scholarly journals Transcriptomic Analysis Reveals Cu/Zn SODs Acting as Hub Genes of SODs in Hylocereus undatus Induced by Trypsin during Storage

Antioxidants ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 162 ◽  
Author(s):  
Xinyue Pang ◽  
Xinling Li ◽  
Xueru Liu ◽  
Luning Cai ◽  
Bairu Li ◽  
...  
Keyword(s):  
Metal Ion ◽  
Antioxidant Activities ◽  
Defense Responses ◽  
Transcriptomic Analysis ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Free Network ◽  
Hylocereus Undatus

It has been revealed by us that superoxide scavenging is a new activity of trypsin. In this study, the synergistic mechanisms of trypsin and superoxide dismutases (SODs) were evaluated in Hylocereus undatus (pitaya). Trypsin significantly improved the storage quality of H. undatus, including weight loss impediment and decrease of cellular injury. The regulatory mechanisms of 16 SOD genes by trypsin were revealed using transcriptomic analysis on H. undatus. Results revealed that important physiological metabolisms, such as antioxidant activities or metal ion transport were induced, and defense responses were inhibited by trypsin. Furthermore, the results of protein–protein interaction (PPI) networks showed that besides the entire ROS network, the tiny SODs sub-network was also a scale-free network. Cu/Zn SODs acted as the hub that SODs synergized with trypsin during the storage of H. undatus.

scholarly journals TulsiPIN: an interologous protein interactome ofOcimum tenuiflorum

2019 ◽  
Author(s):  
Vikram Singh ◽  
Gagandeep Singh ◽  
Vikram Singh
Keyword(s):  
Small World ◽  
Ppi Network ◽  
Modular Architecture ◽  
High Confidence ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Ppi Networks ◽  
A Genome ◽  
Transcription Regulators

AbstractOcimum tenuiflorum, commonly known as holy basil or tulsi, is globally recognized for its multitude of medicinal properties. However, a comprehensive study revealing the complex interplay among its constituent proteins at subcellular level is still lacking. To bridge this gap, a genome scale interologous protein-protein interaction (PPI) network, TulsiPIN, is developed using 49 template plants. The reported network consists of 13, 660 nodes and 327, 409 binary interactions. A high confidence PPI network consisting of 7, 719 nodes having 95, 532 interactions was inferred using domain-domain interaction information along with interolog based statistics, and its reliability was further assessed using functional homogeneity and protein colocalization. 1, 625 vital proteins are predicted by statistically evaluating this high confidence TulsiPIN with two ensembles of corresponding random networks, each consisting of 10, 000 realizations of Erdős-Rényi and Barabási-Albert models. Topological features of TulsiPIN including small-world, scale-free and modular architecture are inspected and found to resemble with other plant PPI networks. Finally, numerous regulatory proteins like transcription factors, transcription regulators and protein kinases are profiled in TulsiPIN and a sub-network of proteins participating in 10 secondary metabolite biosynthetic pathways is studied. We believe, the methodology developed and insights imparted would be useful in understanding regulatory mechanisms in various plant species.

scholarly journals Bipartite subgraphs and the signless Laplacian matrix

2011 ◽  
Vol 5 (1) ◽  
pp. 1-13 ◽  
Author(s):  
Steve Kirkland ◽  
Debdas Paul
Keyword(s):  
Laplacian Matrix ◽  
Rayleigh Quotient ◽  
Scale Free Network ◽  
Power Law Distribution ◽  
Laplacian Eigenvalue ◽  
Scale Free ◽  
Protein Protein Interaction ◽  
Signless Laplacian Matrix ◽  
Signless Laplacian ◽  
Free Network

For a connected graph G, we derive tight inequalities relating the smallest signless Laplacian eigenvalue to the largest normalized Laplacian eigenvalue. We investigate how vectors yielding small values of the Rayleigh quotient for the signless Laplacian matrix can be used to identify bipartite subgraphs. Our results are applied to some graphs with degree sequences approximately following a power law distribution with exponent value 2:1 (scale-free networks), and to a scale-free network arising from protein-protein interaction.

Transcriptomic analysis reveals key genes related to antioxidant mechanisms of Hylocereus undatus quality improvement by trypsin during storage

2019 ◽  
Vol 10 (12) ◽  
pp. 8116-8128 ◽  
Author(s):  
Xin Li ◽  
Xueru Liu ◽  
Yong Yin ◽  
Huichun Yu ◽  
Min Zhang ◽  
...  
Keyword(s):  
Antioxidant Enzymes ◽  
Quality Improvement ◽  
Transcriptomic Analysis ◽  
Ppi Network ◽  
Storage Quality ◽  
System P ◽  
Key Genes ◽  
Hylocereus Undatus ◽  
Antioxidant Mechanisms

The synergistic effect of trypsin with antioxidant enzymes can improve the storage quality of H. undatus. Transcriptomic analysis and PPI network indicated that CAT is the key one among the enzymes of the complicated antioxidant system.

scholarly journals NAUTICA: classifying transcription factor interactions by positional and protein-protein interaction information

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Stefano Perna ◽  
Pietro Pinoli ◽  
Stefano Ceri ◽  
Limsoon Wong
Keyword(s):  
Protein Interaction ◽  
Positional Information ◽  
Ppi Network ◽  
Interaction Prediction ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Physical Interactions ◽  
Novel Interactions ◽  
Factor Interactions

Abstract Background Inferring the mechanisms that drive transcriptional regulation is of great interest to biologists. Generally, methods that predict physical interactions between transcription factors (TFs) based on positional information of their binding sites (e.g. chromatin immunoprecipitation followed by sequencing (ChIP-Seq) experiments) cannot distinguish between different kinds of interaction at the same binding spots, such as co-operation and competition. Results In this work, we present the Network-Augmented Transcriptional Interaction and Coregulation Analyser (NAUTICA), which employs information from protein-protein interaction (PPI) networks to assign TF-TF interaction candidates to one of three classes: competition, co-operation and non-interactions. NAUTICA filters available PPI network edges and fits a prediction model based on the number of shared partners in the PPI network between two candidate interactors. Conclusions NAUTICA improves on existing positional information-based TF-TF interaction prediction results, demonstrating how PPI information can improve the quality of TF interaction prediction. NAUTICA predictions - both co-operations and competitions - are supported by literature investigation, providing evidence on its capability of providing novel interactions of both kinds. Reviewers This article was reviewed by Zoltán Hegedüs and Endre Barta.

scholarly journals Chitosan biopolymer improves the fruit quality of litchi (Litchi chinensis Sonn.)

2019 ◽  
Vol 72 (2) ◽  
Author(s):  
Md. Shabab Mehebub ◽  
Nur Uddin Mahmud ◽  
Mosaddiqur Rahman ◽  
Musrat Zahan Surovy ◽  
Dipali Rani Gupta ◽  
...  
Keyword(s):  
Antioxidant Activity ◽  
Ascorbic Acid ◽  
Fruit Quality ◽  
Antioxidant Activities ◽  
Defense Responses ◽  
Butylated Hydroxytoluene ◽  
Natural Defense ◽  
Litchi Fruit ◽  
Chitosan Biopolymer

Chitosan (CHT) is a natural compound that has been used to control postharvest pathogenic diseases due to its capability of eliciting natural defense responses in plants. The aim of this study was to investigate the effect of foliar CHT application on yield and quality of the litchi fruit. Chitosan was applied by spraying on to fruit and foliage just after fruit set four times at 7-day intervals with four varying doses viz. 100, 250, 500, and 1,000 µg L<sup>−1</sup> and a control (0 µg L<sup>−1</sup>). Although the application of CHT had no significant effect on the size of the fruits (length and width), the total contents of phenolics, flavonoids, and ascorbic acid and the antioxidant activity of litchi fruit arils were significantly increased in CHT-treated fruit compared with the untreated control. The highest phenolic, flavonoid, and ascorbic acid contents were 334 µg gallic acid g<sup>−1</sup>, 881 μg quercetin g<sup>−1</sup>, and 178 µg g<sup>−1</sup>, respectively, in fruits treated with 500 µg L<sup>−1</sup> CHT. However, the highest antioxidant activity (622 μg butylated hydroxytoluene g<sup>−1</sup>) was recorded in 250 µg L<sup>−1</sup> CHT-treated fruits. Our findings revealed that the application of low doses of CHT in a litchi orchard might improve fruit quality by increasing the content of antioxidants and antioxidant activities.

Research on new evolution model of scale-free network

2009 ◽  
Vol 29 (5) ◽  
pp. 1230-1232
Author(s):  
Hao RAO ◽  
Chun YANG ◽  
Shao-hua TAO
Keyword(s):  
Evolution Model ◽  
Scale Free Network ◽  
Scale Free ◽  
Free Network

Brief Survey of Biological Network Alignment and a Variant with Incorporation of Functional Annotations

2018 ◽  
Vol 14 (1) ◽  
pp. 4-10
Author(s):  
Fang Jing ◽  
Shao-Wu Zhang ◽  
Shihua Zhang
Keyword(s):  
Gene Ontology ◽  
Topological Structure ◽  
Metabolic Networks ◽  
Biological Network ◽  
Genomic Sequence ◽  
Network Alignment ◽  
Future Directions ◽  
Functional Annotations ◽  
Protein Protein Interaction ◽  
Ppi Networks

Background:Biological network alignment has been widely studied in the context of protein-protein interaction (PPI) networks, metabolic networks and others in bioinformatics. The topological structure of networks and genomic sequence are generally used by existing methods for achieving this task.Objective and Method:Here we briefly survey the methods generally used for this task and introduce a variant with incorporation of functional annotations based on similarity in Gene Ontology (GO). Making full use of GO information is beneficial to provide insights into precise biological network alignment.Results and Conclusion:We analyze the effect of incorporation of GO information to network alignment. Finally, we make a brief summary and discuss future directions about this topic.

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.

scholarly journals Identification of candidate biomarkers of liver hydatid disease via microarray profiling, bioinformatics analysis, and machine learning

2021 ◽  
Vol 49 (3) ◽  
pp. 030006052199398
Author(s):  
Jinwu Peng ◽  
Zhili Duan ◽  
Yamin Guo ◽  
Xiaona Li ◽  
Xiaoqin Luo ◽  
...  
Keyword(s):  
Random Forest ◽  
Hydatid Disease ◽  
Characteristic Curve ◽  
Receiver Operator Characteristic Curve ◽  
Random Forest Model ◽  
Hepatic Hydatid Disease ◽  
Forest Model ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Microarray Profiling

Objectives Liver echinococcosis is a severe zoonotic disease caused by Echinococcus (tapeworm) infection, which is epidemic in the Qinghai region of China. Here, we aimed to explore biomarkers and establish a predictive model for the diagnosis of liver echinococcosis. Methods Microarray profiling followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed in liver tissue from patients with liver hydatid disease and from healthy controls from the Qinghai region of China. A protein–protein interaction (PPI) network and random forest model were established to identify potential biomarkers and predict the occurrence of liver echinococcosis, respectively. Results Microarray profiling identified 1152 differentially expressed genes (DEGs), including 936 upregulated genes and 216 downregulated genes. Several previously unreported biological processes and signaling pathways were identified. The FCGR2B and CTLA4 proteins were identified by the PPI networks and random forest model. The random forest model based on FCGR2B and CTLA4 reliably predicted the occurrence of liver hydatid disease, with an area under the receiver operator characteristic curve of 0.921. Conclusion Our findings give new insight into gene expression in patients with liver echinococcosis from the Qinghai region of China, improving our understanding of hepatic hydatid disease.

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