斑马鱼肌肉高表达基因近端非编码元件分析及功能检测

为鉴定鱼类肌肉组织特异性顺式调控元件,通过分析斑马鱼多个组织的转录组数据,筛选出肌肉高表达基因及低表达基因.通过MEME对肌肉高表达基因和低表达基因非编码区序列特征进行分析,在5个肌肉高表达基因的转录起始位点上游发现了序列保守的DNA区域,包含6个排列顺序一致的DNA基序.将其中一段目标片段插入具有Tol2转座子元件的...     

Time-Series Clustering of lncRNA-mRNA Expression during the Adipogenic Transdifferentiation of Porcine Skeletal Muscle Satellite Cells

Skeletal muscle satellite cells (SMSCs), which are multifunctional muscle-derived stem cells, can differentiate into adipocytes. Long-chain non-coding RNA (lncRNA) has diverse biological functions, including the regulation of gene expression, chromosome silencing, and nuclear transport. However, the regulatory roles and mechanism of lncRNA during adipogenic transdifferentiation in muscle cells have not been thoroughly investigated. Here, porcine SMSCs were isolated, cultured, and induced for adipogenic differentiation. The expressions of lncRNA and mRNA at different time points during transdifferentiation were analysed using RNA-seq analysis. In total, 1005 lncRNAs and 7671 mRNAs showed significant changes in expression at differential differentiation stages. Time-series expression analysis showed that the differentially expressed (DE) lncRNAs and mRNAs were clustered into 5 and 11 different profiles with different changes, respectively. GO, KEGG, and REACTOME enrichment analyses revealed that DE mRNAs with increased expressions during the trans-differentiation were mainly enriched in the pathways for lipid metabolism and fat cell differentiation. The genes with decreased expressions were mainly enriched in the regulation of cell cycle and genetic information processing. In addition, 1883 DE mRNAs were regulated by 193 DE lncRNAs, and these genes were related to the controlling in cell cycle mainly. Notably, three genes in the fatty acid binding protein (FABP) family significantly and continuously increased during trans-differentiation, and 15, 13, and 11 lncRNAs may target FABP3, FABP4, and FABP5 genes by cis- or trans-regulation, respectively. In conclusion, these studies identify a set of new potential regulator for adipogenesis and cell fate and help us in better understanding the molecular mechanisms of trans-differentiation.     

Graph-based identification of cancer signaling pathways from published gene expression signatures using PubLiME

Gene expression technology has become a routine application in many laboratories and has provided large amounts of gene expression signatures that have been identified in a variety of cancer types. Interpretation of gene expression signatures would profit from the availability of a procedure capable of assigning differentially regulated genes or entire gene signatures to defined cancer signaling pathways. Here we describe a graph-based approach that identifies cancer signaling pathways from published gene expression signatures. Published gene expression signatures are collected in a database (PubLiME: Published Lists of Microarray Experiments) enabled for cross-platform gene annotation. Significant co-occurrence modules composed of up to 10 genes in different gene expression signatures are identified. Significantly co-occurring genes are linked by an edge in an undirected graph. Edge-betweenness and k-clique clustering combined with graph modularity as a quality measure are used to identify communities in the resulting graph. The identified communities consist of cell cycle, apoptosis, phosphorylation cascade, extra cellular matrix, interferon and immune response regulators as well as communities of unknown function. The genes constituting different communities are characterized by common genomic features and strongly enriched cis-regulatory modules in their upstream regulatory regions that are consistent with pathway assignment of those genes.