1. Introduction
It is estimated that, in 2023, there were 10.8 million new tuberculosis (TB) cases globally, with an incidence rate of 134/100,000and 1.25 million deaths due to TB globally, and TB again became the top cause of death from a single infectious disease globally [1]. Thus, TB prevention and control are still critical. TB is an infectious disease caused by Mycobacterium tuberculosis (MTB), which is mostly found in the lungs, but it can also colonize other parts of the body and can seriously affect the organism itself [2,3,4]. Macrophages play an essential role in the research of MTB. Some research works have shown that MTB preferentially colonizes macrophages and interacts with them, such as MTB infection of macrophages regulating the activation of related immune signaling pathways within macrophages [5], MTB evading the immune clearance of macrophages using its unique cell surface lipids [6], etc. This interaction is crucial in the determination of whether MTB can successfully infect [5,6,7,8,9]. Therefore, a clear understanding of the interaction mechanism between MTB and macrophages is conducive to developing effective diagnosis and treatment plans for MTB. The study of MTB is limited by the fact that MTB is pathogenic, infectious, and has a long growth cycle. A biosafety shelter laboratory-2 (BSL-2) or BSL-3 experimental environment is required to carry out relevant experiments on MTB. These factors have limited a more in-depth study of MTB. Therefore, finding a safe alternative to MTB for related research is very necessary. Currently, Mycobacterium smegmatis (M. smeg) is a commonly used alternative to MTB. M. smeg, as an alternative bacterium to MTB, has a fast growth cycle, no pathogenicity, and its internal composition is similar to that of MTB; thus, it is the preferred research object chosen by researchers as an alternative to MTB [10,11].
Currently, the research on M. smeg mainly focuses on introducing some components of MTB into M. smeg to construct M. smeg recombinant bacteria with some elements of MTB to detect the specific physiological functions of MTB-related components [12]. However, the research on the physiological functions related to M. smeg is uncommon. It mainly focuses on using it as an alternative model for the in vitro MTB cell infection model to verify the physiological functions related to MTB [13,14,15]. Therefore, exploring the physiological functions of M. smeg itself is significant for analogizing the related functions of MTB. Since MTB mainly interacts with macrophages, and most studies on the interaction between M. smeg and macrophages remain at the aspect of phenotypic changes [16], there have not been many reports on the interaction mechanism between M. smeg and macrophages. The rise in transcriptomic research has also brought new detection techniques for studying the interaction between M. smeg and macrophages. Transcriptomics is the main means to study gene expression, and the analysis of transcriptomics can provide a clear understanding of the changes in all mRNAs of the samples to be tested at the overall level to screen the relevant cell signaling pathways or related proteins [17,18].
In this study, M. smeg was used to infect macrophages to study the main physiological functions of M. smeg in macrophages. Transcriptomic sequencing was used to analyze whether the related genes or molecular signaling pathways in the cells changed, and the main physiological functions of M. smeg in macrophages were summarized. It provides a clear research direction for future studies using M. smeg as a substitute for MTB bacteria to explain the related molecular action mechanism of MTB. After stimulation by M. smeg, the transcriptome sequencing results showed that the immune response and the activation of related signaling pathways were the main physiological functions of M. smeg in regulating macrophage production. Meanwhile, the expressions of representative immune-related cytokines interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) were significantly increased, further verifying the above sequencing results. Therefore, this study found that M. smeg can regulate the immune response of macrophages and has a similar ability to regulate the immune response of macrophages as MTB. This also indicates that when studying the interaction between mycobacteria and macrophages, the activation of the immune response remains a direction that requires attention. The analysis of the interaction mechanism between M. smeg and macrophages revealed that the ability of M. smeg or MTB to regulate the immune response of macrophages is vital for whether MTB can successfully infect the host.
2. Materials and Methods
2.1. Source and Culture of Cells and Strains
Macrophages (Raw264.7) were kept in our laboratory and cultured in sterile DMEM medium (319-005-CL, WISENT, Nanjing, China) with 10% FBS (085-150, WISENT, Nanjing, China) and 1% penicillin–streptomycin mixture (450-201-EL, WISENT, Nanjing, China) at 37 °C in 5% CO2. The standard strain Mycobacterium smegmatis (M. smeg) mc2-155 was purchased from the American Typical Collection Center (ATCC) (ATCC700084, VA, USA) and kept in the strain bank of Beijing Chest Hospital of Capital Medical University. M. smeg was cultured in 7H9 liquid medium (271310, BD, NJ, USA) supplemented with 10% OACD (211886, BD, NJ, USA), 0.05% Tween 80 (P1754, Merck, Darmstadt, Germany), and 0.5% glycerol (137028, Merck, Darmstadt, Germany).
2.2. Mycobacterium smegmatis Infection of Macrophages
A total of 5 × 105 Raw264.7 cells were spread on 6-well plates, and 18ߝ24 h later, centrifuging the M. smeg cultured in 7H9 liquid medium, the amount of M. smeg was calculated and centrifuged at a ratio of MOI = 10 for 10 min at 1,000× g, followed by centrifugation for 10 min using 1× PBS for washing the precipitate and then centrifugation at 1000× g. The precipitate was resuspended using DMEM and added to Raw264.7 cells, and the culture was continued by replacing the medium with fresh DMEM after 4 h.
2.3. Transcriptomics Assay
After M. smeg infected macrophages for 24 h, the culture supernatant was discarded, the cells were washed twice with 1× PBS, and 1 mL of Trizol solution (15596018CN, Thermo Fisher, Waltham, MA, USA) was added; the samples were collected for RNA extraction, and then the cDNA library was constructed. The cDNA library construction kit we used is VAHTS Universal V6 RNA-seq Library Prep Kit for Illumina (NR604-01, Vazyme, Nanjing, China) and AMPure XP system (N411-02, Vazyme, Nanjing, China). Before the sequencing began, we conducted quality inspections on the extracted sample RNA and the library to ensure the sequencing quality. The samples were sequenced and subsequently analyzed with the Illunima platform. After obtaining the original data, we removed the connector sequence, low-quality reads with an average base mass value of less than 20, and reads with an undetermined number of base information greater than 5 to ensure the quality of the detection data for subsequent analysis.
2.4. qPCR
After M. smeg infected macrophages for 24 h, the culture supernatant was discarded, the cells were washed twice with 1× PBS, and RNA was extracted using an RNA extraction kit (RC112-01, Vazyme, Nanjing, China) according to the manufacturer’s instructions. In this study, after extracting RNA, we configured the reaction system using DNase I (SL20761, Coolaber, Beijing, China) as per the manufacturer’s instructions to remove DNA from the RNA samples. RNA reverse transcription (G3337-100, Servicebio, Wuhan, China) was performed. Primers were designed with IL-6, TNF-α, and GAPDH as target genes (see Table S1); the GAPDH primer sequences were designed on the Primer Bank website (Gene ID: 14433). The IL-6 primer sequences were synthesized by referring to the literature published by Yanguas-Casás et al. [19], and the TNF-α primer sequences were synthesized by referring to the literature published by Hu et al. [20]. All primers were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China). A qPCR kit (F0107, Forscience, Beijing, China) was used to perform the reaction, and data collection was conducted using a real-time fluorescence quantitative PCR instrument (ABI 7500, Thermo Fisher, MA, USA) according to the manufacturer’s instructions. In this manner, reactions were performed, and data were collected.
2.5. ELISA
After M. smeg infected macrophages for 24 h, culture supernatants were collected, and the experiments were performed using IL-6 (E-EL-M0044, Elabscience, Wuhan, China) and TNF-α ELISA (E-EL-M3063, Elabscience, Wuhan, China) kits according to the manufacturer’s instructions; the results were collected and analyzed using an enzyme marker (Multiskan FC, Thermo Fisher, MA, USA) under OD450 nm.
2.6. Statistical Analysis
Each group of experiments was repeated 3 times, n = 3. DESeq2 was used for the differential expression analysis of genes, and the p-values obtained from the original hypothesis tests were corrected. The padj less than 0.05 and |log2foldchange| greater than 1 were taken as the criteria for significance of differences. GO functional annotation (p value < 0.05) and KEGG pathway enrichment analysis were performed through Fisher’s Exact Test. The GO function annotation uses the Blast2GOv2.5 software, and the parameters are e-value = 1 × 10-6, we set the p-value corrected by multiple hypothesis testing as FDR, and its value range is [0, 1]. KEGG pathway enrichment analysis was conducted using KOBAS 3.0 software, with the parameters being e-value = 1 ×10-10. Screening was conducted in the AnimalTFDB/PlantTFDB database based on gene ID, and transcription factors were annotated for the differentially expressed genes. GraphPad Prism software 8.0 was used for data processing and figure generation. Normal distribution was analyzed with one-way analysis of variance (ANOVA), and the test criterion p < 0.05 was considered statistically significant.
3. Results
3.1. Mycobacterium smegmatis Can Regulate the Activation of Macrophage Immune Responses
When conducting sequencing result analysis, it is necessary to test the correlation among the sequenced samples to ensure the accuracy of the analyzed results. We detected the correlation of gene expression levels among samples through Pearson correlation analysis and principal component analysis (PCA). The Pearson correlation analysis included the M. smeg-stimulated and non-stimulated macrophage groups. The color within the stimulated or non-stimulated groups was redder, and the correlation of gene expression among the samples was stronger. However, the color between the stimulated and non-stimulated groups was lighter, and the correlation of gene expression among the samples was weaker (Figure 1A). There was a significant difference between the stimulated and non-stimulated groups in the PCA, and the percentages of PC1 and PC2 were 37.49% and 16.72%, respectively. There was a significant segregation between the unstimulated and stimulated macrophage groups of M. smeg (Figure 1B). The above results indicate that the correlation of gene expression levels among the samples to be analyzed is good, and subsequent in-depth analysis can be conducted.
To explore the physiological functions of M. smeg in macrophages, we first conducted gene expression analysis on the M. smeg-stimulated macrophage group and the non-stimulated macrophage group. We plotted the results of differentially expressed gene analysis on a volcano map. After M. smeg stimulated macrophages, the up-regulation and down-regulation of intracellular gene expression were evident. There were 10,551 up-regulated significantly differentially expressed genes (DEGs) and 5527 down-regulated DEGs (Figure 2). GO enrichment analysis of the above genes revealed that up-regulated DEG mainly exerted the immune response function (Figure 3A), down-regulated DEG mainly exerted the chromosome segregation function (Figure 3B), and overall DEG also occupied a significant position in the immune response function (Figure S1 and Table S2). KEGG enrichment analysis of the above genes revealed that up-regulated DEG was mainly involved in the regulation of multiple signaling pathways, such as the RIG-I receptor signaling pathway, IL-17 signaling pathway, NF-kappa B signaling pathway, etc. (Figure 4A), while down-regulated DEG was mainly involved in biological regulation related to nucleic acids and metabolism, such as AminoacI-tRNA biosynthesis, Fatly acid metabolism, Cell cycle-yeast, etc. (Figure 4B). Overall DEG occupy an integral part in the regulation of signal pathways such as Toll-like receptor signaling pathway, TNF signaling pathway, p53 signaling pathway, and biological processes related to signal pathways (Figure S2 and Table S3). After GO and KEGG enrichment analyses, the expressions of transcription factors such as Interferon regulatory factor (IRF) and zinc finger in macrophages at the transcription factor level were activated (Table S4). The above results indicate that M. smeg can regulate macrophages to produce a strong immune response.
3.2. Mycobacterium smegmatis Regulates Production of IL-6 and TNF-α
The above sequencing results show that M. smeg can regulate the immune response of macrophages, and multiple immune-related molecular signaling pathways and transcription factor IRF are regulated and activated. Therefore, we intend to verify the sequencing results at the molecular biological level. From the above GO analysis and KEGG analysis results, as well as the transcription factor sequencing results, it is demonstrated that M. smeg can regulate the immune response of macrophages and the activation of related immune signaling molecular pathways. The immune signaling molecular pathway and the transcription factor IRF can promote the expression of various cytokines. Studies have shown that cytokines IL-6 and TNF-α can activate macrophage immune responses regulated by MTB, and they are closely related to the activation of cellular immune signaling pathways [21]. Therefore, in this study, we detected the changes in the expression levels of IL-6 and TNF-α in macrophages after infection with M. smeg by qPCR and ELISA. The results showed that macrophages infected with M. smeg could regulate the production of cytokines IL-6 and TNF-α (Figure 5). The above results indicate that M. smeg can regulate the activation of macrophage immune responses and are consistent with the results of transcriptome sequencing.
4. Discussion
The results of this study indicate that M. smeg stimulates the activation of immune direction-related signaling pathways in macrophages, suggesting that immune regulation is the primary regulatory mode of interaction between M. smeg and macrophages. It also provides a basis for subsequent research on the physiological functions related to MTB using M. smeg as a substitute for MTB [22,23,24,25]. When conducting GO enrichment analysis on sequencing results, GO enrichment analysis can describe the properties of genes and gene products in organisms from the following three aspects: Including the involved Biological processes (BPs), Molecular functions (MFs), and Cellular components (CCs), and in the final analysis results, we found that the GO enrichment results of biological process attributes accounted for the most significant part of the entire results. Therefore, in this study, we presented the GO enrichment analysis results of biological process attributes, and the main purpose of this study was to explore the physiological functions of M. smeg in a single type of macrophage. Therefore, although we present the GO enrichment analysis results of up-regulated or down-regulated enriched genes and the GO enrichment analysis results of the overall biological process attributes, this does not conflict with our research purpose and can still provide a direction for us to obtain the conclusion that M. smeg can regulate the immune response of macrophages.
This study used transcriptome sequencing to investigate the interaction between M. smeg and a single macrophage. Transcriptome sequencing has the advantages of low cost, high throughput, complete information obtained, and the ability to conduct quantitative expression analysis of the research object [17,18]. However, the amount of data it acquires is enormous and complex, requiring a specific professional knowledge background for analysis. In addition to transcriptome sequencing, to determine the physiological functions regulated by M. smeg in macrophages, other sequencing methods can also be selected, such as single-cell sequencing [26], metabolomic sequencing [27], proteomic sequencing [28,29,30], and so on. Single-cell sequencing has the advantages of high sequencing throughput, fast cycle, and comprehensive results obtained. However, it has specific requirements for cell activity and is relatively costly. Metabolome sequencing has the advantages of simple operation, low cost, and strong universality, but the sequencing results obtained cannot be used to analyze intermolecular interactions. Proteomic sequencing technology has also become increasingly mature, with multiple sequencing modes that can be used for corresponding proteomic sequencing according to different needs. However, the cost of some proteomic sequencing is relatively high. Other sequencing methods target different detection objects. When conducting related research, selecting an appropriate sequencing scheme is necessary. Currently, multi-omics analysis is a very popular means of analysis [31,32], which can help researchers to understand the in-depth mechanism of the study from multiple perspectives and levels. In a follow-up study, we could include the research directions of other genomics to conduct a more accurate study of the regulation of macrophage function by M. smeg.
Since MTB can persist in macrophages for a relatively long time, studies have found that the distribution of MTB in different types of macrophages varies [22,23,24]. Among them, the activation of classical macrophages is of great significance for the clearance of MTB [23]. Therefore, clarifying the physiological functions of MTB in a single type of macrophage is necessary for understanding MTB’s survival pattern in vivo and formulating effective treatment plans for MTB. In this study, in addition to using transcriptome sequencing technology to investigate the function of M. smeg in macrophages, the expressions of immune cytokines IL-6 and TNF-α were also verified. Studies have shown that these two cytokines, as anti-inflammatory cytokines, are related to the polarization of macrophages towards M1-type macrophages, and M1-type macrophages are crucial for the clearance of MTB. Therefore, we also speculate that the macrophages used in this paper can be regulated to polarize towards type M1 after being stimulated by M. smeg. Of course, confirming the elevated expression of immune cytokines IL-6 and TNF-α is not sufficient to prove the production of M1-type macrophages. In the subsequent research, we still need to conduct in-depth studies on the polarization types of macrophages. Macrophages can be classified into various types, including alveolar type, interstitial type, etc. Although this study initially confirmed that M. smeg has a similar ability to regulate immune responses to MTB, does M. smeg stimulate macrophages to secrete only anti-inflammatory cytokines and pro-inflammatory factors? However, studies also report that MTB can persist in macrophages and evade immune responses. What is the specific mechanism causing this phenomenon? Or is it related to the type of macrophages? These problems still require further study by us.
Thus, studying macrophage regulation by M. smeg can help us fully understand the ability of M. smeg to regulate immune responses and simultaneously gain a deeper understanding of the related physiological functions of M. smeg, which can then be correlated to the ability to regulate the immune responses of macrophages of MTB.
5. Conclusions
In this study, through transcriptome sequencing analysis and expression detection of cytokines IL-6 and TNF-α in macrophages infected with M. smeg, it was found that M. smeg can regulate the immune response within macrophages and the activation of multiple cellular signaling pathways. Since the physiological state of M. smeg is similar to that of MTB, it plays a crucial role in the research of replacing the related physiological functions of MTB. The research results showed that M. smeg and MTB also have similar physiological functions in immune regulation. This also indicates that M. smeg has significant advantages as an alternative bacteria to the MTB model and can provide an effective reference value for our in-depth exploration of the immune functions related to MTB.
Conceptualization, Z.S.; methodology, H.S.; software, Y.H.; validation, D.L.; formal analysis, N.T.; investigation, W.W.; data curation, W.X.; writing—original draft preparation, H.S.; writing—review and editing, Z.S.; project administration, Z.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data used are contained within the article.
The authors declare no conflicts of interest.
The following abbreviations are used in this manuscript:
| MTB | Mycobacterium tuberculosis |
| M. smeg | Mycobacterium smegmatis |
| TB | tuberculosis |
| IL-6 | Interleukin-6 |
| TNF-α | Tumor Necrosis Factor-α |
| IRF | Interferon Regulatory Factor |
Footnotes
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Figure 1 The strong inter-sample correlation between Mycobacterium smegmatis-infected macrophages in the infected group compared to the uninfected group. (A) Pearson’s coefficient analysis. (B) PCA result analysis. M. smeg-represents the uninfected macrophage group, M. smeg+ represents the infected macrophage group, and 1, 2, and 3 represent the parallel experimental groups under the same group. Each group of experiments was repeated 3 times, n = 3.
Figure 2 Differentially expressed genes expression enrichment analysis. (A) Results of volcano plot analysis. (B) Differential gene clustering analysis. M. smeg-represents the uninfected macrophage group, and M. smeg+ represents the infected macrophage group.
Figure 3 GO expression enrichment analysis. Up-regulation results (A) and down-regulation results (B). M. smeg-represents the uninfected macrophage group, and M. smeg+ represents the infected macrophage group.
Figure 4 KEGG expression enrichment analysis. Up-regulation results (A), down-regulation results (B). M. smeg-represents uninfected macrophage group, M. smeg+ represents infected macrophage group.
Figure 5 Mycobacterium smegmatis regulates the increased expression of macrophage cytokines IL-6 and TNF-α. A, C. TNF-α expression results: ELISA (A) and qPCR (C). B, D. IL-6 expression results: ELISA (B) and qPCR (D). Each group of experiments was repeated 3 times, n = 3. **, p < 0.01.
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; Liu Dingyi; Sun Zhaogang