Proteomic Identification of PFKFB3 and PFKFB4 Associated with Coenzyme Metabolism and Redox Imbalance in Dairy Cows with Clinical Mastitis

TL;DR

This study identifies PFKFB3 and PFKFB4 as key proteins linked to coenzyme metabolism and oxidative stress in dairy cows with mastitis.

Contribution

The study reveals novel associations between PFKFB3/PFKFB4 and coenzyme metabolism in bovine mastitis.

Findings

CM cows showed reduced glutathione and coenzyme levels, along with increased oxidative stress.

PFKFB3 and PFKFB4 were differentially expressed and localized in mammary epithelial cells.

Pathway analysis suggests a regulatory network linking inflammation, ROS, and PFKFB3/PFKFB4 expression.

Abstract

In this study, we identified a number of biological processes, pathways, and key protein targets associated with coenzyme metabolism in bovine clinical mastitis (CM). The expression patterns and subcellular localization of key proteins were examined to characterize their potential association with oxidative stress and inflammatory responses in mammary gland tissues. The CM group exhibited collapsed and atrophied mammary acini, inflammatory cell infiltration, increased reactive oxygen species fluorescence signals, and a significant reduction in glutathione content. Levels of key coenzymes, including nicotinamide adenine dinucleotide and flavin adenine dinucleotide, decreased significantly. Bioinformatic analysis identified four biological processes related to coenzyme metabolism and 20 key differentially expressed proteins associated with the glycolysis pathway. Among them, 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase 3 (PFKFB3) and PFKFB4 were identified as key hub proteins involved in fructose and mannose metabolism and AMP-activated protein kinase (AMPK) signaling pathways. PFKFB3 and PFKFB4 were primarily localized in the cytoplasm of mammary epithelial cells, and the CM group showed significantly upregulated and downregulated expression at both the gene and protein levels. Molecular mechanism analysis based on pathway enrichment suggested a putative regulatory network in which pathogen-induced inflammation may be associated with ROS–AMPK-related signaling, potentially contributing to dysregulated PFKFB3/PFKFB4 expression, glycolytic imbalance, impaired coenzyme metabolism, and mammary epithelial cell injury.

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Full text

1. Introduction

Bovine mastitis is a localized inflammatory response in the mammary gland (MG) tissue of dairy cows and is caused by various factors such as pathogenic microorganisms, metabolic imbalance, or mechanical injury. It is among the most prevalent diseases in the global dairy industry, characterized by high incidence and severe economic losses [1]. Clinical mastitis (CM), a subtype of bovine mastitis, seriously affects cow health, milk quality, longevity, and culling rate. At the pathological level, CM is typically accompanied by excessive inflammatory cell infiltration, structural tissue damage, and pronounced oxidative stress, which disrupts mammary gland homeostasis [2]. According to epidemiological survey data, CM causes economic losses of approximately 13 billion USD worldwide each year, and the annual direct economic loss attributed to CM in China alone is estimated to exceed six billion RMB [1]. The etiology of mastitis is complex; however, bacterial infection is the primary causative factor, with Escherichia coli, Staphylococcus aureus, and other opportunistic pathogens being the most frequently isolated agents [2]. Antibiotic therapy is one of the primary methods used to prevent and treat CM in dairy cows. However, prolonged and excessive use of antibiotics has led to the emergence of antibiotic-resistant strains and increased drug residues, posing a threat to food safety and human health [3,4]. Although various approaches, such as bacterial isolation and identification, development of alternative antibiotics, and biological therapies, have been employed for the prevention and treatment of mastitis [3,5,6], this disease remains prevalent on large-scale farms. Therefore, a deeper understanding of molecular therapeutic targets and their underlying mechanisms in bovine CM is essential for the development of effective and sustainable intervention strategies.

Coenzymes are a class of small organic molecules that reversibly associate with enzymes to facilitate electron or functional group transfer, thereby sustaining enzymatic activity and cellular metabolism [7,8]. The system must not only achieve the dispatching objectives of pumping stations and gate stations along the line, but also meet the operational constraints of each project and provide feedback on control deviations [9,10]. Disruption of redox-related coenzymes, such as coenzyme I/nicotinamide adenine dinucleotide (NAD^+^), impairs intracellular redox balance and promotes excessive reactive oxygen species (ROS) accumulation, thereby exacerbating bovine mastitis [11]. In parallel, impaired participation of coenzyme A (CoA) in fatty acid β-oxidation results in systemic metabolic imbalance and increases susceptibility to mastitis [10]. During mastitis progression, abnormal coenzyme metabolism alters the activity of long-chain fatty acyl-CoA synthetases, leading to disturbances in fatty acid activation and inflammatory lipid signaling [12]. Collectively, this evidence indicates that coenzyme metabolism is not only affected by mastitis-associated oxidative stress but may also actively contribute to the modulation of inflammatory and redox processes in MG tissue of dairy cows.

In recent years, restoration of metabolic and redox homeostasis in tissues through exogenous supplementation or endogenous regulation of coenzymes has emerged as a promising host-oriented strategy to prevent or attenuate infection-triggered inflammatory progression in bovine mastitis. Coenzyme Q10 (CoQ10), a key component of the mitochondrial electron transport chain, exhibits potent antioxidant activity by scavenging free radicals and preserving mitochondrial integrity, thereby limiting oxidative stress-driven inflammatory damage [13]. Notably, CoQ10 supplementation has been reported to reduce the expression of inflammatory markers, including C-reactive protein, interleukin (IL)-6, and tumor necrosis factor-alpha (TNF-α) [14,15]. Similarly, NAD^+^, an essential cofactor for the deacetylase sirtuin-1 (SIRT1), suppresses the release of pro-inflammatory factors such as TNF-α, IL-1β, and IL-6 by inhibiting the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway, highlighting its critical role in linking energy metabolism, oxidative stress, and inflammation [16]. Despite these advances, the dynamic reprogramming of coenzyme metabolism in mammary tissues during CM and how these changes mechanistically determine the balance between effective host defense and pathological inflammation remain largely unclear.

Data-independent acquisition (DIA)-based proteomics has emerged as a powerful high-throughput strategy for comprehensive and reproducible profiling of protein expression across complex biological samples [17]. In recent years, proteomic approaches have been widely applied in bovine mastitis research, enabling the identification of differentially expressed proteins and dysregulated biological processes related to metabolic reprogramming [10], inflammatory responses [18], cell death [19], and vascular dysfunction [17] in the MG tissue. These studies have provided valuable insights into the pathogenesis of mastitis and facilitated the discovery of potential biomarkers and therapeutic targets, underscoring the indispensable role of proteomics in advancing the prevention and treatment of bovine mastitis.

This study aimed to systematically identify the biological processes (BPs), pathways, and differentially expressed proteins (DEPs) associated with coenzyme metabolism using DIA proteomics data and bioinformatics analysis. By integrating proteomic profiling with molecular biology approaches, we sought to elucidate the regulatory mechanisms of coenzyme metabolism in the onset and progression of CM in dairy cows, and to validate the expression patterns and subcellular localization of key DEPs. Collectively, the results of this study provide mechanistic insights into the pathogenesis of bovine CM and offer a theoretical basis for the development of targeted antioxidant and metabolic intervention strategies.

2. Materials and Methods

2.1. Sample Preparation and Collection

The MG tissue samples were collected from lactating Holstein cows (5–6 years old, 3rd to 5th lactation, 60–250 days in milk) raised on large-scale commercial dairy farms in Wuzhong City, Ningxia Hui Autonomous Region, and Zhangye City, Gansu Province, China. All dairy cows were clinically examined and diagnosed with mastitis by a licensed veterinarian based on typical clinical symptoms, as previously described [18]. Milk samples were collected to carry out the somatic cell count (SCC) and the California mastitis test (CMT), as well as pathogen isolation and identification (Supplementary Figure S1). Holstein cows in the control (without pathogens) and CM group cows induced using Escherichia coli were screened following established protocols. Cows without typical clinical symptoms, with SCC ≤ 1 × 10^5^ cells/mL, and negative CMT results, were classified as healthy cows (control group, Con). In contrast, cows exhibiting typical clinical symptoms of mastitis, with SCC ≥ 1.3 × 10^6^ cells/mL, and positive CMT results, were classified as clinical mastitis cows (Clinical Mastitis group, CM). To minimize biological variability, cows in both groups were selected to have comparable lactation stages and parity under similar management conditions. Eligible cows were transported to slaughterhouses, and MG tissue samples were collected immediately after slaughter (n = 4/group). Portions of the tissues were fixed in 4% paraformaldehyde for histological analyses, while the remaining tissues were snap-frozen in liquid nitrogen and stored at −80 °C for subsequent molecular analyses. All experiments were approved by the Ethics Committee of Gansu Agricultural University, Lanzhou, China (No. GSAU-Eth-VMC-2021-020).

2.2. Hematoxylin-Eosin (H&E) and ROS Staining

Fixed MG tissues were processed using standard paraffin embedding procedures and sectioned into 5 µm thick slices (Leica, Wetzlar, Germany). H&E staining was performed as previously described [19]. Stained sections were examined and imaged using an optical microscope (Nikon, Tokyo, Japan), and six randomly selected fields were analyzed per sample. For ROS detection, frozen MG tissue sections were prepared and stained using a dihydroethidium (DHE) staining kit according to the manufacturer’s instructions [20]. Nuclei were counterstained with 0.5 µg/mL 4′,6-diamidino-2-phenylindole (DAPI; Solarbio, Beijing, China). Fluorescence images were captured using a fluorescence microscope (Olympus, Tokyo, Japan), with six randomly selected fields analyzed per sample. The integrated optical density (IOD) of the positive signals was quantified using the ImageJ v1.44p software (NIH, Bethesda, MD, USA).

2.3. Glutathione (GSH) Detection

Frozen MG tissues were homogenized in protein removal reagent M followed by centrifugation to collect the supernatants. Total glutathione content was determined using a commercial glutathione/glutathione disulfide (GSH/GSSG) detection kit according to the manufacturer’s instructions [21]. Briefly, the supernatants were incubated with a GSH-scavenging auxiliary solution and working solution, and absorbance was measured using a microplate reader.

2.4. RNA Extraction, cDNA Synthesis and Quantitative Polymerase Chain Reaction (qPCR) Assays

Total RNA was extracted from 20 mg of MG tissue using TransZol Up reagent (TransGen, Beijing, China), and RNA concentration was determined using a NanoDrop-8000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). Subsequently, 500 ng of total RNA was reverse-transcribed into single-stranded cDNA using the Evo M-MLV kit (AG, Changsha, China) as previously described [18,22]. Primers for bovine β-actin, 6-Phosphofructo-2-Kinase/Fructose-2,6-Bisphosphatase 3 (PFKFB3), PFKFB4, Nicotinamide Mononucleotide Adenylyltransferase 1 (NMNAT1), and Riboflavin Kinase (RFK) (Table S1) were designed using Primer3 (v4.1.0) and synthesized by Qingke Biotech (Xi’an, Shanxi, China). qPCR was performed using cDNA as a template, as previously described [22]. β-actin was used as the internal reference gene for normalization. Relative gene expression was calculated using the 2^−∆∆Ct^ method [23]. All qPCRs were performed at least in triplicate.

2.5. Bioinformatic Analysis

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses (p < 0.05) were performed based on the DIA proteomic data obtained from MG tissue samples from Holstein cows (ProteomeXchange accession number: IPX0013254001/PXD068044). Peptide-level and protein-level identifications were filtered at a false discovery rate (FDR) ≤ 1%, and only peptides meeting this criterion were used for protein quantification. Protein intensities were normalized across samples using the local normalization method implemented in the Spectronaut software (version 18.4, Biognosys AG, Schlieren, Switzerland), which incorporates the Pulsar search engine, to reduce technical variation. Proteins with missing values in more than 50% of the samples were excluded, and the remaining missing values were imputed using the missing value imputation (MVI) approach prior to downstream analyses. DEPs were explicitly defined as proteins with a fold change > 1.2 and a nominal p value < 0.05. For multiple-testing correction in exploratory analyses, the Benjamini–Hochberg method was applied, and an FDR < 0.1 was considered statistically significant. Bioinformatics analysis was conducted to screen for BPs and key DEPs related to coenzyme metabolism, and to identify the core pathways associated with coenzymes. Enrichment circle plots, heat maps, volcano plots, and Sankey diagrams were generated using R language and the OmicShare online platform (https://www.omicshare.com/tools, accessed on 28 June 2025) [18]. Additionally, the protein–protein interaction (PPI) network of key DEPs was constructed using STRING v.12.0 (EMBL, Heidelberg, Germany) and Cytoscape 3.9.1, including the ClueGO plugin (Cytoscape Consortium, La Jolla, CA, USA) [10,17].

2.6. Immunohistochemistry (IHC) Staining

The sections were baked, dewaxed, hydrated, and subjected to antigen retrieval using a sodium citrate buffer (Service Bio, Wuhan, China). After blocking with 3% H_2_O_2_ and 5% donkey serum (Solarbio, Beijing, China), sections were incubated with rabbit anti-PFKFB3 and anti-PFKFB4 primary antibodies (1:200 dilution; Proteintech, Wuhan, China, Table S2). As a negative control, the primary antibodies were replaced with PBS. Secondary antibody incubation and chromogenic detection were performed using a streptavidin-biotin complex (SABC) kit (Boster, Wuhan, China) and a 3-diaminobenzidine (DAB) substrate kit (Solarbio, Beijing, China), as described previously [18,24]. The sections were examined and imaged using a microscope (Nikon, Tokyo, Japan), and six randomly selected fields were analyzed per sample. The IOD of the positive signals was quantified using ImageJ v1.44p software (National Institutes of Health, Bethesda, MD, USA). All immunostaining assays and measurements were performed in triplicates.

2.7. Immunofluorescence (IF) Assay

Sections were incubated with rabbit anti-cytokeratin (CK)-18, anti-PFKFB3, and anti-PFKFB4 primary antibodies (1:100 dilution; Proteintech, Wuhan, China) as described previously [17]. Subsequently, fluorescent labeled secondary antibodies (Alexa Fluor^®^ 488 for PFKFB3, Alexa Fluor^®^ 594 for PFKFB4, and Alexa Fluor^®^ 647 for CK-18; 1:350 dilution) were then added for incubation. Nuclei were counterstained with DAPI (0.5 µg/mL), and sections were mounted with an anti-fade mounting medium. Fluorescence images were captured using a fluorescence microscope (Olympus, Tokyo, Japan), and six randomly selected fields were analyzed per sample. The IOD of the positive signals was quantified using ImageJ v1.44p software (NIH, Bethesda, MD, USA). All staining assays and measurements were performed in triplicates.

2.8. Western Blotting

Total protein was extracted from 80 mg of tissue samples stored at −80 °C using a Radioimmunoprecipitation Assay (RIPA) kit (Solarbio, Beijing, China) and quantified using a Bicinchoninic Acid (BCA) Protein Assay kit (Solarbio), according to the manufacturer’s instructions [18,22]. The protein expression levels of PFKFB3, PFKFB4 (1:2000 dilution), and β-actin (1:4000 dilution; Bioss, Beijing, China) were assessed in the control and CM groups as previously described [18,19]. Immunobands were analyzed using the ImageJ v1.44p software (NIH, Bethesda, MD, USA). All immunoblot assays were performed in triplicates.

2.9. Statistical Analysis

Statistical analysis was performed using the SPSS v23.0 software (SPSS Inc., Chicago, IL, USA). Comparisons between two groups were performed using Student’s t-test, while comparisons among multiple groups were analyzed using one-way ANOVA. The results were expressed as mean ± standard deviation (X ± SD). Statistical graphs were generated using GraphPad Prism 9.0 (San Diego, CA, USA) and Adobe Illustrator 2024 (Adobe Inc., San Jose, CA, USA).

3. Results

3.1. Morphological and Coenzyme Metabolic Changes in the MG Tissue of Holstein Cows

H&E staining revealed that the MG tissue structure was intact in the control group, with clear acinar cavities, normal morphology, and well-organized layering of mammary epithelial cells (MECs). In contrast, the MG tissue structure in the CM group was disorganized, exhibiting acinar degeneration and atrophy, accompanied by MEC detachment and infiltration of inflammatory cells, including neutrophils, lymphocytes, and macrophages (Figure 1A). ROS staining revealed weak fluorescence signals in the control group, whereas markedly enhanced ROS fluorescence was observed in the mammary alveolar regions in the CM group (Figure 1B). Quantitative analysis of IOD demonstrated that the relative ROS content in the CM group was significantly higher than that in the control group (p < 0.01, Figure 1C). Consistently, the GSH content in MG tissue was significantly decreased in the CM group compared with the control group, the GSH content in MG tissue was significantly decreased in the CM group compared with the controls (p < 0.01, Figure 1D). qPCR analysis showed that the relative expression levels of NMNAT1 and RFK in MG tissues were significantly downregulated in the CM group compared to those in the control group (p < 0.01, Figure 1E,F). These findings demonstrated that CM is associated with enhanced oxidative stress, impaired coenzyme metabolism, and disrupted MG tissue homeostasis.

3.2. Screening and Identification Coenzyme Metabolism-Associated BPs and DEPs

Analysis of DIA proteomics and functional enrichment data identified four coenzyme metabolism-related BPs (p < 0.05, Table S3), including the coenzyme metabolic process, coenzyme biosynthetic process, oxidative-reduction coenzyme metabolic process, and regulation of the coenzyme metabolic process (Figure 2A). A Venn diagram revealed that 18 DEPs were common to all four BPs (Figure 2B). The volcano plot showed that among the 18 DEPs, 12 were upregulated and six were downregulated in the CM group compared to those in the control group (Figure 2C). The heatmap confirmed the significant differential expression of these 18 DEPs between the control and CM groups (Figure 2D). PPI network analysis showed that 11 of the DEPs exhibited predicted protein–protein interactions, with PFKFB3 and PFKFB4 emerging as central hub proteins (Figure 2E). These results suggest that the candidate DEPs may affect the CM process of dairy cows by regulating oxidative-reduction coenzyme metabolic processes and that PFKFB3 and PFKFB4 play key regulatory roles.

3.3. Analysis of KEGG Pathways and DEPs Associated with PFKFB3 and PFKFB4

Based on the KEGG pathway enrichment data, signaling pathways and related DEPs involving PFKFB3 and PFKFB4 were systematically identified (p < 0.05, Table S4). The enrichment circle diagram indicated that PFKFB3 is involved in fructose and mannose metabolism, the AMP-activated protein kinase (AMPK) signaling pathway, metabolic pathway, and hypoxia-inducible factor-1 signaling pathway, and that PFKFB3 and PFKFB4 were co-enriched in fructose and mannose metabolism and the AMPK signaling pathway (Figure 3A). A heat map revealed significant differences in the expression of 43 DEPs within these two pathways between the control and CM groups (Figure 3B). The Volcano plot shows that among the 43 key DEPs, 15 were upregulated and 28 were downregulated (Figure 3C). The PPI network revealed interactions among 36 DEPs, with PFKFB3 and PFKFB4 exhibiting strong interactions with proteins, including triosephosphate isomerase 1 (TPI1), protein kinase AMP-activated non-catalytic subunit gamma 1 (PRKAG1), fructose-1,6-bisphosphatase 1 (FBP1), and phosphofructokinase 1 (PFKL) (Figure 3D). These results suggest that PFKFB3 and PFKFB4 influence the pathogenesis of CM by regulating fructose and mannose metabolism and the AMPK signaling pathway.

3.4. Integrated Analysis of Candidate BPs and KEGG Pathways

Venn diagram analysis of four BPs involving 285 DEPs and two KEGG pathways (p < 0.05, Table S5) involving 43 DEPs identified 20 shared DEPs associated with coenzyme biosynthesis and metabolism (Figure 4A). Among these 20 shared DEPs, eight were upregulated, and 12 were downregulated (Figure 4B). The heatmap revealed significant differential expression of these 20 DEPs between the control and CM groups (Figure 4C). Clue GO analysis revealed that 20 DEPs exhibited direct and indirect functional associations. Among these, PFKFB3 and PFKFB4 were identified as key regulators of glucose and energy metabolism, with network connectivity to proteins such as FBP1 and PFKL, and they exert crucial functions by modulating the AMPK signaling pathway, coenzyme metabolism, and glycolytic processes (Figure 4D). The Sankey diagram further highlighted PFKFB3 and PFKFB4 as central nodes within the coenzyme metabolism-related functional network, connecting oxidoreductase coenzyme metabolism and the regulation of coenzyme metabolic processes (Figure 4E). These results indicate that PFKFB3 and PFKFB4 represent key nodes within the dysregulated coenzyme metabolism and redox-associated metabolic networks during CM in dairy cows.

3.5. Correlation Analysis of Candidate Targets with Clinical Mastitis and Coenzyme-Related Parameters

Correlation analysis between candidate DEPs and clinical mastitis, and coenzyme metabolic indicators revealed that eight proteins, including PFKFB3, FBP1, protein kinase AMP-activated catalytic subunit alpha 1 (PRKAA1), and TPI1, were positively correlated with SCC and differential somatic cell count (DSCC). Conversely, 12 proteins, including PFKFB4, acetyl-CoA carboxylase alpha (ACACA), aldolase C (ALDOC), and fatty acid synthase (FASN), were negatively correlated with SCC and DSCC (Figure 5A). The expression level of PFKFB3 positively correlated with ROS but negatively correlated with GSH, NAD^+^, and flavin adenine dinucleotide (FAD). In contrast, the expression level of PFKFB4 negatively correlated with ROS levels and positively correlated with GSH, NAD^+^, and FAD levels (Figure 5B). These findings indicate that the expression levels of PFKFB3 and PFKFB4 are closely associated with clinical indicators of mastitis and coenzyme metabolism. Notably, PFKFB3 expression was positively correlated with inflammatory and oxidative stress markers, whereas PFKFB4 expression exhibited the opposite correlation patterns.

3.6. Localization and Expression Patterns of PFKFB3 and PFKFB4 in Bovine Mammary Tissue

IHC staining revealed that, compared with the control group, the CM group exhibited markedly enhanced PFKFB3 staining and reduced PFKFB4 staining in the MG tissue. In contrast, the negative control group showed no specific staining for either protein (Figure 6A). Quantitative IOD analysis confirmed that PFKFB3 protein expression was significantly upregulated (p < 0.01), whereas PFKFB4 protein expression was significantly downregulated (p < 0.01) in the CM group compared to the controls (Figure 6B). IF staining revealed signals for CK-18, PFKFB3, and PFKFB4 in both groups. In the CM group, the fluorescence intensity of the epithelial cell marker CK-18 was reduced, the MECs were irregularly shaped, and the alveoli showed degenerative atrophy with cavitation. Concurrently, PFKFB3 expression was enhanced, whereas PFKFB4 expression decreased (Figure 6C). Colocalization analysis demonstrated that CK-18, PFKFB3, and PFKFB4 were primarily colocalized in the cytoplasm of MECs. qPCR analysis showed that PFKFB3 mRNA was significantly upregulated, whereas PFKFB4 mRNA was significantly downregulated in the CM group compared to the control group (p < 0.01, Figure 6D). WB analysis confirmed the presence of PFKFB3 and PFKFB4 proteins in MG tissues from both groups (Figure 6E). Densitometric analysis of band intensities further demonstrated that PFKFB3 protein expression was significantly increased and PFKFB4 expression was significantly decreased in the CM group (p < 0.01; Figure 6F). These results suggest that PFKFB3 and PFKFB4 are closely associated with MEC function and the occurrence and progression of CM in dairy cows.

4. Discussion

4.1. Coenzyme Metabolic Dysregulation Contributes to Oxidative Stress and Inflammatory Injury in Bovine Mastitis

Coenzymes are critically involved in the pathogenesis of various inflammatory diseases by regulating inflammatory signaling, redox homeostasis, and immune responses [25,26]. In the mammary gland, these processes are tightly coupled with cellular energy metabolism and oxidative balance, particularly under pathogen-induced inflammatory stress such as Escherichia coli infection. Investigating the functions and regulatory mechanisms of key proteins involved in coenzyme metabolism during the pathogenesis of bovine mastitis is crucial for developing novel antioxidant-based interventions and effective prevention strategies for dairy mastitis.

Morphological changes in MECs and disruption of the blood-milk barrier are direct features of the inflammatory response in bovine MG tissues [27]. Dysregulation of coenzyme metabolism (e.g., NAD^+^) can lead to cytoskeletal depolymerization [28], impair epithelial tight junctions, and facilitate inflammatory cell infiltration [16,29], thereby aggravating the inflammatory response. These observations align with our H&E staining results, suggesting that coenzyme metabolic dysregulation is an underlying factor that contributes to CM in dairy cows. Furthermore, coenzyme metabolic dysregulation promotes elevated ROS production, diminishes antioxidant capacity, and intensifies oxidative stress [30], which are characteristic consequences of bacterial infection-induced inflammatory responses. The observed increases in ROS levels and decreases in GSH levels in the MG tissues of the CM group further support the notion that a coenzyme metabolic imbalance contributes to mammary epithelial barrier disruption, thereby exacerbating oxidative stress and inflammatory injury. As a crucial cofactor for multiple dehydrogenases in glycolysis, a decline in NAD^+^ levels can lead to cellular energy dysregulation and impaired DNA repair, ultimately amplifying the inflammatory response [31,32]. Similarly, FAD serves as a cofactor for succinate dehydrogenase, a key enzyme in the mitochondrial respiratory chain. Reduced FAD leads to disordered energy metabolism, increased ROS generation and activation of inflammatory signaling pathways such as NF-κB and the NOD-like receptor family, pyrin domain-containing 3 (NLRP3), which are commonly activated during Escherichia coli-induced mastitis and promote the expression of pro-inflammatory factors including IL-1β and TNF-α [33]. The results indicated that the levels of NAD^+^ and FAD in MG tissues were significantly downregulated in the CM group, suggesting that the pathological process of mastitis is accompanied by coenzyme metabolic disorders. However, the precise molecular mechanisms linking coenzyme metabolism to oxidative stress and inflammation in the CM remain unclear.

4.2. Identification of PFKFB3 and PFKFB4 as Central Regulators of Coenzyme Metabolism in CM

Based on previous DIA proteomic data, DEPs involved in coenzyme metabolism were systematically screened from BPs, KEGG pathways, and key DEPs. These results revealed 20 coenzyme-related DEPs that were simultaneously implicated in regulating coenzyme-associated metabolic processes. PFKFB3 and PFKFB4 have been identified as key candidate molecules that may participate in the progression of bovine CM by regulating coenzymes, glycolysis, and energy metabolism. It has been demonstrated that the upregulation of PFKFB3 in fibroblast-like synoviocytes in rheumatoid arthritis promotes glycolysis and enhances the release of pro-inflammatory factors such as IL-6 and IL-8 [34]. Conversely, downregulation of PFKFB4 impairs the glycolysis-driven energy supply through CHIP-mediated ubiquitination and degradation, leading to insufficient NADPH generation and ROS accumulation, thereby promoting the development of chronic inflammatory diseases such as endometriosis [35]. Collectively, these findings suggest that PFKFB3 and PFKFB4 exacerbate the inflammatory response by driving glycolysis and redox imbalance, particularly under pathogen-induced inflammatory stress, thereby contributing to metabolic dysregulation and the pathogenesis of bovine mastitis.

ClueGO analysis revealed that other coenzyme metabolism-related DEPs are also implicated in inflammatory processes. As a key rate-limiting enzyme in gluconeogenesis, FBP1 deficiency leads to the accumulation of glycolytic intermediates [36]. This accumulation activates the NLRP3 inflammasome, leading to increased tissue inflammation and damage [37]. The TP53-Induced Glycolysis and Apoptosis Regulator (TIGAR), a p53 target gene, suppresses glycolysis and promotes the pentose phosphate pathway, thereby reducing ROS levels and inhibiting inflammation [38]. Notably, our findings demonstrate that coenzyme metabolism-related DEPs interact with both PFKFB3 and PFKFB4. This interaction suggests that PFKFB3 and PFKFB4 act as central regulators of coenzyme metabolism and may mediate bovine mammary inflammation via glycolysis and the pentose phosphate pathway.

4.3. Roles of PFKFB3 and PFKFB4 in MEC Metabolism and Inflammatory Regulation

MECs generate ATP and NADPH through glycolysis and the pentose phosphate pathway, thereby providing the energy and reducing power required for milk synthesis and secretion [39,40]. Furthermore, the maintenance of glucose and coenzyme metabolic homeostasis supplies essential intermediates and cofactors (e.g., NAD^+^/NADH, NADP^+^/NADPH) for antimicrobial peptide synthesis and tight junction protein assembly [41], which are critical for host defense against invading pathogens such as Escherichia coli, thus reinforcing the mammary barrier and protecting against pathogen invasion. The localization of PFKFB3 and PFKFB4 within bovine MECs suggests their involvement in the regulation of both physiological and pathological processes in these cells. As bifunctional enzymes, PFKFB3 and PFKFB4 possess both kinase and phosphatase activities within a single polypeptide, enabling them to catalyze the synthesis and degradation of fructose-2,6-bisphosphate. In endothelial cells, elevated expression of PFKFB3 enhances glycolysis, activates NF-κB, and induces the expression of inflammatory genes such as IL-8 and monocyte chemoattractant protein-1 (MCP-1), thereby amplifying the inflammatory response [42]. By contrast, the absence of PFKFB4 in hepatocytes leads to the accumulation of fructose and excessive glycolysis, resulting in an abnormal increase in the NADH/NAD^+^ ratio, inhibition of mitochondrial β-oxidation, and the induction of fatty acid metabolism disorders and inflammation [43]. These findings are consistent with our observations of PFKFB3 upregulation and PFKFB4 downregulation in CM mammary tissues, suggesting that an imbalance in PFKFB3/PFKFB4 expression contributes to oxidative stress, coenzyme metabolic dysfunction, and inflammatory damage in MECs.

4.4. Proposed Metabolic Mechanism of PFKFB3/PFKFB4 Imbalance in CM

In summary, we propose a hypothetical and integrative model, based on our proteomic and bioinformatic analyses, to illustrate how PFKFB3 and PFKFB4 may be involved in the metabolic dysregulation observed in Escherichia coli-induced CM in dairy cows (Figure 7). Upon pathogen invasion of the mammary tissue, the immune system is activated, and inflammatory cells (such as macrophages and neutrophils) are recruited [44]. Activated immune cells generate large amounts of ROS through a respiratory burst, leading to mitochondrial damage and an ensuing energy crisis. Previous studies have shown that cellular energy stress and oxidative stress are capable of engaging the AMPK signaling pathway [45], which is known to regulate glucose metabolism by modulating the expression and activity of glycolytic regulators such as PFKFB3 [46] and PFKFB4 [47]. In the present study, enrichment of the AMPK signaling pathway was identified based on DIA proteomic data; however, AMPK activation itself was not directly assessed. Therefore, the proposed involvement of AMPK is inferred from pathway enrichment analysis and existing literature, suggesting that dysregulated PFKFB3/PFKFB4 expression may be linked to inflammation-associated metabolic signaling. This imbalance may bias metabolic flux toward glycolysis [48], disrupt coenzyme homeostasis (including GSH, NAD^+^, and FAD), and ultimately contribute to mammary epithelial cell injury and lactation failure. This model should be regarded as a working hypothesis rather than direct evidence of AMPK functional activation.

4.5. Limitations and Future Perspectives

Several limitations of this study should be noted. First, the sample size was relatively limited (n = 4 per group), which may have reduced the statistical power and restricted the ability to fully capture biological variability among animals. Second, all CM cases analyzed were caused by Escherichia coli, potentially limiting the generalizability of our findings to mastitis induced by other pathogens. Third, in vitro functional rescue and loss-of-function experiments are lacking. Fourth, although pathway enrichment analysis implicated the AMPK signaling pathway, AMPK activation was not directly assessed in this study (e.g., phosphorylation status or kinase activity). Therefore, conclusions regarding AMPK involvement are based on bioinformatic inference rather than functional validation. Lastly, the feasibility of targeting PFKFB3 and PFKFB4 as therapeutic candidates has not yet been systematically evaluated. Addressing these issues strengthens the validity and translational relevance of the research findings.

These findings suggest that coenzyme metabolic regulation represents a host-directed adjunctive strategy for mastitis control. The modulation of coenzyme metabolism may help preserve mammary epithelial barrier integrity, reduce oxidative stress, and limit inflammatory damage during infection, thereby supporting vaccine efficacy by improving host immunity and redox homeostasis. In antibiotic therapy, coenzyme-related interventions are more likely to act as adjuncts than as alternatives, with local modulation potentially applicable to intramammary treatment and systemic modulation in severe cases requiring injectable antibiotics. However, the safety, efficacy, and optimal delivery routes for these approaches require further investigation.

5. Conclusions

In conclusion, dairy cows with CM exhibited reduced levels of key coenzymes, including GSH, NAD^+^, and FAD, which are associated with increased oxidative stress, mammary tissue damage, and inflammatory responses. DIA proteomic analysis identified two key pathways and 20 DEPs associated with PFKFB3 and PFKFB4, highlighting their roles as central regulators of coenzyme metabolic glycolysis and energy metabolism in the CM. Immunolocalization results showed that PFKFB3 and PFKFB4 were localized to MECs in both the control and CM groups. Notably, compared with the control group, PFKFB3 expression was upregulated, whereas PFKFB4 expression was downregulated in the CM group, suggesting that their aberrant expression is closely associated with mammary tissue inflammation and MEC dysfunction. Based on these results, this study proposes a potential regulatory mechanism for PFKFB3 and PFKFB4 in the pathogenesis of bovine CM, demonstrating that PFKFB3 and PFKFB4 may participate in the inflammatory process in bovine MG tissues by modulating coenzyme metabolism, glycolysis, and energy metabolism. However, further functional studies are required to validate their causal roles and clarify the underlying molecular mechanisms. Overall, these findings provide preliminary insights into the involvement of PFKFB3 and PFKFB4 in the metabolic alterations associated with bovine mastitis and suggest that coenzyme metabolic regulation may represent a potential host-directed adjunct strategy for mastitis control, warranting further investigation.