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Stefano Patalano
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BBBD
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BBBD: Blood Biomarkers for Brain Diseases

Overview

This project analyzes blood-based biomarkers for brain diseases, particularly focusing on Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). The analysis uses gene expression data from blood samples and cross-references it with tissue-specific expression data to identify brain-enriched genes.

Datasets

GEO AddNeuroMed Cohort

  • Batch 1 (GSE63060)
  • 145 AD samples
  • 80 MCI samples

  • 104 healthy controls (CTL)

  • Batch 2 (GSE63061)

  • 175 AD samples
  • 78 MCI samples
  • 135 healthy controls (CTL)

GTEx Data

  • Version: V8
  • Scope: Bulk tissue expression
  • Coverage: 56,200 genes across 49 tissues (including 18 brain tissues)
  • Measurement: Gene TPM (Transcripts Per Million)

Methodology

Data Preparation

  1. Batch Normalization
  2. Cross-batch normalization for GEO datasets

  3. Standardization of expression values

  4. Brain-Enriched Gene Filtering
    Based on NCBI definition: genes expressed at least 4x higher in brain compared to other organs

Two filtering approaches:
- Filtering 1: mean(brain subtissues) > 4 * mean(other tissues)
- Filtering 2: brain subtissue > 4 * mean(other tissues)

Data Processing Pipeline

  1. Initial Filtering
  2. Row means filtering
  3. Interquartile Range (IQR) filtering

  4. Log Fold Change (LogFC) filtering

  5. Statistical Analysis

  6. P-value computation
  7. P-value adjustment for multiple testing
  8. Significance filtering (threshold = 0.01)

Results

An enrichment analysis was conducted on the resulting genes, using Enrichr and EnrichrKG. Finding a strong (p-val: e-28) correlation between 2 out of 11 genes involved in ATP synthesis mitochondrial processes with many brain diseases.

Recent articles confirm (using different methods: LASSO, SVM) these two genes are candidates to predict LO-AD and MCI.

Further analysis will be conducted on other GWAS datasets as ADNI.
Also, as partial inhibition of mitochondrial-complex-I has been exploited as therapeuthic target for AD, further analysis can be conducted on these 2-11 genes using MIENTURENET to evaluate the potential RNA therapeutic approaches for AD.

Future Directions

Planned Extensions

  1. ADNI Dataset Integration
  2. Additional validation of findings

  3. Cross-cohort analysis

  4. GWAS Analysis

  5. Integration with genetic variant data
  6. Investigation of genetic associations

Data Access

  • GEO datasets: GSE63060, GSE63061
  • GTEx data: V8 release

Author

Stefano Patalano (2024)