scholarly journals Deciphering GRINA/Lifeguard1: Nuclear Location, Ca2+ Homeostasis and Vesicle Transport

2019 ◽  
Vol 20 (16) ◽  
pp. 4005
Author(s):  
Jiménez-González ◽  
Ogalla-García ◽  
García-Quintanilla ◽  
García-Quintanilla
Keyword(s):  
Ribosome Biogenesis ◽  
Cell Cycle Progression ◽  
Interaction Network ◽  
Sterol Synthesis ◽  
Protein Protein Interaction ◽  
Rich Domain ◽  
Nuclear Location ◽  
Binding Sequence ◽  
First Time ◽  
Nuclear Localization Sequences

The Glutamate Receptor Ionotropic NMDA-Associated Protein 1 (GRINA) belongs to the Lifeguard family and is involved in calcium homeostasis, which governs key processes, such as cell survival or the release of neurotransmitters. GRINA is mainly associated with membranes of the endoplasmic reticulum, Golgi, endosome, and the cell surface, but its presence in the nucleus has not been explained yet. Here we dissect, with the help of different software tools, the potential roles of GRINA in the cell and how they may be altered in diseases, such as schizophrenia or celiac disease. We describe for the first time that the cytoplasmic N-terminal half of GRINA (which spans a Proline-rich domain) contains a potential DNA-binding sequence, in addition to cleavage target sites and probable PY-nuclear localization sequences, that may enable it to be released from the rest of the protein and enter the nucleus under suitable conditions, where it could participate in the transcription, alternative splicing, and mRNA export of a subset of genes likely involved in lipid and sterol synthesis, ribosome biogenesis, or cell cycle progression. To support these findings, we include additional evidence based on an exhaustive review of the literature and our preliminary data of the protein–protein interaction network of GRINA.

scholarly journals Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dilara Uzuner ◽  
Yunus Akkoç ◽  
Nesibe Peker ◽  
Pınar Pir ◽  
Devrim Gözüaçık ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Regulatory Networks ◽  
Metastatic Cancer ◽  
Interaction Network ◽  
Current Treatment ◽  
Cellular Mechanisms ◽  
Protein Protein Interaction ◽  
Cancer Dormancy ◽  
Protein Protein Interaction Networks ◽  
First Time

AbstractPrimary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.

scholarly journals Genome-wide identification of the GATA transcription factor family and their expression patterns under temperature and salt stress in Aspergillus oryzae

AMB Express ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunmiao Jiang ◽  
Gongbo Lv ◽  
Jinxin Ge ◽  
Bin He ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Salt Stress ◽  
Expression Patterns ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Gata Transcription Factor ◽  
Starting Point ◽  
Evolutionary Features ◽  
First Time

AbstractGATA transcription factors (TFs) are involved in the regulation of growth processes and various environmental stresses. Although GATA TFs involved in abiotic stress in plants and some fungi have been analyzed, information regarding GATA TFs in Aspergillusoryzae is extremely poor. In this study, we identified and functionally characterized seven GATA proteins from A.oryzae 3.042 genome, including a novel AoSnf5 GATA TF with 20-residue between the Cys-X2-Cys motifs which was found in Aspergillus GATA TFs for the first time. Phylogenetic analysis indicated that these seven A. oryzae GATA TFs could be classified into six subgroups. Analysis of conserved motifs demonstrated that Aspergillus GATA TFs with similar motif compositions clustered in one subgroup, suggesting that they might possess similar genetic functions, further confirming the accuracy of the phylogenetic relationship. Furthermore, the expression patterns of seven A.oryzae GATA TFs under temperature and salt stresses indicated that A. oryzae GATA TFs were mainly responsive to high temperature and high salt stress. The protein–protein interaction network of A.oryzae GATA TFs revealed certain potentially interacting proteins. The comprehensive analysis of A. oryzae GATA TFs will be beneficial for understanding their biological function and evolutionary features and provide an important starting point to further understand the role of GATA TFs in the regulation of distinct environmental conditions in A.oryzae.

scholarly journals Protein–Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome

2019 ◽  
Vol 20 (12) ◽  
pp. 2959 ◽  
Author(s):  
Balqis Ramly ◽  
Nor Afiqah-Aleng ◽  
Zeti-Azura Mohamed-Hussein
Keyword(s):  
Network Analysis ◽  
Protein Interaction ◽  
Ribosome Biogenesis ◽  
Antigen Processing ◽  
Polycystic Ovarian Syndrome ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Ovarian Syndrome

Based on clinical observations, women with polycystic ovarian syndrome (PCOS) are prone to developing several other diseases, such as metabolic and cardiovascular diseases. However, the molecular association between PCOS and these diseases remains poorly understood. Recent studies showed that the information from protein–protein interaction (PPI) network analysis are useful in understanding the disease association in detail. This study utilized this approach to deepen the knowledge on the association between PCOS and other diseases. A PPI network for PCOS was constructed using PCOS-related proteins (PCOSrp) obtained from PCOSBase. MCODE was used to identify highly connected regions in the PCOS network, known as subnetworks. These subnetworks represent protein families, where their molecular information is used to explain the association between PCOS and other diseases. Fisher’s exact test and comorbidity data were used to identify PCOS–disease subnetworks. Pathway enrichment analysis was performed on the PCOS–disease subnetworks to identify significant pathways that are highly involved in the PCOS–disease associations. Migraine, schizophrenia, depressive disorder, obesity, and hypertension, along with twelve other diseases, were identified to be highly associated with PCOS. The identification of significant pathways, such as ribosome biogenesis, antigen processing and presentation, and mitophagy, suggest their involvement in the association between PCOS and migraine, schizophrenia, and hypertension.

scholarly journals Machine Learning and Bioinformatics Framework Integration to Potential Familial DCM-Related Markers Discovery

Genes ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1946
Author(s):  
Concetta Schiano ◽  
Monica Franzese ◽  
Filippo Geraci ◽  
Mario Zanfardino ◽  
Ciro Maiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
Expression Profiles ◽  
Nyha Class ◽  
Molecular Targets ◽  
Interaction Network ◽  
Left Ventricular ◽  
Class Iii ◽  
Protein Protein Interaction ◽  
Network Methods ◽  
First Time

Objectives: Dilated cardiomyopathy (DCM) is characterized by a specific transcriptome. Since the DCM molecular network is largely unknown, the aim was to identify specific disease-related molecular targets combining an original machine learning (ML) approach with protein-protein interaction network. Methods: The transcriptomic profiles of human myocardial tissues were investigated integrating an original computational approach, based on the Custom Decision Tree algorithm, in a differential expression bioinformatic framework. Validation was performed by quantitative real-time PCR. Results: Our preliminary study, using samples from transplanted tissues, allowed the discovery of specific DCM-related genes, including MYH6, NPPA, MT-RNR1 and NEAT1, already known to be involved in cardiomyopathies Interestingly, a combination of these expression profiles with clinical characteristics showed a significant association between NEAT1 and left ventricular end-diastolic diameter (LVEDD) (Rho = 0.73, p = 0.05), according to severity classification (NYHA-class III). Conclusions: The use of the ML approach was useful to discover preliminary specific genes that could lead to a rapid selection of molecular targets correlated with DCM clinical parameters. For the first time, NEAT1 under-expression was significantly associated with LVEDD in the human heart.

scholarly journals Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yongcheng Dong ◽  
Qifan Kuang ◽  
Xu Dai ◽  
Rong Li ◽  
Yiming Wu ◽  
...  
Keyword(s):  
Human Papillomavirus ◽  
Protein Interaction ◽  
Cell Cycle Progression ◽  
Interaction Network ◽  
Support Vector ◽  
Human Papillomavirus 16 ◽  
Protein Protein Interaction ◽  
Topological Features ◽  
Broad Array ◽  
Human Proteins

The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM) model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.

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.

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
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