Exploring the transcriptome of ciliated cells using in silico dissection of human tissues

Abstract

Cilia are cell organelles that play important roles in cell motility, sensory and developmental functions and are involved in a range of human diseases, known as ciliopathies. Here, we search for novel human genes related to cilia using a strategy that exploits the previously reported tendency of cell type-specific genes to be coexpressed in the transcriptome of complex tissues. Gene coexpression networks were constructed using the noise-resistant WGCNA algorithm in 12 publicly available microarray datasets from human tissues rich in motile cilia: airways, fallopian tubes and brain. A cilia-related coexpression module was detected in 10 out of the 12 datasets. A consensus analysis of this module's gene composition recapitulated 297 known and predicted 74 novel cilia-related genes. 82% of the novel candidates were supported by tissue-specificity expression data from GEO and/or proteomic data from the Human Protein Atlas. The novel findings included a set of genes (DCDC2, DYX1C1, KIAA0319) related to a neurological disease dyslexia suggesting their potential involvement in ciliary functions. Furthermore, we searched for differences in gene composition of the ciliary module between the tissues. A multidrug-and-toxin extrusion transporter MATE2 (SLC47A2) was found as a brain-specific central gene in the ciliary module. We confirm the localization of MATE2 in cilia by immunofluorescence staining using MDCK cells as a model. While MATE2 has previously gained attention as a pharmacologically relevant transporter, its potential relation to cilia is suggested for the first time. Taken together, our large-scale analysis of gene coexpression networks identifies novel genes related to human cell cilia.

Figure 1. Flowchart of analysis.

Marks in italics - databases, programs and analysis types. Red marks - tables (“T”), figures (“F”) and supplementary tables (“S”). A. Search in the GEO database: selection of data pertaining to brain, airways and reproductive tracts. B. Construction of coexpression networks in each dataset using WGCNA algorithm: identification of coexpression modules. C. Generation of a consensus ciliary module: identification of genes shared by the tissues. D to G: validation and characterization of genes in the consensus signature. D. Discrimination between known, candidate and novel ciliary genes (CilDB and PubMed databases). E. Determination which genes from the consensus signature are up-regulated in ‘ciliated’ tissues compared to ‘non-ciliated’ tissues (GEO database). F. Determination which proteins from the consensus signature have characteristic patterns of subcellular localization in ciliated cells (Protein Atlas database). G. Linking genes to cellular functions and human diseases using literature mining (Anni 2.1 program). H. Differential coexpression analysis: identification of genes which represent members of the ciliary module in only a subset of ciliated tissues.

Figure 2. Heatmaps of consensus ciliary signature in 10 contributing datasets.

Red - high, green - low level of expression. Columns – samples, rows – genes. Samples were clustered separately in each dataset. Genes were ordered by the number of datasets in which they belonged to the ciliary module: the gene order is constant across the datasets. Genes, that lacked measurements in a subset of the experiments, were excluded from the heatmaps.

Figure 3. Tissue specificity analysis of the signature genes.

(A) Top 20 tissues (out of the total 104 human tissues available in GSE7307) with highest mean expression level of the 326 signature genes. The “Sam” column describes the number of samples available for each tissue in the dataset. The bars show mean Z-score expression level of the signature genes in the respective tissues. An asterisk marks tissues that were previously reported to contain cells with motile cilia. A double asterisk marks testis that contains spermatozoa with motile flagellum (an organelle related to motile cilia). (B) Percentage of signature genes up-regulated in ciliated tissues – by literature-based categories. Up-regulated genes were detected using a permutation-based test that compared expression level of a given gene in the union of trachea, bronchus, lung, fallopian tube, endometrium and testis versus the rest of the tissues in the GSE7307 dataset (P<0.05, Methods).

Figure 4. Subcellular localization types of the signature proteins.

The images were obtained by immunohistochemical staining of airways and fallopian tubes with protein-specific antibodies in the Protein Atlas project . The antibodies were targeted at the following proteins: (A) C11orf66 (a protein with unknown function), (B) FOXJ1 (a transcription factor known to regulate cilium biogenesis), (C) TSGA10 (a sperm tail protein), (D) RBKS (ribokinase, a ribose metabolism enzyme). Brown corresponds to the antibody-based staining, blue – to staining of nuclei with DAPI. Note that in airways ciliated cells form a continuous layer, while in fallopian tubes they are separated from each other by non-ciliated epithelial cells.

Figure 5. Associations of signature genes with cell functions and diseases suggested by literature mining.

A. Cell functions. B. Diseases and syndromes. The plots depict contributions (%) of each biological term (a specific cell function or disease) into the overall similarity between contexts in which the signature genes are mentioned in the literature . A high contribution value for a given term implies that multiple genes from the signature co-occur with this term in a large number of literature abstracts. The disease categories include the following individual diseases: ‘Ciliary motility disorders’ - ciliary dyskinesias (diseases that manifest mainly in dysfunctions of motile cilia), ‘Ciliopathy syndromes’ - Bardet-Biedl syndrome, Meckel syndrome, Joubert syndrome (diseases that manifest in a broader range of dysfunctions, including those related to non-motile cilia); ‘Kidney diseases’ - nephronophthisis, polycystic kidney disease; ‘Retinal diseases’ - retinitis pigmentosa, retinal dystrophy, Leber amaurosis.

Figure 6. MATE2 co-localizes with acetylated α-tubulin in the primary cilia of MDCK cells.

Immunofluorescence staining was performed with antibodies against MATE2 (green) and acetylated α-tubulin (red). Nuclei were stained with DAPI (blue). Co-localization of the antibody signals in the primary cilia is observed. A – MDCK immunofluorescence image; B - enlarged fragment of the image (marked with a white box in A). Arrows in B mark fluorescence from the antibodies specific to MATE2.