scholarly journals Deciphering transcriptional regulators of banana fruit ripening by regulatory network analysis

2020 ◽  
Author(s):  
Jian‐Fei Kuang ◽  
Chao‐Jie Wu ◽  
Yu‐Fan Guo ◽  
Dirk Walther ◽  
Wei Shan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Fruit Ripening ◽  
Regulatory Network ◽  
Transcriptional Regulators ◽  
Banana Fruit

Transcriptional regulatory network analysis of developing human erythroid progenitors reveals patterns of coregulation and potential transcriptional regulators

2006 ◽  
Vol 28 (1) ◽  
pp. 114-128 ◽  
Author(s):  
M. A. Keller ◽  
S. Addya ◽  
R. Vadigepalli ◽  
B. Banini ◽  
K. Delgrosso ◽  
...  
Keyword(s):  
Network Analysis ◽  
Blood Cell ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Transcriptional Regulators ◽  
Erythroid Progenitors ◽  
Hematopoietic Stem ◽  
Transcriptional Regulatory

Deciphering the molecular basis for human erythropoiesis should yield information benefiting studies of the hemoglobinopathies and other erythroid disorders. We used an in vitro erythroid differentiation system to study the developing red blood cell transcriptome derived from adult CD34+ hematopoietic progenitor cells. mRNA expression profiling was used to characterize developing erythroid cells at six time points during differentiation ( days 1, 3, 5, 7, 9, and 11). Eleven thousand seven hundred sixty-three genes (20,963 Affymetrix probe sets) were expressed on day 1, and 1,504 genes, represented by 1,953 probe sets, were differentially expressed (DE) with 537 upregulated and 969 downregulated. A subset of the DE genes was validated using real-time RT-PCR. The DE probe sets were subjected to a cluster metric and could be divided into two, three, four, five, or six clusters of genes with different expression patterns in each cluster. Genes in these clusters were examined for shared transcription factor binding sites (TFBS) in their promoters by comparing enrichment of each TFBS relative to a reference set using transcriptional regulatory network analysis. The sets of TFBS enriched in genes up- and downregulated during erythropoiesis were distinct. This analysis identified transcriptional regulators critical to erythroid development, factors recently found to play a role, as well as a new list of potential candidates, including Evi-1, a potential silencer of genes upregulated during erythropoiesis. Thus this transcriptional regulatory network analysis has yielded a focused set of factors and their target genes whose role in differentiation of the hematopoietic stem cell into distinct blood cell lineages can be elucidated.

scholarly journals Signature RNAS and related regulatory roles in type 1 diabetes mellitus based on competing endogenous RNA regulatory network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qinghong Shi ◽  
Hanxin Yao
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Network Analysis ◽  
Type 1 Diabetes Mellitus ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Competing Endogenous Rna ◽  
Pathway Enrichment

Abstract Background Our study aimed to investigate signature RNAs and their potential roles in type 1 diabetes mellitus (T1DM) using a competing endogenous RNA regulatory network analysis. Methods Expression profiles of GSE55100, deposited from peripheral blood mononuclear cells of 12 T1DM patients and 10 normal controls, were downloaded from the Gene Expression Omnibus to uncover differentially expressed long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs). The ceRNA regulatory network was constructed, then functional and pathway enrichment analysis was conducted. AT1DM-related ceRNA regulatory network was established based on the Human microRNA Disease Database to carry out pathway enrichment analysis. Meanwhile, the T1DM-related pathways were retrieved from the Comparative Toxicogenomics Database (CTD). Results In total, 847 mRNAs, 41 lncRNAs, and 38 miRNAs were significantly differentially expressed. The ceRNA regulatory network consisted of 12 lncRNAs, 10 miRNAs, and 24 mRNAs. Two miRNAs (hsa-miR-181a and hsa-miR-1275) were screened as T1DM-related miRNAs to build the T1DM-related ceRNA regulatory network, in which genes were considerably enriched in seven pathways. Moreover, three overlapping pathways, including the phosphatidylinositol signaling system (involving PIP4K2A, INPP4A, PIP4K2C, and CALM1); dopaminergic synapse (involving CALM1 and PPP2R5C); and the insulin signaling pathway (involving CBLB and CALM1) were revealed by comparing with T1DM-related pathways in the CTD, which involved four lncRNAs (LINC01278, TRG-AS1, MIAT, and GAS5-AS1). Conclusion The identified signature RNAs may serve as important regulators in the pathogenesis of T1DM.

scholarly journals The HrpG/HrpX Regulon of Xanthomonads—An Insight to the Complexity of Regulation of Virulence Traits in Phytopathogenic Bacteria

2021 ◽  
Vol 9 (1) ◽  
pp. 187
Author(s):  
Doron Teper ◽  
Sheo Shankar Pandey ◽  
Nian Wang
Keyword(s):  
Regulatory Network ◽  
Regulatory Networks ◽  
Focal Point ◽  
Genetic Regulation ◽  
Transcriptional Regulators ◽  
Secretion System ◽  
In Planta ◽  
Type 3 Secretion System ◽  
Type 3 ◽  
Made In

Bacteria of the genus Xanthomonas cause a wide variety of economically important diseases in most crops. The virulence of the majority of Xanthomonas spp. is dependent on secretion and translocation of effectors by the type 3 secretion system (T3SS) that is controlled by two master transcriptional regulators HrpG and HrpX. Since their discovery in the 1990s, the two regulators were the focal point of many studies aiming to decipher the regulatory network that controls pathogenicity in Xanthomonas bacteria. HrpG controls the expression of HrpX, which subsequently controls the expression of T3SS apparatus genes and effectors. The HrpG/HrpX regulon is activated in planta and subjected to tight metabolic and genetic regulation. In this review, we cover the advances made in understanding the regulatory networks that control and are controlled by the HrpG/HrpX regulon and their conservation between different Xanthomonas spp.

Expression profiles of a MhCTR1 gene in relation to banana fruit ripening

2012 ◽  
Vol 56 ◽  
pp. 47-55 ◽  
Author(s):  
Huei-Lin Hu ◽  
Yi-Yin Do ◽  
Pung-Ling Huang
Keyword(s):  
Fruit Ripening ◽  
Expression Profiles ◽  
Banana Fruit

Multiple forms of α-expansin genes are expressed during banana fruit ripening and development

2007 ◽  
Vol 45 (2) ◽  
pp. 184-192 ◽  
Author(s):  
Asha ◽  
Vidhu A. Sane ◽  
Aniruddha P. Sane ◽  
Pravendra Nath
Keyword(s):  
Fruit Ripening ◽  
Banana Fruit ◽  
Multiple Forms

Study on some of the physiological and biochemical indexes under the age of development of banana fruit (Musa paradisiaca L.) grown in Thanh Tri, Hanoi

2021 ◽  
Vol 66 (1) ◽  
pp. 87-95
Author(s):  
Trong Le Van ◽  
Khanh Nguyen Nhu
Keyword(s):  
Fruit Ripening ◽  
Starch Content ◽  
Sugar Content ◽  
Maximum Size ◽  
Research Process ◽  
Scientific Basis ◽  
Carotenoid Content ◽  
Banana Fruit ◽  
Fruit Formation ◽  
Physiological And Biochemical

Research to determine the ripening time of the fruit is the scientific basis for better harvesting and preservation. Physiological and biochemical methods were used to analyze the changes of some indicators according to the growth and development of banana fruit grown in Thanh Liet commune, Thanh Tri district, Hanoi from the time of its formation until the fruit ripening. The results showed that the banana reached the maximum size at 16 weeks old, at this time the peel was yellow due to the decrease in chlorophyll and increased carotenoid content. The content of vitamin C and total organic acid content reached their maximum when the fruit at 12 weeks old, then decreased gradually. Starch content increased to 14 weeks old, then decreased. Reduced sugar content increased gradually to 16 weeks old and then decreased. Protein content decreased gradually from fruit formation until fruit ripening, lipid content increased gradually to 15 weeks old, then decreased. Through the research process, we have determined that the physiological ripe time of banana fruit was 16 weeks old, this is the time when the fruit stops growing and accumulates many nutrients.

PAGeneRN

2020 ◽  
pp. 1052-1075 ◽  
Author(s):  
Dina Elsayad ◽  
A. Ali ◽  
Howida A. Shedeed ◽  
Mohamed F. Tolba
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Network Analysis ◽  
Gene Regulatory Network ◽  
Gene Expression Data ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Data ◽  
Gene Expression Data Analysis ◽  
Gene Regulatory

The gene expression analysis is an important research area of Bioinformatics. The gene expression data analysis aims to understand the genes interacting phenomena, gene functionality and the genes mutations effect. The Gene regulatory network analysis is one of the gene expression data analysis tasks. Gene regulatory network aims to study the genes interactions topological organization. The regulatory network is critical for understanding the pathological phenotypes and the normal cell physiology. There are many researches that focus on gene regulatory network analysis but unfortunately some algorithms are affected by data size. Where, the algorithm runtime is proportional to the data size, therefore, some parallel algorithms are presented to enhance the algorithms runtime and efficiency. This work presents a background, mathematical models and comparisons about gene regulatory networks analysis different techniques. In addition, this work proposes Parallel Architecture for Gene Regulatory Network (PAGeneRN).

scholarly journals Banana Ovate Family Protein MaOFP1 and MADS-Box Protein MuMADS1 Antagonistically Regulated Banana Fruit Ripening

PLoS ONE ◽  
2015 ◽  
Vol 10 (4) ◽  
pp. e0123870 ◽  
Author(s):  
Juhua Liu ◽  
Jing Zhang ◽  
Wei Hu ◽  
Hongxia Miao ◽  
Jianbin Zhang ◽  
...  
Keyword(s):  
Fruit Ripening ◽  
Banana Fruit ◽  
Mads Box ◽  
Family Protein
Sign in / Sign up

Export Citation Format

Share Document