Colonization of selective probiotics in gastrointestinal tract might lead to immune modulation within gut microenvironment by communications with mucosal immune cells (Belkaid and Hand, 2014). Furthermore, the crosstalk between probiotics and immune cells could affect the status of various physiological conditions of humans (Kumari et al., 2021). According to this, several connections between gut and specific organs such as gut-brain, gut-liver, and gut-lung axes have been proposed to explain the functional importance of selective probiotics (Dumas et al., 2018; Powell et al., 2017; Tripathi et al., 2018).
Probiotics can modulate immune responses by mainly interacting with pattern recognition receptors (PRRs) expressed on professional antigen presenting cells (pAPCs) or epithelial cells (Fukata and Arditi, 2013). After interacting with PRRs, specific receptor-mediated signal transduction pathway can turn on and change the phenotype of pAPCs (Fukata and Arditi, 2013). Besides dendritic cells, macrophages are a major population of pAPCs that can modulate inflammatory responses by either triggering inflammation or suppressing inflammation for tissue repair (Lawrence and Natoli, 2011). Therefore, two types of macrophages, inflammatory macrophages (M1 macrophages) and anti-inflammatory macrophages (M2 macrophages), have been proposed depending on their functions (Lawrence and Natoli, 2011). Recently, many observations have demonstrated that M1 or M2 macrophage skewing conditions might affect several pathologic conditions in human diseases (Shapouri-Moghaddam et al., 2018).
Among probiotics, Weissella cibaria is considered as a good starter of Kimchi fermentation because it has many beneficial effects including anti-microbial activities, antioxidant activities and immunomodulatory effects (Lim et al., 2017; Yu et al., 2018; Zhu et al., 2022). Latilactobacillus sakei is also used for the food fermentation or preservation. Especially, L. sakei is known for a commercial meat starter of meat fermentation (Leroy et al., 2006). Therefore, W. cibaria and L. sakei are good resources for food industry.
In the present study, we profiled mRNA expression patterns in macrophages after treatment with two different strains of probiotics isolated from Kimchi, W. cibaria WIKIM28 and L. sakei WIKIM50 (Lim et al., 2017). Results of this study might provide valuable data to understand the molecular mechanisms of macrophage polarization triggered by different types of probiotics.
Materials and Methods
Isolation and species identification of W. cibaria WIKIM28 from Gat Kimchi have been described previously (Lim et al., 2017). WIKIM50 was isolated from Baechu Kimchi and identified as L. sakei based on 16S rRNA gene sequencing according to a previously established method (Lim et al., 2017). WIKIM28 and WIKIM50 were sub-cultured more than 10 times before treating peritoneal macrophages (PMs) for RNA-Seq data analyses.
PMs were isolated according to a previously established method (Pineda-Torra et al., 2015). Briefly, 3% thioglycolate (Becton Dickinson, Sparks, MD, USA) in phosphate-buffered saline (PBS) was intraperitoneally injected into C57BL/6 mice (Orient Bio, Seongnam, Korea) at 1 mL per mouse. On day 3, thioglycolate-injected mice were sacrificed to harvest peritoneal lavages. Cell pellets were then acquired after centrifuging peritoneal lavages at 400×g for 5 min at 4°C and resuspended in DMEM/F-12 (Thermo Fisher Scientific, Waltham, MA, USA) medium supplemented with 10% (v/v) fetal bovine serum and 1% penicillin/streptomycin (DMEM/F-12-10). After resuspension, cells were plated into tissue culture plates and incubated in a humidified 5% CO2 atmosphere at 37°C. After overnight culture, adherent cells were harvested and counted. The purity of PMs was validated by flow cytometry analyses using anti-mouse CD11b (eBioscience, San Diego, CA, USA) and anti-mouse F4/80 (BioLegend, San Diego, CA, USA) antibodies. More than 90% of cells were CD11b+F4/80+ cells (data not shown). After isolation, PMs (2×106 cell/well) were seeded into a 12-well plate in 1 mL of DMEM/F-12-10. Heat killed WIKIM28 or WIKIM50 (2×107 CFU/mL) was co-cultured with PMs in a humidified 5% CO2 atmosphere at 37°C for 24 hours. As controls, vehicle (PBS) was used to treat PMs without incubation with probiotics.
Total RNAs were isolated from vehicle (PBS)-, WIKIM28-, or WIKIM50-treated PMs using TRIzol (Thermo Fisher Scientific) according to the manufacturer’s protocol. The quality and quantity of each RNA sample were measured using an ND-1000 spectrophotometer (NanoDrop, Wilmington, DE, USA) and an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Gene chip analyses were then performed using Agilent SurePrint G3 Mouse gene expression V2 microarrays (8×60K) according to the manufacturer’s protocol (Agilent Technology, V 6.5, 2010). Briefly, total RNAs from each sample were linearly amplified and labeled with Cy3-dCTP. These labeled cRNAs were then purified with an RNAeasy mini kit (Qiagen, Hilden, Germany). The concentration and specific activity of the labeled cRNAs (pmol Cy3/μg cRNA) were measured with an ND-1000 spectrophotometer. For hybridization of microarray, each labeled cRNA was fragmented by adding 5 μL of 10×blocking agent and 1 μL of 25×fragmentation buffer and then heated at 60°C for 30 min. After heating, 25 μL of 2×hybridization buffer was added to dilute the labeled cRNA. Subsequently, 40 μL of hybridization solution was loaded into the gasket slide and assembled to microarray slides (Agilent Technologies). After assembling, microarray slides were incubated at 65°C in an Agilent hybridization oven (Agilent Technologies) for 17 hours. After incubation, microarray slides were washed at room temperature using washing buffer provided by manufacturer (Agilent Technologies). The hybridized array was then immediately scanned with an Agilent Microarray Scanner D (Agilent Technologies).
Raw data were extracted using a Feature Extraction Software (v18.104.22.168) provided by Agilent (Agilent Technologies). Comparative analysis between test sample and control sample was carried out using paired t-test. False discovery rate was controlled by adjusting p-value using Benjamini-Hochberg algorithm (Benjamini and Hochberg, 1995). For a differentially expressed genes (DEG) set, hierarchical cluster analysis was performed using complete linkage and Euclidean distance as a measure of similarity.
To confirm gene chip analyses, qRT-PCR were conducted as described previously (Choi et al., 2018). Briefly, total RNAs from vehicle (PBS)-, WIKIM28-, or WIKIM50-treated PMs were isolated for synthesizing cDNAs using a First-Strand cDNA Synthesis Kit with oligo-dT primers (SuperScript RT; Thermo Fisher Scientific). Subsequently, quantitative real-time PCR was performed using one μg of each total RNA with QGreenTM 2X qPCR Master Mix (GenDEPOT, Katy, TX, USA) on a Bio-Rad CFX96 Real-Time Detection System (Bio-Rad, Hercules, CA, USA). Expression of each mRNA transcript was compared to the expression level of mouse Gapdh to obtain relative gene expression. Primer sequences for mouse gapdh were 5′-ATGGTGAAGGTCGGTGTGAA-3′ (sense) and 5′-GGTCGTTGATGGCAACAATCTC-3′ (anti-sense), resulting in a 100 bp product (Kang et al., 2022). PCR primers of M1 or M2 marker genes used in this study are listed in Table 1.
Results and Discussion
Compared with vehicle (PBS)-treated PMs, PMs treated with WIKIM28 and WIKIM50 showed a total of 889 and 432 DEGs with expression differences of at least 4 folds, respectively. Among them, 652 from WIKIM28-treated cells and 375 genes from WIKIM50-treated cells were up-regulated compared to those in vehicle (PBS)-treated PMs. Contrarily, 237 genes from WIKIM28-treated cells and 57 genes from WIKIM50-treated cells were down-regulated compared to those in vehicle (PBS)-treated cells.
DEGs with at least 2-fold expression difference in WIKIM50-treated cells compared to those in WIKIM28-treated cells are listed in Table 2. Among them, 25 genes were up-regulated, and 21 genes were down-regulated in WIKIM50-treated cells compared to those in WIKIM28-treated cells. Interestingly, several M2 macrophage marker genes such as anxa1, mafb, and sepp1 were up-regulated in WIKIM50-treated cells, whereas several M1 macrophage marker genes such as hdac9, ptgs2, and socs3 were down-regulated in WIKIM50-treated cells compared with those in WIKIM28-treated cells (Arnold et al., 2014; Barrett et al., 2015; Kim, 2017; Liu et al., 2021; Lu et al., 2017; Moraes et al., 2017). These results indicate that WIKIM28-treated macrophages are more likely to be polarized into M1 phenotype whereas WIKIM50-treated macrophages might exhibit M2 phenotype. Supporting this idea, mRNA transcripts of pro-inflammatory cytokines such as tumor necrosis factor-α and IL-1α were significantly decreased in WIKIM50-treated macrophages compared to those in WIKIM28-treated macrophages (Table 2).
Besides genes involved in macrophage polarization, genes encoding several cell surface proteins including PRRs, chemokine receptors, cytokine receptors were also identified as DEGs (Table 2). However, there was no clear physiological relevance in these DEGs.
To validate gene chip data, mRNA expression levels of DEGs related to macrophage polarization were monitored by qRT-PCR (Fig. 1). Results of qRT-PCR analyses clearly showed that M2 macrophage marker genes such as anxa1, mafb, and sepp1 were significantly decreased in WIKIM28-treated cells, whereas M1 macrophage marker genes such as hdac9, ptgs2, and socs3 were significantly increased in WIKIM28-treated PMs compared with those in WIKIM50-treated PMs.
Similar to our study, previous reports have demonstrated modulation of macrophage polarization by several different probiotics (Wang et al., 2020). Therefore, consumption of particular probiotics which shifts macrophage into M1 or M2 phenotype might help alleviate specific pathologic conditions of human diseases. For example, oral uptake of several different strains of probiotics can ameliorate colitis or hepatic steatosis by inducing M2 macrophage polarization in a mouse model (Jang et al., 2014; Sohn et al., 2015). However, not many studies have attempted to dissect the molecular mechanism of how certain strains can induce macrophage polarization. Here, we provide evidence indicating that signature genes of macrophage polarization are changed depending on probiotic strains. These results might be useful for finding the molecular clue of macrophage polarization caused by probiotics. Further studies will be required to define molecular signaling pathways involved in WIKIM28- or WIKIM50-mediated macrophage polarizations.