ObjectiveTo conduct a bioinformatics analysis of gene expression profiles in frontal lobe of patients with Parkinson disease (PD), in order to explore the potential mechanism related to depression in PD.MethodsAll the bioinformatics data before March 20th 2019 were acquired from Gene Expression Omnibus (GEO) database, using " Parkinson disease” as the key word. The species was limited to human (Homo sapiens), and the detective method was limited to expression profiling by array. ImgGEO (Integrative Gene Expression Meta-Analysis from GEO database), DAVID (the Database for Annotation, Visualization and Integrated Discovery), STRING and Cytoscape 3.6.1 software were utilized for data analysis.ResultsTotally, 45 samples (24 PD cases and 21 healthy controls) were obtained from 2 datasets. We identified 236 differentially expressed genes (DEGs) in the post-mortem frontal lobe between PD cases and healthy controls, in which 146 genes were up-regulated and 90 genes were down-regulated. Based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis, the DEGs were mainly enriched in the structures of postsynaptic membrane, cell membrane component, postsynaptic membrane dense area, and myelin sheath, and were involved in the occurrence of PD, depression, and other diseases. These genes were involved in the biological processes of dopaminergic, glutamate-nergic, GABA-nergic synapses, and some other synapses, as well as several signaling pathways (e.g. mitogen- activated protein kinase signal pathway, p53 signal pathway, and Wnt signal pathway), which were associated with PD and depression pathogenesis. Besides, we found that NFKBIA, NRXN1, and RPL35A were the Hub proteins.ConclusionsGene expression in frontal lobe of patients with PD is associated with the pathogenesis of PD. This study provides a theoretical basis for understanding the mechanism of PD occurrence and progression, as well as the potential mechanism of depression in PD.
Objective To explore the pathogenesis of acute respiratory disease syndrome (ARDS) by bioinformatics analysis of neutrophil gene expression profile in order to find new therapeutic targets. Methods The gene expression chips include ARDS patients and healthy volunteers were screened from the Gene Expression Omnibus (GEO) database. The differentially expressed genes were carried out through GEO2R, OmicsBean, STRING, and Cytoscape, then enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathways was conducted to investigate the biological processes involved in ARDS via DAVID website. Results Bioinformatics analysis showed 86 differential genes achieved through the GEO2R website. Eighty-one genes were included in the STRING website for protein interaction analysis. The results of the interaction were further analyzed by Cytoscape software to obtain 11 hub genes: AHSP, ALAS2, CD177, CLEC4D, EPB42, GPR84, HBD, HVCN1, KLF1, SLC4A1, and STOM. GO analysis showed that the differential gene was enriched in the cellular component, especially the integrity of the plasma membrane. KEGG analysis showed that multiple pathways especially the cytokine receptor pathway involved in the pathogenesis of ARDS. Conclusions A variety of genes and pathways have been involved in the pathogenesis of ARDS. Eleven hub genes are screened, which may be involved in the pathogenesis of ARDS and can be used in subsequent studies.
The rapid development of high-throughput chromatin conformation capture (Hi-C) technology provides rich genomic interaction data between chromosomal loci for chromatin structure analysis. However, existing methods for identifying topologically associated domains (TADs) based on Hi-C data suffer from low accuracy and sensitivity to parameters. In this context, a TAD identification method based on spatial density clustering was designed and implemented in this paper. The method preprocessed the raw Hi-C data to obtain normalized Hi-C contact matrix data. Then, it computed the distance matrix between loci, generated a reachability graph based on the core distance and reachability distance of loci, and extracted clustering clusters. Finally, it extracted TAD boundaries based on clustering results. This method could identify TAD structures with higher coherence, and TAD boundaries were enriched with more ChIP-seq factors. Experimental results demonstrate that our method has advantages such as higher accuracy and practical significance in TAD identification.