Calciprotein particle counts associate with vascular remodelling in chronic kidney disease

Abstract

Aims: Calciprotein particles (CPPs) are circulating calcium and phosphate nanoparticles associated with the development of vascular calcification (VC) in chronic kidney disease (CKD). Although recent studies have been focusing on associations of CPPs with the presence of VC in CKD, insights in the underlying processes and mechanisms by which CPPs might aggravate VC and vascular dysfunction in vivo are currently lacking. Here, we assessed the overall burden of abdominal VC in healthy kidney donors and CKD patients and subsequently performed transcriptome profiling in the vascular tissue obtained from these subjects, linking outcome to CPP counts and calcification propensity.

Methods and results: Calcification scores were quantified in renal arteries, iliac arteries, and abdominal aorta using computed tomography (CT) scans of kidney donors and CKD patients. The vascular tissue was collected from kidney donors (renal artery) and CKD patients (iliac artery), after which bulk RNA sequencing and gene set enrichment analysis (GSEA) were performed on a subset of patients. Calcification propensity (crystallization time, T50) was measured using nephelometry and CPP counts with microparticle flow cytometric analysis. Increased calcification scores (based on CT) were found in CKD patients compared to kidney donors. Transcriptome profiling revealed enrichment for processes related to endothelial activation, inflammation, extracellular matrix (ECM) remodelling, and ossification in CKD vascular biopsies compared to kidney donors. Calcification propensity was increased in CKD, as well as CPP counts, with the latter being significantly associated with markers of vascular remodelling.

Conclusion: Our findings reveal that CKD is characterized by systemic VC with increased calcification propensity and CPP counts. Transcriptome profiling showed altered vascular gene expression with enrichment for endothelial activation, inflammation, ECM remodelling, and ossification. Moreover, we demonstrate, for the first time, that vascular remodelling processes are associated with increased circulating CPP counts. Interventions targeting CPPs are promising avenues for alleviating vascular remodelling and VC in CKD.

Keywords: Calcification propensity (crystallization time, T50); Calciprotein particles (CPPs); Chronic kidney disease (CKD); Endothelial activation; Vascular calcification; Vascular remodelling.

Graphical Abstract

Figure 1

Computed tomography-assessed (macro)calcifications in healthy kidney donors and CKD patients. (A, B) Representative image of the computed tomography (CT) scan of (A) healthy kidney donors or (B) CKD patients. Arrows indicate spots of (macro)calcification. (C–E) Quantification of the CT-assessed (macro)calcifications resulted in a total abdominal artery calcification score (sum of all seven abdominal arteries) (C), renal artery calcification score (sum left and right renal artery) (D), and iliac artery calcification score (sum external and common iliac arteries, left and right) (E). Graphs show medians with interquartile ranges and individual data points (kidney donors: n = 16; CKD patients: n = 23). P < 0.05 was considered statistically significant and tested using a Mann–Whitney U test.

Figure 2

Histological analysis of microcalcifications in healthy kidney donors and CKD patients. (A, B) Representative image of Alizarin red staining (A) and von Kossa staining (B) on vascular biopsies of healthy kidney donors (renal artery) and CKD patients (iliac artery) to assess microcalcifications. Calcification is visible as red-coloured calcium-rich deposits (Alizarin red staining) or black-coloured calcium-phosphate-rich deposits (von Kossa staining). Scale bar represents 200 μm. (C, D) Quantification of Alizarin red (C) or von Kossa (D) staining on the vascular tissue of healthy kidney donors and CKD patients (kidney donors: n = 28; CKD patients: n = 35). Data are shown in stacked bar graphs: score 0 (no calcification), score 1 (traces of microcalcification), score 2 (mild microcalcification), score 3 (moderate microcalcification), and score 4 (severe microcalcification).

Figure 3

VSMC dedifferentiation and calcification marker gene expression assessed with qRT-PCR. (A–C) mRNA expression of VSMC osteochondrogenic dedifferentiation markers ACTA2 (A), MYH11 (B), and Transgelin (TAGLN) (C). (D–I) mRNA expression of VSMC calcification markers ALPL (D), IBSP (E), MGP (F), MSX2 (G), RUNX2 (H), and SOX9 (I). Data are shown as relative expression (2^−dCp) (means ± SEM) and individual data points (n = 15–33, variable since not all genes were analysed on all samples). Significance is indicated P < 0.05 and tested with a Mann–Whitney U test.

Figure 4

Bulk RNA sequencing on vascular biopsies of healthy kidney donors and CKD patients. (A) PCA plot showing the distribution of the data of healthy kidney donors and chronic kidney disease (CKD) patients. (B) Volcano plot showing differentially expressed genes using Log₂ fold difference >1 and an FDR-adjusted P-value < 0.05. Significantly increased genes in CKD are indicated in red (n = 147), while significantly decreased genes in CKD are indicated in blue (n = 19). Plot shows the ratio CKD vs. kidney donors. (C, D) Enrichment plots showing GO term inflammatory response (C), blood vessel remodelling (D), and ossification (E). Normalized enrichment score and FDR-adjusted P-value are shown. Black vertical bars indicate all individual hits for the GO term. The enrichment profile is indicated with a green line. Data included healthy kidney donors (n = 6) and CKD patients (n = 6).

Figure 5

Serum calcification propensity, CPP radius, and CPP count in healthy kidney donors and CKD patients. (A) Serum calcification propensity measured as T₅₀ values and expressed in minutes (min). (B) CPP2 diameter expressed in nanometer (nm). (C) Serum CPP1 count expressed as 10⁴ particles per millilitre serum (mL). (D) Serum CPP2 count expressed as 10⁴ particles per mL serum. All parameters measured in serum of healthy kidney donors (n = 17) and CKD patients (n = 34). Data show means ± SEM and individual data points. Significance is indicated P < 0.05 and tested with a Mann–Whitney U test. (E) Reactome2022 enrichment analysis for differently expressed RNA sequencing genes, which were significantly associated with CPP1 counts (67 genes, n = 6 healthy kidney donors and n = 6 CKD patients). (F) Reactome2022 enrichment analysis for differently expressed RNA-sequencing genes, which were significantly associated with CPP2 counts (51 genes, n = 6 healthy kidney donors and n = 6 CKD patients). Rank of the processes is indicated on the y-axis (i.e. the higher the rank the more enriched). The adjusted P-value is indicated on the x-axis. The size of the dots indicates the number of genes involved within the specific GO term, shown as overlap ratio.