| Literature DB >> 35681479 |
Marie Claes1, Emiel Geeraerts1, Stéphane Plaisance2, Stephanie Mentens1,3,4, Chris Van den Haute5,6, Lies De Groef3, Lut Arckens4, Lieve Moons1.
Abstract
One important facet of glaucoma pathophysiology is axonal damage, which ultimately disrupts the connection between the retina and its postsynaptic brain targets. The concurrent loss of retrograde support interferes with the functionality and survival of the retinal ganglion cells (RGCs). Previous research has shown that stimulation of neuronal activity in a primary retinal target area-i.e., the superior colliculus-promotes RGC survival in an acute mouse model of glaucoma. To build further on this observation, we applied repeated chemogenetics in the superior colliculus of a more chronic murine glaucoma model-i.e., the microbead occlusion model-and performed bulk RNA sequencing on collicular lysates and isolated RGCs. Our study revealed that chronic target stimulation upon glaucomatous injury phenocopies the a priori expected molecular response: growth factors were pinpointed as essential transcriptional regulators both in the locally stimulated tissue and in distant, unstimulated RGCs. Strikingly, and although the RGC transcriptome revealed a partial reversal of the glaucomatous signature and an enrichment of pro-survival signaling pathways, functional rescue of injured RGCs was not achieved. By postulating various explanations for the lack of RGC neuroprotection, we aim to warrant researchers and drug developers for the complexity of chronic neuromodulation and growth factor signaling.Entities:
Keywords: DREADDs; FACS; RNA sequencing; chemogenetics; glaucoma; neuromodulation; neuroprotection; postsynaptic target area; retinal ganglion cells; superior colliculus
Mesh:
Year: 2022 PMID: 35681479 PMCID: PMC9179903 DOI: 10.3390/cells11111784
Source DB: PubMed Journal: Cells ISSN: 2073-4409 Impact factor: 7.666
Figure 1Brief overview of the study design. To explore the role of chronic target activation on the health of retinal ganglion cells (RGCs) upon glaucomatous injury, the microbead occlusion model was used to induce a glaucomatous-like pathology in the murine eye and the DREADD toolbox was applied in the superior colliculus as a chronic chemogenetic neuromodulation tool. Next, the molecular underpinning of these techniques was evaluated via bulk RNA sequencing on collicular lysates and isolated RGCs, whilst probing the functionality of injured RGCs.
Figure 2Schematic diagram of the experimental design. Two weeks after DREADD or null vector injections in the superior colliculus, ocular hypertension (OHT) was induced via intracameral injection of magnetic microbeads versus saline for SHAM controls. All mice received clozapine-N-oxide (CNO) injections (i.p., 3 mg/kg, 3 days/week), starting 1 day before glaucoma induction until five weeks later. As depicted, four experimental groups were included in this study: “SHAM”, “OHT”, “SHAM + DREADD” and “OHT + DREADD”. Assessed read-outs were longitudinal intraocular pressure measurements (IOP) and several endpoint (five weeks post injury) measurements: optical coherence tomography (OCT), electroretinography (ERG), and bulk RNA sequencing. For the bulk RNA sequencing study, both collicular lysates, as well as isolated RGCs via fluorescence-activated cell sorting (FACS) were collected. Downstream analysis was performed with EdgeR and Ingenuity Pathway Analysis (IPA).
Figure 3Overview of the biological questions uncovered in this study and the detected differentially expressed genes (DEGs) of these dataset comparisons. DEGs were identified based on the amplitude and statistical significance of the log fold change expression level (FDR < 0.1 and |log fold change| > 1.3 for the RGC samples vs. > 0.5 for the collicular samples). Up- and downregulated DEGs are shown in green and red, respectively, together with the unaltered genes in black.
Figure 4Canonical Pathway analysis in the glaucomatous RGC transcriptome (“OHT” vs. “SHAM” RGCs). (a) Overarching categories to which the altered canonical pathways belong. (b) List of altered signaling pathways relevant to the biological question (FDR ≤ 0.1, −log (p-value) ≥ 1.30, Fisher’s exact test). A list of all significantly altered canonical pathways is provided in Supplementary File S2. IPA predictions are algorithm- (based on z-score, filled bars) or user-defined (striped bars). Pathways that are predicted to be upregulated are shown in orange, those predicted downregulated in blue.
Upstream regulators identified as significantly inhibited in the “OHT” RGCs as compared to “SHAM” RGCs (FDR ≤ 0.1, p-value of overlap ≤ 0.01, |z-score| ≥ 1.95).
| Upstream | Expr Log Ratio | Molecule Type | Activation z-Score | Target Molecules in Dataset | |
|---|---|---|---|---|---|
| BMP2 | growth factor | −2.58 | 3.71 × 10−3 |
| |
| STAT3 | 0.19 | transcription regulator | −2.50 | 2.12 × 10−4 |
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| SMARCB1 | 0.02 | transcription regulator | −2.24 | 7.12 × 10−3 |
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| IFNAR1 | −0.23 | transmembrane receptor | −2.21 | 8.66 × 10−3 |
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| IL12B | −4.31 | cytokine | −2.20 | 5.00 × 10−4 |
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| IL4 | −0.11 | cytokine | −2.12 | 2.29 × 10−4 |
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| IL6 | cytokine | −2.02 | 2.77 × 10−5 |
| |
| BRD4 | kinase | −1.98 | 4.40 × 10−5 |
|
Figure 5Optimization of the chronic DREADD toolbox in the retinocollicular system. (a) An hM3D(Gq) DREADD vector was unilaterally injected in the superior colliculus at a dilution series to exclude retrograde retinal transduction. Upon a dilution of 1:8, no transduction of RGCs was observed. For this dilution, representative images of the superior colliculus are shown, revealing a baseline c-Fos expression 2 h after CNO injection(s) upon null vector injection, versus a clear upregulation of c-Fos expression upon DREADD expression and CNO injection(s). Scale bar = 500 µm and 50 µm for magnified panels of the retina/superior colliculus. (b) Quantitative measurements of c-Fos+ density confirmed this qualitative observation: repeated CNO injections still elicit an upregulation of c-Fos expression, identical to an acute injection, independent of the CNO (3 vs. 1 mg/kg bodyweight) concentration or stimulation scheme (single injection vs. two or three times per week for three consecutive weeks). Control conditions included naive, dark-adapted mice with saline stimulation (dark grey), mice transduced with DREADD vector and saline stimulations (light grey) and mice transduced with null vector together with repeated CNO injections (striped bars), all revealing baseline neuronal activity. Uppercase letters were used to indicate statistical significance, with different letters representing significant differences (two-way ANOVA, n= 4–6 mice, p ≤ 0.05).
Figure 6Canonical Pathway analysis in the “OHT + DREADD” vs. “OHT” dataset of the collicular transcriptome. (a) Overarching categories to which the altered canonical pathways belong. (b) List of altered signaling pathways, relevant to the biological question (FDR ≤ 0.1, −log (p-value) ≥ 1.30, Fisher’s exact test). A list of all significantly altered canonical pathways is provided in Supplementary File S3. IPA predictions are algorithm- (based on z-score, filled bars) or user-defined (striped bars). Pathways that are predicted to be upregulated are shown in orange, those predicted downregulated in blue. A grey bar indicates that no prediction could be made.
Top 20 upstream regulators identified as significantly activated in the “OHT + DREADD” superior colliculus lysates as compared to “OHT” lysates (FDR ≤ 0.1, p-value of overlap ≤ 0.01, |z-score| ≥ 1.95). An entire list of upstream regulators is provided in Supplementary File S3.
| Upstream | Expr Log Ratio | Molecule Type | Activation z-Score | Target Molecules in Dataset | |
|---|---|---|---|---|---|
| CREB1 | −0.23 | transcription regulator | 3.91 | 9.08 × 10−11 |
|
| EGF | growth factor | 3.63 | 4.62 × 10−9 |
| |
| IL1B | 0.40 | cytokine | 3.51 | 7.74 × 10−5 |
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| Insulin | group | 3.12 | 9.86 × 10−8 |
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| CREB | group | 3.02 | 1.71 × 10−9 |
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| GPER1 | 0.28 | G-protein coupled receptor | 2.91 | 6.92 × 10−13 |
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| IGF1 | 0.16 | growth factor | 2.90 | 2.10 × 10−4 |
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| TGFB1 | 0.31 | growth factor | 2.88 | 2.81 × 10−5 |
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| MAPK3 | −0.08 | kinase | 2.77 | 2.97 × 10−9 |
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| F2 | peptidase | 2.72 | 4.79 × 10−6 |
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| TNF | 0.36 | cytokine | 2.66 | 9.25 × 10−5 |
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| FOXO3 | −0.13 | transcription regulator | 2.63 | 2.55 × 10−5 |
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| RELA | 0.27 | transcription regulator | 2.61 | 2.34 × 10−4 |
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| GNAS | enzyme | 2.60 | 9.85× 10−9 |
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| IL6 | cytokine | 2.60 | 6.17× 10−3 |
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| HGF | 0.01 | growth factor | 2.58 | 1.63× 10−3 |
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| Pkc(s) | group | 2.57 | 1.07 × 10−5 |
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| BDNF | 0.03 | growth factor | 2.57 | 2.35 × 10−6 |
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| CSF1 | 0.24 | cytokine | 2.54 | 3.36 × 10−5 |
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| NGF | −0.27 | growth factor | 2.48 | 3.04 × 10−7 |
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Figure 7Heatmaps of the differentially expressed genes (DEGs) detected in the “OHT” vs. “SHAM” and “OHT + DREADD” vs. “OHT” comparisons of the RGC transcriptome. (a) Comparing the gene expression of all DEGs—i.e., unique and shared DEGs between both datasets—showed that chronic target stimulation partially reversed the disease signature. (b) This reversal of disease signature is emphasized when only plotting the shared DEGs. Red and blue colors denote a decreased (log fold change < −1.3) and increased (log fold change > 1.3) gene expression, respectively, whereas a white color represents genes that were not differentially expressed.
Figure 8Canonical Pathway analysis in the “OHT + DREADD” vs. “OHT” dataset of the RGC transcriptome. (a) Overarching categories to which the altered canonical pathways belong. (b) List of the altered signaling pathways, relevant to the biological question (FDR ≤ 0.1, −log (p-value) ≥ 1.30, Fisher’s exact test). A list of all significantly altered canonical pathways is provided in Supplementary File S4. IPA predictions are algorithm- (based on z-score, filled bars) or user-defined (striped bars). Pathways that are predicted to be upregulated are shown in orange, those predicted downregulated in blue. A grey bar indicates that no prediction could be made.
Upstream regulators identified as significantly activated in the “OHT + DREADD” RGCs as compared to “OHT” RGCs (FDR ≤ 0.1, p-value of overlap ≤ 0.01, |z-score| ≥ 1.95).
| Upstream | Expr Log Ratio | Molecule Type | Activation z-Score | Target Molecules in Dataset | |
|---|---|---|---|---|---|
| HIF1A | 0.47 | transcription regulator | 2.88 | 1.12 × 10−3 |
|
| BDNF | 0.26 | growth factor | 2.60 | 8.93 × 10−3 |
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| AGT | 1.70 | growth factor | 2.56 | 3.54 × 10−4 |
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| TGFBR2 | kinase | 2.55 | 7.71 × 10−3 |
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| TNF | cytokine | 2.53 | 2.76 × 10−6 |
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| FOXO3 | −0.31 | transcription regulator | 2.52 | 1.05 × 10−4 |
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| IL6 | cytokine | 2.45 | 1.44 × 10−4 |
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| NGF | growth factor | 2.40 | 1.59 × 10−3 |
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| MAPK3 | 0.01 | kinase | 2.39 | 3.20 × 10−4 |
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| TGFB3 | 0.04 | growth factor | 2.36 | 1.65 × 10−5 |
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| CSF2 | cytokine | 2.34 | 3.46 × 10−5 |
| |
| CHUK | −0.16 | kinase | 2.21 | 6.41 × 10−5 |
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| Ngf | group | 2.21 | 1.44 × 10−3 |
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| FGF21 | growth factor | 2.21 | 1.09 × 10−3 |
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| EP300 | 1.00 | transcription regulator | 2.20 | 1.26 × 10−4 |
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| IKBKB | −0.44 | kinase | 2.16 | 9.56 × 10−4 |
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| HGF | 1.12 | growth factor | 2.06 | 3.51 × 10−3 |
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| CXCL12 | 0.36 | cytokine | 2.05 | 1.41 × 10−6 |
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| IL3 | cytokine | 2.01 | 1.17 × 10−6 |
| |
| IKBKG | −0.42 | kinase | 1.97 | 3.25 × 10−4 |
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| NTRK1 | 0.38 | kinase | 1.96 | 1.88 × 10−4 |
|
Figure 9One-way ANOVA and estimation statistics (Hedges’ g) on different read-outs to probe glaucoma induction and progression (n = 18–24 mice). (a) Cumulative IOP, defined as the area under the curve of IOP measured at 2, 3 and 4 weeks post injury, was elevated upon OHT induction—i.e., in “OHT” and “OHT + DREADD mice”—and unaltered in “SHAM + DREADD” mice as compared with “SHAM” controls. (b) Similarly, anterior chamber depth was elevated five weeks after OHT induction, yet not upon DREADD stimulation alone as compared with “SHAM” controls. (c) “SHAM + DREADD” mice showed a similar pSTR response as “SHAM” mice, whereas this response was significantly lower in “OHT” and “OHT + DREADD” mice as compared with “SHAM” mice. The pSTR response was identical in “OHT” and “OHT + DREADD” mice, revealing that DREADD stimulation of the superior colliculus did not rescue RGC functionality in mild glaucomatous mice. Mice with a pupil diameter < 1 mm were excluded. Key: ns = non-significant, * p ≤ 0.05, *** p ≤ 0.001 and **** p ≤ 0.0001. Mean values and estimation statistics are listed in Supplementary File S6, Tables S1 and S2.