Comparative analysis of macrophage transcriptomes reveals a key mechanism of the immunomodulatory activity of Tricholoma matsutake polysaccharide

  • Authors:
    • Xiang Ding
    • Jian Li
    • Yiling Hou
    • Wanru Hou
  • View Affiliations

  • Published online on: May 18, 2016     https://doi.org/10.3892/or.2016.4814
  • Pages: 503-513
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Abstract

In the present study, we performed a proliferation assay, phagocytosis assay and cell cycle analysis of macrophages and sequenced the transcriptomes of control group macrophages and TMP-A group macrophages using Illumina sequencing technology to identify differentially expressed genes (DEGs) and determine the molecular mechanisms associated with differences in the immunomodulatory activity of TMP-A in macrophages. The results showed that TMP-A exhibits strong proliferation activity and phagocytosis activity in RAW264.7 cells in vitro and could also promote the proliferation of macrophage cells by abolishing cell-cycle arrest in the G0/G1 phase and promoting the cell cycle in the G2/M phase, which may induce cell division. A total of 12,616,096 and 11,798,839 bp paired-end reads were obtained for the control group and TMP-A group, respectively, and they corresponded to a total size of 12.5 G bp and 11.7 G bp, respectively, after the low-quality reads and adapter sequences were removed. Approximately 79.8% of the total number of genes (10,191) were expressed (RPKM ≥1), and more than 1,372 genes were highly expressed (RPKM >60) in the TMP-A group. A total of 1,043 unigenes were identified as DEGs, and approximately 486 genes were upregulated, whereas 557 genes were down-regulated, which might have contributed to the proliferation activity and phagocytosis activity of TMP-A in the RAW264.7 cells in vitro. A Gene Ontology (GO) enrichment analysis generated 13,042 assignments to cellular components, 13,094 assignments to biological processes, and 13,135 assignments to molecular functions. A KEGG pathway enrichment analysis showed that the MAPK and NF-κB signaling pathways are significantly enriched for DEGs between the two cell groups. Based on the experimental data, we believe that the significant antitumor activities of TMP-A in vivo involve the MAPK and NF-κB signaling pathways because the two signaling pathways intersect.

Introduction

The efficacy of chemotherapeutic treatments for the majority of cancer types has improved in the last three decades, although the highly toxic effects of chemotherapeutic drugs still cause severe reductions in quality of life that present serious problems in clinical medicine (1). Therefore, developing effective low-toxicity anticancer drugs, including those based on natural products, is important. In recent years, polysaccharides from natural sources have received increasing attention as an efficient herbal medicine for the prevention and treatment of cancer because of their antitumor and immunomodulatory activities and low toxicity (2,3). The antitumor properties are generally related to their ability to induce tumor cell apoptosis and activate macrophages (4).

Macrophages occupy a unique position in the immune system because they can initiate natural immune responses and then act as effector cells that help manage immune responses (5,6), such as inflammation, angiogenesis and fighting an infection. Macrophages can eliminate the advanced stage of tumors because of their powerful functions, including phagocytosis and the release of numerous proinflammatory cytokines [interleukin (IL) and tumor necrosis factor (TNF)] and cytotoxic and inflammatory molecules [nitric oxide (NO) and reactive oxygen species (ROS)] that contribute to direct and/or indirect antitumor activities (79).

Recently, polysaccharides obtained from microorganisms, fungi and plants have become regarded as the most effective immune-regulating substances, and they have been shown to be clinically effective. Polysaccharides have anti-inflammatory, antihypoglycemic, antibacterial, and antitumor activities, and the basic mechanisms underlying the therapeutic effects of fungal polysaccharides, including their antitumor and immunostimulatory activities, likely occur through the modulation and stimulation of the complement system via macrophages (9). Since the discovery that Letinan, a polysaccharide from Lentinus edodes (Berk.) Sing, inhibited mouse sarcoma 180 and displayed low toxicity compared with chemical antitumor drugs (10), a number of polysaccharides with immunostimulatory and antitumor activities from species such as Coriolus versicolor, Agaricus blazei and Panax ginseng have been reported (1113).

Tricholoma matsutake is a fungus belonging to the subgenus Tricholoma. As a traditionally edible fungus in Asian countries, particularly China, Japan and South Korea, Tricholoma matsutake has been used for the prevention and treatment of disease for several thousand years (1416). Our group recently isolated a novel polysaccharide from Tricholoma matsutake named TMP-A, which has a backbone of 1,4-β-glucopyranose that branches at O-6, is composed of an (1➝3)-α-galactopyranose residue and terminates with an α-xylopyranose residue (17). TMP-A also exhibits significant antitumor activities in vivo. However, the immunomodulatory activity and mechanism of TMP-A remain unclear. Here, we performed a proliferation assay, phagocytosis assay and cell cycle analysis of macrophages and sequenced the transcriptomes of macrophages of a control group and TMP-A group using Illumina sequencing technology. The goal of the present study was to identify differentially expressed genes (DEGs) in macrophages between the control group and TMP-A group to help determine the molecular mechanisms underlying the immunomodulatory activity of TMP-A in macrophages.

Materials and methods

Materials

The reagent 2-(2-methoxy-4-nitrophenyl-)3-(4-nitrophenyl)-5-(2,4-disulfonic acid benzene)-2H-tetrazolium monosodium salt (CCK-8) was purchased from Dojindo Molecular Technologies, Inc. (Tokyo, Japan); lipopolysaccharide (LPS), D-Hanks solution, RPMI-1640 medium, fetal calf serum (FCS) and dimethyl sulfoxide (DMSO) were purchased from Gibco (Grand Island, NY, USA). Penicillin G and streptomycin were purchased from Sigma (Shanghai, China). All other chemicals and solvents were of analytical grade, and TMP-A was prepared in our laboratory as previously described (17).

Cell lines and reagents

The RAW264.7 cell line was cultured in RPMI-1640 medium containing 10% fetal bovine serum (FBS), 1% penicillin (100 IU/ml) and streptomycin (100 mg/l) in a humidified atmosphere with 5% CO2 at 37°C before use.

RAW264.7 cell proliferation assay

The cytotoxic effects of TMP-A on the RAW264.7 cells were determined by the CCK-8-based colorimetric method. Briefly, RAW264.7 cells suspended in RPMI-1640 medium at a density of 1×105 cells/ml were pipetted into a 96-well plate (100 µl/well) and inoculated at 37°C in a humidified 5% CO2 atmosphere. After incubation for 24 h, 100 µl of the test sample at different concentrations was separately added into each well and incubated at 37°C in a humidified 5% CO2 atmosphere for 48 h. RPMI-1640 medium and 10 µg/ml LPS were used as the negative and positive controls, respectively. Subsequently, 20 µl of CCK-8 reagent was added to each well, and the plate was further incubated for another 1–4 h. The absorbance of the colored solution at 490 nm was measured on a 96-well microplate reader (Bio-Rad Laboratories, Tokyo, Japan). All of the experiments were performed in triplicate, and the inhibitory rate was calculated as follows: Cell proliferation activity (%) = [A2−A0]/[A1−A0] × 100 where A2 is the average optical density of TMP-A-treated cells, A0 is the average optical density of the control wells (culture medium without cells), and A1 is the average optical density of the negative control (culture medium containing cells). Each value is presented as the mean ± SD (n=4); *P<0.05 and **P<0.01 (vs. control).

RAW264.7 cell phagocytosis assay

RAW264.7 cells were inoculated in the presence of varying concentrations of TMP-A as described above. RPMI-1640 medium and LPS were used as the negative and positive controls, respectively. After 24 h, the supernatants were removed, 100 µl of 0.075% neutral red solution was added to each well, and the cells were cultured for an additional 1 h. The plate was then washed three times with phosphate-buffered saline (PBS) and patted gently with tissues to allow the plates to drain. Finally, 100 µl of cell lysis buffer (0.1 mol/l acetic acid and ethanol in a 1:1 ratio) was added to each well at 4°C for 2 h. The absorbance at 540 nm was determined using a microplate ELISA reader. All of the analyses were conducted in triplicate. Each value is presented as the mean ± SD (n=4); *P<0.05 and **P<0.01 (vs. control).

RAW264.7 cell cycle analysis by flow cytometry

The effect of TMP-A on the cell cycle distribution was assessed by flow cytometry after staining the cells with propidium iodide (PI). RAW264.7 cells were seeded in 6-well plates (5×105 cells/well) and allowed to grow for one day before being exposed to TMP-A (1, 5 or 10 µg/ml) for 72 h. After incubation, the treated cells were harvested, washed twice with PBS and fixed in cold 70% ethanol for 4 h or overnight at 4°C. After an additional wash in cold PBS, the cells were resuspended in 0.5 ml of staining buffer containing 10 µl of RNase and 25 µl of PI, then incubated for 30 min in the dark at 37 °C. The DNA content of the cells was measured using a flow cytometer (Becton-Dickinson, Franklin Lakes, NJ, USA), and the population of cells in each phase was calculated using the ModFit LT software program. Each experiment was conducted three times.

RNA extraction, library preparation and sequencing

TRIzol reagent (Invitrogen, Burlington, ON, Canada) was used to extract the total RNA, and 1% agarose gels were used to investigate the RNA contamination and degradation. RNA purity was detected on a NanoPhotometer spectrophotometer (Implen, Inc., Westlake Village, CA, USA). After examining the RNA purity and concentration, the RNA 6000 Nano Assay kit with NanoDrop 2000 (Thermo Scientific NanoDrop 2000c) was used to assess the RNA integrity. A total of 3 µg of RNA per sample was used for the RNA sample preparations as input material (18). Following the manufacturer's recommendations, the transcriptome libraries were generated using the Illumina TruSeq™ RNA Sample Preparation kit (Illumina, San Diego, CA, USA). Clustering of the index-coded samples was completed using the TruSeq PE Cluster kit v3-cBot-HS (Illumina) on a cBot Cluster Generation System. The libraries were sequenced on an Illumina HiSeq 2000 platform after clustering, and 100 bp paired-end reads were generated (18).

Transcriptome data analysis

In-house Perl scripts were used to process the raw data in FASTQ format to remove low quality reads, which contained poly-N stretches (partially un-sequenced regions) and adapter sequences. All of the downstream analyses are based on the high-quality clean sequences.

Differential expression and quantification analysis of the transcripts

Prior to performing the differential gene expression analysis, the read counts were adjusted using an edgeR program package for each sequenced library through one scaling normalized factor. The reads per kilobase per million reads (RPKM) method was used to quantify the transcript expression, and HTSeq v. 0.5.3 was used to count the number of reads mapped to each transcript. The RPKM value was calculated based on the mapped transcript fragments, sequencing depth and transcript length (18). The edgeR Bioconductor was used to complete the read counts with one scaling normalized factor before the analysis of differential gene expression, which was completed using the DEGSeq R package, release 1.12.0. A log2-fold change of ±1 and a P-value of 0.005 were set as the threshold of statistically significant differential expression. A large fold-change value (|log2-fold-change| >5) was also used to identify DEGs.

GO annotation and GO/KEGG enrichment analyses

The protein functions of all of the genes were annotated using BLASTX and InterProScan against the NCBI database. The resulting BLAST and InterPro annotations were then converted into Gene Ontology (GO) annotations. All of the GO terms were mapped to the GO slim categories. Fisher's exact test within Blast2GO [false discovery rate (FDR) <0.05] was used to determine the statistical significance of the functional GO slim enrichment. A hyper geometric test and the Benjamini-Hochberg FDR correction were used to identify significantly enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways with KOBAS 2.0 (18).

Results

Proliferation of RAW264.7 cells following TMP-A treatment in vitro

The cytotoxicity or stimulation of TMP-A on RAW264.7 cells is shown in Fig. 1A and B. The cell proliferation activity was lowest when the macrophages were exposed to medium alone, whereas the incubation of these cells with increasing concentrations of TMP-A showed a dose-dependent increase in cell proliferation. The highest concentration of TMP-A significantly promoted RAW264.7 cell proliferation compared with the control group (0.3125 µg/ml, P<0.05; 0.625–10 µg/ml, P<0.01). Furthermore, the cell proliferation activity at a concentration of 10 µg/ml TMP-A was even greater than the activity elicited by 10 µg/ml LPS.

Phagocytosis activity of RAW264.7 cells following TMP-A treatment in vitro

The most striking feature of macrophage activation is the increase in pinocytic activity. The pinocytic activity of RAW264.7 cells following TMP-A treatment was examined by neutral red uptake activity (0.075%). As shown in Fig. 1C, after 24-h incubation with varying concentrations of TMP-A, the phagocytosis activity of RAW264.7 was enhanced by TMP-A in the tested dose range in a dose-dependent manner compared with the negative control. Furthermore, the pinocytic activity at 5–10 µg/ml TMP-A was comparable to or even greater than the activity elicited by 10 µg/ml LPS, a positive control.

Effects of TMP-A on the cell cycle distribution of RAW264.7 cells

To examine the effects of TMP-A on cell cycle progression, a cell cycle analysis was performed on RAW264.7 cells using flow cytometry. Fig. 2 shows the effects of TMP-A on the cell cycle phase (G0/G1, S and G2/M) distribution of RAW264.7 cells using flow cytometry with PI staining. The treatment of RAW264.7 cells with TMP-A at 2.5, 5 and 10 µg/ml for 72 h induced a significant and concentration-dependent increase in the G2/M phase population from 17.1% of the control group to 15.3, 16.6 and 16.8%, respectively (P<0.05 or P<0.01), with a concomitant decrease in the percentage of cells in the G0/G1 phase from 67.4% of the control group to 61.6, 61.0 and 59.6%, respectively. At the tested concentrations, TMP-A also induced a significant change in the S phase population, from 4.7% of the control group to 13.0, 10.2 and 11.1%, respectively. These results suggested that TMP-A could promote the proliferation of macrophage cells by abolishing cell cycle arrests in the G0/G1 and G2/M phases and promoting cell cycle progression in S phase, which might induce cell division.

Transcriptome sequencing and de novo assembly

To explore differences in the RAW264.7 cell transcriptomes between the control group and the TMP-A group, two cell groups were selected for analysis. Two cDNA libraries were constructed with the respective total RNA from the control group and TMP-A group. The prepared libraries were sequenced on an Illumina HiSeq 2000 platform. After quality control, a total of 12,616,096 and 11,798,839 bp paired-end reads were obtained for the control and TMP-A groups, respectively, which corresponded to a total size of 12.5 G bp and 11.7 G bp, respectively, after the low-quality reads and adapter sequences were removed (Table I and Fig. 3). We mapped the clean reads to the RAW264.7 cell reference genome. The proportion of total reads in the two RAW264.7 cell transcriptome libraries that mapped to the genome ranged from 41.04 to 43.29%. A sequencing saturation analysis showed that the number of genes detected by the library was saturated (Fig. 4). A 5′-3′ sequence preference statistical analysis showed that the sequencing was mainly focused on the gene body region, and the bias at the two ends was limited (Fig. 5). The distribution of gene coverage is shown in Fig. 6 and provides a good basis for the follow-up analysis.

Table I

Summary of the mapping results (mapping to reference genome).

Table I

Summary of the mapping results (mapping to reference genome).

Sample IDTotal readsTotal base pairsTotal mapped readsPerfect match≤2 bp mismatchUnique matchMulti-position matchTotal unmapped reads
Control 12,498,414
(100.00%)
612,422,286
(100.00%)
5,129,004
(41.04%)
985,094
(7.88%)
4,143,910
(33.16%)
3,166,326
(25.33%)
1,962,678
(15.70%)
7,369,410
(58.96%)
TMP-A 11,665,609
(100.00%)
571,614,841
(100.00%)
5,050,408
(43.29%)
944,319
(8.09%)
4,106,089
(35.20%)
3,045,421
(26.11%)
2,004,987
(17.19%)
6,615,201
(56.71%)
Transcriptome profiles of the two RAW264.7 cell groups

The abundance of all the genes was calculated and normalized using uniquely mapped reads by the RPKM method. The distribution of the expression levels of all the genes was similar for the two groups. Genes with RPKMs over 60 were considered to be expressed at a high level, whereas genes with RPKMs in the interval 0-1 were considered to be expressed at low levels or not at all. The results showed that in the control group, ~81.8% of the total number of genes (10,038) were expressed (RPKM ≥1) and more than 1,333 genes were highly expressed (RPKM >60), whereas in the TMP-A group, ~79.8% of the total number of genes (10,191) were expressed (RPKM ≥1) and >1,372 genes were highly expressed (RPKM >60).

The results also showed that eight genes (Eef1α1, Fth1, Rpl23, Rps24, Rps6, Tpt1, Rpl5 and Rplp2) were extremely highly expressed (RPKM >10,000) in the control group (Table II), whereas seven genes (Fth1, Eef1α1, Rps24, Rpl23, Rps6, Rplp2 and Tpt1) were extremely highly expressed (RPKM >10,000) in the TMP-A group (Table III). It is worth noting that the RPKM of the Fth1 gene was 16,044 in the control group and 78,552 in the TMP-A group. The Fth1 gene encodes the heavy subunit of ferritin, which is the major intracellular iron storage protein in eukaryotes and composed of 24 subunits of light and heavy ferritin chains. Changes in the ferritin subunit composition may affect iron absorption and release in different tissues. One of the major functions of ferritin is iron storage in a non-toxic and soluble state (19,20). These results are consistent with the proliferation and phagocytosis activities of RAW264.7 cells following TMP-A treatment in vitro.

Table II

Quantification of gene expression in the control group (RPKM >10,000).

Table II

Quantification of gene expression in the control group (RPKM >10,000).

Gene IDUniq_reads _numLengthCoverageRPKMSymbolDescriptionKEGG orthology
171361175276173797.64%27073.88Eef1a1Eukaryotic translation elongation factor 1 α 1K03231
253194951582899.88%16044.80Fth1Ferritin, heavy polypeptide 1K00522
8176340233106992.33%10097.94Rpl5Ribosomal protein L5K02932
293043920280182.90%13131.18Rps6Ribosomal protein S6K02991
1166463532379495.59%11936.17Tpt1Tumor protein, translationally-controlled 1
292822957051889.38%15316.15Rpl23Ribosomal protein L23K02894
817762475846693.56%14254.68248Rps24Ribosomal protein S24K02974
14061917945386.31%11359.41Rplp2Ribosomal protein, large P2K02943

Table III

Quantification of gene expression in the TMP-A group (RPKM > 10,000).

Table III

Quantification of gene expression in the TMP-A group (RPKM > 10,000).

Gene IDUniq_reads_numLengthCoverageRPKMSymbolDescriptionKEGG orthology
2531923726882899.88%78552.08078Fth1Ferritin, heavy polypeptide 1K00522
171361143447173797.58%22638.13Eef1a1 Eukaryotic translation elongation factor 1 α 1K03231
293043419680181.52%11702.85Rps6Ribosomal protein S6K02991
1166462991779495.59%10328.72Tpt1Tumor protein, translationally-controlled 1
292822791251890.15%14771.01Rpl23Ribosomal protein L23K02894
817762604146693.56%15318.65Rps24Ribosomal protein S24K02974
1406621729545386.31%10465.77Rplp2Ribosomal protein, large P2K02943
Differentially expressed genes between the control and TMP-A groups

The reads were adjusted using the edgeR program with one scaling normalized factor, and the DEGs between the two cell groups were identified using the DEGSeq R package. Values of FDR ≤0.001 and |log2 ratio| ≥1 were set as the thresholds for significant differential expression. Our research group performed hierarchical clustering for all of the DEGs based on the log10 RPKMs of the two cells groups to observe the gene expression patterns (Fig. 7). A total of 1,043 unigenes were identified as DEGs, and ~486 genes were upregulated, whereas 557 genes were downregulated (Fig. 7), which might have contributed to the proliferation and phagocytosis activities of RAW264.7 cells following TMP-A treatment in vitro. The numbers of DEGs in the control vs. TMP-A were 316 for transcripts detected with |log2-fold-change| >2 and 35 for transcripts detected with |log2-fold-change| >5. Among the DEGs within the |log2-fold-change| >5 threshold, 24 genes were upregulated, including Ifi44, Ifit1, Ifit3, Il13rα2 and Il1α, among others, whereas 11 genes were downregulated, including RT1-Da, RT1-Db2 and C1qa, among others (Tables IV and Table V).

Table IV

Differentially expressed genes: upregulated (|log2-fold-change| >5).

Table IV

Differentially expressed genes: upregulated (|log2-fold-change| >5).

Gene IDGene_lengthlog2 ratio (TMP-A/control)SymbolDescriptionKEGG orthology
17049687613.15758Lcn2Lipocalin 2K01830; K03999
2875618079.84101Ccl7Chemokine (C-C motif) ligand 7K05509
5011626459.109831MregMelanoregulin
31096929228.814769Ifi44Interferon-induced protein 44
2459937938.784161Nos2Nitric oxide synthase 2, inducibleK13241
2444323678.743156HdcHistidine decarboxylaseK01590
30547539528.676823Osbp2Oxysterol binding protein 2K08283
30506441658.446367Ptpn14Protein tyrosine phosphatase, non-receptor type 14K01104
5682421748.411298Ifit1Interferon-induced protein with tetratricopeptide repeats 1K14217
36058013718.321534Dusp14Dual specificity phosphatase 14K04459
11399223517.935806Ugt1a6UDP glucuronosyltransferase 1 family, polypeptide A6K00699
17109116927.924924Zbp1Z-DNA binding protein 1K12965
6505417407.678115Aqp9Aquaporin 9K09877
30952620537.64592Ifit3Interferon-induced protein with tetratricopeptide repeats 3K14217
17106019157.539858Il13ra2Interleukin 13 receptor, α 2K05077
29291223557.241475Rasip1Ras interacting protein 1K05702
6519036287.165516Rsad2Radical S-adenosyl methionine domain containing 2K15045
36046831547.11958Emp2Epithelial membrane protein 2K08341
817805707.086246Ccl5Chemokine (C-C motif) ligand 5K12499
29362419857.010151Irf7Interferon regulatory factor 7K09447
7925330146.190835AvilAdvillinK08017
29869815706.075358Angptl6Angiopoietin-like 6K10104; K05467
6438741145.180711Ccdc80Coiled-coil domain containing 80
2449319925.045914Il1aInterleukin 1 αK04383

Table V

Differentially expressed genes: downregulated (|log2-fold-change| >5).

Table V

Differentially expressed genes: downregulated (|log2-fold-change| >5).

Gene IDGene_lengthlog2 ratio (TMP-A/control)SymbolDescriptionKEGG orthology
2942691212−11.7903RT1-DaRT1 class II, locus DaK06752
3626341060−9.05405C1qc Complement component 1, q subcomponent, C chainK03988
5543531299−8.76072Gpr34G protein-coupled receptor 34K08383
249811134−8.7343RT1-Db2RT1 class II, locus Db2K06752
3096211145−7.87593RT1-BaRT1 class II, locus BaK06752
244994529−7.5208Il6rInterleukin 6 receptorK05055
2952832887−7.30369Ankrd34aAnkyrin repeat domain 34A
1710561318−6.26366Cx3cr1Chemokine (C-X3-C motif) receptor 1K04192
2913275091−6.09829Mrc1Mannose receptor, C type 1K06560
3610862886−5.58375ScelSciellinK06084
2985661025−5.36135C1qaComplement component 1, q subcomponent, A chainK03986

IL-1 is involved in the regulation of immune response, inflammatory response and hematopoietic function. Interferons (IFNs) are a set of signals released by the host cells in response to pathogen release proteins, such as those from bacteria, parasites, viruses or tumor cells. IFNs belong to a category of proteins called cytokines, which protect and promote the immune system and help eliminate pathogens (21,22).

IFNs also have a variety of other functions, including the activation of immune cells, such as macrophages and the regulation of the immune system. Both of these functions are important against antiviral infection. By interacting with specific receptors, IFNs activate signal transducer and activator of transcription (STAT) complexes. STATs are a family of transcription factors that regulate the expression of certain immune system genes. Type I IFNs further activate p38 mitogen-activated protein kinase to promote gene transcription (23). The antiproliferative and antiviral effects of type I IFNs are derived from p38 mitogen-activated protein kinase (MAPK) signaling. The phosphatidylinositol 3-kinase signaling pathway is also regulated by type I and II IFNs (24,25). Based on experimental data and the results from our previous study, we believe that the significant antitumor activities of TMP-A in vivo may involve the MAPK signaling pathway of macrophages.

GO and KEGG enrichment analyses of the differentially expressed genes

GO analyses were used to confirm the functional classifications of the annotated unigenes and classify the transcripts with known proteins. A total of 39,271 genes were annotated with GO terms, which were converted to generic GO slim terms. The GO enrichment analysis was performed using Fisher's exact test in Blast2GO to analyze the gene functions of the DEGs. The analysis generated 13,042 assignments to cellular components, 13,094 assignments to biological processes, and 13,135 assignments to molecular functions. In the category of cellular components (Table III), 98.40 and 98.40% of the unigenes were located in cell parts (GO:0044464) and cells (GO:0005623), respectively. Most of the biological process categories were related to cellular processes (GO:0009987, 77.70%) and metabolic processes (GO:0008152, 58.00%). Under the molecular functions, the majority of the GO terms were grouped into binding (GO:0005488, 86.60%) and catalytic activity (GO:0008152, 45.20%) (Table VI). These results suggested that the immune mechanisms may present additional differences between the control group and TMP-A group.

Table VI

Gene Ontology enrichment analysis of the DEGs.

Table VI

Gene Ontology enrichment analysis of the DEGs.

Gene Ontology termCluster frequencyGenome frequency of useCorrected P-value
Cell
GO:0044464887 out of 901 genes, 98.4%12602 out of 13042 genes, 96.6%0.08999
Cell part
GO:0005623887 out of 901 genes, 98.4%12602 out of 13042 genes, 96.6%0.08999
Cellular process
GO:0009987682 out of 878 genes, 77.7%8844 out of 13094 genes, 67.5%8.64e-09
Metabolic process
GO:0008152509 out of 878 genes, 58.0%6505 out of 13094 genes, 49.7%0.00035
Binding
GO:0005488753 out of 870 genes, 86.6%9931 out of 13135 genes, 75.6%4.17e-14
Catalytic activity
GO:0008152393 out of 870 genes, 45.2%4776 out of 13135 genes, 36.4%7.59e-06

The pathway analysis was conducted using the KEGG pathway database to further understand the biological function of the gene products. The KEGG pathway enrichment analysis was performed using KOBAS (KEGG Orthology Based Annotation System, v2.0). A KEGG analysis records the molecular interaction networks in cells with variants that are specific to particular organisms. We found that the MAPK signaling pathway (45 DEGs with pathway annotation: 5.10%) (Fig. 8A) and the NF-κB signaling pathway (20 DEGs with pathway annotation: 2.27%) (Fig. 8B) are significantly enriched in the DEGs between the two cell groups (Table VII). This result also supported our previous hypothesis that the significant antitumor activities of TMP-A in vivo might involve the MAPK signaling pathway of macrophages and might also include the NF-κB signaling pathway because there are intersections between the two signaling pathways.

Table VII

KEGG pathway enrichment analysis of the DEGs.

Table VII

KEGG pathway enrichment analysis of the DEGs.

PathwayDEGs with pathway annotation (883)All genes with pathway annotation (13697)P-valueQ-valuePathway ID
MAPK signaling pathway45 (5.1%)357 (2.61%)1.150435e-051.794679e-04ko04010
NF-κB signaling pathway20 (2.27%)144 (1.05%)0.00092557786.016256e-03ko04064
Cell cycle34 (3.85%)150 (1.1%)7.470169e-115.826732e-09ko04110

It is worth noting that the cell cycle between the two cell groups (34 DEGs with pathway annotation: 3.85%) is also significantly enriched for DEGs. These results indicated that TMP-A could promote the proliferation of macrophage cells by abolishing cell cycle arrest in the G0/G1 phase and promoting cell cycle progression in the G2/M phase, which might induce cell division.

Discussion

High-throughput and low-cost NGS technologies, such as RNA-Seq, have become popular and useful not only for de novo genome assembly and genome diversity studies but also to investigate gene expression profiles and discover pharmacological activity mechanisms. In our previous studies, the polysaccharide TMP-A exhibited significant antitumor activities in vivo. The inhibitory rate in mice treated with 80 mg/kg TMP-A reached 68.422%, which might be comparable to the effects of mannatide. However, the immunomodulatory activity and mechanism of TMP-A remain unclear. Here, we performed a proliferation assay, phagocytosis assay and cell cycle analysis of macrophages, and the results showed that TMP-A exhibits strong proliferation and phagocytosis activities on RAW264.7 cells in vitro and could also promote the proliferation of macrophage cells by abolishing cell cycle arrest in the G0/G1 and G2/M phases and promoting cell cycle progression in S phase, which might induce cell division. To determine the mechanisms underlying the TMP-A effects on RAW264.7 cells and antitumor and immune activity, we sequenced the transcriptomes of macrophages of the control group and the TMP-A group using Illumina sequencing technology.

Our analysis identified 45 DEGs in the MAPK signaling pathway, 25 of which were upregulated in the TMP-A group (Fig. 8A), including EGF [K04357: 294559 (2.8)], c-Myc [K04377: 24577 (4.9)], IL-1 [K04383: 24493 (5.0); K04519: 24494 (3.5)] and TNF [K03156: 24835 (1.1)], whereas 20 were downregulated, including c-JUN [K03283: 24468 (−1.0)] and p38 [K04441: 81649 (−1.6)]. The MAPK signaling cascade is a common signal transduction module that connects different receptors/sensors to nuclear and cellular responses. The classical MAPK signaling cascade consists of three types of phosphorylated kinases: MAPK, MAPK kinase (MAPKK/MEK), and MAPK kinase kinase (MAPKKK/MEKK) (25). In the MAPK signaling pathway, EGF acts by binding to epidermal growth factor receptors (EGFRs) on the cell surface, which leads to cell proliferation, differentiation and survival. This process stimulates ligand dimerization and starts a signal transduction cascade reaction that results in a series of biochemical changes in cells as well as increased intracellular calcium levels, glycolysis and protein synthesis, with these changes eventually causing cell proliferation and DNA synthesis (26). This process would adequately explain the mechanism of the TMP-A proliferation activity on macrophages.

The IL-1 family is produced by macrophages, fibroblasts, monocytes, and these proteins play a significant role in the regulation of inflammatory and immune responses to infections. Thus, the upregulated DEGs in the MAPK signaling pathway in the TMP-A group might be associated with the high immunomodulatory activity of TMP-A on RAW264.7 cells in vitro.

Moreover, of the 20 DEGs in the NF-κB signaling pathway (Fig. 8B), 17 were upregulated in the TMP-A group, including IL-1β [K04519: 24494 (3.5)], TNFα [K03156: 24835 (1.1)], TRAF6 [K03175: 114635 (1.0)] and COX2 [K11987: 29527 (2.2)], whereas only 3 were downregulated, including LTB [K03157: 361795 (−1.2)]. The NF-κB family plays important roles in the immune system by regulating the expression of cytokines, inducible nitric oxide synthase (iNOS), cyclooxygenase 2 (COX-2) and growth factors. Under normal circumstances, the activation of NF-κB occurs because of the release of IκB molecules (27). In the classical activation pathway, signaling occurs by tumor necrosis factor receptor (TNFR), interleukin-1 receptor (IL-1R) and Toll-like receptors (TLRs). TNFα and IL-1β are the classic signaling molecules that can activate the IκB kinase complex (28), which binds to other components and interacts with upstream signaling kinases. A number of stimuli can be produced by activating the IKK complex through different mechanisms, such as the phosphorylation of IKKs by upstream kinases or through the self-activation of IKK-dimers by mutual phosphorylation (29). IL-1β is a member of the IL-1 cytokine family produced by macrophages, and it is an important mediator involved in a variety of cellular activities, including cell differentiation, cell proliferation and cell apoptosis (30).

TNF (which is also known as cachectin or TNFα) is a cytokine with a wide variety of functions and is involved in the cytolysis of certain tumor cell lines, the induction of cachexia, and onset of fever by direct action or by stimulating IL-1 secretion. The upregulation of TNF genes in both the MAPK signaling pathway and the NF-κB signaling pathway indicate that both pathways can be initiated (31,32). First, in the activation of NF-κB, TRAF2 recruits the protein kinase IKK, which is activated by serine-threonine kinase (33). IκBα is an inhibitory protein that binds to NF-κB and prevents its translocation, and it is phosphorylated by IKK and degraded to release NF-κB. NF-κB translocates into the nucleus and mediates the transcription of proteins involved in cell proliferation and survival (34). Second, during MAPK activation, TNF induces strong JNK activation and elicits p38-MAPK responses, which is important for ERK activation. TRAF2/Rac activates the upstream kinases of MEKK1 and MLK2/MLK3 induced by JNK (35), and then JNK translocates to the nucleus and activates transcription factors such as c-Jun and ATF2 (36,37). The JNK pathway is also involved in cell differentiation, cell proliferation, and cell apoptosis. Thus, the upregulated DEGs in the TMP-A group might be associated with the strong effects of TMP-A on the proliferation activity, phagocytosis activity and cell cycle distribution of RAW264.7 cells in vitro, and these results can adequately explain the mechanism underlying the significant antitumor activities of TMP-A in the immune system.

In conclusion, we performed a proliferation assay, phagocytosis assay and cell cycle analysis of macrophages. Low cell proliferation activity and phagocytosis activity were observed when the macrophages were exposed to the medium alone, whereas a dose-dependent increase in cell proliferation activity and phagocytosis activity was observed after the cells were incubated with increasing concentrations of TMP-A. The cell proliferation activity induced by TMP-A was also time-dependent. The cell cycle analysis indicated that TMP-A could promote the proliferation of macrophage cells by abolishing cell cycle arrest in the G0/G1 and G2/M phases and promoting cell cycle proliferation in S phase, which might induce cell division. We then sequenced and characterized the transcriptomes of the macrophages of the control and TMP-A groups using Illumina sequencing technology, which enabled us to examine gene expression profiles and differential expression profiles and select functional genes related to the molecular mechanism of the immunomodulatory activity of TMP-A in macrophages. Based on the experimental data and the results from our previous study, we believe that the significant antitumor activities of TMP-A in vivo may involve the MAPK and NF-κB signaling pathways because the two signaling pathways intersect. Our results provide a foundation for understanding the molecular mechanisms underlying the antitumor activity and immune activity of polysaccharides.

Acknowledgments

The present study was supported by the National Natural Science Foundation of China (31400016 and 31200012), the Application Foundation Project of Sichuan Province (2013JY0094), the Science and Technology Support Project of Sichuan Province (2014SZ0020 and 2014FZ0024), the Cultivate Major Projects of Sichuan Province (14CZ0016), the Open Foundation of Microbial Resources and Drug Development of Key Laboratory of Guizhou Province (GZMRD-2014-002), and the Doctor Startup Foundation Project of China West Normal University (11B019 and 11B020).

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July 2016
Volume 36 Issue 1

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APA
Ding, X., Li, J., Hou, Y., & Hou, W. (2016). Comparative analysis of macrophage transcriptomes reveals a key mechanism of the immunomodulatory activity of Tricholoma matsutake polysaccharide. Oncology Reports, 36, 503-513. https://doi.org/10.3892/or.2016.4814
MLA
Ding, X., Li, J., Hou, Y., Hou, W."Comparative analysis of macrophage transcriptomes reveals a key mechanism of the immunomodulatory activity of Tricholoma matsutake polysaccharide". Oncology Reports 36.1 (2016): 503-513.
Chicago
Ding, X., Li, J., Hou, Y., Hou, W."Comparative analysis of macrophage transcriptomes reveals a key mechanism of the immunomodulatory activity of Tricholoma matsutake polysaccharide". Oncology Reports 36, no. 1 (2016): 503-513. https://doi.org/10.3892/or.2016.4814