scholarly journals Transcriptome Analysis of Porcine PBMCs Reveals the Immune Cascade Response and Gene Ontology Terms Related to Cell Death and Fibrosis in the Progression of Liver Failure

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
YiMin Zhang ◽  
Li Shao ◽  
Ning Zhou ◽  
JianZhou Li ◽  
Yu Chen ◽  
...  
Keyword(s):  
Cell Death ◽  
Gene Ontology ◽  
Liver Failure ◽  
Transcriptome Analysis ◽  
Receptor Interaction ◽  
Failure Group ◽  
Kegg Pathways ◽  
Gene Sets ◽  
Baseline Group ◽  
Go Terms

Background. The key gene sets involved in the progression of acute liver failure (ALF), which has a high mortality rate, remain unclear. This study aims to gain a deeper understanding of the transcriptional response of peripheral blood mononuclear cells (PBMCs) following ALF. Methods. ALF was induced by D-galactosamine (D-gal) in a porcine model. PBMCs were separated at time zero (baseline group), 36 h (failure group), and 60 h (dying group) after D-gal injection. Transcriptional profiling was performed using RNA sequencing and analysed using DAVID bioinformatics resources. Results. Compared with the baseline group, 816 and 1,845 differentially expressed genes (DEGs) were identified in the failure and dying groups, respectively. A total of five and two gene ontology (GO) term clusters were enriched in 107 GO terms in the failure group and 154 GO terms in the dying group. These GO clusters were primarily immune-related, including genes regulating the inflammasome complex and toll-like receptor signalling pathways. Specifically, GO terms related to cell death, including apoptosis, pyroptosis, and autophagy, and those related to fibrosis, coagulation dysfunction, and hepatic encephalopathy were enriched. Seven Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, cytokine-cytokine receptor interaction, hematopoietic cell lineage, lysosome, rheumatoid arthritis, malaria, and phagosome and pertussis pathways were mapped for DEGs in the failure group. All of these seven KEGG pathways were involved in the 19 KEGG pathways mapped in the dying group. Conclusion. We found that the dramatic PBMC transcriptome changes triggered by ALF progression was predominantly related to immune responses. The enriched GO terms related to cell death, fibrosis, and so on, as indicated by PBMC transcriptome analysis, seem to be useful in elucidating potential key gene sets in the progression of ALF. A better understanding of these gene sets might be of preventive or therapeutic interest.

scholarly journals Gene Ontology and KEGG Enrichment Analyses of Genes Related to Age-Related Macular Degeneration

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Zhang ◽  
ZhiHao Xing ◽  
Mingming Ma ◽  
Ning Wang ◽  
Yu-Dong Cai ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Feature Selection ◽  
Macular Degeneration ◽  
Classification System ◽  
Underlying Disease ◽  
Age Related Macular Degeneration ◽  
Disease Genes ◽  
Kegg Pathways ◽  
Age Related ◽  
Go Terms

Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.

Analysis of Four Types of Leukemia Using Gene Ontology Term and Kyoto Encyclopedia of Genes and Genomes Pathway Enrichment Scores

2020 ◽  
Vol 23 (4) ◽  
pp. 295-303
Author(s):  
Jing Lu ◽  
YuHang Zhang ◽  
ShaoPeng Wang ◽  
Yi Bi ◽  
Tao Huang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Lymphoblastic Leukemia ◽  
Feature Selection Method ◽  
Lymphocytic Leukemia ◽  
Myelogenous Leukemia ◽  
Ontology Term ◽  
Kegg Pathways ◽  
Different Types ◽  
First Time ◽  
Go Terms

Aim and Objective: Leukemia is the second common blood cancer after lymphoma, and its incidence rate has an increasing trend in recent years. Leukemia can be classified into four types: acute lymphoblastic leukemia (ALL), acute myeloid leukemia (AML), chronic lymphocytic leukemia (CLL), and chronic myelogenous leukemia (CML). More than forty drugs are applicable to different types of leukemia based on the discrepant pathogenesis. Therefore, the identification of specific drug-targeted biological processes and pathways is helpful to determinate the underlying pathogenesis among such four types of leukemia. Methods: In this study, the gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were highly related to drugs for leukemia were investigated for the first time. The enrichment scores for associated GO terms and KEGG pathways were calculated to evaluate the drugs and leukemia. The feature selection method, minimum redundancy maximum relevance (mRMR), was used to analyze and identify important GO terms and KEGG pathways. Results: Twenty Go terms and two KEGG pathways with high scores have all been confirmed to effectively distinguish four types of leukemia. Conclusion: This analysis may provide a useful tool for the discrepant pathogenesis and drug design of different types of leukemia.

scholarly journals Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Zhen Li ◽  
Bi-Qing Li ◽  
Min Jiang ◽  
Lei Chen ◽  
Jian Zhang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Disease Free Survival ◽  
Computational Method ◽  
The Other ◽  
Free Survival ◽  
Kegg Pathways ◽  
Incremental Feature Selection ◽  
Optimal Feature ◽  
Go Terms ◽  
Disease Free

One of the most important and challenging problems in biomedicine is how to predict the cancer related genes. Retinoblastoma (RB) is the most common primary intraocular malignancy usually occurring in childhood. Early detection of RB could reduce the morbidity and promote the probability of disease-free survival. Therefore, it is of great importance to identify RB genes. In this study, we developed a computational method to predict RB related genes based on Dagging, with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). 119 RB genes were compiled from two previous RB related studies, while 5,500 non-RB genes were randomly selected from Ensemble genes. Ten datasets were constructed based on all these RB and non-RB genes. Each gene was encoded with a 13,126-dimensional vector including 12,887 Gene Ontology enrichment scores and 239 KEGG enrichment scores. Finally, an optimal feature set including 1061 GO terms and 8 KEGG pathways was obtained. Analysis showed that these features were closely related to RB. It is anticipated that the method can be applied to predict the other cancer related genes as well.

scholarly journals Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Hang Yin ◽  
ShaoPeng Wang ◽  
Yu-Hang Zhang ◽  
Yu-Dong Cai ◽  
Hailin Liu
Keyword(s):  
Pancreatic Cancer ◽  
Gene Ontology ◽  
Feature Selection Method ◽  
Selection Method ◽  
Biological Processes ◽  
Kegg Pathways ◽  
Extensive Analysis ◽  
The World ◽  
Serious Disease ◽  
Go Terms

Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.

scholarly journals Analysis of Protein–Protein Functional Associations by Using Gene Ontology and KEGG Pathway

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Fei Yuan ◽  
Xiaoyong Pan ◽  
Lei Chen ◽  
Yu-Hang Zhang ◽  
Tao Huang ◽  
...  
Keyword(s):  
Gene Ontology ◽  
Kegg Pathway ◽  
Final Analysis ◽  
Biological Pathways ◽  
Extended Version ◽  
Multiple Sources ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
The Difference ◽  
Go Terms

Protein–protein interaction (PPI) plays an extremely remarkable role in the growth, reproduction, and metabolism of all lives. A thorough investigation of PPI can uncover the mechanism of how proteins express their functions. In this study, we used gene ontology (GO) terms and biological pathways to study an extended version of PPI (protein–protein functional associations) and subsequently identify some essential GO terms and pathways that can indicate the difference between two proteins with and without functional associations. The protein–protein functional associations validated by experiments were retrieved from STRING, a well-known database on collected associations between proteins from multiple sources, and they were termed as positive samples. The negative samples were constructed by randomly pairing two proteins. Each sample was represented by several features based on GO and KEGG pathway information of two proteins. Then, the mutual information was adopted to evaluate the importance of all features and some important ones could be accessed, from which a number of essential GO terms or KEGG pathways were identified. The final analysis of some important GO terms and one KEGG pathway can partly uncover the difference between proteins with and without functional associations.

scholarly journals Transcriptome Analysis of the Sepia pharaonis: Identification of salt stress-related information and Microsatellite Markers

2020 ◽  
Author(s):  
Zhiming Ren ◽  
Yun Zhao ◽  
Weiwei Song ◽  
Chunlin Wang ◽  
Changkao Mu ◽  
...  
Keyword(s):  
Salt Stress ◽  
Transcriptome Analysis ◽  
Transcriptome Sequencing ◽  
Control Group ◽  
Protein Database ◽  
Kegg Pathways ◽  
Related Information ◽  
Sepia Pharaonis ◽  
Paired End Sequencing ◽  
Go Terms

Abstract Background: Sepia pharaonis has great commercial value for aquaculture. However, it is sensitive to salinity fluctuations and lacking in genomic information. The present work utilized throughput transcriptome sequencing to assess the factors associated with salt stress in Sepia pharaonis. Results: Based on the Illumina paired-end sequencing results, 203,852,818 raw reads were produced, and 130,857 unigenes were assembled having an average of 784.72 bp in length. Transcriptome analysis identified 16013 and 24119 unigenes in the Swiss-Prot protein database and NCBI non-redundant database, respectively. Of note, 12717 unigenes were grouped into 64 Gene Ontology (GO) terms, 5237 unigenes were classified into 332 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, 13808 unigenes were subcategorized into 25 Cluster of orthologous groups for eukaryotic complete genomes (KOG) functional categories based on functional analysis. Besides, 6153 genes were identified as differentially expressed (p≤0.05), of which 3340 were increased and 2813 were decreased in treatment group relative to the control group. Subsequently, these DEGs were allocated to 226 KEGG pathways and 491 GO terms. Analysis of the transcriptome sequences and DEGs identified several unigenes and pathways involved in salt stress regulation. Moreover, the Sepia pharaonis carried 101576 simple sequence repeats (SSRs). Conclusions: This is the first time osmoregulation in Sepia pharaonis has been explored by transcriptome sequencing. The data presented here reveals key insights into the genetic markers of salt stress in Sepia pharaonis.

scholarly journals Transcriptome Analysis Reveals Impaired Fertility and Immunity Under Salinity Exposure in Juvenile Grass Carp

2021 ◽  
Vol 8 ◽  
Author(s):  
Jingjing Zhang ◽  
Zhi Wu ◽  
Yujie He ◽  
Xinhui Li ◽  
Jie Li
Keyword(s):  
Signaling Pathway ◽  
Transcriptome Analysis ◽  
Grass Carp ◽  
Enrichment Score ◽  
Receptor Interaction ◽  
Receptor Signaling ◽  
Steroid Biosynthesis ◽  
Kegg Pathways ◽  
Risk Of Death ◽  
Receptor Signaling Pathway

Grass carp (Ctenopharyngodon idellus) is one of the most economically important aquaculture species and is widely cultured in China. However, its wild populations in many rivers are increasingly declining, and seawater intrusion is one of the most important threats to their survival. However, the mechanisms underlying the decline due to salinity pressure are still unknown. Here, we performed a comparative transcriptome analysis of C. idellus larvae in response to salinity exposures; a total of 481 differentially expressed genes (DEGs) were identified. These DEGs were significantly enriched in eight Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, among which steroid biosynthesis was the most important one, with the highest enrichment score. The pathway plays an important role in the development of the testes and ovary. Interestingly, all DEGs in steroid biosynthesis showed a down regulation, indicating that salinity exposure may pose damage to the fertility of C. idellus. Furthermore, three immunity-associated pathways (cytokine–cytokine receptor interaction, Toll-like receptor signaling pathway, and NOD-like receptor signaling pathway) were also significantly enriched, suggesting impaired immunity and a high risk of disease infection under salinity exposure. Overall, damage to both fertility and immunity would decrease the number of offspring and increase the risk of death due to disease infection. Our results provide a potential molecular mechanism underlying the decline of wild C. idellus populations in the Pearl River.

scholarly journals Graph-based exploitation of gene ontology using GOxploreR for scrutinizing biological significance

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Kalifa Manjang ◽  
Shailesh Tripathi ◽  
Olli Yli-Harja ◽  
Matthias Dehmer ◽  
Frank Emmert-Streib
Keyword(s):  
Gene Ontology ◽  
Structural Information ◽  
Theoretical Perspective ◽  
Biological Significance ◽  
Structural Features ◽  
Directed Acyclic Graphs ◽  
Direct Access ◽  
Gene Sets ◽  
Or Gene ◽  
Go Terms

Abstract Gene ontology (GO) is an eminent knowledge base frequently used for providing biological interpretations for the analysis of genes or gene sets from biological, medical and clinical problems. Unfortunately, the interpretation of such results is challenging due to the large number of GO terms, their hierarchical and connected organization as directed acyclic graphs (DAGs) and the lack of tools allowing to exploit this structural information explicitly. For this reason, we developed the package . The main features of are (I) easy and direct access to structural features of GO, (II) structure-based ranking of GO-terms, (III) mapping to reduced GO-DAGs including visualization capabilities and (IV) prioritizing of GO-terms. The underlying idea of is to exploit a graph-theoretical perspective of GO as manifested by its DAG-structure and the containing hierarchy levels for cumulating semantic information. That means all these features enhance the utilization of structural information of GO and complement existing analysis tools. Overall, provides exploratory as well as confirmatory tools for complementing any kind of analysis resulting in a list of GO-terms, e.g., from differentially expressed genes or gene sets, GWAS or biomarkers. Our package is freely available from CRAN.

Different cell death mechanisms are triggered by acute or chronic right heart failure-induced liver failure

2011 ◽  
Vol 49 (01) ◽  
Author(s):  
K Herzer ◽  
G Kneiseler ◽  
F Post ◽  
M Schlattjan ◽  
T Neumann ◽  
...  
Keyword(s):  
Heart Failure ◽  
Cell Death ◽  
Liver Failure ◽  
Right Heart Failure ◽  
Right Heart ◽  
Cell Death Mechanisms
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