OPP samples were prepared according to the methods described in Sambanthamurthi et al. [22
]. OPP contain numerous phenolic acids. Three isomers of caffeoylshikimic acid are major components of the extract [23
]. Other phenolic acids include caffeic acid, protocatechuic acid and p
-hydroxybenzoic acid. The detailed composition of OPP is as described earlier [24
Animal feeding and sample collection
All male inbred BALB/c mice that were designated for this study were purchased from the Institute of Medical Research, Kuala Lumpur, Malaysia, at around 5 weeks of age just after weaning. All animal procedures were approved by the Animal Care and Use Committee of the University of Malaya, Kuala Lumpur, Malaysia. The animals were randomly assigned into cages (n = 5 per cage) and acclimatized for 1 week, during which a standard chow diet purchased from the University of Malaya and distilled water were given ad libitum. At the start of the experiment, the diet of the animals was changed to a custom-made low-fat normal diet (58.2 % kcal/kcal carbohydrate, 27.2 % kcal/kcal protein and 14.6 % kcal/kcal fat, including cellulose, mineral mix, vitamin mix and DL-methionine) or a custom-made high-fat atherogenic diet (40.5 % kcal/kcal carbohydrate, 19.0 % kcal/kcal protein and 40.5 % kcal/kcal fat, including 0.15 % w/w cholesterol, cellulose, mineral mix, vitamin mix and DL-methionine) for 6 weeks ad libitum. The ND + DW group (n = 10) and the AD + DW group (n = 10) were supplemented with distilled water, while the AD + OPP group (n = 10) was supplemented with OPP, as drinking fluid ad libitum. The phenolic content of the OPP given was around 1,500 ppm gallic acid equivalent. Food and fluid were changed daily. During the animal feeding process, body weights were monitored every week, while fluid intake was monitored every day for 6 weeks. Food intake and fecal output were monitored for seven consecutive days in the middle of the designated feeding period, between week two to week three. The mice were killed via euthanasia with diethyl ether followed by exsanguination after 6 weeks of feeding following an overnight food fast (with fluids still provided). Blood samples were collected via cardiac puncture. Three major organs including livers, spleens and hearts were excised, blotted, weighed, snap-frozen in liquid nitrogen and stored at −80 °C until the total RNA extraction process.
Hematology and clinical biochemistry analyses
Hematology and clinical biochemistry analyses were carried out by the Clinical Biochemistry and Hematology Laboratory, Department of Veterinary Pathology and Microbiology, Faculty of Veterinary Medicine, Universiti Putra Malaysia, Serdang, Selangor, Malaysia, using the Animal Blood Counter Vet Hematology Analyzer (Horiba ABX, France) and the Roche/Hitachi 902 Chemistry Analyzer (Roche/Hitachi, Switzerland), respectively. Aliquots (200 μL) of whole blood samples (n = 4) were stored in tubes containing ethylenediaminetetraacetic acid for hematology analysis. In order to obtain sera, blood samples were allowed to clot at room temperature for 2 h before being centrifuged at 1,000×g for 5 min, after which the supernatant layers were collected and stored at −20 °C. A portion (100 μL) of each serum sample (n = 6 per group) was kept in aliquots for cytokine profiling and antioxidant analysis. The remaining serum samples (around 200 μL per replicate) were subjected to clinical biochemistry analysis for eleven parameters. Two samples in the ND + DW group, two samples in the AD + DW group and three samples in the AD + OPP group were excluded due to blood lysis.
Total RNA extraction
Total RNA isolation from mouse organs was carried out using the RNeasy Mini Kit (Qiagen, Inc., Valencia, CA) and QIAshredder homogenizers (Qiagen, Inc., Valencia, CA), preceded by grinding in liquid nitrogen using mortars and pestles. The total RNA samples obtained were subjected to NanoDrop 1000A Spectrophotometer (Thermo Fisher Scientific, Waltham, MA) measurement for yield and purity assessment. Integrity of the total RNA samples was then assessed using the Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA) and Agilent RNA 6000 Nano Chip Assay Kit (Agilent Technologies, Santa Clara, CA). Four total RNA samples with the highest RNA Integrity Numbers and 28S/18S rRNA ratios within each condition were then selected for microarray studies.
Microarray gene expression analysis
Amplification of total RNA samples that were of high yield, purity and integrity was carried out using the Illumina TotalPrep RNA Amplification Kit (Ambion, Inc., Austin, TX). The biotinylated cRNA produced was then hybridized to the Illumina MouseRef-8 Version 1 Expression BeadChip (Illumina, Inc., San Diego, CA), using the Direct Hybridization Kit (Illumina, Inc., San Diego, CA). Microarray hybridization, washing and scanning were carried out according to the manufacturer’s instructions. The raw gene expression data obtained are available at Gene Expression Omnibus [28
] (Accession number: GSE30908).
Quality control of the hybridization, microarray data extraction and initial analysis were carried out using the Illumina BeadStudio software (Illumina, Inc., San Diego, CA). Outlier samples were removed via hierarchical clustering analysis provided by the Illumina BeadStudio software and also using the TIGR MeV software (The Institute for Genomic Research, Rockville, MD) [29
]. A minimum of three replicates per condition (with outliers removed) was then considered for further analysis. For livers, spleens and hearts of mice in the ND + DW group, four replicates were analyzed for each organ, respectively. On the other hand, for livers, spleens and hearts of mice in the AD + DW and AD + OPP groups, three replicates were analyzed for each organ, respectively, with the exception of livers in the AD + DW group, in which four replicates were analyzed. Three comparisons were made in this study, with the first comparison to find out gene expression changes caused by the atherogenic diet (AD + DW:ND + DW), the second comparison to identify gene expression changes caused by OPP (AD + OPP:AD + DW) and the third to identify genes that were differentially regulated by the two factors. The first two comparisons were carried out separately before the third comparison was made.
For the first two comparisons, gene expression values were normalized using the rank invariant method and genes that had Detection Levels of more than 0.99 in either condition (control or treatment) were considered significantly detected. To filter the data for genes that changed significantly in terms of statistics, the Illumina Custom error model was used and genes were considered significantly changed at a |Differential Score| of more than 20, which was equivalent to a P
value of less than 0.01 [30
]. The stringency of this filtering criterion was lowered to a |Differential Score| of more than 13, which was equivalent to a P
value of less than 0.05, if less than 100 genes were found significantly changed. Since the results of this statistical analysis were to be used for functional analysis, it was relevant to include more genes by using a lower threshold to give statistical power to the functional analysis. The genes and their corresponding data were then exported into the Microsoft Excel software (Microsoft Corporation, Redmond, WA) for further analysis. To calculate fold changes, an arbitrary value of 10 was given to expression values which were less than 10. Fold changes were then calculated by dividing means of Signal Y (treatment) with means of Signal X (control) if the genes were up-regulated and vice versa if the genes were down-regulated. Two-way (gene and sample) hierarchical clustering of the significant genes was then performed using the TIGR MeV software to ensure that the replicates of each condition were clustered to each other. The Euclidean distance metric and average linkage method were used to carry out the hierarchical clustering analysis. Changes in biological pathways and gene ontologies were assessed via functional enrichment analysis, using the GenMAPP [31
] and MAPPFinder [32
] softwares (University of California at San Francisco, San Francisco, CA). The MAPPFinder software ranks GenMAPPs (pathways) and gene ontologies based on hypergeometric distribution. GenMAPPs and gene ontologies that had Permuted P
values of less than 0.01, Numbers of Genes Changed of more than or equal to 2 and Z Scores of more than 2 were considered significant. A Permuted P
value of less than 0.05 was used when genes were selected using a |Differential Score| of more than 13, in order to identify more GenMAPPs and gene ontologies affected. Boxes colored yellow indicate genes that were up-regulated, while those colored blue indicate genes that were down-regulated. The fold changes are indicated next to the boxes. Individual boxes that have different shadings within them indicate the presence of multiple probes (splice transcripts) within a single gene. Changes in regulatory networks were also analyzed through the use of the Ingenuity Pathways Analysis software (Ingenuity®
Systems, Redwood City, CA). A network is a graphical representation of the molecular relationships between genes or gene products. Genes or gene products were represented as nodes, and the biological relationship between two nodes was represented as an edge (line). The intensity of the node color indicates the degree of up-regulation (red) or down-regulation (green). Nodes were displayed using various shapes that represented the functional class of the gene product. Edges were displayed with various labels that described the nature of the relationship between the nodes.
In order to assess how OPP affected genes changed by the atherogenic diet, the significantly changed genes obtained in the first comparison were intersected with significantly changed genes obtained in the second comparison in order to obtain a set of genes that were significantly regulated by both factors (atherogenic diet and OPP), hence the third comparison. The directions of fold changes for genes in this intersecting set were then compared in order to identify the number of genes that were differentially regulated by both factors in terms of direction.
Real-time qRT-PCR validation
Two-step real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was carried out on six target genes from the second comparison (Online Resource 1 in Supplementary Material 1), selected to represent the different organs used in the microarray experiments, using TaqMan Gene Expression Assays (Applied Biosystems, Foster City, CA). The same aliquots of total RNA samples used in the microarray experiments were utilized for this analysis. Primer and probe sets for the selected genes were obtained from the ABI Inventoried Assays-On-Demand (Applied Biosystems, Foster City, CA).
Briefly, reverse transcription to generate first-strand cDNA from total RNA was carried out using the High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA). Real-time PCR was then carried out on the first-strand cDNA generated using a 25 μL reaction volume in an Applied Biosystems 7000 Real-Time PCR System (Applied Biosystems, Foster City, CA) with the following conditions: 50 °C, 2 min, 1 cycle; 95 °C, 10 min, 1 cycle; 95 °C, 15 s and 60 °C, 1 min, 40 cycles. For gene expression measurements, reactions for each biological replicate and non-template control (NTC) were carried out in duplicates. For amplification efficiency determination, reactions were carried out in triplicates.
Quality control of the replicates used, real-time qRT-PCR data extraction and initial analysis were carried out using the 7000 Sequence Detection System software (Applied Biosystems, Foster City, CA). A manual threshold of 0.6000 and an auto baseline were applied in order to obtain the threshold cycle (Ct) for each measurement taken. The threshold was chosen as it intersected the exponential phase of the amplification plots [33
]. The criteria for quality control of the data obtained include
Ct less than 0.5 between technical replicates and
Ct more than 5.0 between samples and NTCs [34
Relative quantification of the target genes of interest was carried out using the qBase 1.3.5 software (Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium) [35
], which takes into account the calculations of amplification efficiencies and multiple housekeeping genes. Expression levels of target genes were normalized to the geometric mean of three housekeeping genes, Sfrs9
. Stability of these housekeeping genes was assessed using the geNorm 3.5 software (Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium) [36
Cytokine profiling on serum samples was performed using the Bio-Plex Suspension Array System (Bio-Rad Laboratories, Hercules, CA), available at the Faculty of Medicine, University of Malaya. This assay was carried out using the Bio-Plex Mouse Cytokine 23-Plex Cytokine Panel (Bio-Rad Laboratories, Hercules, CA), which contains antibody-conjugated beads (25× concentration) for 23 types of mouse cytokines including IL-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-17, eotaxin, G-CSF (granulocyte colony-stimulating factor), GM-CSF (granulocyte–macrophage colony-stimulating factor), IFN-γ (interferon-γ), KC (keratinocyte-derived chemokine), MCP-1 (monocyte chemoattractant protein-1 or also known as monocyte chemotactic and activating factor MCAF), MIP-1α (macrophage inflammatory protein-1α), MIP-1β, RANTES (regulated on activation, normal T cell expressed and secreted) and TNF-α (tumor necrosis factor-α). The kit also contains the necessary components such as detection antibodies (25× concentration) and standards. The experiment was carried out according to the manufacturer’s instructions. Each serum sample (n = 6) was tested in duplicates. The data were analyzed using the Bio-Plex Manager Version 4.0 software (Bio-Rad Laboratories, Hercules, CA). Generation of standard curves, averaging of duplicate fluorescence readings of each serum sample, background subtraction with the blank and calculation of concentration for each cytokine were carried out by the Bio-Plex Manager software. The averaged concentration readings were exported into the Microsoft Excel software (Microsoft Corporation, Redmond, WA) for statistical analysis.
Serum antioxidant analysis was carried out using four assays, including the total phenolics content by Folin-Ciocalteu reagent (TP-FCR) assay [37
], the ferric reducing ability of plasma (FRAP) assay [39
], the 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging activity assay [40
] and the Trolox equivalent antioxidant capacity (TEAC) assay [42
]. All these assays were carried out using the Infinite M200 microplate reader (Tecan, Austria). Each serum sample (n
= 6) was tested in duplicates. Measurement settings and data acquisition were carried out using the Magellan Version 6.2 software (Tecan, Austria). Generation of standard curves, averaging of duplicate absorbance readings of each sample, background subtraction with the blank, calculation of concentration and statistical analysis for each assay were carried out in the Microsoft Excel software (Microsoft Corporation, Redmond, WA).
Statistical analysis was carried out by using the two-tailed unpaired Student’s t test available in the Microsoft Excel software (Microsoft Corporation, Redmond, WA) unless otherwise stated. Differences with P values of less than 0.05 were considered statistically significant.