Multi-Tissue Cell Membrane Proteomic Profiling of Metabolic Disease Progression and Exercise in Mice Identifies Hypothalamic GPCR Regulators of Energy Balance
This repository provides selected R scripts and analysis workflows associated with our study of cell membrane protein (CMP) remodeling across mouse tissues during metabolic disease progression and exercise intervention.
In this study, we constructed a multi-tissue CMP proteomic atlas across 22 tissues from male mice and profiled CMP proteomic and transcriptomic changes across metabolically relevant tissues in mouse models of obesity, type 2 diabetes, and exercise intervention. The study characterizes tissue-specific CMP remodeling, with a particular focus on GPCRs, receptor tyrosine kinases (RTKs), and their associated protein ligands/prohormones, and identifies hypothalamic PTH1R and PTH2R as regulators of feeding, body weight, and exercise-associated metabolic adaptation.
Current manuscript title:
Multi-Tissue Cell Membrane Proteomic Profiling of Metabolic Disease Progression and Exercise in Mice Identifies Hypothalamic GPCR Regulators of Energy Balance
Preprint:
Lang X. et al. Cell membrane proteome profiling uncovers molecular signatures and GPCR regulators underlying diet-induced obesity and exercise-mediated adaptation.
https://doi.org/10.64898/2026.04.23.720492
The repository contains example R scripts for selected analyses described in the manuscript.
| Script | Analysis |
|---|---|
01_fig1d_cv.R |
Replicate-level coefficient of variation analysis (Fig. 1d) |
02_fig1e_enriched_enhanced.R |
Tissue-enriched and tissue-enhanced CMP analysis (Fig. 1e) |
03_fig3e_pca_ttest.R |
PCA and statistical testing of PC1/PC2 scores (Fig. 3e) |
04_fig3f_limma_decmp.R |
Processing and integration of differential CMP analysis results (Fig. 3f) |
05_fig3f_tissue_specific_shared.R |
Tissue-specific and tissue-shared differential CMP analysis (Fig. 3f) |
06_fig4d_reverse_trend.R |
Reverse-trend CMP analysis and restoration index calculation (Fig. 4d) |
07_fig4e_pathway_redundancy.R |
Redundancy filtering for pathway enrichment results (Fig. 4e) |
08_si_fig5d_deseq2.R |
DESeq2-based transcriptomic analysis (Supplementary Fig. 5d) |
R >= 4.2 is recommended.
Rscript install_dependencies.R
Rscript run_example.RIndividual scripts can also be run separately as needed.
Proteomics data generated in this study have been deposited in iProX under dataset identifier IPX0011997000. Transcriptomics data have been deposited in the CNGB Sequence Archive (CNSA) under project CNP0007469. Please refer to the manuscript for detailed data-access information.
The code provided here is intended as example/reference code for selected customized analyses and does not constitute a complete end-to-end reproduction of every analysis in the study. Most analyses were performed using established open-source R packages.
For complete information on data processing, statistical analysis, parameter settings, thresholds, and software used in the study, please refer to the Methods section of the manuscript.