The identification of dysfunctional crosstalk of pathways in Parkinson disease

Parkinson disease (PD) is the second most common neurodegenerative disorder after Alzheimer's disease, affecting 1–2% of the population over the age of 65. Both genetic and environmental factors trigger risks of and protection from PD. However, the molecular mechanism of PD is far from being clear. In this study, we downloaded the gene expression profile of PD from Gene Expression Omnibus and identified differentially expressed genes (DEGs) and dysfunctional pathways in PD patients compared with controls. To further understand how these pathways act together to account for the initiation of PD, we constructed a pathway crosstalk network by calculating the Jaccard index among pathways. A total of 873 DEGs and 16 dysfunctional pathways between PD patients and controls were identified. Through constructing a network of pathways, the relationships among PD pathways were visually presented by their interactions. Our results demonstrate the existence of crosstalk between different pathways in PD pathogenesis. These results not only may explain the causes of PD, but could also open the door to new therapeutic approaches for this disease.     

Gene co-expression network analysis for identifying genetic markers in Parkinson's disease - a three-way comparative approach

Parkinson's disease (PD) is a neurodegenerative disorder involving progressive deterioration of dopaminergic neurons. Although few genetic markers for familial PD are known, the etiology of sporadic PD remains poorly understood. Microarray data was analysed for induced pluripotent stem cells (iPSCs) derived from PD patients and mature neuronal cells (mDA) differentiated from these iPSCs. Combining expression and semantic similarity, a highly-correlated PD interactome was constructed that included interactions of established Parkinson's disease marker genes. A novel three-way comparative approach was employed, delineating topologically and functionally important genes. These genes showed involvement in pathways like Parkin-ubiquitin proteosomal system (UPS), immune associated biological processes and apoptosis. Of interest are three genes, eEF1A1, CASK, and PSMD6 that are linked to PARK2 activity in the cell and thereby form attractive candidate genes for understanding PD. Network biology approach delineated in this study can be applied to other neurodegenerative disorders for identification of important genetic regulators.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Identification of potential key genes associated with osteosarcoma based on integrated bioinformatics analyses

Due to high rates of metastasis and poor clinical outcomes for patients, it is important to study the pathomechanisms of osteosarcoma. However, due to the fact that osteosarcoma shows significant interindividual variation and high heterogeneity, the identification of differentially expressed genes (DEGs) at the population level cannot answer many important questions related to osteosarcoma tumorigenesis. Therefore, a new strategy to identify dysregulated genes in osteosarcoma samples is required. The aim of this study was to improve our understanding of osteosarcoma pathogenesis by identifying genes with universal aberrant expression in osteosarcoma samples. Because the relative expression ordering of genes is stable in normal bone tissues but is disrupted in osteosarcoma tissues, we used the RankComp algorithm to identify DEGs in normal and osteosarcoma tissue samples. We then calculated the dysregulation frequency for each gene. Genes with deregulation frequencies above 80% were deemed to be universal DEGs. Next, coexpression, pathway enrichment, and protein-protein interaction network analyses were performed to characterize the functions of these genes. From 188 samples of osteosarcoma obtained from four datasets measured on different platforms, 51 universal DEGs were identified, including 4 universally upregulated genes and 47 universally downregulated genes. Genes that were differentially coexpressed with these universal DEGs were found to be enriched in 46 cancer-related pathways. In addition, functional and network analyses showed that genes with high dysregulation frequencies were involved in cancer-related functions. Thus, the commonly aberrant genes identified in osteosarcoma tissues may be important targets for osteosarcoma diagnosis and therapy.     

Gene microarray analysis of expression profiles in Suberoyllanilide hyroxamic acid-treated Dendritic cells

Objective

The purpose of this study is to provide a further theoretical basis for the role of Suberoyllanilide hyroxamic acid (SAHA) affect on Dendritic cells (DCs).

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Characterization of a high‐growth‐rate mutant strain of Pyropia yezoensis using physiology measurement and transcriptome analysis

A mutant strain of Pyropia yezoensis, strain E, was isolated from the free‐living conchocelis of a pure strain (NA) treated with ethyl methane sulfonate. The incremental quantities of young strain E blades were higher than those of NA after 14 d of cultivation, indicating that young blades of mutant strain E released more archeospores. The mean length and weight of large E blades were both over three times greater than those of NA after 4 weeks of cultivation. The photosynthetic parameters (Fv/Fm, Y[I], Y[II], and O₂ evolution rate) and pigment contents (including phycoerythrin and phycocyanin) of strain E blades were higher than those of NA (P < 0.05). The cellular respiratory rate of strain E blades was lower than that of NA (P < 0.05). In order to investigate the causes of changes in strain E blades, total RNA in strain E and NA blades were sequenced using the Illumina Hiseq platform. Compared with NA, 1,549 unigenes were selected in strain E including 657 up‐regulated and 892 down‐regulated genes. According to the physiology measurement and differentially expressed genes analysis, cell respiration in strain E might decrease, whereas anabolic‐like photosynthesis and protein biosynthesis might increase compared with NA. This means substance accumulation might be greater than decomposition in strain E. This might explain why strain E blades showed improved growth compared with NA. In addition, several genes related to stress resistance were up‐regulated in strain E indicating that strain E might have a higher stress resistance. The sequencing dataset may be conducive to Pyropia yezoensis molecular breeding research.     

慢性酒精中毒大鼠在学习记忆方面差异基因的生物信息学分析

借助基因芯片获取慢性酒精中毒大鼠海马相关基因的表达数据集,通过生物信息学的分析方法对差异表达基因进行筛选与分析。从分子水平揭示慢性酒精中毒对大鼠大脑海马体的影响,为慢性酒精中毒的损伤机制以及相关疾病发病机制的基础研究与临床治疗提供新的方向。同时,还通过Y迷宫实验对实验大鼠的学习记忆功能进行了检测,借助电镜拍摄其线粒体。结果显示,我们一共筛选出208个差异表达基因,其中51个表达上调,157个表达下调。其中涉及的主要信号通路有氧化磷酸化通路、D-谷氨酰胺和谷氨酸代谢通路、阿尔茨海默病信号通路、帕金森病信号通路、膀胱癌信号通路、B细胞受体信号通路和亨廷顿病信号通路等。由此我们得出结论,慢性酒精中毒可能影响了海马多个基因的表达,其中包括Rpsa、Wdr31、Rps11、Rps9、Ndufa2、Mrto4、Rpl6、Dap3、Ndufb8、Ndufb6、Ephb2、Cox6c、Prkcd、Rela、Raf1、Ubd、Mrps28、Mrpl35等关键基因,进而损伤了电子传递链复合体Ⅰ,最终损伤线粒体,导致大鼠学习记忆能力的损伤。     

Identifying significant genes and functionally enriched pathways in familial hypercholesterolemia using integrated gene co-expression network analysis

Familial hypercholesterolemia (FH) is a monogenic lipid disorder which promotes atherosclerosis and cardiovascular diseases. Owing to the lack of sufficient published information, this study aims to identify the potential genetic biomarkers for FH by studying the global gene expression profile of blood cells. The microarray expression data of FH patients and controls was analyzed by different computational biology methods like differential expression analysis, protein network mapping, hub gene identification, functional enrichment of biological pathways, and immune cell restriction analysis. Our results showed the dysregulated expression of 115 genes connected to lipid homeostasis, immune responses, cell adhesion molecules, canonical Wnt signaling, mucin type O-glycan biosynthesis pathways in FH patients. The findings from expanded protein interaction network construction with known FH genes and subsequent Gene Ontology (GO) annotations have also supported the above findings, in addition to identifying the involvement of dysregulated thyroid hormone and ErbB signaling pathways in FH patients. The genes like CSNK1A1, JAK3, PLCG2, RALA, and ZEB2 were found to be enriched under all GO annotation categories. The subsequent phenotype ontology results have revealed JAK3I, PLCG2, and ZEB2 as key hub genes contributing to the inflammation underlying cardiovascular and immune response related phenotypes. Immune cell restriction findings show that above three genes are highly expressed by T-follicular helper CD4⁺ T cells, naïve B cells, and monocytes, respectively. These findings not only provide a theoretical basis to understand the role of immune dysregulations underlying the atherosclerosis among FH patients but may also pave the way to develop genomic medicine for cardiovascular diseases.