Abstract
Qi-Jing-Sheng-Bai granule (QJSB) is a newly developed traditional
Chinese medicine (TCM) formula. Clinically, it has been used for the
treatment of leucopenia. However, its pharmacological mechanism needs
more investigation. In this study, we firstly tested the effects of
QJSB on leucopenia using mice induced by cyclophosphamide. Our results
suggested that QJSB significantly raised the number of peripheral white
blood cells, platelets and nucleated bone marrow cells. Additionally,
it markedly enhanced the cell viability and promoted the colony
formation of bone marrow mononuclear cells. Furthermore, it reversed
the serum cytokines IL-6 and G-CSF disorders. Then, using
transcriptomics datasets and metabonomic datasets, we integrated
transcriptomics-based network pharmacology and metabolomics
technologies to investigate the mechanism of action of QJSB. We found
that QJSB regulated a series of biological processes such as
hematopoietic cell lineage, homeostasis of number of cells, lymphocyte
differentiation, metabolic processes (including lipid, amino acid, and
nucleotide metabolism), B cell receptor signaling pathway, T cell
activation and NOD-like receptor signaling pathway. In a summary, QJSB
has protective effects to leucopenia in mice probably through
accelerating cell proliferation and differentiation, regulating
metabolism response pathways and modulating immunologic function at a
system level.
Keywords: Qi-Jing-Sheng-Bai granule, leucopenia, transcriptomics,
metabolomics, network pharmacology
Introduction
Chemotherapy has been widely used for the treatment of cancers.
However, chemotherapy usually induces many adverse effects, including
hematologic toxicity and neurotoxicity ([37]Magge and DeAngelis, 2015;
[38]Ratti and Tomasello, 2015; [39]Zhou et al., 2018). Among them,
hematologic toxicity, like leucopenia, is a very common adverse
reaction that can delay the subsequent therapy, induce the risk of
cancer metastasis, and even lead to life-threatening events ([40]Xu et
al., 2011; [41]Cui et al., 2015). Hence the hematologic indexes,
including the white blood cells (WBC), neutrophile granulocytes, blood
platelets and monocytes, are important objective indexes for cancer
patients after chemotherapy. Studies have shown that granulocyte
colony-stimulating factor (G-CSF) and related agents have clinical
efficacy. They are recommended to prevent leucopenia ([42]Xu et al.,
2011). However, G-CSF only reduces the neutropenia duration for 1–2
days while also increases adverse reactions and the cost of treatment
notably ([43]Dygai et al., 2012; [44]Aarts et al., 2013). Traditional
Chinese medicine (TCM) formulae have been widely used in China for the
treatment of leucopenia ([45]Liu et al., 2014). Formulae usually
consist of several types of Chinese medicines. Among them, one herb is
the principal component, and the others serve as adjuvant ones to
assist the function of the principal component. It is believed that
multiple components contained in the formulae could hit multiple
targets and exert synergistic effects ([46]Jiang, 2005).
Qi-Jing-Sheng-Bai granule (QJSB) is a modern TCM formula. It is made
from extracts of nine Chinese medicines, namely, Astragalus
membranaceus (Huangqi), Panax quinquefolium (Xiyangshen), Epimedium
brevicornum (Yinyanghuo), Angelica sinensis (Danggui), Polygonatum
sibiricum (Huangjing), Eclipta pwstmta (Mohanlian), Lycium barbarum
(Gouqi), Psoralea corylifolia (Buguzhi), and Spatholobus suberectus
(Jixueteng), as well as one raw material component of Cervi Cornus
Colla, at a ratio of 6:2:3:2:2:3:2:2:6:1. Both A. membranaceus and
Cervi Cornus Colla are principal components. In a clinical study, QJSB
has been used for the treatment of leucopenia ([47]Wu et al., 2018). In
our previous study, we identified 143 compounds, including 56
flavonoids, 51 saponins, and 36 other compounds, from QJSB ([48]Wu et
al., 2018). It has been accepted that the effective ingredients can be
detected in serum after medicine administration. After the oral
administration of QJSB, 42 compounds, including 24 prototype compounds
and 18 metabolites, have been detected in the serum of rats ([49]Wu et
al., 2018). Among the 42 compounds, many ingredients have been proven
to have bioactivities. For example, ferulic acid improves hematopoietic
cell recovery in whole-body gamma irradiated mice and increases levels
of granulocyte-colony stimulating factor (G-CSF) ([50]Ma et al., 2011);
several flavonoids including formononetin, ononin, calycosin, and
calycosin-7-O-β-D-glucoside, induce the expression of erythropoietin in
human embryonic kidney fibroblasts via the accumulation of
hypoxia-inducible factor-1α ([51]Zheng et al., 2011); quercitrin
protects endothelial progenitor cells from oxidative damage via
inducing autophagy through extracellular signal-regulated kinase
([52]Zhi et al., 2016); icaritin improves the hematopoietic function in
cyclophosphamide-induced myelosuppression mice ([53]Sun et al., 2018).
The identification of compounds absorbed into blood revealed the
effective substance of QJSB to some extent. However, the therapeutic
mechanism of QJSB in leucopenia remains unclear. Thus, the effect on
the treatment of leucopenia needs further detailed investigation.
Systems biology studies the pharmacological mechanism of TCM by
integrating transcriptomic, proteomic and metabolomic profiles ([54]Su
et al., 2011; [55]Meierhofer et al., 2014). Network pharmacology is a
new system biology approach, generally describing the association of
multiple components with multiple targets and multiple pathways
([56]Ning et al., 2017). Recently, the application of integrated
systems biology and network pharmacology is a promising approach for
the delineation of effects and mechanisms of TCM formulae.
In this study, we firstly investigated the effect of QJSB on leucopenia
using mice induced by cyclophosphamide. We further investigated the
therapeutic mechanism of QJSB using the transcriptomics datasets
derived from the bone marrow and metabonomic datasets from plasma.
Finally, we applied transcriptomics-based network pharmacology and
metabolomics technologies to study the mechanism of QJSB for the
treatment of leucopenia.
Materials and Methods
Chemical Reagents
Cyclophosphamide was purchased from Zaiqi Biotechnology Corporation
(Shanghai, China). QJSB granules were kindly provided by Zhendong
Pharmaceutical (Shanxi, China).
Animals and Treatments
All animal studies were performed according to the institutional
ethical guidelines of animal care and were approved by the Committee on
the Ethics of Animal Experiments of the Second Military Medical
University, China. Male ICR mice (18–22 g) were obtained from
Laboratory Animal Company (Shanghai, China). The mice were acclimated
for 2–3 days under conditions of controlled temperature (24 ± 2°C),
relative humidity of 60 ± 5%, 12 h light/dark cycle, and ad libitum
access to standard laboratory food and water. All the mice were
randomly allocated into four groups: normal group and 3 leucopenia
model groups (cyclophosphamide, 80 mg/kg/day). The model groups were
treated with vehicle, leucogen (20 mg/kg/day) and QJSB (3 g/kg/day),
respectively. The mice in each group were orally administered with
respective medicines for 1 week, and an equivalent volume (0.2 ml/10 g)
of 0.9% saline solution was used for normal group and model group.
Next, the animals in each group were sacrificed by dislocation of the
cervical vertebra and were prepared for subsequent experiments. In each
group, the whole blood of 10 mice was used for routine blood
examination and bone marrow cells in the femurs were used for the bone
marrow nuclear cell count, cell viability assay and colony-forming unit
assay. The sera of another 10 mice were used to detect the levels of
IL-6 and G-CSF. Bone marrow cells in the femurs of three of these mice
were collected for RNA isolation, sequencing and real-time quantitative
PCR (RT-qPCR). The plasma of the mice was collected for metabonomic
analysis. After 2 weeks of treatment, the mice were sacrificed by
dislocation of the cervical vertebra and the whole blood was used for
routine blood examination (n = 10).
Leucopenia Model
The leucopenia model was established as follows: for 1 week of
treatment, the mice in the model groups were treated with 80 mg/kg/day
of cyclophosphamide intraperitoneally for 3 consecutive days (from day
5 to day 7), and the normal group was treated with an equivalent volume
(0.1 ml/10 g) of normal saline. For 2 weeks of treatment, the mice were
treated with cyclophosphamide (80 mg/kg/day) intraperitoneally for 6
days (3 consecutive days per week), and the normal group was treated
with an equivalent volume (0.1 ml/10 g) of normal saline.
Cell Viability Assay
Cell viability was measured using the Cell Counting Kit-8 (CCK8)
reagent (Dojindo, Japan). The mice were sacrificed by dislocation of
the cervical vertebra and the femurs were immediately collected. Bone
marrow was eluted from the shaft by RPMI 1640 medium, and filtered
through a 70 micron filter. Bone marrow nuclear cells were dispensed in
96-well culture plates (100 μL/well) at a density of 5 × 10^5 /mL.
Next, the cells were incubated with 10 μl of CCK8 reagent. Finally, the
absorbance at 450 nm was measured.
Colony-Forming Units Assay
The mice were sacrificed by dislocation of the cervical vertebra and
the femurs were immediately harvested. Next, bone marrow cells were
eluted from the shaft by RPMI 1640 medium, and filtered through a 70
micron filter. Thereafter, bone marrow mononuclear cells were obtained
by centrifugation in a Ficoll density gradient. The cells were diluted
with M3434 methylcellulose medium (StemCell Technologies, Canada) at a
density of 3 × 10^4/mL, dispensed into 6-well culture plates (1
mL/well), and cultured in an atmosphere of 5% CO[2] at 37°C for 12
days. Colonies consisting of 50 cells or more were counted.
The Detection of IL-6 and G-CSF
Whole blood was collected from mice by removing the eyeballs. Blood
samples were placed to clot for 2 h at room temperature before
centrifuging for 20 min at 2000 ×g. Serum was collected and stored in
aliquots at -80°C for later use. Commercially available sandwich
enzyme-linked immunosorbent assay (ELISA) kits (eBiosciences, San
Diego, CA, United States) were used for the quantitation of IL-6 and
G-CSF. The optical density of each sample at 450 nm was measured.
Cytokine levels were quantified using standard curves, and the values
were expressed in units of pg/ml.
RNA Isolation, Library Preparation, and Sequencing
The total RNA of bone marrow cells from 12 mice (leucopenia group, n =
3, QJSB group, n = 3, normal group, n = 3, and leucogen group, n = 3)
were extracted using TRIzol (Invitrogen, Carlsbad, CA, United States)
reagent for RNA sequencing and were purified according to the
manufacturer’s instructions. Strand-specific libraries were prepared
using the VAHTS Total RNAseq Library PrepKit for Illumina (Vazyme,
China) following the manufacturer’s instructions. Using Ribo-Zero rRNA
removal beads, ribosomal RNA was removed from total RNA. Following
purification, the mRNA was fragmented into small pieces using divalent
cations under 94°C for 8 min. The cleaved RNA fragments were copied
into first strand cDNA using reverse transcriptase and random primers.
This is followed by second strand cDNA synthesis using DNA polymerase I
and RNase H. These cDNA fragments then went through an end repair
process, the addition of a single “A” base, and then ligation of the
adapters. The products were then purified and enriched with PCR to
create the final cDNA library. Purified libraries were quantified by
Qubit^® 2.0 Fluorometer (Life Technologies, United States) and
validated by Agilent 2100 bioanalyzer (Agilent Technologies, United
States) to confirm the insert size and calculate the mole
concentration. Cluster was generated by cBot with the library diluted
to 10 pM, followed by sequencing on the Illumina HiSeq 2500 (Illumina,
United States).
Data Analysis for Gene Expression
Sequencing raw reads were preprocessed by filtering out rRNA reads,
sequencing adapters, short-fragment reads and other low-quality reads
using Seqtk. Hisat2 (version 2.0.4) was used to map the cleaned reads
to the mouse GRCm38.p4 (mm10) reference genome with two mismatches.
Gene expression was evaluated in FPKM (fragments per kilobase per
million mapped fragments) from RNAseq data. The formula to calculate
FPKM was as follows: FPKM = (number of mapping fragments) × 10^3 ×
10^6/[(length of transcript) × (number of total fragments)].
Differential expression analysis of two groups was performed using the
“DESeq2” R package at the cutoff of | log2 fold change| > 0.585 and
P-value < 0.05 ([57]Love et al., 2013). Then, we constructed a
pre-ranked gene list of all differentially expressed genes ordered by
the absolute value of log2 fold change and selected the top 300 genes
for further analysis.
Pathway Enrichment Analysis and GO Analysis
We firstly used R package “clusterProfiler” to perform pathway
enrichment analysis to identify KEGG ([58]Kanehisa and Goto, 2000)
(Kyoto Encyclopedia of Genes and Genomes) pathways enriched with the
top 300 differentially expressed genes. Significant pathways with
P-value < 0.05 were selected. Next, GO (Gene Ontology) enrichment
analysis was also performed to explore the biological processes of the
top 300 differentially expressed genes. At a cutoff of P < 0.05.
Evaluation of Drug’s Effects by the Network Scores
The background protein-protein interaction (PPI) network was downloaded
from the STRING database v10.5^[59]1 and the organism was chosen as
“Mus musculus.” All genes were standardized by mapping to the Entrez ID
for further analysis. In this paper, the top 300 DEGs under the
treatment of a drug were regarded as the drug’s potential target genes.
Similarly, the top 300 DEGs between the model and normal groups were
regarded as disease associated genes. We applied the algorithm random
walk with restart (RWR) to measure the seed genes’ influence on the
background network. Specifically, the drug’s potential target genes and
disease associated genes were used as seed nodes, respectively.
Additionally, genes in the background network were scored by RWR. Next,
we calculated the Pearson correlation coefficient between the network
scores based on each gene set to estimate the relevance of the drug’s
potential target genes and disease associated genes. The relevance was
estimated as follows:
[MATH: Relevance=cor(Scoresdrug
,Scoresdisease)
:MATH]
where cor represents the Pearson correlation coefficient and Scores
represents the genes’ network scores. To evaluate the significance of
the correlation between the drug’s target genes and disease genes, a
reference distribution was built. Genes with the same number of drug’s
target genes were randomly selected from the background network and the
correlation coefficient was calculated between the disease genes and
random set. We performed 100 repetitions to generate the reference
perturbation distribution. The mean and standard deviation of the
random correlation coefficients were denoted by μ[Relevance] and
σ[Relevance], respectively. The Z-score was finally calculated and the
absolute value of the Z-score larger than 3 suggests that the drug’s
effect on the disease was statistically significant.
[MATH:
Zscore=|Relevance−μRelevance
|σRel
evance :MATH]
Sample Preparation and LC-MS Conditions for Metabonomic Analysis
Because the endogenous metabolites play an essential role in the
physiology of hosts, we explored the host metabolic profiling in the
plasma of a subset of 27 subjects by liquid chromatography-mass
spectrometry (LC/MS). The detailed method was as follows. Each plasma
sample was thawed at 4°C and vortexed for 5 s at room temperature.
Next, 100 μL of plasma was transferred into another 1.5 mL tube with
300 μL of methanol and was mixed 45 s in a vortex. Thereafter, the
sample was centrifuged for 10 min (12000 rpm, 4°C). Finally, the
supernatant was transferred into auto-sampler vials and stored at -80°C
for LC-MS analysis. The QC sample was prepared by mixing 10 μL of
aliquot from the six above prepared samples, respectively. The QC
samples were injected every six samples.
Chromatographic separation was performed on a ACQUITY UPLC^® HSS T3
(2.1 × 100 mm, 1.8 μm, Waters, United States) using an ACQUITY Ultra
Performance LC system (Waters corp., Milford, MA, United States). The
column was maintained at 40°C. The flow rate was set at 0.4 mL/min, and
the sample injection was 1 μL. The optimal mobile phase consisted of a
linear gradient system of water mixed with 0.1% formic acid (phase A)
and acetonitrile (phase B): 0–6.0 min, 5–100% B, 6.0–8.0 min, 100% B;
8.0–9.0 min, 100–90% B; 9.0–14.0 min, 90–80% B; 14.0–14.1 min, 80–5% B;
14.1–19.0 min, 5% B. MS detection was acquired on a Micromass
Quadrupole (Q) SYNAPT G2-Si high-resolution mass spectrometer (Waters
Corp., Milford, MA, United States) equipped with an electrospray ion
(ESI) source. Both positive and negative modes were utilized in the
current research. The temperature of the ion source was 120°C. The
capillary voltage and cone voltage were 2000 V and 49V, respectively.
The desolvation gas temperature and flow were 350°C and 750 L/h,
respectively. The cone gas was set at 50 L/h. Data were collected
between 50 and 1200 m/z with a 0.2 s scan time and a 0.02 s interscan
delay. All analyses were conducted using the lock spray to ensure the
accuracy and precision of the mass information for compound
identification. Leucine encephalin [(M + H)^+ = 556.2771, (M-H)^- =
554.2615] was used as the lock spray at a concentration of 1 μg/mL, and
the flow rate was set at 5 μL/min. Additionally, mass spectrometry
elevated energy (MS^E) collection was applied for compound
identification. This technique obtains precursor ion information
through low collision energy and full-scan accurate mass fragment
information through the ramp of the high collision energy. The
collision energy of MS^E was set from 15 to 35 V.
Metabonomic Data Processing and Analysis
The raw plasma LC-MS data were pre-processed using Waters Progenesis QI
2.0 software (Non-linear Dynamics, Newcastle, United Kingdom).
Progenesis QI includes the steps of importing data, reviewing
alignment, experiment design setup, picking peaks, identifying and
reviewing compounds, and performing compound statistical analysis.
Then, the data were exported into SICMA 14.1 (Umetric, Umeå, Sweden)
for multivariable statistical analysis. The multivariate statistical
analysis (MVA) included principal component analysis (PCA), partial
least squares discrimination analysis (PLS-DA), and orthogonal partial
least square-discriminant (OPLS-DA) models, which were used to observe
the classifications for different groups. Thereafter, based on the
OPLS-DA plot, the ions were filtered by VIP > 1 (variable importance in
the projection) to identify the metabolites contributing to the
classifications. Next, ions with P-values < 0.05 were regarded as the
differential metabolite ions. Subsequently, the differential
metabolites ions with the two filters were structurally identified and
interpreted based on the metabonomic associated databases:
METLIN^[60]2, HMDB^[61]3, and KEGG^[62]4. Finally, using the
MetaboAnalyst 4.0, we performed pathway analysis for the metabolites
contributing to the classifications and identified the most relevant
pathways.
RNA Extraction and Real-Time Quantitative PCR
Total RNA was extracted in bone marrow cells from three groups
(leucopenia, QJSB and normal groups) mice using the Trizol reagent
(Thermo Fisher Scientific) according to the manufacturer’s
instructions. And cDNA was generated using the High-Capacity cDNA
Reverse Transcription Kit (Thermo Fisher Scientific). RT-qPCR was
performed using the Stratagene Mx3005P RT-PCR System (Applied
Biosystems) and the PowerUp^TM SYBR^® Green Master Mix (Thermo Fisher
Scientific), according to the protocol. Melt curves were analyzed at
the end of each assay to confirm the specificity. Fold change was
determined using the 2^-ΔΔCT method normalized with endogenous control
GAPDH. The PCR primers used are listed in [63]Supplementary Table S1.
Statistical Analysis
GraphPad was used for statistical analysis of the biochemical data. The
animals were randomly assigned by using the random permutations table.
The data were expressed as the means ± standard deviation (SD). The
data were analyzed using by two-tailed Student’s t-test or one-way
analysis of variance (ANOVA). P < 0.05 was considered statistically
significant. The transcriptomics data were processed by R package with
“DESeq2” and “clusterProfiler.” SICMA 14.1 (Umetric, Umeå, Sweden) was
used for MVA. Cytoscape was used to trace the associated gene–enzyme
relationship using KEGG database.
Results
QJSB Increased Peripheral WBCs and Platelets in Leucopenia Model Mice
After treatment with cyclophosphamide, the peripheral WBCs and
platelets were significantly decreased. After 1 week of treatment, QJSB
increased peripheral WBCs (P < 0.001) and platelets (P < 0.001)
significantly (approximately 2.6-fold and 2.4-fold, respectively)
compared with those in model group ([64]Figure 1A,B). Administration of
QJSB or cyclophosphamide had no significant effect on the other
peripheral hemogram parameters such as RBCs and hemoglobin
concentration ([65]Figure 1C,D). We also got the similar results when
the leucopenia model animals were treated by QJSB for 2 weeks
([66]Figure 1E,F). For WBC differential counts at 1 week of treatment,
cyclophosphamide decreased the percentage of monocytes and increased
the percentage of eosinophils. The percentage of eosinophils returned
to normal after being administrated with QJSB and leucogen. In
addition, an increase was observed in the percentage of basophils in
QJSB and leucogen groups ([67]Figure 2).
FIGURE 1.
[68]FIGURE 1
[69]Open in a new tab
The effect of QJSB on the peripheral WBC (A), platelet (B), RBC (C),
and HGB (D) parameters in leucopenia mice induced by cyclophosphamide.
After 1 week of treatment, QJSB increased peripheral WBCs (A) and
platelets (B) compared with model group, but had no significant effect
on the other peripheral hemogram parameters such as RBCs (C) and
hemoglobin concentration (D). After 2 week of treatment, QJSB also
increased peripheral WBCs (E) and platelets (F). Data are presented as
means ± SD. ^###Represent P < 0.001 vs. normal group and ^∗, ^∗∗, and
^∗∗∗ represent P < 0.05, 0.01, and 0.001 vs. model group, respectively.
FIGURE 2.
[70]FIGURE 2
[71]Open in a new tab
The effect of QJSB on WBC differential counts: neutrophil (A),
lymphocyte (B), monocyte (C), eosinophil (D), and basophil (E) in
leucopenia mice induced by cyclophosphamide. Cyclophosphamide increased
the percentage of eosinophils (D), and QJSB returned it to normal. An
increase was observed in the percentage of basophils (E) in QJSB group.
Data are presented as means ± SD. ^#Represent P < 0.05 vs. normal group
and ^∗, ^∗∗, and ^∗∗∗ represent P < 0.05, 0.01, and 0.001 vs. model
group, respectively.
QJSB Increased Bone Marrow Nuclear Cells and Enhanced Cell Viability in
Leucopenia Model Mice
The cell number and cell viability of bone marrow nuclear cells were
significantly decreased in the cyclophosphamide treatment group (P <
0.05). QJSB significantly increased the number of bone marrow nuclear
cells, and enhanced the cell viability (P < 0.001) ([72]Figure
3A–[73]C).
FIGURE 3.
[74]FIGURE 3
[75]Open in a new tab
The effect of QJSB on the cell number and cell viability of bone marrow
nuclear cells, colony formation of bone marrow mononuclear cells and
cytokines secretion. QJSB significantly increased the number of bone
marrow nuclear cells (A,B), enhanced the cell viability (C) and
promoted colony formation of bone marrow mononuclear cells (D). QJSB
significantly reversed the increases of G-CSF (E) and IL-6 (F) induced
by cyclophosphamide. Data are presented as means ± SD. ^###Represent P
< 0.001 vs. normal group and ^∗ and ^∗∗∗ represent P < 0.05 and 0.001
vs. model group, respectively.
QJSB Promoted Bone Marrow Mononuclear Cells Colony Formation
To determine the effect of QJSB on bone marrow hemopoietic
stem/progenitor cells, we performed the methylcellulose semisolid
colony-forming units assay. Mononuclear cells were extracted from the
bone marrow of ICR mice. Colony formation of bone marrow mononuclear
cells was significantly decreased by cyclophosphamide. After 1 week of
treatment, the colony number and colony size were both significantly
increased by QJSB (P < 0.001) ([76]Figure 3D).
QJSB Reversed Cytokines Secretion in Serum of Leucopenia Model Mice
The hematopoiesis-related cytokines are important factors for the
regulation of hematopoietic function ([77]Alexander, 1998). Both G-CSF
and IL-6 in serum were detected. Compared with the normal control
group, after cyclophosphamide treatment, the levels of G-CSF and IL-6
in serum were dramatically increased from 100.6 and 5.5 pg/ml to 1143.0
and 89.0 pg/ml, respectively. QJSB significantly reversed the increases
of G-CSF (from 1143 to 345 pg/ml, P < 0.001) ([78]Figure 3E) and IL-6
(from 89.0 to 9.4 pg/ml, P < 0.001) ([79]Figure 3F) induced by
cyclophosphamide.
Differential Expression Genes Identification and Functional Analysis
In order to identify potential molecular mechanisms, high-throughput
sequencing was used to identify the affected gene by QJSB in bone
tissue. The raw data of fastq format of RNAseq are available through
the National Center for Biotechnology Information’s Gene Expression
Omnibus (GEO^[80]5), and the GEO series accession number is
[81]GSE120707
. Then, the top 300 differentially expressed genes between QJSB
treatment and model group were identified, which included 172
upregulated and 128 downregulated genes. Comparing the leucopenia
groups with the normal groups, the top 300 differentially expressed
genes, with 14 upregulated and 286 downregulated genes, were detected.
Additionally, comparing the leucogen groups with the leucopenia groups,
the top 300 differentially expressed genes, with 177 upregulated and
123 downregulated genes, were also identified. The details of the top
300 differentially expressed genes are listed in [82]Supplementary
Table S2. To identify potential affected pathways of QJSB, KEGG pathway
enrichment analysis was performed using the top 300 differentially
expressed genes between QJSB and model group. We found that
“hematopoietic cell lineage,” “osteoclast differentiation, “B cell
receptor signaling pathway, “NOD-like receptor signaling pathway,”
“arachidonic acid metabolism,” and “Ferroptosis” were significantly
enriched (P < 0.05) ([83]Figure 4, left). To further identify the
biological processes, we did GO terms enrichment analysis and found the
most significantly enriched terms are “homeostasis of number of cells,”
“cellular response to TGF-β stimulus,” “TGF-β receptor signaling
pathway,” “response to TGF-β,” “lymphocyte differentiation,”
“regulation of immune effector process,” and “T cell activation”
([84]Figure 4, right). The detail parameters of pathways and GO terms
are shown in [85]Tables 1, [86]2. These results indicate that QJSB may
influence these pathways and biological process, thus increasing
peripheral WBCs and platelets in leucopenia model mice.
FIGURE 4.
[87]FIGURE 4
[88]Open in a new tab
Pathway enrichment analysis and GO analysis. KEGG pathway enrichment
analysis for top 300 differentially expressed genes in QJSB-treated
mice is shown left. GO analysis for top 300 differentially expressed
genes in QJSB-treated mice is shown right.
Table 1.
Pathways enrichment analysis of differently expressed genes in bone
marrow cells of QJSB-treated mice.
ID Description GeneRatio P-value
mmu04380 Osteoclast differentiation 0.0550 0.0035
mmu04662 B cell receptor signaling pathway 0.0390 0.0049
mmu04621 NOD-like receptor signaling pathway 0.0550 0.0148
mmu04640 Hematopoietic cell lineage 0.0390 0.0154
mmu04216 Ferroptosis 0.0240 0.0248
mmu00590 Arachidonic acid metabolism 0.0310 0.0480
mmu04974 Protein digestion and absorption 0.0310 0.0497
[89]Open in a new tab
Table 2.
GO enrichment analysis of differently expressed genes in bone marrow
cells of QJSB-treated mice.
ID Description GeneRatio P-value
GO:0002697 Regulation of immune effector process 0.0540 0.0000
GO:0030098 Lymphocyte differentiation 0.0540 0.0001
GO:0042110 T cell activation 0.0610 0.0001
GO:0019369 Arachidonic acid metabolic process 0.0220 0.0001
GO:0048872 Homeostasis of number of cells 0.0430 0.0003
GO:0071560 Cellular response to TGF-β stimulus 0.0320 0.0003
GO:0071559 Response to TGF-β 0.0320 0.0004
GO:0007179 TGF-β receptor signaling pathway 0.0280 0.0004
[90]Open in a new tab
RWR-Based Evaluation of QJSB
As mentioned above, the top 300 differentially expressed genes were
identified from leucopenia-normal groups, leucogen-leucopenia groups,
QJSB-leucopenia groups, respectively. We took the top 300 genes of each
set as seeds to apply the RWR algorithm. In total, 153 genes from
leucopenia-normal groups were mapped to the background network and set
as disease associated genes, while 252 genes from QJSB and 233 genes
from leucogen were mapped and set as drug’s potential target genes. We
calculated the Z-score as described in Materials and Methods. The
results are listed in [91]Table 3. As shown in [92]Table 3, the Z-score
for QJSB is 6.156 (larger than 3), and it is 10.823 for leucogen. These
results suggest that QJSB and leucogen have significant effects against
leucopenia in mice from perspective of network analysis.
Table 3.
The effect scores of QJSB and leucogen on leukopenia.
Drug QJSB Leucogen
Overlap 252 233
Revelance 0.1248 0.1590
Z-score 6.1560 10.8230
[93]Open in a new tab
Differential Metabolites Identificationand Metabolic Pathway Analysis
The plasma samples were subjected to LC/MS analysis in both the
positive ion mode (ESI+) and negative ion mode (ESI-). To discriminate
the metabolic profiles among normal, model control and QJSB group, we
performed clustering analysis using PCA, and the supervised PLS-DA and
OPLS-DA. The plasma sample from different groups tended to separated
according to the PCA plots either in ESI+ or ESI-mode ([94]Figure 5A).
Furthermore, the PLS-DA score scatter plots further evidenced the
significant separation among the normal, model and QJSB groups either
in ESI+ or ESI-mode ([95]Figure 5B). To further identify the
significant metabolites contributing to the classifications among these
three groups, supervised OPLS-DA was adopted between two groups either
in the ESI+ or ESI- modes together. The permutations plot was used to
assess the OPLS-DA model and the results showed the model was highly
significant and non-overfitting ([96]Supplementary Figure S1). The
quality of the OPLS-DA model was shown in [97]Supplementary Table S3.
The result suggested that there was the remarkable separation in model
vs. normal and model vs. QJSB. There were 51 and 47 metabolites
differently regulated in leucopenia (vs. normal) and QJSB treatment
(vs. leucopenia model) mice, respectively ([98]Supplementary Table S4).
Then, in order to identify the key metabolic pathway, we did metabolic
pathway enrichment analysis using MetaboAnalyst 4.0 ([99]Tables 4,
[100]5). As shown in [101]Figure 6A, in leucopenia model (vs. normal),
the differential metabolites primarily participate in
glycerophospholipid metabolism, primary bile acid biosynthesis
phenylalanine, tyrosine and tryptophan biosynthesis and phenylalanine
metabolism. These metabolic anomalies were found to be primarily
involved in lipid metabolism and amino acid metabolism. After QJSB
treatment (vs. leucopenia model), the differential metabolites also
were primarily enriched in lipid metabolism (glycerophospholipid
metabolism, ether lipid metabolism, and linoleic acid metabolism) and
amino acid metabolism (tryptophan metabolism) ([102]Figure 6B). The
results indicate that QJSB is likely involved in the modulation of the
metabolic disorders.
FIGURE 5.
[103]FIGURE 5
[104]Open in a new tab
Analysis of metabolic profiles from normal, model and QJSB groups based
on UPLC-QTOF-MS. The SIMCA-derived PCA (A) and PLS-DA (B) score plot
among three groups either in ESI+ or ESI– mode, respectively.
Table 4.
Pathways enrichment analysis of differential metabolites in bone marrow
cells of model mice.
Term Total Expected Hits Raw P-value
Phenylalanine, tyrosine, and tryptophan biosynthesis 4 0.0565 1 0.0553
Glycerophospholipid metabolism 30 0.4234 2 0.0650
D-Glutamine and D-glutamate metabolism 5 0.0706 1 0.0687
Taurine and hypotaurine metabolism 8 0.1129 1 0.1077
Limonene and pinene degradation 8 0.1129 1 0.1077
Phenylalanine metabolism 11 0.1553 1 0.1452
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis 14 0.1976 1
0.1812
Citrate cycle (TCA cycle) 20 0.2823 1 0.2489
Butanoate metabolism 22 0.3105 1 0.2702
Alanine, aspartate, and glutamate metabolism 24 0.3387 1 0.2910
Primary bile acid biosynthesis 46 0.6493 1 0.4855
Purine metabolism 68 0.9598 1 0.6286
Aminoacyl-tRNA biosynthesis 69 0.9739 1 0.6341
Steroid hormone biosynthesis 72 1.0162 1 0.6501
[105]Open in a new tab
Table 5.
Pathways enrichment analysis of differential metabolites in bone marrow
cells of QJSB-treated mice.
Term Total Expected Hits Raw P-value
Glycerophospholipid metabolism 30 0.3387 3 0.0040
Synthesis and degradation of ketone bodies 5 0.0565 1 0.0553
Linoleic acid metabolism 6 0.0677 1 0.0660
Alpha-Linolenic acid metabolism 9 0.1016 1 0.0974
Ether lipid metabolism 13 0.1468 1 0.1378
Glycosylphosphatidylinositol (GPI)-anchor biosynthesis 14 0.1581 1
0.1476
Butanoate metabolism 22 0.2484 1 0.2225
Steroid biosynthesis 35 0.3952 1 0.3312
Arachidonic acid metabolism 36 0.4065 1 0.3390
Tryptophan metabolism 40 0.4517 1 0.3691
Purine metabolism 68 0.7678 1 0.5467
[106]Open in a new tab
FIGURE 6.
[107]FIGURE 6
[108]Open in a new tab
Metabolic pathway analysis of the differential metabolites using the
MetaboAnalyst 4.0. (A) Representative pathway analysis of the
metabolites in leucopenia mice. (B) Representative pathway analysis of
the metabolites in QJSB-treated mice. All the matched pathways are
displayed as circles. The color and size of each circle was based on
the P-value and pathway impact value, respectively. The smaller P-value
means the pathway with higher levels of significance.
Correlation Networks Construction of Differential Genes and Metabolites
In order to obtain a comprehensive view of the complex mechanisms of
QJSB, we combined the transcriptomics-based network pharmacology and
metabolomics data to obtain a system-wide view of the therapeutic
mechanism of QJSB. Using the Metscape plugin of Cytoscape, we
constructed the correlation network between the differential genes and
differential metabolites regulated in QJSB-treated mice to analyze
their potential relationships. The results suggested that a lot of
metabolites and genes were in the same metabolic pathways, including
glycerophospholipid metabolism, linoleate metabolism, squalene and
cholesterol biosynthesis, glycine, serine, alanine and threonine
metabolism, histidine metabolism, tryptophan metabolism, purine and
pyrimidine metabolism, etc. As shown in [109]Figure 7, these metabolic
pathways mainly were mainly grouped into three classes: lipid
metabolism, amino acid metabolism and nucleotide metabolism.
FIGURE 7.
FIGURE 7
[110]Open in a new tab
Correlation networks construction, including lipid metabolism (A),
amino acid metabolism (B), and nucleotide metabolism (C), between the
differential metabolites and differentially expressed genes. The dark
red hexagonal represents detected metabolites, the shallow red
hexagonal represents in-direct metabolites. The green square represents
protein (enzyme). The blue circle represents genes that code for
corresponding proteins.
In the lipid metabolic pathways, the levels of metabolites such as
phosphatidylcholine (lecithin) and 2-lysolecithin were elevated, and
the expression of some genes encoding important metabolic enzymes like
Pcyt1a, Phospho1, Cyp4f18, Alox5, and Ggt5 were also augmented by QJSB
treatment. Amino acids and nucleotides are essential components of
proteins and nucleic acids, respectively, and amino acid metabolism and
nucleotide metabolism play an important role in biological synthesis
and metabolism of amino acids and nucleotides ([111]Gutteridge and
Coombs, 1977; [112]Ballantyne, 2001; [113]Bender, 2012). In these
metabolic pathways, the level of the metabolite tryptamine and the
expression of GGTLA1, HDC, NDST1, and ATP6V1C2 were increased, while
the level of the metabolite deoxyadenosine and the expression of
ATP6V1G2, ATP2B2, and POLR3H were decreased. These results suggested
that QJSB might affect biological synthesis and metabolism of amino
acids and nucleotides by regulating amino acid metabolism and
nucleotide metabolic pathways.
QJSB Increased the Gene Expression of Ggt5, Cyp4f18, Pcyt1a, Alox5, and
Phospho1 in Leucopenia Model Mice
To further investigate the effects of QJSB on the regulation of
metabolic pathways, we measured the expression of five genes, Ggt5,
Cyp4f18, Pcyt1a, Alox5, and Phospho1, according to our previous data.
They are key genes encoding important metabolic enzymes. RT-qPCR was
performed in bone marrow cells from leucopenia model mice induced by
cyclophosphamide. Compared to the normal control, the expressions of
Ggt5, Cyp4f18, Pcyt1a, Alox5, and Phospho1 were significantly decreased
in leucopenia model groups. QJSB significantly increased the mRNA
levels of these genes ([114]Figure 8, P < 0.01). These results are
consistent with our data from transcriptomics analysis.
FIGURE 8.
[115]FIGURE 8
[116]Open in a new tab
The effects of QJSB on the expression of five lipid metabolism related
genes (Ggt5, Cyp4f18, Phospho1, Alox5, and Pcyt1a) in bone marrow cells
from leucopenia model mice. Data are presented as means ± SD. ##
represents P < 0.01 vs. normal group and ^∗∗ represents P < 0.01 vs.
model group.
Discussion
Leucopenia is a very common adverse reaction induced by chemotherapy
([117]Xu et al., 2011; [118]Cui et al., 2015). In China, TCM formulae
have been widely used to treat leucopenia. Among them, QJSB is a newly
developed TCM formula and has been used clinically. In this study, our
experiments conclude that QJSB has protective effects against
leucopenia. As the theory of pharmachemistry of TCM, more scientists
accept the viewpoint that the effective constituents may be detected in
serum ([119]Wang, 2006). We have identified 24 prototype compounds in
the serum of rats after the oral administration of QJSB ([120]Wu et
al., 2018), which revealed the potential active substances of the
formula to some extent ([121]Supplementary Table S5). The network
pharmacology technology is a powerful tool to investigate the
therapeutic effects and molecular mechanisms ([122]Zhao et al., 2009;
[123]Zhang et al., 2017). We also employed a transcriptomics-based
network pharmacology approach to determine that the mechanism was
involved in the cell proliferation and differentiation, metabolism
response and immunologic function. Functional enrichment analysis was
performed to explore the biological processes of the top 300
differentially expressed genes. Usually, the top 300 differentially
expressed genes under the treatment of a drug are regarded as the
drug’s potential target genes, and the top 300 differentially expressed
genes between the model and control groups are regarded as disease
associated genes. The relevant parameters between the drug’s potential
target genes and disease associated genes and the Z-score were
calculated to evaluate the effect of drugs. A Z-score value greater
than 3 often indicates a statistically significant deviation between
the actual value and the random ones ([124]Fang et al., 2017). In this
study, the Z-score was 6.156. Thus, QJSB might have significant effects
against leucopenia disease. Using the transcriptomics-based network
pharmacology and metabonomics technology, we propose a model for QJSB
multi-pathways treatment mechanism ([125]Figure 9). We concluded that
QJSB mainly participates in the metabolism response, cell proliferation
and differentiation, and the immune response, etc.
FIGURE 9.
FIGURE 9
[126]Open in a new tab
Multi-pathways treatment mechanism of QJSB on leucopenia in mice. The
orange rectangle represents the key gene and the black rectangle
represents the key metabolite. Red arrow represents up-regulated.
The hematopoietic cell lineage is mainly involved in blood cells
development progresses from a rare population of hematopoietic stem
cells (HSCs). HSCs can undergo either self-renewal or differentiation
into multilineage committed progenitor cells: common lymphoid
progenitors (CLPs) or common myeloid progenitors (CMPs), and
successively become more restricted in their differentiation capacity.
They finally generate functionally mature cells such as lymphocytes,
granulocytes, monocytes, and erythrocytes, et al. Among them,
lymphocytes, including B and T cells, constitute a major proportion
(more than 80%) of leukocytes ([127]Dintzis and Treuting, 2011;
[128]O’Connell et al., 2015). B and T cells are the primary effector
cells during the adaptive immune response ([129]Medzhitov and Janeway,
1997; [130]Iwasaki and Medzhitov, 2015). B cell receptor signaling
pathway is involved in B lymphocyte proliferation, differentiation,
survival and activation ([131]Jacob et al., 2002; [132]Schweighoffer et
al., 2013; [133]Reth and Nielsen, 2014). T cell activation is a vital
event for immune system, and only the activated T cell can exert an
efficient immune response. Cd3d and Tnfsf13b are key genes in these
pathways. The protein encoded by Cd3d is part of the T-cell
receptor/CD3 complex and is involved in T-cell activation and signal
transduction. Cd3d-deficient patients show a complete block in T cell
development. Deficiency of Cd3d also impairs T cell-dependent functions
of B cells and causes severe immunodeficiency ([134]de Saint Basile et
al., 2004; [135]Gil et al., 2011; [136]Munoz-Ruiz et al., 2016). The
protein encoded by Tnfsf13b plays roles in the survival and maturation
of both of B and T cells ([137]Pfister et al., 2011; [138]Liu et al.,
2016). Besides lymphocytes, both eosinophils and basophils are also
involved in the immune response ([139]Jacobsen et al., 2011;
[140]Voehringer, 2011). The imbalance of eosinophils and basophils
might also affect the hematopoiesis ([141]Tebbi et al., 1980;
[142]Enokihara et al., 1996; [143]Schneider et al., 2010). In this
study, the expression levels of Cd3d and Tnfsf13b were both
up-regulated in QJSB group and recovered the abnormity of eosinophils
and basophils induced by cyclophosphamide. These data indicate that
QJSB might participate in the regulation of the immune effector
process.
Although most HSCs normally exist in a quiescent or dormant state
([144]Wilson et al., 2008), some of them divide and support the
production of all mature blood cell types through multiple intermediate
progenitor stages, during the steady state, and in response to
urgencies to maintain blood cell number homeostasis ([145]Busch et al.,
2015; [146]Sawai et al., 2016; [147]Grinenko et al., 2018). Itgam is
mainly involved in adhesion and migration of leukocytes. It is
necessary for HSCs expansion in vitro and engraftment in vivo
([148]Prashad et al., 2015). Patients with Itgam variants have reduced
switched memory B-cell counts ([149]Maggadottir et al., 2015). Ets1 is
a key transcription factor required for CD8 T cell differentiation
([150]Zamisch et al., 2009). It is a critical regulator of group 2
innate lymphoid cells expansion and cytokines production ([151]Zook et
al., 2016). In this study, the different expression genes of Itgam and
Ets1 are simultaneously enriched in QJSB group (vs. leucopenia group).
These data indicate that HSCs expansion, lymphocyte differentiation and
cytokines production may also be involved in the protective mechanism
of QJSB.
Hematopoiesis-related cytokines are important factors for the
regulation of hematopoietic function ([152]Alexander, 1998). For
example, IL-6 was first identified and characterized as a
lymphocyte-stimulating factor according to its ability to promote the
activation and population expansion of T cells, the differentiation and
survival of B cells, and the regulation of the acute-phase response
([153]Hunter and Jones, 2015). G-CSF, also known as colony-stimulating
factor 3 (Csf3), is the major hematopoietic growth factor involved in
the control of neutrophil development. G-CSF supports the
proliferation, survival, and differentiation of neutrophilic progenitor
cells in vitro and provides non-redundant signals for the maintenance
of steady-state neutrophil levels in vivo ([154]van de Geijn et al.,
2003). G-CSF also participates in the development of other myeloid
lineages, the mobilization of HSCs and myeloid cell migration
([155]Liongue et al., 2009). To determine how G-CSF was regulated by
QJSB, we constructed a subnetwork by extracting the links between G-CSF
and differentially expressed genes under the treatment of QJSB from our
background PPI network, i.e., the STRING network ([156]Supplementary
Figure S2). This network shows that G-CSF interacts with a group of
differentially expressed genes, including Itgam, Il7r, Il18, Ccr2,
Dpp4, Jun, and Ltf. Among these genes, Itgam and G-CSF receptor (CSF3R)
are two markers of granulocyte differentiation and it was found that
G-CSF could decrease Itgam expression ([157]Lantow et al., 2013;
[158]Lin et al., 2015). IL-18 (interleukin-18) is involved in the
hematopoietic progenitor cell growth and stimulates the secretion of
IL-6 and the expression of G-CSF mRNA in splenic adherent cells
([159]Ogura et al., 2001). Additionally, IL-18 treatment increases the
serum G-CSF level in C57BL/6 mice ([160]Kinoshita et al., 2011). It was
reported that when immortalized bone marrow progenitors are induced by
G-CSF to differentiate into mature neutrophils, the CCR2 gene is
strongly activated and CCR2 play a critical role in monocyte
recruitment ([161]Iida et al., 2005). G-CSF also increases CCR2 protein
expression of THP-1 monocytes ([162]Chen et al., 2008). As our data
shows, Itgam was up-regulated while Il18 and Ccr2 were down-regulated
by QJSB, a finding that was consistent with the decrease in the serum
G-CSF level. Excessive activation and release of cytokines impair the
hematopoietic function, and exhaust the production of hematopoietic
factor ([163]Hara et al., 2004). QJSB reversed the excessive exhaustion
of certain cytokines induced by cyclophosphamide, which might be
beneficial for the recovery of leucopenia.
Qi-Jing-Sheng-Bai granule also modulates the metabolism response,
including lipid metabolism, amino acid metabolism and nucleotide
metabolism. In lipid metabolism, the levels of metabolites such as
phosphatidylcholine (lecithin) and 2-lysolecithin are elevated, and the
expression of some genes encoding important metabolic enzymes like
Pcyt1a, Phospho1, Cyp4f18, Alox5, and Ggt5 are also augmented by QJSB
treatment. Additionally, RT-qPCR was performed to verify that QJSB
upregulated their mRNA levels. Phosphatidylcholine participates in a
series of biological activities such as biological membranes synthesis,
cell proliferation and platelet activation ([164]Ridgway, 2013; [165]Li
et al., 2014; [166]O’Donnell et al., 2014). Metabolic enzymes encoded
by Pcyt1a regulate the biological synthesis of phosphatidylcholine
([167]Haider et al., 2018). The high expression of Pcyt1a causes
elevated levels of phosphatidylcholine, which may result in accelerated
biological membranes synthesis and cell proliferation of WBCs and
platelets. Additionally, Cyp4f18, Alox5, and Ggt5 are involved in the
processes of generation, transformation and degradation of leukotriene
([168]Christmas et al., 2006; [169]Rådmark et al., 2015). Thus, QJSB
may promote lipid production by regulating lipid metabolism, and
regulate immune and inflammatory responses by affecting the generation,
transformation and degradation of leukotriene.
Amino acids and nucleotides are essential components of proteins and
nucleic acids, respectively. They are indispensable for cell
proliferation, survival and development. Leucogen and vitamin B4 are
very commonly used for the treatment of leucopenia. Leucogen is an
analog of cysteine while vitamin B4 is a precursor of adenine
([170]Lecoq, 1957; [171]Whelan, 2005; [172]Zheng et al., 2006;
[173]Langhammer et al., 2011). Therefore, regulating amino acid
metabolism and nucleotide metabolism have been confirmed to cure
leucopenia. Our data indicate that QJSB participate in the biological
synthesis and metabolism of energy, nutrition and genetic materials,
which are essential for cell proliferation, development and maturation.
Conclusion
In summary, our data reveal the therapeutic mechanism of QJSB by
integrative application of transcriptomics-based network pharmacology
and metabolomics technologies. QJSB exerts protective effect against
leucopenia in mice through participating in multi-pathways, mainly
including accelerating cell proliferation and differentiation,
regulating metabolism response pathways and modulating immunologic
function.
Ethics Statement
All animal studies were performed according to the institutional
ethical guidelines of animal care and were approved by the Committee on
the Ethics of Animal Experiments of the Second Military Medical
University, China.
Author Contributions
ST, PH, and YG collected and analyzed the data, and drafted and revised
the manuscript. JY, RW, and JZ collected and revised the manuscript. WZ
and A-JL designed the study, collected the data, and revised the
manuscript. All authors read and approved the final manuscript.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest. The handling Editor declared a shared
affiliation, though no other collaboration, with several of the authors
at the time of review.
Funding. The work was supported by Professor of Chang Jiang Scholars
Program, NSFC (81520108030 and 21472238), Shanghai Engineering Research
Center for the Preparation of Bioactive Natural Products (16DZ2280200),
the Scientific Foundation of Shanghai China (13401900103 and
13401900101), and the National Key Research and Development Program of
China (2017YFC1700200).
^1
[174]https://string-db.org/
^2
[175]https://metlin.scripps.edu/
^3
[176]www.hmdb.ca
^4
[177]https://www.genome.jp/kegg/
^5
[178]http://www.ncbi.nlm.nih.gov/geo/
Supplementary Material
The Supplementary Material for this article can be found online at:
[179]https://www.frontiersin.org/articles/10.3389/fphar.2019.00408/full
#supplementary-material
FIGURE S1
Distinct discrimination by comparison between Normal vs. Model (A,C),
Model vs. QJSB (B,D) either in ESI+ and ESI-. OPLS-DA was used to
distinguish the cluster and its permutations plot was used to assess
the current OPLS-DA model.
[180]Click here for additional data file.^ (1.2MB, zip)
FIGURE S2
The protein-protein network of Csf3 (G-CSF) with differentially
expressed genes between QJSB and model group. Csf3 (G-CSF) was
surrounded by differentially expressed genes based on the STRING
database.
[181]Click here for additional data file.^ (1.2MB, zip)
TABLE S1
The primers of the key genes encoding important metabolic enzymes.
[182]Click here for additional data file.^ (1.2MB, zip)
TABLE S2
The top 300 differentially expressed genes were identified in Model vs.
Normal, QJSB vs. Model and Leucogen vs. Model groups.
[183]Click here for additional data file.^ (1.2MB, zip)
TABLE S3
Summary of the LC-MS data sets used in OPLS-DA modeling. R2X (cum)
represents the cumulative X-variation modeled after components, R2Y
means the fraction of Y-variation modeled in the component, and Q2
expresses overall cross-validated R2Y for the component and is used to
an estimate the model prediction. Cumulative values of R2X, R2Y, and Q2
close to 1 indicate an excellent model.
[184]Click here for additional data file.^ (1.2MB, zip)
TABLE S4
Differential metabolites of plasma in Model vs. Normal and QJSB vs.
Model were identified by OPLS-DA on SIMCA software. RT is retention
time on gas chromatograph. MZ is the ratio of protons and charge
number. Fold change is the ratio of relative abundance of differential
metabolites. The data were calculated by t test.
[185]Click here for additional data file.^ (1.2MB, zip)
TABLE S5
The active substances of absorbed prototype compounds in QJSB and
theirs evidence.
[186]Click here for additional data file.^ (1.2MB, zip)
References