scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

AbstractL-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins-SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

<p>L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins- SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.</p>

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins-SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.

scholarly journals Protein-Protein Interaction Network Analysis and Identification of Key Players in nor-NOHA and NOHA Mediated Pathways for Treatment of Cancer through Arginase Inhibition: Insights from Systems Biology

2018 ◽  
Author(s):  
Ishtiaque Ahammad
Keyword(s):  
Systems Biology ◽  
Cancer Progression ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Cancer Cell Proliferation ◽  
Biological Processes ◽  
Ppi Network ◽  
Protein Protein Interaction ◽  
Key Players ◽  
Protein Protein Interaction Network

<p>L-arginine is involved in a number of biological processes in our bodies. Metabolism of L-arginine by the enzyme arginase has been found to be associated with cancer cell proliferation. Arginase inhibition has been proposed as a potential therapeutic means to inhibit this process. N-hydroxy-nor-L-Arg (nor-NOHA) and N (omega)-hydroxy-L-arginine (NOHA) has shown promise in inhibiting cancer progression through arginase inhibition. In this study, nor-NOHA and NOHA-associated genes and proteins were analyzed with several Bioinformatics and Systems Biology tools to identify the associated pathways and the key players involved so that a more comprehensive view of the molecular mechanisms including the regulatory mechanisms can be achieved and more potential targets for treatment of cancer can be discovered. Based on the analyses carried out, 3 significant modules have been identified from the PPI network. Five pathways/processes have been found to be significantly associated with nor-NOHA and NOHA associated genes. Out of the 1996 proteins in the PPI network, 4 have been identified as hub proteins- SOD, SOD1, AMD1, and NOS2. These 4 proteins have been implicated in cancer by other studies. Thus, this study provided further validation into the claim of these 4 proteins being potential targets for cancer treatment.</p>

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.

scholarly journals A Network Pharmacology Approach to Uncover the Mechanisms of Shen-Qi-Di-Huang Decoction against Diabetic Nephropathy

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Sha Di ◽  
Lin Han ◽  
Qing Wang ◽  
Xinkui Liu ◽  
Yingying Yang ◽  
...  
Keyword(s):  
Diabetic Nephropathy ◽  
Signaling Pathway ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Protein Protein Interaction ◽  
Regulation Of Blood Pressure ◽  
Protein Protein Interaction Network

Shen-Qi-Di-Huang decoction (SQDHD), a well-known herbal formula from China, has been widely used in the treatment of diabetic nephropathy (DN). However, the pharmacological mechanisms of SQDHD have not been entirely elucidated. At first, we conducted a comprehensive literature search to identify the active constituents of SQDHD, determined their corresponding targets, and obtained known DN targets from several databases. A protein-protein interaction network was then built to explore the complex relations between SQDHD targets and those known to treat DN. Following the topological feature screening of each node in the network, 400 major targets of SQDHD were obtained. The pathway enrichment analysis results acquired from DAVID showed that the significant bioprocesses and pathways include oxidative stress, response to glucose, regulation of blood pressure, regulation of cell proliferation, cytokine-mediated signaling pathway, and the apoptotic signaling pathway. More interestingly, five key targets of SQDHD, named AKT1, AR, CTNNB1, EGFR, and ESR1, were significant in the regulation of the above bioprocesses and pathways. This study partially verified and predicted the pharmacological and molecular mechanisms of SQDHD on DN from a holistic perspective. This has laid the foundation for further experimental research and has expanded the rational application of SQDHD in clinical practice.

An integrative C. elegans protein–protein interaction network with reliability assessment based on a probabilistic graphical model

2016 ◽  
Vol 12 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Xiao-Tai Huang ◽  
Yuan Zhu ◽  
Leanne Lai Hang Chan ◽  
Zhongying Zhao ◽  
Hong Yan
Keyword(s):  
Protein Interaction ◽  
Graphical Model ◽  
Reliability Assessment ◽  
Interaction Network ◽  
Probabilistic Graphical Model ◽  
Ppi Network ◽  
C Elegans ◽  
Protein Protein Interaction ◽  
Reliability Score ◽  
Protein Protein Interaction Network

We construct an integrative protein–protein interaction (PPI) network in Caenorhabditis elegans, which is weighted by our proposed reliability score based on a probability graphical model (RSPGM) method.

scholarly journals The molecular mechanisms associated with PIN7, a protein-protein interaction network of seven pleiotropic proteins

2019 ◽  
Author(s):  
Jarmila Nahálková
Keyword(s):  
Signaling Pathway ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
P53 Signaling ◽  
Age Related ◽  
P53 Signaling Pathway ◽  
Protein Protein Interaction Network

The protein-protein interaction network of seven pleiotropic proteins (PIN7) contains proteins with multiple functions in the aging and age-related diseases (TPPII, CDK2, MYBBP1A, p53, SIRT6, SIRT7, and BSG). At the present work, the pathway enrichment, the gene function prediction and the protein node prioritization analysis were applied for the examination of main molecular mechanisms driving PIN7 and the extended network. Seven proteins of PIN7 were used as an input for the analysis by GeneMania, a Cytoscape application, which constructs the protein interaction network. The software also extends it using the interactions retrieved from databases of experimental and predicted protein-protein and genetic interactions. The analysis identified the p53 signaling pathway as the most dominant mediator of PIN7. The extended PIN7 was also analyzed by Cytohubba application, which showed that the top-ranked protein nodes belong to the group of histone acetyltransferases and histone deacetylases. These enzymes are involved in the reverse epigenetic regulation mechanisms linked to the regulation of PTK2, NFκB, and p53 signaling interaction subnetworks of the extended PIN7. The analysis emphasized the role of PTK2 signaling, which functions upstream of the p53 signaling pathway and its interaction network includes all members of the sirtuin family. Further, the analysis suggested the involvement of molecular mechanisms related to metastatic cancer (prostate cancer, small cell lung cancer), hemostasis, the regulation of the thyroid hormones and the cell cycle G1/S checkpoint. The additional data-mining analysis showed that the small protein interaction network MYBBP1A-p53-TPPII-SIRT6-CD147 controls Warburg effect and MYBBP1A-p53-TPPII-SIRT7-BSG influences mTOR signaling and autophagy. Further investigations of the detail mechanisms of these interaction networks would be beneficial for the development of novel treatments for aging and age-related diseases.

scholarly journals Integrating protein networks and machine learning for disease stratification in the Hereditary Spastic Paraplegias

2021 ◽  
Author(s):  
Nikoleta Vavouraki ◽  
James E. Tomkins ◽  
Eleanna Kara ◽  
Henry Houlden ◽  
John Hardy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Topological Analysis ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Hereditary Spastic Paraplegias ◽  
Core Proteins ◽  
Protein Protein Interaction Network

AbstractThe Hereditary Spastic Paraplegias are a group of neurodegenerative diseases characterized by spasticity and weakness in the lower body. Despite the identification of causative mutations in over 70 genes, the molecular aetiology remains unclear. Due to the combination of genetic diversity and variable clinical presentation, the Hereditary Spastic Paraplegias are a strong candidate for protein-protein interaction network analysis as a tool to understand disease mechanism(s) and to aid functional stratification of phenotypes. In this study, experimentally validated human protein-protein interactions were used to create a protein-protein interaction network based on the causative Hereditary Spastic Paraplegia genes. Network evaluation as a combination of both topological analysis and functional annotation led to the identification of core proteins in putative shared biological processes such as intracellular transport and vesicle trafficking. The application of machine learning techniques suggested a functional dichotomy linked with distinct sets of clinical presentations, suggesting there is scope to further classify conditions currently described under the same umbrella term of Hereditary Spastic Paraplegias based on specific molecular mechanisms of disease.

scholarly journals Partners in crime: POPX2 phosphatase and its interacting proteins in cancer

2020 ◽  
Vol 11 (10) ◽  
Author(s):  
Pu Rum Kim ◽  
Songjing Zhang ◽  
Muhammad Bakhait Rahmat ◽  
Cheng-Gee Koh
Keyword(s):  
Cancer Progression ◽  
Interaction Network ◽  
Interacting Proteins ◽  
Cellular Functions ◽  
Cancer Cell Motility ◽  
Protein Protein Interaction ◽  
Binding Partners ◽  
Intracellular Signal ◽  
The Impact ◽  
Protein Protein Interaction Network

Abstract Protein phosphorylation and dephosphorylation govern intracellular signal transduction and cellular functions. Kinases and phosphatases are involved in the regulation and development of many diseases such as Alzheimer’s, diabetes, and cancer. While the functions and roles of many kinases, as well as their substrates, are well understood, phosphatases are comparatively less well studied. Recent studies have shown that rather than acting on fewer and more distinct substrates like the kinases, phosphatases can recognize specific phosphorylation sites on many different proteins, making the study of phosphatases and their substrates challenging. One approach to understand the biological functions of phosphatases is through understanding their protein–protein interaction network. POPX2 (Partner of PIX 2; also known as PPM1F or CaMKP) is a serine/threonine phosphatase that belongs to the PP2C family. It has been implicated in cancer cell motility and invasiveness. This review aims to summarize the different binding partners of POPX2 phosphatase and explore the various functions of POPX2 through its interactome in the cell. In particular, we focus on the impact of POPX2 on cancer progression. Acting via its different substrates and interacting proteins, POPX2’s involvement in metastasis is multifaceted and varied according to the stages of metastasis.

Construction and analysis of mRNA and lncRNA regulatory networks reveal the key genes associated with prostate cancer related fatigue during localized radiation therapy

2020 ◽  
Vol 15 ◽  
Author(s):  
Yechen Wu ◽  
Yaping Gui ◽  
Denglong Wu ◽  
Qiang Wu
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Biological Processes ◽  
Protein Protein Interaction ◽  
Cancer Related Fatigue ◽  
Non Coding Rnas ◽  
New Biomarkers ◽  
Protein Protein Interaction Network

Background: Localized radiation therapy is the first line option for the treatment of non-metastatic prostate cancer (PCa). Previous studies revealed long non-coding RNAs (lncRNAs) had crucial roles in diseases progression. However, the mechanisms of lncRNAs underlying prostate cancer related fatigue remained largely unclear. Objective: The present study aimed to uncover the hub genes related to PCa related fatigue during localized radiation therapy by constructing mRNA and lncRNA regulatory networks. Methods: We analyzed GSE30174, which included 10 control samples and 40 PCa related fatigue samples, to identify differently expressed lncRNAs and mRNAs in PCa related fatigue. Protein-protein interaction network was constructed to uncover the interactions among mRNAs. Co-expression network analysis was appled to identify the key lncRNAs and reveal the functions of these lncRNAs in PCa related fatigue. Results and Discussion: This research found 1271 dysregulated mRNAs and 205 dysregulated lncRNAs in PCa related fatigue using GSE30174. Bioinformatics analysis showed PCa related fatigue related mRNAs and lncRNAs were associated with inflammatory response and immune response related biological processes. Furthermore, we constructed PPI network and lncRNA co-expression network related to fatigue in PCa. Of note, we observed the dysregulated lncRNAs and mRNAs, such as SEC61A2, ADCY6, LPAR5, COL7A1, ALB, COL1A1, SNHG1, LINC01215, LINC00926, GNG4, LMO7, and COL4A6, in PCa related fatigue could predict the outcome of PCa patients. Conclusions: This research could provide novel mechanisms underlying fatigue and identify new biomarkers for the prognosis of PCa.

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