# Diabetic Nephropathy Meta-Analysis Using Gene Expression Atlas Knowledge Graph
## Chat Transcript
👤 **User**
@gene-expression-atlas-okn: Suggest 3 specific use cases for this KG
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🧠 **Assistant**
I'll explore the gene-expression-atlas-okn knowledge graph to understand its structure and suggest specific use cases.
After exploring the Gene Expression Atlas Knowledge Graph, here are **3 specific use cases** for this resource:
## 1. **Cross-Study Disease Gene Expression Meta-Analysis**
**Use Case:** Identify consistently dysregulated genes across multiple studies of the same disease to discover robust biomarkers or therapeutic targets.
**Example Application:** A researcher studying diabetic nephropathy could query all studies examining kidney tissue from diabetic patients to find genes that show significant differential expression across multiple independent experiments. This cross-study validation strengthens confidence in candidate genes compared to single-study findings.
**Key Features Utilized:**
- 243 studies with 797 assays across diverse diseases
- Differential expression data with statistical metrics (effect sizes, p-values)
- Disease-to-study-to-gene relationships
- Ability to filter by anatomical entities (e.g., kidney tissue)
---
## 2. **Tissue-Specific Gene Expression Pattern Discovery**
**Use Case:** Characterize how specific genes or gene families are expressed differently across anatomical tissues, cell types, and developmental stages to understand tissue-specific functions.
**Example Application:** A developmental biologist could investigate how a particular gene's expression changes across different life stages (embryonic, postnatal, adult) in various tissues to understand its role in development and maturation. Or compare expression patterns between healthy and diseased states in specific cell types.
**Key Features Utilized:**
- 152,879 genes profiled
- Anatomical entity and cell type annotations
- Life stage and biological sex metadata
- Test vs. reference group comparisons in assays
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## 3. **Pathway and Biological Process Enrichment Analysis**
**Use Case:** Connect differentially expressed genes to biological pathways and molecular processes to understand the functional consequences of experimental perturbations.
**Example Application:** After identifying genes upregulated in response to a drug treatment or genetic manipulation, researchers could trace connections to biological processes (GO terms), molecular pathways, and protein domains to understand the mechanistic basis of the observed effects. This is particularly useful for understanding mind-body interventions, stress responses, or drug mechanisms of action (as suggested by studies in the KG like "Genomic Counter-Stress Changes Induced by Mind-Body Practice").
**Key Features Utilized:**
- Links between genes and BiologicalProcess entities
- Pathway and ProteinDomain associations
- Experimental factor metadata (treatments, conditions)
- Technology platform and array design information for methodology validation
Each of these use cases leverages the knowledge graph's structured integration of gene expression measurements with rich biological context, enabling systematic exploration across studies that would be difficult with individual datasets alone.
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👤 **User**
Create a prompt for the first use case
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🧠 **Assistant**
I've created a comprehensive prompt for the **Cross-Study Disease Gene Expression Meta-Analysis** use case. The prompt includes:
**Key Components:**
- Clear objective and background on the knowledge graph
- Step-by-step methodology for conducting the meta-analysis
- Example SPARQL queries ready to use
- Success criteria and expected outputs
- Specific disease examples to explore (diabetic nephropathy, cancer, IBD, etc.)
- Practical tips and next steps for validation
**The prompt guides users to:**
1. Find relevant disease studies in the knowledge graph
2. Extract differential expression data with statistical metrics
3. Identify genes with consistent patterns across studies
4. Integrate biological context (pathways, processes)
5. Generate actionable insights for biomarker discovery
This template can be adapted for any disease by simply replacing the disease keywords in the SPARQL queries.
[Prompt document](https://github.com/sbl-sdsc/mcp-proto-okn/blob/main/docs/examples/cross_study_metaanalysis_prompt.md)
---
👤 **User**
Now run the prompt with "diabetic nephropathy" as the DISEASE OF INTEREST
---
🧠 **Assistant**
I'll now execute the cross-study meta-analysis for diabetic nephropathy. Let me work through the steps systematically.
## Step 1: Identify Relevant Studies
I searched the knowledge graph for diabetic nephropathy studies and found:
**Primary Study Analyzed:**
- **Study ID:** E-GEOD-1009
- **Title:** "Gene expression profiling in glomeruli from human kidneys with diabetic nephropathy"
- **PubMed ID:** 15042541
- **Technology:** DNA microarray
- **Experimental Design:**
- Test Group: Glomeruli from kidneys with diabetic nephropathy
- Reference Group: Normal kidney glomeruli
While I searched broadly for related diabetes and kidney studies, only one study specifically examining diabetic nephropathy in kidney tissue was available in the knowledge graph.
## Step 2: Extract Differential Expression Data
The study contains one assay comparing diabetic nephropathy glomeruli to normal glomeruli. The data includes:
- log2 fold change (log2fc) values
- Adjusted p-values (adj_p_value)
## Step 3: Results Summary
### Overall Statistics
| Metric | Count |
|--------|-------|
| **Total Differentially Expressed Genes (adj. p < 0.05)** | 200 |
| **Upregulated Genes** | 1 (0.5%) |
| **Downregulated Genes** | 199 (99.5%) |
### Top 30 Most Strongly Downregulated Genes
| Gene ID | Log2 Fold Change | Adj. P-Value | Direction |
|---------|------------------|--------------|-----------|
| 53405 (CLIC5) | -4.3 | 5.27e-05 | Down |
| 2246 (FGF1) | -4.2 | 6.43e-05 | Down |
| 5800 (PTPRO) | -4.1 | 5.32e-05 | Down |
| 10529 (NEBL) | -3.9 | 1.15e-05 | Down |
| 2152 (F3) | -3.3 | 2.02e-05 | Down |
| 2296 (FOXC1) | -3.3 | 6.43e-05 | Down |
| 6943 (TCF21) | -3.2 | 3.82e-04 | Down |
| 51196 (PLCE1) | -3.1 | 6.91e-06 | Down |
| 23109 (DDN) | -3.1 | 5.65e-05 | Down |
| 9863 (MAGI2) | -3.1 | 5.10e-05 | Down |
| 51232 (CRIM1) | -3.0 | 5.10e-05 | Down |
| 4868 (NPHS1) | -2.7 | 5.27e-05 | Down |
| 7422 (VEGFA) | -2.8 | 7.16e-05 | Down |
| 4311 (MME) | -2.9 | 3.06e-05 | Down |
### The Single Upregulated Gene
| Gene ID | Gene Symbol | Log2 Fold Change | Adj. P-Value |
|---------|-------------|------------------|--------------|
| 2203 | FBP1 | +1.0 | 6.87e-04 |
## Key Biological Insights
### Critical Kidney Function Genes Downregulated
1. **NPHS1 (Nephrin)** - Log2FC: -2.7
- Critical podocyte slit diaphragm protein
- Essential for glomerular filtration barrier
- Loss indicates severe podocyte damage
2. **VEGFA (Vascular Endothelial Growth Factor A)** - Log2FC: -2.8
- Surprising finding: typically thought to be upregulated
- Glomerular-specific downregulation may represent endothelial dysfunction
3. **MAGI2** - Log2FC: -3.1
- Scaffolding protein in tight junctions
- Loss contributes to barrier dysfunction
4. **MME (Neprilysin)** - Log2FC: -2.9
- Kidney metallopeptidase
- Loss affects peptide metabolism
### Transcription Factors
5. **FOXC1 (Forkhead Box C1)** - Log2FC: -3.3
- Essential for kidney development
- Loss suggests dedifferentiation
6. **TCF21 (Transcription Factor 21)** - Log2FC: -3.2
- Regulates kidney epithelial development
- Downregulation indicates loss of differentiated state
### Novel Candidates
7. **CLIC5 (Chloride Channel 5)** - Log2FC: -4.3
- **Strongest downregulated gene**
- Role in chloride transport and cell shape
- Potential biomarker candidate
8. **DDN (Dendrin)** - Log2FC: -3.1
- Podocyte protein
- May contribute to filtration barrier integrity
### Metabolic Stress Response
9. **FBP1 (Fructose-1,6-Bisphosphatase 1)** - Log2FC: +1.0
- The only upregulated gene
- Gluconeogenesis enzyme
- Indicates metabolic stress response
## Interpretation
The overwhelming predominance of **downregulated genes** (99.5%) suggests diabetic nephropathy is characterized by:
1. **Global transcriptional suppression** in glomerular cells
2. **Loss of normal kidney function** genes
3. **Cellular stress** and dysfunction
4. **Podocyte damage** (loss of NPHS1, MAGI2, structural proteins)
5. **Vascular dysfunction** (VEGFA downregulation)
6. **Dedifferentiation** (loss of transcription factors FOXC1, TCF21)
7. **Metabolic crisis** (FBP1 upregulation)
## Clinical Significance
These molecular changes align with known diabetic nephropathy pathology:
- **Proteinuria:** Loss of filtration barrier proteins
- **Glomerulosclerosis:** Cytoskeletal and structural protein loss
- **Progressive kidney failure:** Loss of differentiated function
- **Metabolic dysfunction:** Altered glucose metabolism
## Limitations
1. **Single Study:** Only one study available in the knowledge graph
2. **No cross-study validation possible** at this time
3. **Microarray technology:** Older platform, limited dynamic range
4. **Tissue heterogeneity:** Mixed cell types in glomeruli
## Recommendations
1. **Validation:** RT-qPCR and protein validation of top genes
2. **Single-cell analysis:** Resolve cell-type specific changes
3. **Functional studies:** Test CLIC5, DDN, CRIM1 as therapeutic targets
4. **External databases:** Search GEO, ArrayExpress for additional studies
I've created two comprehensive documents with the complete analysis and all 200 genes ranked by effect size.
[Diabetic nephropathy metaanalysis report](https://github.com/sbl-sdsc/mcp-proto-okn/blob/main/docs/examples/diabetic_nephropathy_metaanalysis_report.md)
[Supplementary_gene_list](https://github.com/sbl-sdsc/mcp-proto-okn/blob/main/docs/examples/supplementary_gene_list.md)
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👤 **User**
Create transcript
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*Created by [mcp-proto-okn](https://github.com/sbl-sdsc/mcp-proto-okn) 0.5.0 on 2026-01-15*
Model: Claude Sonnet 4.5 (claude-sonnet-4-5-20250929)
