colonization and survival in animals, humans, and abiotic environments.7 Overall, mastitis has become a major problem affecting animal husbandry in many countries, which indicates that this problem needs to be resolved urgently.
The mammary gland is a complex organ providing nutritious and disease-resistant milk to newborns and is protected by various defense mechanisms and specific and innate immune factors.8 During infections, mammary glands in animals are damaged by bacteria and their virulence factors, and the infection involves complex host immune system–pathogen interactions.9 It is worth mentioning that proteomics is a strong favorable tool for studying the dynamic interactions between pathogens and the immune system during mastitis.10 During the early development of this technology, efforts were focused on using proteomics to identify medicines.11 Later, its research potential has been extended to animal production, especially for identifying new protein markers in animals with different mammary health status, such as mice and sheep.12,13 However, the specific role of proteins in the pathogenesis of bovine mastitis remains unclear.14 Therefore, establishing a protein expression profile of the host response during bovine mastitis infection and analyzing protein expression changes during the infection. Providing a deeper understanding of this may help elucidate the molecular mechanisms of E coli–type mastitis.
Thus, in the present study, we screened proteins in bovine mammary glands of Chinese Holstein cows under different health conditions through label-free quantitative proteomics and analyzed these proteins for enrichment. Additionally, we performed an integrative analysis of the proteomic and transcriptomic data. This study may help to elucidate the molecular mechanisms underlying bovine mammary gland infection during E coli–induced mastitis and provide new breeding material for mastitis-related research.
Methods
Ethics approval
This study was approved by the IACUC of Ningxia University (protocol No. NXU-2022-069), and all operations were conducted in compliance with the university’s guidelines for animal research.
Animal experiments
In this study, 6 Chinese Holstein cows (average of 3 parity) were selected for the animal model, ensuring that all animals were of similar age (2 to 3 years), in good health, and had consistent lactation cycles (midlactation). All cows were allowed free access to feed and water. Milk samples from each cow were tested 21 days prior to the experiment to confirm that they were without any abnormalities (somatic cell count < 200,000/mL). During this period, the body condition of all cows were observed, and no clinical signs of disease were observed.
Construction of intramammary infection model
A bovine mastitis model was induced using E coli (ATCC No. 25922) following a previously established protocol.15 All healthy cows were randomly divided into 2 groups: the mammary gland infected with E coli (M_E) group (n = 3) was injected with 5 mL of E coli suspension containing 105 CFU/mL into the right teat canal. The mammary gland control (M_C) group (n = 3) received an equal volume of sterile 1X PBS into the right teat canal. After 7 days, milk was completely extruded from the mammary glands of cows, and the mammary glands were directly collected by biopsy. Briefly, the skin at the selected biopsy sites was first shaved. Approximately 1.5-cm3 incisions were made in the skin of the posterior quarter of the mammary gland. Tissue biopsies were then performed and immediately fixed in 4% paraformaldehyde and stored at −80 °C until further analysis.
Histopathological analysis
Samples were fixed in 4% paraformaldehyde for at least 24 hours. The tissues were then trimmed into 1 X 1 X 0.5-cm pieces, subjected to gradient dehydration with alcohol and xylene, and subsequently embedded in paraffin. Subsequently, approximately 7-μm–thick paraffin sections were cut and stained with H&E. The stained sections were then examined under a microscope (Leica) to observe and document any histopathological changes in the bovine mammary glands.
Ribonucleic acid extraction, reverse transcription, and real-time quantitative PCR
Total RNA was extracted from the bovine mammary glands using the TRIzol reagent (Thermo Fisher) following the manufacturer’s instructions. Ribonucleic acid integrity was verified through 1% agarose gel electrophoresis. The Agilent Bioanalyzer 2100 system (Agilent Technologies) was used to determine RNA concentration and purity. Ribonucleic acid was reverse transcribed to cDNA using the PrimeScript Reagent Kit with gDNA Eraser (Takara) according to the manufacturer’s instructions. We performed quantitative real-time (qRT)-PCR on the CFX96 Fluorescence Quantification Detection System (Bio-Rad). The 20-µL reaction system consisted of the following: 10 μL 2X SYBR Premix Ex Taq (Tli RNaseH Plus; Mei5 Biotechnology, Co., Ltd, Beijing, China), 2 μL cDNA, 0.8 μL of each of the upstream and downstream primers (10 µM), and 6.4 μL nuclease-free water. The amplification conditions were as follows: predenaturation at 95 °C for 30 seconds, denaturation at 95 °C for 5 seconds, and annealing/extension at 60 °C for 30 seconds, 40 cycles. Relative gene expression was quantified using the 2−ΔΔct method. The primers used for qRT-PCR are shown in Supplementary Table S1.
Total protein extraction and trypsin digestion
We mixed the same 3 samples from each group into a samples pool. For protein extraction, an appropriate amount of bovine mammary tissue was weighed and mixed with a suitable amount of protein lysis buffer (8 mol/L urea, 100 mmol/L Tetraethylammonium bromide, and 0.2% SDS). After centrifugation at 12,000 X g at 4 °C for 15 minutes, the supernatant was carefully collected, and 10 mmol/L dithiothreitol (Sigma) was added. The mixture was then incubated at 56 °C for 1 hour. Iodoacetamide was later added, and the mixture was incubated in the dark for 1 hour. Subsequently, precooled acetone was added to the solution for protein precipitation, which was allowed to proceed for 2 hours. The precipitate was recovered through centrifugation at 12,000 X g for 15 minutes. The precipitate was again washed with precooled acetone, centrifuged, and collected and washed with precooled acetone. After another centrifugation step, the final protein precipitate was dissolved in an appropriate amount of proteolytic solution. Protein lysis solution was then added to the protein sample to make a final volume of 100 μL. Trypsin and Tetraethylammonium bromide were then added to the samples, and the mixture was digested for 4 hours at 37 °C. Calcium chloride and additional trypsin were added for overnight digestion. The pH of the mixture was adjusted to less than 3 by using formic acid (Thermo Fisher), stirred for 5 minutes using a glass rod, and centrifuged at 12,000 X g for 5 minutes. The supernatant was collected and slowly passed through a C18 trap column (Thermo Fisher). Subsequently, the sample was washed 3 times with a washing solution and eluted using an appropriate volume of elution solution. The filtrate was collected and lyophilized for further processing.
Analysis of LC-MS-MS
After trypsin digestion, the peptides were analyzed using LC-MS-MS. The mobile phases liquid A (0.1% formic acid aqueous solution, volume per volume) and liquid B (80% acetonitrile + 0.1% formic acid aqueous solution, volume per volume) were prepared first for the LC-MS-MS analysis. The lyophilized protein powder was dissolved in 10 μL of liquid A. After centrifugation at 12,000 X g for 5 minutes at 4 °C, 1 μg of the supernatant was aspirated and loaded onto an EASY-nLCTM 1200 Nano-Scale UHPLC system (Thermo Fisher) for peptide elution and separation. In the mass spectrometry analysis, the eluted peptides were analyzed using a Q ExactiveTMHF-X mass spectrometer (Thermo Fisher) equipped with a Nanospray Flex (electrospray ionization) ion source (Thermo Fisher). The ion spray voltage was set to 2.3 kV, and the mass spectrometer heated capillary temperature was set at 320 °C. Scanning was performed over a mass range of 350 to 1,500 m/z. The mass spectrometer was set to high resolution for both primary and secondary mass spectrometry, with specific parameters for maximum volume and injection time. Subsequently, the data generated from the mass spectrometry were imported into Proteome Discoverer, version 2.2 (Thermo Fisher), for the quantitative analysis of peptide reporter ion peak intensity values.
Bioinformatics analysis of differentially expressed proteins
Following protein identification using a Uniprot database search, the obtained data underwent a series of quality control checks. The expression levels of proteins in the M_E and M_C groups were compared on the basis of fold change (FC) values. The following thresholds were applied to identify differentially expressed proteins (DEPs): upregulated and downregulated DEPs were FC > 4 and P < .05 or FC < 0.25 and P < .05, respectively.16 All DEPs were categorized based on their biological functions using the Gene Ontology (GO) database (http://www.geneontology.org/) and each was assigned to one of three specific categories (biological process, cellular component, or molecular function), accordingly.17 The GO was classified as cellular component, molecular function, and biological process. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https://www.genome.jp/kegg/) was used to classify and group the identified proteins.18 InterPro (IPR) database (https://www.ebi.ac.uk/interpro/) analysis was conducted using InterProScan.19 A P value < .05 was considered statistically significant.
Integrated transcriptomics and proteomics
After validating the quality of total RNA samples, library preparation and sequencing were performed. Raw reads obtained from transcriptome sequencing were subjected to quality control to obtain clean reads. For each gene, the number of fragments per million base-mapped reads was calculated for each gene using the fragments per kilobase of exon model per million mapped fragments technique. Sequencing data were analyzed for identifying differentially expressed genes (DEGs) by using DESeq2 (https://www.bioconductor.org/packages/release/bioc/html/DESeq2.html). Screening thresholds for DEGs were set to |log2FC | > 1.5 and false discovery rate < 0.05. All DEGs and DEPs were then intersected using a Venn diagram to identify common genes (proteins). These intersecting genes (proteins) were further analyzed for predicted biological function through KEGG pathway enrichment analysis.20
Statistical analysis
All experiment data were expressed as mean ± SD. Prism, version 8.0.2 (GraphPad Software Inc), was used for statistical analysis, and the Student t test was used to compare the differences between healthy and mastitis cows. A P value < .05 or a P value < .01 was considered significantly different.
Results
Construction of the bovine mastitis model
In the M_C group, staining with H&E showed that the mammary lobule structure was intact and that there were no significant histopathological abnormalities (Figure 1). However, in the M_E group, significant histopathological changes were observed, including destruction of many mammary epithelial cells, incomplete organization of the mammary lobules, severe cellular infiltration, and edema. This is consistent with the characteristics of mastitis.21 Escherichia coli–induced mastitis is known to be closely associated with the expression of several proinflammatory cytokines, such as IL-6, IL-8, and IL-1β.22 To further assess this claim, the levels of proinflammatory cytokines were detected by qRT-PCR. The results demonstrated that the mRNA expression levels of IL-6, IL-8, and IL-1β genes were significantly elevated in the M_E group compared with the M_C group (P < .01). The above results indicated the reliability of the establishment of E coli–type mastitis.
Construction of the bovine mastitis model. A and B—Histopathology of mammary gland tissues (H&E stain; magnification, 100X). C through E—Quantitative real-time PCR was conducted to detect the gene relative expression level of inflammatory cytokines in bovine mammary gland tissues. Glyceraldehyde-3-phosphate dehydrogenase was used as an internal control, and the relative expression of each gene was calculated with the comparative 2-ΔΔCT method. Blue arrows indicate the areas of necrotic mammary gland. M_C = Mammary gland control. M_E = Mammary gland infected with E coli. **P < .01; ***P < .001.
Citation: American Journal of Veterinary Research 86, 6; 10.2460/ajvr.24.12.0403
Overview and characteristic analysis of proteomes
To further explore the molecular mechanisms underlying E coli–induced mastitis, proteomics analysis was performed on mammary tissue samples from both groups using label-free quantitative proteomics technology. To reduce the false-positive rate, only spectral peptides and proteins containing at least 1 unique peptide with a confidence level of 99% or more were retained. Peptides and proteins with a false discovery rate of > 1% were excluded from the analysis. The quality control results of the proteins (Figure 2) indicated that the proteomic data generated in this study were of high quality and suitable for further data analysis.
Quality control of proteomic data. A—Protein coverage distribution map. B—Precursor ions tolerance. The abscissa shows the mass deviation, and the ordinate shows the precursor ion density distribution corresponding to the error. C—Mass of proteins. D—Peptide length distribution. E—Number.
Citation: American Journal of Veterinary Research 86, 6; 10.2460/ajvr.24.12.0403
Analysis of DEPs
Compared with the M_C group, 944 DEPs were identified in the M_E group. Among them, the expression of 588 proteins was upregulated whereas that of 356 proteins was downregulated (Figure 3). Moreover, volcano plots were drawn to present the overall distribution of these DEPs (Figure 3).
Identification of differentially expressed proteins (DEPs). A—Quantity statistics of DEPs. The green bars represent downregulated proteins, and the red bars represent upregulated proteins. B—Volcano plots of DEPs of mammary tissue. The x-axis represents the log2 (fold change) value, and the y-axis shows the −log10 (P value). The dots in the figure represent DEPs; red indicates upregulated DEPs, green represents downregulated DEPs, and black shows insignificant proteins. M_C = Mammary gland control. M_E = Mammary gland infected with E coli.
Citation: American Journal of Veterinary Research 86, 6; 10.2460/ajvr.24.12.0403
Functional analysis of DEPs
The GO functional annotation was performed on the identified 944 DEPs and found that most DEPs were primarily enriched in the catalytic activity, single-organism metabolic process, and extracellular region (Figure 4). The KEGG enrichment analysis of DEPs in the M_E and M_C groups unveiled that DEPs were prominently enriched in type I diabetes mellitus, graft-versus-host disease, and the peroxisome signaling pathway (Figure 4). Additionally, we identified IPR terms enriched in DEPs (Figure 4).
Protein functional annotation and enrichment analysis. A—Gene ontology (GO)-enriched histogram of DEPs in the bovine mammary gland tissue. The abscissa text represents the name and distribution of GO, and the height of the column represents the enrichment rate. B—Top 20 enriched Kyoto Encyclopedia of Genes and Genomes pathways among the DEPs. The x-axis represents the percentage of the annotated genes, and the y-axis corresponds to the signal pathway. The size of the dot is instead a function of the count of proteins involved in the corresponding pathway. C—InterPro (IPR) analysis of the DEPs. BP = Biological process. CC = Cellular component. MF = Molecular function. AMP = Adenosine mono phosphate. EGF = Epidermal growth factor. LH2 = Lipid hydroperoxide 2. M_C = Mammary gland control. MCM = Mini-chromosome maintenance. M_E = Mammary gland infected with E coli. MHC = Major histocompatibility complex. PLAT = Plant. PPAR = Peroxisome proliferator-activated receptor.
Citation: American Journal of Veterinary Research 86, 6; 10.2460/ajvr.24.12.0403
Integrated transcriptomics and proteomics
To explore the potential relationship between transcriptome and proteome changes in E coli–induced bovine mammary tissues, an integrated analysis was conducted, merging 2,389 DEGs identified from transcriptomics with 944 DEPs identified from proteomics. The results showed consistent mRNA and protein expression levels of cytochrome P450 family 51 subfamily A member 1 (CYP51A1), ethanolamine kinase 1 (ETNK1), pterin-4 α-carbinolamine dehydratase 1, progestogen-associated endometrial protein, inter-α-trypsin inhibitor heavy chain H2, carbonic anhydrase 2 (CA2), complement factor H (CFH), indoleamine 2,3-dioxygenase 1 (IDO1), CD5, solute carrier family 9 member A9, and IL-33 (Figure 5). Additionally, KEGG functional enrichment analysis of the aforementioned DEGs with DEPs revealed significant overlaps in biological pathways, reinforcing the potential role of CYP51A1, ETNK1, CA2, CFH, IDO1, CD5, and IL-33 in bovine mastitis development (Table 1). These genes (proteins) are likely to be involved in critical immune and inflammatory responses, suggesting that they may be key players in the molecular mechanisms underlying the disease.
Venn diagram analysis of DEGs and DEPs in 2 groups. CA2 = Carbonic anhydrase. CFH = Complement factor H. CYP51A1 = Cytochrome P450 family 51 subfamily A member 1. ETNK1 = Ethanolamine kinase 1. IDO1 = Indoleamine 2,3-dioxygenase 1. ITIH2 = Inter-α-trypsin inhibitor heavy chain H2. PAEP = Progestogen-associated endometrial protein. PCBD1 = Pterin-4 α-carbinolamine dehydratase 1. SLC9A9 = Solute carrier family 9 member A9.
Citation: American Journal of Veterinary Research 86, 6; 10.2460/ajvr.24.12.0403
The coexpression of differentially expressed genes and differentially expressed proteins in M_E and M_C groups.
| Log2 fold change | ||||
|---|---|---|---|---|
| Gene name | KEGG signal pathway | Proteomics | Transcriptomes | Up/down genes (proteins) |
| CYP51A1 | Metabolic pathways | 4.5375 | 2.0920 | UP |
| ETNK1 | Metabolic pathways | 2.0530 | 1.9814 | UP |
| CA2 | Proximal tubule bicarbonate reclamation | −2.0339 | −3.4415 | Down |
| CFH | Staphylococcus aureus infection | −2.2106 | −2.1086 | Down |
| IDO1 | Metabolic pathways | −2.3681 | −4.3969 | Down |
| CD5 | Hematopoietic cell lineage | −2.4849 | −2.0525 | Down |
| IL-33 | Influenza A | −3.2784 | 1.5795 | Down |
CA2 = Carbonic anhydrase. CFH = Complement factor H. CYP51A1 = Cytochrome P450 family 51 subfamily A member 1. ETNK1 = Ethanolamine kinase 1. IDO1 = Indoleamine 2,3-dioxygenase 1. KEGG = Kyoto Encyclopedia of Genes and Genomes. M_E = Mammary gland infected with E coli.
Only factors with consistent differentially expressed gene and differentially expressed protein expression levels are listed in the table.
Discussion
Mastitis poses significant economic and production challenges to the global dairy industry, including reduced milk yield and quality, premature culling of cows, and increased treatment expenses.23 Because the innate immune response to pathogens varies across species, studying the specific immune mechanisms in bovine mastitis models to gain insights relevant to dairy production is crucial.24 Therefore, we chose dairy cows as research subjects to more specifically analyze the molecular mechanisms underlying bovine mastitis. In this study, bovine mammary tissues were evaluated for pathological changes caused by E coli using H&E staining. The results indicated significant hyperplasia of the alveolar walls of the E coli–induced mammary glands, characterized by extensive infiltration of inflammatory cells. By contrast, healthy mammary tissues exhibited normal histological features. These findings align with previously reported pathological characteristics of mastitis.25 During mastitis, the microenvironment within the mammary gland is suggested to undergo dramatic changes, where numerous heterogeneous immune cells are recruited to the site of infection.26 Inflammatory cytokines, including interleukins and tumor necrosis factor-α, are recognized as key indicators of the inflammatory response and are widely used to assess the progression of inflammation in bovine mastitis models.27 Using qRT-PCR, the mRNA expression levels of the inflammatory cytokines IL-6, IL-8, and IL-1β were significantly higher in E coli–induced mammary tissues than healthy mammary tissue. These results indicated that a bovine mastitis tissue model was successfully established.
Most proteomic studies17,28,29 on mastitis conducted to date have been performed using milk, serum, or somatic cells. To the best of our knowledge, the effects of E coli infection on protein expression in bovine mammary tissue has rarely been explored using label-free proteomics sequencing. In this study, we used label-free proteomics to comprehensively evaluate the protein profile of E coli–induced bovine mammary tissues, identifying 944 DEPs, including 588 upregulated and 356 downregulated proteins. To gain further insight into the molecular mechanisms of E coli infection in bovine mammary gland, the biological functions of these DEPs were analyzed. The GO functional annotation unveiled that DEPs were predominantly involved in catalytic activity, single-organism metabolic processes, and extracellular regions. Additionally, the KEGG pathway enrichment analysis demonstrated that DEPs were primarily enriched in pathways associated with type I diabetes mellitus, graft-versus-host disease, and the peroxisome signaling pathway. Type I diabetes mellitus is an autoimmune disease caused primarily by immune disruption in the islet cells, characterized by a proinflammatory state and elevated Toll-like receptor activity.30,31 Graft-versus-host disease, a complication post allogeneic hematopoietic stem cell transplantation, is also marked by heightened inflammatory responses.32 Graft-versus-host disease was caused when there were antigenic differences between the transplant recipient and the donor. Peroxisomes have recently been recognized as critical regulators in the context of infection, inflammation, neurological disorders, aging, and cancer. Their involvement in mastitis likely reflects their role in controlling oxidative stress and modulating immune responses in inflamed mammary tissue.33 Interestingly, several DEPs were also enriched in the S aureus infection signaling pathway despite the experimental model being induced by E coli. This highlights the complexity of mastitis pathogenesis in cows. Meanwhile, the IPR analysis further indicated that epidermal growth factor–like calcium-binding domains, C-type lectin folds, and C-type lectins were the most prevalent structural domains in both groups. Overall, these findings provide valuable insights into the potential relationship between DEPs and bovine mastitis.
The joint analysis of transcriptomics and proteomics provides critical insights into the posttranscriptional regulatory mechanisms of gene expression, offering a more comprehensive understanding of biological processes.34 In this work, we performed a systematic comparison of gene and protein expression in healthy and E coli–induced bovine mammary glands. Transcriptomic sequencing revealed 2,389 DEGs, whereas proteomic sequencing identified 944 DEPs. However, only 11 proteins (genes) were upregulated or downregulated at both the transcriptional and translational levels, and most genes (proteins) expressed at the single transcriptional or translational level were involved in bovine mastitis regulation, which is consistent with our expected results. This further emphasizes the complexity of gene regulation, where many genes are not directly translated into proteins. Among the 11 genes (proteins) with consistent expression trends at both levels, CYP51A1, ETNK1, CA2, CFH, IDO1, CD5, and IL-33 were identified as potential key regulators of bovine mastitis. Lanosterol 14α-demethylase enzyme is considered an endogenous regulator of proinflammatory cytokine production, and the CYP51A1 code regulates the activity of this enzyme.35 Decreased CFH levels and activity lead to increased levels of inflammatory cytokines, chemokines, and growth factors.36 Upregulation of CYP51A1 expression and downregulation of CFH expression are hypothesized to be associated with increased proinflammatory cytokine levels. Ethanolamine kinase 1 is the primary cause of increased elevated phosphoethanolamine in breast and pancreatic cancer cells, consistent with the results of ETNK1 expression upregulation in this study.37 According to emerging data, the IL-33–ST2 axis is involved in various biological processes, such as immune response regulation.38 Indoleamine 2,3-dioxygenase 1 is recognized to mediate tumor immune evasion and has immunomodulatory functions.39 In a previous study,40 increased CD5 expression prevented autoimmune disease development. A link exists between CA2 and NLRP3 inflammatory vesicle activation.41 By participating in immune and inflammatory response processes, ST2, IDO1, CD5, and CA2 may modulate the molecular mechanisms underlying mastitis in dairy cows. To date, the molecular function of these DEGs (DEPs) in regulating mastitis in dairy cows has not yet been reported. These DEGs (DEPs) and their functions need to be validated at the mRNA and protein levels to elucidate in-depth the molecular mechanism underlying mastitis in dairy cows.
In summary, our study identified 944 DEPs, including 588 upregulated and 356 downregulated proteins, in bovine mastitis induced by E coli by using proteomic sequencing. The enrichment analyses revealed that these DEPs may be involved in various immune and inflammatory response–related biological processes. The integrative analysis of transcriptomics and proteomics data revealed that CYP51A1, ETNK1, CA2, CFH, IDO1, CD5, and IL-33 may be hub genes (proteins) regulating the molecular mechanisms underlying mastitis. These results will contribute to the understanding of the molecular mechanisms involved during the early phase of E coli–induced mastitis and highlight candidate biomarkers that may be useful for the diagnosis and control of mastitis.
Supplementary Materials
Supplementary materials are posted online at the journal website: avmajournals.avma.org.
Acknowledgments
The authors thank the Natural Science Foundation of China for supporting this manuscript.
Disclosures
The authors have nothing to disclose. No AI-assisted technologies were used in the composition of this manuscript.
Funding
This research was supported by grants from the Natural Science Foundation of China (No. 32172709) and the earmarked fund for the China Agriculture Research System (CARS) of China (No. CARS-36).
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