286 whereas that following the single-dose treatment was domi- nated by the top three modes (Fig. 2a, right part). The mode sets for the CCX, the HIP, and the AMY are presented in Fig. 2b–d, right part. These figures revealed a complex pattern of expression, with oscillations at different time points of treatment. The temporal patterns of gene expression after DMI treatment described by the use of modes varied among the brain regions and between treatment types (repeated and single dose). However, there were some genes that could be grouped together (described by a common mode), although their temporal expression profiles differed between treatment types (the modes that contributed the most to their expression have different time-courses). After CIT treatment, the numb- ers of characteristic modes were generally smaller than after DMI treatment. No modes could be generated for the PCX after repeated CIT treatment and for the AMY after repeated and single-dose CIT treatment because the number of genes selected in ANOVA for a specific treatment type was smaller than the number of the time points. Comparisons between drug and brain regions indicated multiple time-dependent changes in gene expression The strategy of a detailed time-course study of gene expres- sion alterations upon antidepressant treatment was applied to verify the question whether acute and repeated adminis- tration of an AD results in similar effects. To analyze the dynamics of the changes and the influence of the time and treatment type-specific factors, two-way ANOVA was per- formed. The genes for which expression changes reached the level of statistical significance (p < 0.05 for each factor after multiple comparison corrections) are presented in Venn diagrams in Fig. 2a–h, left part. The ANOVA was followed by the analyses of the biological function of the obtained gene sets by examining over-representation of GO terms and TFBSs (Table 2). Functional analyses of genes exhibiting significant changes during the treatment period and the identification of transcription factor binding sites GO terms were identified separately for each dataset (two-way ANOVA filtered), specific brain region and drug, according to the observed temporal gene expression changes (p<0.05 for the time factor after correction for multiple comparisons). The results for each dataset are presented in Table 2. To find molecular factors involved in the transcriptional control of the observed gene expression changes over time, an in silico method of TFBS identification was used. We ana- lyzed gene promoters on the assumption that a subset of the genes with altered expression following a specific treatment might be coregulated by common transcription factors. For this purpose, we used the cREMaG database. The results are Psychopharmacology (2012) 223:281–297 Fig. 2 The left parts of the pictures include Venn diagrams comparing b changes in gene expression depending on the drug and the brain region. Venn diagrams present genes for which changes in expression reached the level of statistical significance estimated by two-way ANOVA following FDR correction for multiple comparisons after DMI (a–d) or CIT (e–h) treatment in the PCX (a, e), the CCX (b, f), the HIP (c, g), and the AMY (d, h). Genes in circles represent significant changes in gene expression by each factor (treatment type and time), as well as their interaction. The complete results of the two- way ANOVA and p values are presented in Supplementary files 1 and 2. The right part of each picture present comparative SVD analysis of structure-dependent gene expression following repeated and single- dose DMI and CIT treatments. The datasets obtained from repeatedly and acutely treated rats were transformed separately by SVD after DMI (a–d) and CIT (e–h) treatment for each brain region: the PCX (a, e), the CCX (b, f), the HIP (c, g), and the AMY (d, h). No modes could be generated for the PCX after repeated CIT treatment and for the AMY after repeated and single-dose CIT treatment because the number of genes selected in ANOVA for a specific treatment type was smaller than the number of the time points. The genes for which the expression profiles were most similar to the global SVD mode are listed in Table 1 presented in Table 2. Surprisingly, the over-representation of binding sites was similar between brain regions and between drugs. After DMI treatment, the TFBSs for myocyte enhancer factor 2 (MEF2A) and serum response factor (SRF) were over- represented. In addition to SRF and MEF2A, PAX5, ESR2, FOXF2, and CEBPA factors were also over-represented in promoters of genes with altered expression in CCX and CREB1 factor was over-represented in the HIP and AMY. Following CIT treatment, the over-represented TFBSs partly overlapped with those identified for DMI treatment in the same brain regions, with the SRF, MEF2A, and PAX families of transcription factors being affected in both cases. Interest- ingly, the CREB1 binding site was over-represented in all brain structures after CIT treatment. Dynamics in gene expression at the level of individual genes The analyses described above revealed the diversity in the global gene expression profiles depending on the brain structure and the drug used. Although they provide an overall view, they give no answer about the time-course of single, specific genes. We have chosen a subset of genes for which expression was significantly altered according to two- way ANOVA. Figure 3 presents the changes in the mRNA levels of certain transcription factor genes for which the observed changes were the greatest and were statistically significant in almost all regions. Interestingly, a high corre- lation was observed in the expression profiles between Fos and Nr4a1 and, to a lesser extent, Egr1. In the PCX, CCX, and AMY, the initial decrease in Fos mRNA was observed, with the subsequent increase at day 7, compared to the control level. Differences between the repeated and single- dose treatments were apparent. Similar changes were ob- served for Nr4a1 in the PCX and CCX. Significant