Bioinformatics-based Screening of Key Genes for Saponin Metabolism in Quinoa

Abstract Quinoa saponins have complex, diverse and evident physiologic activities. However, the key regulatory genes for quinoa saponin metabolism are not yet well studied. The purpose of this study was to explore genes closely related to quinoa saponin metabolism. In this study, the significantly differentially expressed genes in yellow quinoa were firstly screened based on RNA-seq technology. Then, the key genes for saponin metabolism were selected by gene set enrichment analysis (GSEA) and principal component analysis (PCA) statistical methods. Finally, the specificity of the key genes was verified by hierarchical clustering. The results of differential analysis showed that 1654 differentially expressed genes were achieved after pseudogenes deletion. Therein, there were 142 long non-coding genes and 1512 protein-coding genes. Based on GSEA analysis, 116 key candidate genes were found to be significantly correlated with quinoa saponin metabolism. Through PCA dimension reduction analysis, 57 key genes were finally obtained. Hierarchical cluster analysis further demonstrated that these key genes can clearly separate the four groups of samples. The present results could provide references for the breeding of sweet quinoa and would be helpful for the rational utilization of quinoa saponins.

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AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.1773 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1037.2-1038

Author(s):

X. Sun ◽

S. X. Zhang ◽

S. Song ◽

T. Kong ◽

C. Zheng ◽

...

Keyword(s):

Gene Expression ◽

Signaling Pathways ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Shanxi Province ◽

Receptor Interaction ◽

Term Therapy ◽

Pathway Enrichment Analysis ◽

Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

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Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

PeerJ ◽

10.7717/peerj.8831 ◽

2020 ◽

Vol 8 ◽

pp. e8831 ◽

Cited By ~ 1

Author(s):

Xiaojiao Guan ◽

Yao Yao ◽

Guangyao Bao ◽

Yue Wang ◽

Aimeng Zhang ◽

...

Keyword(s):

Gene Expression ◽

Esophageal Cancer ◽

Esophageal Carcinoma ◽

Differentially Expressed Genes ◽

Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Diagnostic Model ◽

Link Type ◽

Key Genes

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

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Differentially expressed genes in the femur cartilage transcriptome clarify the understanding of femoral head separation in chickens

Scientific Reports ◽

10.1038/s41598-021-97306-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ludmila Mudri Hul ◽

Adriana Mércia Guaratini Ibelli ◽

Igor Ricardo Savoldi ◽

Débora Ester Petry Marcelino ◽

Lana Teixeira Fernandes ◽

...

Keyword(s):

Articular Cartilage ◽

Femoral Head ◽

Transcriptome Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Economic Losses ◽

Rna Seq ◽

Cellular Activation ◽

Bone Anomalies

AbstractLocomotor problems are among one of the main concerns in the current poultry industry, causing major economic losses and affecting animal welfare. The most common bone anomalies in the femur are dyschondroplasia, femoral head separation (FHS), and bacterial chondronecrosis with osteomyelitis (BCO), also known as femoral head necrosis (FHN). The present study aimed to identify differentially expressed (DE) genes in the articular cartilage (AC) of normal and FHS-affected broilers by RNA-Seq analysis. In the transcriptome analysis, 12,169 genes were expressed in the femur AC. Of those, 107 genes were DE (FDR < 0.05) between normal and affected chickens, of which 9 were downregulated and 98 were upregulated in the affected broilers. In the gene-set enrichment analysis using the DE genes, 79 biological processes (BP) were identified and were grouped into 12 superclusters. The main BP found were involved in the response to biotic stimulus, gas transport, cellular activation, carbohydrate-derived catabolism, multi-organism regulation, immune system, muscle contraction, multi-organism process, cytolysis, leukocytes and cell adhesion. In this study, the first transcriptome analysis of the broilers femur articular cartilage was performed, and a set of candidate genes (AvBD1, AvBD2, ANK1, EPX, ADA, RHAG) that could trigger changes in the broiler´s femoral growth plate was identified. Moreover, these results could be helpful to better understand FHN in chickens and possibly in humans.

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A Differential Host Response to Viral Infection Defines a Subset of Earlier-Onset Diverticulitis Patients

Journal of Gastrointestinal and Liver Diseases ◽

10.15403/jgld.2014.1121.273.sch ◽

2018 ◽

Vol 27 (3) ◽

pp. 249-255 ◽

Cited By ~ 1

Author(s):

Kathleen M Schieffer ◽

Bryan P Kline ◽

Leonard R Harris ◽

Sue Deiling ◽

Walter A Koltun ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Principal Component ◽

Differentially Expressed ◽

Rna Seq ◽

Viral Pathogen ◽

Viral Response ◽

Elevated Expression ◽

Bioinformatic Approaches ◽

Relationship Of ◽

The Relationship

Background & Aims: Diverticulitis is the chronic inflammation of diverticula. Whether the pathophysiology of earlier-onset patients differs from later-onset patients is unknown. We profiled the colonic transcriptomes of these two patient populations to gain insight into the molecular underpinnings of diverticulitis. Methods: We conducted deep RNA sequencing (RNA-seq) on colonic segments surgically resected from earlier-onset (<42 years old, n=13) and later-onset (>65 years old, n=13) diverticulitis patients. We used bioinformatic approaches to cluster the patients based on the relationship of differentially expressed genes and to inform on the molecular pathways that segregated the clusters. Results: Principal component analysis identified three patient clusters; diverticulitis later-onset (DVT-LO), diverticulitis mixed-onset (DVT-MO), and diverticulitis earlier-onset (DVT-EO). The patients comprising DVT-EO, which was the majority of earlier-onset patients, displayed increased expression of anti-viral response genes. This finding was confirmed using an independent weighted co-expression network analysis (WGCNA) of differentially expressed genes. Conclusions: We found that the majority of patients with earlier-onset disease contained elevated expression of host genes involved in the anti-viral response. Thus, susceptibility to a viral pathogen may offer one explanation why some individuals develop diverticulitis at an earlier age.

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Identification of nine key genes by bioinformatics analysis for predicting poor prognosis in smoking-induced lung adenocarcinoma

Lung Cancer Management ◽

10.2217/lmt-2020-0009 ◽

2020 ◽

Vol 9 (2) ◽

pp. LMT30

Author(s):

Chuanli Ren ◽

Weixiu Sun ◽

Xu Lian ◽

Chongxu Han

Keyword(s):

Lung Adenocarcinoma ◽

Differentially Expressed Genes ◽

Interaction Network ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Kaplan Meier ◽

Key Genes ◽

Core Genes

Aim: To screen and identify key genes related to the development of smoking-induced lung adenocarcinoma (LUAD). Materials & methods: We obtained data from the GEO chip dataset GSE31210. The differentially expressed genes were screened by GEO2R. The protein interaction network of differentially expressed genes was constructed by STRING and Cytoscape. Finally, core genes were screened. The overall survival time of patients with the core genes was analyzed by Kaplan–Meier method. Gene ontology and Kyoto encyclopedia of genes and genomes bioaccumulation was calculated by DAVID. Results: Functional enrichment analysis indicated that nine key genes were actively involved in the biological process of smoking-related LUAD. Conclusion: 23 core genes and nine key genes among them were correlated with adverse prognosis of LUAD induced by smoking.

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Bayesian analysis of fMRI data and RNA-Seq time course experiment data

10.32469/10355/57756 ◽

2015 ◽

Author(s):

◽

Yuan Cheng

Keyword(s):

Model Selection ◽

Bayesian Analysis ◽

Differentially Expressed Genes ◽

Time Course ◽

Selection Procedure ◽

Principal Component ◽

Differentially Expressed ◽

Rna Seq ◽

Hemodynamic Response Function ◽

Model Selection Procedure

The present dissertation contains two parts. In the first part, we develop a new Bayesian analysis of functional MRI data. We propose a novel triple gamma Hemodynamic Response Function (HRF) including the component to describe the initial dip. We use HRF to inform voxel-wise neuronal activities. Then we devise a new model selection procedure with a nonlocal pMOM prior for joint detection of neuronal activation and estimation of HRF, in order to time the activation time difference between visual and motor areas in the brain. In the second part, we develop a new Bayesian analysis of RNA-Seq Time Course experiments data. We propose to use Bayesian Principal Component regression model and based on that, devise a model selection procedure by using nonlocal piMOM prior in order to identify differentially expressed genes. Most current existing methods for RNA-Seq Time Course experiments data are from static view of point and cannot predict temporal patterns. Our method estimate the posterior differentially expressed probability for each gene by borrowing information across all subjects. Use of nonlocal prior in the model selection procedure reduces false discovered differentially expressed genes.

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Expression Signatures of Long Noncoding RNAs in Left Ventricular Noncompaction

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.763858 ◽

2021 ◽

Vol 8 ◽

Author(s):

Qingshan Tian ◽

Hanxiao Niu ◽

Dingyang Liu ◽

Na Ta ◽

Qing Yang ◽

...

Keyword(s):

Differentially Expressed Genes ◽

Noncoding Rnas ◽

Enrichment Analysis ◽

Left Ventricular ◽

Long Noncoding Rnas ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Control Group ◽

Left Ventricular Noncompaction ◽

Ventricular Noncompaction

Long noncoding RNAs have gained widespread attention in recent years for their crucial role in biological regulation. They have been implicated in a range of developmental processes and diseases including cancer, cardiovascular, and neuronal diseases. However, the role of long noncoding RNAs (lncRNAs) in left ventricular noncompaction (LVNC) has not been explored. In this study, we investigated the expression levels of lncRNAs in the blood of LVNC patients and healthy subjects to identify differentially expressed lncRNA that develop LVNC specific biomarkers and targets for developing therapies using biological pathways. We used Agilent Human lncRNA array that contains both updated lncRNAs and mRNAs probes. We identified 1,568 upregulated and 1,141 downregulated (log fold-change > 2.0) lncRNAs that are differentially expressed between LVNC and the control group. Among them, RP11-1100L3.7 and XLOC_002730 are the most upregulated and downregulated lncRNAs. Using quantitative real-time reverse transcription polymerase chain reaction (RT-QPCR), we confirmed the differential expression of three top upregulated and downregulated lncRNAs along with two other randomly picked lncRNAs. Gene Ontology (GO) and KEGG pathways analysis with these differentially expressed lncRNAs provide insight into the cellular pathway leading to LVNC pathogenesis. We also identified 1,066 upregulated and 1,017 downregulated mRNAs. Gene set enrichment analysis (GSEA) showed that G2M, Estrogen, and inflammatory pathways are enriched in differentially expressed genes (DEG). We also identified miRNA targets for these differentially expressed genes. In this study, we first report the use of LncRNA microarray to understand the pathogenesis of LVNC and to identify several lncRNA and genes and their targets as potential biomarkers.

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Characterization and Duodenal Transcriptome Analysis of Chinese Beef Cattle With Divergent Feed Efficiency Using RNA-Seq

Frontiers in Genetics ◽

10.3389/fgene.2021.741878 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chaoyun Yang ◽

Liyun Han ◽

Peng Li ◽

Yanling Ding ◽

Yun Zhu ◽

...

Keyword(s):

Beef Cattle ◽

Differentially Expressed Genes ◽

Feed Intake ◽

Feed Efficiency ◽

Muscle Development ◽

Dietary Factors ◽

Differentially Expressed ◽

Rna Seq ◽

Biological Regulation ◽

Key Genes

Residual feed intake (RFI) is an important measure of feed efficiency for agricultural animals. Factors associated with cattle RFI include physiology, dietary factors, and the environment. However, a precise genetic mechanism underlying cattle RFI variations in duodenal tissue is currently unavailable. The present study aimed to identify the key genes and functional pathways contributing to variance in cattle RFI phenotypes using RNA sequencing (RNA-seq). Six bulls with extremely high or low RFIs were selected for detecting differentially expressed genes (DEGs) by RNA-seq, followed by conducting GO, KEGG enrichment, protein-protein interaction (PPI), and co-expression network (WGCNA, n = 10) analysis. A total of 380 differentially expressed genes was obtained from high and low RFI groups, including genes related to energy metabolism (ALDOA, HADHB, INPPL1), mitochondrial function (NDUFS1, RFN4, CUL1), and feed intake behavior (CCK). Two key sub-networks and 26 key genes were detected using GO analysis of DEGs and PPI analysis, such as TPM1 and TPM2, which are involved in mitochondrial pathways and protein synthesis. Through WGCNA, a gene network was built, and genes were sorted into 27 modules, among which the blue (r = 0.72, p = 0.03) and salmon modules (r = −0.87, p = 0.002) were most closely related with RFI. DEGs and genes from the main sub-networks and closely related modules were largely involved in metabolism; oxidative phosphorylation; glucagon, ribosome, and N-glycan biosynthesis, and the MAPK and PI3K-Akt signaling pathways. Through WGCNA, five key genes, including FN1 and TPM2, associated with the biological regulation of oxidative processes and skeletal muscle development were identified. Taken together, our data suggest that the duodenum has specific biological functions in regulating feed intake. Our findings provide broad-scale perspectives for identifying potential pathways and key genes involved in the regulation of feed efficiency in beef cattle.

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Insights into the Synthesis, Secretion and Curing of Barnacle Cyprid Adhesive via Transcriptomic and Proteomic Analyses of the Cement Gland

Marine Drugs ◽

10.3390/md18040186 ◽

2020 ◽

Vol 18 (4) ◽

pp. 186

Author(s):

Guoyong Yan ◽

Jin Sun ◽

Zishuai Wang ◽

Pei-Yuan Qian ◽

Lisheng He

Keyword(s):

Differentially Expressed Genes ◽

Molecular Mechanisms ◽

Lysyl Oxidase ◽

Enrichment Analysis ◽

Salivary Secretion ◽

Cement Gland ◽

Differentially Expressed ◽

Model Organisms ◽

Rna Seq ◽

Secretion Pathway

Barnacles represent one of the model organisms used for antifouling research, however, knowledge regarding the molecular mechanisms underlying barnacle cyprid cementation is relatively scarce. Here, RNA-seq was used to obtain the transcriptomes of the cement glands where adhesive is generated and the remaining carcasses of Megabalanus volcano cyprids. Comparative transcriptomic analysis identified 9060 differentially expressed genes, with 4383 upregulated in the cement glands. Four cement proteins, named Mvcp113k, Mvcp130k, Mvcp52k and Mvlcp1-122k, were detected in the cement glands. The salivary secretion pathway was significantly enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of the differentially expressed genes, implying that the secretion of cyprid adhesive might be analogous to that of saliva. Lysyl oxidase had a higher expression level in the cement glands and was speculated to function in the curing of cyprid adhesive. Furthermore, the KEGG enrichment analysis of the 352 proteins identified in the cement gland proteome partially confirmed the comparative transcriptomic results. These results present insights into the molecular mechanisms underlying the synthesis, secretion and curing of barnacle cyprid adhesive and provide potential molecular targets for the development of environmentally friendly antifouling compounds.

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Bayesian analysis for detecting differentially expressed genes from RNA-seq data

10.32469/10355/48204 ◽

2014 ◽

Author(s):

◽

Shiqi Cui

Keyword(s):

Differentially Expressed Genes ◽

Time Course ◽

Markov Models ◽

Single Gene ◽

Principal Component ◽

Integrated Approach ◽

Differentially Expressed ◽

Monte Carlo Algorithm ◽

Rna Seq ◽

Paired Data

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation introduces hmmSeq, a model-based hierarchical Bayesian technique for detecting differentially expressed genes from RNA-seq data. Our novel hmmSeq methodology uses hidden Markov models to account for potential co-expression of neighboring genes. In addition, hmmSeq employs an integrated approach to studies with technical or biological replicates, automatically adjusting for any extra-Poisson variability. Moreover, for cases when paired data are available, hmmSeq includes a paired structure between treatments that incorporates subject-specific effects. To perform parameter estimation for the hmmSeq model, we develop an efficient Markov chain Monte Carlo algorithm. Further, we develop a procedure for detection of differentially expressed genes that automatically controls false discovery rate. A simulation study shows that the hmmSeq methodology performs better than competitors in terms of receiver operating characteristic curves. Finally, the analyses of three publicly available RNA-Seq datasets demonstrate the power and flexibility of the hmmSeq methodology. This dissertation also introduces an empirical Bayesian approach to detect differentially expressed genes in time course RNA-seq experiments. The proposed Bayesian method identifies major variation in gene expression profile by Bayesian principal component regression. The expression data are normalized for each gene, and the high dimentionality of time course data is first reduced by principal component analysis. The proposed model assumes a mixture distribution of expression parameters for differentially and nondifferentially expressed genes, borrows strength by sharing same variance across multiple subjects for each single gene, as well as shares information across genes by assuming gene-wise probabilities of being differentially expressed from the common beta prior distribution.

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