scholarly journals Network-based analysis on the pharmacological mechanisms underlying the anti-diabetic property of Coix lachryma-jobi

2021 ◽  
Author(s):  
Arvin A Tanquiqui ◽  
Angelu Mae A Ferrer ◽  
Janella Rayne A David ◽  
Custer C Deocaris ◽  
Malona Velasco Alinsug
Keyword(s):  
Diabetes Mellitus ◽  
Bioactive Compounds ◽  
Target Genes ◽  
Interaction Network ◽  
Bioactive Compound ◽  
Regulation Of Transcription ◽  
Protein Protein Interaction ◽  
Treatment Of Diabetes ◽  
Go Terms ◽  
Protein Protein Interaction Network

Diabetes mellitus (DM) is the most common endocrine disorder and among the top 10 leading diseases causing death worldwide. Coicis semen [CS] (Coix lachryma-jobi L.), also known as adlay have been reported to display anti-diabetic properties. Unfortunately, studies on the pharmacological mechanisms involving adlay for the treatment of diabetes are nil. Thus, this study was conducted to evaluate the interactions and mechanisms of the bioactive compound targets of adlay in the treatment of diabetes using network analysis. Adlay bioactive compounds and potential target genes were obtained from SymMap. Diabetes related target genes were collected from CTD. Protein-Protein Interaction Network was analyzed using the STRING database. GO and KEGG pathway enrichment analyses were performed using DAVID to further explore the mechanisms of adlay in treating diabetes. PPI and compound-target-pathway were visualized using Cytoscape. A total of 25 bioactive compounds, 201 corresponding targets, and 35839 diabetes mellitus associated targets were obtained while 200 were considered potential therapeutic targets. The 9 bioactive compounds studied were berberine, oleic acid, beta-sitosterol, sitosterol, linoleic acid, berberrubine, jatrorrhizine, thalifendine, and stigmasterol. The identified 5 core targets were ESR1, JUN, MAPK14, and RXRA. Adlai targets enriched in GO terms were mostly involved with positive regulation of transcription, response to drugs, and negative regulation of apoptosis. This study provides novel research insights into the clinical properties of adlay in diabetes melitus treatment.

scholarly journals Pharmacological mechanisms on the anti-breast cancer property of Coix lacryma-jobi: A network-based analysis

2021 ◽  
Author(s):  
Angelu Mae A Ferrer ◽  
Janella Rayne A David ◽  
Arvin A Taquiqui ◽  
Arci A Bautista ◽  
Custer C Deocaris ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Rna Polymerase Ii ◽  
Bioactive Compounds ◽  
Target Genes ◽  
Interaction Network ◽  
Regulation Of Transcription ◽  
Protein Protein Interaction ◽  
Anti Cancer ◽  
Pathway Analyses ◽  
Go Terms

Breast cancer is considered as one of the three most common cancers around the world and the second leading cause of cancer deaths among women. Coix lachrymal jobi, commonly known as Jobs tears or adlay has been reported to possess anti-cancer properties. Despite evidences provided by clinical data, the usage of Coix lacryma-jobi in treating cancer, particularly breast cancer, has been scarce. Thus, this study was conducted to determine the pharmacological mechanisms underlying its anti-breast cancer property using various network pathway analyses. Bioactive compounds from Coix lacryma-jobi and its potential target genes were obtained from SymMap. Breast cancer-related target genes were collected from CTD. Protein-protein interaction network was analyzed using the STRING database. GO and KEGG pathway enrichment analyses were performed using DAVID to further explore the mechanisms of Coix lacryma-jobi in treating breast cancer. PPI and compound-target-pathway were visualized using Cytoscape. A total of 26 bioactive compounds, 201 corresponding targets, 36625 breast cancer-associated targets were obtained, and 200 common targets were considered potential therapeutic targets. The 9 bioactive compounds identified were berberine, oleic acid, beta-sitosterol, sitosterol, linoleic acid, berberrubine, jatrorrhizine, thalifendine, and stigmasterol. The identified 5 core targets were ESR1, JUN, MAPK14, and RXRA. Coix lacryma-jobi targets enriched in GO terms were mostly involved in regulation of transcription from RNA polymerase II promoter, drug response, steroid hormone receptor activity, and protein binding. This study elucidates on the pharmacological underpinnings on the potency of adlay against breast cancer. Its subsequent drug development will be worth a step forward for a breast cancer-free society.

scholarly journals Exploring Heat-Response Mechanisms of MicroRNAs Based on Microarray Data of Rice Post-meiosis Panicle

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yan Peng ◽  
Xianwen Zhang ◽  
Yuewu Liu ◽  
Xinbo Chen
Keyword(s):  
Heat Stress ◽  
Target Genes ◽  
Expression Profiles ◽  
Time Lag ◽  
Interaction Network ◽  
Biological Processes ◽  
Functional Identification ◽  
Protein Protein Interaction ◽  
Heat Response ◽  
Protein Protein Interaction Network

To explore heat response mechanisms of mircoRNAs (miRNAs) in rice post-meiosis panicle, microarray analysis was performed on RNA isolated from rice post-meiosis panicles which were treated at 40°C for 0 min, 10 min, 20 min, 60 min, and 2 h. By integrating paired differentially expressed (DE) miRNAs and mRNA expression profiles, we found that the expression levels of 29 DE-miRNA families were negatively correlated to their 178 DE-target genes. Further analysis showed that the majority of miRNAs in 29 DE-miRNA families resisted the heat stress by downregulating their target genes and a time lag existed between expression of miRNAs and their target genes. Then, GO-Slim classification and functional identification of these 178 target genes showed that (1) miRNAs were mainly involved in a series of basic biological processes even under heat conditions; (2) some miRNAs might play important roles in the heat resistance (such as osa-miR164, osa-miR166, osa-miR169, osa-miR319, osa-miR390, osa-miR395, and osa-miR399); (3) osa-miR172 might play important roles in protecting the rice panicle under the heat stress, but osa-miR437, osa-miR418, osa-miR164, miR156, and miR529 might negatively affect rice fertility and panicle flower; and (4) osa-miR414 might inhibit the flowering gene expression by downregulation of LOC_Os 05g51830 to delay the heading of rice. Finally, a heat-induced miRNA-PPI (protein-protein interaction) network was constructed, and three miRNA coregulatory modules were discovered.

Exploring Shared Pathogenesis of Alzheimer’s Disease and Type 2 Diabetes Mellitus via Co-expression Networks Analysis

2020 ◽  
Vol 17 (6) ◽  
pp. 566-575 ◽  
Author(s):  
Yukun Zhu ◽  
Xuelu Ding ◽  
Zhaoyuan She ◽  
Xue Bai ◽  
Ziyang Nie ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Protein Interaction Network ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Background: Alzheimer’s Disease (AD) and Type 2 Diabetes Mellitus (T2DM) have an increased incidence in modern society. Although increasing evidence has supported the close linkage between these two disorders, the inter-relational mechanisms remain to be fully elucidated. Objective: The primary purpose of this study is to explore the shared pathophysiological mechanisms of AD and T2DM. Methods: We downloaded the microarray data of AD and T2DM from the Gene Expression Omnibus (GEO) database and constructed co-expression networks by Weighted Gene Co-Expression Network Analysis (WGCNA) to identify gene network modules related to AD and T2DM. Then, Gene Ontology (GO) and pathway enrichment analysis were performed on the common genes existing in the AD and T2DM related modules by clusterProfiler and DOSE package. Finally, we utilized the STRING database to construct the protein-protein interaction network and found out the hub genes in the network. Results: Our findings indicated that seven and four modules were the most significant with AD and T2DM, respectively. Functional enrichment analysis showed that AD and T2DM common genes were mainly enriched in signaling pathways such as circadian entrainment, phagosome, glutathione metabolism and synaptic vesicle cycle. Protein-protein interaction network construction identified 10 hub genes (CALM1, LRRK2, RBX1, SLC6A1, TXN, SNRPF, GJA1, VWF, LPL, AGT) in AD and T2DM shared genes. Conclusions: Our work identified common pathogenesis of AD and T2DM. These shared pathways might provide a novel idea for further mechanistic studies and hub genes that may serve as novel therapeutic targets for diagnosis and treatment of AD and T2DM.

scholarly journals Network pharmacology approach to reveals therapeutic mech-anism of traditional plants formulation used by Malaysia in-digenous ethnics in coronaviruses infection

2022 ◽  
Author(s):  
Fui Fui Lem ◽  
Dexter Jiunn Herng Lee ◽  
Fong Tyng Chee ◽  
Su Na Chin ◽  
Kai Min Lin ◽  
...  
Keyword(s):  
Bioactive Compounds ◽  
Target Genes ◽  
Interaction Network ◽  
Bioactive Compound ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Pathway Network ◽  
Pharmacological Mechanism ◽  
Target Network ◽  
Compound Target

Network pharmacology analysis can act as a strategy to identify the pharmacological effect of plant-based bioactive compounds against coronavirus diseases. This study aimed to investigate the potential pharmacological mechanism of a local ethnomedicine (Costus speciosus, Hibiscus rosa-sinensis and Phyllanthus niruri) of Northern Borneo against coronaviruses known as CHP. Compounds in CHP were extracted from databases and screened for their oral bioavailability and drug-likeness before a compound-target network was built. Furthermore, the protein-protein interaction network and pathway enrichment were constructed and analyzed. A compound-target network consisting of 48 putative bioactive compounds targeting 587 candidate genes was identified. A total of 186 coronavirus-related genes were extracted and TP53, STAT3, HSP90AA1, STAT1, and EP300 were predicted to be the key targets. Notably, mapping of these target genes into the target-pathway network illustrated that functional enrichment was on viral infection and regulation of inflammation pathways. Urinatetralin is predicted, for the first time, as a bioactive compound that solely targets STAT3. The results from this study indicate that compounds present in CHP employ STAT3 and its connected pathways as the mechanism of action against coronaviruses. In conclusion, urinatetralin should be further investigated for its potential application against coronavirus infections.

Protein function prediction from protein–protein interaction network using gene ontology based neighborhood analysis and physico-chemical features

2018 ◽  
Vol 16 (06) ◽  
pp. 1850025 ◽  
Author(s):  
Sovan Saha ◽  
Abhimanyu Prasad ◽  
Piyali Chatterjee ◽  
Subhadip Basu ◽  
Mita Nasipuri
Keyword(s):  
Functional Groups ◽  
Protein Function ◽  
Functional Group ◽  
Protein Function Prediction ◽  
Interaction Network ◽  
Function Prediction ◽  
Protein Protein Interaction ◽  
Physico Chemical ◽  
Go Terms ◽  
Protein Protein Interaction Network

Protein Function Prediction from Protein–Protein Interaction Network (PPIN) and physico-chemical features using the Gene Ontology (GO) classification are indeed very useful for assigning biological or biochemical functions to a protein. They also lead to the identification of those significant proteins which are responsible for the generation of various diseases whose drugs are still yet to be discovered. So, the prediction of GO functional terms from PPIN and sequence is an important field of study. In this work, we have proposed a methodology, Multi Label Protein Function Prediction (ML_PFP) which is based on Neighborhood analysis empowered with physico-chemical features of constituent amino acids to predict the functional group of unannotated protein. A protein does not perform functions in isolation rather it performs functions in a group by interacting with others. So a protein is involved in many functions or, in other words, may be associated with multiple functional groups or labels or GO terms. Though functional group of other known interacting partner protein and its physico-chemical features provide useful information, assignment of multiple labels to unannotated protein is a very challenging task. Here, we have taken Homo sapiens or Human PPIN as well as Saccharomyces cerevisiae or yeast PPIN along with their GO terms to predict functional groups or GO terms of unannotated proteins. This work has become very challenging as both Human and Yeast protein dataset are voluminous and complex in nature and multi-label functional groups assignment has also added a new dimension to this challenge. Our algorithm has been observed to achieve a better performance in Cellular Function, Molecular Function and Biological Process of both yeast and human network when compared with the other existing state-of-the-art methodologies which will be discussed in detail in the results section.

Bioinformatics Analysis Reveals Functions of MicroRNAs in Rice Under the Drought Stress

2021 ◽  
Vol 15 (8) ◽  
pp. 927-936 ◽  
Author(s):  
Yan Peng ◽  
Yuewu Liu ◽  
Xinbo Chen
Keyword(s):  
Drought Stress ◽  
Target Genes ◽  
Hot Spot ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Protein Protein Interaction ◽  
Promising Tool ◽  
Differentially Expressed Mirnas ◽  
Aba Biosynthesis

Background: Drought is one of the most damaging and widespread abiotic stresses that can severely limit the rice production. MicroRNAs (miRNAs) act as a promising tool for improving the drought tolerance of rice and have become a hot spot in recent years. Objective: In order to further extend the understanding of miRNAs, the functions of miRNAs in rice under drought stress are analyzed by bioinformatics. Method: In this study, we integrated miRNAs and genes transcriptome data of rice under the drought stress. Some bioinformatics methods were used to reveal the functions of miRNAs in rice under drought stress. These methods included target genes identification, differentially expressed miRNAs screening, enrichment analysis of DEGs, network constructions for miRNA-target and target-target proteins interaction. Results: (1) A total of 229 miRNAs with differential expression in rice under the drought stress, corresponding to 73 rice miRNAs families, were identified. (2) 1035 differentially expressed genes (DEGs) were identified, which included 357 up-regulated genes, 542 down-regulated genes and 136 up/down-regulated genes. (3) The network of regulatory relationships between 73 rice miRNAs families and 1035 DEGs was constructed. (4) 25 UP_KEYWORDS terms of DEGs, 125 GO terms and 7 pathways were obtained. (5) The protein-protein interaction network of 1035 DEGs was constructed. Conclusion: (1) MiRNA-regulated targets in rice might mainly involve in a series of basic biological processes and pathways under drought conditions. (2) MiRNAs in rice might play critical roles in Lignin degradation and ABA biosynthesis. (3) MiRNAs in rice might play an important role in drought signal perceiving and transduction.

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.

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