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. 2024 May 9:18:1369951.
doi: 10.3389/fncel.2024.1369951. eCollection 2024.

Recovery of synaptic loss and depressive-like behavior induced by GATA1 through blocking of the neuroinflammatory response

Koeul Choi  1 , Joonhee Lee  1 , Gukdo Kim  1 , Younghyun Lim  1 , Hyo Jung Kang  1
Affiliations

Recovery of synaptic loss and depressive-like behavior induced by GATA1 through blocking of the neuroinflammatory response

Koeul Choi et al. Front Cell Neurosci. .

Abstract

GATA1, a member of the GATA transcription factor family, is a critical factor in hematopoietic system development. In a previous study, we demonstrated the increased expression of GATA1 in the dorsolateral prefrontal cortex (dlPFC) of patients suffering from depression and described its role as a transcriptional repressor of synapse-related genes. In this study, we investigated how GATA1 globally altered gene expression using multi-omics approaches. Through the combined analyses of ChIPseq, mRNAseq, and small RNAseq, we profiled genes that are potentially affected by GATA1 in cultured cortical neurons, and Gene Ontology (GO) analysis revealed that GATA1 might be associated with immune-related functions. We hypothesized that GATA1 induces immune activation, which has detrimental effects including synapse loss and depressive-like behavior. To test this hypothesis, we first performed a microglial morphometric analysis of a brain having overexpression of GATA1 because microglia are the resident immune cells of the central nervous system. Fractal analysis showed that the ramification and process length of microglia decreased in brains having GATA1 overexpression compared to the control, suggesting that GATA1 overexpression increases the activation of microglia. Through flow cytometry and immunohistochemical analysis, we found that activated microglia showed pro-inflammatory phenotypes characterized by the expression of CD86 and CD68. Finally, we demonstrated that the effects of GATA1 overexpression including synapse loss and depressive-like behavior could be blocked by inhibiting microglial activation using minocycline. These results will elucidate the regulatory mechanisms of GATA1 that affect pathophysiological conditions such as depression and provide a potential target for the treatment of depression.

Keywords: GATA1; depression; inflammation; microglia; multi-omics.

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Conflict of interest statement

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.

Figures

Figure 1
Figure 1
Functional annotation of genes for unique peaks in the cultured cortical neurons overexpressed with GATA1. (A) Genes for unique peaks of H3K4me3 in the promoter regions were functionally categorized under the biological process. (B) Top 3,000 genes by peak score for unique peaks of H3K27me3 in the promoter regions were functionally categorized under the biological process. The horizontal axis shows the cluster number in enrichment score order. The vertical axis shows the negative of the base 10 logarithm of the Benjamini adjusted p-value. Node color represents the subcategory and node size represents the gene count. Benjamini <0.1 were considered significant. (C) Genes for unique peaks of H3K4me3 (upper) and H3K27me3 (lower) in the promoter regions were functionally categorized under KEGG pathway. The horizontal axis shows the enrichment score of each cluster. Node color represents the Benjamini adjusted p-value, while node size represents the gene count. Benjamini <0.1 were considered significant.
Figure 2
Figure 2
Functional annotation of DEGs in the cultured cortical neurons with overexpressed GATA1 and comparison with ChIPseq data. (A) Up-regulated genes (left panel) and down-regulated genes (right panel) were functionally categorized under biological process (GO_BP), cellular component (GO_CC), and molecular function (GO_MF). The horizontal axis shows the enrichment score of each cluster. Node color represents the p-value, and node size represents the gene count. p-values <0.05 were considered significant. (B) Representative peak images of differentially expressed genes (up-regulated genes; Havcr2 and Rbm47, down-regulated genes; Gpc3 and Cdh18) overlapped with ChIPseq result. IGV track displayed ChIPseq coverage for H3K4me3 (blue) and H3K27me3 (red) across the genome of each gene. Boxes represent exonic regions, and lines represent intronic regions. The arrows indicate the direction of transcription.
Figure 3
Figure 3
Relationships of the identified genes and GO terms between each omics data. (A,D) Venn diagram showing the number of overlapping genes that were activated or repressed by GATA1 overexpression between each sequencing data. (B,E) Venn diagram showing the number of overlapping GO terms in biological process between each sequencing data. ChIPseq, GATA1-unique peak genes; RNAseq, differentially expressed genes; small RNAseq, predicted target genes of differentially expressed miRNAs. (C,F) List of overrepresented GO terms overlapped in GO analysis of all sequencing data. GO terms were aligned in ascending order according to the enrichment score and p-value in the results of mRNAseq GO analysis.
Figure 4
Figure 4
Microglial morphometric analysis. (A) Schematic illustration of the experimental timeline and confirmation of GATA1 overexpression by qRT-PCR. All Ct values were normalized to those of Gapdh (fold change = 58.405, p = 2.63 × 10−5). Data are shown as mean ± SEM (-dCt) of gene in control (n = 12) and GATA1 (n = 10) overexpressed mice. *p < 0.05 upon comparison of GATA1-overexpressed mice to the control using unpaired t-test. (B) Pre-processing of cell digital image. Fluorescent image transformed into a binary image comprising filled image and outlined image. (C) Comparison of morphological parameters between microglial cells in the Control and GATA1 group. Graph shows mean ± SEM of parameters of each cell on Control (n = 95) or GATA1 (n = 90). Asterisks indicate significant differences (p < 0.0001) by unpaired t-test. (D) Hierarchical cluster analysis of microglial cells. Dendrogram for 185 cells, where the abscissa represents individual cells, and the ordinate corresponds to the linkage distance measured by Euclidean distance. Clusters were color-coded white (cluster 1), light gray (cluster 2), and dark gray (cluster 3). (E) Number and percentage of cells belonging to each cluster and representative cell image of each cluster.
Figure 5
Figure 5
Phenotype of activated microglia in the brain of GATA1-overexpressed mice. (A) Gating strategy used for sorting of microglia and their subtype. (B) Graph displaying the calculated percentage of homeostatic (CD86−CD206−), pro-inflammatory (CD86+CD206−), and anti-inflammatory (CD86−CD206+) microglia. Pro-inflammatory microglia were significantly increased in the brain of GATA1-overexpressed mice, while homeostatic microglia were significantly decreased. Data shown as mean ± SEM (percentage) in control (n = 10) and GATA1 (n = 10). (C) Quantitative analysis of Iba1+CD68+ co-localized cells. CD68+ microglia were increased in the brain of GATA1-overexpressed mice, while there is no difference in total number of microglia. Data shown as mean ± SEM (percentage) in control (n = 16) and GATA1 (n = 21). (D) Immunofluorescence detection of microglial cells in the mPFC infected with Control or GATA1 virus (scale bar = 20 μm). Microglial cells were labeled with Iba1 (red) and CD68 (cyan). GFP signal represents virus-infected cells. Merge images represent the interaction between activated microglia and neurons (white box). *p < 0.05 upon comparison of GATA1-overexpressed mice to the control using unpaired t-test.
Figure 6
Figure 6
Effects of minocycline treatment on behavior and dendritic spine analysis in GATA1-overexpressed mice. (A) Experimental schedule of the study design. After acclimation for a week, mice were subjected to stereotaxic surgery and recovery over a week. After minocycline administration for 3 weeks, behavior tests were performed. (B) Sucrose preference, which represents anhedonic behavior, was measured in the SPT. (C) Immobility times, which represent behavioral despair, were measured in the FST. Data shown as mean ± SEM (sucrose preference or immobility time) in CT_NS (n = 9), CT_MIN (n = 9), GT_NS (n = 10), and GT_MIN (n = 10). (D, E) Effect of spine density on minocycline treatment in the mPFC of GATA1-overexpressed mice (D), and representative confocal image (E). Data shown as mean ± SEM (spines/µm) in CT_NS (n = 16), CT_MIN (n = 11), GT_NS (n = 14) and GT_MIN (n = 11). Comparative analyses were performed using two-way ANOVA followed by Tukey’s multiple comparison test (*p < 0.05, **p < 0.01, ***p < 0.0001, ****p < 0.0001).
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Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (NRF-2018M3C7A1024150, NRF-2021R1A2C2014123), and the Korea Brain Research Institute (KBRI) Basic Research Program (24-BR-04-03).

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