Integrated transcriptomic and proteomic profiling of colonic tissue in interleukin-10-deficient mice

Abstract

Interleukin (IL)-10, a prominent anti-inflammatory cytokine predominantly secreted by various immune cells, plays a crucial role in the pathophysiological mechanisms of inflammatory bowel disease (IBD). Mice deficient in IL-10 (IL-10^-/-) progressively develop features of idiopathic enterocolitis as they mature. To advance our understanding of the molecular mechanisms underpinning chronic enterocolitis in IL-10^-/- mice, we performed an extensive analysis of the transcriptome and proteome of colonic tissue from these mice manifesting colitis. The study employed bulk RNA sequencing (RNA-seq) and four-dimensional (4D) label-free mass spectrometry (MS) to facilitate an integrated analysis of the resultant omics data. Following an extensive series of quality control evaluations, 635 genes and 1,071 proteins were identified as differentially expressed. The principal aim of this integrated analysis was to elucidate novel signaling pathways and identify potential therapeutic targets for IBD treatment.

Fig. 1

Workflow of colonic samples preparation and data analysis. (a) The establishment of IL-10^−/− chronic colitis mice model and acquirement of the colons from WT and IL-10^−/− mice. Two groups of mice were both executed at 24 weeks of age (n = 3). (b) The process of colonic tissue preparation for proteomic and transcriptomic sequencing. Subsequently, the integrated analysis of proteomic and transcriptomic data were performed.

Fig. 2

Assessment of chronic colitis in IL-10^−/− mice. (a) PCR-based identification of WT and IL-10^−/− mice. (b) Changes in body weight of two groups of mice during 24 weeks. (c) Changes in disease activity index (DAI) of two groups of mice during 24 weeks. (d) Comparison of colon length between two groups of mice at 24 weeks of age. (e) Histological scoring of colons in two groups of mice. n = 3, All values represent the mean with SD; a two-sided t test. *p < 0.05.

Fig. 3

Protein gel stained with Coomassie Brilliant Blue.

Fig. 4

Quality control of proteomic and transcriptomic data. (a) Transcriptomic principal component analysis (PCA) of the six samples in two groups. (b) Differential expression genes (DEGs) between two groups. (c) Venn diagram of overlapping DEGs. (d) Pearson correlation between samples. Two groups contained six samples, and every two samples were compared. (e) The distribution of peptide counts and protein molecular weight. (f) The cluster heatmap of DEPs. (g) The Pearson’s test of proteomics. (h) Distribution of quantitative proteomic data before missing values imputation in each sample. Y-axis represented quantitative values exponentially. (i) Distribution of quantitative proteomic data after missing values imputation in each sample following normalization and logarithmic transformation. (j,k) The intensity of peptides and proteins.

Fig. 5

Comparison of quantitative information between two samples within each group. In the same group, every two replicates were compared.