Abstract

   Techniques for analyzing genome-wide expression profiles, such as the
   microarray technique and next-generation sequencers, have been
   developed. While these techniques can provide a lot of information
   about gene expression, selection of genes of interest is complicated
   because of excessive gene expression data. Thus, many researchers use
   statistical methods or fold change as screening tools for finding gene
   sets whose expression is altered between groups, which may result in
   the loss of important information. In the present study, we aimed to
   establish a combined method for selecting genes of interest with a
   small magnitude of alteration in gene expression by coupling with
   proteome analysis. We used hypercholesterolemic rats to examine the
   effects of a crude herbal drug on gene expression and proteome
   profiles. We could not select genes of interest by using standard
   methods. However, by coupling with proteome analysis, we found several
   effects of the crude herbal drug on gene expression. Our results
   suggest that this method would be useful in selecting gene sets with
   expressions that do not show a large magnitude of alteration.

   Keywords: Proteome, Transcriptome, Crude drug, Pathway

Highlights

     * •
       Herbal crude drug SJG had mild effects on the gene expression
       profile.
     * •
       It was difficult to select genes altered by SJG with conventional
       methods.
     * •
       We established analytical method for transcriptome by using
       proteome experiments.
     * •
       We identified genes of interest that could not be selected by
       conventional methods.

1. Introduction

   Recently, methods for analyzing genome-wide expression profiles, such
   as microarrays and next-generation sequencing, have been developed.
   These methods enable us to obtain a large amount of gene expression
   data, easily and inexpensively. While the massive amount of data
   provides an overview of certain biological phenomena on the basis of
   suitable statistical analyses, there are several limitations. In
   general, analysis of genome-wide expression data is carried out by: (1)
   normalization of the expression data, (2) selection of genes of
   interest from the data, and (3) visualization of expression profiles
   [37][1]. The most important step is the selection of genes of interest
   and determining the biological significance of those selected gene set.
   In addition, most researchers select genes with statistically altered
   expression or when the magnitude of the fold change between groups is
   large, because it is difficult to limit the numbers of genes selected
   by using small thresholds [38][2], [39][3], [40][4]. However, such
   methods for the selection of genes often result in the loss of
   information because genes that demonstrate large variation in
   expression within groups or minor changes in expression between groups
   are not selected as genes of interest. Two-dimensional difference gel
   electrophoresis (2D-DIGE) is a new technique that compares the
   abundance of a large number of proteins, and many researchers have
   attempted to use this technique in various areas of research [41][5],
   [42][6]. However, compared to studies utilizing model organisms that
   have sufficient genomic information, there are limitations in analyzing
   the proteome of non-model organisms whose genomes have not been
   sequenced [43][7], and the throughput of the experiment is lower than
   that of transcriptome analysis. Additionally, both proteome and
   transcriptome analyses have a similar problem in the selection of
   candidates, that is, loss of information. Thus, transcriptome and
   proteome analyses have both merits and demerits. It is necessary to
   develop new methods for analyzing transcriptome and proteome data
   without the loss of information.

   In our previous study, we used microarray analysis to determine the
   effects of a combined extract of Sasa albo-marginata leaves (kumazasa;
   Sasa kurinensis Makino), Japanese red pine leaves (Pinus densiflora
   Sieb. et Zucc), and ginseng roots (Panax ginseng C.A. Meyer) (SJG) on
   high-cholesterol diet (HCD)-induced hypercholesterolemia [44][8]. In
   that study, we finally confirmed that expression of the gene encoding
   cytochrome P450 7A1 was altered in response to HCD and HCD with SJG,
   although we could not select the gene as a candidate gene of interest
   by using standard microarray analysis because of the minor change in
   gene expression [45][8]. This result suggests that it may be difficult
   to determine the molecular mechanism underlying the action of crude
   drugs using standard methods because changes in gene or protein
   expression caused by crude herbal drugs may be smaller than those
   caused by most pharmaceutical drugs. However, several plant polyphenols
   are known to play a major role in reducing risk of arteriosclerosis
   [46][9], [47][10], [48][11], [49][12], [50][13], suggesting that small
   molecules from plants have useful health effects. In this study, we
   aimed to select genes of interest whose transcript or protein
   expression profiles showed small changes by using transcriptome
   analysis coupled with proteome analysis and used crude herbal
   drug-treated rats as model animals.

2. Materials and methods

2.1. Reagents

   Immobilized pH gradient strip (pH 3–10), pharmalytes, drystrip cover
   fluid, bromophenol blue, agarose, Cy2, Cy3, and Cy5 were purchased from
   GE Healthcare (Little Chalfont, UK). Protease inhibitor cocktail
   (Pefabloc SC and Pefabloc SC protector) was purchased from Roche
   (Mannheim, Germany). Sequence-grade trypsin was obtained from Promega
   UK (Southampton, Hants, UK). All other chemicals used in this study
   were of the highest grade available and were purchased from GE
   Healthcare, Dojin Chemical Japan (Osaka, Japan), Sigma (St. Louis, MO),
   Wako (Osaka, Japan), Nacalai Tesque (Kyoto, Japan), or Kanto (Tokyo,
   Japan). The crude herbal drug SJG was prepared by Wakanyaku Medical
   Institute, Ltd. (Maebashi, Japan). SJG is composed of a water extract
   of kumazasa leaves and ethanol extracts of Japanese red pine leaves and
   ginseng roots in the ratio 8:1:1 [51][14]. SJG was supplied as a liquid
   preparation and diluted using tap water to 50% (v/v). We previously
   analyzed the component of SJG and found that SJG contained several
   compounds like tricin, p-coumaric acid, ginsenoside [52][8].

2.2. Animals and treatment

   Twenty male Wistar rats (age, 13 weeks) were placed in a room with
   controlled lighting (12-h light/dark cycle), temperature (23±1 °C), and
   humidity (55±10%). All rats were provided with food and tap water or
   50% SJG ad libitum throughout the experiment. We divided the rats into
   four groups (Groups 1, 2, 3, and 4). After 1 week of habituation, all
   rats were fed a standard diet (23% protein, 5% fat, and 55%
   nitrogen-free extract; MF, Oriental Yeast Co., Tokyo, Japan) and given
   water (Groups 1 and 3) or 50% SJG (Groups 2 and 4) for 12, 30, or 52
   weeks (experimental phase I, [53]Fig. 1). The rats were then provided
   with food and water as follows: standard diet and water, Group 1;
   standard diet and 50% SJG, Group 2; HCD and water, Group 3; and HCD and
   50% SJG, Group 4 for three weeks (experimental phase II, [54]Fig. 1).
   HCD was the standard diet supplemented with 0.5% cholesterol and 0.5%
   cholic acid. Then, the rats were anesthetized with diethyl ether and
   sacrificed. The plasma was collected for measuring the levels of
   biochemical parameters, such as glucose, total cholesterol,
   high-density lipoprotein (HDL) cholesterol, low-density lipoprotein
   (LDL) cholesterol, cholesteryl ester, free cholesterol, triglyceride
   (TG), free fatty acid (FFA), phospholipids, and total lipids, and the
   livers were collected for RNA extraction. The animal experiments were
   approved by the Animal Research Committee of Wakanyaku Medical
   Institute, Ltd., and performed in accordance with the Guidelines for
   Care and Use of Laboratory Animals at Wakanyaku Medical Institute, Ltd.
   and the Guidelines for Proper Conduct of Animal Experiments from
   Science Council of Japan.

Fig. 1.

   [55]Fig. 1
   [56]Open in a new tab

   Scheme of the experimental schedule. All rats were acclimatized for a
   week and fed a standard diet throughout habituation and the
   experimental phase I (12, 30, and 52 weeks). After habituation, the
   rats were divided into four groups. Groups 1 and 3 received water
   throughout the experimental phase, while groups 2 and 4 received 50%
   SJG. In experimental phase II (3 weeks), groups 3 and 4 were fed a
   high-cholesterol diet. Finally, all the rats were sacrificed, and
   plasma and liver were collected for analysis.

2.3. Plasma biochemical parameters

   Blood samples were obtained from the postcaval vein by using a vacuum
   blood collection tube containing heparin. Plasma levels of glucose,
   total cholesterol, HDL cholesterol, LDL cholesterol, cholesteryl ester,
   free cholesterol, TG, FFA, phospholipids, and total lipids were
   determined using the Glucose test Wako (Wako), L-type Wako cholesterol
   (Wako), Cholestest N HDL (Sekisui Medical Co., Ltd, Tokyo, Japan),
   Cholestest LDL (Sekisui), L-type Wako cholesterol (Wako) and L-type
   Wako free cholesterol (Wako), L-type Wako free cholesterol (Wako),
   L-type Wako TG (Wako), NEFA-SS Eiken (Eiken Chemical Co., Ltd, Tokyo,
   Japan), L-type Wako phospholipids (Wako), and Total lipids reagent
   Kokusai (Sysmex, Kobe, Japan), respectively.

2.4. RNA isolation and microarray analysis

   Isolation of total RNA from the liver and microarray analyses were
   performed according to the methods described in our previous study
   [57][8]. Total RNA was extracted from frozen rat livers by using the
   High Pure RNA Isolation Kit (Roche) and subjected to oligonucleotide
   array analysis (Agilent Microarray Design ID 028279; Agilent, Santa
   Clara, CA, USA) by using a single-color labeling system (Low Input
   Quick Amp Labeling Kit for One Color; Agilent). We performed t tests or
   Benjamini and Hochberg's method to compare signals between two groups,
   and a Venn diagram was created to extract genes whose expression was
   altered by the intake of HCD, and the expression was further modified
   by SJG. All analyses for microarray data were performed using linear
   models for microarray data (limma) package [58][15] or Subio platform
   software, and we finally identified cholesterol-regulated genes whose
   expressions were altered by SJG (Supplementary [59]Fig. S1A and B). To
   select the genes whose expression patterns were similar to certain
   gene, first of all, we obtained average vector of the gene. After that
   we calculated the Peason's correlation between the gene and remaining
   all genes on one-on-one level. Finally, we selected the genes which had
   high correlation (r>0.85).

2.5. 2D-DIGE

   2D-DIGE was performed as described by Yamanaka et al. [60][16]. In
   brief, liver samples were homogenized in 10 volumes of lysis buffer
   that contained 4% (w/v) CHAPS, 2 M thiourea, 8 M urea, 10 mM Tris–HCl
   (pH 8.8), 4 mM Pefabloc SC, 20 mM Pefabloc SC protector on ice by using
   a Potter type homogenizer, followed by sonication. After centrifugation
   was performed at 20,800×g for 20 min at 10 °C, the supernatant was
   collected as the protein sample. Protein concentration of each sample
   was determined using the dye-binding method [61][17] with transferrin
   as the standard, and protein samples (50 μg) were labeled with 200 pmol
   of N-hydroxy succinimidyl ester-derivatives of cyanine dyes Cy3 or Cy5.
   A mixture of all protein samples was labeled with Cy2 and used as the
   standard on all gels. Labeled samples and the standard were run on the
   same gel, and 2-dimensional electrophoresis was performed. We used
   wide-range immobilized pH gradient strips (pH 3–10, 24 cm). Separation
   in the first dimension was performed using Multiphor II (GE Healthcare)
   at 54 kV h and 20 °C under darkness. Then, we used 12% T, 7.5% C gels
   (24 cm×20 cm) for separation in the second dimension with Ettan DALT
   twelve (GE Healthcare) at 2 W/gel and 20 °C. The gels were scanned
   directly with the 2920 2D-Master Imager (GE Healthcare), and the
   scanned images were analyzed using the Decyder (Version 4.00) software
   (GE Healthcare) to quantify the amount of proteins. We performed t
   tests to compare signals between two groups, and a Venn diagram was
   created to select the proteins whose levels were altered by HCD intake,
   and the levels were further altered by SJG (Supplementary [62]Fig.
   S1C). Then, gel electrophoresis for mass spectrometry (MS) was
   performed using 600 μg of a mixture of all liver protein samples by
   using the same above mentioned procedure. After fixation and staining
   of the gels, selected proteins were obtained from the gels by using an
   automated spot picker (GE Healthcare), and the gels were washed with
   50 mM ammonium bicarbonate and acetonitrile. Then, the gels were
   incubated with 12.5 ng/μl trypsin (Promega) at 30 °C for 15 h. The
   peptides were eluted from the gels with 50 mM ammonium bicarbonate,
   followed by 10% (v/v) formic acid and acetonitrile. The solvent was
   changed to 0.1% (v/v) formic acid. The peptides were concentrated and
   desalted using the C18 PepMap column (300-μm id/5 mm in length; LC
   Packings, San Francisco, CA), followed by MS/MS sequencing with an
   automated MS-to-MS/MS switching protocol. Spectrum processing and peak
   annotations were performed, and processed spectra were searched using
   the Mascot database (Matrix Science, London, UK).

2.6. Statistical analysis

   The microarray and proteome data are presented as means of
   log-normalized expression±SEM. Differences between the groups were
   analyzed using Kruskal-Wallis analysis with Scheffe's
   multiple-comparison post-tests for non-normally distributed values or
   one-way analysis of variance (ANOVA) with Bonferroni's
   multiple-comparison post-tests for normally distributed values to
   evaluate the significance of the differences. A value of P<0.05 was
   considered statistically significant.

3. Results

3.1. Effects of SJG on plasma and liver parameters

   HCD rats were untreated or treated with SJG for 12 (short-term), 30
   (medium-term), or 52 (long-term) weeks. Total cholesterol levels were
   highest in rats from the long-term experiment and lowest in rats from
   the short-term experiment, suggesting that cholesterol levels become
   elevated with age. HCD-induced hypercholesterolemia was improved by
   treatment with SJG in rats from the medium-term experiment ([63]Table
   1). Similar tendencies were observed for the short- and long-term
   experiments. Thus, we examined the genome-wide expression profiles in
   all three experiments. Treatment with SJG did not affect HCD-induced
   elevation of LDL cholesterol and free cholesterol levels. We also found
   that treatment with SJG did not affect glucose, TG, FFA, phospholipids,
   or total lipid levels. In addition, HCD induced fatty liver, and
   treatment with SJG did not ameliorate this effect ([64]Table 1).

Table 1.

   Several parameters in plasma and liver.
   Sample Parameters Period Experimental conditions
     __________________________________________________________________
     __________________________________________________________________

   MF+water MF+SJG HCD+water HCD+SJG
   Blood Glucose (mg/dl) 12week 110.4±4.8 a 107.4±3.4 a 105.0±4.4 a
   111.8±7.4 a
   30week 122.8±9.8 a 125.6±6.8 a 115.0±5.2 a 126.4±4.2 a
   52week 111.6±4.2 a 110.8±7.2 a 111.0±6.4 a 101.8±1.2 a
   Total cholesterol (mg/dl) 12week 66.0±0.8 a 63.8±3.0 a 93.8±5.1 b
   91.2±5.2 b
   30week 73.6±3.8 a 79.8±6.3 a 112.6±4.8 b 91.0±4.3 a
   52week 108.2±5.4 a 87.6±8.6 a 124.6±19.5 a 113.2±3.9 a
   HDL cholesterol (mg/dl) 12week 17.6±0.4 a 17.8±0.4 a 22.0±0.5 b
   19.2±0.6 a
   30week 17.6±0.7 a 18.0±0.7 a 22.4±1.1 b 18.0±0.8 a
   52week 22.6±0.8 a 20.8±1.5 a 22.8±3.5 a 23.0±1.2 a
   LDL cholesterol (mg/dl) 12week 5.8±0.2 a 6.0±0.4 a 18.4±1.7 b 21.4±2.0
   b
   30week 6.0±0.3 a 6.8±0.7 a 20.8±1.4 b 17.6±1.1 b
   52week 12.8±1.0 a 10.0±0.9 a 29.2±5.8 b 25.8±1 b
   Cholesteryl ester (mg/dl) 12week 56.2±0.6 a 54.4±2.3 a 79.8±4.3 b
   78.4±4.5 b
   30week 54.6±2.9 a 59.4±4.5 ac 89.4±3.8 b 71.2±3.5 c
   52week 89.0±4.7 a 74.2±6.8 a 104.0±16.5 a 95.8±3.4 a
   Free cholesterol (mg/dl) 12week 9.8±0.4 a 9.4±0.7 a 14.0±0.8 b 12.8±0.7
   b
   30week 19.0±0.9 a 20.4±1.9 a 23.2±1.0 a 19.8±0.9 a
   52week 19.2±1.2 a 13.4±1.8 a 20.6±3.0 a 17.4±0.5 a
   TG (mg/dl) 12week 128.6±18.4 a 109.8±15.4 a 86.2±6.8 a 88.4±7.3 a
   30week 161.4±29.4 a 148.4±18.6 a 121.8±9.2 a 103.8±13.2 a
   52week 290.2±38.8 a 218.2±20.5 ac 156.8±22.2 bc 176.2±9.2 bc
   Free fatty acid (μEq/l) 12week 839.6±59.7 a 838.6±69.4 a 738.4±55.7 a
   809.0±79.1 a
   30week 787.2±42.4 a 845.0±74.7 a 911.8±65.4 a 736.0±45.8 a
   52week 964.6±54.7 a 981.0±46.7 a 869.2±89.0 a 928.6±52.3 a
   Phospholipids (mg/dl) 12week 113.2±2.8 a 109.4±4.3 a 113.2±3.5 a
   106.0±4.0 a
   30week 119.8±4.8 a 125.2±8.1 a 125.6±5.0 a 111.6±5.2 a
   52week 166.4±7.6 a 140.6±10.8 a 131.4±14.9 a 125.8±5.1 a
   Total lipids (mg/dl) 12week 308.4±17.2 a 287.0±18.8 a 337.8±15.7 a
   344.4±22.5 a
   30week 391.0±29.5 a 392.2±32.9 a 451.6±14.5 a 383.0±17.7 a
   52week 563.4±37.1 a 484.0±35.4 a 501.4±58.9 a 499.6±15.6 a
   Liver Total cholesterol (mg/100 g) 12week 506.8±9.8 a 488.0±8.8 a
   3582.5±271.0 b 4119.3±298.5 b
   30week 504.3±15.0 a 512.8±6.9 a 4302.0±190.9 b 3988.7±203.1 b
   52week 760.2±20.6 a 739.6±20.1 a 5112.5±373.1 b 4303.4±154.5 b
   [65]Open in a new tab

   Each value is the mean±SEM of four individuals. Different letters (a,
   b, and c) denote a statistically significant difference in means
   (P<0.05; one-way ANOVA with Bonferroni's multiple-comparison post-tests
   for multiple comparisons).

3.2. Effects of SJG on the genome-wide expression profile

   We performed microarray analyses to identify gene sets whose expression
   was altered in the presence of of HCD, and then further affected by the
   presence of SJG. We identified only one gene in the long-term
   experiment by using Limma software (Supplementary [66]Fig. S1A), and
   44, 29, and 163 genes in the short-, medium-, and long-term
   experiments, respectively (Supplementary [67]Fig. S1B), by using the
   Subio software. We did not identify any genes whose expression patterns
   were the identical or somewhat similar in the three experiments
   ([68]Fig. 2).

Fig. 2.

   [69]Fig. 2
   [70]Open in a new tab

   Results of genome-wide expression analysis of the liver of diet-induced
   hypercholesterolemic rats. Venn diagrams showing the number of genes
   altered by cholesterol or a combination of cholesterol and SJG in the
   short- (12 weeks), medium- (30 weeks), and long-term (52 weeks)
   experiments. The total number of genes whose expressions were altered
   by HCD intake and further modified by SJG was 44, 29, and 163 in the
   short-, medium-, and long-term experiments, respectively. No genes
   showed similar expression patterns in all three experiments.

3.3. Effects of SJG on the protein expression profile and comparison with the
microarray results

   We examined the protein expression profiles in the livers of rats from
   the long-term experiment because this group had the most affected genes
   in the standard microarray analysis (163 genes; Supplementary [71]Fig.
   S1B). Then, we selected the proteins whose levels were altered by the
   intake of HCD, and were further affected by treatment with SJG, using a
   similar procedure for standard microarray analysis (Supplementary
   [72]Fig. S1C), and identified paraoxonase 3 (PON3) and protein
   disulfide isomerase (PDIA1) as candidates ([73]Fig. 3A and B,
   respectively). We found that expression of PON3 and PDIA1 transcripts
   ([74]Fig. 3C and D, respectively) were comparable ([75]Fig. 3A versus
   3C; 3B versus 3D) to the protein expression. While PON3 protein levels
   and transcript levels of PON3 and PDIA1 in each experimental group
   showed normally distributed values, PDIA1 protein levels had non-normal
   distribution. The mean value and SEM of processed PON3 protein levels
   were 0.118 and 0.025 (MF+Water), −0.024 and 0.037 (MF+SJG), −0.106 and
   0.067 (HCD+Water), and −0.019 and 0.032 (HCD+SJG), respectively. The
   mean value and SEM of processed PON3 transcript levels were 0.014 and
   0.026 (MF+Water), 0.028 and 0.026 (MF+SJG), −0.056 and 0.036
   (HCD+Water), and 0.014 and 0.028 (HCD+SJG), respectively. The mean
   value and SEM of processed PDIA1 transcript levels were 0.029 and 0.033
   (MF+Water), 0.041 and 0.019 (MF+SJG), −0.056 and 0.021 (HCD+Water), and
   −0.015 and 0.015 (HCD+SJG), respectively. The median value, 25th
   percentile, and 75th percentile of processed PDIA1 protein levels were
   0.084, 0.059, and 0.112 (MF+Water), −0.004,−0.029, and 0.025 (MF+SJG),
   −0.075, −0.146, and −0.056 (HCD+Water), and 0.045, 0.033, and 0.053
   (HCD+SJG), respectively. We analyzed the PON3 protein levels and
   transcript levels of both genes using one-way ANOVA with Bonferroni's
   post-hoc test, and analyzed PDIA1 protein levels using Kruskal-Wallis
   test with Scheffe's post-hoc test. We found that protein levels of PON3
   and PDIA1 were altered significantly between the groups while we did
   not observe any significant differences in transcript levels of either
   gene. SJG was potent enough to recover HCD-induced reduction of PON3
   and PDIA1 levels.

Fig. 3.

   [76]Fig. 3
   [77]Open in a new tab

   Results of proteome and transcriptome analyses of the liver of
   diet-induced hypercholesterolemic rats. Two-dimensional gel
   electrophoresis of rat liver proteins was performed using immobilized
   pH 3–10 gradient strips, followed by SDS-PAGE (12% T, 7.5% C). Panels A
   and B represent quantified data for proteome analysis of PON3 and
   PDIA1, respectively. Panels C and D represent quantified data for
   transcriptome analysis of PON3 and PDIA1, respectively. The vertical
   axis represents the standardized quantity of the target protein or
   transcript for each sample (n=5 for proteome analysis and n=4 for
   transcriptome analysis). The open circle represents the standardized
   quantity of each sample. The line represents the average value for each
   treatment group. Different letters denote a statistically significant
   difference in means (P<0.05; Kruskal-Wallis analysis with Scheffe's
   multiple-comparison post-tests or one-way ANOVA with Bonferroni's
   multiple comparison post-test for multiple comparison).

   We searched for data about PDIA1 in the Kyoto Encyclopedia of Genes and
   Genomes (KEGG) database. This gene is associated with “protein
   processing in endoplasmic reticulum” (accession number, rno04141). We
   collected expression data for 30,451 transcripts by using microarray.
   Of the 30,451 genes, 184 were associated with the rno04141 pathway, and
   3998 had expression patterns that were similar to that of PDIA1
   (Pearson's correlation, r>0.85, [78]Table 2). Of the 3998 genes, 37
   genes (Supplementary [79]Table S1) were associated with the rno04141
   pathway ([80]Table 2). By analyzing these data with the Fisher's exact
   test, we found that the genes with expression patterns similar to that
   of PDIA1 were significantly enriched in the rno04141 pathway ([81]Table
   2, P<0.01). These results suggest that intake of HCD reduces the
   expression of genes in this pathway and that SJG may play a role in the
   recovery of that expression.

Table 2.

   Pathway enrichment analysis using Fisher’s exact test.
   Number of genes associated with the KEGG pathway rno04141 Number of
   genes not associated with the KEGG pathway rno04141 Total
   Number of genes whose expression was similar to PDIA1 37 3961 3998
   Number of genes whose expression was not similar to PDIA1 147 26,306
   26,453
   Total 184 30,267 30,451
   [82]Open in a new tab

   Genes whose expression was similar to that of PDIA1 were selected
   according to Peason's correlation (>0.85), and were enriched
   significantly in KEGG pathway rno04141 (protein processing in
   endoplasmic reticulum). P<0.01 (Fisher's exact test, two-tailed).

   PON3 is a member of the arylesterase/paraoxonase family, and it acts as
   an anti-atherosclerotic agent by binding to HDL [83][18]. We could not
   find atherosclerosis-related pathways in the KEGG database. Thus, we
   examined the expression profiles of genes related to the formation of
   atherosclerotic lesions, such as PON1, PON2, PON3, matrix
   metalloproteinase 9 (MMP9), scavenger receptor AI (SR-AI), CD36,
   lectin-type oxidized LDL receptor 1 (LOX1), and endothelial nitric
   oxide synthase (eNOS), and found that HCD-induced changes in the
   expression of these genes tended to improve with SJG intake ([84]Fig.
   4). However, the expression of several other genes with possible roles
   in the formation of atherosclerotic lesions, such as intercellular
   adhesion molecule 1 (ICAM1), vascular cell adhesion molecule 1 (VCAM1),
   C-C chemokine receptor type 2, macrophage colony-stimulating factor
   (MCSF), monocyte chemotactic protein-1, interleukin-6, and tumor
   necrosis factor alpha (TNF-α), were not affected (data not shown).

Fig. 4.

   [85]Fig. 4
   [86]Open in a new tab

   Expression profiles of the genes related to the formation of
   atherosclerotic lesions. Data were extracted from the microarray
   results. The panels represent the expression profiles of PON1 (A), PON2
   (B), PON3 (C), MMP9 (D), SR-AI (E), CD36 (F), LOX1 (G), and eNOS (H).
   The vertical axis represents the ratio of the quantity of the target
   gene transcript for each sample to that of group 1 samples as a
   magnitude of induction (fold change) after global normalization (n=4).
   Each value is mean±SEM. Different letters denote a statistically
   significant difference in means (P<0.05; one-way ANOVA with
   Bonferroni's multiple-comparison post-tests for multiple comparisons).

4. Discussion

   In the present study, we aimed to establish a combined method for
   analyzing data from transcriptome experiments coupled with proteome
   analyses, which enabled us to identify genes of interest that could not
   be selected using standard methods. Crude herbal drugs are usually
   prepared from several plants and are thought to have mild effects when
   compared to the effects of pharmaceutical agents. Thus, changes caused
   by treating organisms with herbal crude drugs may not be large enough,
   making it difficult to obtain useful information from genome-wide
   experiments using standard methods. First, we examined several serum
   parameters and found that total cholesterol and HDL cholesterol levels
   were elevated by HCD intake and that the HCD-induced increases of total
   cholesterol or HDL cholesterol were recovered by SJG in the medium-tern
   experiment or short- and medium-term experiments, respectively.
   Although the effect of SJG was not consistent among the short-,
   medium-, and long-term experiments, it is possible that SJG has
   beneficial effects on cholesterol homeostasis in liver.

   Next, we attempted to extract gene sets from genome-wide expression
   profiles using standard methods. However, we could not find gene(s)
   with expression commonly altered in short-, medium-, and long-term
   experiments ([87]Fig. 2). Additionally, PON3 and PDIA1, which were not
   selected by standard methods, were selected from proteome analysis.
   These results suggested that, at least in this study, it is difficult
   to obtain useful candidates using standard methods. Therefore, we
   performed proteome analysis of samples from the long-term experiment
   and found that PON3 and PDIA1 levels were reduced by HCD intake and
   that SJG recovered the levels ([88]Fig. 3A and B). Gene expression
   patterns of PON3 and PDIA1 in the long-term experiment were comparable
   to the protein expression patterns ([89]Fig. 3C and D). Based on these
   results, it is evident that we were unable to identify PON3 and PDIA1
   as genes of interest using standard methods. However, we could identify
   PON3 and PDIA1 as candidate proteins from the proteome analyses and
   found a correlation between the transcript and protein levels of both
   these genes. There might be several cases in which expression levels
   are different between the protein and the transcript because the
   protein functions are altered due to post-translational modifications
   like acetylation, methylation, or phosphorylation. Although, in such
   cases, it will be more difficult to identify the genes of interest from
   transcriptomic data, the combined analytical method is considered a
   powerful tool because it provides a large amount of data.

   We demonstrated that genes with expression patterns similar to that of
   PDIA1 were significantly enriched in the “protein processing in
   endoplasmic reticulum” pathway, suggesting that protein processing was
   decreased by HCD intake and that SJG had the ability to recover the
   HCD-induced decrease, even if the magnitude of the change was small
   ([90]Table 2).

   PON1 and PON3 had similar expression patterns ([91]Fig. 4). PON1 and
   PON3 are synthesized mainly in the liver and bind to HDL [92][18].
   PON1-HDL and PON3-HDL have antioxidant activity and prevent the
   oxidation of LDL during oxidative stress [93][19], [94][20]. Oxidized
   LDL plays a role in the occurrence or progression of atherosclerosis by
   acting as a signal molecule. Oxidative stress increases the migration
   of monocytes and upregulates the expression of adhesion molecules,
   including ICAM1 and VCAM1 [95][21]. After binding to adhesion
   molecules, monocytes are stimulated by MCSF to move into the
   subendothelial region [96][22]. Then, the monocytes are further
   stimulated by oxidized LDL in the subendothelial region, and they
   differentiate into macrophages [97][22]. Scavenger receptors, such as
   SR-AI, CD36, and LOX1, play an important role in these steps [98][22].
   In addition, inflammation, migration of smooth muscle cells, and
   interactions with the extracellular matrix increase cytokine levels,
   which causes the macrophages to form foam cells [99][23], [100][24],
   [101][25], [102][26], [103][27], [104][28]. MMP9 induces the rupture of
   atherosclerotic lesions and causes ischemic heart disease [105][22],
   [106][29]. Thus, the effects of SJG on HCD-induced reduction of PON1
   and PON3 may lead to the improvement of atherosclerosis. The expression
   of genes related to the formation of atherosclerosis, including
   scavenger receptors, adhesion molecules, and MMPs, was improved after
   treatment with SJG.

   In conclusion, we established a combined method for the analysis of
   transcriptome experiments by coupling with them with proteome
   experiments, and we identified genes of interest that were not
   identified using standard methods. These methods will be useful in
   identifying gene sets that do not show large changes in expression.

Acknowledgements