Dataset Information

Detecting splicing variants in idiopathic pulmonary fibrosis from non-differentially expressed genes.

ABSTRACT: Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease of unknown cause that lacks a proven therapy for altering its high mortality rate. Microarrays have been employed to investigate the pathogenesis of IPF, but are presented mostly at the gene-expression level due to technologic limitations. In as much as, alternative RNA splicing isoforms are increasingly identified as potential regulators of human diseases, including IPF, we propose a new approach with the capacity to detect splicing variants using RNA-seq data. We conducted a joint analysis of differential expression and differential splicing on annotated human genes and isoforms, and identified 122 non-differentially expressed genes with a high degree of "switch" between major and minor isoforms. Three cases with variant mechanisms for alternative splicing were validated using qRT-PCR, among the group of genes in which expression was not significantly changed at the gene level. We also identified 35 novel transcripts that were unique to the fibrotic lungs using exon-exon junction evidence, and selected a representative for qRT-PCR validation. The results of our study are likely to provide new insight into the pathogenesis of pulmonary fibrosis and may eventuate in new treatment targets.

SUBMITTER: Deng N

PROVIDER: S-EPMC3699530 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Detecting splicing variants in idiopathic pulmonary fibrosis from non-differentially expressed genes.

Deng Nan N Sanchez Cecilia G CG Lasky Joseph A JA Zhu Dongxiao D

PloS one 20130702 7

Idiopathic pulmonary fibrosis (IPF) is an interstitial lung disease of unknown cause that lacks a proven therapy for altering its high mortality rate. Microarrays have been employed to investigate the pathogenesis of IPF, but are presented mostly at the gene-expression level due to technologic limitations. In as much as, alternative RNA splicing isoforms are increasingly identified as potential regulators of human diseases, including IPF, we propose a new approach with the capacity to detect spl ...[more]

PMID: 23844188

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Project description:Background Using bioinformatic methods to explore the differentially expressed genes (DEGs) of human idiopathic pulmonary fibrosis (IPF) and to elucidate the pathogenesis of IPF from the genetic level. Methods The GSE110147 gene expression profile was downloaded from the GEO database. The data of lung adenocarcinoma (LUAD) samples, lung squamous cell carcinoma (LUSC) samples and normal samples were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. DEGs between IPF patients and healthy donors were analyzed using the GEO2R tool. Use the “clusterprofiler” package in R software to perform gene ontology (GO) and KEGG pathway enrichment analysis, and then perform function annotation and protein-protein interaction (PPI) network construction in the STRING online tool. The Genome Browser tool of the university of california santa cruz (UCSC) online website was used to predict transcription factors (TFs) of genes. In the final, the results were analyzed synthetically. Results A total of 9,183 DEGs were identified, of which 4,545 genes were down-regulated, and 4638 were up-regulated. MMP1, SPP1, and BPIFB1 were the top three DEGs with the highest significant up-regulation. These DEGs played an important role in the occurrence of IPF through the MAPK (mitogen-activated protein kinase) signaling pathway. Furthermore, 50 DEGs were enriched in the expression of PD-L1 and the PD-1 checkpoint pathway in cancer, of which 11 genes were re-enriched in the pathway of non-small cell lung cancer. The expression of the 11 genes were extensively regulated by CTCFL, SP2 and ZNF341. Most of them were differentially expressed between lung cancers and normal lung tissues. The overall survival (OS) curve of LUAD were significantly stratified by AKT2, KRAS, PIK3R1, meanwhile the OS curve of LUAC was significantly stratified by MAPK3. Conclusions Bioinformatics analysis revealed that DEGs including MPP1 might be potential targets and biomarkers of IPF, and the MAPK signaling pathway is related to the occurrence and development of IPF. The development of IPF lung cancer complications may be related to the activation of genes enriched in PD-L1 expression and PD-1 checkpoint pathway, which provides clues to the pathogenesis of IPF combined with lung cancer.

Project description:BackgroundThe underlying molecular pathways of idiopathic pulmonary fibrosis (IPF), a progressive lung condition with a high death rate, are still mostly unknown. By using microarray datasets, this study aims to identify new genetic targets for IPF and provide light on the genetic factors that contribute to the development of IPF.MethodWe conducted a comprehensive analysis of three independent IPF datasets from the Gene Expression Omnibus (GEO) database, employing R software for data handling and normalization. Our evaluation of the relationships between differentially expressed genes (DEGs) and IPF included differential expression analysis, expression quantitative trait loci (eQTL) analysis, and Mendelian Randomization(MR) analyses. Additionally, we used Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to explore the functional roles and pathways of these genes. Finally, we validated the results obtained for the target genes.ResultsWe identified 486 highly expressed genes and 468 lowly expressed genes that play important roles in IPF. MR analysis identified six significantly co-expressed genes associated with IPF, specifically C12orf75, SPP1, ZG16B, LIN7A, PPP1R14A, and TLR2. These genes participate in essential biological processes and pathways, including macrophage activation and neural system regulation. Additionally, CIBERSORT analysis indicated a unique immune cell distribution in IPF, emphasized the significance of immunological processes in the disease. The MR analysis was consistent with the results of the analysis of variance in the validation cohort, which strengthens the reliability of our MR findings.ConclusionOur findings provide new insights into the molecular basis of IPF and highlight the promise of therapeutic interventions. They emphasize the potential of targeting specific molecular pathways for the treatment of IPF, laying the foundation for further research and clinical work.

Project description:Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Dataset Information

Detecting splicing variants in idiopathic pulmonary fibrosis from non-differentially expressed genes.

Publications

Detecting splicing variants in idiopathic pulmonary fibrosis from non-differentially expressed genes.

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