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Study of the inflammatory activating process in the early stage of Fusobacterium nucleatum infected PDLSCs. Int J Oral Sci 2023; 15:8. [PMID: 36754953 PMCID: PMC9908923 DOI: 10.1038/s41368-022-00213-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 11/15/2022] [Accepted: 11/27/2022] [Indexed: 02/10/2023] Open
Abstract
Fusobacterium nucleatum (F. nucleatum) is an early pathogenic colonizer in periodontitis, but the host response to infection with this pathogen remains unclear. In this study, we built an F. nucleatum infectious model with human periodontal ligament stem cells (PDLSCs) and showed that F. nucleatum could inhibit proliferation, and facilitate apoptosis, ferroptosis, and inflammatory cytokine production in a dose-dependent manner. The F. nucleatum adhesin FadA acted as a proinflammatory virulence factor and increased the expression of interleukin(IL)-1β, IL-6 and IL-8. Further study showed that FadA could bind with PEBP1 to activate the Raf1-MAPK and IKK-NF-κB signaling pathways. Time-course RNA-sequencing analyses showed the cascade of gene activation process in PDLSCs with increasing durations of F. nucleatum infection. NFκB1 and NFκB2 upregulated after 3 h of F. nucleatum-infection, and the inflammatory-related genes in the NF-κB signaling pathway were serially elevated with time. Using computational drug repositioning analysis, we predicted and validated that two potential drugs (piperlongumine and fisetin) could attenuate the negative effects of F. nucleatum-infection. Collectively, this study unveils the potential pathogenic mechanisms of F. nucleatum and the host inflammatory response at the early stage of F. nucleatum infection.
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Patra BG, Soltanalizadeh B, Deng N, Wu L, Maroufy V, Wu C, Zheng WJ, Roberts K, Wu H, Yaseen A. An informatics research platform to make public gene expression time-course datasets reusable for more scientific discoveries. Database (Oxford) 2020; 2020:baaa074. [PMID: 33247935 PMCID: PMC7698665 DOI: 10.1093/database/baaa074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 07/17/2020] [Accepted: 08/10/2020] [Indexed: 11/13/2022]
Abstract
The exponential growth of genomic/genetic data in the era of Big Data demands new solutions for making these data findable, accessible, interoperable and reusable. In this article, we present a web-based platform named Gene Expression Time-Course Research (GETc) Platform that enables the discovery and visualization of time-course gene expression data and analytical results from the NIH/NCBI-sponsored Gene Expression Omnibus (GEO). The analytical results are produced from an analytic pipeline based on the ordinary differential equation model. Furthermore, in order to extract scientific insights from these results and disseminate the scientific findings, close and efficient collaborations between domain-specific experts from biomedical and scientific fields and data scientists is required. Therefore, GETc provides several recommendation functions and tools to facilitate effective collaborations. GETc platform is a very useful tool for researchers from the biomedical genomics community to present and communicate large numbers of analysis results from GEO. It is generalizable and broadly applicable across different biomedical research areas. GETc is a user-friendly and efficient web-based platform freely accessible at http://genestudy.org/.
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Affiliation(s)
- Braja Gopal Patra
- Department of Biostatistics and Data Science, School of Public Health,The University of Texas Health
Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
| | - Babak Soltanalizadeh
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health
Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
| | - Nan Deng
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health
Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
| | - Leqing Wu
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health
Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
| | - Vahed Maroufy
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health
Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
| | - Canglin Wu
- TechWave International. Inc., Houston, TX, USA and
| | - W Jim Zheng
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
| | - Kirk Roberts
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
| | - Hulin Wu
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health
Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin St. Suite 600, Houston, TX 77030, USA
| | - Ashraf Yaseen
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health
Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
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Soltanalizadeh B, Gonzalez Rodriguez E, Maroufy V, Zheng WJ, Wu H. Modelling of hypoxia gene expression for three different cancer cell lines. ACTA ACUST UNITED AC 2020; 13:124-143. [PMID: 32153660 DOI: 10.1504/ijcbdd.2020.10026794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Gene dynamic analysis is essential in identifying target genes involved pathogenesis of various diseases, including cancer. Cancer prognosis is often influenced by hypoxia. We apply a multi-step pipeline to study dynamic gene expressions in response to hypoxia in three cancer cell lines: prostate (DU145), colon (HT29), and breast (MCF7) cancers. We identified 26 distinct temporal expression patterns for prostate cell line, and 29 patterns for colon and breast cell lines. The module-based dynamic networks have been developed for all three cell lines. Our analyses improve the existing results in multiple ways. It exploits the time-dependence nature of gene expression values in identifying the dynamically significant genes; hence, more key significant genes and transcription factors have been identified. Our gene network returns significant information regarding biologically important modules of genes. Furthermore, the network has potential in learning the regulatory path between transcription factors and the downstream genes. In addition, our findings suggest that changes in genes BMP6 and ARSJ expression might have a key role in the time-dependent response to hypoxia in breast cancer.
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Affiliation(s)
- Babak Soltanalizadeh
- Department of Biostatistics & Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Erika Gonzalez Rodriguez
- Center for translational Injury Research, Department of Surgery, McGovern Medical School, UT Houston, Houston, TX, USA
| | - Vahed Maroufy
- Department of Biostatistics & Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - W Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Hulin Wu
- Department of Biostatistics & Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA
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4
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Liu ZP. Towards precise reconstruction of gene regulatory networks by data integration. QUANTITATIVE BIOLOGY 2018. [DOI: 10.1007/s40484-018-0139-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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5
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Zhang Y, Topham DJ, Thakar J, Qiu X. FUNNEL-GSEA: FUNctioNal ELastic-net regression in time-course gene set enrichment analysis. Bioinformatics 2018; 33:1944-1952. [PMID: 28334094 PMCID: PMC5939227 DOI: 10.1093/bioinformatics/btx104] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Accepted: 02/17/2017] [Indexed: 01/26/2023] Open
Abstract
Motivation Gene set enrichment analyses (GSEAs) are widely used in genomic research to identify underlying biological mechanisms (defined by the gene sets), such as Gene Ontology terms and molecular pathways. There are two caveats in the currently available methods: (i) they are typically designed for group comparisons or regression analyses, which do not utilize temporal information efficiently in time-series of transcriptomics measurements; and (ii) genes overlapping in multiple molecular pathways are considered multiple times in hypothesis testing. Results We propose an inferential framework for GSEA based on functional data analysis, which utilizes the temporal information based on functional principal component analysis, and disentangles the effects of overlapping genes by a functional extension of the elastic-net regression. Furthermore, the hypothesis testing for the gene sets is performed by an extension of Mann-Whitney U test which is based on weighted rank sums computed from correlated observations. By using both simulated datasets and a large-scale time-course gene expression data on human influenza infection, we demonstrate that our method has uniformly better receiver operating characteristic curves, and identifies more pathways relevant to immune-response to human influenza infection than the competing approaches. Availability and Implementation The methods are implemented in R package FUNNEL, freely and publicly available at: https://github.com/yunzhang813/FUNNEL-GSEA-R-Package. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yun Zhang
- Department of Biostatistics and Computational Biology
| | - David J Topham
- Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, USA
| | - Juilee Thakar
- Department of Biostatistics and Computational Biology.,Department of Microbiology and Immunology, University of Rochester, Rochester, NY 14642, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology
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Weishaupt H, Johansson P, Engström C, Nelander S, Silvestrov S, Swartling FJ. Loss of Conservation of Graph Centralities in Reverse-engineered Transcriptional Regulatory Networks. Methodol Comput Appl Probab 2017. [DOI: 10.1007/s11009-017-9554-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Liu ZP. Quantifying Gene Regulatory Relationships with Association Measures: A Comparative Study. Front Genet 2017; 8:96. [PMID: 28751908 PMCID: PMC5507966 DOI: 10.3389/fgene.2017.00096] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 06/28/2017] [Indexed: 11/13/2022] Open
Abstract
In this work, we provide a comparative study of the main available association measures for characterizing gene regulatory strengths. Detecting the association between genes (as well as RNAs, proteins, and other molecules) is very important to decipher their functional relationship from genomic data in bioinformatics. With the availability of more and more high-throughput datasets, the quantification of meaningful relationships by employing association measures will make great sense of the data. There are various quantitative measures have been proposed for identifying molecular associations. They are depended on different statistical assumptions, for different intentions, as well as with different computational costs in calculating the associations in thousands of genes. Here, we comprehensively summarize these association measures employed and developed for describing gene regulatory relationships. We compare these measures in their consistency and specificity of detecting gene regulations from both simulation and real gene expression profiling data. Obviously, these measures used in genes can be easily extended in other biological molecules or across them.
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Affiliation(s)
- Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong UniversityJinan, China
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Module Anchored Network Inference: A Sequential Module-Based Approach to Novel Gene Network Construction from Genomic Expression Data on Human Disease Mechanism. Int J Genomics 2017; 2017:8514071. [PMID: 28197408 PMCID: PMC5286469 DOI: 10.1155/2017/8514071] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2016] [Accepted: 12/15/2016] [Indexed: 11/26/2022] Open
Abstract
Different computational approaches have been examined and compared for inferring network relationships from time-series genomic data on human disease mechanisms under the recent Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge. Many of these approaches infer all possible relationships among all candidate genes, often resulting in extremely crowded candidate network relationships with many more False Positives than True Positives. To overcome this limitation, we introduce a novel approach, Module Anchored Network Inference (MANI), that constructs networks by analyzing sequentially small adjacent building blocks (modules). Using MANI, we inferred a 7-gene adipogenesis network based on time-series gene expression data during adipocyte differentiation. MANI was also applied to infer two 10-gene networks based on time-course perturbation datasets from DREAM3 and DREAM4 challenges. MANI well inferred and distinguished serial, parallel, and time-dependent gene interactions and network cascades in these applications showing a superior performance to other in silico network inference techniques for discovering and reconstructing gene network relationships.
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Sun X, Hu F, Wu S, Qiu X, Linel P, Wu H. Controllability and stability analysis of large transcriptomic dynamic systems for host response to influenza infection in human. Infect Dis Model 2016; 1:52-70. [PMID: 29928721 PMCID: PMC5963324 DOI: 10.1016/j.idm.2016.07.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2016] [Accepted: 07/08/2016] [Indexed: 12/20/2022] Open
Abstract
Background Gene regulatory networks are complex dynamic systems and the reverse-engineering of such networks from high-dimensional time course transcriptomic data have attracted researchers from various fields. It is also interesting and important to study the behavior of the reconstructed networks on the basis of dynamic models and the biological mechanisms. We focus on the gene regulatory networks reconstructed using the ordinary differential equation (ODE) modelling approach and investigate the properties of these networks. Results Controllability and stability analyses are conducted for the reconstructed gene response networks of 17 influenza infected subjects based on ODE models. Symptomatic subjects tend to have larger numbers of driver nodes, higher proportions of critical links and lower proportions of redundant links than asymptomatic subjects. We also show that the degree distribution, rather than the structure of networks, plays an important role in controlling the network in response to influenza infection. In addition, we find that the stability of high-dimensional networks is very sensitive to randomness in the reconstructed systems brought by errors in measurements and parameter estimation. Conclusions The gene response networks of asymptomatic subjects are easier to be controlled than those of symptomatic subjects. This may indicate that the regulatory systems of asymptomatic subjects are easier to recover from disease stimulations, so these subjects are less likely to develop symptoms. Our results also suggest that stability constraint should be considered in the modelling of high-dimensional networks and the estimation of network parameters.
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Affiliation(s)
- Xiaodian Sun
- Biostatistics and Bioinformatics Core, Sylvester Comprehensive Cancer Center, University of Miami, Miami, USA
| | - Fang Hu
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | - Shuang Wu
- Genus PLC, ABS Global, Deforest, WI, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry, Rochester, NY, USA
| | | | - Hulin Wu
- Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
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Liu ZP. Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data. Curr Genomics 2015; 16:3-22. [PMID: 25937810 PMCID: PMC4412962 DOI: 10.2174/1389202915666141110210634] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2014] [Revised: 09/05/2014] [Accepted: 09/05/2014] [Indexed: 12/17/2022] Open
Abstract
Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.
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Affiliation(s)
- Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong 250061, China
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Abstract
Mathematical models of natural systems are abstractions of much more complicated processes. Developing informative and realistic models of such systems typically involves suitable statistical inference methods, domain expertise, and a modicum of luck. Except for cases where physical principles provide sufficient guidance, it will also be generally possible to come up with a large number of potential models that are compatible with a given natural system and any finite amount of data generated from experiments on that system. Here we develop a computational framework to systematically evaluate potentially vast sets of candidate differential equation models in light of experimental and prior knowledge about biological systems. This topological sensitivity analysis enables us to evaluate quantitatively the dependence of model inferences and predictions on the assumed model structures. Failure to consider the impact of structural uncertainty introduces biases into the analysis and potentially gives rise to misleading conclusions.
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