scholarly journals Metatranscriptomic Analysis of Multiple Environmental Stresses Identifies RAP2.4 Gene Associated with Arabidopsis Immunity to Botrytis cinerea

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Arjun Sham ◽  
Hibatullah Al-Ashram ◽  
Kenna Whitley ◽  
Rabah Iratni ◽  
Khaled A. El-Tarabily ◽  
...  
Keyword(s):  
Botrytis Cinerea ◽  
Plant Immunity ◽  
Interaction Network ◽  
Defense Responses ◽  
Plant Responses ◽  
Breeding Programs ◽  
Protein Protein Interaction ◽  
Genetic Components ◽  
Protein Protein Interaction Network

AbstractIn this study, we aimed to identify common genetic components during stress response responsible for crosstalk among stresses, and to determine the role of differentially expressed genes in Arabidopsis-Botrytis cinerea interaction. Of 1,554 B. cinerea up-regulated genes, 24%, 1.4% and 14% were induced by biotic, abiotic and hormonal treatments, respectively. About 18%, 2.5% and 22% of B. cinerea down-regulated genes were also repressed by the same stress groups. Our transcriptomic analysis indicates that plant responses to all tested stresses can be mediated by commonly regulated genes; and protein-protein interaction network confirms the cross-interaction between proteins regulated by these genes. Upon challenges to individual or multiple stress(es), accumulation of signaling molecules (e.g. hormones) plays a major role in the activation of downstream defense responses. In silico gene analyses enabled us to assess the involvement of RAP2.4 (related to AP2.4) in plant immunity. Arabidopsis RAP2.4 was repressed by B. cinerea, and its mutants enhanced resistance to the same pathogen. To the best of our knowledge, this is the first report demonstrating the role of RAP2.4 in plant defense against B. cinerea. This research can provide a basis for breeding programs to increase tolerance and improve yield performance in crops.

scholarly journals The diminishing role of hubs in dynamical processes on complex networks

2013 ◽  
Vol 10 (88) ◽  
pp. 20130568 ◽  
Author(s):  
Rick Quax ◽  
Andrea Apolloni ◽  
Peter M. A. Sloot
Keyword(s):  
Interaction Network ◽  
Macroscopic System ◽  
Protein Protein Interaction ◽  
Equilibrium Dynamics ◽  
Qualitative Evidence ◽  
Individual Unit ◽  
System Of Units ◽  
Protein Protein Interaction Network

It is notoriously difficult to predict the behaviour of a complex self-organizing system, where the interactions among dynamical units form a heterogeneous topology. Even if the dynamics of each microscopic unit is known, a real understanding of their contributions to the macroscopic system behaviour is still lacking. Here, we develop information-theoretical methods to distinguish the contribution of each individual unit to the collective out-of-equilibrium dynamics. We show that for a system of units connected by a network of interaction potentials with an arbitrary degree distribution, highly connected units have less impact on the system dynamics when compared with intermediately connected units. In an equilibrium setting, the hubs are often found to dictate the long-term behaviour. However, we find both analytically and experimentally that the instantaneous states of these units have a short-lasting effect on the state trajectory of the entire system. We present qualitative evidence of this phenomenon from empirical findings about a social network of product recommendations, a protein–protein interaction network and a neural network, suggesting that it might indeed be a widespread property in nature.

scholarly journals Diversified Regulation of Cytokinin Levels and Signaling During Botrytis cinerea Infection in Arabidopsis

2021 ◽  
Vol 12 ◽  
Author(s):  
Beibei Li ◽  
Ruolin Wang ◽  
Shiya Wang ◽  
Jiang Zhang ◽  
Ling Chang
Keyword(s):  
Botrytis Cinerea ◽  
Response Regulator ◽  
Plant Immunity ◽  
Defense Responses ◽  
Cytokinin Oxidase ◽  
Plant Defense Responses ◽  
Necrotrophic Pathogen ◽  
Transcriptional Changes ◽  
Complex Regulation

Cytokinins (CKs) can modulate plant immunity to various pathogens, but how CKs are involved in plant defense responses to the necrotrophic pathogen Botrytis cinerea is still unknown. Here, we found that B. cinerea infection induced transcriptional changes in multiple genes involved in the biosynthesis, degradation, and signaling of CKs, as well as their contents, in pathogen-infected Arabidopsis leaves. Among the CKs, the gene expression of CYTOKININ OXIDASE/DEHYDROGENASE 5 (CKX5) was remarkably induced in the local infected leaves and the distant leaves of the same plant without pathogen inoculation. Cis-zeatin (cZ) and its riboside (cZR) accumulated considerably in infected leaves, suggesting an important role of the cis-zeatin type of CKs in the plant response to B. cinerea. Cytokinin double-receptor mutants were more susceptible to B. cinerea infection, whereas an exogenous CK treatment enhanced the expression levels of defense-related genes and of jasmonic acid (JA) and ethylene (ET), but not salicylic acid (SA), resulting in higher resistance of Arabidopsis to B. cinerea. Investigation of CK responses to B. cinerea infection in the JA biosynthesis mutant, jar1-1, and ET-insensitive mutant, ein2-1, showed that CK signaling and levels of CKs, namely, those of isopentenyladenine (iP), isopentenyladenine riboside (iPR), and trans-zeatin (tZ), were enhanced in jar1-1-infected leaves. By contrast, reductions in iP, iPR, tZ, and tZ riboside (tZR) as well as cZR contents occurred in ein2-1-infected leaves, whose transcript levels of CK signaling genes were likewise differentially regulated. The Arabidopsis Response Regulator 5 (ARR5) gene was upregulated in infected leaves of ein2-1 whereas another type-A response regulator, ARR16, was significantly downregulated, suggesting the existence of a complex regulation of CK signaling via the ET pathway. Accumulation of the cis-zeatin type of CKs in B. cinerea-infected leaves depended on ET but not JA pathways. Collectively, our findings provide evidence that CK responds to B. cinerea infection in a variety of ways that are differently modulated by JA and ET pathways in Arabidopsis.

scholarly journals The Highlighted Role of GAPDH and Nitric-Oxide Synthase Regulator Activity in Proton Beam Irradiated Melanoma BLM Cells

2019 ◽  
Vol 10 (5) ◽  
pp. S68-S72
Author(s):  
Mohammad-Mehdi Zadeh-Esmaeel ◽  
Shabnam Shahrokh ◽  
Mona Zamanian Azodi ◽  
Nayebali Ahmadi
Keyword(s):  
Nitric Oxide ◽  
Nitric Oxide Synthase ◽  
Proton Therapy ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Oxide Synthase ◽  
Protein Protein Interaction Network ◽  
Cancer Bioinformatics

Introduction: The human melanoma is a type of invasive tumor the treatment of which is challenging. To better understand the proton irradiation mechanisms as one of the widely applied therapy for this type of cancer, bioinformatics analysis of proteomics outcome could be beneficial. Methods: Protein-protein interaction network analysis of the differentially expressed proteins (DEPs) of melanoma BLM (BRO lung metastasis) cells in the treatment of 3 Gy dosage proton therapy was performed in this study via Cytoscape V.3.7.2. and its integrated plug-ins. Results: Eighteen DEPs were searched for network constructions and limited numbers of query +neighbor proteins were found central. The hub-bottlenecks (i.e. central nodes) were GAPDH, ACTB, ALB, AKT1, TP53, and EGFR. The fist mentioned proteins were from DEPs. The enrichment analysis of these elements identified nitric-oxide synthase regulator activity and the positive regulation of the norepinephrine uptake that may be the key to the mechanisms of proton therapy. Conclusion: In conclusion, the identified central nodes (EGFR, TP53, ALB, AKT1, GAPDH, and ACTB) and the related biological terms are the critical affected genes and biological terms in the irradiated melanoma cells.

Applications of random graphs

2017 ◽  
Author(s):  
A.C.C. Coolen ◽  
A. Annibale ◽  
E.S. Roberts
Keyword(s):  
Social Networks ◽  
Random Graphs ◽  
Interaction Network ◽  
Power Grids ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
First Case ◽  
The Stability ◽  
Protein Protein Interaction Network

This chapter reviews graph generation techniques in the context of applications. The first case study is power grids, where proposed strategies to prevent blackouts have been tested on tailored random graphs. The second case study is in social networks. Applications of random graphs to social networks are extremely wide ranging – the particular aspect looked at here is modelling the spread of disease on a social network – and how a particular construction based on projecting from a bipartite graph successfully captures some of the clustering observed in real social networks. The third case study is on null models of food webs, discussing the specific constraints relevant to this application, and the topological features which may contribute to the stability of an ecosystem. The final case study is taken from molecular biology, discussing the importance of unbiased graph sampling when considering if motifs are over-represented in a protein–protein interaction network.

scholarly journals Diabetic Retinopathy and Laser Therapy in Rats: A Protein-Protein Interaction Network Analysis

2017 ◽  
Vol 8 (Suppl 1) ◽  
pp. S20-S21 ◽  
Author(s):  
Akram Safaei ◽  
Mostafa Rezaei Tavirani ◽  
Mona Zamanian Azodi ◽  
Alireza Lashay ◽  
Seyed Farzad Mohammadi ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Network Analysis ◽  
Laser Therapy ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

scholarly journals Short loop functional commonality identified in leukaemia proteome highlights crucial protein sub-networks

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Sun Sook Chung ◽  
Joseph C F Ng ◽  
Anna Laddach ◽  
N Shaun B Thomas ◽  
Franca Fraternali
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Interaction Network ◽  
Protein Protein Interactions ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Short Loop ◽  
New Strategy ◽  
Loop Network ◽  
Protein Protein Interaction Network

Abstract Direct drug targeting of mutated proteins in cancer is not always possible and efficacy can be nullified by compensating protein–protein interactions (PPIs). Here, we establish an in silico pipeline to identify specific PPI sub-networks containing mutated proteins as potential targets, which we apply to mutation data of four different leukaemias. Our method is based on extracting cyclic interactions of a small number of proteins topologically and functionally linked in the Protein–Protein Interaction Network (PPIN), which we call short loop network motifs (SLM). We uncover a new property of PPINs named ‘short loop commonality’ to measure indirect PPIs occurring via common SLM interactions. This detects ‘modules’ of PPI networks enriched with annotated biological functions of proteins containing mutation hotspots, exemplified by FLT3 and other receptor tyrosine kinase proteins. We further identify functional dependency or mutual exclusivity of short loop commonality pairs in large-scale cellular CRISPR–Cas9 knockout screening data. Our pipeline provides a new strategy for identifying new therapeutic targets for drug discovery.

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