Abstract

   Many polysaccharides have been shown to be bioactive, with the addition
   of sulfate often enhancing or altering this bioactivity. In previous
   studies, masson pine pollen polysaccharides, to include a sulfate
   derivative, have been shown to promote macrophage proliferation
   similarly to LPS. However, the exact metabolic mechanisms promoting
   this proliferation remain unclear. In this study, RAW264.7 macrophage
   cells were treated with a purified masson pine pollen polysaccharide
   (PPM60-D), a sulfate derivative (SPPM60-D), or LPS. Proliferation
   levels at a variety of concentrations were examined using MTT assay,
   with optimal concentration used when performing metabolomic analysis
   via ^1H nuclear magnetic resonance (^1H-NMR). This process resulted in
   the identification of thirty-five intracellular metabolites. Subsequent
   multivariate statistical analysis showed that both LPS and SPPM60-D
   promote RAW264.7 proliferation by promoting aerobic respiration
   processes and reducing processes associated with glycolysis. While some
   insight was gained regarding the mechanistic differences between
   SPPM60-D and LPS, the specific mechanisms governing the effect of
   SPPM60 on RAW264.7 cells will require further elucidation. These
   findings show that both LPS and SPPM60-D effectively promote RAW264.7
   proliferation and may have beneficial uses in maintaining cellular
   vitality or inhibiting cancer.

   Keywords: ^1H nuclear magnetic resonance, metabolomics, sulfated
   polysaccharides

1. Introduction

   Masson pine pollen polysaccharides serve various functions and have
   varying degrees of activity. When these polysaccharides are
   artificially modified, such as with the addition of sulfate, their
   activity can be improved due to their branched chain now carrying
   negative charges [[28]1]. This alteration can also impact the
   bioactivity of the polysaccharide and confer valuable properties, such
   as antioxidant, antiviral, antitumor, anticoagulant or pro-immunity
   [[29]2]. In our previous study, we showed that sulfated polysaccharides
   from masson pine pollen could promote spleen lymphocyte and macrophage
   proliferation and could induce the generation of immune-related
   cytokines [[30]3].

   The field of metabolomics seeks to identify and quantify metabolites in
   biological systems. Differential metabolites can be identified by
   utilizing multivariate statistical analyses, and their identification
   can aid in early disease diagnosis or facilitate the identification of
   upstream pathways or corresponding genes. At present, high performance
   liquid chromatography (HPLC), gas chromatography (GC), mass
   spectrometry (MS), nuclear magnetic resonance (NMR), and infrared (IR)
   spectroscopy are commonly used detection approaches [[31]4]. NMR offers
   high sensitivity, reproducibility, and suitability for detection within
   mixed samples and in vitro or in vivo experimentation. Furthermore, NMR
   offers a reduced cost compared with the alternatives and can easily
   identify metabolite changes; thus, it is an indispensable tool in
   metabolomic research [[32]5].

   Macrophages are a part of innate immunity and play an important role in
   immune responses by releasing cytokines and signal factors in vivo.
   While research has been performed to examine the mechanisms of
   macrophage activation using metabolomic focused technologies
   [[33]6,[34]7,[35]8], the examination of RAW264.7 macrophage activation
   by masson pine pollen polysaccharides using NMR has not been previously
   performed. Polysaccharides have been shown to act on relatively complex
   pathways, with the addition of sulfate enhancing or sometimes altering
   these actions. Herein, masson pine pollen polysaccharides or a sulfate
   derivative were utilized to induce RAW264.7 cell proliferation, with
   the mechanisms of this outcome examined using the ^1H-NMR metabolomic
   approach.

2. Material and Methods

2.1. Materials and Reagents

   Broken masson pine (Pinus massonian) pollen was provided by the Yantai
   New Era Health Industry Company (broken rate < 95%). Broken masson pine
   pollen polysaccharides were extracted using a water extract-alcohol
   precipitation method, with the coarse polysaccharides (PPM60) obtained
   by ethanol precipitation with 60% ethanol. Proteins were removed using
   the trichloroacetic acid precipitation method. PPM60-D was then
   purified from PPM60 using Sephacryl S-400HR and was composed of
   mannose, galactose, trehalose, and an unknown monosaccharide at molar
   ratios of 0.75:1:0.68:2.37 [[36]9]. A sulfate derivative,
   namedSPPM60-D, was then obtained using chlorosulfonic- acid pyridine.
   Ultraviolet spectrum analysis and the barium sulfate turbidity method
   were used to assess sulfur content, with the degree of substitution
   then calculated.

   RAW264.7 cells were purchased from the Chinese Type Culture Collection
   (CTCC, Shanghai, China). Dulbecco’s modified Eagle’s medium (DMEM) was
   obtained from Gibco (New York, NY, USA); fetal calf serum from Zhejiang
   Tianhang Biotechnology Co. (Huzhou, China); and
   100×penicillin-streptomycin, MTT, DMSO, lipopolysaccharide (LPS), and
   phosphate buffer from Solarbio (Beijing, China). Sephacryl S-400 HR was
   purchased from Pharmacia (Helsingborg, Sweden); 5 mm ST500-7 NMR sample
   tubes from Norell (Morganton, NC, USA); and deuterium oxide with 0.05%
   TSP, Dextran T10, Dextran T70, Dextran T110, and Dextran T500 from
   Sigma Aldrich (Saint Louis, MO, USA). Other analytical reagents were
   prepared in-house.

2.2. Instrument

   The instrumentation used included a TU-1810SPC UV-VIS spectrophotometer
   (Beijing Purkinje General Instrument co., LTD., Beijing, China), 5804R
   high-speed refrigerated centrifuge (Eppendorf, Germany), SB-1000 rotary
   evaporator (EYELA, Tokyo, Japan), FDU-1200 freeze dryer (EYELA, Japan),
   carbon dioxide incubator (Nuaire, USA),VC 130PB ultrasonic cell
   disrupter system (Sonic, Branson, MO,, USA), Avance 600 III nuclear
   magnetic resonance spectrometer (Bruker, Karlsruhe, Germany), and U3000
   high performance liquid chromatography with refractive index detector
   (Diane, Sunnyvale, CA, USA).

2.3. Effects of SPPM60-D on RAW264.7 Cell Proliferation

   RAW264.7 cell proliferation was assessed in the presence of SPPM60-D,
   PPM60-D, or LPS (positive control) using MTT assay. RAW264.7 cells were
   plated at a concentration of 1 × 10^4 cells/mL in a 96-well plate.
   Samples were then treated with PPM60-D or SPPM60-D at a final
   concentration of 100, 200, 400, or 800 μg/mL or with LPS (10 μg/mL) for
   48 h. Next, MTT (20 μL) was added to each well and allowed to incubate
   for 4h. At last, the plate was centrifuged to remove the supernatants,
   DMSO (150 μL) was added to each well, and measurements were obtained at
   an absorbance of 490 nm.

2.4. ^1H-NMR Metabolomics Analysis of the Effects of SPPM60-D on RAW264.7
Cells

   RAW264.7 cells were plated at a concentration of 1 × 10^4 cells/mL,
   treated with either SPPM60-D (400 μg/mL) or LPS (10 μg/mL) for 48 h,
   and rinsed twice with PBS. A negative control group that was only
   cultured in DMEM was also used. Cells were quenched using cold methanol
   and detached using cell lifters. The intracellular fluid was extracted
   using a dual phase extraction procedure with methanol–chloroform and
   ultrapure water as previously described [[37]10]. The supernatants were
   transferred to new vials, with the hydrophilic metabolites obtained
   after drying and dissolved in 600 μL D[2]O. The ^1H-NMR measurements
   were performed on a Bruker Avance 600 III nuclear magnetic resonance
   spectrometer, with 550 μL of each sample solution added to a 5 mm NMR
   tube for analysis.

2.5. Data Analysis

   A MestReNova 6.1.1 (Mestrelab Research SL, Santiago De Compostela,
   Spain) was used to add exponential window function (exponential: 0.5)
   and to improve the signal to noise ratio (SNR). Manual phase baseline
   correction with TSP calibration was used to remove the water peak at
   4.65–5.15 ppm and to eliminate the effect of water on the integral. The
   integral width (bin) was 0.002. The total area of the spectrogram was
   normalized, and the saved format of the integral data was a .txt file.
   To open the excel table, manually we input packet information and
   sample number, remove the water peak section and import the
   SIMCA-P+12.0 software (Umetrics Inc., Umea, Sweden) for partial least
   squares discriminant analysis (PLS-DA) and orthogonal partial least
   squares discriminant analysis (OPLS-DA). The reliability of the PLS and
   OPLS-DA model was verified by permutation test and CV-ANOVA, and the
   difference variables were screened. The main criterion was the variable
   importance in projection (VIP) value and load weight and correlation
   coefficient (Pcorr) for the first prediction principal component.

3. Results

3.1. SPPM60-D Characterization

   The average molecular weight of SPPM60-D was determined using HPLC. The
   columns were calibrated using various dextran standards and a
   calibration curve was derived based on the equation Log (Mw) = −0.2582t
   + 11.757 (R^2 = 0.951) and showed SPPM60-D to have a Mw = 40.5 kDa.
   Furthermore, fourier-transform infrared spectroscopy (FTIR) was
   performed and showed that SPPM60-D had been successfully sulfated
   ([38]Figure 1), with the barium sulfate turbidity method showing the
   degree of substitution to be 1.212.

Figure 1.

   [39]Figure 1
   [40]Open in a new tab

   The infrared spectrum of PPM60-D (A) and SPPM60-D (B).

3.2. Effects of PPM60-D and SPPM60-D on RAW264.7 Cell Proliferation

   The MTT assay showed that both PPM60-D and SPPM60-D could promote
   RAW264.7 proliferation in a dose-dependent manner ([41]Figure 2).
   PPM60-D only showed significant proliferation levels relative to the
   negative control at a concentration of 800 μg/mL (p < 0.05), while
   SPPM60-D showed significantly increased proliferation levels at all
   concentrations in a dose-dependent manor (p < 0.01). The highest
   proliferation levels in the SPPM60-D group were obtained at
   concentrations of 400 and 800 μg/mL, but no significant differences
   were noted; thus, a concentration of 400 μg/mL was utilized for
   subsequent experimentation.

Figure 2.

   [42]Figure 2
   [43]Open in a new tab

   The different concentration of PPM60-D and SPPM60-D effect on RAW264.7
   cell proliferation (n = 6). Note: * p < 0.05, ** p < 0.01 compared with
   the control.

3.3. Intracellular Metabolites Identified by Nuclear Magnetic Resonance
Spectroscopy (NMR)

   An ^1H-NMR spectrum was constructed for the LPS group ([44]Figure 3A)
   and the SPPM60-D group ([45]Figure 3B), with associated blank control
   groups. To identify the indicated intracellular metabolites, relevant
   literature, the human metabolome database (HMDB), and the biological
   magnetic resonance database (BMRB) were consulted and thirty-five
   identities were assigned ([46]Table 1). When comparing the LPS and
   SPPM60-D spectra, no apparent differences were noted, but the
   concentrations of some of the metabolites differed.

Figure 3.

   [47]Figure 3

   [48]Figure 3
   [49]Open in a new tab

   ^1H-NMR spectra of RAW264.7 cell from the LPS groups, SPPM60-D groups
   and control groups (The arrows show the differential metabolites in
   [50]Table 1). (A) The LPS group vs. Control group (B) The SPPM60-D
   group vs. Control group.

Table 1.

   Nuclear magnetic resonance spectroscopy (NMR) data for RAW264.7 cells.
   No. Metabolites Group δH (Multiplicity) Loading (SPPM60-D vs. Control)
   Loading (LPS Group vs. Control)
   1 Isoleucine CH[3] 0.94 (t) 64.9 9.64
   CH[3] 0.99 (d)
   CH[2] 1.28 (m)
   CH[2] 1.46 (m)
   CH 1.99 (m)
   CH 3.66 (d)
   2 Leucine CH[3] 0.96 (d) 10.73 22.66
   CH[3] 0.97 (d)
   CH 1.7 (m)
   CH[2] 3.77 (m)
   CH 3.722 (m)
   3 Valine CH[3] 0.99 (d) 11.7 20.34
   CH[3] 1.05 (d)
   CH 2.25 (m)
   CH 3.61 (d)
   4 Ethanol CH[3] 1.19 (t) −150.81 146.23
   CH[2] 3.65 (q)
   5 (R)-Lactate CH[3] 1.33 (d) −1175 −1020.86
   CH 4.11 (q)
   6 Threonine CH[3] 1.33 (d) 41.35 40.5984
   CH 3.58 (d),
   CH 4.25 (m)
   7 Alanine CH[3] 1.48 (d) 51.01 137.05
   CH 3.78 (q)
   8 Acetate CH[3] 1.92 (s) 76.87 98.77
   9 Proline CH[2] 2.01 (m) 476.19 22.39
   CH[2] 2.05 (m)
   CH[2] 2.34 (m)
   CH[2] 3.42 (td)
   CH[2] 3.45 (m)
   CH 4.12 (m)
   10 L-Glutamic acid CH[2] 2.04 (m) −15.76 −17.08
   CH[2] 2.11 (m)
   CH[2] 2.34 (m)
   CH 3.77 (m)
   11 L-Glutamine CH[2] 2.13 (m) −18.93 −35.01
   CH[2] 2.44 (m)
   CH 3.77 (m)
   12 Tyrosine CH[2] 3.06 (dd) 2.306 6.27
   CH[2] 3.15 (dd)
   CH 3.94 (dd)
   3,5-CH 6.91 (d)
   2,6-CH 7.2 (d)
   13 Choline N(CH[3])[3] 3.23 (s) 306.01 263.07
   N CH[2] 3.56 (s)
   O CH[2] 4.07 (t)
   14 Succinate CH[2] 2.393 (s) 10.04 17.12
   15 Pyruvate CH[3] 2.46 (s) 10.48 28.31
   16 Sarcosine CH[3] 2.73 (s) 32.18 11.84
   CH[2] 3.6 (s)
   17 Aspartate CH[2] 2.68 (m) 67.57 118.274
   CH[2] 2.82 (m)
   CH 3.91 (m)
   18 Phosphocreatine CH[3] 3.05 (s) 201.21 142.58
   CH[2] 3.95 (s)
   19 Creatine CH[3] 3.04 (s) 259.98 226.48
   CH[2] 3.94 (s)
   20 Taurine CH[2]SO[3] 3.25 (t) −304.83 −244.785
   NCH[2] 3.43 (t)
   21 Trimethylamine N-oxide (TMAO) CH[3] 3.26 (s) 54.23 −189.45
   22 Betaine CH[3] 3.27 (s) 294.88 529.127
   CH[2] 3.89 (s)
   23 Methanol CH[3] 3.352 (s) −14.48 29.20
   24 Glycine CH[2] 3.56 (s) 306.01 263.07
   25 Uridine CH[2] 3.81 (d) 23.93 118.274
   CH[2] 3.92 (d)
   4-CH 4.18 (q)
   3-CH 4.25 (t)
   2-CH 4.38 (t)
   5-CH 5.95 (d)
   6-CH 5.97 (d)
   1-CH 7.87 (d)
   26 Uridine diphosphate
   (UDP) CH[2] 4.22 (m) 12.87 23.33
   4-CH 4.28 (m)
   3-CH 4.37 (m)
   2-CH 4.37 (m)
   5-CH 5.98 (d)
   6-CH 5.99 (d)
   1-CH 7.96 (d)
   27 Fumaric acid 4,5-CH 5.52 (s) 2.66 1.91
   28 α-glucose 1-CH 5.24 (s) −1.05 −11.43
   29 Cytidine 3-CH 6.09 (d) 1.81 2.09
   2-CH 7.87 (d)
   30 Fumarate CH 6.522 (s) 2.20 2.07
   31 Niacinamide 5-CH 7.6 (dd) −2.28 0.75
   6-CH 8.72 (d)
   32 Adenine 2-CH 8.11 (s) 4.47 4.78
   6-CH 8.12 (s)
   33 Hypoxanthine 2-CH 8.22 (s) 2.31 37.64
   7-CH 8.272 (s)
   34 Formate CH 8.46 (s) 34 −105.611
   35 NADP 6-CH 8.84 (d) 4.54 1.71
   4-CH 9.15 (d)
   [51]Open in a new tab

   Multiplicity: singlet (s), doublets (d), triplets (t), doublet of
   doublets (dd), multiplets (m); quartets (q).

3.4. Data Analysis

3.4.1. Supervised Partial Least Squares Discriminate Analysis (PLS-DA)

   The PLS-DA results showed that the values of all the distances between
   sample-points and the X-line were less than the linear value of 1.35
   ([52]Figure 4A,B), thus indicating that there were no abnormal points.
   The PLS-DA score plots showed that sample points from the SPPM60-D and
   LPS groups and their associated blank control groups were
   distinguishable at the first principal component (PC1; [53]Figure
   5A,B). Furthermore, the substitution permutation test ([54]Figure 6A,B)
   showed that the model was reliable and had no over fitting. Overall,
   this approach was shown to be able to reliably predict data, with the
   four groups of samples being within the 95% confidence interval.

Figure 4.

   [55]Figure 4

   [56]Figure 4
   [57]Open in a new tab

   Distance to model X line Plot prediction in samples. (A) LPS and
   control groups; (B) SPPM60-D and control groups.

Figure 5.

   [58]Figure 5
   [59]Open in a new tab

   PLS-DA score plot of RAW264.7 cells from treatment and control groups.
   (A) LPS and control groups (B) SPPM60-D and control groups.

Figure 6.

   [60]Figure 6

   [61]Figure 6
   [62]Open in a new tab

   Permutation test of RAW264.7 cells from treatment and control groups.
   (A) LPS and control groups (B) SPPM60-Dand control groups.

3.4.2. Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)

   The OPLS-DA score plots showed that the groups were clearly
   distinguishable ([63]Figure 7A,B). To determine significance, an ANOVA
   of the cross-validated residuals (CV-ANOVA) was performed with both the
   LPS (p < 0.046) and SPPM60-D (p < 0.0018) sets. Then based on the
   variable importance in the projection (VIP) values, correlation
   coefficients (r) and OPLS-DA plot diagrams ([64]Figure 8A,B) of
   differential metabolites were identified ([65]Table 2). While the types
   of metabolic pathways between the LPS and SPPM60-D groups were very
   similar, the concentrations of some of the metabolites did differ. When
   comparing the LPS group with the blank control group, eighteen
   differential metabolites were identified, while fifteen were identified
   in the SPPM60-D group relative to the control. These differential
   metabolites were amino acid, organic acid, choline, alcohol, and other
   substances involved in the Krebs cycle, anaerobic respiration, and the
   synthesis and degradation of amino acids.

Figure 7.

   [66]Figure 7
   [67]Open in a new tab

   Orthogonal partial least Squares discriminant analysis OPLS-DA score
   plot of RAW264.7 cells from treatment and control groups. (A) LPS and
   control groups (B) SPPM60-Dand control groups.

Figure 8.

   [68]Figure 8
   [69]Open in a new tab

   S-Plot of OPLS-DA analysis of RAW264.7 cells from treatment and control
   groups. (A) LPS and control groups (B) SPPM60-Dand control groups.

Table 2.

   Identified differential metabolites.
   LPS vs. Control SPPM60-D vs. Control
   NO. Metabolites VIP Trend Metabolites VIP Trend
   1 Lactate 25.36 ↓ Lactate 24.06 ↓
   2 Alanine 14.19 ↑ Betaine 12.84 ↑
   3 Choline 6.61 ↑ Proline 7.58 ↑
   4 Proline 5.46 ↑ Creatine phosphate 6.36 ↑
   5 Betaine 4.27 ↑ Creatine 6.14 ↑
   6 Glycine 4.23 ↑ choline 5.90 ↑
   7 Creatine 4.07 ↓ Glycine 5.89 ↑
   8 Taurine 3.84 ↓ Alanine 4.36 ↑
   9 Acetate 2.45 ↑ Taurine 3.21 ↓
   10 Aspartate 1.82 ↑ Acetate 3.10 ↑
   11 Uridine 1.62 ↑ Aspartate 2.81 ↑
   12 Threonine 1.67 ↑ Trimethylamino oxide (TMAO) 2.48 ↑
   13 Formate 1.51 ↓ Isoleucine 2.34 ↑
   14 Trimethylamine N-oxide (TMAO) 1.43 ↓ Threonine 1.23 ↑
   15 Methanol 1.40 ↑ Ethanol 1.05 ↓
   16 leucine 1.31 ↑
   17 Ethanol 1.22 ↑
   18 Glutamine 1.13 ↓
   [70]Open in a new tab

3.4.3. Analysis of Differential Metabolites

   Based on the pattern recognition analysis and the multivariate
   statistical results, the Metabo Analyst software and the Kyoto
   Encyclopedia of Genes and Genomes (KEGG) were utilized to analyze
   pathways associated with the identified differential metabolites
   ([71]Table 2). The results showed that in the LPS group, the most
   significant cellular metabolism pathways associated with the
   differential metabolites included protein synthesis; glycine, serine,
   and threonine metabolism; methionine metabolism; betaine metabolism;
   and ammonia recycling, ([72]Figure 9 and [73]Figure 10; [74]Table 3).
   When analyzing the SPPM60-D group, the most significant pathway
   associations included glycine, serine, and threonine metabolism;
   methionine metabolism; arginine and proline metabolism; and betaine
   metabolism ([75]Figure 11 and [76]Figure 12; [77]Table 4).

Figure 9.

   [78]Figure 9
   [79]Open in a new tab

   The pathway analysis of all differential metabolites between LPS and
   control groups.

Figure 10.

   [80]Figure 10
   [81]Open in a new tab

   Pathway enrichment of differences metabolites between LPS and control
   groups.

Table 3.

   Pathway analysis associated with the differential LPS metabolites.
   Name Total Expected Hits Raw p −LOG (p)
   Glycine, serine and threonine metabolism 31 0.39379 5 2.52E-05 10.589
   Methane metabolism 9 0.11433 3 0.00013807 8.8877
   Aminoacyl-tRNA biosynthesis 69 0.8765 5 0.001237 6.6951
   Glycolysis or Gluconeogenesis 26 0.33028 3 0.003731 5.5911
   Nitrogen metabolism 9 0.11433 2 0.0052073 5.2577
   Arginine and proline metabolism 44 0.55893 3 0.01646 4.1068
   Pyruvate metabolism 23 0.29217 2 0.032939 3.4131
   Alanine, aspartate and glutamate metabolism 24 0.30487 2 0.035665
   3.3336
   D-Glutamine and D-glutamate metabolism 5 0.063514 1 0.062007 2.7805
   Cyanoamino acid metabolism 6 0.076217 1 0.073964 2.6042
   Pyrimidine metabolism 41 0.52082 2 0.09334 2.3715
   Taurine and hypotaurine metabolism 8 0.10162 1 0.097448 2.3284
   Primary bile acid biosynthesis 46 0.58433 2 0.11353 2.1757
   Valine, leucine and isoleucine biosynthesis 11 0.13973 1 0.13162 2.0278
   Glyoxylate and dicarboxylate metabolism 18 0.22865 1 0.20667 1.5766
   Glutathione metabolism 26 0.33028 1 0.28493 1.2555
   Porphyrin and chlorophyll metabolism 27 0.34298 1 0.29419 1.2235
   Glycerophospholipid metabolism 30 0.38109 1 0.32127 1.1355
   Valine, leucine and isoleucine degradation 38 0.48271 1 0.38878 0.94474
   Purine metabolism 68 0.8638 1 0.58963 0.52826
   [82]Open in a new tab

Figure 11.

   [83]Figure 11
   [84]Open in a new tab

   The pathway analysis of all differential metabolites between SPPM60-D
   and control groups.

Figure 12.

   [85]Figure 12
   [86]Open in a new tab

   Pathway enrichment of differences metabolites between SPPM60-Dand
   control groups.

Table 4.

   Pathway analysis associated with the differential SPPM60-D metabolites.
   Name Total Expected Hits Raw p −LOG (p)
   Glycine, serine and threonine metabolism 31 0.35004 5 1.32E-05 11.232
   Glycolysis or Gluconeogenesis 26 0.29358 3 0.0026238 5.9431
   Methane metabolism 9 0.10162 2 0.0041113 5.494
   Arginine and proline metabolism 44 0.49682 3 0.011797 4.4399
   Pyruvate metabolism 23 0.2597 2 0.026347 3.6364
   Aminoacyl-tRNA biosynthesis 69 0.77911 3 0.039232 3.2383
   Cyanoamino acid metabolism 6 0.067749 1 0.065978 2.7184
   Taurine and hypotaurine metabolism 8 0.090332 1 0.087048 2.4413
   Primary bile acid biosynthesis 46 0.51941 2 0.092722 2.3782
   Nitrogen metabolism 9 0.10162 1 0.097415 2.3288
   Glyoxylate and dicarboxylate metabolism 18 0.20325 1 0.18588 1.6827
   Alanine, aspartate and glutamate metabolism 24 0.271 1 0.24027 1.426
   Glutathione metabolism 26 0.29358 1 0.25762 1.3563
   Porphyrin and chlorophyll metabolism 27 0.30487 1 0.26616 1.3236
   Glycerophospholipid metabolism 30 0.33874 1 0.29123 1.2336
   [87]Open in a new tab

4. Discussion

   In RAW264.7 macrophages, LPS can induce inflammatory reactions. LPS was
   shown to be involved in macrophage activation and the production of
   inflammation factors [[88]11]. Furthermore, LPS has been shown to act
   through the TLR2/TLR4-PLCγ2 pathway and can promote [Ca^2+][i] in
   macrophages (Aki et al., 2008). Additionally, one study indicated that
   LPS can induce Ca^2+ flow into cells by way of Ca^2+ release-activated
   Ca^2+ (CRAC) channels in mice macrophages [[89]12]. In a previous
   study, Geng et al. examined the PPM60-D sulfated derivative SPPM60-D in
   RAW264.7 macrophage cells and showed that it also promoted
   proliferation similar to LPS by activating Ca^2+ signaling via
   toll-like receptor 4 (TLR4) and CRAC channels [[90]3]. However, this
   study did not examine the possible mechanistic differences between LPS
   and SPPM60-D.

   In the present study, metabolite pathway enrichment analysis (MPEA)of
   the LPS group showed that pathway inductions were focused on protein
   synthesis and betaine, taurine, hypotaurine, glycine, serine, and
   threonine metabolism. Most of these metabolites showed increased
   concentrations, with the exception of taurine and hypotaurine which
   were decreased. MPEA was also performed for the SPPM60-D group and
   showed an induction of pathways associated with protein synthesis and
   methionine, glycine, serine, threonine, betaine, taurine, and
   hypotaurine metabolism. Thus, similar trends were seen in both
   LPS-induced and SPPM60-D induced proliferation in RAW264.7 cells.

   Both LPS and SPPM60-D exposure resulted in decreased lactic acid
   levels. Due to the fact the RAW246.7 cells are an immortalized cell
   line, you would expect them to exhibit similarities to cancer cells,
   such as obtaining energy predominantly by glycolysis and producing
   large amounts of lactic acid [[91]13]. These results show that the
   masson pine pollen polysaccharides and LPS can inhibit glycolysis,
   thereby potentially preventing cell cancerization. In one study, it was
   suggested that the cancer cells choosing an inefficient energy process
   may be due to impaired mitochondria [[92]14]. Therefore, we postulate
   that the masson pine pollen polysaccharides and LPS may have a
   protective effect in mitochondria.

   Also, in the LPS and SPPM60-D groups, taurine pathways were decreased.
   Taurine is an abundant essential non-protein amino acid in animals that
   is widely distributed in various tissues and is not involved in protein
   synthesis. Taurine can be synthesized in the liver and also can be
   obtained through diet. Taurine has a wide range of biological
   functions, such as regulating normal physiological activities,
   maintaining osmotic pressure balance, modulating cellular calcium
   balance, improving immune functions, and enhancing the antioxidant
   capacity of the cell membrane [[93]15]. Cells can also transport the
   extracellular taurines into cells through transporters, with one study
   showing that alanine can have an antagonistic action on taurine
   transport [[94]16]. In agreement with this, both the LPS and SPPM60-D
   groups showed significantly higher levels than the control group, thus
   possibly inhibiting taurine transport. Taurine can be synthesized from
   cysteine and methionine by cysteine sulfinic acid decarboxylase (CSAD),
   which produces hypotaurine and then taurine via oxidation. Thus, it
   would seem that the reduction in taurine may be attributed to reduced
   CSAD levels.

   Other similarities seen in the LPS and SPPM60-D groups included
   increased proline, methionine, and arginine concentrations, with these
   amino acids being associated with cell viability and cellular
   metabolism. The concentrations of cholines and choline phosphates,
   which are important in the process of energy shuttling, were also
   significantly increased in the two groups relative to the control.
   Betaine concentrations were also significantly increased, with betaine
   metabolism mainly occurring in the mitochondria of liver and kidney
   cells. Betaine can regulate cellular osmotic pressure and in the
   betaine homocysteine S-methyltransferase (BHMT) pathway, it serves as a
   methyl donor for homocysteine (Hcy), which is then utilized to generate
   methionine, a participant in protein and fat metabolism [[95]17]. These
   results show that LPS and SPPM60-D utilize many of the same mechanisms
   when promoting RAW246.7 proliferation.

   Despite many commonalities being noted following treatment with LPS or
   SPPM60-D, some differences were also present. For example, in the
   SPPM60-D group, ethanol concentrations were reduced relative to the
   blank control, while the LPS group showed higher levels. In
   macrophages, transient receptor potential vanilloid subtype 1 (TRPVl)
   expression, which can be activated by ethanol, has been associated with
   inflammatory responses [[96]18]. TRPV1 is a nonselective calcium ion
   channel and is associated with heat and pain responses [[97]19].
   Furthermore, TRPV4, another member of the TRPV family, has been
   associated with the regulation of thermogenesis and inflammation in fat
   cells [[98]20]. Thus, we speculate that during LPS exposure, ethanol
   may act as an activator of TRPV1, thereby mediating inflammatory
   related calcium ion cellular influx and reducing the release of
   inflammatory cytokines. However, in the SPPM60-D group, ethanol levels
   were reduced and the TRPV1 channels were not activated. These findings
   were consistent with a previous study that found that SPPM60-D could
   promote the release of macrophage inflammatory factors to a higher
   level than could LPS [[99]3]. However, further study is required to
   determine if this is associated with the metabolic differences between
   the two groups.

   Another differential factor between the groups was trimetlylamine oxide
   (TMAO), which was increased in the SPPM60-D group and decreased in the
   LPS group. TMAO can promote protein folding and ligand binding and
   enhance protein stability [[100]18]. Additionally, TMAO also has
   physiological and biochemical properties, such as regulating osmotic
   pressure. These results suggest that SPPM60-D can offer a protective
   effect by stimulating macrophages to produce TMAO.

   Overall, the present study identified thirty-five intracellular
   metabolites via NMR spectrometry. Multivariate statistical analyses
   showed that LPS and SPPM60-D can promote RAW264.7 cell proliferation by
   promoting aerobic respiration processes and reducing glycolysis. While
   some insight was gained regarding mechanistic differences between
   SPPM60-D and LPS, the specific mechanisms governing the effect of
   SPPM60 on RAW264.7 proliferation require further elucidation.

Author Contributions

   Project administration, writing—review and editing, Y.G.; methodology,
   investigation, writing—original draft preparation, F.S.; methodology,
   investigation, M.S.

Funding

   This research received no external funding.

Conflicts of Interest

   The authors declare no conflict of interest.

Footnotes

   Sample Availability: Samples of the compounds PPM60-D are available
   from the authors.

References