Edited by: Robert Rozeske, INSERM U862, France
Reviewed by: Philip Holmes, Princeton University, USA; Lindsey MacGillivray, University of Toronto, Canada
*Correspondence: Monika Fleshner, Department of Integrative Physiology and the Center for Neuroscience, University of Colorado Boulder, 1725 Pleasant Street, Clare Small 114, 354 UCB, Boulder, CO 80309-0354, USA. e-mail:
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and subject to any copyright notices concerning any third-party graphics etc.
Serotonin (5-HT) is implicated in the development of stress-related mood disorders in humans. Physical activity reduces the risk of developing stress-related mood disorders, such as depression and anxiety. In rats, 6 weeks of wheel running protects against stress-induced behaviors thought to resemble symptoms of human anxiety and depression. The mechanisms by which exercise confers protection against stress-induced behaviors, however, remain unknown. One way by which exercise could generate stress resistance is by producing plastic changes in gene expression in the dorsal raphe nucleus (DRN). The DRN has a high concentration of 5-HT neurons and is implicated in stress-related mood disorders. The goal of the current experiment was to identify changes in the expression of genes that could be novel targets of exercise-induced stress resistance in the DRN. Adult, male F344 rats were allowed voluntary access to running wheels for 6 weeks; exposed to inescapable stress or no stress; and sacrificed immediately and 2 h after stressor termination. Laser capture micro dissection selectively sampled the DRN. mRNA expression was measured using the whole genome Affymetrix microarray. Comprehensive data analyses of gene expression included differential gene expression, log fold change (LFC) contrast analyses with False Discovery Rate correction, KEGG and Wiki Web Gestalt pathway enrichment analyses, and Weighted Gene Correlational Network Analysis (WGCNA). Our results suggest that physically active rats exposed to stress modulate expression of twice the number of genes, and display a more rapid and strongly coordinated response, than sedentary rats. Bioinformatics analyses revealed several potential targets of stress resistance including genes that are related to immune processes, tryptophan metabolism, and circadian/diurnal rhythms.
Depression and anxiety frequently coexist and are the most common mood disorders affecting society. The World Health Organization estimates that 121 million people currently suffer from depression. Individuals suffering from depression have significant impairment in quality of life (Rapaport et al.,
Stressful life events often precede the onset of depression (Kendler et al.,
To investigate the neural circuitry underlying stress-related mood disorders, researchers use animal models (Krishnan and Nestler,
Numerous components of the serotonergic system such as serotonin (5-HT) receptors, the 5-HT transporter, and extracellular 5-HT levels are sensitive to stress. Serotonergic nerve terminals and receptors also occupy regions of the brain involved in neuroendocrine and behavioral responses to stress (Chaouloff,
The DRN receives afferent, and provides efferent, projections to brain regions involved in fear, anxiety, and depression. These regions include the prefrontal cortex, striatum, bed nucleus of the stria terminalis (BNST), amygdala, and locus coeruleus (LC). Efferent DRN projections render these regions susceptible to stress-induced 5-HT activity in the DRN. Furthermore, these regions are themselves sensitive to stress (Cullinan et al.,
Also important to consider is that within the DRN, interactions between diverse cell populations may influence stress-induced 5-HT activity. The DRN is not just a homogenous structure of 5-HT neurons. Other populations of neurons containing the neurotransmitters γ-aminobutyric acid (Belin et al.,
Non-neuronal cell types, such as astrocytes and microglia, may also influence DRN neural activity. Microglia are the resident “immune cells” of the brain and are sensitive to stress-induced elevation of glucocorticoids (Nair and Bonneau,
Overall, the DRN is an important region of investigation in studying the neurobiological mechanisms of stress-related mood disorders. Elucidation of variables influencing the serotonergic response to stress within the DRN may provide a better understanding of the development of these disorders. Furthermore, identification of interventions that prevent or manipulate the serotonergic response to stress and/or influence the various factors capable of modulating 5-HT activity within the DRN, may lead to the identification of novel therapeutic targets.
In humans, physical activity is one factor known to influence an individual's response to stress. Exercise reduces the risk of developing stress-related depression and anxiety (Fox,
In particular, the 5-HT1
The protective effect of wheel running could also occur indirectly, through a non-serotonergic route. One possibility is through neuropeptides. Wheel running increases brain-derived neurotropic factor (BDNF) (Neeper et al.,
Another method by which exercise may confer protection is through an immune-related mechanism. Evidence suggests a role of cytokines in human mood disorders (Maes,
Though the precise mechanisms are not fully understood, the protective effect of exercise likely involves preventing stress-induced alterations in the serotonergic system, either by directly constraining activity of 5-HT neurons within the DRN or indirectly, through altering other neurotransmitter systems or neuropeptides within the DRN that are capable of modulating 5-HT neurons. Furthermore, DRN 5-HT neurons may be influenced by exercise-induced plastic changes that reduce afferent input to the DRN, activate afferent inhibition of the DRN during stress (Greenwood and Fleshner,
One approach to reveal novel targets is by employing the use of microarray technology. Microarray technology permits the investigation of the expression of tens of thousands of genes simultaneously, at the level of mRNA transcription. Predesigned chips that contain sequences, known as probes, derived from every gene within a specified genome can be probed with mRNAs obtained from experimental samples in order to gain information about gene expression under the given conditions (Cox et al.,
The purpose of this experiment was to investigate the effect of exercise and/or stress on gene expression within the DRN. We hypothesized that wheel running produces changes in mRNA transcription within the DRN, and physically active rats exposed to stress have different gene expression profiles compared to sedentary rats exposed to stress. The differences in gene expression patterns within the DRN between physically active and sedentary rats exposed to stress may underlie the molecular mechanisms by which exercise protects against behaviors produced by inescapable stress exposure. Whole genome Affymetrix microarray analysis was used to assess gene expression. Our goal was to use an exploratory approach to (1) systematically organize the transcriptome (17,170 genes) obtained from the microarray analysis into a more manageable and focused gene set and (2) extrapolate physiological implications from this focused gene set by identifying novel targets of exercise-induced stress resistance within the DRN. To ensure a comprehensive assessment of the data, the organizational process involved two approaches, (1) identification of genes based on changes in differential expression in response to exercise and/or stress (2) identification of genes based on changes in coexpression in response to exercise and/or stress. For the differential expression analysis, two measures of significance were utilized. In a more conservative approach, genes were identified by log fold changes in gene expression. In a second, less stringent approach, genes statistically significantly differentially expressed by
The University of Colorado Boulder Animal Care and Use Committee approved all protocols for this study. A total of 48 adult, male Fisher 344 rats weighing 170–180 grams at time of arrival (Harlan Laboratories) were used in this experiment. Upon arrival, animals were individually housed in Nalgene Plexiglas cages (45 × 25.2 × 14.7 cm). The housing environment was maintained on a 12:12 h light:dark cycle, controlled for humidity, and held at a constant temperature of 22°C. Rats were allowed ad libitum access to food and water and were weighed weekly to ensure each animal remained healthy. Following arrival, animals were acclimated to the housing conditions for 1 week before experimental manipulation.
Animals were randomly assigned to remain sedentary (Sed,
Animals were randomly assigned to remain in their home cages (HC) or receive inescapable tail shock (Stress). The stress procedure occurred between 0700 and 1200. Animals subjected to stress were restrained in acrylic cylinders (23.4 × 7 cm diameter). The tail projected from the back of the restraint device. An electrode was positioned 3 cm from the base of the tail and served as the vehicle by which shock was delivered. The shock procedure consisted of 100, 5-s tail shocks administered on a random 60-s inter-trial interval. Rats received 1.0 mA tail shocks for 50 min, at which time the intensity of shock was increased to 1.5 mA tail shocks for the remainder of the session. The entire stress procedure lasted 1 h and 48 min. Rats were sacrificed by rapid decapitation immediately following termination of tail shock (Stress0) or 2 h post termination of tail shock (Stress2). The sacrificing of rats that remained in their home cage was time matched with those animals subjected to tail shock.
RNAse free conditions were maintained throughout tissue processing. After rats were sacrificed, brains were extracted and flash frozen at −20°C, in 2-Methylbutane (Fisher Scientific), for 4 min. Brains were stored at −80°C prior to sectioning. Brains were prepared with M-1 embedding matrix before sectioning at −21°C with a cryostat (Leica CM1850). Tissue was sectioned to a thickness of 20 um through the rostral to mid-caudal (approximately −7.3 to −8.2 mm relative to Bregma) portions of the DRN. This specific region of the DRN was targeted because it is involved in modulating stress- and anxiety-like behaviors (Hale et al.,
Laser capture microdissection was performed to procure a precise sample of the DRN from each rat. Slides containing sections of DRN were allowed to thaw for 20 s prior to being fixed in 75% ethanol, subjected to a Histogene Stain (for visualization purposes), and dehydrated in graded ethanol concentrations, in accordance with the Arcturus Histogene LCM Frozen Section Staining Kit protocols (Applied Biosystems). Following staining procedures, slides were loaded into the laser capture microdissection system (Arcturus XT, Life Technologies). The regions of DRN targeted for capture were the dorsal and ventral portions of the rostral to mid-caudal DRN. Samples were captured so that each sample contained the entire portion of the dorsal and ventral portion of the DRN at the given rostral-caudal level. DRN samples were obtained by using an infrared laser to adhere the tissue to a cap coated with a thermoplastic film (Capsure Macro LCM Caps, Applied Biosystems). An ultraviolet laser was used to separate the DRN from the rest of the brain section. An average of 23 DRN samples, ranging in size from 300,000 to 800,000 um2 (depending on rostral to caudal level), were successfully dissected and pooled for each rat to ensure maximal total RNA yield. Following laser capture microdissection, caps were incubated in RNA extraction buffer (Applied Biosystems) for 30 min and frozen at −80°C until future use. RNA was isolated using the Arcturus Picopure RNA Isolation Kit (Applied Biosystems) in accordance with kit protocols. Samples were stored in Elution Buffer (Applied Biosystems) at −80°C until microarray analysis.
Samples were sent to the Genomics and Microarray Core Facility at the University of Colorado Denver for whole genome analysis using microarray. RNA integrity was evaluated with the Agilent Bioanalyzer 2100 and RNA 6000 Nano/Pico Kit (Agilent Technologies). Concentrations of extracted RNA were assessed with the Nanodrop spectrophotometer (Nanodrop Technologies). One sample was removed from further processing due to poor integrity of RNA (
The Bioconductor toolset within the R statistical software program was used to format the raw microarray data. This pre-processing was completed using the ‘expresso’ option in the ‘affy’ package of the Bioconductor toolset and included background adjustment, log fold transformation, and normalization. To control for inter-array variability, the dataset was normalized using the Robust Multi Array Average method. Gene chip and RNA quality were assessed by examining total mRNA expression for each animal.
Following pre-processing and normalization, a standardized expression value was obtained for each gene for each rat. The expression values for each gene were averaged for each experimental group. The LIMMA package was used to generate nine contrasts between experimental groups. These contrasts included [(SedStress0 v. SedHC) v. (RunStress0 v. RunHC)], [(SedStress2 v. SedHC) v. (RunStress2 v. RunHC)], RunHC v. SedHC, SedStress0 v. SedHC, SedStress2 v. SedHC, RunStress0 v. RunHC, RunStress2 v. RunHC, RunStress0 v. SedSress0, and RunStress2 v. SedSress2. For each contrast, the difference in the expression level of each individual gene was calculated by subtracting the expression level of the 2nd group in each contrast from the expression level of the 1st group in each contrast. For example, the contrast RunHC v. SedHC indicates that the expression level of gene X in the SedHC group was subtracted from the expression level of gene X in the RunHC group, or (RunHC—SedHC). The first two contrasts represent the interaction between exercise and stress at each time point. For each contrast,
In an initial approach, genes differentially expressed by a LFC ≥ ± 1.1 were identified for each contrast. A second approach was performed utilizing the same contrasts as previously described. However, less stringent requirements for statistical significance were utilized (
Given that genes often operate in a coordinated manner to accomplish a physiological function, a more sophisticated approach utilizing Weighted Gene Correlational Network Analysis (WGCNA) was also performed. That is, in the absence of absolute differences in gene expression, the coexpression of genes may differ across experimental conditions. The WGCNA package within the R software program was used to perform this analysis. Following standard preprocessing and normalization of the data, a gene expression profile was available for each rat. Based on this expression profile, rats were clustered hierarchically within a dendogram based on Euclidian distance, or similarity between expression profiles. The dendogram was visualized to see how the physical traits (experimental conditions) related to the various clusters. Next, modules of highly coexpressed genes were identified and related to physical traits. Importantly, the genes within each module are more highly correlated with each other than to the rest of the transcriptome. Physical traits were categorized by experimental group (Sed.HC, Sed.Stress0, Sed.Stress2, Run.HC, Run.Stress0, Run.Stress2) and differences between groups (RunvsSed.HC, Stress0vsHC.Sed, Stress2vsHC.Sed, Stress0vsHC.Run, Stress2vsHC.Run, RunvsSed.Stress0, RunvsSed.Stress2). A correlation value and
A repeated measures ANOVA was used to analyze body weights. Repeated measures ANOVA analysis revealed statistically significant main effects of time [
To verify microarray chip quality and mRNA integrity, boxplots were constructed that represented total mRNA expression for each rat. Visual inspection of boxplots revealed four outliers. One sample was previously identified with spectrophotometry analysis. The additional three outliers were dropped from further analysis. Final group totals were SedHC (
In a conservative initial approach, genes differentially expressed by a LFC ≥ ±1.1 in response to exercise and/or stress were identified. Overall, relatively few genes had LFCs in expression ≥ ±1.1. The effect of stress on LFCs in gene expression ≥ ±1.1 is not different between sedentary and physically active rats immediately following or 2 h after stress exposure. When considering the effect of exercise, only one gene was statistically significantly altered. This gene was transthyretin, which was downregulated in physically active rats. Following stress exposure, transythretin remained downregulated in physically active rats immediately after, but not 2 h post stress. Compared to home cage controls, stress produced alterations in gene expression regardless of physical activity status at both time points. Figure
Figure
A second less stringent approach utilized
Figure
Genes that were differentially expressed at
Olfactory transduction:04740 | 70 | 1.65e–13 |
Ribosome:03010 | 18 | 9.81e–12 |
Metabolic pathways:01100 | 69 | 1.48e–10 |
MAPK signaling pathway:04010 | 23 | 6.84e–07 |
Neuroactive ligand receptor interaction:04080 | 20 | 1.92e–05 |
Pathways in cancer:05200 | 23 | 2.34e–05 |
Endocytosis:04144 | 17 | 5.79e–05 |
TGF beta signaling pathway:04350 | 10 | 6.16e–05 |
ErbB signaling pathways:04012 | 10 | 0.0001 |
Arachidonic acid metabolism:00590 | 9 | 0.0010 |
Olfactory transduction:04740 | 66 | 1.78e–21 |
Allograft rejection:05330 | 8 | 7.41e–06 |
Pathways in cancer:05200 | 18 | 1.16e–05 |
Autoimmune thyroid disease:05320 | 8 | 2.27e–05 |
Graft-vs.-host disease:05332 | 7 | 5.39e–05 |
C21-Steroid hormone metabolism:00140 | 4 | 4.44e–05 |
Androgen and estrogen metabolism:00150 | 6 | 3.77e–05 |
Type I diabetes mellitus:04940 | 7 | 0.0001 |
Non-small cell lung cancer:05223 | 6 | 0.0003 |
VEGF signaling pathway:04370 | 7 | 0.0003 |
Metabolic pathways:01100 | 141 | 3.24e–17 |
Olfactory transduction:04740 | 115 | 4.84e–12 |
MAPK signaling pathway:04010 | 48 | 3.22e–12 |
Pathways in cancer:05200 | 53 | 2.28e–11 |
Ribosome:03010 | 23 | 6.46e–10 |
FC epsilon RI signaling pathway:04664 | 21 | 2.54e–09 |
Cell cycle:04110 | 27 | 3.83e–09 |
Long term depression:04730 | 19 | 5.84e–09 |
Gap junction:04540 | 21 | 1.65e–08 |
Vascular smooth muscle contraction:04270 | 24 | 4.09e–08 |
MAPK signaling pathway:04010 | 58 | 1.10e–21 |
Metabolic pathways:01100 | 104 | 2.33e–09 |
VEGF signaling pathway:04370 | 19 | 5.06e–09 |
Neurotropin signaling pathway:04722 | 25 | 9.26e–09 |
Pathways in cancer:05200 | 42 | 2.58e–08 |
Chronic myeloid leukemia:05220 | 17 | 4.77e–07 |
Toll-like receptor signaling pathway:04620 | 18 | 7.50e–07 |
GnRH signaling pathway:04912 | 16 | 1.93e–05 |
Leukocyte transendothelial migration:04670 | 18 | 2.29e–05 |
FC epsilon RI signaling pathways:04664 | 14 | 2.75e–05 |
MAPK signaling pathway:04010 | 56 | 9.54e–19 |
Pathways in cancer:05200 | 53 | 8.51e–13 |
Adipocytokine signaling pathway:04920 | 22 | 1.95e–12 |
Metabolic pathways:01100 | 116 | 3.16e–11 |
VEGF signaling pathway:04370 | 19 | 1.68e–08 |
Neuroactive ligand receptor interaction:04080 | 36 | 1.80e–07 |
Jak-STAT signaling pathway:04630 | 25 | 2.22e–07 |
Leukocyte transendothelial migration:04670 | 22 | 3.05e–07 |
Regulation of actin cytoskeleton:04810 | 31 | 4.89e–07 |
Toll-like receptor signaling pathway:04620 | 19 | 4.78e–07 |
MAPK signaling pathway:04010 | 63 | 2.05e–23 |
Metabolic pathways:01100 | 125 | 9.13e–14 |
Pathways in cancer:05200 | 50 | 5.84e–11 |
Cytokine-cytokine receptor interaction:04060 | 35 | 5.59e–10 |
Adipocytokine signaling pathway:04920 | 17 | 6.23e–08 |
Focal adhesion:04510 | 31 | 8.65e–08 |
P53 signaling pathway:04115 | 17 | 3.64e–07 |
Neuroactive ligand receptor interaction:04080 | 35 | 7.63e–07 |
Calcium signaling pathway:04020 | 28 | 1.25e–06 |
Neutrotrophin signaling pathway:04722 | 22 | 3.15e–06 |
Pathways in cancer:05200 | 74 | 3.25e–25 |
MAPK signaling pathway:04010 | 62 | 3.30e–22 |
Metabolic pathways:01100 | 127 | 8.15e–14 |
Jak-STAT signaling pathway:04630 | 35 | 1.15e–13 |
Neuroactive ligand receptor interaction:04080 | 44 | 4.00e–11 |
Chronic myeloid leukemia:05220 | 23 | 6.70e–11 |
Cytokine-cytokine receptor interaction:04060 | 37 | 6.25e–11 |
Focal adhesion:04510 | 35 | 8.43e–10 |
Prostate cancer:05215 | 23 | 1.01e–09 |
Pancreatic cancer:05212 | 19 | 1.20e–08 |
Cytokine-cytokine receptor interaction:04060 | 12 | 7.82e–05 |
Pathways in cancer:05200 | 15 | 0.0003 |
Chemokine signaling pathway:04062 | 9 | 0.0018 |
MAPK signaling pathway:04010 | 11 | 0.0038 |
Toll-like receptor signaling pathway:04620 | 6 | 0.0038 |
Olfactory transduction:04740 | 28 | 0.0032 |
Apoptosis:04210 | 6 | 0.0047 |
Arachidonic acid metabolism:00590 | 5 | 0.0058 |
Jak-STAT signaling pathway:04630 | 7 | 0.0086 |
TGF beta signaling pathway:04350 | 5 | 0.0103 |
Neuroactive ligand receptor interaction:04080 | 23 | 2.38e–07 |
Prostate cancer:05215 | 13 | 6.23e–07 |
Pathways in cancer:05200 | 24 | 4.38e–06 |
Ribosome:03010 | 11 | 1.22e–05 |
Focal adhesion:04510 | 16 | 4.16e–05 |
Regulation of actin cytoskeleton:04810 | 17 | 3.59e–05 |
Melanoma:05218 | 9 | 8.38e–05 |
Olfactory transduction:04740 | 44 | 0.0004 |
Cell adhesion molecule:04514 | 12 | 0.0006 |
Wnt signaling pathway:04310 | 11 | 0.0011 |
KEGG analysis revealed that genes that were differentially expressed between sedentary and physically active rats in response to stress were related to functional categories including metabolic pathways, mitogen-activated protein kinase (MAPK) signaling, neuroactive ligand receptor interaction, transforming growth factor-β (TGF-β) signaling, epidermal growth factor family of receptor tyrosine kinases (ErbB) signaling, and vascular endothelial growth factor (VEGF) signaling immediately following and or 2 h post stress.
Six weeks of wheel running modulated the expression of genes involved in physiological processes including metabolic activity, olfactory transduction, MAPK signaling, cell cycle, and long term depression.
Compared to home cage non-stressed controls, both sedentary and physically active rats exposed to stress had significant enrichment of functional categories related to MAPK signaling, metabolic pathways, adipocytokine signaling, and neuroactive ligand receptor interaction. Significant enrichment of functional categories including VEGF signaling and toll-like receptor signaling was exclusive to sedentary stressed rats compared to home cage non-stressed controls and occurred at both time points. Compared to home cage non-stressed controls, stress modulated the expression of genes involved in cytokine-cytokine receptor interaction exclusively in physically active rats at both time points.
A direct comparison of sedentary and physically active rats exposed to stress, revealed enrichment differences in functional categories related to cytokine-cytokine receptor interaction, chemokine signaling, MAPK signaling, toll-like receptor signaling, apoptosis, janus kinase-signal transducer and activator of transcription (Jak-Stat) signaling, TGF-β signaling, neuroactive ligand receptor interaction, cell adhesion molecules, and wingless-type mouse mammary tumor virus integration site (WNT) signaling either immediately following and/or 2 h post stress.
Genes that were differentially expressed at
Cytoplasmic ribosomal proteins:WP30 | 17 | 3.08e–09 |
MAPK signaling pathway:WP358 | 14 | 2.17e–05 |
IL-5 signaling pathway:WP44 | 9 | 4.29e–05 |
Insulin signaling:WP439 | 13 | 7.33e–05 |
B cell receptor signaling pathway:WP285 | 13 | 0.0001 |
Diurnally regulated genes with circadian orthologs:WP1306 | 6 | 0.0005 |
TGF beta receptor signaling pathway:WP362 | 11 | 0.0005 |
Adipogenesis:WP155 | 10 | 0.0008 |
Fas pathway and stress induction of HSP regulation:WP89 | 6 | 0.0007 |
IL-6 signaling pathway:WP135 | 9 | 0.0008 |
Biosynthesis of aldosterone and cortisol:WP508 | 2 | 0.0038 |
Diurnally regulated genes with circadian orthologs:WP1306 | 4 | 0.0037 |
Steroid biosynthesis:WP66 | 2 | 0.0070 |
TNF alpha NF-kB signaling pathway:WP457 | 6 | 0.0579 |
GPCRs, class A rhodopsin-like:WP473 | 7 | 0.0485 |
Kit receptor signaling pathway:WP147 | 4 | 0.0206 |
Inflammatory response pathway:WP40 | 2 | 0.0666 |
Cytokines and inflammatory response:WP271 | 2 | 0.0494 |
Ovarian infertility genes:WP263 | 2 | 0.0535 |
Metapathway biotransformation:WP1286 | 5 | 0.0379 |
MAPK signaling pathway:WP358 | 29 | 2.69e–09 |
EGFR1 signaling pathway:WP5 | 29 | 3.80e–08 |
TNF alpha NF-KB signaling pathway:WP457 | 30 | 3.17e–08 |
Insulin signaling:WP439 | 25 | 4.32e–07 |
Renin-angiotensin system:WP376 | 13 | 3.97e–07 |
Myometrial relaxation and contraction pathways:WP140 | 24 | 5.98e–07 |
Regulation of actin cytoskeleton:WP351 | 24 | 6.89e–07 |
G protein signaling pathways:WP73 | 18 | 1.14e–06 |
IL-5 signaling pathway:WP44 | 15 | 3.31e–06 |
B cell receptor signaling pathway:WP285 | 24 | 5.24e–06 |
MAPK signaling pathway:WP358 | 36 | 2.63e–16 |
Insulin signaling:WP439 | 31 | 1.25e–12 |
TGF beta receptor signaling pathway:WP362 | 25 | 2.96e–09 |
GPCRs, class A rhodopsin-like:WP473 | 32 | 4.84e–09 |
Adipogenesis:WP155 | 23 | 6.55e–09 |
EGFR1 signaling pathway:WP5 | 26 | 7.67e–08 |
IL-6 signaling pathway:WP135 | 19 | 1.53e–07 |
Toll-like receptor signaling pathway:WP1309 | 16 | 5.76e–07 |
IL-3 signaling pathway:WP319 | 18 | 5.39e–07 |
B cell receptor signaling pathway:WP285 | 23 | 1.21e–06 |
MAPK signaling pathway:WP358 | 38 | 6.26e–17 |
Adipogenesis:WP155 | 28 | 5.99e–12 |
EGFR1 signaling pathway:WP5 | 33 | 1.42e–11 |
B cell receptor signaling pathway:WP285 | 28 | 5.19e–09 |
Insulin signaling:WP439 | 27 | 4.64e–09 |
IL-3 signaling pathway:WP319 | 21 | 1.29e–08 |
IL-6 signaling pathway:WP135 | 21 | 1.93e–08 |
Toll-like receptor signaling pathway:WP1309 | 18 | 5.10e–08 |
Delta notch signaling pathway:WP199 | 18 | 5.10e–08 |
TNF alpha NF-KB signaling pathway:WP457 | 28 | 6.69e–08 |
MAPK signaling pathway:WP358 | 33 | 8.27e–13 |
Insulin signaling:WP439 | 31 | 1.27e–11 |
Adipogenesis:WP155 | 27 | 5.08e–11 |
EGFR1 signaling pathway:WP5 | 31 | 4.54e–10 |
Apoptosis mechanisms:WP284 | 21 | 1.50e–09 |
Apoptosis:WP1290 | 21 | 1.91e–09 |
Diurnally regulated genes with circadian orthologs:WP1306 | 13 | 6.73e–09 |
Cardiovascular signaling:WP590 | 14 | 3.15e–08 |
Toll-like receptor signaling pathway:WP1309 | 24 | 7.80e–08 |
T cell receptor signaling pathway:WP352 | 23 | 9.49e–08 |
MAPK signaling pathway:WP358 | 40 | 4.75e–18 |
Adipogenesis:WP155 | 33 | 1.25e–15 |
EGFR1 signaling pathway:WP5 | 39 | 2.01e–15 |
Insulin signaling:WP439 | 35 | 2.93e–14 |
B cell receptor signaling pathway:WP285 | 33 | 5.96e–12 |
IL-6 signaling pathway:WP135 | 26 | 5.32e–12 |
Delta notch signaling pathway:WP199 | 21 | 3.62e–10 |
TGF beta signaling pathways:WP505 | 17 | 6.00e–10 |
GPCRs, class A rhodopsin-like:WP473 | 34 | 5.30e–09 |
TGF beta receptor signaling pathway:WP362 | 26 | 6.09e–09 |
Delta notch signaling pathway:WP199 | 8 | 2.30e–05 |
Kit receptor signaling pathway:WP147 | 7 | 6.36e–05 |
IL-5 signaling pathway:WP44 | 7 | 7.03e–05 |
IL-3 signaling pathway:WP319 | 7 | 0.0007 |
IL-6 signaling pathway:WP135 | 7 | 0.0007 |
TGF beta signaling pathways:WP505 | 5 | 0.0011 |
Toll-like receptor signaling pathway:WP1309 | 6 | 0.0012 |
Notch signaling pathway:WP517 | 4 | 0.0017 |
Endochondral ossification:WP1308 | 5 | 0.0017 |
Hedgehog signaling pathway:WP574 | 3 | 0.0026 |
Adipogenesis:WP155 | 11 | 0.0001 |
GPCRs, class A rhodopsin-like:WP473 | 15 | 0.0002 |
B cell receptor signaling pathway:WP285 | 12 | 0.0004 |
Hypothetical network for drug addiction:WP1281 | 5 | 0.0007 |
IL-6 signaling pathway:WP135 | 9 | 0.0006 |
Regulation of actin cytoskeleton:WP351 | 11 | 0.0006 |
Calcium regulation in the cardiac cell:WP326 | 10 | 0.0009 |
Cytoplasmic ribosomal proteins:WP30 | 9 | 0.0017 |
Androgen receptor signaling pathway:WP68 | 9 | 0.0019 |
Myometrial relaxation and contraction pathways:WP140 | 10 | 0.0019 |
Wiki analysis revealed that genes that were differentially expressed between sedentary and physically active rats in response to stress were related to functional categories including metabolic pathways, MAPK signaling, adipogenesis, biosynthesis of aldosterone and cortisol, and diurnally regulated genes with circadian orthologs. In addition, various immune-associated categories were also identified including those related to the inflammatory and cytokine response as well as signaling pathways for interleukin-5 (IL-5), IL-6, B-cell receptor, TGF-β receptor, and TNF-α-nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB).
Six weeks of wheel running modulated the expression of genes involved in physiological processes related to signaling pathways for MAPK, epidermal growth factor receptor 1 (EGFR1), TNF-α-NF-κB, Insulin, G-protein, IL-5, and B-cell receptor. Compared to home cage non-stressed controls, both sedentary and physically active rats exposed to stress had enrichment of functional categories related to signaling pathways for MAPK, insulin, TGF-β receptor, IL-6, EGFR1, delta notch, and toll-like receptor. Other genes affected by stress were related to functional categories including G protein coupled receptors (GPCRs) and adipogenesis. Compared to home cage non-stressed controls, stress modulated the expression of genes involved in interleukin-3 (IL-3) signaling exclusively in sedentary rats at both time points. Significant enrichment of categories related to apoptosis, diurnally regulated genes with circadian orthologs, cardiovascular signaling, and T-cell receptor signaling was exclusive to physically active stressed rats compared to home cage non-stressed controls.
A direct comparison of sedentary and physically active rats exposed to stress, revealed significant differences in functional categories related to signaling pathways including delta notch, kit receptor, IL-3, IL-5, IL-6, TGF-β receptor, toll-like receptor, and B-cell receptor. Significant enrichment of functional categories related to adipogenesis and GPCRs was also observed.
The effect of stress on gene expression in the DRN is different depending on physical activity status. Figure
Genes that were differentially expressed at both time points (
Olfactory transduction:04740 | 19 | 2.80en–08 |
Pathways in cancer:05200 | 8 | 0.0001 |
Metabolic pathways:01100 | 15 | 0.0001 |
Allograft rejection:05330 | 4 | 0.0001 |
MAPK signaling pathway:04010 | 7 | 0.0002 |
Autoimmune thyroid disease:05320 | 4 | 0.0003 |
VEGF signaling pathway:04370 | 4 | 0.0004 |
Intestinal immune network for IgA production:04672 | 3 | 0.0010 |
C21-Steroid hormone metabolism:00140 | 2 | 0.0014 |
Graft-vs. host disease:05332 | 3 | 0.0022 |
Diurnally regulated genes with circadian orthologs:WP1306 | 3 | 0.0006 |
B cell receptor signaling pathway:WP285 | 4 | 0.0040 |
Cytokine and inflammatory response:WP271 | 2 | 0.0040 |
Tryptophan metabolism:WP270 | 2 | 0.0128 |
Regulation of actin cytoskeleton:WP351 | 3 | 0.0201 |
TGF beta signaling pathway:WP505 | 2 | 0.0195 |
MAPK signaling pathway:WP358 | 3 | 0.0238 |
Kit receptor signaling pathway:WP147 | 2 | 0.0309 |
IL-5 signaling pathway:WP44 | 2 | 0.0318 |
GPCRs, class A rhodopsin-like:WP473 | 3 | 0.0564 |
Genes that were differentially expressed at both time points (
Select genes from Wiki functional pathway categories were targeted for analysis by ANOVA to reveal the main effect of wheel running, main effect of stress, and exercise x stress interaction on gene expression in the DRN immediately following and 2 h post stress. Table
In order to construct modules of highly correlated genes that were related to exercise and/or stress exposure, a WGCNA was performed. Hierarchical clustering was used to categorize rats based on their individual expression profile of the genes within the transcriptome (17,170). First, a dendogram was constructed to cluster rats based on gene expression profile. Rats that were closer in distance within the dendogram were considered to have a more closely related gene expression profile. After the dendogram was constructed, clusters of the dendogram were related to physical traits whereby experimental condition (exercise and stress) were considered physical traits. With the exception of the outliers (
Modules of highly coexpressed genes were identified that were also highly correlated (either positively or negatively) to exercise and/or stress. Eleven modules were derived from the transcriptome and were related to the various physical traits. Each module was assigned an arbitrary color. The number of genes contributing to each module was: yellow-199, blue-1077, purple-36, magenta-46, turquoise-3350, red-99, black-67, brown-373, green-153, pink-53, and grey-11,717. A correlation value and
Of the 11 modules, 2 modules, the brown and black, were responsive to stress in both physically active and sedentary rats. The brown module was highly positively correlated to stress, indicating a strong increase in expression of genes within the brown module in response to stress. The physically active rats had a greater correlation value immediately following stress (0.98) compared to sedentary rats (0.83), suggesting that there was a more coordinated response among genes in the brown module in physically active rats. The black module was also highly positively correlated to stress, indicating a strong increase in expression of genes within the black module in response to stress. For both time points, physically active rats had a greater correlation value (0.96 and 0.83) compared to sedentary rats (0.8 and 0.72), suggesting that there was a more coordinated response among genes in the black module in physically active rats in response to stress.
Five additional modules were also identified. Genes within the blue, purple, and green modules were responsive to stress only in the sedentary rats. The blue module was negatively correlated (−0.5) with stress in sedentary rats, indicating a decrease in expression of genes within the blue module in response to stress. The purple module was negatively correlated (−0.5 and −0.65) with stress in sedentary rats immediately following and 2 h post stress, indicating a decrease in expression of genes within the purple module in response to stress. The green module was positively correlated (0.48) immediately following stress in sedentary rats, indicating an increase in expression of genes within the green module in response to stress.
Expression of genes within the purple and turquoise modules was associated with physical activity. The purple module was negatively correlated with physical activity (−0.48) indicating a decrease in expression of genes within the purple module in response to wheel running. The turquoise module was positively correlated with physical activity (0.53) indicating an increase in expression of genes within the turquoise module in response to wheel running.
Finally, the magenta module was positively correlated (0.53) with physically active rats 2 h post stress compared to sedentary rats 2 h post stress. This suggests that relative to sedentary rats, physically active rats have increased expression of genes within the magenta module 2 h following stress exposure.
Modules of interest (correlational strength
ANOVA analysis of the magenta module revealed a statistically significant main effect of exercise [
For modules found to be statistically significant through ANOVA analysis, genes that contributed to each module were imported into Web Gestaldt bioinformatics system and subjected to analysis with KEGG functional terms. Table
Focal adhesion:04510 | 3 | 0.0014 |
Type II diabetes mellitus:04930 | 2 | 0.0015 |
Pancreatic cancer:05212 | 2 | 0.0031 |
Colorectal cancer:05210 | 2 | 0.0047 |
Pathways in cancer:05200 | 3 | 0.0061 |
Neurotropin signaling pathway:04722 | 2 | 0.0099 |
Insulin signaling pathway:04910 | 2 | 0.0099 |
Ubiquitin mediated proteolysis:04120 | 2 | 0.0091 |
Metabolic Pathways:01100 | 2 | 0.3704 |
Metabolic Pathways:01100 | 231 | 3.92e–38 |
Ribosome:03010 | 43 | 7.08e–24 |
Ubiquitin mediated proteolysis:04120 | 45 | 9.21e–18 |
Axon guidance:04360 | 42 | 6.30e–15 |
Huntington's disease:05016 | 56 | 2.63e–13 |
Alzheimer's disease:05010 | 57 | 7.96e–13 |
Oxidative phosphorylation:00190 | 43 | 3.01e–12 |
Regulation of actin cytoskeleton:04810 | 51 | 3.72e–12 |
Spliceosome:03040 | 37 | 3.89e–12 |
Cell cycle:04110 | 37 | 5.05e–12 |
Cytokine-cytokine receptor interaction:04060 | 4 | 0.0005 |
Prion Diseases:05020 | 2 | 0.0020 |
Wnt signaling pathway:04310 | 3 | 0.0023 |
MAPK signaling pathway:04010 | 4 | 0.0014 |
Chemokine signaling pathway:04062 | 3 | 0.0039 |
Toll-like receptor signaling pathway:04620 | 2 | 0.0127 |
VEGF signaling pathway:04370 | 2 | 0.0087 |
GnRH signaling pathway:04912 | 2 | 0.0132 |
Apoptosis:04210 | 2 | 0.0137 |
Small cell lung cancer:05222 | 2 | 0.0127 |
MAPK signaling pathway:04010 | 23 | 3.94e–15 |
Cytokine-cytokine receptor interaction:04060 | 13 | 9.53e–08 |
Jak-Stat signaling pathway:04630 | 10 | 2.02e–06 |
Adipocytokine signaling pathway:04920 | 7 | 4.19e–06 |
Pathways in cancer:05200 | 14 | 6.26e–06 |
Hematopoietic cell lineage:04640 | 7 | 1.07e–05 |
P53 signaling pathway:04115 | 6 | 9.48e–05 |
Focal adhesion:04510 | 9 | 0.0002 |
Chronic myeloid leukemia:05220 | 6 | 0.0002 |
ErbB signaling pathway:04012 | 6 | 0.0002 |
KEGG analysis revealed that genes within the black module were related to functional categories including prion diseases, Wnt signaling, chemokine signaling, toll-like receptor signaling, VEGF signaling, gonadotropin-releasing hormone (GnRH) signaling, apoptosis, and small cell lung cancer. Genes within the brown module were related to functional categories including Jak-Stat signaling, adipocytokine signaling, pathways in cancer, hematopoietic cell lineage, p53 signaling, focal adhesion, chronic myeloid leukemia, and ErbB signaling. Both the black and brown modules contained genes that were related to functional categories of pathways involving cytokine-cytokine receptor interaction and MAPK signaling.
Genes within the magenta and turquoise modules were related to functional categories including metabolic pathways and ubiquitin mediated proteolysis. Refer to Table
For modules found to be statistically significant through ANOVA analysis, genes that contributed to each module were imported into Web Gestaldt bioinformatics system and subjected to analysis with Wiki functional terms. Table
Insulin signaling:WP439 | 3 | 0.0005 |
Apoptosis:WP1290 | 2 | 0.0039 |
IL-3 signaling pathway:WP319 | 2 | 0.0048 |
Apoptosis mechanisms:WP284 | 2 | 0.0038 |
mRNA processing:WP529 | 41 | 2.45e–17 |
Electron transport chain:WP59 | 35 | 6.06e–17 |
TNF alpha NF-KB signaling pathway:WP457 | 47 | 2.77e–14 |
EGFR1 signaling pathway:WP5 | 42 | 6.39e–12 |
Regulation of actin cytoskeleton:WP351 | 37 | 2.25e–11 |
TGF beta receptor signaling pathway:WP362 | 35 | 1.04e–10 |
B cell receptor signaling pathway:WP285 | 38 | 1.96e–10 |
G protein signaling pathway:WP73 | 27 | 2.60e–10 |
Oxidative phosphorylation:WP1283 | 19 | 3.28e–09 |
Proteasome degradation:WP302 | 19 | 1.51e–08 |
Hypertrophy Model:WP442 | 3 | 4.38e–06 |
Insulin signaling:WP429 | 4 | 0.0001 |
GPCRs, class A rhodopsin-like:WP473 | 4 | 0.0005 |
Small ligand GPCRs:WP161 | 2 | 0.0004 |
Prostaglandin synthesis and regulation:WP303 | 2 | 0.0012 |
Myometrial relaxation and contraction pathways:WP140 | 3 | 0.0018 |
MAPK signaling pathway:WP358 | 3 | 0.0022 |
TGF beta receptor signaling pathway:WP362 | 3 | 0.0016 |
Diurnally regulated genes with circadian orthologs:WP1306 | 2 | 0.0022 |
Peptide GPCRs:WP131 | 2 | 0.0043 |
MAPK signaling pathway:WP358 | 15 | 1.29e–11 |
Adipogenesis:WP155 | 12 | 1.41e–09 |
Insulin signaling:WP429 | 9 | 1.04e–05 |
TGF beta receptor signaling pathway:WP362 | 8 | 3.95e–05 |
IL-6 signaling pathway:WP135 | 7 | 4.22e–05 |
Triacylglyercide synthesis:WP356 | 4 | 4.69e–05 |
ErbB signaling pathway:WP1299 | 5 | 7.64e–05 |
P38 MAPK signaling pathway:WP294 | 4 | 0.0002 |
GPCRs, class A rhodopsin-like:WP473 | 9 | 0.0002 |
Wnt signaling pathway and pluripotency:WP1288 | 6 | 0.0002 |
TNF alpha NF-KB signaling pathway:WP457 | 5 | 7.31e–05 |
Electron transport chain:WP59 | 3 | 0.0013 |
Androgen receptor signaling pathway:WP68 | 3 | 0.0029 |
Cytoplasmic ribosomal proteins:WP30 | 3 | 0.0028 |
Oxidative phosphorylation:WP1283 | 2 | 0.0070 |
Proteasome degradation:WP302 | 2 | 0.0081 |
G1 to S cell cycle control:WP348 | 2 | 0.0110 |
Wnt signaling pathway:WP375 | 2 | 0.0227 |
Wiki analysis revealed that genes within the brown module were related to functional categories including adipogenesis, IL-6 signaling, triacylglyceride synthesis, ErbB signaling, p38 MAPK signaling, and Wnt signaling and pluripotency. Genes within the black module were related to functional categories including hypertrophy model, small ligand GPCRs, prostaglandin synthesis and regulation, myometrial relaxation and contraction, diurnally regulated genes with circadian orthologs, and peptide GPCRs. Both the black and brown modules contained genes that were related to functional categories of pathways in insulin signaling, MAPK signaling, GPCRs of class A rhodopsin-like, and TGF-β receptor signaling.
For the magenta and turquoise modules, functional categories associated with immune pathways such as IL-3 signaling, insulin signaling, TGF-β receptor signaling, B-cell receptor signaling, and TNF-α-NF-κB signaling were identified.
The mechanism by which exercise protects against the behavioral consequences of inescapable stress is unknown. The current data suggest that rats with 6 weeks of prior access to a running wheel have a different physiological response to stress, as measured by gene expression in the DRN, than sedentary rats. Here we report that (1) relative to home cage non-stressed controls, physically active rats have a greater number of genes differentially expressed in response to stress both immediately following and 2 h after stress exposure than sedentary rats (2) modules made up of genes that are highly coexpressed and responsive to stress operate in a more strongly coordinated manner in response to stress in physically active rats compared to sedentary rats (3) many of the stress-responsive genes within the DRN are known to be involved in various immune-related pathways, such as cytokine signaling and inflammatory processes.
These data demonstrate that in response to stress, physically active rats mount a more active response, at the level of mRNA transcription in the DRN. Relative to home cage non-stressed controls, physically active rats had a greater number of genes altered by a LFC ≥ ±1.1 and a greater number of genes significantly differentially altered by
Interestingly, many genes within the DRN that are altered in response to stress are involved in immune-related signaling processes including the signaling of proteins (MAPK) involved in the stimulation of proinflammatory factors, immune cell receptors (toll-like, B cell, T cell), cytokines (IL-3, IL-4, IL-5, IL-6, TGF-β, TNF-α) and cytokine receptors (TGF-β1), chemokines, and regulatory pathways involved in the immune response to infection (NF-κB). These various immune-related functional categories were identified in both differential expression and WGCNA analysis of genes and in both KEGG and Wiki pathway databases. It is important to note that identification of specific functional pathways does not necessarily imply that such processes are occurring within the DRN in response to exercise and/or stress. Rather, pathway identification is a means of organizing the thousands of genes that are significantly differentially expressed or coexpressed in response to exercise and/or stress, by functional relationships. Genes known to be involved in B cell receptor signaling, for example, were significantly altered in the DRN in response to stress. However, B cells are only present at very low levels in a healthy brain (Anthony et al.,
Overall, these data suggest that at the level of mRNA transcription in the DRN, there is a colossal response to stress. A history of physical activity, changes, but does not necessarily dampen this response. Furthermore, there is evidence of stress-induced induction of inflammatory processes, though it is not clear whether there is an overall pro-inflammatory, anti-inflammatory, or balanced response. Additionally, it is not apparent if the overall inflammatory response is different depending on physical activity status. Regardless, these data provide evidence for a stress-induced inflammatory response originating in a region of the brain implicated in stress-related mood disorders, and exemplify the importance of investigating novel theories, such as the cytokine-induced hypothesis of depression.
The goal of this experiment was to identify novel gene targets of exercise-induced stress resistance. The “novel” component was considered to be of particular importance and therefore, the data were analyzed without the guidance of an a priori hypothesis. Differential gene expression analysis was employed to narrow the transcriptome of 17,170 genes to those most likely involved in stress resistance. Specifically, contrasts were made between experimental groups to identify genes that were significantly differentially expressed by
ANOVA analysis was performed in order to assess the differential effect of stress-induced changes within these genes in physically active compared to sedentary rats. Table
Transcription factor: can inhibit or activate transcription | |
Enzyme: participates in variety of cellular processes by reversible protein phosphorylation | |
Not clear: potential oncogene and regulator of latent HIV | |
Enzyme: catalyzes the synthesis of retinoic acid from retinaldehyde | |
Enzyme: catalyzes 1st and rate-limiting step in a major pathway of tryptophan metabolism, L-tryptophan > n-formyl kynurenine | |
Substrate: role in antigen receptor signaling, potential role in regulation of gene expression | |
Enzyme: catalyze hydrolysis of phospholipids | |
Ligase: negatively regulates T-cell and B-cell receptors | |
Enzyme: may help couple Fc receptor to activation of respiratory burst, potential role in neutrophil migration and degranulation of neutrophils | |
Cytokine: forms a gene cluster w/ IL-3, IL-5, IL-13 on chromosome 5q, costimulator of DNA synthesis, induces expression of MHC II on B-cells | |
Cytokine: controls proliferation, differentiation, regulation of other growth factors | |
Receptor: related to type 1 receptors of TGF-β family |
Of particular interest are
It is important to consider that in the context of this particular analysis, the identification of novel targets of exercise-induced stress resistance was restricted to those genes that were differentially expressed due to the interaction between exercise and stress. However, it is possible that the mechanism by which exercise confers protection is not opposite of the mechanism by which stress produces negative consequences. Exercise-induced stress resistance could occur through a non-stress responsive route. Furthermore, identification of stress-resistant genes relied solely on the genes being differentially expressed in physically active rats compared to sedentary rats exposed to stress. However, genes often work in coordinated manners to carry out physiological functions. In the absence of absolute differences in gene expression, differences in the coexpression of gene networks in response to stress may underlie the protective effect of exercise. Identification of hub genes critically important to the modules detected with WGCNA will address this possibility, and may lead to the identification of additional targets. (Identification of hub genes is discussed in greater detail in Future Directions.)
The greatest advantage of microarray analysis is that it enables the simultaneous exploration of the expression of thousands of genes. Therefore, it is particularly useful in studying complex processes, such as the stress response, whereby thousands of genes are affected. When used in conjunction with laser capture microarray technology, microarray has the potential to yield whole genome expression data about an organism's response to an environmental manipulation, such as exposure to stress or voluntary exercise, in a specific region of the brain or cell type within that region. It is important to point out that microarray analysis only provides information at the level of mRNA transcription, which is not necessarily indicative of protein production. Nevertheless, transcription initiation is the most widely used means of gene regulation in eukaryotes (Cox et al.,
The exploratory approach to microarray data analysis, however, is not without its limitations. This approach relies solely on statistical significance to identify often thousands of genes that in turn, must be organized in such a way that allows for interpretation. The organization of statistically significant genes, usually by functional categories derived from bioinformatics databases, may fail to identify genes that play a crucial role in the regulation of a given process. The 5HT1
Overall, both the advantages and limitations of microarray technology are the product of the colossal dataset that a microarray experiment yields. Organization of the data is required so that statistically significant differences observed in thousands of genes can be focused into more manageable and interpretable gene sets. This process of data distillation, however, is not without a price and in the process, information may be lost. Given this reality, it is of paramount importance that multiple statistical and analytical strategies are executed. The traditional analysis of differential expression should be used in addition to the more sophisticated analysis of coexpression with WGCNA. Although both approaches return long lists of genes that must be further mined, each method assesses different regulatory processes. Finally, when identifying functional categories related to genes of interest, multiple bioinformatics databases should be explored. Inconsistencies in the information returned by bioinformatics databases (Soh et al.,
The overwhelming amount of data obtained in microarray experiments can be a challenge to manage, however, the results are powerful and provide researchers with a wealth of novel processes and genes to explore. To identify additional targets of exercise-induced stress resistance, hub genes should be identified within the modules of highly coexpressed genes detected by the WGCNA. Research suggests that a gene's position within a given network of genes, or module, is indicative of its functional significance to that module (Miller et al.,
Further analysis of significantly regulated genes with assignment of cell type may also add richly to the dataset. More specifically, a database containing information on the genes enriched in a given cell type, such as neurons, astrocytes, and oligodendrocytes, can be used to assign cell type specificity to significantly differentially expressed or coexpressed genes (Cahoy et al.,
Future experiments should include validating the novel targets identified with microarray analysis with PCR and/or in-situ hybridization. In-situ hybridization, in particular, could provide information on the anatomical specificity of the observed differences in mRNA expression in response to exercise and/or stress within the DRN. Additionally, novel gene targets of exercise-induced stress resistance should be tested by means of behavioral pharmacology. That is manipulation of the proteins encoded by these genes with specific agonists or antagonists in the context of inescapable stress exposure and/or exercise may reveal information on the therapeutic potential of these genes. Delivery of pharmacological agonsits of targets of upregulated “stress-resistance genes” to sedentary rats, for example, would be expected to confer protection against stress-induced behaviors in these rats in the absence of exercise.
Androgens and circadian regulationare additional topics that may warrant further investigation for having a role in the mechanisms by which exercise produces stress resistance. Our data suggest that pathways of genes related to androgen receptor signaling and diurnal regulation are differentially expressed in the DRN in physically active compared to sedentary rats following stress. Androgens promote neurogenesis (Spiritzer and Galea,
In conclusion, when the data are organized effectively, microarray experiments have the ability to yield a rich amount of information on the molecular activities underlying physiological processes. When used in combination with laser capture microdissection, this information can be obtained from a specific region or cell type within an organism. Thus microarray technology is particularly useful in studying the neurobiological mechanisms underlying the complex pathophysiology of stress-related mood disorders. This experiment was designed to reveal novel targets by which exercise produces resistance to stress-related mood disorders, specifically within the DRN, using microarray and laser capture microdissection technology. The current data reveal evidence for different profiles of gene expression in the DRN of physically active rats exposed to stress compared to sedentary rats exposed to stress. Physically active rats have a more active and more strongly coordinated response to stress than sedentary rats. Specifically, Tdo2, a gene encoding an enzyme involved in tryptophan metabolism, may have a role in the mechanism by which exercise protects against the behavioral consequences of inescapable stress. In addition, an inflammatory-related gene encoding for the cytokine TGF-ß1 was particularly responsive to stress and this response was different depending on physical activity status. Overall, an inflammatory theme was revealed consistently across multiple analyses, suggesting a large effect of stress on inflammatory-related processes in cells of the DRN. The consequence of stress-induced inflammatory processes in the DRN should be further investigated.
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.
Studies were funded by National Institutes of Mental Health (R01-MH068283 and R03-MH086665) and Defense Advanced Research Projects Agency Award# W911NF-10-1-0050.