Molecular analysis of the mouse brain exposed to chronic mild stress: The influence of hepatocyte nuclear factor 4α on physiological homeostasis
- Authors:
- Published online on: May 11, 2017 https://doi.org/10.3892/mmr.2017.6577
- Pages: 301-309
Abstract
Introduction
Major depressive disorder (MDD) is one of the most prevalent mental disorders in developed countries such as Japan, and its frequency is rapidly increasing (1,2). However, the etiological and psychobiological mechanisms for the development of MDD remain unclear, even though pharmacological agents for MDD have been extensively investigated, with a particular focus on serotonergic, adrenergic and/or dopaminergic dysfunction (3). Clinical and basic research of depressive status has indicated that there are close associations and strong interactions among the prefrontal cortex (PFC), thalamus and hippocampus (4–7). For example, stressful conditions increase activity in the PFC and limbic or prelimbic regions, resulting in hyperactivity of the hypothalamic-pituitary-adrenal axis and hippocampus (4,5). Despite such associations between these parts of the brain in healthy people and in depressive subjects, the mechanism underlying their interactions remains unknown.
Previous studies have indicated significant associations between MDD and biomarkers that can be detected in serum, including cytokines, brain-derived neurotrophic factor (BDNF) and hormones in patients treated with corticosteroids or interferon therapy (8–12). For example, levels of serum BDNF decreased and levels of inflammatory cytokines increased in patients with MDD (13–15). However, these factors were also affected by diurnal variation or by physical conditions (16,17). Therefore, there is still insufficient evidence to support their validity and applicability as diagnostic markers or as biological indicators for assessing the severity of MDD. Thus, even though MDD is a disorder associated with psychological, sociocultural or neurobiological factors and also with more pervasive physiological dysfunctions, the underlying mechanisms for interactions between these factors have yet to be elucidated.
The group have previously performed molecular analyses to assess the associations between MDD and dysregulation of physiological homeostasis. Hepatocyte nuclear factor 4α (Hnf4α) was identified as a candidate central regulator that has crucial influence on immunological function, lipid metabolism, coagulation, hormonal activity and the synthesis of amines (18). As Hnf4α is a transcription factor, it is difficult to measure in serum, and the target molecules regulated by Hnf4α that are detectable in serum have not been examined. Therefore, the influence of Hnf4α on the development of clinical MDD or depressive status in humans has not been determined.
The aim of the present study was to investigate: i) the molecular mechanism of the depressive state in the PFC; and ii) the involvement of genes extracted from comparative gene expression analysis that may mediate the associations between MDD and physiological diseases using a chronic mild stress (CMS) animal model of MDD. In addition, the present study determined whether specific molecules regulated by Hnf4α may exist in the PFC and serum, and examined the possibility that these molecules may be reliable indicators of clinical MDD.
Materials and methods
Details regarding the experimental animals, the CMS procedure, sample collection, RNA purification, microarray analysis, reverse transcription-quantitative polymerase chain reaction (RT-qPCR), western blotting and Ingenuity Pathway Analysis (IPA) have been described in our previous studies (18,19).
Animals
A total of 50 experimentally naïve male C57BL/6N mice (Japan SLC, Inc., Shizuoka, Japan) were used. Mice were 9–10 weeks old and weighed 22.3 g on average at the start of the experiment. Mice were housed in groups of 3, 4 or 5 in polycarbonate cages that were placed in a colony room maintained at a constant temperature (22±1°C) and humidity (50–60%), under a 12-h light/dark cycle (lights on at 7:00 am) with free access to food and water.
Animals were randomly assigned to one of two groups: The control group (C group) mice and the chronic mildly stressed (CMS) group mice, as previously described (18). The procedure to induce CMS is described in Table I and was performed over 4 weeks. A total of 16 mice from each group were euthanized by decapitation using a guillotine, and brain samples were collected for molecular analyses.
Animal experiments were conducted according to the Guide for Care and Use of Laboratory Animals published by the National Institutes of Health, and was approved by the Ethical committee of Behavioral and Medical Science Research Consortium (Hyōgo, Japan; approval IDs: 2012-B-09 and 2012-B-10). All efforts were made to minimize the number of mice used and the suffering of animals.
RNA purification
Total RNA was purified from the mouse brain using a Sepasol-RNA I Super kit (Nacalai Tesque, Inc., Kyoto, Japan), according to the manufacturer's instructions, and was treated with 5 units of RNase-free DNase I at 37°C for 30 min to remove genomic DNA contamination. Following phenol/chloroform (Wako Pure Chemical Industries, Ltd., Osaka, Japan) extraction and 100% ethanol (Wako Pure Chemical Industries, Ltd.) precipitation, as previously described (18,19), total RNA was dissolved in deionized distilled water. RNA concentrations were determined using a NanoDrop-1000 spectrophotometer (NanoDrop Technologies; Thermo Fisher Scientific, Inc., Pittsburgh, PA, USA).
Microarray analysis
The microarray analysis of the whole genome was outsourced to Takara Bio, Inc. (Mie, Japan). The protocol details are described below (series entry, GSE49867).
RNA quality check
RNA was re-quantified using a NanoDrop-2000 spectrophotometer (NanoDrop Technologies; Thermo Fisher Scientific, Inc.), according to the manufacturer's protocol, and the quality was monitored with an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA, USA).
Labeling protocol (1 color)
Cyanine-3 (Cy3)-labeled cRNA was prepared from 0.1 µg total RNA using the Low Input Quick Amp Labeling kit (Agilent Technologies, Inc.), followed by RNeasy column purification (Qiagen, Inc., Valencia, CA, USA), according to the manufacturer's protocol. Dye incorporation and the cRNA yield were checked with a NanoDrop ND-2000 spectrophotometer (NanoDrop Technologies; Thermo Fisher Scientific, Inc.).
Hybridization protocol
A total of 0.6 µg Cy3-labeled cRNA was fragmented at 60°C for 30 min in a reaction volume of 25 µl containing 1X Agilent fragmentation buffer and 2X Agilent blocking agent, following the manufacturer's instructions (Agilent Technologies, Inc.). Upon completion of the fragmentation reaction, 25 µl 2X Agilent hybridization buffer was added to the fragmentation mixture and hybridized to an Agilent SurePrint G3 Mouse GE 8×60 K array (cat. no. G4858A-028005) for 17 h at 65°C in a rotating Agilent hybridization oven (Agilent Technologies, Inc.).
Following hybridization, microarrays were washed for 1 min at room temperature with GE Wash buffer 1 (Agilent Technologies, Inc.) and for 1 min at 37°C with GE Wash buffer 2 (Agilent Technologies, Inc.).
Scanning protocol
Following washing, slides were immediately scanned on the Agilent DNA Microarray Scanner (cat. no. G2565CA; Agilent Technologies, Inc.) using a one-color scan setting for 8×60 K array slides (scan area, 61×21.6 mm; scan resolution, 3-µm; the dye channel set was to green, and the green photomultipier was set to 100%).
Data processing
The scanned images were analyzed with Feature Extraction Software v10.10.1.1 (Agilent Technologies, Inc.) using default parameters to obtain background subtracted and spatially detrended processed signal intensities.
Value definition
Scaled signal intensities were adjusted to an average intensity value of 2,500 (in arbitrary units).
IPA
Microarray data were analyzed using IPA software version spring 2016 (Ingenuity® Systems; http://www.ingenuity.com), to provide functionality for the interpretation of gene expression data. The network explorer of IPA was used to identify relevant interactions, functions and diseases among the CMS and C group genes, and to determine the shortest direct paths between genes. Firstly, to investigate the molecular mechanism of MDD and the influence of Hnf4α, core analysis settings of microarray results were performed as follows: Network, Interaction; Data Sources, all; Confidence, Experimentally Observed; Species, human and mice; Tissues, Cerebral cortex; Mutation, all. Subsequently, for investigating novel molecules that are able to be detected in serum, core analysis settings were set to default. Analysis was performed as described in our previous studies (18,19).
RT-qPCR
To validate the results obtained by the microarray analysis and IPA, RT-qPCR was performed. Total RNA (10 ng/reaction) extracted from the CMS and C groups was used with the RNA-direct SYBR® Green Real-Time PCR Master mix: One-step qPCR kit (Toyobo Co., Ltd., Osaka, Japan), according to manufacturer's protocol. Samples were run in duplicate reactions in 96-well plates. Median threshold cycle values were used to calculate fold changes (FCs) between the samples of the two groups. FC values were normalized to GAPDH expression, using the relative standard curve method. qPCR was performed using an Applied Biosystems 7500 Real-Time PCR System (Thermo Fisher Scientific, Inc.), under the following thermocycling conditions: 30 sec at 90°C and 20 min at 61°C for reverse transcription according to the manufacturer's protocol, followed by 45 cycles of 98°C for 1 sec, 67°C for 15 sec and 74°C for 35 sec. The primer sequences for RT-qPCR are presented in Table II, and the production of these primers was outsourced to Sigma-Aldrich; Merck KGaA (Darmstadt, Germany).
Western blotting
For western blotting, mouse brains were minced in Lysis buffer [1% Nonidet P-40, 20 mM Tris-HCl (pH 8.0), 150 mM NaCl, 10% glycerol] containing a protease inhibitor cocktail (Complete™; Roche Diagnostics, Tokyo, Japan). They were then homogenized on ice using a sonicator (Sonifier II; Branson; Emerson Ultrasonics, CT, USA), and each lysate was centrifuged at 4°C in 13,000 × g for 3 min and the supernatant was collected. The protein concentration in each specimen was determined with a Bradford protein assay kit (Bio-Rad Laboratories, Inc., Hercules, CA, USA), according to the manufacturer's protocol. Samples were denatured in Laemmli's sample buffer (cat. no. #09499-14; Nacalai Tesque, Inc.) for 5 min at 95°C, electrophoresed in a 12.5% sodium dodecyl sulfate polyacrylamide gel, and transferred onto a polyvinylidene difluoride membrane (Hybond-P; Amersham; GE Healthcare Life Sciences, Chalfont, UK). Membranes were blocked for 1 h at room temperature with 1% bovine serum albumin in phosphate-buffered saline (PBS) containing 0.1% Triton X-100 (T-PBS), then incubated with primary antibodies at 4°C overnight. The membranes were probed with polyclonal rabbit anti-insulin-like growth factor 1 (IGF1; dilution 1:100; cat. no. #NBP1-45641; Novus Biologicals, LLC, Littleton, CO, USA), polyclonal rabbit anti-transthyretin (TTR; dilution 1:200; cat. no. sc-13098; Santa Cruz Biotechnology, Inc., Dallas, TX, USA), polyclonal rabbit anti-S100 calcium-binding protein A9 (S100A9; dilution 1:1,000; cat. no. #NB110-89726; Novus Biologicals, LLC), polyclonal rabbit anti-α2-HS-glycoprotein (AHSG; dilution 1:250; cat. no. #bs-2922R; BIOSS, Beijing, China), monoclonal rabbit anti-β-actin (ACTB; dilution 1:1,000; cat. no. #5125S; Cell Signaling Technology, Inc., Danvers, MA, USA) and monoclonal rabbit anti-GAPDH (dilution 1:1,000; cat. no. #3683S; Cell Signaling Technology, Inc.) antibodies. Membranes were then incubated for 3 h at room temperature with horseradish peroxidase-conjugated donkey anti-rabbit immunoglobulin G secondary antibody (dilution 1:2,000; cat. no. #NA9340V; GE Healthcare Life Sciences). Washing with T-PBS was performed following each treatment. Antibody reactions were captured using the photo-image analyzer, LAS-4010 (Fujifilm Corporation, Tokyo, Japan). The density of specific protein bands was measured twice using ImageJ software version 1.6 (National Institutes of Health, Bethesda, MD, USA). AHSG and S100A9 expression was normalized to GAPDH, whereas IGF1 and TTR expression was normalized to ACTB. The mean of the measured bands in controls was set to one. The present study also assessed HL-60 whole cell lysate (cat. no. #NB800-PC3, Novus Biologicals, LLC) and mature liver lysate, isolated from the same mice, as positive controls of S100A9 and AHSG, respectively.
Statistical analysis
All results are expressed as the mean ± standard deviation. Sigmaplot™ (version 11.0; Systat Software, Inc., San Jose, CA, USA) was used for all statistical analyses. Differences between the two groups were analyzed by Student's t-test or the Mann-Whitney U-test. P<0.05 was considered to indicate a statistically significant difference. All analyses were performed >2 times to confirm the results.
Results
Isolation and classification of cortex-specific genes in CMS mice
The microarray results have been published previously (18). A total of 494 genes whose expression was >2X or <1/2 that of the C group were extracted from the CMS group. The IPA results from the microarray data using the first settings are shown in Table III. In this analysis, 5 genes were identified, coagulation factor II (F2), Igf1, plasminogen (Plg), Ttr and serine (or cysteine) peptidase inhibitor, clade A, member 3 (Serpina3). In addition, Igf1, Plg, Serpina3 and Ttr were affected by Hnf4α (Fig. 1A).
Investigation of novel genes with a connection between depression and physiological homeostasis
To investigate novel molecules that are associated with physiological homeostasis, are able to be detected in serum and are regulated directly by Hnf4α, a number of molecules from all of the extracted genes were chosen automatically (Fig. 1B). In the present study, the main focus was the analysis of S100a9 and Ahsg as they were directly affected by only Hnf4a (thus excluding multiple regulation) and may be detected in serum with simple probes in microarray analysis.
IPA analysis of 494 genes indicated that S100a9 and Ahsg may affect the development of a number of physical diseases. S100a9 affects arteriosclerosis associated with vascular diseases or ischemia of the brain and rheumatic diseases (Table IV). Ahsg is associated with lipid concentration, rheumatic diseases and glucose tolerance (Table V).
RT-qPCR
Previously, Spearman's rank collection test identified a significant correlation between the microarray and RT-qPCR data for S100a9 and Ahsg in the PFC (18). Additional comparisons for F2, Igf1, Plg and Ttr in the PFC between groups are shown in Table VI. S100a9 expression was significantly decreased in the hippocampus, although not in the thalamus (Fig. 2A). By contrast, Ahsg expression was significantly increased in the thalamus, although not in the hippocampus (Fig. 2B).
 | Table VI.Comparison of gene expression levels as determined by microarray and reverse transcription-quantitative polymerase chain reaction experiments. |
Western blotting
When the protein levels of IGF1 and TTR were measured in the PFC, thalamus and hippocampus, no differences were observed between the C and CMS groups (Fig. 3A-C). The augmented expression of S100A9 and AHSG in the PFC, thalamus and hippocampus of CMS mice was further examined. In accordance with the microarray and RT-qPCR results, quantitative analysis of the representative blots indicated enhanced synthesis of these two proteins in the PFC of the CMS mice. S100A9 expression in the PFC of the CMS group was higher compared with that in the C group (Fig. 4A and B). In contrast with the mRNA levels in the hippocampus, S100A9 levels were significantly higher in the hippocampus of CMS mice when compared with those of the C group; however, there was no difference in S100A9 levels in the thalamus (Fig. 4A and B). Similar to the microarray and RT-qPCR results, ASHG levels in the PFC of the CMS group were significantly increased when compared with those in the C group (Fig. 4A and C). By contrast, no difference was observed in the thalamus or hippocampus (Fig. 4A and C).
Discussion
The present study revealed the following clinical and pathophysiological features of MDD: i) F2 and Plg, which are strongly regulated by Hnf4α, and Serpina3 may be induced in the depressive state PFC through Hnf4α; and ii) S100A9 and AHSG are potential biomarkers for the development of physical disease in patients with MDD.
A total of 5 genes, F2, Igf1, Plg, Serpina3 and Ttr, were extracted from the IPA analyses according to the molecular mechanisms of MDD in the PFC. As shown in Table III, 2 out of 10 annotations, ‘cell death of cerebral cortex cells’ and ‘cell death’, were associated with cell death processes such as apoptosis (20–25). F2, Plg and Serpina3 may increase ‘cell death’, and F2 and Serpina3 may also be associated with ‘apoptosis of neurons’ (23–25). F2 and Plg were categorized as ‘coagulation’, whose dysfunction may be closely associated with MDD (18,26,27). In addition, postmortem studies of depressed patients have revealed morphometric changes, such as smaller sized cell bodies in PFC regions (28). Ttr mRNA in the PFC was higher in CMS with analgesia models, which is consistent with the finding that serum levels of TTR in patients with depression were higher compared with those of healthy people (29,30). In addition, Igf1, Plg, Serpina3 and Ttr were affected by Hnf4α (Fig. 1A) (31–35). These results indicated that Igf1, Plg, Serpina3, and Ttr, which are strongly regulated by Hnf4α, and F2 may affect the molecular mechanism of MDD development in the PFC.
S100a9 in the hippocampus is affected by exposure to chronic or repeated social stress, which may promote the migration of leukocytes to the brain (36,37). In addition to regulating inflammation, S100a9 also regulates responses to fibrosis, arteriosclerosis and infarction (38–43). S100A9 levels in the CMS group were higher compared with those in the C group, which was consistent with the mRNA levels in the PFC. In the hippocampus, S100A9 levels in the CMS group were higher compared with those in the C group, which was inconsistent with the mRNA levels. According to the Allen Brain Atlas (http://mouse.brain-map.org/), S100a9 is expressed at low levels in the hippocampal formation and thalamus of a normal mouse (http://mouse.brain-map.org/gene/show/19965) (44). S100A9, which is regulated by Hnf4α was upregulated in the PFC and hippocampus (18,32). Thus, S100a9/S100A9 may be upregulated in the depressive state through Hnf4α/HNF4A. This suggests that S100a9/S100A9 may have a role in chronic social stress and also in the development of physical diseases, such as inflammation or ischemia of the brain.
Clinical research has revealed a significant association between Ahsg and cognitive dysfunctions that are frequently observed in patients with MDD (45). Ahsg was categorized in immune disorders, and also in physical functions of glucose and lipid homeostasis (46,47). Our previous results indicated that the CMS model had hypertriglycemia, and that there may be a significant association between MDD and metabolic disorders (18). The present study revealed that Ahsg levels in the PFC and thalamus of the CMS group were higher compared with those of the C group (Fig. 2B). According to the Allen Brain Atlas (http://mouse.brain-map.org/), Ahsg is expressed at relatively low levels in a normal mouse cortex and thalamus (http://mouse.brain-map.org/gene/show/11412) (44). In addition, there were significant group differences in the level of AHSG in the PFC, similar to those of HNF4A (18). Thus, AHSG may affect physiological homeostasis and lead to physical diseases, such as metabolic disorders, under MDD conditions.
In a meta-analysis of clinical studies, levels of interleukin (IL)-6 and tumor necrosis factor (TNF)-α were significantly higher in patients with MDD when compared with those in normal controls (13). This finding supports a potentially close association between MDD and the inflammatory response. In our previous study, levels of inflammatory cytokines, such as IL-5 and TNF-α, were higher in the CMS group when compared with those in the C group (18). These results were consistent with previous findings from clinical and animal studies, and support the occurrence of inflammation during the course of MDD (13,48). In addition, a clinical study revealed that serum S100A9 levels were greater in patients with an autoimmune disorder or rheumatoid arthritis when compared with healthy controls (41). From the IPA results, S100a9, Tnfα and Il12 may regulate each other (49–51), which further supports the close association between MDD and the inflammatory response. As serum AHSG levels are associated with the probability of metabolic syndrome and insulin resistance (52,53), these two molecules may increase the risk of developing physical diseases in patients with MDD.
Regarding the limitations of the present study, S100A9 and AHSG were measured in only three brain regions in an animal model. To more thoroughly examine our hypotheses and to verify clinical relevance, particularly in association with physiological functions, metabolism should be analyzed in peripheral organs. In addition, measurement of the S100A9 and AHSG serum levels in this model would clarify the interactions between depression and other diseases; however, this was not possible in the present study due to a lack of serum. Therefore, further studies are required to evaluate these roles and the potential associations between other molecules associated with MDD and physiological homeostasis, including lipid metabolism or immune reactions. Finally, Serpina3 was not measured by RT-qPCR, as different subtypes were detected multiple times on the microarray (GSE49867 on the Gene Expression Omnibus web page; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE49867).
In conclusion, the present study demonstrated that a number of molecules directly regulated by Hnf4α in the PFC may be closely associated with the development of MDD. S100A9 and AHSG were clearly expressed in the brain and may link depression with physiological homeostasis. Though there were a number of limitations in the present study, the results may help to clarify the mechanism mediating the interactions between MDD and physiological homeostasis in humans.
Acknowledgements
The authors would like to thank Mr Nobutaka Okamura (Department of Neuropsychiatry, Hyōgo College of Medicine, Hyōgo, Japan), Mrs. Naomi Gamachi (Laboratory of Tumor Immunology and Cell Therapy, Hyōgo College of Medicine, Hyōgo, Japan) and Ms. Emi Yamaguchi (Laboratory of Tumor Immunology and Cell Therapy, Hyōgo College of Medicine, Hyōgo, Japan) for their technical support; Mr Nobutaka Okamura for his assistance in the care of animals and collection of samples, Mrs. Naomi Gamachi for skilled western blotting, and Ms. Emi Yamaguchi for RNA purification and clerical support. In addition, the authors are grateful to the staff in the Research Facilities for Common Use, Hyōgo College of Medicine (Hyōgo, Japan), for allowing the use of their resources for RT-qPCR and western blotting. Finally, the authors thank the editing staff of Edanz Group Japan (Fukuoka, Japan) for their editorial support and proofreading.
Glossary
Abbreviations
Abbreviations:
|
ACTB |
β-actin |
|
Ahsg/AHSG |
α2-HS-glycoprotein |
|
Bdnf |
brain-derived neurotrophic factor |
|
CMS |
chronic mild stress |
|
F2 |
coagulation factor II |
|
Hnf4α/HNF4A |
hepatocyte nuclear factor 4α |
|
Igf1/IGF1 |
insulin-like growth factor 1 |
|
Il/IL |
interleukin |
|
IPA |
Ingenuity Pathway Analysis |
|
MDD |
major depressive disorder |
|
PFC |
prefrontal cortex |
|
Plg |
plasminogen |
|
RT-qPCR |
quantitative reverse transcription polymerase chain reaction |
|
S100a9/S100A9 |
S100 calcium-binding protein A9 |
|
Serpina3 |
serine (or cysteine) peptidase inhibitor, clade A, member 3 |
|
Tnfa/TNFA |
tumor necrosis factor α |
|
Ttr/TTR |
transthyretin |
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