Identification of key genes associated with Schmid-type metaphyseal chondrodysplasia based on microarray data

A Collagen10a1 mutation disrupts cell polarity in a medaka model for metaphyseal chondrodysplasia type Schmid

Heterozygous mutations in COL10A1 lead to metaphyseal chondrodysplasia type Schmid (MCDS), a skeletal disorder characterized by epiphyseal abnormalities. Prior analysis revealed impaired trimerization and intracellular retention of mutant collagen type X alpha 1 chains as cause for elevated endoplasmic reticulum (ER) stress. However, how ER stress translates into structural defects remained unclear. We generated a medaka (Oryzias latipes) MCDS model harboring a 5 base pair deletion in col10a1, which led to a frameshift and disruption of 11 amino acids in the conserved trimerization domain. col10a1Δ633a heterozygotes recapitulated key features of MCDS and revealed early cell polarity defects as cause for dysregulated matrix secretion and deformed skeletal structures. Carbamazepine, an ER stress-reducing drug, rescued this polarity impairment and alleviated skeletal defects in col10a1Δ633a heterozygotes. Our data imply cell polarity dysregulation as a potential contributor to MCDS and suggest the col10a1Δ633a medaka mutant as an attractive MCDS animal model for drug screening.

Application of weighted gene co-expression network analysis to identify novel key genes in diabetic nephropathy

AIMS/INTRODUCTION

Diabetic nephropathy (DN) is among the leading causes of end-stage renal disease worldwide. DN pathogenesis remains largely unknown. Weighted gene co-expression network analysis is a powerful bioinformatic tool for identifying key genes in diseases.

MATERIALS AND METHODS

The datasets GSE30122, GSE104948, GSE37463 and GSE47185 containing 23 DN and 23 normal glomeruli samples were obtained from the National Center for Biotechnology Information Gene Expression Omnibus database. After data pre-processing, weighted gene co-expression network analysis was carried out to cluster significant modules. Then, Gene Set Enrichment Analysis-based Gene Ontology analysis and visualization of network were carried out to screen the key genes in the most significant modules. The connectivity map analysis was carried out to find the significant chemical compounds. Finally, some key genes were validated in in vivo and in vitro experiments.

RESULTS

A total of 454 upregulated and 392 downregulated genes were identified. A total of 16 modules were clustered, and the most significant modules (green, red and yellow modules) were determined. The green module was associated with extracellular matrix organization, the red module was associated with immunity reaction and the yellow module was associated with kidney development. We found several key genes in these three modules separately, and part of them were validated in vivo and in vitro successfully. We found the top 15 chemical compounds that could perturb the overall expression of key genes in DN.

CONCLUSION

Weighted gene co-expression network analysis was applied to DN expression profiling in combination with connectivity map analysis. Several novel key genes and chemical compounds were screened out, providing new molecular targets for DN.

Identification of two novel COL10A1 heterozygous mutations in two Chinese pedigrees with Schmid-type metaphyseal chondrodysplasia

Background

Schmid-type metaphyseal chondrodysplasia (MCDS) is an autosomal dominant disorder caused by COL10A1 mutations, which is characterized by short stature, waddling gait, coxa vara and bowing of the long bones. However, descriptions of the expressivity of MCDS are rare.

Methods

Two probands and available family members affected with MCDS were subjected to clinical and radiological examination. Genomic DNA of all affected individuals was subjected to whole-exome sequencing, and candidate mutations were verified by Sanger sequencing in all available family members and in 250 healthy donors. A spatial model of the type X collagen (α1) C-terminal noncollagenous (NC1) domain was further constructed.

Results

We found that the phenotype of affected family members exhibited incomplete dominance. Mutation analysis indicated that there were two novel heterozygous missense mutations, [c.1765 T > A (p.Phe589Ile)] and [c.1846A > G (p.Lys616Glu)] in the COL10A1 gene in family 1 and 2, respectively. The two novel substitution sites were highly conserved and the mutations were predicted to be deleterious by in silico analysis. Furthermore, protein modeling revealed that the two substitutions were located in the NC1 domain of collagen X (α1), which potentially impacted the trimerization of collagen X (α1) and combination with molecules in the pericellular matrix.

Conclusion

Two novel mutations were identified in the present study, which will facilitate diagnosis of MCDS and further expand the spectrum of the COL10A1 mutations associated with MCDS patients. In addition, our research revealed the phenomenon of incomplete dominance in MCDS.

A network-based variable selection approach for identification of modules and biomarker genes associated with end-stage kidney disease

AIMS

Intervention for end-stage kidney disease (ESKD), which is associated with adverse prognoses and major economic burdens, is challenging due to its complex pathogenesis. The study was performed to identify biomarker genes and molecular mechanisms for ESKD by bioinformatics approach.

METHODS

Using the Gene Expression Omnibus dataset GSE37171, this study identified pathways and genomic biomarkers associated with ESKD via a multi-stage knowledge discovery process, including identification of modules of genes by weighted gene co-expression network analysis, discovery of important involved pathways by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses, selection of differentially expressed genes by the empirical Bayes method, and screening biomarker genes by the least absolute shrinkage and selection operator (Lasso) logistic regression. The results were validated using GSE70528, an independent testing dataset.

RESULTS

Three clinically important gene modules associated with ESKD, were identified by weighted gene co-expression network analysis. Within these modules, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed important biological pathways involved in ESKD, including transforming growth factor-β and Wnt signalling, RNA-splicing, autophagy and chromatin and histone modification. Furthermore, Lasso logistic regression was conducted to identify five final genes, namely, CNOT8, MST4, PPP2CB, PCSK7 and RBBP4 that are differentially expressed and associated with ESKD. The accuracy of the final model in distinguishing the ESKD cases and controls was 96.8% and 91.7% in the training and validation datasets, respectively.

CONCLUSION

Network-based variable selection approaches can identify biological pathways and biomarker genes associated with ESKD. The findings may inform more in-depth follow-up research and effective therapy.

A Venezuelan Case of Schmid-Type Metaphyseal Chondrodysplasia with a Novel Mutation in COL10A1

Schmid-type metaphyseal chondrodysplasia (MIM 156500) is an uncommon autosomal dominant skeletal dysplasia caused by heterozygous mutations in the COL10A1 gene (MIM 120110) encoding the α1(X) chains of type X collagen. We report an 8-year-old girl with waddling gait, short stature, mild dorsal scoliosis, coxa vara, short lower limbs, bowing of the femurs, genu varum, and metaphyseal fraying and splaying, who is a carrier of a novel heterozygous 2-bp (c.1894_1895dupTA; p.Leu633Thrfs*45) duplication in exon 3 of the COL10A1 gene.

Identification of potential functional genes in papillary thyroid cancer by co-expression network analysis

Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co-expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co-expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co-expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC-related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes.