Identification of psychoneuroimmune drug targets in hiPSC-derived astrocytes Open Access
Siciliano, Benjamin (Spring 2025)
Abstract
Major Depressive Disorder (MDD) and Alzheimer’s Disease (AD) represent two highly prevalent and debilitating brain disorders, yet their shared pathophysiological mechanisms remain poorly understood. Recent epidemiological studies suggest a bidirectional link between MDD and AD, highlighting the need for novel therapeutic strategies. This research focuses on astrocytes, key glial cells implicated in neuroinflammation, synaptic maintenance, and the blood-brain barrier (BBB), to investigate how astrocyte-specific dysfunction may drive disease processes in MDD and AD. Using human induced pluripotent stem cell (hiPSC) technology, astrocytes were derived from MDD patients, categorized as responders (R) or nonresponders (NR) to selective serotonin reuptake inhibitors (SSRIs), and from individuals with familial AD (fAD) and healthy controls. Comprehensive transcriptomic and kinomic analyses revealed distinct and overlapping molecular signatures across these astrocyte groups. Notably, NR astrocytes exhibited pronounced dysregulation in chemotaxis-related genes and upregulated stress-responsive pathways, whereas fAD astrocytes displayed impairment in extracellular matrix (ECM) organization, neuroinflammation, and lipid metabolism. Further transcription factor (TF) activity inference identified differential modulation of factors such as HIF3A, KLF17 and kinase pathways such as DDR2, PI3K associated with SSRI responsiveness and AD pathogenesis. Functional network analyses underscored astrocytes’ active roles in synaptic modulation and inflammatory cascades, supporting the hypothesis that astrocyte dysfunction contributes to both treatment resistance in MDD and neurodegenerative processes in AD. By querying the Library of Integrated Network-based Cellular Signatures (LINCS), this work uncovered several FDA-approved drugs, including certain antidepressants and anti-inflammatory agents, capable of reversing pathological astrocyte phenotypes in vitro. These findings highlight promising avenues for drug repurposing and astrocyte-targeted intervention. Overall, this dissertation advances the emerging concept of astrocytes as central players in neuropsychiatric and neurodegenerative disorders. It identifies novel molecular candidates and pathways for therapeutic development, emphasizing the need for a precision medicine approach tailored to astrocyte-specific dysfunction. Collectively, these results contribute to our understanding of how glial dysregulation underpins MDD and AD, providing a foundation for future translational efforts aimed at improving clinical outcomes.
Table of Contents
Table of Contents 6
List of Abbreviations 12
List of Figures 13
Chapter 1: Introduction 1
Major depressive disorder and treatment-resistant depression 1
Sporadic and familial Alzheimer's disease 3
Epidemiological link between major depressive disorder and Alzheimer's disease 4
Precision medicine approaches to major depressive disorder and Alzheimer's disease 6
Modeling major depressive disorder and Alzheimer's disease using hiPSC-derived neurons 7
hiPSC-derived astrocytes models of major depressive disorder and Alzheimer's disease 9
Chapter 2: Astrocyte-mediated neuroimmune dysfunction and extracellular matrix remodeling define a cellular biomarker of SSRI-resistant depression 15
Abstract 15
Introduction 16
Materials and methods 18
Subjects 18
hiPSC culture and astrocyte differentiation 19
Immunocytochemistry 20
RNA extraction and sequencing 21
Differential gene expression analysis 21
Pathway enrichment analysis 22
Weighted gene coexpression network analysis 22
Protein-protein interaction network analysis of divergent modules 23
Transcription factor activity inference 24
Transcription factor activity overlap analysis 24
Correlation analysis of transcription factor activity across conditions 25
Protein-protein interaction network analysis of differentially active transcription factors 25
Real-time PCR quantification of spliced X-box binding protein 1 26
Analysis of transcription factor target expression 27
LINCS-based identification of discordant perturbagens 27
Statistical analysis and data visualization 28
Results 29
Astrocytic molecular signatures linked to SSRI responsiveness in MDD 29
Gene co-expression networks associated with the SSRI response in MDD astrocytes 36
Key genes driving the distinct transcriptional signatures between SSRI responders and non-responders in astrocytes 40
Altered XBP1 signaling in SSRI-nonresponder astrocytes 45
Identification of discordant perturbagens in SSRI-responder and nonresponder astrocytes 48
Discussion 52
Conclusions 57
Chapter 3: Proinflammatory transcriptomic and kinomic alterations in astrocytes derived from patients with familial Alzheimer's disease 59
Abstract 59
Introduction 60
Materials and methods 62
Culture of human iPSCs and differentiation into astrocytes 62
Sample preparation and processing for transcriptomics 64
Sample preparation and processing for kinomics 65
Transcriptomic analysis 66
Transcriptomic pathway analysis 66
Protein-protein interaction network analysis 66
Transcription factor activity inference 67
Kinomic analysis 67
Multiomic integration 68
Sample size and statistical analyses 69
Results 69
Characterization of hiPSC-derived astrocytes 69
Transcriptomic profiling reveals key dysregulated pathways in fAD astrocytes 70
Identification of astrocyte-specific molecular targets in fAD 74
Kinomic profiling reveals dysregulated kinase activity in fAD astrocytes 77
Multiomics integration highlights dysregulated pathways in fAD astrocytes 80
Discussion 83
Conclusions 87
Chapter 4: Shared and distinct psychoneuroimmune dysregulation in hiPSC astrocyte transcriptomes across depressive and neurodegenerative disorders 89
Abstract 89
Introduction 90
Materials and methods 93
Data acquisition and processing 93
Differential gene expression analysis 93
Correlation analysis of differential gene expression 94
Functional enrichment analysis 94
Clustering of differentially expressed genes in overlapping biological processes 94
Transcription factor activity inference 95
Kinase enrichment analysis 95
Results 96
Differential gene expression and functional enrichment in hiPSC-derived astrocytes 96
Integrated transcription factor and kinase activity analysis in hiPSC-derived astrocytes 100
LINCS-derived mechanisms of action and perturbagens in R, NR, and fAD hiPSC-derived astrocytes 105
Discussion 109
Conclusions 116
Chapter 5: Conclusions 118
Summary of key findings 118
Mechanistic insights into astrocyte dysfunction in MDD and AD 125
Therapeutic implications and drug repurposing opportunities 127
Comparative analysis with existing literature 129
Limitations of the current research 131
Future directions for astrocyte-centric research 133
Concluding remarks 134
References 136
List of Figures
Figure 2.1. Transcription factor-based differentiation of astrocytes from human induced pluripotent stem cells. 31
Figure 2.2. Astrocytic gene expression signatures associated with depression with differential antidepressant responses. 34
Figure 2.3. Differentially expressed gene clustering in healthy control, responder, and non-responder hiPSC-derived astrocytes. 35
Figure 2.4. Hierarchical clustering of genes and module detection in hiPSC-derived astrocytes. 36
Figure 2.5. Identification of significant modules in hiPSC-derived astrocytes from SSRI-responders and nonresponders. 39
Figure 2.6. Key transcription factors driving the astrocytic signatures of SSRI-responders and non-responders. 43
Figure 2.7. Alterations in XBP1 signaling and XBP1 target genes in hiPSC-derived astrocytes. 46
Figure 2.8. Identification of potential therapeutics using LINCS analysis. 50
Figure 3.1. Study design and characterization of hiPSC-derived astrocytes. 70
Figure 3.2. Transcriptomic analysis of fAD versus HC hiPSC-derived astrocytes. 73
Figure 3.3. Identification of astrocyte-specific molecular targets in fAD. 76
Figure 3.4. Differential kinase activity in fAD astrocytes versus healthy control astrocytes. 80
Figure 3.5. Identification of molecular pathways altered in fAD astrocytes via multiomic integration. 82
Figure 4.1. Comparative analysis of differential gene expression and functional enrichment in NR, R, and fAD hiPSC-derived astrocytes. 97
Figure 4.2. Integrated analysis of transcription factor and kinase activities in R, NR, and fAD hiPSC-derived astrocytes. 102
Figure 4.3. LINCS-derived MOAs and perturbagens in R, NR, and fAD hiPSC-derived astrocytes. 106
Figure 5.1. Distinct astrocyte states in SSRI-responders and non-responders. 119
Figure 5.2. Transcriptomic and kinomic dysregulation in fAD astrocytes. 122
Figure 5.3. Shared Molecular Dysregulation in NR and fAD Astrocytes. 123
About this Dissertation
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Primary PDF
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Identification of psychoneuroimmune drug targets in hiPSC-derived astrocytes () | 2025-04-25 16:03:13 -0400 |
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Supplemental Files
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Supplementary Information (Supplementary Figures and Table Descriptions) | 2025-04-25 16:03:20 -0400 |
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Supplementary Table S1 (DEGs in iA vs iPSC) | 2025-04-25 16:03:36 -0400 |
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Supplementary Table S2 (DEGs in R vs HC) | 2025-04-25 16:03:58 -0400 |
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Supplementary Table S3 (DEGs in NR vs HC) | 2025-04-25 16:04:24 -0400 |
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Supplementary Table S4 (DEGs in NR vs R) | 2025-04-25 16:04:47 -0400 |
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Supplementary Table S5 (ORA in R vs HC) | 2025-04-25 16:05:09 -0400 |
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Supplementary Table S6 (ORA in NR vs HC) | 2025-04-25 16:05:32 -0400 |
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Supplementary Table S7 (ORA in NR vs R) | 2025-04-25 16:05:56 -0400 |
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Supplementary Table S8 (WGCNA) | 2025-04-25 16:06:17 -0400 |
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Supplementary Table S9 (TFs in R vs HC) | 2025-04-25 16:06:38 -0400 |
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Supplementary Table S10 (TFs in NR vs HC) | 2025-04-25 16:06:58 -0400 |
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Supplementary Table S11 (iLINCs in R vs HC) | 2025-04-25 16:07:22 -0400 |
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Supplementary Table S12 (iLINCs in NR vs HC) | 2025-04-25 16:07:43 -0400 |
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Supplementary Table S13 (Core Nodes in R vs HC) | 2025-04-25 16:08:06 -0400 |
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Supplementary Table S14 (Core Nodes in NR vs HC) | 2025-04-25 16:08:26 -0400 |
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Supplementary Table S15 (TF Network Metrics) | 2025-04-25 16:08:48 -0400 |
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