scholarly journals Biological pathway expression complementation contributes to biomass heterosis in Arabidopsis

2021 ◽  
Vol 118 (16) ◽  
pp. e2023278118
Author(s):  
Wenwen Liu ◽  
Guangming He ◽  
Xing Wang Deng
Keyword(s):  
Expression Patterns ◽  
Transcriptional Networks ◽  
Hub Genes ◽  
Pathway Expression ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Dynamic Growth ◽  
Hybrid Growth ◽  
Growth Vigor ◽  
Coexpression Network Analysis

The mechanisms underlying heterosis have long remained a matter of debate, despite its agricultural importance. How changes in transcriptional networks during plant development are relevant to the continuous manifestation of growth vigor in hybrids is intriguing and unexplored. Here, we present an integrated high-resolution analysis of the daily dynamic growth phenotypes and transcriptome atlases of young Arabidopsis seedlings (parental ecotypes [Col-0 and Per-1] and their F1 hybrid). Weighted gene coexpression network analysis uncovered divergent expression patterns between parents of the network hub genes, in which genes related to the cell cycle were more highly expressed in one parent (Col-0), whereas those involved in photosynthesis were more highly expressed in the other parent (Per-1). Notably, the hybrid exhibited spatiotemporal high-parent–dominant expression complementation of network hub genes in the two pathways during seedling growth. This suggests that the integrated capacities of cell division and photosynthesis contribute to hybrid growth vigor, which could be enhanced by temporal advances in the progression of leaf development in the hybrid relative to its parents. Altogether, this study provides evidence of expression complementation between fundamental biological pathways in hybrids and highlights the contribution of expression dominance in heterosis.

scholarly journals High-resolution analysis of central nervous system expression patterns in zebrafish Gal4 enhancer-trap lines

2015 ◽  
Vol 244 (6) ◽  
pp. 785-796 ◽  
Author(s):  
Hideo Otsuna ◽  
David A. Hutcheson ◽  
Robert N. Duncan ◽  
Adam D. McPherson ◽  
Aaron N. Scoresby ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
High Resolution ◽  
Expression Patterns ◽  
Enhancer Trap ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Enhancer Trap Lines

Very High Resolution Analysis of the Dynamics of a Coronal Plasmoid

1994 ◽  
Vol 144 ◽  
pp. 593-596
Author(s):  
O. Bouchard ◽  
S. Koutchmy ◽  
L. November ◽  
J.-C. Vial ◽  
J. B. Zirker
Keyword(s):  
High Resolution ◽  
Solar Eclipse ◽  
Total Solar Eclipse ◽  
Field Of View ◽  
Small Field ◽  
High Resolution Analysis ◽  
Ccd Observations ◽  
Resolution Analysis ◽  
Very High ◽  
Small Field Of View

AbstractWe present the results of the analysis of a movie taken over a small field of view in the intermediate corona at a spatial resolution of 0.5“, a temporal resolution of 1 s and a spectral passband of 7 nm. These CCD observations were made at the prime focus of the 3.6 m aperture CFHT telescope during the 1991 total solar eclipse.

scholarly journals How to Effectively Control Energy Consumption Growth in China’s 29 Provinces: A Paradigm of Multi-Regional Analysis Based on EAALMDI Method

2021 ◽  
Vol 13 (3) ◽  
pp. 1093
Author(s):  
Yunlong Zhao ◽  
Geng Kong ◽  
Chin Hao Chong ◽  
Linwei Ma ◽  
Zheng Li ◽  
...  
Keyword(s):  
Energy Consumption ◽  
High Resolution ◽  
Energy Policy ◽  
Policy Making ◽  
Regional Analysis ◽  
Policy Implications ◽  
Consumption Growth ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Consumption Control

Controlling energy consumption to reduce greenhouse gas emissions has become a global consensus in response to the challenge of climate change. Most studies have focused on energy consumption control in a single region; however, high-resolution analysis of energy consumption and personalized energy policy-making, for multiple regions with differentiated development, have become a complicated challenge. Using the logarithmic mean Divisia index I (LMDI) decomposition method based on energy allocation analysis (EAA), this paper aims to establish a standard paradigm for a high-resolution analysis of multi-regional energy consumption and provide suggestions for energy policy-making, taking 29 provinces of China as the sample. The process involved three steps: (1) determination of regional priorities of energy consumption control by EAA, (2) revealing regional disparity among the driving forces of energy consumption growth by LMDI, and (3) deriving policy implications by comparing the obtained results with existing policies. The results indicated that 29 provinces can be divided into four groups, with different priorities of energy consumption control according to the patterns of coal flows. Most provinces have increasing levels of energy consumption, driven by increasing per capita GDP and improving living standards, while its growth is restrained by decreasing end-use energy intensity, improving energy supply efficiency, and optimization of industrial structures. However, some provinces are not following these trends to the same degree. This indicates that policy-makers must pay more attention to the different driving mechanisms of energy consumption growth among provinces.

scholarly journals A Back Migration from Asia to Sub-Saharan Africa Is Supported by High-Resolution Analysis of Human Y-Chromosome Haplotypes

2002 ◽  
Vol 70 (5) ◽  
pp. 1197-1214 ◽  
Author(s):  
Fulvio Cruciani ◽  
Piero Santolamazza ◽  
Peidong Shen ◽  
Vincent Macaulay ◽  
Pedro Moral ◽  
...  
Keyword(s):  
High Resolution ◽  
Y Chromosome ◽  
Sub Saharan Africa ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Sub Saharan ◽  
Human Y Chromosome

scholarly journals Shared Blood Transcriptomic Signatures between Alzheimer’s Disease and Diabetes Mellitus

Biomedicines ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Taesic Lee ◽  
Hyunju Lee
Keyword(s):  
Diabetes Mellitus ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Expression Patterns ◽  
Protein Protein Interactions ◽  
Hub Genes ◽  
Gene Modules ◽  
Gene Regulatory ◽  
The Brain

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

scholarly journals The potential role of chemotaxis and the complement system in the formation and progression of thoracic aortic aneurysms inferred from the weighted gene coexpression network analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Chuxiang Lei ◽  
Dan Yang ◽  
Wenlin Chen ◽  
Haoxuan Kan ◽  
Fang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Clinical Significance ◽  
Immune Cells ◽  
Complement System ◽  
Aortic Wall ◽  
Gene Coexpression Network ◽  
Hub Genes ◽  
Gene Coexpression ◽  
The Complement System ◽  
Coexpression Network Analysis

Abstract Background Thoracic aortic aneurysm (TAA) can be life-threatening due to the progressive weakening and dilatation of the aortic wall. Once the aortic wall has ruptured, no effective pharmaceutical therapies are available. However, studies on TAA at the gene expression level are limited. Our study aimed to identify the driver genes and critical pathways of TAA through gene coexpression networks. Methods We analyzed the genetic data of TAA patients from a public database by weighted gene coexpression network analysis (WGCNA). Modules with clinical significance were identified, and the differentially expressed genes (DEGs) were intersected with the genes in these modules. Gene Ontology and pathway enrichment analyses were performed. Finally, hub genes that might be driving factors of TAA were identified. Furthermore, we evaluated the diagnostic accuracy of these genes and analyzed the composition of immune cells using the CIBERSORT algorithm. Results We identified 256 DEGs and two modules with clinical significance. The immune response, including leukocyte adhesion, mononuclear cell proliferation and T cell activation, was identified by functional enrichment analysis. CX3CR1, C3, and C3AR1 were the top 3 hub genes in the module correlated with TAA, and the areas under the curve (AUCs) by receiver operating characteristic (ROC) analysis of all the hub genes exceeded 0.7. Finally, we found that the proportions of infiltrating immune cells in TAA and normal tissues were different, especially in terms of macrophages and natural killer (NK) cells. Conclusion Chemotaxis and the complement system were identified as crucial pathways in TAA, and macrophages with interactive immune cells may regulate this pathological process.

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