Transcriptome and metabolome profiling reveal the effect of MTB virulence protein Rv3874 and Rv3875 on macrophage Sphingolipid metabolism

doi:10.21203/rs.3.rs-2111121/v1

Download PDF

Research Article

Transcriptome and metabolome profiling reveal the effect of MTB virulence protein Rv3874 and Rv3875 on macrophage Sphingolipid metabolism

https://doi.org/10.21203/rs.3.rs-2111121/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background

Normal lipid metabolism is a key process to maintaining cellular immune function and energy metabolism of macrophages, but Mycobacterium tuberculosis (MTB) infection can cause lipid metabolism disorder of macrophages, resulting in granuloma or intracellular retention.

Methods

Recombinant BCG with virulence protein Rv3874 or Rv3875(rBCG-Rv3874 and rBCG-Rv3875)were constructed. Transcriptomics and High-performance liquid chromatography-mass spectrometry (HPLC-MS) metabonomics studies were performed using rBCG infected macrophage samples. Extensive and variation difference analyses were conducted on transcription and lipid metabolism levels compared with the control group.

Results

A fact is that 7100 genes were changed in the rBCG-Rv3874 group and 1263 genes were changed in the rBCG-Rv3875 group at the RNA-seq level. A total of 612 and 485 substances were detected in the non-targeted positive and negative ion mode of lipid metabolism, and a total of 1240 and 210 metabolites were detected in the targeted positive and negative ion mode of lipid metabolism. Finally, it was found that the lipid changes related to the sphingomyelin (SM) metabolic pathway were the most obvious. 220 kinds of SM metabolites were detected by positive ion detection, and it was found that more ceramide, ceramide-1-phosphate, and SM were enriched in the rBCG-Rv3874 group than in rBCG-Rv3875 group.

Conclusion

MTB virulence proteins Rv3874 and Rv3875 can affect the sphingolipid metabolism of macrophages and it's associated with phagocytosis, apoptosis, and inflammatory processes.

Rv3874

Rv3875

macrophage

omics analysis

sphingolipid

Macrophages are specialized phagocytes, the front line of defense against foreign substances and pathogens. It plays a vital role in the immune process against tuberculosis. The interaction between Mycobacterium tuberculosis (MTB) and macrophages often determines the development and onset of tuberculosis. MTB and its secreted proteins can regulate macrophage functions such as cytokine expression, antigen presentation and glycolipid metabolism by inhibiting the phagolysosome fusion and acidification, autophagy and apoptosis and antigen presentation of macrophage [1]. and leading to the occurrence of inflammation, cell death, granuloma formation and immune escape [2].

Lipids are major components of biological membranes and can also serve as energy sources and signaling molecules during infection and inflammation [3]. The strictly balanced lipid metabolism of macrophages is an important guarantee for regulating cell polarization and immune function [4]. Macrophages undergo extensive lipid remodeling during MTB infection, and changes in cellular lipid metabolism in turn affect their susceptibility to tuberculosis and the outcome of infection [5]. MTB provides a nutrient source for its long-term survival in vivo through the regulation of cellular lipid homeostasis [6]. MTB infection can interfere with the normal lipid metabolism of macrophages through a variety of mechanisms, such as inducing the increase of CD36 expression, resulting in the absorption and accumulation of lipids, hindering the maturation of phagolysosomes [7]. Recent study shown that lipid droplets play an important role in regulating macrophage metabolism and activating pro-inflammation [8][9]. This may be related to the pattern recognition receptors (PRRs) and lipid components on the membrane of macrophage. TLR2 and TLR4 are the main receptors that recognize MTB, and sphingolipid types can specifically regulate the proinflammatory phase or the late escape phase of TLR4 activation [10].

Rv3874 and Rv3875, as the major secreted virulence proteins encoded by the MTB RD1 region, play a variety of roles in regulating the host immune response to infection and causing bacterial escape to the host cytoplasm, and can also serve as strong host (human) T cell antigens [11]. However, how Rv3874 and Rv3875 affect cellular lipid metabolism during MTB infection of macrophages has not been reported. In this study, the effects of Rv3874 and Rv3875 on immune function and sphingolipid metabolism of macrophage were investigated.

Construction of recombinant BCG strains

BCG strain (Beijing Institute of biological products, China), pMV-261 shuttle vector (comatebio, China), and the gene fragment of protein Rv3874 and Rv3875 were provided by Jilin Agricultural University. The gene fragment of protein Rv3874 and Rv3875 were inserted into the multiple clonal site of empty shuttle vector pMV-261 respectively, in order to obtain the recombinant plasmid pMV-261-Rv3874 and pMV-261-Rv3875. The recombinant plasmids were transferred into BCG by electrotransformation assay under 1350V, respectively. And recombinant BCG rBCG-Rv3874 and rBCG-Rv3875 were constructed by Kan⁺ resistance (Sangon Biotech, China) 7H11 solid culture (guidechem, China) screened. Target gene was verified by PCR. Services such as primer synthesis and genetic sequence sequencing were provided by Sangon Biotech,(China).

Sample grouping and processing method

Samples of mouse macrophage RAW264.7 cells treated with BCG, rBCG-Rv3874 and rBCG-Rv3875 are defined as group ZB, Z4 and Z5, respectively. And the cells blank control group was defined as ZC. Separate biological replicates were prepared, each containing 3 or more treated macrophage RAW264.7 material. Total RNA and metabolites were extracted from the same batch of cultured strains and cells to ensure the parallelism of samples and the credibility of data. Each sample was set up with 18 replicates and divided into transcriptome and metabolome. Each group of transcriptome samples had 9 replicates, and samples were taken after mixing every 3 replicates to reduce the differences between the groups of cells.

Macrophage infection experiment

Mouse macrophage RAW264.7 cell line was purchased from Shanghai Yaji Biotechnology Co.,Ltd. (Https://yaji.biomart.cn), product number: YS3639C. RAW264.7 cells were plated on a six-well plate at a concentration of 5×10⁵ cells/mL, and the original medium was aspirated and discarded when the cells grew to about 1×10⁶ cells/well (cell density reached 80% -90%) after 24 hours. 1mL of DMEM high glucose medium (Gibco, China) containing recombinant BCG was added, and the culture was continued for 3H at 37℃ in a 5% CO2 incubator. The colony CFU test showed that the number of colonies was 3.0×10⁸ when the OD value was 6. Taking 1mL of bacterial solution with the OD value of 0.6, centrifuge 6000g for 10min, and discard the supernatant. The cells were resuspended in 1mL of DMEM medium preheated at 37℃ and counted, and the infection was carried out according to the infection multiplicity of 10:1 (bacteria: cells), that is the concentration of bacteria was 1×10⁷/mL. After 3H of infection, the original medium was discarded, gently washed once with preheated DMEM solution at 37℃, and 2mL of DMEM complete medium (89% DMEM medium, 10% fetal bovine serum (Gibco, China), and 1% penicillin and streptomycin resistance (Gibco, China)) was added for further 5H.

Preparation and analysis of transcriptome cell samples

After the cell re-culture, the supernatant was collected and washed twice with DMEM; 1mL TransZol Up (Transgen, China) reagent was added per 10cm² of culture area (equivalent to one well of a six-well plate); repeatedly blown with a 1mL lance tip to allow TransZol Up to contact the surface of all culture flasks with cells and digest them thoroughly; after mixing every three wells into one well, transfer it into a 1.5mL centrifuge tube of RNase-free, and repeatedly beat the cells with a disposable syringe until no agglomerated cell mass can be seen. The whole solution is clear and not viscous. After quick freezing with liquid nitrogen, it is stored at -80℃. Transcriptome samples were treated with TransZol Up, immediately frozen with liquid nitrogen, preserved with dry ice, and sent to Beijing Oveson Biotechnology Co., Ltd. (Http://tech.allwegene.com) for detection. The samples were divided into 4 groups, with a total of 12 samples and 3 biological replicates in each group. Using Illumina II high-throughput sequencing platform and PE150 sequencing strategy, 300436322 raw read pairs were measured, and 295614239 clean reads pairs were obtained after quality control. Alignment and transcript splicing analysis were performed by star and Cufflinks software respectively, and then all genes were quantitatively analyzed to identify the differential genes, and 10357 differential genes were identified. These differential genes were subsequently subjected to GO enrichment analysis (http://www.geneontology.org) and KEGG enrichment analysis (http://www.genome.jp/kegg) to explore their functions.

Preparation and Analysis of Metabolome Cell Samples

After the cells were re-cultured, the supernatant was collected and washed twice with PBS (solarbio, China) buffer; 1mL of trypsin (solarbio, China) reagent was added per 10cm² of culture area for digestion for 1min, and then 1mL of DMEM complete culture medium was added to stop digestion, and 1mL of the gun head was used to purge until the cells were completely shed, and every three wells were combined into one well for mixing, and then counted with a blood cell counting plate. Transfer 1×10⁶ cells/mL to 1.5mL centrifuge tubes, centrifuge at 1000g for 5min, discard the supernatant, wash the cells twice with PBS buffer, centrifuge at 1000g for 5min, and discard the supernatant; the cell samples were used for lipidome analysis.

Some samples were sent to Kangcheng Biological Company (aksomics. Co, China) for non-targeted qualitative analysis, and the main metabolites were analyzed by mass spectrometry. The other part of the sample extracted metabolites. 1×10⁶ cells of each group were resuspended and lysed in 200µL chloroform: ethanol = 1:1. After centrifugation at 12000g for 10min, the supernatant was removed and transferred to a new 200µL Eppendorf tube, and the lipid content of the sample was detected by multiple reaction monitoring (MRM) using the SCIEX-TripleQuad system. Rinse with the mobile phase for 10min, and then place the sample extracted from the cells into the autosampler. The injection volume was set to 2µL, and each sample was repeated in six groups to detect the lipid components in the sample. The sample was injected into a C18 column (100×2.1mm, 1.9µm) with a linear gradient of 65min at a flow rate of 0.1mL/min. Eluent A (20% ME, 0.5‰ FAA, 0.15‰ FA in water) and Eluent B (50% ACE, 50% Pro, 0.5‰ FAA, 0.15‰ FA). The solvent gradient was set to: 0min 30% B; 5min 45% B; 12min 60% B; 17min 70% B, 22min 75% B, 27min 80% B, 47min 95% B, 50min 99% B; 63min 99% B; 64min 30% B; positive/negative polarity mode, maximum pressure 40MPa. HPLC-MS generated raw data files for peak alignment, peak selection, and quantification of each metabolite using SCIEX OS software. Peak matching with mzCloud, mzVault, and MassList databases was then performed to obtain accurate qualitative and relative quantitative results. Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) analyses were performed using SIMCA-P 14.1. Metabolites with a VIP value of > 1, a p-value of < 0.05, and a fold change of > 2 were considered to be differential metabolites. KEGG enrichment analysis and lipid enrichment analysis of differentially accumulated metabolites were performed using KOBAS 2.0 software and MetaboAnalyst5.0 website.

Combined transcriptome and metabolome analysis

Differentially expressed genes (DEGs) and differentially accumulated metabolites from transcriptome and metabolome were mapped to the KEGG pathway for integration. Correlation analysis was performed on the data to explore the correlation between DEGs and metabolites. Joint enrichment analysis was performed with DEGs and KEGG IDs of differentially accumulated metabolites, which were visualized by generating relevant signal pathway maps.

Real-time qPCR

Real-time quantitative PCR (RT-qPCR) was performed using the Mx3000P system according to the SYBR PreMix Ex Taq instructions (Takara, China). For each reaction, 0.5µL of forward and reverse primers and 2µL of cDNA template were added. All primers used in this study are listed in Table S1. Relative gene expression levels were calculated using the 2^−ΔΔCt method.

Statistical analysis

SPSS 20.0, Origin 2021, CIMCA, and Cytoscape software were used for statistical analysis. All data were expressed as mean ± S. E. M. The difference was statistically significant when P < 0.05. RT-qPCR results were analyzed by MxPro software. P < 0.05 means a significant difference.

1. Construction and characterization of recombinant BCG

rBCG-Rv3874 and rBCG-Rv3875 were successfully constructed by electroporation. The recombinant BCG was lysed and the plasmid was extracted. The PCR method was used to verify whether the target genes were successfully inserted in the plasmids and the recombinant BCGs were successfully constructed (Fig. 1A). rBCG-Rv3874 and rBCG-Rv3875 were cultured to determine their logarithmic growth phase, and the growth curves of BCG, rBCG-rv3874, and rBCG-rv3875 were drawn (Fig. 1B). And that CFU in each milliliter of the BCG liquid in the logarithmic growth phase is determined by a multiple proportion dilution method. Recombinant strain was used to infect mouse macrophage RAW264.7 to obtain transcriptome and metabolome samples. Sample grouping and processing method were shown in Fig. 1C.

2. Quality control of RNA-seq reads

This study compared the transcriptome of rBCG-Rv3874 and rBCG-Rv3875 samples by RNA-seq analysis, mainly analyzing mRNA accumulation information obtained from cDNA libraries. The sequencing of each cell sample generated more than 100 million reads. After filtering out the adapter and low-quality reads, got high-quality reads, which account for more than 97% of the original reads in Table S2. To prove the repeatability of the experiment and the reliability of the evaluation results, RNA-Seq correlation test and PCA analysis between groups were carried out. The results showed that the correlation coefficient between samples in a group was greater than 0.98. The closer the correlation coefficient was to 1, the higher the similarity of expression patterns between samples was, and the significant differences between groups were observed. The results of the PCA analysis showed that 60.04% of the components in the ZB, Z4, and Z5 groups were similar in PC1, and only 20.2% of the components in PC2 and the Z4 group were significantly different from the other three groups (Figs. 2A and B). According to the cluster analysis at the transcriptome level, the Z4 group was significantly different from the other three groups, while the ZB and Z5 groups were more similar in transcription level than the Z4 group (Fig. 2E).

3. Differentially expressed genes of rBCG-Rv3874 and rBCG-Rv3875

The degree of difference between the Z4, Z5, ZB, and ZC samples is shown in the volcano and cluster plots. The results showed that compared with the ZB group, there were 3698 genes up-regulated and 3402 genes down-regulated in the Z4 group, and 576 genes up-regulated and 687 genes down-regulated in the Z5 group (Figs. 2C and D). All results are shown in Table S3.

To find the gene function of DEGs in the Z4 and Z5 and ZB groups, this study performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. GO is an international standard classification system for gene function. All DEGs can be classified into three categories: biological process (BP), cellular component (CC), and molecular function. As shown in the histogram of GO enrichment after the classification of GO-enriched items, the Z4 compared with the ZB, according to the GO analysis of transcriptionally up-regulated mRNA, The most significantly enriched BPs were “organic substance metabolic process”, “primary metabolic process”, “cellular metabolic process”, “organic cyclic compound metabolic process”.The most significantly enriched CCs were “cellular anatomical entity”, “intracellular organelle”, “membrane-bounded organelle”, “intracellular membrane-bounded organelle”、 “intrinsic component of membrane”. The most significantly enriched MFs were “catalytic activity”, “organic cyclic compound binding”, “heterocyclic compound binding”, “protein binding”, “Ion binding”, “nucleic acid binding” and “transferase activity” (Figure 3A). In addition, the histogram of mRNA down-regulated by transcription is shown. The results showed that the most significantly enriched BPs were “macromolecule modification”, “organic substance transport”, and “macromolecule localization”. The most enriched MFs were “catalytic activity”, “ion binding”, “hydrolase activity”, and “transferase activity”. The most enriched CCs were “proton-transporting V-type ATPase”, “V1 domain”, “endomembrane system”, “peptidase complex”, and “Golgi apparatus”༈Figure 3B༉. All enrichment results are shown in Table S4.

For the KEGG analysis of DEGs, Pathways associated with macrophage function and metabolism that were significantly enriched were “Apoptosis”, “TNF signaling pathway”, “Endocytosis”, “Toll-like receptor signaling pathway”, “Phagosome, Sphingolipid metabolism” (Fig. 3E). All enriched KEGG pathways containing DEGs are given in Table.S5.

The most significant BPs were “metabolic process”, “organic substance metabolic process” and “primary metabolic process” in the go enrichment analysis of the Z5 compared with the ZB transcriptionally up-regulated mRNA. The most significant CCs enrichments are “cellular component”, “cellular metabolic process”, and “intracellular membrane-bounded organelle”. The most significantly enriched MFs were “ion binding”, “nucleic acid binding”, “protein binding” and “DNA binding” (Fig. 3C). In addition, “localization”, “ion transport”, and “transmembrane transport” were the most significant enrichment for BPs, as shown by the histogram of down-regulated mRNA. The most significantly enriched MFs were “catalytic activity”, “transferase activity”, and “transporter activity”. The most significant enrichment of CCs was “proton-transporting V-type ATPase”, “host cytoskeleton”, and “small-subunit processome” (Fig. 3D). All enrichment results are shown in Table S6. For the KEGG analysis of DEGs, Pathways associated with macrophage function and metabolism that were significantly enriched were “Steroid biosynthesis”, “TNF signaling pathway”, “Apoptosis”, “NF-kappa B signaling pathway”, “Toll-like receptor signaling pathway”、 “Cholesterol metabolism”、 “Cell cycle”(Figure 3F). Table S7 presents all enriched KEGG pathways containing DEGs.

4. Differential accumulation of metabolites between rBCG-Rv3874 and rBCG-Rv3875

The non-targeted qualitative results are shown in Table S8, in which 612 substances were detected by positive ions and 485 substances were detected by negative ions. The main components were Cer, phosphatidic acid, phosphatidylcholine, phosphatidylethanolamine, and phosphatidylglycerol .

The composition of metabolites in RAW264.7 macrophages infected by the ZB, Z4, and Z5 was determined by HPLC-MS, and the lipid metabolic components of macrophages showed obvious and extensive changes. 210 and 1240 compounds were analyzed in the positive and negative ion modes, respectively (Table S9), including PC, Cer, PE, SM, lysophosphatidylcholine, cholesteryl ester, phosphatidylinositol, arachidonic acid, vitamin D, and vitamin K.

The total lipid metabolites of the cells were based on the OPLS-DA method to classify the samples according to the key metabolite information, and there were significant differences in the lipid metabolism of the cells. The OPLS-DA score plot showed significant separation between the treatment group samples, as shown in Fig. 4A. To better understand the differences between Rv3874 and Rv3875, this study plotted the cluster heat maps of four groups of 24 samples (4 groups of 6 samples averaged) in positive and negative ion modes (Fig. 4B). Most of the differentially accumulated metabolites detected in the Z4 and Z5 groups were Cer and PC, which had a higher accumulation. Metabolites were accumulated in the Z4 group compared with the ZB group, of which 79 were up-regulated and 73 were down-regulated, mainly Cer and PC. Metabolites were accumulated in the Z5 group compared with the ZB group, of which 88 were up-regulated and 70 were down-regulated, mainly CE, Cer, and PC (only substances with a change of more than 1.5 times were counted). Table S10 for specific results.

This study extracted VIP values from the importance map of the OPLS-DA model to search for differentially accumulated metabolites in the Z4 and Z5 relative to the ZB. Differentially accumulated metabolites were screened according to VIP > 1. According to the above criteria, a total of 353 metabolites were screened from the positive ion mode and the negative ion mode (Fig. 4C). Table S11 for the results. The most obvious changes are PC (O-16: 0–16: 0), PC (O-18: 0–19: 1), PC (O-20: 0–18: 2), LCER (16:0), Cer (D18: 0–18: 0) and Cer (T20: 0–24: 02OH). Cer(t18:0)-24:02OH)、SM(d18:2–22:0).

A database such as LIPID MAPS, PubChem, HMDB, and MBROLE2.0 was used to query the PubChem ID and HMDB ID of metabolites. Metabolite data with HMDB ID were entered into the MetaboAnalyst5.0 database for SMPDB pathway enrichment analysis, respectively. As shown in Fig. 4D, the detected metabolites are involved in six metabolic pathways, including “α-linolenic acid and linoleic acid metabolic pathways”, “phospholipid biosynthesis pathway”, “sphingolipid metabolic pathway”, “steroid biosynthesis pathway”, “arachidonic acid metabolic pathway”, and “tyrosine metabolic pathway”. This study show that the sphingolipid metabolic pathway is the most significant, and the Enrichment Ratio can reach 0.31, which is much higher than other metabolic pathways. This indicates that the types and quantities of substances involved in sphingolipid metabolism are dominant in the detected metabolites.

Then the HPLC-MS method was used to quantitatively detect the accumulated metabolites in the SM metabolic pathway. It was found that 220 compounds were detected in the SM metabolic pathway in the positive ion mode (Table S12). The most abundant metabolites examined included SM, Cer, and C1P. As shown in Fig. 5A, the SM metabolites of the cells were classified based on OPLS-DA, and the OPLS-DA score plot showed significant separation between the treatment group samples. To better understand the differences between Rv3874 and Rv3875 in SM metabolic pathway-related products, this study plotted the cluster heat map of four groups of samples (24 samples in four groups, 6 samples in each group were averaged) in the positive ion mode (Fig. 5B). It was found that the Z4 had a greater impact on the SM metabolic pathway than the Z5. From the data point of view, both groups had an upward trend in the SM metabolic pathway-related products, but the proportion and degree of increase in the Z4 group were significantly higher than those in the Z5 group. Here we can also judge that the effect of the Z4 group on the SM metabolic pathway is higher than that of the Z5 group.

VIP values were extracted from the importance map of the OPLS-DA model to search for differentially accumulated metabolites in the Z4 and Z5 relative to the ZB. A total of 63 metabolites were screened according to VIP > 1, and 14 metabolites were screened according to VIP > 1.5, including 10 Cers, 1 C1P, 1 SMs, and 1 LCer. The results are shown in Figs. 5C and D (Table S13). Among the 220 lipids detected in the SM metabolic pathway, 191 substances in the Z4 and Z5 groups changed more than 1.5 times compared with the ZB group, indicating that Cer, C1P, and SM accumulated in cells. So this study focused on the SM metabolic pathway to observe the effect of the Z4 and Z5 on macrophage SM metabolism and how they further affect cell function.

5. Transcriptomics and metabonomics were combined to analyze the differences in sphingomyelin metabolic pathways.

A total of 258 genes were found to be involved in the SM metabolic pathway, of which 172 genes were detected in the transcriptome and 86 genes were not detected. Compared with the Z5 group, Rv3874 had a greater impact on SM metabolism in the Z4 group. Aloxe3, A3galt2, Bcl2, Ccn1, Naaa, Sgms2, and Map3k5 were the most up-regulated genes in the Z4 group, which mainly regulated sphingolipid metabolism, cell adhesion, and apoptosis. Naaa and Fut7 were significantly up-regulated in the Z5 group, which mainly regulated fatty acid metabolism and cell adhesion. The most down-regulated genes in the Z4 group were Fads3, Map2k1, Hexb, and Smpd5. It mainly regulates the biosynthesis of polyunsaturated fatty acids, signal regulation, and glycosphingolipid metabolism. In the Z5 group, Smpd5 was most significantly down-regulated, mainly regulating the hydrolysis of membrane SM to phosphorylcholine and Cer (Fig. 6A). The transcriptome trend was consistent with the metabolome law (only genes with a fold change greater than 1 were counted).

The obtained Z4 and Z5 differential genes related to SM metabolism and the lipid changes detected by the metabolome were integrated into the SM metabolism KEGG network (Figs. 6B and C). Up-regulation of sphingomyelin synthase 1 (Sgms1), ceramide kinase (Cerk), galactosylceramidase (Galc), and sphingosine-1-phosphate phosphatase 2 (Sgpp2) in Z4vsZB group was found in transcriptome differential gene data. While sphingosine kinase 1 (Sphk1), ceramide synthase 6 (Cers6), and UDP-glucosylceramide glucosyltransferase (Ugcg) were down-regulated, which favored the synthesis and accumulation of Cer, C1P, and SM, which was also confirmed in the metabolome data. Cer, C1P, and SM were also up-regulated in the Z5vsZB group, but the number of up-regulated genes and metabolites was far less than that in the Z4 group. It was concluded that Z4 treatment of macrophages resulted in reprogramming of macrophage lipid metabolism toward SM synthesis and C1P enrichment. Compared with the ZB group, the metabolic direction of the Z4 and Z5 groups changed significantly. The metabolic direction of the ZBvsZC group was significantly more inclined to the de novo synthesis pathway of SM, from serine and palmitoyl-Coa synthesis to sphingosine, sphingosine-1-phosphate synthesis and glycosphingolipid synthesis (Figure S1). However, the metabolic pathway of SM was reprogrammed in the Z4 and Z5 groups, which reduced the synthetic direction of Cer, C1P, and SM in the ZB group, and the change was the most obvious in the Z4 group, while the change was not significant in the Z5 group.

Cytoscape was used to draw the GO network of SM metabolism, sphingolipid catabolic pathway, Cer metabolism, and the DEGs of Z4vsZB and Z5vsZB groups in transcriptome data were introduced into the network to observe the enrichment of metabolic pathways. For the SM metabolic pathway and sphingolipid decomposition pathway (Figs. 7A and B), almost all of them were enriched, and the sphingosine catabolic process, Cer phosphoethanolamine decomposition process, and C1P catabolic process were not enriched. For the Cer metabolism process (Fig. 7C), only the glycoside biosynthesis process and the glucosylceramide positive/negative regulation process were not enriched, and the other processes were all involved.

6. Real-time quantitative polymerase chain reaction (RT-qPCR) validation of mRNA expression

To verify the accuracy of RNA-seq results and provide a basis for further study, some genes were selected from cell inflammation, phagosome maturation, and SM metabolic pathways for RT-qPCR analysis. The expression levels of these genes were confirmed by RT-qPCR and transcriptome sequencing. All validation results fully demonstrated the reliability and accuracy of transcriptome sequencing data.

From the RNA-seq data and RT-qPCR results, Macrophage phagocytic receptor CD63, phagolysosome maturation genes Rab5b and Rab7, phagolysosome acidification gene Atp6v1h, Lamp2, an MHC II-mediated foreign antigen presentation gene, and Pik3ap1, a signaling factor that links Toll-like receptor signaling to PI3K activation, showed a downward trend relative to the ZB group (Figs. 8A and B), indicating that macrophage-related immune functions were suppressed. It may provide favorable conditions for the survival of recombinant strains in macrophages. TLR pathway-related receptors TLR2/4 and nuclear transcription factor genes AP-1 and NFκB were increased, indicating that the TLR pathway was involved in recognition and response, and the expressed cytokines were involved in immune regulation and pathogen clearance (Figs. 8C and D). Apoptosis-related genes Bax, Caspase3, and Caspase7 were up-regulated in all groups, with the highest degree of apoptosis in the Z4 group (Figs. 8E and F). The results of RT-qPCR showed that Sgms2, Cerk, Galc, and Sphk2 were up-regulated. Ugcg and N-acylsphingosine amidohydrolase 1 (Asah1) were down-regulated, and the trend was consistent with the transcriptome and metabolic data (Figs. 8G and H). Cer, C1P, and SM tended to accumulate more in the Z4 group than in the Z5 group. Cer can regulate apoptosis through cathepsin-BID-Bax and ASK1-JNK/P38-Bax pathways, which also proves the reason why the expression of apoptosis-related genes is higher in the Z4 group.

The relationship between MTB and immune cells is complex and changeable. There are many mechanisms for MTB to escape the killing of immune cells. Studies have shown that MTB protein Rv0888 has powerful sphingomyelinase activity, which can cleave SM into Cer and phosphorylcholine providing essential nutrients for the long-term survival of MTB in the body [12], but the functions of Rv3874 and Rv3875 have not been reported. In this study, the effects of MTB virulence protein Rv3874 and Rv3875 on the metabolism of sphingolipids in macrophages were studied by metabolic process and transcriptome analysis. The results showed that rBCG-Rv3874 and rBCG-Rv3875 could induced the reprogramming of RAW264.7 sphingolipid metabolic, and may associated with phagocytosis, apoptosis and inflammatory processes of macrophages.

Granuloma often occurs in the lung tissue of tuberculosis patients. The disorder of lipid metabolism of macrophages often lead to the formation of foamy macrophages and finally transformed into granuloma [13]. The formation of foamy macrophages may also cause related complications such as atherosclerosis, which is partly due to the disorder of sphingolipid metabolism in the arterial wall and the increase of Cer and SM levels [14]. In this study, we found that Rv3874 and Rv3875 enriched SM in macrophages, which may also be the reason for the foam formation of macrophages.

It has been reported that the proteins related to the RD1 region of MTB constitute the ESX-1 type secretion system, in which Rv3874 and Rv3875 are secreted outside the cell as dimers with the assistance of other proteins in the RD1 region, and play an immune escape function as virulent proteins [15]. However, how Rv3874 and Rv3875 act on macrophages and alter their function is not well understood. Our study proposed that Rv3874 and Rv3875 might affect the function of macrophages and the immune evasion of MTB by changing their lipid metabolism reprogramming.

The cell membrane and organelle membrane of macrophage are rich in lipids, and lipid changes can cause changes in cell functions of macrophages[16]. Sphingolipids are a kind of very important lipids involved in a variety of cellular processes as well as the development and progression of many diseases. Sphingolipid metabolism is composed of many enzyme-controlled reactions. Cer is central to sphingolipid metabolism and is involved in the regulation of signal transduction processes [17]. What’s more, Cer plays an important role in the regulation of autophagy, cell differentiation, survival, and inflammatory responses [17, 18]. As is shown in our study, the Z4 group had the highest enrichment of Cer and the highest relative expression of apoptosis-related genes. In addition, increased Cer levels can promote autophagosome-lysosome fusion, but lysosomal surface V-ATPase is required for proton transport and acidification of intracellular compartments after phagocytosis-lysosome fusion, thereby digesting pathogens. In this study, although Cer levels were increased, lysosomal acidification and antigen presentation were inhibited to varying degrees, indicating the increased fusion of phagosomes and lysosomes did not directly change the immune process.

SM plays an important role in cell signal transduction through the interaction of catabolic metabolites with cholesterol and glycosphingolipids to form lipid rafts [19]. SM, known as cholesterol-binding molecules, plays an important role in cholesterol efflux from macrophages. Decreased levels of SM in the plasma membrane of macrophages increase the levels of ABCA1 and ABCG1 proteins on the surface of macrophages, thereby increasing cholesterol efflux and preventing the formation of foam cells caused by excessive accumulation of cholesterol in macrophages [20]. There was a negative correlation between cholesterol efflux and inflammation in macrophages, and the more cholesterol efflux, the less inflammation in macrophages. However, in this study, Z4 and Z5 groups were more inclined in the direction of SM synthesis, which might lead to cholesterol influx and excessive accumulation of SM, resulting in the formation of foamy cells, increasing apoptosis and inflammation, resulting in cell death.

This study shows that Rv3874 and Rv3875 can change the direction of sphingolipid metabolism in macrophages, leading to the enrichment of SM and Cer, resulting in changes in inflammation, and apoptosis and immune function of macrophages. This may also be one of the mechanisms of MTB causing lipid metabolism disorder and immune escape of macrophages in vivo. This study provides a new idea for revealing the potential molecular mechanism between the virulence proteins Rv3874 and Rv3875 of MTB and immune cells and also demonstrates the potential of metabolism-related enzymes as therapeutic targets for tuberculosis, that will lay a foundation for the screening of biomarkers for TB diagnosis [21, 22].

Mycobacterium tuberculosis (MTB) ; High performance liquid chromatography mass spectrometry (HPLC-MS) ; Regions of difference (RD) ; Pattern recognition receptors (PRRs) ; multiple reaction monitoring (MRM) ; principal component analysis (PCA) ; Orthogonal Partial Least Squares Discrimination Analysis (OPLS-DA) ; Differentially expressed genes (DEGs) ; Real-time quantitative PCR (RT-qPCR) ; Gene Ontology (GO) ; Kyoto Encyclopedia of Genes and Genomes (KEGG) ; biological process (BP) ; cellular component (CC) ; ceramide (Cer) ; sphingomyelin (SM) ; ceramide-1-phosphate (C1P) ;sphingomyelin synthase 1 (Sgms1) ; ceramide kinase (Cerk) ; galactosylceramidase (Galc) ; and sphingosine-1-phosphate phosphatase 2 (Sgpp2) ; sphingosine kinase 1 (Sphk1) ； ceramide synthase 6 (Cers6) ； and UDP-glucosylceramide glucosyltransferase (Ugcg); N-acylsphingosine amidohydrolase 1 (Asah1)

Funding

This work was supported by the Science & Technology Development Plan of Jilin Province [No. 20220101326JC].

Availability of data and materials

All data generated or analysed during this study are included in this published article and its supplementary information files.

Authors’ contributions

LZ ,LF ,PZ, and MQ conceived the study. PZ wrote the manuscript. PZ and MQ carried out sample preparation and data analyses.PZ and MQ carried out untargeted UPLC-MS metabolomic and metabolite attribution. PZ and MQ performed metabolite validation. PZ and MQ performed metabolomic data processing and statistical data analyses. All authors have given approval for the final version of the manuscript.

Ethics approval

Not applicable.

Competing interests

The authors declare no competing interests

Author details

¹Jilin Agricultural University, School of Life science, Changchun, China.

²Zaozhuang University, School of Food Science and Pharmaceutical Engineering, Zaozhuang, China

Meena LS. Rajni. Survival mechanisms of pathogenic Mycobacterium tuberculosis H37Rv.FEBS. J. 2010;277(11):2416–27.
Olson GS, Murray TA, Jahn AN, et al. Type I interferon decreases macrophage energy metabolism during mycobacterial infection. Cell Rep. 2021;35(9):109195.
Bae M, Cassilly CD, Liu X, et al. Akkermansia muciniphila phospholipid induces homeostatic immune responses. Nature. 2022;608(7921):168–73.
Sottero B, Testa G, Gamba P, et al. Macrophage polarization by potential nutraceutical compounds: A strategic approach to counteract inflammation in atherosclerosis. Free Radic Biol Med. 2022;181:251–69.
Pawełczyk J, Brzostek A, Minias A, et al. Cholesterol-dependent transcriptome remodeling reveals new insight into the contribution of cholesterol to Mycobacterium tuberculosis pathogenesis. Sci Rep. 2021;11(1):12396. Published 2021 Jun 11.
Genoula M, Marín Franco JL, Dupont M, et al. Formation of Foamy Macrophages by Tuberculous Pleural Effusions Is Triggered by the Interleukin-10/Signal Transducer and Activator of Transcription 3 Axis through ACAT Upregulation. Front Immunol. 2018;9:459.
Dodd CE, Pyle CJ, Glowinski R, Rajaram MV, Schlesinger LS. CD36-Mediated Uptake of Surfactant Lipids by Human Macrophages Promotes Intracellular Growth of Mycobacterium tuberculosis. J Immunol. 2016;197(12):4727–35.
Bosch M, Sánchez-Álvarez M, Fajardo A, et al. Mammalian lipid droplets are innate immune hubs integrating cell metabolism and host defense. Science. 2020;370(6514):eaay8085.
Laval T, Chaumont L, Demangel C. Not too fat to fight: The emerging role of macrophage fatty acid metabolism in immunity to Mycobacterium tuberculosis. Immunol Rev. 2021;301(1):84–97.
Olona A, Hateley C, Muralidharan S, Wenk MR, Torta F, Behmoaras J. Sphingolipid metabolism during Toll-like receptor 4 (TLR4)-mediated macrophage activation. Br J Pharmacol. 2021;178༈23༉:4575–87.
Renshaw PS, Panagiotidou P, Whelan A, et al. Conclusive evidence that the major T-cell antigens of the Mycobacterium tuberculosis complex ESAT-6 and CFP-10 form a tight, 1:1 complex and characterization of the structural properties of ESAT-6, CFP-10, and the ESAT-6*CFP-10 complex. Implications for pathogenesis and virulence. J Biol Chem. 2002;277(24):21598–603.
Speer A, Sun J, Danilchanka O, et al. Surface hydrolysis of sphingomyelin by the outer membrane protein Rv0888 supports replication of Mycobacterium tuberculosis in macrophages. Mol Microbiol. 2015;97(5):881–97.
Reichmann MT, Tezera LB, Vallejo AF, et al. Integrated transcriptomic analysis of human tuberculosis granulomas and a biomimetic model identifies therapeutic targets. J Clin Invest. 2021;131(15):e148136.
Edsfeldt A, Dunér P, Ståhlman M, et al. Sphingolipids Contribute to Human Atherosclerotic Plaque Inflammation. Arterioscler Thromb Vasc Biol. 2016;36(6):1132–40.
Tiwari S, Casey R, Goulding CW, Hingley-Wilson S, Jacobs WR Jr. Infect and Inject: How Mycobacterium tuberculosis Exploits Its Major Virulence-Associated Type VII Secretion System, ESX-1. Microbiol Spectr. 2019;7(3):10.1128/microbiolspec.BAI-0024-2019.
Yan J, Horng T. Lipid Metabolism in Regulation of Macrophage Functions. Trends Cell Biol. 2020;30(12):979–89.
Gomez-Larrauri A, Presa N, Dominguez-Herrera A, Ouro A, Trueba M, Gomez-Muñoz A. Role of bioactive sphingolipids in physiology and pathology. Essays Biochem. 2020;64(3):579–89.
Lai M, La Rocca V, Amato R, Freer G, Pistello M. Sphingolipid/Ceramide Pathways and Autophagy in the Onset and Progression of Melanoma: Novel Therapeutic Targets and Opportunities. Int J Mol Sci. 2019;20(14):3436.
Nicolson GL, Ferreira de Mattos G. A Brief Introduction to Some Aspects of the Fluid-Mosaic Model of Cell Membrane Structure and Its Importance in Membrane Lipid Replacement. Membranes (Basel). 2021;11༈12༉:947.
Jiang T, Ren K, Chen Q, et al. Leonurine Prevents Atherosclerosis Via Promoting the Expression of ABCA1 and ABCG1 in a Pparγ/Lxrα Signaling Pathway-Dependent Manner. Cell Physiol Biochem. 2017;43(4):1703–17.
Chandra P, He L, Zimmerman M, et al. Inhibition of Fatty Acid Oxidation Promotes Macrophage Control of Mycobacterium tuberculosis. mBio. 2020;11(4):e01139-20.
Chen JX, Han YS, Zhang SQ, et al. Novel therapeutic evaluation biomarkers of lipid metabolism targets in uncomplicated pulmonary tuberculosis patients. Signal Transduct Target Ther. 2021;6(1):22.

No competing interests reported.

Additionalfile.zip
Figure.S1. ZBvsZC Sphingolipid metabolism（mmu：00600） Table.S1. Primer sequences of differential expressed genes in Z4vsZ5 Table.S2. Summary of RNA-seq data and mapped reads for mRNA. Table.S3. Differential genes expression Table.S4. Z4vsZB.DEG_GO_enrichment_result_all Table.S5. Z4vsZB_all.DEG_KEGG_pathway_enrichment_result Table.S6. Z5vsZB.DEG_GO_enrichment_result_all Table.S7. Z5vsZB_all.DEG_KEGG_pathway_enrichment_result Table.S8. Non targeted qualitative analysis Table.S9. Detection of foreign bodies with metabolism by positive and negative ions Table.S10. Difference multiple of metabolites between Z4 and Z5 group Table.S11. VIP value of differential metabolite is greater than 1 Table.S12. Sphingomyelin metabolites were detected by positive ions Table.S13. Sphingomyelin metabolic pathway difference metabolite VIP value is greater than 1

Download PDF

Version 1

posted

You are reading this latest preprint version

Transcriptome and metabolome profiling reveal the effect of MTB virulence protein Rv3874 and Rv3875 on macrophage Sphingolipid metabolism

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

Introduction

Materials And Methods

Construction of recombinant BCG strains

Sample grouping and processing method

Macrophage infection experiment

Preparation and analysis of transcriptome cell samples

Preparation and Analysis of Metabolome Cell Samples

Combined transcriptome and metabolome analysis

Real-time qPCR

Statistical analysis

Results

1. Construction and characterization of recombinant BCG

2. Quality control of RNA-seq reads

3. Differentially expressed genes of rBCG-Rv3874 and rBCG-Rv3875

4. Differential accumulation of metabolites between rBCG-Rv3874 and rBCG-Rv3875

5. Transcriptomics and metabonomics were combined to analyze the differences in sphingomyelin metabolic pathways.

6. Real-time quantitative polymerase chain reaction (RT-qPCR) validation of mRNA expression

Discussion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1