Expression correlation attenuates within and between key signaling pathways in chronic kidney disease

Abstract

Background: Compared to the conventional differential expression approach, differential coexpression analysis represents a different yet complementary perspective into diseased transcriptomes. In particular, global loss of transcriptome correlation was previously observed in aging mice, and a most recent study found genetic and environmental perturbations on human subjects tended to cause universal attenuation of transcriptome coherence. While methodological progresses surrounding differential coexpression have helped with research on several human diseases, there has not been an investigation of coexpression disruptions in chronic kidney disease (CKD) yet.

Methods: RNA-seq was performed on total RNAs of kidney tissue samples from 140 CKD patients. A combination of differential coexpression methods were employed to analyze the transcriptome transition in CKD from the early, mild phase to the late, severe kidney damage phase.

Results: We discovered a global expression correlation attenuation in CKD progression, with pathway Regulation of nuclear SMAD2/3 signaling demonstrating the most remarkable intra-pathway correlation rewiring. Moreover, the pathway Signaling events mediated by focal adhesion kinase displayed significantly weakened crosstalk with seven pathways, including Regulation of nuclear SMAD2/3 signaling. Well-known relevant genes, such as ACTN4, were characterized with widespread correlation disassociation with partners from a wide array of signaling pathways.

Conclusions: Altogether, our analysis reported a global expression correlation attenuation within and between key signaling pathways in chronic kidney disease, and presented a list of vanishing hub genes and disrupted correlations within and between key signaling pathways, illuminating on the pathophysiological mechanisms of CKD progression.

Keywords: Chronic kidney disease; Correlation attenuation; Differential co-expression; Pathway crosstalk.

Fig. 1

Overlap of resultant entity across kidney transcriptome datasets of different sources. a statistical significance of set intersection between dataset pairs. Hypergeometric probability model was employed to calculate the p-value of obtaining the actual or a greater number of shared entities. Bar height symbolizes the inverse of p-values, thus the higher the more significant. GSE6, GSE62792; GSE3, GSE37171; GSE3.x, a derived dataset originating from GSE37171, with balanced sample sizes (20 vs. 21). b empirical cumulative density function curves for the 369 pathway-wise p-values determined by GSNCA in each dataset. The 21 disease samples in each GSE3.x dataset were randomly selected from the whole set of 63 samples, and these selected disease samples may share in part among the five derived datasets. c statistical significance of intersection between top-ranking pathways from different datasets. Top-ranking pathways were gradually enlarged from 5 to 150 (40.1% of all pathways) at an interval of 5 (row labels). Color shade is proportional to log10(p), where p is the p-value calculated under hypergeometric probability model. Red signifies high portion of intersection entities unexpected by random cases

Fig. 2

Global expression correlation attenuation and extremely low hub retention of pathways. a breakdown of differentially co-expressed gene links (DCLs). Each DCL is characterized with a pair of correlation values corresponding to the two comparator conditions, respectively, and DCLs are categorized into four types on account of the signs and changing trend of the paired correlation values. Diff signed, DCLs of two extreme correlation values in opposite signs. Same signed negative, DCLs of two negative correlation values. Increased positive, DCLs showing correlation increment toward extreme positive values. Decreased positive, DCLs showing correlation decrease from extreme positive values. b breakdown of pathways by predominant correlation change direction. Dissolved, more gene pairs have decreased correlation. Consolidated, more gene pairs have increased correlation. Maintained, even share of gene pairs with increased correlation and gene pairs with decreased correlation. c one hundred times of random permutation of patients’ class labels were performed and GSNCA was implemented on the permutated datasets, with respect to all 369 covered pathways. The real hub constancy rate (3/27) and hub retention rate (1/44) was compared against the empirical distributions resulting from permutations. d hub constancy rates and hub retention rates in real data analysis (red line) and permuted analyses (grey histogram), where one hundred times of random permutation of patients’ pathway annotations preceded GSNCA running. Technically, permuting patients’ pathway annotations was equivalent to shuffling the gene-to-pathway mapping relations, thus achieving random organization of genes to meaningless pseudo-pathways while maintaining the same pathway size profile. The real hub constancy rate (3/27) and hub retention rate (1/44) was compared against the empirical distributions resulting from permutations

Fig. 3

Universal correlation attenuation within individual pathways. Rows and columns represent genes of the concerned pathway, arranged in identical order. Cells denote the expression correlation values between the row gene and the column gene, with the lower triangle and the upper triangle indicating the early CKD and late CKD phenotypes, respectively

Fig. 4

Three genes lost hub status in transcriptome rewiring of their respective pathways in CKD advancement. In each panel, left denotes early CKD and right denotes late CKD. a MAP 2 K7. b ARF6. c SRCAP. Red, hub genes in early CKD. Blue, hub genes in late CKD. Node size, vertex degree. Edge width, absolute correlation

Fig. 5

Disruption of pathway crosstalk in CKD progression. a pathway crosstalks present in early CKD were disrupted in late CKD. Node size and edge width are proportional to the statistical significance of correlation loss (extremity of p value). b attenuated cross-pathway gene correlation links incident to the affected pathways. For clarity, only links pertaining to Differentially Coexpressed Genes or hub genes were shown. Node size, vertex degree. Node color, pathway membership. Red text, pathway hub genes. Asterisk (*), differentially expressed genes (FDR < 0.3)