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. 2022 Dec 15;13(12):2370.
doi: 10.3390/genes13122370.

Proteomic and Transcriptomic Landscapes of Alström and Bardet-Biedl Syndromes

Urszula Smyczynska  1 , Marcin Stanczak  1 , Miljan Kuljanin  2 , Aneta Włodarczyk  3 , Ewelina Stoczynska-Fidelus  4 , Joanna Taha  5 , Bartłomiej Pawlik  6   7 , Maciej Borowiec  8 , Joseph D Mancias  2 , Wojciech Mlynarski  6 , Piotr Rieske  3 , Wojciech Fendler  1   2 , Agnieszka Zmysłowska  8
Affiliations

Proteomic and Transcriptomic Landscapes of Alström and Bardet-Biedl Syndromes

Urszula Smyczynska et al. Genes (Basel). .

Abstract

Alström syndrome (ALMS) and Bardet-Biedl syndrome (BBS) are rare genetic diseases with a number of common clinical features ranging from early-childhood obesity and retinal degeneration. ALMS and BBS belong to the ciliopathies, which are known to have the expression products of genes, encoding them as cilia-localized proteins in multiple target organs. The aim of this study was to perform transcriptomic and proteomic analysis on cellular models of ALMS and BBS syndromes to identify common and distinct pathological mechanisms present in both syndromes. For this purpose, epithelial cells were isolated from the urine of patients and healthy subjects, which were then cultured and reprogrammed into induced pluripotent stem (iPS) cells. The pathways of genes associated with the metabolism of lipids and glycosaminoglycan and the transport of small molecules were found to be concomitantly downregulated in both diseases, while transcripts related to signal transduction, the immune system, cell cycle control and DNA replication and repair were upregulated. Furthermore, protein pathways associated with autophagy, apoptosis, cilium assembly and Gli1 protein were upregulated in both ciliopathies. These results provide new insights into the common and divergent pathogenic pathways between two similar genetic syndromes, particularly in relation to primary cilium function and abnormalities in cell differentiation.

Keywords: Alström syndrome; Bardet–Biedl syndrome; cilia; proteomics; transcriptomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Characterization of exemplary induced pluripotent stem cells (iPSCs) derived from urine cells isolated from ALMS patient by means of reprogramming. The detection of pluripotency-associated markers showed that cell colonies were positive for OCT3/4 transcription factor and displayed TRA-1-60 expression on their surface. Cells presented typical morphology for pluripotent stem cells. Images were captured using Eclipse Ci-S epifluorescence microscope.
Figure 2
Figure 2
Transcriptomic characteristics of ALMS and BBS. Principal component analysis (PCA) of transcripts in ALMS, BBS and control (A); Venn diagram showing number of transcripts significantly up- and downregulated in ALMS and BBS (B); volcano plot for differential gene expression in ALMS (C); volcano plot for BBS (D); hierarchical clustering heatmap of the top 50 genes with the highest variance (E).
Figure 3
Figure 3
Enrichment maps on transcriptomics data presenting reactome gene set significance (FDR < 0.05) in ALMS (A) and BBS (B). Lines connecting pathways represent genes common to these pathways.
Figure 4
Figure 4
Proteomic characteristics of ALMS and BBS. Principal component analysis (PCA) of proteins abundance in ALMS and control (A); PCA of proteins abundance in BBS and control (B); PCA of ALMS and BBS patient samples calculated on protein to mean control ratios (C); hierarchical clustering of top 25 proteins in ALMS with the highest p value in a t-test (D); hierarchical clustering of top 25 proteins in BBS with the highest p value in a t-test (E); volcano plot of ALMS (F); volcano plot of BBS (G); Venn diagram showing number of proteins significantly up- and downregulated in ALMS and BBS (H).
Figure 5
Figure 5
Compounds causing changes opposite to ALMS- and BBS-proteins with FDR < 0.15 and 0.67 > FC > 1.5.
Figure 6
Figure 6
Enrichment maps on proteomic data presenting the reactome gene sets significant in ALMS vs. controls (A) and BBS vs. controls comparisons (B).
Figure 7
Figure 7
Comparison of proteomic and transcriptomic findings. Venn diagrams show significantly regulated proteins and genes in ALMS (A) and BBS (B). Reactome gene sets and their normalized enrichment scores calculated on proteomics and transcriptomics data for ALMS (C) and BBS (D). Significance of gene sets according to the color key: Brown—significant in proteomics; Green—significant in transcriptomics; Grey—not significant; Black—significant in both. (E) Enrichment map of pathways significant (FDR < 0.05) both in proteomics and transcriptomics between ALMS and BBS. Inner part of the circle shows NES in ALMS, outer part depicts NES in BBS accordingly with the color code: significant upregulation in both proteomics and transcriptomics is shown in red and, accordingly, downregulation is in blue, no significant expression or mismatch between proteomics and transcriptomics is coded with white.
Figure 8
Figure 8
Enrichment map presenting similarities and differences in reactome gene sets expression between proteomics and transcriptomics data in ALMS (A) and BBS (B).
Figure 9
Figure 9
GSEA results on proteomics data of pathways associated with the Gli1 protein. Enrichment plot and a heatmap of the reactome degradation of Gli1 by the proteasome pathway in ALMS (A) and BBS (B). Enrichment plot and a heatmap of reactome Hedgehog ON state in ALMS (C) and BBS (D). Enrichment plot and a heatmap of reactome Hedgehog OFF state in ALMS (E) and BBS (F).
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