scholarly journals Identification of Potential Gene Regulatory Pathways Affecting the Ratio of Four-Seed Pod in Soybean

2021 ◽  
Vol 12 ◽  
Author(s):  
Ting Fang ◽  
Yiwei Bai ◽  
Wenxuan Huang ◽  
Yueying Wu ◽  
Zhihui Yuan ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Calcium Binding ◽  
Recombinant Inbred Lines ◽  
Differentially Expressed Gene ◽  
Differentially Expressed ◽  
Growth Stages ◽  
Individual Plant ◽  
Regulatory Pathways ◽  
Gene Regulatory

The number of four-seed pods is one of the most important agronomic traits affected by gene and environment that can potentially improve soybean (Glycine max) yield. However, the gene regulatory network that affects the ratio of four-seed pod (the ratio of the number of four-seed pods to the total number of pods in each individual plant) is yet unclear. Here, we performed bulked segregant RNA sequencing (BSR-seq) on a series of recombinant inbred lines (RILs) derived from hybrid progenies between Heinong 48 (HN48), a cultivar with a high ratio of four-seed pod, and Henong 64 (HN64), a cultivar with a low ratio of four-seed pod. Two tissues, flower bud and young pod, at two different growth stages, R1 and R3, were analyzed under the ratios of four-seed pod at less than 10% and greater than 30%, respectively. To identify the potential gene regulation pathways associated with the ratio of soybean four-seed pod, we performed differentially expressed analysis on the four bulked groups. A differentially expressed gene (DEG) encoding a photosystem II 5-kDa protein had the function of participating in the energy conversion of photosynthesis. In addition, 79 common DEGs were identified at different developmental stages and under different ratios of four-seed pod. Among them, four genes encoding calcium-binding proteins and a WRKY transcription factor were enriched in the plant–pathogen interaction pathway, and they showed a high level of expression in roots. Moreover, 10 DEGs were identified in the reported quantitative trait locus (QTL) interval of four-seed pod, and two of them were significantly enriched in the pentose and glucuronate interconversion pathway. These findings provide basic insights into the understanding of the underlying gene regulatory network affected by specific environment and lay the foundation for identifying the targets that affect the ratio of four-seed pod in soybean.

scholarly journals Identification of differentially expressed genes and signaling pathways in gestational diabetes mellitus by integrated bioinformatics analysis

2021 ◽  
Author(s):  
Basavaraj Mallikarjunayya Vastrad ◽  
Chanabasayya Mallikarjunayya Vastrad
Keyword(s):  
Diabetes Mellitus ◽  
Gestational Diabetes ◽  
Gestational Diabetes Mellitus ◽  
Gene Regulatory Network ◽  
Differentially Expressed Genes ◽  
Regulatory Network ◽  
Molecular Pathogenesis ◽  
Differentially Expressed ◽  
Hub Gene ◽  
Gene Regulatory

Gestational diabetes mellitus (GDM) is a metabolic disorder during pregnancy. Numerous biomarkers have been identified that are linked with the occurrence and development of GDM. The aim of this investigation was to identify differentially expressed genes (DEGs) in GDM using a bioinformatics approach to elucidate their molecular pathogenesis. GDM associated expression profiling by high throughput sequencing dataset (GSE154377) was obtained from Gene Expression Omnibus (GEO) database including 28 normal pregnancy samples and 33 GDM samples. DEGs were identified using DESeq2. The gene ontology (GO) and REACTOME pathway enrichments of DEGs were performed by g:Profiler. Protein-protein interaction (PPI) networks were assembled with Cytoscape software and separated into modules using the PEWCC1 algorithm. MiRNA-hub gene regulatory network and TF-hub gene regulatory network were performed with the miRNet database and NetworkAnalyst database. Receiver Operating Characteristic (ROC) analyses was conducted to validate the hub genes. A total of 953 DEGs were identified, of which 478 DEGs were up regulated and 475 DEGs were down regulated. Furthermore, GO and REACTOME pathway enrichment analysis demonstrated that these DEGs were mainly enriched in multicellular organismal process, cell activation, formation of the cornified envelope and hemostasis. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB were identified as key genes in the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network. TRIM54, ELAVL2, PTN, KIT, BMPR1B, APP, SRC, ITGA4, RPA1 and ACTB in GDM were validated using ROC analysis. This investigation provides further insights into the molecular pathogenesis of GDM, which might facilitate the diagnosis and treatment of GDM.

scholarly journals Genome-scale Transcriptomic Insights Into the Gene-regulatory Network of Seed Abortion in Triploid Siraitia Grosvenorii

2021 ◽  
Author(s):  
Rongchang Wei ◽  
Dongping Tu ◽  
Xiyang Huang ◽  
Zuliang Luo ◽  
Xiaohua Huang ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Developmental Stages ◽  
Differentially Expressed ◽  
Seed Abortion ◽  
Submicroscopic Structure ◽  
Medicinal Value ◽  
Siraitia Grosvenorii ◽  
Gene Regulatory ◽  
Genome Scale

Abstract BackgroundSiraitia grosvenorii (Swingle) C. Jeffrey, also known as Luohanguo or monk fruit, is a famous traditional Chinese medicine ingredient with important medicinal value and broad development prospects. Diploid S. grosvenorii has too many seeds. Thus, studying the molecular mechanism of seed abortion in triploid S. grosvenorii, identifying the abortion-related genes, and regulating their expression will be a new direction to obtain seedless S. grosvenorii. Herein, we examined the submicroscopic structure of triploid S. grosvenorii seeds during abortion. ResultsBy measuring the content of endogenous hormones, we found that abscisic acid (ABA) and trans-zeatin (ZR) levels were significantly downregulated at days 15 and 20 after flowering. RNA-seq of triploid seeds at different developmental stages was performed to identify the key genes regulating abortion of triploid S. grosvenorii seeds. Multiple differentially expressed genes between adjacent stages were identified; seven genes were differentially expressed across all stages. Weight gene co-expression network analysis revealed that the enhancement of monoterpene and terpene metabolic processes might lead to seed abortion by reducing substrate flow to ABA and ZR.ConclusionsThese findings provide insights into the gene-regulatory network of seed abortion in triploid S. grosvenorii from different perspectives, thereby facilitating the innovation of the breeding technology of S. grosvenorii.

Integrative Analysis of Whole-genome Expression Profiling and Regulatory Network Identifies Novel Biomarkers for Insulin Resistance in Leptin Receptor-deficient Mice

2020 ◽  
Vol 16 (5) ◽  
pp. 635-642 ◽  
Author(s):  
Yuchi Zhang ◽  
Xinyu Wu ◽  
Cong Zhao ◽  
Kai Li ◽  
Yi Zheng ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Gene Regulatory Network ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Regulatory Network ◽  
Differentially Expressed ◽  
Qrt Pcr ◽  
Genome Expression ◽  
Gene Regulatory ◽  
Whole Genome Expression

Background: Molecular characterization of insulin resistance, a growing health issue worldwide, will help to develop novel strategies and accurate biomarkers for disease diagnosis and treatment. Objective: Integrative analysis of gene expression profiling and gene regulatory network was exploited to identify potential biomarkers early in the development of insulin resistance. Methods: RNA was isolated from livers of animals at three weeks of age, and whole-genome expression profiling was performed and analyzed with Agilent mouse 4×44K microarrays. Differentially expressed genes were subsequently validated by qRT-PCR. Functional characterizations of genes and their interactions were performed by Gene Ontology (GO) analysis and gene regulatory network (GRN) analysis. Results: A total of 197 genes were found to be differentially expressed by fold change ≥2 and P < 0.05 in BKS-db +/+ mice relative to sex and age-matched controls. Functional analysis suggested that these differentially expressed genes were enriched in the regulation of phosphorylation and generation of precursor metabolites which are closely associated with insulin resistance. Then a gene regulatory network associated with insulin resistance (IRGRN) was constructed by integration of these differentially expressed genes and known human protein-protein interaction network. The principal component analysis demonstrated that 67 genes in IRGRN could clearly distinguish insulin resistance from the non-disease state. Some of these candidate genes were further experimentally validated by qRT-PCR, highlighting the predictive role as biomarkers in insulin resistance. Conclusions: Our study provides new insight into the pathogenesis and treatment of insulin resistance and also reveals potential novel molecular targets and diagnostic biomarkers for insulin resistance.

Research on Gastric Cancer’s Drug-resistant Gene Regulatory Network Model

2020 ◽  
Vol 15 (3) ◽  
pp. 225-234
Author(s):  
Zhi Li ◽  
Tianyue Zhang ◽  
Haojie Lei ◽  
Liyan Wei ◽  
Yuanning Liu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Drug Resistance ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Differentially Expressed Gene ◽  
Multi Drug Resistance ◽  
Core Network ◽  
Valuable Data ◽  
Gene Regulatory ◽  
Primary Gene

Objective: Based on bioinformatics, differentially expressed gene data of drug-resistance in gastric cancer were analyzed, screened and mined through modeling and network modeling to find valuable data associated with multi-drug resistance of gastric cancer. Methods: First, data sets were preprocessed from three aspects: data processing, data annotation and classification, and functional clustering. Secondly, based on the preprocessed data, each classified primary gene regulatory network was constructed by mining interactions among the genes. This paper computed the values of each node in each classified primary gene regulatory network and ranked these nodes according to their scores. On the basis of this, the appropriate core node was selected and the corresponding core network was developed. Results and Conclusion:: Finally, core network modules were analyzed, which were mined. After the correlation analysis, the result showed that the constructed network module had 20 core genes. This module contained valuable data associated with multi-drug resistance in gastric cancer.

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