You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction
============

Mastitis is defined as inflammation of the mammary gland that principally occurs in response to the invasion of pathogenic microorganisms, such as *Staphylococcus aureus* ([@B19]). Notably, the invasion of *S. aureus*, and its subsequent effect upon the host's immune system, is an important cause of mastitis in dairy cows ([@B11]). Bacteriophage-based therapy therefore appears to represent a potent and safe alternative tool for the treatment of such bacterial infections.

Data from experimental bacteriophage therapy indicate that successful treatment can correct the increased levels of proinflammatory cytokines associated with bacterial infections both *in vivo* and *in vitro* ([@B20]; [@B27]; [@B10]; [@B7]; [@B16]). Moreover, bacteriophages with both Gram-positive and Gram-negative hosts are able to induce an (innate) immune response *in vitro*. This observed immune response was shown to be endotoxin-independent and predominantly anti-inflammatory ([@B18]). However, it is important to consider how bacteriophages can affect the production of cytokines. One mechanism that might be responsible for mediating such effects could be related to the activity of nuclear factor (NF)-κB ([@B6]; [@B1]).

Previous work has shown that bacteriophages can penetrate MAC-T bovine mammary epithelial cells and are able to clear intracellular *S. aureus* in a time-dependent manner ([@B26]). The aim of the present study was to use real-time polymerase chain reaction (PCR) and Western blotting to investigate the inflammatory effects and mechanisms by the virulent of *S. aureus* bacteriophage, vB_SauM_JS25, in MAC-T cells. Our results show that bacteriophages can reduce *S. aureus*- or lipopolysaccharide (LPS)-induced levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, IL-10, and regulated on activation, normal T cell expressed and secreted (RANTES) mRNA in MAC-T cells, in a manner that is unrelated to its antibacterial action. Moreover, the current data highlight the fact that the *S. aureus* bacteriophage, vB_SauM_JS25, can suppress the LPS-induced phosphorylation of NF-κB p65.

Materials and Methods {#s1}
=====================

Antibodies and Reagents
-----------------------

Anti-phospho-NF-κB p65 (Ser536) (93H1) rabbit monoclonal antibody was purchased from Cell Signaling Technology, Inc. (Beverly, MA, United States). Anti-NF-κB p65 (H-286) and anti-β-actin (C4) were purchased from Santa Cruz Biotechnology (Santa Cruz, CA, United States), while LPS (from *Escherichia coli* O55:B5) was acquired from Sigma (St. Louis, MO, United States).

Culture of *S. aureus* Strains
------------------------------

*Staphylococcus aureus* strains American Type Culture Collection (ATCC; United States) 6538 and JYG2 were routinely cultivated at 37°C using tryptic soy broth (TSB, QingDao Hope Bio-technology Co., Ltd., Qingdao, China). *S. aureus* ATCC 6538 was routinely used as the host bacterial strain for phage propagation. *S. aureus* JYG2 was isolated from milk samples acquired from a dairy farm and used to infect the MAC-T cells used in this study ([@B25]).

Bacteriophage Preparation
-------------------------

This study used the bacteriophage vB_SauM_JS25, a broad-spectrum virulent bacteriophage belonging to the family *Myoviridae* ([@B24]). Stock bacteriophage was prepared using the double-agar overlay method ([@B8]). Phage lysates were further purified using cesium chloride (CsCl) centrifugation to removal toxins, as described previously ([@B24]). Then, bacteriophages underwent ultra-filtration through a cellulose membrane (Millipore, Billerica, MA, United States) with a nominal molecular mass limit of 100 kDa to remove the CsCl. Previous work has reported that CsCl gradient ultra-centrifuged bacteriophages are free from residual DNA, RNA, and bacterial proteins released during the lysis of bacterial cells ([@B14]).

Cell Culture and Stimulation
----------------------------

MAC-T bovine mammary epithelial cells were cultured in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal calf serum (Sigma-Aldrich, MO, United States), and were maintained at 37°C with 5% CO~2~. MAC-T cells were then cultured overnight in six-well plates and then infected with *S. aureus* JYG2 (10^6^ bacteria/well) or LPS (1 μg/ml). One or five hours later, cells were treated with purified bacteriophage vB_SauM_JS25 (10^8^ PFU/ml). At the indicated time points, real-time quantitative PCR (Q-PCR) was used to investigate the effects of the bacteriophage on *S. aureus*- or LPS-induced inflammation in MAC-T cells.

Real-Time Q-PCR
---------------

Total RNA was extracted from cellular samples using an RNA kit (OMEGA, Shanghai, China) in accordance with the manufacturer's instructions. Then, 500 ng of total RNA was used in a 10-μl PrimeScript^TM^ RT Master Mix (TaKaRa, Tokyo, Japan) as described in the manufacturer's instructions. Next, the cDNA was used as a template for real-time Q-PCR, which was performed on a LightCycler 480 (Roche Diagnostic, IN, United States). The primers used for bovine GAPDH, TNF-α, IL-1β, IL-8, IL-6, IL-10, and RANTES have been described previously (**Table [1](#T1){ref-type="table"}**) ([@B5]; [@B22]; [@B23]). For each sample, data were normalized to the expression level of GAPDH ([@B23]).

###### 

Primers used in this study for real-time quantitative polymerase chain reaction.

  Gene     Primer       Sequence 5′--3′          Product size (bp)
  -------- ------------ ------------------------ -------------------
  GAPDH    Sense        TCAACGGGAAGCTCACTGG      237
           Anti-sense   CCCCAGCATCGAAGGTAGA      
  TNF-α    Sense        CCACGTTGTAGCCGACATC      155
           Anti-sense   CCCTGAAGAGGACCTGTGAG     
  IL-1β    Sense        AGTGCCTACGCACATGTCTTC    114
           Anti-sense   TGCGTCACACAGAAACTCGTC    
  IL-8     Sense        ATGACTTCCAAGCTGGCTGTTG   149
           Anti-sense   TTGATAAATTTGGGGTGGAAAG   
  IL-6     Sense        TGCTGGTCTTCTGGAGTATC     153
           Anti-sense   GTGGCTGGAGTGGTTATTAG     
  IL-10    Sense        GTGATGCCACAGGCTGAGAA     131
           Anti-sense   TGCTCTTGTTTTCGCAGGGCAG   
  RANTES   Sense        GCCAACCCAGAGAAGAAGTG     119
           Anti-sense   CTGCTTAGGACAAGAGCGAGA    
                                                 

GAPDH, glyceraldehyde-3-phosphate dehydrogenase; TNF-α, tumor necrosis factor-α; IL-1β, interleukin-1β; IL-8, interleukin-8; IL-6, interleukin-6; IL-10, interleukin-10; RANTES, regulated on activation, normal T cell expressed and secreted.

Western Blotting
----------------

MAC-T cells were pretreated with bacteriophage vB_SauM_JS25 (10^8^ PFU/well) for 3 h and further incubated with LPS (1 μg/ml) for a further 2 or 5 h. Proteins were then extracted using M-PER mammalian protein extraction reagent (Thermo Fisher Scientific, Waltham, MA, United States) containing protease inhibitors. Whole cell lysates were centrifuged at 14,000 rpm and 4°C for 10 min, and the supernatant was separated by SDS-polyacrylamide gel electrophoresis under reducing conditions. Gels were then transferred onto nitrocellulose membranes that were then blocked with 5% BSA in TBST for 2 h at room temperature. Membranes were then incubated overnight at 4°C with a 1:1000 dilution of each primary antibody in TBST. Each membrane was then washed with TBST and incubated with HRP-conjugated secondary antibody (Abmart). Positive bands were finally visualized with an ECL detection system (Applygen Technologies Inc., Beijing, China) and band intensity quantified using ImageJ software (NIH).

Statistical Analysis
--------------------

Data were analyzed for significance by one-way or two-way analysis of variance (ANOVA) using GraphPad PRISM software (version 5.02 for Windows; GraphPad Software, Inc.). A *p*-value \<0.05 was considered to indicate a statistically significant difference.

Results
=======

Bacteriophage vB_SauM_JS25 Suppressed *S. aureus*-Induced Inflammation in MAC-T Cells
-------------------------------------------------------------------------------------

To examine whether the bacteriophage affected inflammation, MAC-T cells were first infected with *S. aureus* JYG2. One hour later, cells were washed and treated with highly purified bacteriophage vB_SauM_JS25. As shown in **Figure [1](#F1){ref-type="fig"}**, the levels of TNF-α, IL-1β, and RANTES in the *S. aureus*-bacteriophage group gradually increased at 3 h post-treatment (h.p.t.) (*p* \< 0.001) and then rapidly decreased at 6 h.p.t. (*p* \< 0.001) compared to those in the *S. aureus* group. The levels of IL-6 and IL-8 in the *S. aureus*-bacteriophage group were significantly lower than those in the *S. aureus* group (*p* \< 0.001). Furthermore, the bacteriophage alone group induced the expression of low levels of cytokines and chemokine.

![Bacteriophages inhibited the *Staphylococcus aureus*-induced production of inflammatory cytokines and chemokine in MAC-T bovine mammary epithelial cells. MAC-T cells were infected with *S. aureus* JYG2 (10^6^ bacteria/well) and then treated with purified bacteriophage vB_SauM_JS25 (10^8^ PFU/ml). At the indicated time points, the expression of TNF-α **(A)**, IL-1β **(B)**, IL-6 **(C)**, IL-8 **(D)**, and RANTES **(E)** mRNA were determined in MAC-T cells using real-time quantitative polymerase chain reaction. Untreated MAC-T cells were used as a negative control. Data are shown as mean ± standard error of the mean (N.S., not significant, ^∗^*p* \< 0.05, ^∗∗^*p* \< 0.01, ^∗∗∗^*p* \< 0.001 compared to negative control).](fmicb-09-01614-g001){#F1}

Bacteriophage vB_SauM_JS25 Suppressed LPS-Induced Inflammation in MAC-T Cells
-----------------------------------------------------------------------------

To further investigate the effects of bacteriophage on inflammation, LPS was used instead of *S. aureus* to exclude the bactericidal action of bacteriophages that might relieve inflammation. MAC-T cells were treated with LPS. Then, 5 h later, these cells were stimulated with bacteriophages in the absence (-) or presence (+) of LPS for another 3 h. We found that bacteriophage vB_SauM_JS25 significantly inhibited the LPS-induced production of cytokines, including TNF-α, IL-1β, IL-6, IL-8 (*p* \< 0.001), and IL-10 (*p* \< 0.05). Similar results were obtained even in the absence (-) of LPS 5 h post-stimulation (**Figure [2](#F2){ref-type="fig"}**).

![*Staphylococcus aureus* bacteriophage inhibited the lipopolysaccharide (LPS)-induced production of inflammatory cytokines in MAC-T bovine mammary epithelial cells. MAC-T cells were stimulated with LPS (1 μg/ml) for 5 h, and then treated with purified bacteriophage vB_SauM_JS25 (10^8^ PFU/ml) in the absence (--) or presence (+) of LPS for another 3 h. The expression of TNF-α **(A)**, IL-1β **(B)**, IL-6 **(C)**, IL-8 **(D)**, IL-10 **(E)** mRNA in MAC-T cells were determined by real-time quantitative polymerase chain reaction. LPS(--), LPS stimulation for 5 h; LPS(--)+Phage, LPS stimulation for 5 h, bacteriophage treatment for a further 3 h; Phage, bacteriophage treatment for 3 h; LPS(+), LPS stimulation for 5 h, LPS stimulation for another 3 h; LPS(+)+Phage, LPS stimulation for 5 h, LPS plus bacteriophage treatment for another 3 h. Untreated MAC-T cells were used as a negative control. Data are shown as mean ± standard error of the mean (N.S., not significant, ^∗^*p* \< 0.05, ^∗∗^*p* \< 0.01, ^∗∗∗^*p* \< 0.001 compared to negative control).](fmicb-09-01614-g002){#F2}

Next, we investigated whether pre-treatment with bacteriophage could inhibit LPS-induced inflammation. MAC-T cells were pre-treated with bacteriophage for 3 h, and then stimulated with LPS in the absence (-) or presence (+) of bacteriophage for another 5 h. As shown in **Figure [3](#F3){ref-type="fig"}**, consistent treatment with bacteriophage significantly inhibited the LPS-induced production of cytokines (*p* \< 0.001). However, the removal of pre-treated bacteriophage significantly enhanced the LPS-induced production of cytokines (*p* \< 0.001, **Figure [3](#F3){ref-type="fig"}**).

![Pre-treatment with bacteriophage did not inhibit the lipopolysaccharide (LPS)-induced production of inflammatory cytokines in MAC-T bovine mammary epithelial cells. MAC-T cells were pre-treated (10^8^ PFU/ml) with bacteriophage for 3 h and then stimulated with LPS (1 μg/ml) in the absence (--) or presence (+) of bacteriophage for another 5 h. The expression of TNF-α **(A)**, IL-1β **(B)**, IL-6 **(C)**, IL-8 **(D)**, and IL-10 **(E)** mRNA in MAC-T cells was then determined by real-time quantitative polymerase chain reaction. LPS, LPS stimulation for 5 h; Phage(+)+LPS, bacteriophage treatment for 3 h, bacteriophage plus LPS treatment for another 5 h; Phage(--)+LPS, bacteriophage treatment for 3 h, LPS stimulation for 5 h; Phage, bacteriophage treatment for 3 h. Untreated MAC-T cells were used as a negative control. Data are shown as mean ± standard error of the mean (N.S., not significant, ^∗^*p* \< 0.05, ^∗∗^*p* \< 0.01, ^∗∗∗^*p* \< 0.001 compared to negative control).](fmicb-09-01614-g003){#F3}

Inhibition of LPS-Initiated NF-κB Signaling in Response to Bacteriophage vB_SauM_JS25
-------------------------------------------------------------------------------------

To investigate the potential involvement of the NF-κB pathway in the bacteriophage-mediated regulation of LPS-induced inflammation, the NF-κB p65 subunit, and its level of phosphorylation, were investigated by Western blotting. As shown in **Figure [4](#F4){ref-type="fig"}**, LPS increased the levels of NF-κB p65 phosphorylation, but these levels of phosphorylation were significantly suppressed 2 h after LPS stimulation when pre-treated with bacteriophage vB_SauM_JS25 (*p* \< 0.05, **Figure [4](#F4){ref-type="fig"}**). These results imply that the bacteriophage-mediated regulation of LPS-induced inflammation is associated with NF-κB signaling.

![Western blotting analysis showing the inhibition of lipopolysaccharide (LPS)-induced NF-κB activation by *Staphylococcus aureus* bacteriophage. MAC-T cells were pre-treated with bacteriophage vB_SauM_JS25 (10^8^ PFU/well) for 3 h and then further incubated with LPS (1 μg/ml) in the presence of bacteriophage for a further 2 **(A)** or 5 h **(B)**. After immunoblotting, the levels of p65, and the phosphorylation levels of p65, were identified using specific antibodies. Untreated MAC-T cells were used as a negative control. β-Actin was used to ensure equal loading (N.S., not significant, ^∗^*p* \< 0.05 compared to negative control).](fmicb-09-01614-g004){#F4}

Discussion
==========

Bacteriophage therapy can normalize the levels of inflammatory cytokines associated with bacterial infections both *in vivo* and *in vitro* ([@B20]; [@B27]; [@B10]; [@B16]). Different bacteriophages, with both Gram-positive and Gram-negative hosts, have been previously shown to induce an immune response, which was endotoxin-independent and predominantly anti-inflammatory. The addition of endotoxins to highly purified bacteriophages did not cause an immune response comparable to the one induced by the (endotoxin containing) bacteriophage lysate ([@B18]). It is important to consider whether bacteriophage co-existing with bacteria or toxins might dominantly elicit an antiviral innate immune response or alter the inflammatory response induced by bacteria or toxins. In the present study, we investigated whether the *S. aureus* bacteriophage could affect LPS-induced inflammation in MAC-T bovine mammary epithelial cells, in a manner that was unrelated to its antibacterial action (**Figures [2](#F2){ref-type="fig"}--[4](#F4){ref-type="fig"}**). A previous study indicated that bacteriophages are able to reduce lung injury in mice infected with *Pseudomonas aeruginosa*, but not due to lower bacterial loads ([@B16]). Therefore, it is possible that there may be other mechanisms by which bacteriophages directly interact with eukaryotic systems and not just simply by lysing their bacterial hosts.

Several bacterial antigens, known as pathogen-associated molecular patterns (PAMPs), are recognized by pattern recognition receptors on innate immune cells and produce several inflammatory cytokines, such as TNF-α and IL-6 ([@B15]). In particular, families of Toll-like receptors (TLRs) have been shown to be involved in the recognition of bacterial components ([@B4]). These recognize and bind a wide variety of bacterial PAMPs, including LPS (typically recognized by TLR4, although some LPS species can be recognized by TLR2), lipopeptides (TLR1, TLR2, and TLR6), lipoarabinomannan and lipoteichoic acid (TLR2 and other TLRs) ([@B13]). TLR2 and TLR4 are known to play a major role in recognition of Gram-positive and Gram-negative bacteria, respectively ([@B17]). Research has also shown that TLR2 is a functional receptor for both Gram-positive and Gram-negative bacteria and can induce the activation of IL-8 ([@B3]). The myeloid differentiation factor 88 (MyD88)/NF-κB signal transduction pathway has been shown to be involved in both TLR2- and TLR4-mediated production of inflammatory cytokines ([@B21]). Therefore, the responses to *S. aureus* and LPS are quite different. In the present study, different incubation times were used for *S. aureus* and LPS prior to bacteriophage treatment (1 and 5 h, respectively). However, it was found that bacteriophage vB_SauM_JS25 suppressed both *S. aureus*- and LPS-induced inflammation in MAC-T cells (**Figures [1](#F1){ref-type="fig"}--[3](#F3){ref-type="fig"}**). These findings indicate that bacteriophages may affect the common signaling pathway shared by both Gram-positive and Gram-negative bacteria.

Previous studies reported that bacteriophages can diminish cellular infiltration of allogeneic skin allograft in mice, extend its survival and inhibit human T cell activation *in vitro*. Furthermore, T4 phage can abolish the ability of the pathogenic virus to induce NF-κB activity ([@B6]). In order to prove the relationship between the effects induced by bacteriophages and NF-κB, we determined the expression levels and the phosphorylation of the NF-κB p65 subunit were determined by Western blotting. This part of our work demonstrated that pre-treatment with bacteriophage vB_SauM_JS25 significantly suppressed the phosphorylation levels of NF-κB p65 at 2 h post-LPS-stimulation (*p* \< 0.05, **Figure [4](#F4){ref-type="fig"}**). However, once the pre-treatment bacteriophage was removed, the LPS-induced production of cytokines was significantly enhanced (*p* \< 0.001, **Figure [3](#F3){ref-type="fig"}**). As reported previously, the lack of dissemination, and the reduced levels of inflammation caused by the production of prophage-created conditions, could promote persistent infection by *P. aeruginosa* ([@B16]). Moreover, there may be other mechanisms that bacteriophages use to interact directly with eukaryotic systems and thus modulate the immune system. In these scenarios, bacteriophages appear to act an immunomodulator in order to balance inflammation cytokines.

In this study, the levels of TNF-α, IL-1β, and RANTES mRNA in the *S. aureus*-bacteriophage group gradually increased at 3 h.p.t. and then rapidly decreased at 6 h.p.t. compared to those in the *S. aureus* group. This may be the reason for the greater level of toxin exposure when the bacteria were lysed, the same way as antibiotic does ([@B9]). Bacteriophages can then bind to toxins and counteract toxin-induced inflammation, such as T4 gp12-LPS ([@B12]). Another possibility may be an increase in bacteriophage concentration following the replication in *S. aureus*. Moreover, there are other mechanisms by which bacteriophages directly interact with eukaryotic systems. The transcription factor NF-κB has been recognized as a frequent target for immunosuppressive and anti-inflammatory molecules ([@B2]). Our current experiments demonstrated that pre-treatment with bacteriophage significantly suppressed the phosphorylation levels of NF-κB p65 at 2 h post-LPS-stimulation (*p* \< 0.05, **Figure [4](#F4){ref-type="fig"}**). In accordance with the results shown in **Figure [3](#F3){ref-type="fig"}**, bacteriophage treatment significantly inhibited the LPS-induced production of cytokines (*p* \< 0.001) in the Phage(+)+LPS group. Moreover, when cytokine levels were reduced by bacteriophage treatment in the Phage(+)+LPS group at 5 h post-LPS-stimulation (**Figure [3](#F3){ref-type="fig"}**), there was no significant difference in the phosphorylation levels of NF-κB p65 when compared between the LPS and LPS+Phage group at the same time point (**Figure [4](#F4){ref-type="fig"}**). Previous studies have indicated that bacteriophage therapy can correct abnormal levels of inflammatory cytokines ([@B20]; [@B27]; [@B10]). It appears that bacteriophage will not continuously suppress the phosphorylation levels of NF-κB p65 in order to reduce inflammation. Therefore, in our current-case, bacteriophage vB_SauM_JS25 could not promote a persistent infection simply by promoting the survival of bacteria. The ability of vB_SauM_JS25 to influence the immune response highlights the potential development and application of bacteriophage-based therapies and may represent a novel anti-inflammatory therapeutic strategy for mastitis.

Collectively, the results described herein suggest that *S. aureus* bacteriophages can reduce inflammation in MAC-T bovine mammary epithelial cells in a manner unrelated to its antibacterial action. Furthermore, *S. aureus* bacteriophages can suppress the LPS-induced phosphorylation of NF-κB p65, which may represent one of the mechanisms that mediates such effects. In an earlier study, it was found that the bacteriophage vB_SauM_JS25 was able to cross MAC-T cell membranes and reach the nucleus ([@B26]). Our current observations further implicate phage vB_SauM_JS25 as a potential means of reducing infection. The mechanism by which bacteriophages affect the immune response in MAC-T cells will now need to be further elucidated.

Author Contributions
====================

LZ and RW designed the experiments. LZ, XH, LS, TH, RCW, and MP performed the experiments. LZ, XH, LS, and TH analyzed the data. TH, RCW, and MP contributed reagents, materials, and analysis tools. LZ wrote the paper.

Conflict of Interest Statement
==============================

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Funding.** This work was supported by the National Natural Science Foundation of China (Grant No. 31602078 to LZ) and Jiangsu Provincial Natural Science Foundation of China (Grant Nos. BK20140758 to LZ and BK20160585 to LS).

We are grateful to Honglin Jiang for providing the MAC-T cells.

[^1]: Edited by: Pilar García, Consejo Superior de Investigaciones Científicas (CSIC), Spain

[^2]: Reviewed by: Sílvio B. Santos, University of Minho, Portugal; Nayeli Alva-Murillo, Universidad de Guanajuato, Mexico

[^3]: This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology