Analysis Proposal
Analysis Question
Can we validate the relationship between SLC16A1 and neurodegeneration using transcriptomic, proteomic, and network-based approaches?
Datasets
GTEx RNA-seq whole blood and brain tissue expression dataGEO datasets: GSE5281 (hippocampal transcription in AD), GSE7623 (substantia nigra in PD)AMP-AD consortium proteomic data from prefrontal cortex
Methods
Differential expression analysis comparing neurodegeneration cases vs. controls with fold-change and p-value thresholdsWeighted gene co-expression network analysis (WGCNA) to identify SLC16A1 module membership in neurodegeneration-related modulesProtein-protein interaction network analysis using STRING database to map SLC16A1 connectivity to known neurodegeneration proteinsLiterature-based curation using PubMed text mining to quantify reported associations and mechanistic evidence
Expected Outputs
- Significant upregulation or downregulation of SLC16A1 mRNA in affected brain regions (log2FC > 0.5 or < -0.5, adj p-value < 0.05) supporting implicating role
- High module membership correlation (kME > 0.7) linking SLC16A1 to neurodegeneration-associated co-expression modules
- Enrichment of known neurodegeneration proteins (APP, SNCA, MAPT, LRRK2) in SLC16A1 direct interactome ( STRING confidence > 0.7)
- Confirmation of mechanistic literature linking SLC16A1-mediated lactate transport to neuronal metabolic stress in neurodegeneration