scholarly journals A Time-Series Analysis of Severe Burned Injury of Skin Gene Expression Profiles

2018 ◽  
Vol 49 (4) ◽  
pp. 1492-1498 ◽  
Author(s):  
Hai-Ting Xu ◽  
Jian-Chun Guo ◽  
Hua-Zhen Liu ◽  
Wan-wan Jin
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Burn Injury ◽  
Expression Profiles ◽  
Mirna Gene ◽  
Gene Expression Profiles ◽  
Gene Regulation Network ◽  
Soft Clustering ◽  
Major Burn ◽  
Regulation Network

Background/Aims: Major burn injury is one of the main severe forms of wound which lead to a mass of clinical debilitation, this study was to identify core biomarkers for the recovery of severe burned injury. Methods: Gene expression profiles (GSE19743) from the Gene Expression Omnibus (GEO) was downloaded, followed by background correction, normalization of raw microarray dataset and identification of differential expression genes (DEGs) . Soft clustering of DEGs was used for the screening of gene clusters that with sustained increasing (SIG) and decreasing expression (SDG) profiles along with the recovery process of burned injury. The significantly enriched Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of SIGs and SDGs were obtained through the Database for Annotation, Visualization, and Integrated Discovery (DAVID), based on which the miRNA-gene regulation network for SIGs and SDGs were constructed from the miRWalk database. Results: Ten clusters were obtained through soft clustering. The SIGs and SDGs were found to be closely associated with the biological processes of immune system. The miRNA-gene regulation network analysis suggested different roles between SIGs and SDGs in the recovery of severe burned injury. Furthermore, a bunch of important biomarkers were identified, which would be helpful in the treatment of burned patients. Conclusion: Our current findings suggest an interesting molecular link between transcriptional regulation potentially involved in immunosuppressive state after major burn injury, which warrants further exploration for their utilization in the treatment of major burn injury.

scholarly journals Effect of low-dose hydrocortisone on gene expression profiles after severe burn injury

Critical Care ◽  
2014 ◽  
Vol 18 (Suppl 1) ◽  
pp. P75
Author(s):  
J Plassais ◽  
MA Cazalis ◽  
F Venet ◽  
G Monneret ◽  
A Pachot ◽  
...  
Keyword(s):  
Gene Expression ◽  
Low Dose ◽  
Burn Injury ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Severe Burn ◽  
Severe Burn Injury

scholarly journals Deciphering gene regulation from gene expression dynamics using deep neural network

2018 ◽  
Author(s):  
Jingxiang Shen ◽  
Mariela D. Petkova ◽  
Yuhai Tu ◽  
Feng Liu ◽  
Chao Tang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Machine Learning ◽  
Gene Regulation ◽  
Gene Network ◽  
Deep Neural Network ◽  
Biological Functions ◽  
Gene Regulation Network ◽  
Regulation Network ◽  
Gene Expression Dynamics

AbstractComplex biological functions are carried out by the interaction of genes and proteins. Uncovering the gene regulation network behind a function is one of the central themes in biology. Typically, it involves extensive experiments of genetics, biochemistry and molecular biology. In this paper, we show that much of the inference task can be accomplished by a deep neural network (DNN), a form of machine learning or artificial intelligence. Specifically, the DNN learns from the dynamics of the gene expression. The learnt DNN behaves like an accurate simulator of the system, on which one can perform in-silico experiments to reveal the underlying gene network. We demonstrate the method with two examples: biochemical adaptation and the gap-gene patterning in fruit fly embryogenesis. In the first example, the DNN can successfully find the two basic network motifs for adaptation – the negative feedback and the incoherent feed-forward. In the second and much more complex example, the DNN can accurately predict behaviors of essentially all the mutants. Furthermore, the regulation network it uncovers is strikingly similar to the one inferred from experiments. In doing so, we develop methods for deciphering the gene regulation network hidden in the DNN “black box”. Our interpretable DNN approach should have broad applications in genotype-phenotype mapping.SignificanceComplex biological functions are carried out by gene regulation networks. The mapping between gene network and function is a central theme in biology. The task usually involves extensive experiments with perturbations to the system (e.g. gene deletion). Here, we demonstrate that machine learning, or deep neural network (DNN), can help reveal the underlying gene regulation for a given function or phenotype with minimal perturbation data. Specifically, after training with wild-type gene expression dynamics data and a few mutant snapshots, the DNN learns to behave like an accurate simulator for the genetic system, which can be used to predict other mutants’ behaviors. Furthermore, our DNN approach is biochemically interpretable, which helps uncover possible gene regulatory mechanisms underlying the observed phenotypic behaviors.

scholarly journals Semi-soft Clustering of Single Cell Data

2018 ◽  
Author(s):  
Lingxue Zhu ◽  
Jing Lei ◽  
Bernie Devlin ◽  
Kathryn Roeder
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Pairwise Comparison ◽  
Cell Types ◽  
Intermediate Cell ◽  
Soft Clustering ◽  
Membership Matrix ◽  
Cell Data

AbstractMotivated by the dynamics of development, in which cells of recognizable types, or pure cell types, transition into other types over time, we propose a method of semi-soft clustering that can classify both pure and intermediate cell types from data on gene expression or protein abundance from individual cells. Called SOUP, for Semi-sOft clUstering with Pure cells, this novel algorithm reveals the clustering structure for both pure cells, which belong to one single cluster, as well as transitional cells with soft memberships. SOUP involves a two-step process: identify the set of pure cells and then estimate a membership matrix. To find pure cells, SOUP uses the special block structure the K cell types form in a similarity matrix, devised by pairwise comparison of the gene expression profiles of individual cells. Once pure cells are identified, they provide the key information from which the membership matrix can be computed. SOUP is applicable to general clustering problems as well, as long as the unrestrictive modeling assumptions hold. The performance of SOUP is documented via extensive simulation studies. Using SOUP to analyze two single cell data sets from brain shows it produce sensible and interpretable results.

scholarly journals Integrated bioinformatics analysis reveals ASPM and CENPF with prognostic value in lung cancer

2019 ◽  
Author(s):  
jinghang li ◽  
Jing Zhang ◽  
Lin Huang ◽  
Sheng Zhao
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Cell Cycle ◽  
Expression Profiles ◽  
Mirna Gene ◽  
Gene Expression Profiles ◽  
Genetic Alterations ◽  
Potential Candidate ◽  
Expression Microarrays ◽  
Regulation Of Cell Cycle

Abstract Lung cancer (LC) is the most frequent type of cancer in the world. But the mechanism of LC is still largely unknown. In this study, we analyzed three lung cancer gene expression microarrays of different pathologic types to explore the potential candidate genes in LC by Integrated bioinformatical methods. 459 overlapped differentially expressed genes (DEGs) were explored in three GEO gene expression profiles of different pathologic types of lung cancer and function annotation of DEGs were performed. The main biological process of DEGs was regulation of vasculature development and angiogenesis. The most significant molecular function of DEGs was TGF-β receptor activity. The most significant Reactome pathway of DEGs was cell cycle and extracellular matrix organization pathway. The PPI network of the DEGs was constructed and 23 candidate hub genes were identified in the network . Kaplan-Meier survival analysis show 21 genes were associated with the prognosis of LC. The genetic alterations analysis of these genes by using cBioPortal shown ASPM has the highest genetic alteration rate of 9% in main pathological types of 3191 LC patients , CENPF has the second highest alteration rate of 6% in LC patients. ASPM and CENPF also identified have a significant co-occurrence relationship in LC, and the GO analysis shown they both participate in the regulation of cell cycle. In the TF -miRNA-gene network of 21 genes shown CENPF have the most significant value in the network and the most relevant TF are NFYA, E2F1 and MYC.In conclusion, this study explored several key genes about LC and analyzed potential TF of those genes, provides possible therapeutic targets and biomarker for further clinical application.

Adaptive Elman Model of Gene Regulation Network Based on Time Series Data

2019 ◽  
Vol 14 (6) ◽  
pp. 551-561
Author(s):  
Shengxian Cao ◽  
Yu Wang ◽  
Zhenhao Tang
Keyword(s):  
Neural Network ◽  
Gene Expression ◽  
Time Series ◽  
Gene Regulation ◽  
Pearson Correlation ◽  
Series Data ◽  
Expression Data ◽  
Elman Neural Network ◽  
Gene Regulation Network ◽  
Regulation Network

Background:Time series expression data of genes contain relations among different genes, which are difficult to model precisely. Slime-forming bacteria is one of the three major harmful bacteria types in industrial circulating cooling water systems.Objective:This study aimed at constructing gene regulation network(GRN) for slime-forming bacteria to understand the microbial fouling mechanism.Methods:For this purpose, an Adaptive Elman Neural Network (AENN) to reveal the relationships among genes using gene expression time series is proposed. The parameters of Elman neural network were optimized adaptively by a Genetic Algorithm (GA). And a Pearson correlation analysis is applied to discover the relationships among genes. In addition, the gene expression data of slime-forming bacteria by transcriptome gene sequencing was presented.Results:To evaluate our proposed method, we compared several alternative data-driven approaches, including a Neural Fuzzy Recurrent Network (NFRN), a basic Elman Neural Network (ENN), and an ensemble network. The experimental results of simulated and real datasets demonstrate that the proposed approach has a promising performance for modeling Gene Regulation Networks (GRNs). We also applied the proposed method for the GRN construction of slime-forming bacteria and at last a GRN for 6 genes was constructed.Conclusion:The proposed GRN construction method can effectively extract the regulations among genes. This is also the first report to construct the GRN for slime-forming bacteria.

scholarly journals MicroRNA-302a is involved in folate deficiency-induced apoptosis through the AKT-FOXO1-BIM pathway in mouse embryonic stem cells

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Yan Liang ◽  
Dingding Cao ◽  
Yuanyuan Li ◽  
Zhuo Liu ◽  
Jianxin Wu
Keyword(s):  
Stem Cells ◽  
Gene Regulation ◽  
Embryonic Stem Cells ◽  
Mirna Gene ◽  
Embryonic Stem ◽  
Folate Deficiency ◽  
Gene Regulation Network ◽  
Real Time Quantitative Pcr ◽  
Regulation Network ◽  
Induced Apoptosis

Abstract Background Our previous study had shown that microRNA (miR)-302a played a key role in folate deficiency-induced apoptosis in mouse embryonic stem cells. However, details regarding the mechanism remain unclear. Transcription factors (TFs) and miRNAs are two key elements in gene regulation. The aim of this study is to construct the TF-miRNA gene regulation network and demonstrate its possible mechanism. Methods The TF-miRNA gene regulation network was constructed via bioinformatics methods. Chromatin immuno-coprecipitation PCR was selected to confirm the binding between miR-302a and TF. mRNA and protein levels were detected by Real-time quantitative PCR and western blotting. TargetScan prediction and Dual-Luciferase Reporter Assay system were used to confirm whether the miRNA binded directly to the predicted target gene. Results FOXO1 and miR-302a were selected as the key TF and miRNA, respectively. FOXO1 was confirmed to bind directly to the upstream promoter region of miR-302a. Real-time quantitative PCR and immunoblotting showed that in folate-free conditions, miR-302a and AKT were down regulated, while FOXO1 and Bim were up-regulated significantly. Additionally, treatment with LY294002 inhibitor revealed the involvement of the Akt/FOXO1/Bim signaling pathway in folate deficiency-induced apoptosis, rather than the ERK pathway. Finally, TargetScan prediction and double luciferase reporting experiments illustrated the ability of miR-302a to target the Bim 3′UTR region. Conclusion The involvement of miR-302a in folate deficiency-induced apoptosis through the AKT-FOXO1-BIM pathway in mESCs is a unique demonstration of the regulation mechanism of nutrient expression in embryonic development.

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