Subcellular Proteomics to Understand Promotive Effect of Plant-Derived Smoke Solution on Soybean Root

Plant-derived smoke solution enhances soybean root growth; however, its mechanism is not clearly understood. Subcellular proteomics techniques were used for underlying roles of plant-derived smoke solution on soybean root growth. The fractions of membrane and nucleus were purified and evaluated for purity. ATPase and histone were enriched in the fractions of membrane and nucleus, respectively. Principal component analysis of proteomic results indicated that the plant-derived smoke solution affected the proteins in the membrane and nucleus. The proteins in the membrane and nucleus mainly increased and decreased, respectively, by the treatment of plant-derived smoke solution compared with control. In the proteins in the plasma membrane, ATPase increased, which was confirmed by immunoblot analysis, and ATP contents increased through the treatment of plant-derived smoke solution. Additionally, although the nuclear proteins mainly decreased, the expression of RNA polymerase II was up-regulated through the treatment of plant-derived smoke solution. These results indicate that plant-derived smoke solution enhanced soybean root growth through the transcriptional promotion with RNA polymerase II expression and the energy production with ATPase accumulation.

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Pathway Interaction Network Analysis Identifies Dysregulated Pathways in Human Monocytes Infected by Listeria monocytogenes

Computational and Mathematical Methods in Medicine ◽

10.1155/2017/3195348 ◽

2017 ◽

Vol 2017 ◽

pp. 1-8

Author(s):

Wufeng Fan ◽

Yuhan Zhou ◽

Hao Li

Keyword(s):

Listeria Monocytogenes ◽

Rna Polymerase ◽

Rna Polymerase Ii ◽

Interaction Network ◽

Principal Component ◽

Centrality Measures ◽

Network Centrality ◽

Human Monocytes ◽

Pathway Interaction ◽

Pathway Interaction Network

In our study, we aimed to extract dysregulated pathways in human monocytes infected by Listeria monocytogenes (LM) based on pathway interaction network (PIN) which presented the functional dependency between pathways. After genes were aligned to the pathways, principal component analysis (PCA) was used to calculate the pathway activity for each pathway, followed by detecting seed pathway. A PIN was constructed based on gene expression profile, protein-protein interactions (PPIs), and cellular pathways. Identifying dysregulated pathways from the PIN was performed relying on seed pathway and classification accuracy. To evaluate whether the PIN method was feasible or not, we compared the introduced method with standard network centrality measures. The pathway of RNA polymerase II pretranscription events was selected as the seed pathway. Taking this seed pathway as start, one pathway set (9 dysregulated pathways) with AUC score of 1.00 was identified. Among the 5 hub pathways obtained using standard network centrality measures, 4 pathways were the common ones between the two methods. RNA polymerase II transcription and DNA replication owned a higher number of pathway genes and DEGs. These dysregulated pathways work together to influence the progression of LM infection, and they will be available as biomarkers to diagnose LM infection.

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Histone modifications at the transcription start site of non-coding RNA revealed regulatory patterns of activation in Caenorhabditis elegans embryo

10.1101/2020.09.24.311308 ◽

2020 ◽

Author(s):

Megan A. Bandeira ◽

Max E. Boeck

Keyword(s):

Rna Polymerase ◽

Rna Polymerase Ii ◽

Transcription Start Site ◽

Histone Modifications ◽

Principal Component ◽

Integrated Analysis ◽

Regulation Of Transcription ◽

Start Site ◽

Transcription Start ◽

Non Coding Rna

AbstractHistone modifications play an essential role in regulating recruitment of RNA polymerase II and through this regulation of transcription itself. Which modifications are essential for regulating the transcription of non-coding RNA (ncRNA) species and how these patterns differ between the different types of ncRNA remains less studied compared to mRNA. We performed a principal component analysis (PCA) of histone modifications patterns surrounding the transcription start site (TSS) of ncRNA in an attempt to understand how histone modifications predict polymerase recruitment and transcription of ncRNA in early C. elegans development We found that our first PCA axis was a better predictor of polymerase recruitment and expression than any single histone modification for ncRNA and miRNA. This indicates an integrated analysis of many histone modifications is essential for predicting expression based on histone modifications and that each ncRNA species have unique regulation of RNA polymerase recruitment through histone modifications.

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Nutrient-regulated gene expression in eukaryotes

Biochemical Society Symposium ◽

10.1042/bss0730085 ◽

2006 ◽

Vol 73 ◽

pp. 85-96 ◽

Cited By ~ 8

Author(s):

Richard J. Reece ◽

Laila Beynon ◽

Stacey Holden ◽

Amanda D. Hughes ◽

Karine Rébora ◽

...

Keyword(s):

Gene Expression ◽

Rna Polymerase ◽

Rna Polymerase Ii ◽

Transcriptional Control ◽

Direct Interaction ◽

Signalling Pathways ◽

A Cell ◽

One Step ◽

Higher Eukaryotes ◽

Regulated Gene Expression

The recognition of changes in environmental conditions, and the ability to adapt to these changes, is essential for the viability of cells. There are numerous well characterized systems by which the presence or absence of an individual metabolite may be recognized by a cell. However, the recognition of a metabolite is just one step in a process that often results in changes in the expression of whole sets of genes required to respond to that metabolite. In higher eukaryotes, the signalling pathway between metabolite recognition and transcriptional control can be complex. Recent evidence from the relatively simple eukaryote yeast suggests that complex signalling pathways may be circumvented through the direct interaction between individual metabolites and regulators of RNA polymerase II-mediated transcription. Biochemical and structural analyses are beginning to unravel these elegant genetic control elements.

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