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Gollapalli P, Alagundagi D, Ghate SD, Shetty VV, Shetty P, Patil P. Identification of key gene signatures and their characterization by expression correlation with drug sensitivity in smoking-associated oral squamous cell carcinoma. J Cancer Res Ther 2023; 19:1743-1752. [PMID: 38376273 DOI: 10.4103/jcrt.jcrt_1120_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 08/12/2021] [Indexed: 02/21/2024]
Abstract
AIMS Oral squamous cell carcinoma (OSCC), a most frequent type of head-and-neck cancer, is becoming more common and posing a substantial health risk. Using a network biology strategy, this study intended to find and investigate critical genes associated with OSCC. MATERIALS AND METHODS The extended protein-protein interaction networks for differentially expressed genes related to smoking and nonsmoking conditions of OSCC were constructed and visualized using Cytoscape software. The hub genes/proteins were determined based on degree and betweenness centrality measures and then evaluated and validated for expression using the Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and their relationship to the sensitivity of small molecules was discovered utilizing the Gene Set Cancer Analysis (GSCA) web server. RESULTS A total of 596 differentially expressed genes were screened, and four genes, interleukin (IL)-6, JUN, tumor necrosis factor (TNF), and vascular endothelial growth factor A (VEGFA), were identified as hub proteins, and their expression and overall survival in head-and-neck cancers were further investigated using GEPIA2. TNF and VEGFA gene expressions were considerably greater in cancers when compared to normal samples, while JUN and IL-6 gene expressions were not statistically significant. Further, these hub proteins are found to have a substantial favorable correlation with overall survival of head-and-neck cancer patients. Finally, GSCA was used to predict gene-specific potential drugs that act on these molecules by combining mRNA expression and drug sensitivity data from the Genomics of Drug Sensitivity in Cancer and the Cancer Therapeutics Response Portal. CONCLUSIONS The hub genes/proteins identified in this study could help researchers better understand the molecular processes involved in the progression and metastasis of oral cancer in smokers.
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Affiliation(s)
- Pavan Gollapalli
- Central Research Laboratory, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
| | - Dhananjay Alagundagi
- Central Research Laboratory, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
| | - Sudeep D Ghate
- Central Research Laboratory, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
| | - Vijith V Shetty
- Department of Oncology, Justice K S Hegde Charitable Hospital, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
| | - Praveenkumar Shetty
- Department of Biochemistry, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
| | - Prakash Patil
- Central Research Laboratory, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Deralakatte, Mangalore, Karnataka, India
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Gollapalli P, Tamizh Selvan G, Santoshkumar HS, Ballamoole KK. Functional insights of antibiotic resistance mechanism in Helicobacter pylori: Driven by gene interaction network and centrality-based nodes essentiality analysis. Microb Pathog 2022; 171:105737. [PMID: 36038087 DOI: 10.1016/j.micpath.2022.105737] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/05/2022] [Accepted: 08/19/2022] [Indexed: 11/29/2022]
Abstract
Increased antibiotic resistance in Helicobacter pylori (H. pylori), a major human pathogen, constitutes a substantial threat to human health. Understanding the pathophysiology and development of antibiotic resistance can aid our battle with the infections caused by H. pylori. The aim of this study is to discover the high-impact key regulatory mechanisms and genes involved in antimicrobial drug resistance (AMR). In this study, we constructed a functional gene interaction network by integrating multiple sources of data related to antibiotic resistant genes (number-77) from H. pylori. The gene interaction network was assortative, with a hierarchical, scale-free topology enriched in a variety of gene ontology (GO) categories and KEGG pathways. Using an iterative clustering methodology, we identified a number of communities in the AMR gene network that comprised nine genes (sodB, groEL, gyrA, recA, polA, tuf, infB, rpsJ, and gyrB) that were present at the deepest level and hence were key regulators of AMR. Further, an antibiotic-resistant gene network-based centrality analysis revealed superoxide dismutase (sodB) as a bottleneck node in the network. Our findings suggested that sodB is critically enriched in the cellular response to oxidative stress, removal of superoxide radicals, cellular oxidant detoxification processes, cellular component biogenesis, response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process and reactive oxygen species metabolic process. We demonstrated how the sodB, which are involved in the response to reactive oxygen species, urea metabolic process, nitrogen cycle metabolic process, reactive oxygen species metabolic process, regulated by Fur gene/proteins, claim a major authority over regulation and signal propagation in the AMR.
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Affiliation(s)
- Pavan Gollapalli
- Center for Bioinformatics and Biostatistics, Nitte (Deemed to Be University), Mangalore, 575018, Karnataka, India; Central Research Laboratory, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India.
| | - G Tamizh Selvan
- Central Research Laboratory, K. S. Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| | - H S Santoshkumar
- Department of Biotechnology and Bioinformatics, Jnana Sahyadri Campus, Kuvempu University, Shankaraghatta, 577451, Shivamogga, Karnataka, India
| | - Krishna Kumar Ballamoole
- Nitte (Deemed to be University), Division of Infectious Diseases, Nitte University Centre for Science Education and Research (NUCSER), Paneer Campus, Deralakatte, Mangaluru, Karnataka, 575018, India
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Kim YJ, Kim K, Lee H, Jeon J, Lee J, Yoon J. The Protein-Protein Interaction Network of Hereditary Parkinsonism Genes Is a Hierarchical Scale-Free Network. Yonsei Med J 2022; 63:724-734. [PMID: 35914754 PMCID: PMC9344267 DOI: 10.3349/ymj.2022.63.8.724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 04/19/2022] [Accepted: 05/02/2022] [Indexed: 11/27/2022] Open
Abstract
PURPOSE Hereditary parkinsonism genes consist of causative genes of familial Parkinson's disease (PD) with a locus symbol prefix (PARK genes) and hereditary atypical parkinsonian disorders that present atypical features and limited responsiveness to levodopa (non-PARK genes). Although studies have shown that hereditary parkinsonism genes are related to idiopathic PD at the phenotypic, gene expression, and genomic levels, no study has systematically investigated connectivity among the proteins encoded by these genes at the protein-protein interaction (PPI) level. MATERIALS AND METHODS Topological measurements and physical interaction enrichment were performed to assess PPI networks constructed using some or all the proteins encoded by hereditary parkinsonism genes (n=96), which were curated using the Online Mendelian Inheritance in Man database and literature. RESULTS Non-PARK and PARK genes were involved in common functional modules related to autophagy, mitochondrial or lysosomal organization, catecholamine metabolic process, chemical synapse transmission, response to oxidative stress, neuronal apoptosis, regulation of cellular protein catabolic process, and vesicle-mediated transport in synapse. The hereditary parkinsonism proteins formed a single large network comprising 51 nodes, 83 edges, and three PPI pairs. The probability of degree distribution followed a power-law scaling behavior, with a degree exponent of 1.24 and a correlation coefficient of 0.92. LRRK2 was identified as a hub gene with the highest degree of betweenness centrality; its physical interaction enrichment score was 1.28, which was highly significant. CONCLUSION Both PARK and non-PARK genes show high connectivity at the PPI and biological functional levels.
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Affiliation(s)
- Yun Joong Kim
- Department of Neurology, Yonsei University College of Medicine, Seoul, Korea
- Department of Neurology, Yongin Severance Hospital, Yonsei University Health System, Yongin, Korea.
| | - Kiyong Kim
- Department of Electronic Engineering, Kyonggi University, Suwon, Korea.
| | - Heonwoo Lee
- Department of Computer Engineering, Hallym University, Chuncheon, Korea
| | - Junbeom Jeon
- Department of Computer Engineering, Hallym University, Chuncheon, Korea
| | - Jinwoo Lee
- Department of Computer Engineering, Hallym University, Chuncheon, Korea
| | - Jeehee Yoon
- Department of Computer Engineering, Hallym University, Chuncheon, Korea
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Nowakowska AW, Kotulska M. Topological analysis as a tool for detection of abnormalities in protein-protein interaction data. Bioinformatics 2022; 38:3968-3975. [PMID: 35771625 PMCID: PMC9746892 DOI: 10.1093/bioinformatics/btac440] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 03/11/2022] [Accepted: 06/28/2022] [Indexed: 12/24/2022] Open
Abstract
MOTIVATION Protein-protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein-protein interaction data analysis. RESULTS We performed topological analysis of three protein-protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson's disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein-protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein-protein interactions and their multi-omics consequences. AVAILABILITY AND IMPLEMENTATION A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb. CONTACT alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Machine learning aided construction of the quorum sensing communication network for human gut microbiota. Nat Commun 2022; 13:3079. [PMID: 35654892 PMCID: PMC9163137 DOI: 10.1038/s41467-022-30741-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 05/17/2022] [Indexed: 01/02/2023] Open
Abstract
Quorum sensing (QS) is a cell-cell communication mechanism that connects members in various microbial systems. Conventionally, a small number of QS entries are collected for specific microbes, which is far from being able to fully depict communication-based complex microbial interactions in human gut microbiota. In this study, we propose a systematic workflow including three modules and the use of machine learning-based classifiers to collect, expand, and mine the QS-related entries. Furthermore, we develop the Quorum Sensing of Human Gut Microbes (QSHGM) database (http://www.qshgm.lbci.net/) including 28,567 redundancy removal entries, to bridge the gap between QS repositories and human gut microbiota. With the help of QSHGM, various communication-based microbial interactions can be searched and a QS communication network (QSCN) is further constructed and analysed for 818 human gut microbes. This work contributes to the establishment of the QSCN which may form one of the key knowledge maps of the human gut microbiota, supporting future applications such as new manipulations to synthetic microbiota and potential therapies to gut diseases. Microbes communicate with each other by Quorum sensing (QS) languages. Here the authors construct a QS database and the QS communication network to decipher intricate QSbased communications and form one of the key knowledge maps for human gut microbiota.
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Schuster J, Tollefson GA, Zarate V, Agudelo A, Stabila J, Ragavendran A, Padbury J, Uzun A. Protein Network Analysis of Whole Exome Sequencing of Severe Preeclampsia. Front Genet 2022; 12:765985. [PMID: 35719905 PMCID: PMC9201216 DOI: 10.3389/fgene.2021.765985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 11/03/2021] [Indexed: 11/13/2022] Open
Abstract
Preeclampsia is a hypertensive disorder of pregnancy, which complicates up to 15% of US deliveries. It is an idiopathic disorder associated with several different phenotypes. We sought to determine if the genetic architecture of preeclampsia can be described by clusters of patients with variants in genes in shared protein interaction networks. We performed a case-control study using whole exome sequencing on early onset preeclamptic mothers with severe clinical features and control mothers with uncomplicated pregnancies between 2016 and 2020. A total of 143 patients were enrolled, 61 women with early onset preeclampsia with severe features based on ACOG criteria, and 82 control women at term, matched for race and ethnicity. A network analysis and visualization tool, Proteinarium, was used to confirm there are clusters of patients with shared gene networks associated with severe preeclampsia. The majority of the sequenced patients appear in two significant clusters. We identified one case dominant and one control dominant cluster. Thirteen genes were unique to the case dominated cluster. Among these genes, LAMB2, PTK2, RAC1, QSOX1, FN1, and VCAM1 have known associations with the pathogenic mechanisms of preeclampsia. Using bioinformatic analysis, we were able to identify subsets of patients with shared protein interaction networks, thus confirming our hypothesis about the genetic architecture of preeclampsia.
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Affiliation(s)
- Jessica Schuster
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
| | | | - Valeria Zarate
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Anthony Agudelo
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Joan Stabila
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
| | - Ashok Ragavendran
- Center for Computation and Visualization, Brown University, Providence, RI, United States
- Computational Biology of Human Disease, Brown University, Providence, RI, United States
| | - James Padbury
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
| | - Alper Uzun
- Pediatrics, Women and Infants Hospital, Providence, RI, United States
- Pediatrics, Warren Alpert Medical School, Brown University, Providence, RI, United States
- Computational Biology of Human Disease, Brown University, Providence, RI, United States
- Center for Computational Molecular Biology, Brown University, Providence, RI, United States
- *Correspondence: Alper Uzun,
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Sagulkoo P, Suratanee A, Plaimas K. Immune-Related Protein Interaction Network in Severe COVID-19 Patients toward the Identification of Key Proteins and Drug Repurposing. Biomolecules 2022; 12:biom12050690. [PMID: 35625619 PMCID: PMC9138873 DOI: 10.3390/biom12050690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/07/2022] [Accepted: 05/09/2022] [Indexed: 02/05/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is still an active global public health issue. Although vaccines and therapeutic options are available, some patients experience severe conditions and need critical care support. Hence, identifying key genes or proteins involved in immune-related severe COVID-19 is necessary to find or develop the targeted therapies. This study proposed a novel construction of an immune-related protein interaction network (IPIN) in severe cases with the use of a network diffusion technique on a human interactome network and transcriptomic data. Enrichment analysis revealed that the IPIN was mainly associated with antiviral, innate immune, apoptosis, cell division, and cell cycle regulation signaling pathways. Twenty-three proteins were identified as key proteins to find associated drugs. Finally, poly (I:C), mitomycin C, decitabine, gemcitabine, hydroxyurea, tamoxifen, and curcumin were the potential drugs interacting with the key proteins to heal severe COVID-19. In conclusion, IPIN can be a good representative network for the immune system that integrates the protein interaction network and transcriptomic data. Thus, the key proteins and target drugs in IPIN help to find a new treatment with the use of existing drugs to treat the disease apart from vaccination and conventional antiviral therapy.
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Affiliation(s)
- Pakorn Sagulkoo
- Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Bangkok 10330, Thailand;
- Center of Biomedical Informatics, Department of Family Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai 50200, Thailand
| | - Apichat Suratanee
- Department of Mathematics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand;
- Intelligent and Nonlinear Dynamics Innovations Research Center, Science and Technology Research Institute, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, Thailand
| | - Kitiporn Plaimas
- Advance Virtual and Intelligent Computing (AVIC) Center, Department of Mathematics and Computer Science, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Omics Science and Bioinformatics Center, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand
- Correspondence:
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Gollapalli P, Selvan G T, H M, Shetty P, Kumari N S. Genome-scale protein interaction network construction and topology analysis of functional hypothetical proteins in Helicobacter pylori divulges novel therapeutic targets. Microb Pathog 2021; 161:105293. [PMID: 34800634 DOI: 10.1016/j.micpath.2021.105293] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/25/2021] [Accepted: 11/12/2021] [Indexed: 02/07/2023]
Abstract
The emergence and spread of multi-drug resistance among Helicobacter pylori (H. pylori) strain raise more stakes for genetic research for discovering new drugs. The quantity of uncharacterized hypothetical proteins in the genome may provide an opportunity to explore their property and promulgation could act as a platform for designing the drugs, making them an intriguing genetic target. In this context, the present study aims to identify the key hypothetical proteins (HPs) and their biological regulatory processes in H. pylori. This investigation could provide a foundation to establish the molecular connectivity among the pathways using topological analysis of the protein interaction networks (PINs). The giant network derived from the extended network has 374 nodes connected via 925 edges. A total of 43 proteins with high betweenness centrality (BC), 54 proteins with a large degree, and 23 proteins with high BC and large degrees have been identified. HP 1479, HP 0056, HP 1481, HP 1021, HP 0043, HP 1019, gmd, flgA, HP 0472, HP 1486, HP 1478, and HP 1473 are categorized as hub nodes because they have a higher number of direct connections and are potentially more important in understanding HP's molecular interactions. The pathway enrichment analysis of the network clusters revealed significant involvement of HPs in pathways such as flagellar assembly, bacterial chemotaxis and lipopolysaccharide biosynthesis. This comprehensive computational study revealed HP's functional role and its druggability characteristics, which could be useful in the development of drugs to combat H. pylori infections.
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Affiliation(s)
- Pavan Gollapalli
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India.
| | - Tamizh Selvan G
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| | - Manjunatha H
- Department of Biochemistry, Jnana Bharathi Campus, Bangalore University, Bangalore, Karnataka, 560056, India
| | - Praveenkumar Shetty
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
| | - Suchetha Kumari N
- Central Research Laboratory, KS Hegde Medical Academy, Nitte (Deemed to be University), Mangalore, 575018, Karnataka, India
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Abu-Shahba N, Mahmoud M, El-Erian AM, Husseiny MI, Nour-Eldeen G, Helwa I, Amr K, ElHefnawi M, Othman AI, Ibrahim SA, Azmy O. Impact of type 2 diabetes mellitus on the immunoregulatory characteristics of adipose tissue-derived mesenchymal stem cells. Int J Biochem Cell Biol 2021; 140:106072. [PMID: 34455058 DOI: 10.1016/j.biocel.2021.106072] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 08/02/2021] [Accepted: 08/24/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder associated with several complications. Adipose tissue-derived mesenchymal stem cells (AT-MSCs) represent an emerging type of MSCs with high plasticity and immunoregulatory capabilities and are useful for treating inflammation-related disorders such as T2DM. However, the pathogenic microenvironment of T2DM may affect their therapeutic potential. We aimed to examine the impact of the diabetic milieu on the immunomodulatory/anti-inflammatory potential of AT-MSCs. METHODS We assessed the proliferation potential, cell surface expression of MSC-characteristic markers and immunomodulatory markers, along with the gene expression and protein secretion of pro-inflammatory and anti-inflammatory cytokines and adipokines in AT-MSCs derived from T2DM patients (dAT-MSCs) vs. those derived from non-diabetic volunteers (ndAT-MSCs). Furthermore, we evaluated the IFN-γ priming effect on both groups. RESULTS Our data revealed comparable proliferative activities in both groups. Flow cytometric analysis results showed a lower expression of CD200 and CD276 on dAT-MSCs vs. ndAT-MSCs. qPCR demonstrated upregulation of IL-1β associated with a downregulation of IL-1RN in dAT-MSCs vs. ndAT-MSCs. IFN-γ priming induced an elevation in CD274 expression associated with IDO1 and ILRN overexpression and IL-1β downregulation in both groups. ELISA analysis uncovered elevated levels of secreted IL-1β, TNF, and visfatin/NAMPT in dAT-MSCs, whereas IL-1RA and IDO levels were reduced. ELISA results were also evident in the secretome of dAT-MSCs upon IFN-γ priming. CONCLUSIONS This study suggests that the T2DM milieu alters the immunomodulatory characteristics of AT-MSCs with a shift towards a proinflammatory phenotype which may restrain their autologous therapeutic use. Furthermore, our findings indicate that IFN-γ priming could be a useful strategy for enhancing dAT-MSC anti-inflammatory potential.
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Affiliation(s)
- Nourhan Abu-Shahba
- Stem Cell Research Group, Medical Research Centre of Excellence, National Research Centre, Cairo, Egypt; Department of Medical Molecular Genetics, Human Genetics and Genome Research Division, National Research Centre, Cairo, Egypt.
| | - Marwa Mahmoud
- Stem Cell Research Group, Medical Research Centre of Excellence, National Research Centre, Cairo, Egypt; Department of Medical Molecular Genetics, Human Genetics and Genome Research Division, National Research Centre, Cairo, Egypt
| | - Alaa Mohammed El-Erian
- Department of Endocrine Surgery, National Institute of Diabetes and Endocrinology, Cairo, Egypt
| | - Mohamed Ibrahim Husseiny
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs DMRI, Beckman Research Institute, City of Hope, National Medical Center, Durate, CA, USA; Faculty of Pharmacy, Zagazig University, Zagazig, Egypt
| | - Ghada Nour-Eldeen
- Stem Cell Research Group, Medical Research Centre of Excellence, National Research Centre, Cairo, Egypt; Department of Molecular Genetics and Enzymology, Human Genetics and Genome Research Division, National Research Centre, Cairo, Egypt
| | - Iman Helwa
- Department of Immunogenetics, Human Genetics and Genome Research Division, National Resrearch Centre, Egypt
| | - Khalda Amr
- Department of Medical Molecular Genetics, Human Genetics and Genome Research Division, National Research Centre, Cairo, Egypt
| | - Mahmoud ElHefnawi
- Biomedical Informatics and Chemoinformatics Group, Informatics and Systems Department, National Research Centre, Cairo, Egypt
| | - Amel Ibrahim Othman
- Department of Zoology, Faculty of Science, Cairo University, 12613, Giza, Egypt
| | | | - Osama Azmy
- Stem Cell Research Group, Medical Research Centre of Excellence, National Research Centre, Cairo, Egypt; Department of Reproductive Health Research, Medical Research Division, National Research Centre, Cairo, Egypt; Egypt Center for Research and Regenerative Medicine, Cairo, Egypt
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Chen SJ, Cheng JL, Lee SA, Wang TY, Jang JY, Chen KC. Elucidate multidimensionality of type 1 diabetes mellitus heterogeneity by multifaceted information. Sci Rep 2021; 11:20965. [PMID: 34697343 PMCID: PMC8545927 DOI: 10.1038/s41598-021-00388-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 10/08/2021] [Indexed: 12/20/2022] Open
Abstract
Type 1 diabetes (T1D) is an autoimmune disease. Different factors, including genetics and viruses may contribute to T1D, but the causes of T1D are not fully known, and there is currently no cure. The advent of high-throughput technologies has revolutionized the field of medicine and biology, and analysis of multi-source data along with clinical information has brought a better understanding of the mechanisms behind disease pathogenesis. The aim of this work was the development of a data repository linking clinical information and interactome studies in T1D. To address this goal, we analyzed the electronic health records and online databases of genes, proteins, miRNAs, and pathways to have a global view of T1D. There were common comorbid diseases such as anemia, hypertension, vitreous diseases, renal diseases, and atherosclerosis in the phenotypic disease networks. In the protein-protein interaction network, CASP3 and TNF were date-hub proteins involved in several pathways. Moreover, CTNNB1, IGF1R, and STAT3 were hub proteins, whereas miR-155-5p, miR-34a-5p, miR-23-3p, and miR-20a-5p were hub miRNAs in the gene-miRNA interaction network. Multiple levels of information including genetic, protein, miRNA and clinical data resulted in multiple results, which suggests the complementarity of multiple sources. With the integration of multifaceted information, it will shed light on the mechanisms underlying T1D; the provided data and repository has utility in understanding phenotypic disease networks for the potential development of comorbidities in T1D patients as well as the clues for further research on T1D comorbidities.
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Affiliation(s)
- Shaw-Ji Chen
- Department of Psychiatry, Mackay Memorial Hospital, Taitung, Taiwan.,Department of Medicine, Mackay Medical College, New Taipei City, Taiwan
| | - Jen-Liang Cheng
- Department of Medical Informatics, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien, 97004, Taiwan
| | - Sheng-An Lee
- Department of Health Industry Management, Kainan University, Taoyuan, Taiwan
| | - Tse-Yi Wang
- Department of Medical Informatics, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien, 97004, Taiwan
| | - Jyy-Yu Jang
- Department of Medical Informatics, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien, 97004, Taiwan
| | - Kuang-Chi Chen
- Department of Medical Informatics, Tzu Chi University, No. 701, Sec. 3, Zhongyang Rd., Hualien, 97004, Taiwan.
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Maden SF, Acuner SE. Mapping Transcriptome Data to Protein-Protein Interaction Networks of Inflammatory Bowel Diseases Reveals Disease-Specific Subnetworks. Front Genet 2021; 12:688447. [PMID: 34484291 PMCID: PMC8416454 DOI: 10.3389/fgene.2021.688447] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/19/2021] [Indexed: 12/31/2022] Open
Abstract
Inflammatory bowel disease (IBD) is the common name for chronic disorders associated with the inflammation of the gastrointestinal tract. IBD is triggered by environmental factors in genetically susceptible individuals and has a significant number of incidences worldwide. Crohn’s disease (CD) and ulcerative colitis (UC) are the two distinct types of IBD. While involvement in ulcerative colitis is limited to the colon, Crohn’s disease may involve the whole gastrointestinal tract. Although these two disorders differ in macroscopic inflammation patterns, they share various molecular pathogenesis, yet the diagnosis can remain unclear, and it is important to reveal their molecular signatures in the network level. Improved molecular understanding may reveal disease type-specific and even individual-specific targets. To this aim, we determine the subnetworks specific to UC and CD by mapping transcriptome data to protein–protein interaction (PPI) networks using two different approaches [KeyPathwayMiner (KPM) and stringApp] and perform the functional enrichment analysis of the resulting disease type-specific subnetworks. TP63 was identified as the hub gene in the UC-specific subnet and p63 tumor protein, being in the same family as p53 and p73, has been studied in literature for the risk associated with colorectal cancer and IBD. APP was identified as the hub gene in the CD-specific subnet, and it has an important role in the pathogenesis of Alzheimer’s disease (AD). This relation suggests that some similar genetic factors may be effective in both AD and CD. Last, in order to understand the biological meaning of these disease-specific subnets, they were functionally enriched. It is important to note that chemokines—special types of cytokines—and antibacterial response are important in UC-specific subnets, whereas cytokines and antimicrobial responses as well as cancer-related pathways are important in CD-specific subnets. Overall, these findings reveal the differences between IBD subtypes at the molecular level and can facilitate diagnosis for UC and CD as well as provide potential molecular targets that are specific to disease subtypes.
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Affiliation(s)
- Sefika Feyza Maden
- Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
| | - Saliha Ece Acuner
- Department of Bioengineering, Istanbul Medeniyet University, Istanbul, Turkey
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12
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Suresh NT, E R V, U K. Multi-scale top-down approach for modelling epileptic protein-protein interaction network analysis to identify driver nodes and pathways. Comput Biol Chem 2020; 88:107323. [PMID: 32653778 DOI: 10.1016/j.compbiolchem.2020.107323] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 06/04/2020] [Accepted: 06/23/2020] [Indexed: 12/23/2022]
Abstract
Protein - Protein Interaction Network (PPIN) analysis unveils molecular level mechanisms involved in disease condition. To explore the complex regulatory mechanisms behind epilepsy and to address the clinical and biological issues of epilepsy, in silico techniques are feasible in a cost- effective manner. In this work, a hierarchical procedure to identify influential genes and regulatory pathways in epilepsy prognosis is proposed. To obtain key genes and pathways causing epilepsy, integration of two benchmarked datasets which are exclusively devoted for complex disorders is done as an initial step. Using STRING database, PPIN is constructed for modelling protein-protein interactions. Further, key interactions are obtained from the established PPIN using network centrality measures followed by network propagation algorithm -Random Walk with Restart (RWR). The outcome of the method reveals some influential genes behind epilepsy prognosis, along with their associated pathways like PI3 kinase, VEGF signaling, Ras, Wnt signaling etc. In comparison with similar works, our results have shown improvement in identifying unique molecular functions, biological processes, gene co-occurrences etc. Also, CORUM provides an annotation for approximately 60% of similarity in human protein complexes with the obtained result. We believe that the formulated strategy can put-up the vast consideration of indigenous drugs towards meticulous identification of genes encoded by protein against several combinatorial disorders.
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Affiliation(s)
- Nikhila T Suresh
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, India
| | - Vimina E R
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, India.
| | - Krishnakumar U
- Department of Computer Science and IT, Amrita School of Arts and Sciences, Amrita Vishwa Vidyapeetham, Kochi Campus, India
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Pathania S, Randhawa V, Kumar M. Identifying potential entry inhibitors for emerging Nipah virus by molecular docking and chemical-protein interaction network. J Biomol Struct Dyn 2019; 38:5108-5125. [DOI: 10.1080/07391102.2019.1696705] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Shivalika Pathania
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
| | - Vinay Randhawa
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
| | - Manoj Kumar
- Virology Unit and Bioinformatics Centre, Institute of Microbial Technology, Council of Scientific & Industrial Research, Chandigarh, India
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14
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Rahbar Saadat Y, Pourseif MM, Zununi Vahed S, Barzegari A, Omidi Y, Barar J. Modulatory Role of Vaginal-Isolated Lactococcus lactis on the Expression of miR-21, miR-200b, and TLR-4 in CAOV-4 Cells and In Silico Revalidation. Probiotics Antimicrob Proteins 2019; 12:1083-1096. [DOI: 10.1007/s12602-019-09596-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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15
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Wei LK, Quan LS. Biomarkers for ischemic stroke subtypes: A protein-protein interaction analysis. Comput Biol Chem 2019; 83:107116. [PMID: 31561071 DOI: 10.1016/j.compbiolchem.2019.107116] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2019] [Revised: 07/10/2019] [Accepted: 08/26/2019] [Indexed: 01/21/2023]
Abstract
According to the Trial of Org 10172 in Acute Stroke Treatment, ischemic stroke is classified into five subtypes. However, the predictive biomarkers of ischemic stroke subtypes are still largely unknown. The utmost objective of this study is to map, construct and analyze protein-protein interaction (PPI) networks for all subtypes of ischemic stroke, and to suggest the predominant biological pathways for each subtypes. Through 6285 protein data retrieved from PolySearch2 and STRING database, the first PPI networks for all subtypes of ischemic stroke were constructed. Notably, F2 and PLG were identified as the critical proteins for large artery atherosclerosis (LAA), lacunar, cardioembolic, stroke of other determined etiology (SOE) and stroke of undetermined etiology (SUE). Gene ontology and DAVID analysis revealed that GO:0030193 regulation of blood coagulation and GO:0051917 regulation of fibrinolysis were the important functional clusters for all the subtypes. In addition, inflammatory pathway was the key etiology for LAA and lacunar, while FOS and JAK2/STAT3 signaling pathways might contribute to cardioembolic stroke. Due to many risk factors associated with SOE and SUE, the precise etiology for these two subtypes remained to be concluded.
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Affiliation(s)
- Loo Keat Wei
- Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia.
| | - Leong Shi Quan
- Department of Biological Science, Faculty of Science, Universiti Tunku Abdul Rahman, Bandar Barat, 31900 Kampar, Perak, Malaysia
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A network pharmacology approach to investigate the pharmacological effect of curcumin and capsaicin targets in cancer angiogenesis by module-based PPI network analysis. ACTA ACUST UNITED AC 2019. [DOI: 10.1007/s42485-019-00012-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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17
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Chen SJ, Liao DL, Chen CH, Wang TY, Chen KC. Construction and Analysis of Protein-Protein Interaction Network of Heroin Use Disorder. Sci Rep 2019; 9:4980. [PMID: 30899073 PMCID: PMC6428805 DOI: 10.1038/s41598-019-41552-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 03/11/2019] [Indexed: 12/17/2022] Open
Abstract
Heroin use disorder (HUD) is a complex disease resulting from interactions among genetic and other factors (e.g., environmental factors). The mechanism of HUD development remains unknown. Newly developed network medicine tools provide a platform for exploring complex diseases at the system level. This study proposes that protein–protein interactions (PPIs), particularly those among proteins encoded by casual or susceptibility genes, are extremely crucial for HUD development. The giant component of our constructed PPI network comprised 111 nodes with 553 edges, including 16 proteins with large degree (k) or high betweenness centrality (BC), which were further identified as the backbone of the network. JUN with the largest degree was suggested to be central to the PPI network associated with HUD. Moreover, PCK1 with the highest BC and MAPK14 with the secondary largest degree and 9th highest BC might be involved in the development HUD and other substance diseases.
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Affiliation(s)
- Shaw-Ji Chen
- Department of Medicine, Mackay Medical College, New Taipei City, Taiwan.,Department of Psychiatry, Mackay Memorial Hospital, Taitung Branch, Taiwan
| | - Ding-Lieh Liao
- Bali Psychiatric Center, Department of Health, Executive Yuan, New Taipei, Taiwan
| | - Chia-Hsiang Chen
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou and Chang Gung University School of Medicine, Taoyuan, Taiwan
| | - Tse-Yi Wang
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan
| | - Kuang-Chi Chen
- Department of Medical Informatics, Tzu Chi University, Hualien, Taiwan.
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18
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Rani S, Sharma A, Goel M. Insights into archaeal chaperone machinery: a network-based approach. Cell Stress Chaperones 2018; 23:1257-1274. [PMID: 30178307 PMCID: PMC6237683 DOI: 10.1007/s12192-018-0933-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Revised: 08/03/2018] [Accepted: 08/20/2018] [Indexed: 11/30/2022] Open
Abstract
Molecular chaperones are a diverse group of proteins that ensure proteome integrity by helping the proteins fold correctly and maintain their native state, thus preventing their misfolding and subsequent aggregation. The chaperone machinery of archaeal organisms has been thought to closely resemble that found in humans, at least in terms of constituent players. Very few studies have been ventured into system-level analysis of chaperones and their functioning in archaeal cells. In this study, we attempted such an analysis of chaperone-assisted protein folding in archaeal organisms through network approach using Picrophilus torridus as model system. The study revealed that DnaK protein of Hsp70 system acts as hub in protein-protein interaction network. However, DnaK protein was present only in a subset of archaeal organisms and absent from many archaea, especially members of Crenarchaeota phylum. Therefore, a similar network was created for another archaeal organism, Sulfolobus solfataricus, a member of Crenarchaeota. The chaperone network of S. solfataricus suggested that thermosomes played an integral part of hub proteins in archaeal organisms, where DnaK was absent. We further compared the chaperone network of archaea with that found in eukaryotic systems, by creating a similar network for Homo sapiens. In the human chaperone network, the UBC protein, a part of ubiquitination system, was the most important module, and interestingly, this system is known to be absent in archaeal organisms. Comprehensive comparison of these networks leads to several interesting conclusions regarding similarities and differences within archaeal chaperone machinery in comparison to humans.
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Affiliation(s)
- Shikha Rani
- Department of Biophysics, University of Delhi South Campus, Benito Jurarez Road, New Delhi, 110021, India
| | - Ankush Sharma
- Department of Molecular Genetics, Erasmus University Medical Center, Rotterdam, The Netherlands
- Center for Computational Science, University of Miami, Miami, FL, USA
| | - Manisha Goel
- Department of Biophysics, University of Delhi South Campus, Benito Jurarez Road, New Delhi, 110021, India.
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Kalva S, Bindusree G, Alexander V, Madasamy P. Interactome based biomarker discovery for irritable bowel syndrome—A systems biology approach. Comput Biol Chem 2018; 76:218-224. [DOI: 10.1016/j.compbiolchem.2018.07.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2017] [Revised: 11/12/2017] [Accepted: 07/04/2018] [Indexed: 02/07/2023]
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20
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Cri-du-Chat Syndrome interactome network: Correlating genotypic variations to associated phenotypes. GENE REPORTS 2018. [DOI: 10.1016/j.genrep.2018.03.019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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21
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Topology of protein–protein interaction network and edge reduction co-efficiency in VEGF signaling of breast cancer. ACTA ACUST UNITED AC 2017. [DOI: 10.1007/s13721-017-0157-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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22
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Malhotra AG, Jha M, Singh S, Pandey KM. Construction of a Comprehensive Protein-Protein Interaction Map for Vitiligo Disease to Identify Key Regulatory Elements: A Systemic Approach. Interdiscip Sci 2017; 10:500-514. [PMID: 28290051 DOI: 10.1007/s12539-017-0213-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 12/09/2016] [Accepted: 12/29/2016] [Indexed: 12/14/2022]
Abstract
Vitiligo is an idiopathic disorder characterized by depigmented patches on the skin due to progressive loss of melanocytes. Several genetic, immunological, and pathophysiological investigations have established vitiligo as a polygenetic disorder with multifactorial etiology. However, no definite model explaining the interplay between these causative factors has been established hitherto. Therefore, we studied the disorder at the system level to identify the key proteins involved by exploring their molecular connectivity in terms of topological parameters. The existing research data helped us in collating 215 proteins involved in vitiligo onset or progression. Interaction study of these proteins leads to a comprehensive vitiligo map with 4845 protein nodes linked with 107,416 edges. Based on centrality measures, a backbone network with 500 nodes has been derived. This has presented a clear overview of the proteins and processes involved and the crosstalk between them. Clustering backbone proteins revealed densely connected regions inferring major molecular interaction modules essential for vitiligo. Finally, a list of top order proteins that play a key role in the disease pathomechanism has been formulated. This includes SUMO2, ESR1, COPS5, MYC, SMAD3, and Cullin proteins. While this list is in fair agreement with the available literature, it also introduces new candidate proteins that can be further explored. A subnetwork of 64 vitiligo core proteins was built by analyzing the backbone and seed protein networks. Our finding suggests that the topology, along with functional clustering, provides a deep insight into the behavior of proteins. This in turn aids in the illustration of disease condition and discovery of significant proteins involved in vitiligo.
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Affiliation(s)
- Anvita Gupta Malhotra
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Mohit Jha
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Sudha Singh
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Khushhali M Pandey
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India.
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23
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A comprehensive assessment of networks and pathways of hypoxia-associated proteins and identification of responsive protein modules. ACTA ACUST UNITED AC 2016. [DOI: 10.1007/s13721-016-0123-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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24
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Chen C, Shen H, Zhang LG, Liu J, Cao XG, Yao AL, Kang SS, Gao WX, Han H, Cao FH, Li ZG. Construction and analysis of protein-protein interaction networks based on proteomics data of prostate cancer. Int J Mol Med 2016; 37:1576-86. [PMID: 27121963 PMCID: PMC4866967 DOI: 10.3892/ijmm.2016.2577] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Accepted: 04/15/2016] [Indexed: 12/22/2022] Open
Abstract
Currently, using human prostate cancer (PCa) tissue samples to conduct proteomics research has generated a large amount of data; however, only a very small amount has been thoroughly investigated. In this study, we manually carried out the mining of the full text of proteomics literature that involved comparisons between PCa and normal or benign tissue and identified 41 differentially expressed proteins verified or reported more than 2 times from different research studies. We regarded these proteins as seed proteins to construct a protein-protein interaction (PPI) network. The extended network included one giant network, which consisted of 1,264 nodes connected via 1,744 edges, and 3 small separate components. The backbone network was then constructed, which was derived from key nodes and the subnetwork consisting of the shortest path between seed proteins. Topological analyses of these networks were conducted to identify proteins essential for the genesis of PCa. Solute carrier family 2 (facilitated glucose transporter), member 4 (SLC2A4) had the highest closeness centrality located in the center of each network, and the highest betweenness centrality and largest degree in the backbone network. Tubulin, beta 2C (TUBB2C) had the largest degree in the giant network and subnetwork. In addition, using module analysis of the whole PPI network, we obtained a densely connected region. Functional annotation indicated that the Ras protein signal transduction biological process, mitogen-activated protein kinase (MAPK), neurotrophin and the gonadotropin-releasing hormone (GnRH) signaling pathway may play an important role in the genesis and development of PCa. Further investigation of the SLC2A4, TUBB2C proteins, and these biological processes and pathways may therefore provide a potential target for the diagnosis and treatment of PCa.
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Affiliation(s)
- Chen Chen
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Hong Shen
- Department of Modern Technology and Education Center, North China University of Science and Technology and International Science and Technology Cooperation Base of Geriatric Medicine, Tangshan, Hebei 063000, P.R. China
| | - Li-Guo Zhang
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Jian Liu
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Xiao-Ge Cao
- Tianjin Binhai New Area Hangu No. 1 High School, Tianjin 300480, P.R. China
| | - An-Liang Yao
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Shao-San Kang
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Wei-Xing Gao
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Hui Han
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Feng-Hong Cao
- Department of Urology, North China University of Science and Technology Affiliated Hospital, Tangshan, Hebei 063000, P.R. China
| | - Zhi-Guo Li
- Medical Research Center, North China University of Science and Technology and International Science and Technology Cooperation Base of Geriatric Medicine, Tangshan, Hebei 063000, P.R. China
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Zamanian Azodi M, Peyvandi H, Rostami-Nejad M, Safaei A, Rostami K, Vafaee R, Heidari M, Hosseini M, Zali MR. Protein-protein interaction network of celiac disease. GASTROENTEROLOGY AND HEPATOLOGY FROM BED TO BENCH 2016; 9:268-277. [PMID: 27895852 PMCID: PMC5118851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Abstract
AIM The aim of this study is to investigate the Protein-Protein Interaction Network of Celiac Disease. BACKGROUND Celiac disease (CD) is an autoimmune disease with susceptibility of individuals to gluten of wheat, rye and barley. Understanding the molecular mechanisms and involved pathway may lead to the development of drug target discovery. The protein interaction network is one of the supportive fields to discover the pathogenesis biomarkers for celiac disease. MATERIAL AND METHODS In the present study, we collected the articles that focused on the proteomic data in celiac disease. According to the gene expression investigations of these articles, 31 candidate proteins were selected for this study. The networks of related differentially expressed protein were explored using Cytoscape 3.3 and the PPI analysis methods such as MCODE and ClueGO. RESULTS According to the network analysis Ubiquitin C, Heat shock protein 90kDa alpha (cytosolic and Grp94); class A, B and 1 member, Heat shock 70kDa protein, and protein 5 (glucose-regulated protein, 78kDa), T-complex, Chaperon in containing TCP1; subunit 7 (beta) and subunit 4 (delta) and subunit 2 (beta), have been introduced as hub-bottlnecks proteins. HSP90AA1, MKKS, EZR, HSPA14, APOB and CAD have been determined as seed proteins. CONCLUSION Chaperons have a bold presentation in curtail area in network therefore these key proteins beside the other hub-bottlneck proteins may be a suitable candidates biomarker panel for diagnosis, prognosis and treatment processes in celiac disease.
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Affiliation(s)
- Mona Zamanian Azodi
- Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hassan Peyvandi
- Hearing Disorders Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Rostami-Nejad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Akram Safaei
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Kamran Rostami
- Department of Gastroenterology, Milton Keynes University Hospital United Kingdom
| | - Reza Vafaee
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Mostafa Hosseini
- Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Reza Zali
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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Liu Y, Liang Y, Wishart D. PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more. Nucleic Acids Res 2015; 43:W535-42. [PMID: 25925572 PMCID: PMC4489268 DOI: 10.1093/nar/gkv383] [Citation(s) in RCA: 88] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2015] [Accepted: 04/11/2015] [Indexed: 02/01/2023] Open
Abstract
PolySearch2 (http://polysearch.ca) is an online text-mining system for identifying relationships between biomedical entities such as human diseases, genes, SNPs, proteins, drugs, metabolites, toxins, metabolic pathways, organs, tissues, subcellular organelles, positive health effects, negative health effects, drug actions, Gene Ontology terms, MeSH terms, ICD-10 medical codes, biological taxonomies and chemical taxonomies. PolySearch2 supports a generalized ‘Given X, find all associated Ys’ query, where X and Y can be selected from the aforementioned biomedical entities. An example query might be: ‘Find all diseases associated with Bisphenol A’. To find its answers, PolySearch2 searches for associations against comprehensive collections of free-text collections, including local versions of MEDLINE abstracts, PubMed Central full-text articles, Wikipedia full-text articles and US Patent application abstracts. PolySearch2 also searches 14 widely used, text-rich biological databases such as UniProt, DrugBank and Human Metabolome Database to improve its accuracy and coverage. PolySearch2 maintains an extensive thesaurus of biological terms and exploits the latest search engine technology to rapidly retrieve relevant articles and databases records. PolySearch2 also generates, ranks and annotates associative candidates and present results with relevancy statistics and highlighted key sentences to facilitate user interpretation.
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Affiliation(s)
- Yifeng Liu
- Department of Computing Science, University of Alberta, Edmonton, T6G 2E8 Canada
| | - Yongjie Liang
- Department of Computing Science, University of Alberta, Edmonton, T6G 2E8 Canada
| | - David Wishart
- Department of Computing Science, University of Alberta, Edmonton, T6G 2E8 Canada Department of Biological Science, University of Alberta, Edmonton, T6G 2E6 Canada
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27
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Chen H, Zhu Z, Zhu Y, Wang J, Mei Y, Cheng Y. Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers. J Cell Mol Med 2015; 19:297-314. [PMID: 25560835 PMCID: PMC4407592 DOI: 10.1111/jcmm.12447] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Accepted: 08/22/2014] [Indexed: 01/06/2023] Open
Abstract
It is known that a disease is rarely a consequence of an abnormality of a single gene, but reflects the interactions of various processes in a complex network. Annotated molecular networks offer new opportunities to understand diseases within a systems biology framework and provide an excellent substrate for network-based identification of biomarkers. The network biomarkers and dynamic network biomarkers (DNBs) represent new types of biomarkers with protein-protein or gene-gene interactions that can be monitored and evaluated at different stages and time-points during development of disease. Clinical bioinformatics as a new way to combine clinical measurements and signs with human tissue-generated bioinformatics is crucial to translate biomarkers into clinical application, validate the disease specificity, and understand the role of biomarkers in clinical settings. In this article, the recent advances and developments on network biomarkers and DNBs are comprehensively reviewed. How network biomarkers help a better understanding of molecular mechanism of diseases, the advantages and constraints of network biomarkers for clinical application, clinical bioinformatics as a bridge to the development of diseases-specific, stage-specific, severity-specific and therapy predictive biomarkers, and the potentials of network biomarkers are also discussed.
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Affiliation(s)
- Hao Chen
- Department of Cardiothoracic Surgery, Tongji Hospital, Tongji University, Shanghai, China
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28
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Xie W, Sun J, Wu J. Construction and analysis of a protein-protein interaction network related to self-renewal of mouse spermatogonial stem cells. MOLECULAR BIOSYSTEMS 2015; 11:835-43. [PMID: 25566695 DOI: 10.1039/c4mb00579a] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Spermatogonial stem cells (SSCs) are responsible for sustained spermatogenesis throughout the reproductive life of the male. Extensive studies of SSCs have identified dozens of genes that play important roles in sustaining or controlling the pool of SSCs in the mammalian testis. However, there is still limited knowledge of whether or how these key genes interact with each other during SSC self-renewal. Here, we constructed a protein-protein interaction (PPI) network for SSC self-renewal based on interactions between 23 genes essential for SSC self-renewal, which were obtained from a text mining system, and the interacting partners of the 23 key genes, which were differentially expressed in SSCs. The SSC self-renewal PPI network consisted of 246 nodes connected by 844 edges. Topological analyses of the PPI network were conducted to identify genes essential for maintenance of SSC self-renewal. The subnetwork of the SSC self-renewal network suggested that the 23 key genes involved in SSC self-renewal were connected together through other 94 genes. Clustering of the whole network and subnetwork of SSC self-renewal revealed several densely connected regions, implying significant molecular interaction modules essential for SSC self-renewal. Notably, we found the 23 genes to be responsible for SSC self-renewal by forming a continuous PPI network centered on Pou5f1. Our study indicates that it is feasible to explore important proteins and regulatory pathways in biological activities by combining a PPI database with the high-throughput data of gene expression profiles.
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Affiliation(s)
- Wenhai Xie
- Key Laboratory for the Genetics of Developmental & Neuropsychiatric Disorders (Ministry of Education), Bio-X Institutes, Shanghai Jiao Tong University, Shanghai 200240, China.
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Association of polymorphisms of genes involved in lipid metabolism with blood pressure and lipid values in mexican hypertensive individuals. DISEASE MARKERS 2014; 2014:150358. [PMID: 25587205 PMCID: PMC4283438 DOI: 10.1155/2014/150358] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/18/2014] [Revised: 11/12/2014] [Accepted: 11/13/2014] [Indexed: 02/08/2023]
Abstract
Hypertension and dyslipidemia exhibit an important clinical relationship because an increase in blood lipids yields an increase in blood pressure (BP). We analyzed the associations of seven polymorphisms of genes involved in lipid metabolism (APOA5 rs3135506, APOB rs1042031, FABP2 rs1799883, LDLR rs5925, LIPC rs1800588, LPL rs328, and MTTP rs1800591) with blood pressure and lipid values in Mexican hypertensive (HT) patients. A total of 160 HT patients and 160 normotensive individuals were included. Genotyping was performed through PCR-RFLP, PCR-AIRS, and sequencing. The results showed significant associations in the HT group and HT subgroups classified as normolipemic and hyperlipemic. The alleles FABP2 p.55T, LIPC −514T, and MTTP −493T were associated with elevated systolic BP. Five alleles were associated with lipids. LPL p.474X and FABP2 p.55T were associated with decreased total cholesterol and LDL-C, respectively; APOA5 p.19W with increased HDL-C; APOA5 p.19W and FABP2 p.55T with increased triglycerides; and APOB p.4181K and LDLR c.1959T with decreased triglycerides. The APOB p.E4181K polymorphism increases the risk for HT (OR = 1.85, 95% CI: 1.17–2.93; P = 0.001) under the dominant model. These findings indicate that polymorphisms of lipid metabolism genes modify systolic BP and lipid levels and may be important in the development of essential hypertension and dyslipidemia in Mexican HT patients.
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Sinha A, Nagarajaram HA. Nodes occupying central positions in human tissue specific PPI networks are enriched with many splice variants. Proteomics 2014; 14:2242-8. [PMID: 25092398 DOI: 10.1002/pmic.201400249] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 07/04/2014] [Accepted: 08/01/2014] [Indexed: 12/22/2022]
Abstract
The functional repertoire of genes in the eukaryotic organisms is enhanced by the phenomenon of alternative splicing. Hence, a node in a tissue specific protein-protein interaction (TS PPIN) network can be thought of as an ensemble of various spliced protein products of the corresponding gene expressed in that tissue. Here we demonstrate that the nodes that occupy topologically central positions characterized by high degree, betweenness, closeness, and eigenvector centrality values in TS PPINs of Homo sapiens are associated with high number of splice variants. We also show that the high "centrality" of these genes/nodes could in part be explained by the presence of a large number of promiscuous domains.
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Affiliation(s)
- Anupam Sinha
- Laboratory of Computational Biology, Centre for DNA Fingerprinting & Diagnostics (CDFD), Hyderabad, Telangana, India
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Rakshit H, Rathi N, Roy D. Construction and analysis of the protein-protein interaction networks based on gene expression profiles of Parkinson's disease. PLoS One 2014; 9:e103047. [PMID: 25170921 PMCID: PMC4149362 DOI: 10.1371/journal.pone.0103047] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2013] [Accepted: 06/26/2014] [Indexed: 11/29/2022] Open
Abstract
Background Parkinson's Disease (PD) is one of the most prevailing neurodegenerative diseases. Improving diagnoses and treatments of this disease is essential, as currently there exists no cure for this disease. Microarray and proteomics data have revealed abnormal expression of several genes and proteins responsible for PD. Nevertheless, few studies have been reported involving PD-specific protein-protein interactions. Results Microarray based gene expression data and protein-protein interaction (PPI) databases were combined to construct the PPI networks of differentially expressed (DE) genes in post mortem brain tissue samples of patients with Parkinson's disease. Samples were collected from the substantia nigra and the frontal cerebral cortex. From the microarray data, two sets of DE genes were selected by 2-tailed t-tests and Significance Analysis of Microarrays (SAM), run separately to construct two Query-Query PPI (QQPPI) networks. Several topological properties of these networks were studied. Nodes with High Connectivity (hubs) and High Betweenness Low Connectivity (bottlenecks) were identified to be the most significant nodes of the networks. Three and four-cliques were identified in the QQPPI networks. These cliques contain most of the topologically significant nodes of the networks which form core functional modules consisting of tightly knitted sub-networks. Hitherto unreported 37 PD disease markers were identified based on their topological significance in the networks. Of these 37 markers, eight were significantly involved in the core functional modules and showed significant change in co-expression levels. Four (ARRB2, STX1A, TFRC and MARCKS) out of the 37 markers were found to be associated with several neurotransmitters including dopamine. Conclusion This study represents a novel investigation of the PPI networks for PD, a complex disease. 37 proteins identified in our study can be considered as PD network biomarkers. These network biomarkers may provide as potential therapeutic targets for PD applications development.
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Affiliation(s)
- Hindol Rakshit
- Integrated Science Education & Research Centre (ISERC), Visva-Bharati University, Shantiniketan, Birbhum, West Bengal, India
| | - Nitin Rathi
- Cognizant Technology Solutions India Pvt. Ltd., Rajiv Gandhi Infotech Park, MIDC, Hinjewadi, Pune, Maharashtra, India
| | - Debjani Roy
- Department of Biophysics, Bose Institute, Acharya J.C. Bose Centenary Building, Kolkata, West Bengal, India
- * E-mail:
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Handy DE, Loscalzo J, Leopold JA. Systems analysis of oxidant stress in the vasculature. IUBMB Life 2013; 65:911-20. [PMID: 24265198 DOI: 10.1002/iub.1221] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2013] [Accepted: 10/01/2013] [Indexed: 01/11/2023]
Abstract
Systems biology and network analysis are emerging as valuable tools for the discovery of novel relationships, the identification of key regulatory factors, and the prediction of phenotypic changes in complex biological systems. Redox homeostasis in the vasculature is maintained by an intricate balance between oxidant-generating and antioxidant systems. When these systems are perturbed, conditions are permissive for oxidant stress, which, in turn, promotes vascular dysfunction and structural remodeling. Owing to the number of elements involved in redox regulation and the different vascular pathophenotypes associated with oxidant stress, vascular oxidant stress represents an ideal system to study by network analysis. Networks offer a method to organize experimentally derived factors, including proteins, metabolites, and DNA, that are represented as nodes into an unbiased comprehensive platform for study. Through analysis of the network, it is possible to determine essential or regulatory nodes, identify previously unknown connections between nodes, and locate modules, which are groups of nodes located within the same neighborhood that function together and have implications for phenotype. Investigators have only recently begun to construct oxidant stress-related networks to examine vascular structure and function; however, these early studies have provided mechanistic insight to further our understanding of this complicated biological system.
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Affiliation(s)
- Diane E Handy
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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