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Parallel changes in gene expression in peripheral blood mononuclear cells and the brain after maternal separation in the mouse
© Illing et al; licensee BioMed Central Ltd. 2009
- Received: 3 August 2009
- Accepted: 25 September 2009
- Published: 25 September 2009
The functional integration of the neuro-, endocrine- and immune-systems suggests that the transcriptome of white blood cells may reflect neuropsychiatric states, and be used as a non-invasive diagnostic indicator. We used a mouse maternal separation model, a paradigm of early adversity, to test the hypothesis that transcriptional changes in peripheral blood mononuclear cells (PBMCs) are paralleled by specific gene expression changes in prefrontal cortex (PFC), hippocampus (Hic) and hypothalamus (Hyp). Furthermore, we evaluated whether gene expression profiles of PBMCs could be used to predict the separation status of individual animals.
Microarray gene expression profiles of all three brain regions provided substantial evidence of stress-related neural differences between maternally separated and control animals. For example, changes in expression of genes involved in the glutamatergic and GABAergic systems were identified in the PFC and Hic, supporting a stress-related hyperglutamatergic state within the separated group. The expression of 50 genes selected from the PBMC microarray data provided sufficient information to predict treatment classes with 95% accuracy. Importantly, stress-related transcriptome differences in PBMC populations were paralleled by stress-related gene expression changes in CNS target tissues.
These results confirm that the transcriptional profiles of peripheral immune tissues occur in parallel to changes in the brain and contain sufficient information for the efficient diagnostic prediction of stress-related neural states in mice. Future studies will need to evaluate the relevance of the predictor set of 50 genes within clinical settings, specifically within a context of stress-related disorders.
- Gene Expression Change
- Differentially Express
- Differentially Express Gene
- Maternal Separation
- Acute Restraint Stress
The application of microarray techniques has provided insights into the multi-dimensional molecular nature of complex neuropsychiatric disorders. Studies have highlighted the value of using peripheral tissue targets [1, 2], an approach based on the functional integration of neural-, endocrine- and immune-systems . Regulatory exchanges between components of these systems provide a foundation for using peripheral tissue targets as indicators of neuropsychiatric states.
One of the earliest demonstrations that gene expression changes in peripheral blood mononucleoctyes (PBMCs) reflected disease states in the brain, was based on a rat model, where acute neural assaults resulted in gene expression changes in PBMCs within 24 hours . Recent studies have focused on human neuropsychiatric disorders with more subtle disruptions in neurophysiology. Segman et al  were able to predict the onset and progression of post-traumatic stress disorder (PTSD), in recently traumatised patients. Similarly, Tsuang et al  showed that the microarray analysis of peripheral blood samples discriminated between patients clinically diagnosed with schizophrenia or bipolar disorder and healthy controls. Nevertheless, it remains to be established whether gene expression changes in peripheral tissue targets are paralleled by specific transcriptional alterations in neural tissues .
We have used the model of maternal separation, which is known to induce long term alterations in neurophysiology and stress-related behaviours in adult rodents [5, 6] to investigate i) whether parallel changes occur in gene expression in three brain regions (the prefrontal cortex, hippocampus, and hypothalamus) and PBMCs and ii) whether gene expression changes in PBMCs could be used to predict the animal treatment group.
Animals and treatment
Maternal separation was carried out on C57BL/6 mice as previously described  with some modifications. Briefly, MS litters were separated from dams for 3 h a day, starting at 12 h 00 and ending at 15 h 00, from postnatal day (PND) 1 to 14. SH animals underwent brief daily handling. All subsequent procedures were carried out using males only, as the consequences of separation are gender specific .
Acute restraint stress, sacrifice, blood collection and brain dissections
Mice (NMS = 30, NSH = 30) were subjected to 10 min of acute restraint stress and allowed to recover for 20 min prior to sacrifice. Restraint stress was chosen as a means of acutely activating the Hypothalamic-Pituitary-Adrenal (HPA) axis (HPAA), which allowed for an assessment of possible differences in plasma corticosterone profiles (van Heerden et al, submitted manuscript). All mice were sacrificed, by means of cervical dislocation, immediately followed by decapitation and collection of trunk blood. Neural tissues: the (1) prefrontal cortex (PFC), (2) hippocampus (Hic) and (3) hypothalamus (HYP) were immediately dissected and submerged in RNALater® (Qiagen Inc., USA).
Microarray processing and data analysis
Fifty-five samples, 15× PFC (8× MS and 7× SH), 10× Hic and 10× Hyp (5× MS and 5× SH, each and 20× PBMC (10× MS and 10× SH) were used for microarray processing, with a two-colour common reference design. Samples were matched, so that 10 individuals (5× MS and 5× SH) were completely represented in all tissues. A common reference pool was constructed by combining equal amounts (0.75 μg) of PFC and Hic RNA from both groups. Commercial pre-spotted, full mouse genome, microarray slides (OpArray™) were sourced from Operon (Operon Biotechnologies, Germany). Full details of RNA labelling, microarray hybridization, image capture and microarray data processing are given in Additional file 1: Supplementary Methods. Microarray data are available in the ArrayExpress database http://www.ebi.ac.uk/arrayexpress under accession number E-MEXP-2101.
Data normalization was done in R, using the Limma package . Pre-processing and removal of batch effects were done using GEPAS http://www.gepas.org and ASCA-genes  respectively. Differentially expressed genes were identified using a concordance strategy , based on overlap between three statistically divergent approaches. Genes that had a P-value < 0.05, using both the Info statistic, from the ScoreGenes software package http://www.cs.huji.ac.il/labs/compbio/scoregenes/, and the Tusher et al  Significance Analysis of Microarrays (SAM) implementation in the T-Rex module of GEPAS http://www.gepas.org, in addition to an absolute fold-change > 1.2 (where fold change is defined as the fold difference between MS and SH), were considered to be differentially expressed (DE).
All data clustering was done in the Tigr MultiExperiment Viewer V4.1 (TMEV, http://www.tm4.org) using a Pearson correlation metric with average linkage. Functional enrichment of GO terms within differentially expressed gene sets was evaluated using Blast2GO . Gene set enrichment analysis on lists ordered according to SAM statistics was done using FatiScan http://www.babelomics.org . The PFC and Hyp lists were evaluated using 50 partitions, the PBMC list using 55 partitions and the Hic list using 60 partitions.
The efficiency of PBMC gene expression profiles at predicting the treatment class of samples (i.e. MS or SH) was evaluated with the Prophet module in GEPAS http://www.gepas.org  using both the K-nearest neighbour (KNN) and Support Vector machine (SVM) algorithm options. Leave-one-out cross validation was used to counter selection bias whilst simultaneously assessing prediction efficacy.
Microarray data comparing the response of control and MS adult mice to stress was used to investigate the presence of a functional link between gene expression changes in the brain and PBMCs. In the first instance data was analysed to characterise the transcriptional response of three brain regions, the prefrontal cortex, the hippocampus and hypothalamus to stress, and to investigate whether a co-ordinated change in glutamatergic and GABAergic systems occurred in MS mice. Corresponding differences in gene expression in PBMCs of MS mice compared to control mice were also identified. Importantly, these differences could be used to predict the treatment status of mice.
After normalization, replicate merging, removal of flagged features and imputation, the number of genes expressed in each tissue was: (1) PFC, 15 760; (2) Hic, 17 344; (3) Hyp, 15 794 and (4) PBMC, 13 306.
MS produced gene expression differences in all tissues
Gene set enrichment analysis revealed significant functional themes
Response of the glutamergic and GABergic systems in neural tissues after stress
DE genes and enriched functional terms from the PFC datasets highlighted the importance of the glutamatergic and GABAergic systems in the stress-related response of the MS mice. These two neurotransmitter systems constitute the major stimulatory (glutamate) and inhibitory (GABA) mechanisms of neurotransmission, and work counteractively to ensure optimal neuronal activity after stress . Glutamatergic signalling was enhanced in MS mice possibly as a consequence of deficiencies in GABAergic mediated inhibitory mechanisms.
These findings are consistent with the central role of glutamate in the stress-response, in structures such as PFC and hippocampus. Stressors such as acute restraint have been shown to produce dramatic and rapid increases in glutamate levels primarily in the PFC, which ultimately culminates in HPAA activation and glucocorticoid secretion. In addition, the hippocampus is a major site of stress-associated glutamate action. The mechanisms which regulate glutamate action and release within this region function downstream of prefrontal cortical processes, constituting a secondary stress-response phase, which, unlike the PFC, is sensitive to neuroendocrine modulation . The glutamatergic signature found here in both the PFC and hippocampus is therefore consistent with previous work.
Functional significance of gene expression changes in PBMC tissues
A large number of genes (418) were found to be differentially expressed between MS and SH individuals and included several genes whose products are important modulators of immune system function. Examples include Foxp3, an essential modulator of T cell function ; IL-17ra, the receptor target for the IL-17 mediated inflammatory pathway ; and Ccl5 (also known as Rantes), which regulates the activity of several cellular populations within the immune system .
The evidence obtained from the neural transcriptomes (combined with corticosterone and behavioural profiles; van Heerden et al Submitted Manuscript) indicates that pre-weaning treatment (MS or SH) result in differential stress-related profiles. Given this context, the gene expression information derived from the PBMC samples was evaluated in terms of its ability to derive accurate predictions of pre-weaning status of individuals.
PBMC gene expression profiles accurately predict sample classes
Summary of 50 gene predictor set, which classified samples with 95% accuracy*
Operon Oligo ID
Over/Under expressed in MS
RIKEN cDNA 4921528I07 gene
Acetyl-Coenzyme A acetyltransferase 2
A disintegrin-like and metalloprotease with thrombospondin type 1 motif, 9
Adenylate cyclase 8
Actin related protein 2/3 complex, subunit 5-like
ATPase, H+ transporting, lysosomal V0 subunit E2
cDNA sequence BC013672
Bone gamma-carboxyglutamate protein, related sequence 1
Carbonic anhydrase 14
Cytochrome P450, family 2, subfamily c, polypeptide 29
RIKEN cDNA D130054N24 gene
RIKEN cDNA D330050I23 gene
Dedicator of cytokinesis 7
Endothelial differentiation, sphingolipid G-protein-coupled receptor, 5
Forkhead box protein R1 (Forkhead box protein N5)
Homeo box A4
LSM14 protein homolog A (Rap55)
Mitogen-activated protein kinase kinase kinase 9
Mesoderm posterior 2
Matrix-remodelling associated 8
Nitric oxide synthase interacting protein
Olfactory receptor 1495
Olfactory receptor 66
Olfactory receptor 669
Mus musculus polymerase (RNA) II (DNA directed) polypeptide C
PTK2 protein tyrosine kinase 2
Slingshot homolog 3 (Drosophila)
Type 2 lactosamine alpha-2,3-sialyltransferase
Stromal interaction molecule 1
Surfeit gene 5
Transmembrane BAX inhibitor motif containing 1
Transmembrane protein 25
Transmembrane protein 63A
Xin actin-binding repeat containing 2 isoform 2
Zinc finger protein 84
Novel protein (I830077J02Rik)
Of the 50 genes included in the predictor, 46 were functionally annotated. Of particular interest was the identification of 3 genes, Oxt, Cck and Adcy8 (all over-expressed), whose products are known to be important mediators of stress- and anxiety-associated behaviours (Table 1) [26–28]. Both Oxt and Cck are neuroactive hormones with previously described endogenous immunomodulatory properties [29, 30]. These results confirm that the transcriptional profiles of peripheral immune tissues do indeed contain sufficient information for the efficient diagnostic prediction of stress-related neural states in mice. Products of these genes may participate in pathways that are particularly sensitive to stress-induced regulation of the immune system.
This work was supported by the following grants: a SA-Spain Collaboration Grant (UID 65229) held jointly by Dr Joaquin Dopazo (Bioinformatics Department, Centro de Investigación Principe Felipe, Valencia, Spain) and NI, and a National Research Foundations (NRF) Grant (ICD2006071800016) held by NI. JvH was a holder of a NRF Scarce Skills Scholarship. The National Institute of Bioinformatics http://www.inab.org is a platform of Genoma España.
- Segman RH, Shefi N, Goltser-Dubner T, Friedman N, Kaminski N, Shalev AY: Peripheral blood mononuclear cell gene expression profiles identify emergent post-traumatic stress disorder among trauma survivors. Mol Psychiatry. 2005, 10 (5): 500-513. 10.1038/sj.mp.4001636.View ArticlePubMedGoogle Scholar
- Tsuang MT, Nossova N, Yager T, Tsuang MM, Guo SC, Shyu KG, Glatt SJ, Liew CC: Assessing the validity of blood-based gene expression profiles for the classification of schizophrenia and bipolar disorder: a preliminary report. Am J Med Genet B Neuropsychiatr Genet. 2005, 133B (1): 1-5. 10.1002/ajmg.b.30161.View ArticlePubMedGoogle Scholar
- Besedovsky HO, del Rey A: Immune-neuro-endocrine interactions: facts and hypotheses. Endocr Rev. 1996, 17 (1): 64-102.View ArticlePubMedGoogle Scholar
- Tang Y, Lu A, Aronow BJ, Sharp FR: Blood genomic responses differ after stroke, seizures, hypoglycemia, and hypoxia: blood genomic fingerprints of disease. Ann Neurol. 2001, 50 (6): 699-707. 10.1002/ana.10042.View ArticlePubMedGoogle Scholar
- Meaney MJ, Szyf M: Maternal care as a model for experience-dependent chromatin plasticity?. Trends in Neurosciences. 2005, 28 (9): 456-463. 10.1016/j.tins.2005.07.006.View ArticlePubMedGoogle Scholar
- Romeo RD, Mueller A, Sisti HM, Ogawa S, McEwen BS, Brake WG: Anxiety and fear behaviors in adult male and female C57BL/6 mice are modulated by maternal separation. Horm Behav. 2003, 43 (5): 561-567. 10.1016/S0018-506X(03)00063-1.View ArticlePubMedGoogle Scholar
- Smyth GK: Limma: linear models for microarray data. Bioinformatics and Computational Biology Solutions using R and Bioconductor. Edited by: Gentleman R, Carey V, Dudoit S, Irizarry R, Huber W. 2005, New York: Springer, 397-420. full_text.View ArticleGoogle Scholar
- Nueda M, Conesa A, Westerhuis J, Hoefsloot H, Smilde A, Talon M, Ferrer A: Discovering gene expression patterns in time course microarray experiments by ANAVO-SCA. Bioinformatics. 2007, 23 (14): 8-10.1093/bioinformatics/btm251.View ArticleGoogle Scholar
- Guo L, Lobenhofer EK, Wang C, Shippy R, Harris SC, Zhang L, Mei N, Chen T, Herman D, Goodsaid FM, et al: Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol. 2006, 24 (9): 1162-1169. 10.1038/nbt1238.View ArticlePubMedGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98 (9): 5116-5121. 10.1073/pnas.091062498.PubMed CentralView ArticlePubMedGoogle Scholar
- Conesa A, Gotz S, Garcia-Gomez JM, Terol J, Talon M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (18): 3674-3676. 10.1093/bioinformatics/bti610.View ArticlePubMedGoogle Scholar
- Al-Shahrour F, Arbiza L, Dopazo H, Huerta-Cepas J, Minguez P, Montaner D, Dopazo J: From genes to functional classes in the study of biological systems. BMC Bioinformatics. 2007, 8: 114-10.1186/1471-2105-8-114.PubMed CentralView ArticlePubMedGoogle Scholar
- Medina I, Montaner D, Tarraga J, Dopazo J: Prophet, a web-based tool for class prediction using microarray data. Bioinformatics. 2007, 23 (3): 390-391. 10.1093/bioinformatics/btl602.View ArticlePubMedGoogle Scholar
- Sapolsky RM, Romero LM, Munck AU: How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr Rev. 2000, 21 (1): 55-89. 10.1210/er.21.1.55.PubMedGoogle Scholar
- Olson R: GABA. Neuropsychopharmacology: The Fifth Generation of Progress. Edited by: Davis K, Charney D, Coyle J, Nemeroff C. 2002, Philadelphia: Lippincott, Williams & WilkinsGoogle Scholar
- Wirkner K, Gunther A, Weber M, Guzman SJ, Krause T, Fuchs J, Koles L, Norenberg W, Illes P: Modulation of NMDA receptor current in layer V pyramidal neurons of the rat prefrontal cortex by P2Y receptor activation. Cereb Cortex. 2007, 17 (3): 621-631. 10.1093/cercor/bhk012.View ArticlePubMedGoogle Scholar
- Jhamandas JH, Simonin F, Bourguignon JJ, Harris KH: Neuropeptide FF and neuropeptide VF inhibit GABAergic neurotransmission in parvocellular neurons of the rat hypothalamic paraventricular nucleus. Am J Physiol Regul Integr Comp Physiol. 2007, 292 (5): R1872-1880.View ArticlePubMedGoogle Scholar
- Osterweil E, Wells DG, Mooseker MS: A role for myosin VI in postsynaptic structure and glutamate receptor endocytosis. Journal of Cell Biology. 2005, 168 (2): 329-338. 10.1083/jcb.200410091.PubMed CentralView ArticlePubMedGoogle Scholar
- Puig MV, Santana N, Celada P, Mengod G, Artigas F: In vivo excitation of GABA interneurons in the medial prefrontal cortex through 5-HT3 receptors. Cereb Cortex. 2004, 14 (12): 1365-1375. 10.1093/cercor/bhh097.View ArticlePubMedGoogle Scholar
- Crestani F, Lorez M, Baer K, Essrich C, Benke D, Laurent JP, Belzung C, Fritschy JM, Luscher B, Mohler H: Decreased GABAA-receptor clustering results in enhanced anxiety and a bias for threat cues. Nat Neurosci. 1999, 2 (9): 833-839. 10.1038/12207.View ArticlePubMedGoogle Scholar
- Vassilaki A, Lanneau C, Dournand P, de Lecea L, Gardette R, Epelbaum J: Cortistatin affects glutamate sensitivity in mouse hypothalamic neurons through activation of somatostatin receptor subtype. Neuroscience. 1999, 88 (2): 359-364. 10.1016/S0306-4522(98)00413-8.View ArticleGoogle Scholar
- Moghaddam B: Stress activation of glutamate neurotransmission in the prefrontal cortex: implications for dopamine-associated psychiatric disorders. Biological Psychiatry. 2002, 51: 775-787. 10.1016/S0006-3223(01)01362-2.View ArticlePubMedGoogle Scholar
- Kasprowicz DJ, Smallwood PS, Tyznik AJ, Ziegler SF: Scurfin (FoxP3) controls T-dependent immune responses in vivo through regulation of CD4+ T cell effector function. J Immunol. 2003, 171 (3): 1216-1223.View ArticlePubMedGoogle Scholar
- Gaffen SL: An overview of IL-17 function and signaling. Cytokine. 2008, 43 (3): 402-407. 10.1016/j.cyto.2008.07.017.PubMed CentralView ArticlePubMedGoogle Scholar
- Grayson MH, Holtzman MJ: Chemokine complexity: the case for CCL5. Am J Respir Cell Mol Biol. 2006, 35 (2): 143-146. 10.1165/rcmb.f318.PubMed CentralView ArticlePubMedGoogle Scholar
- Ring R, Malberg J, Potestio L, Ping J, Boikess S, Luo B, Schechter L, Rizzo S, Rahman Z, Rosenzweig-Lipson S: Anxiolytic-like activity of oxytocin in male mice: behavioral and autonomic evidence, therapeutic implications. Psychopharmacology. 2006, 185 (2): 218-225. 10.1007/s00213-005-0293-z.View ArticlePubMedGoogle Scholar
- Schaefer ML, Wong ST, Wozniak DF, Muglia LM, Liauw JA, Zhuo M, Nardi A, Hartman RE, Vogt SK, Luedke CE, et al: Altered stress-induced anxiety in adenylyl cyclase type VIII-deficient mice. J Neurosci. 2000, 20 (13): 4809-4820.PubMedGoogle Scholar
- Windle RJ, Shanks N, Lightman SL, Ingram CD: Central oxytocin administration reduces stress-induced corticosterone release and anxiety behavior in rats. Endocrinology. 1997, 138 (7): 2829-2834. 10.1210/en.138.7.2829.PubMedGoogle Scholar
- Csaba G, Pallinger E: In vitro effect of hormones on the hormone content of rat peritoneal and thymic cells. Is there an endocrine network inside the immune system?. Inflamm Res. 2007, 56 (11): 447-451. 10.1007/s00011-007-7021-6.View ArticlePubMedGoogle Scholar
- Meng AH, Ling YL, Zhang XP, Zhang JL: Anti-inflammatory effect of cholecystokinin and its signal transduction mechanism in endotoxic shock rat. World J Gastroenterol. 2002, 8 (4): 712-717.PubMed CentralView ArticlePubMedGoogle Scholar
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