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O'Donnell MS, Edmunds DR, Aldridge CL, Heinrichs JA, Monroe AP, Coates PS, Prochazka BG, Hanser SE, Wiechman LA. Defining biologically relevant and hierarchically nested population units to inform wildlife management. Ecol Evol 2022; 12:e9565. [PMID: 36466138 PMCID: PMC9712811 DOI: 10.1002/ece3.9565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/29/2022] [Accepted: 11/11/2022] [Indexed: 12/05/2022] Open
Abstract
Wildlife populations are increasingly affected by natural and anthropogenic changes that negatively alter biotic and abiotic processes at multiple spatiotemporal scales and therefore require increased wildlife management and conservation efforts. However, wildlife management boundaries frequently lack biological context and mechanisms to assess demographic data across the multiple spatiotemporal scales influencing populations. To address these limitations, we developed a novel approach to define biologically relevant subpopulations of hierarchically nested population levels that could facilitate managing and conserving wildlife populations and habitats. Our approach relied on the Spatial "K"luster Analysis by Tree Edge Removal clustering algorithm, which we applied in an agglomerative manner (bottom-to-top). We modified the clustering algorithm using a workflow and population structure tiers from least-cost paths, which captured biological inferences of habitat conditions (functional connectivity), dispersal capabilities (potential connectivity), genetic information, and functional processes affecting movements. The approach uniquely included context of habitat resources (biotic and abiotic) summarized at multiple spatial scales surrounding locations with breeding site fidelity and constraint-based rules (number of sites grouped and population structure tiers). We applied our approach to greater sage-grouse (Centrocercus urophasianus), a species of conservation concern, across their range within the western United States. This case study produced 13 hierarchically nested population levels (akin to cluster levels, each representing a collection of subpopulations of an increasing number of breeding sites). These closely approximated population closure at finer ecological scales (smaller subpopulation extents with fewer breeding sites; cluster levels ≥2), where >92% of individual sage-grouse's time occurred within their home cluster. With available population monitoring data, our approaches can support the investigation of factors affecting population dynamics at multiple scales and assist managers with making informed, targeted, and cost-effective decisions within an adaptive management framework. Importantly, our approach provides the flexibility of including species-relevant context, thereby supporting other wildlife characterized by site fidelity.
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Affiliation(s)
| | - David R. Edmunds
- U.S. Geological SurveyFort Collins Science CenterFort CollinsColoradoUSA
| | | | - Julie A. Heinrichs
- Natural Resource Ecology Laboratory, U.S. Geological Survey, Fort Collins Science CenterColorado State UniversityFort CollinsColoradoUSA
| | - Adrian P. Monroe
- U.S. Geological SurveyFort Collins Science CenterFort CollinsColoradoUSA
| | - Peter S. Coates
- U.S. Geological SurveyWestern Ecological Research CenterDixonCaliforniaUSA
| | - Brian G. Prochazka
- U.S. Geological SurveyWestern Ecological Research CenterDixonCaliforniaUSA
| | - Steve E. Hanser
- U.S. Geological SurveyFort Collins Science CenterFort CollinsColoradoUSA
| | - Lief A. Wiechman
- U.S. Geological SurveyEcosystems Mission AreaFort CollinsColoradoUSA
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2
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Zhang D, Song J, Dharmarajan S, Jung TH, Lee H, Ma Y, Zhang R, Levenson M. The Use of Machine Learning in Regulatory Drug Safety Evaluation. Stat Biopharm Res 2022. [DOI: 10.1080/19466315.2022.2108135] [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]
Affiliation(s)
- Di Zhang
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Jaejoon Song
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Sai Dharmarajan
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Tae Hyun Jung
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Hana Lee
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Yong Ma
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Rongmei Zhang
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
| | - Mark Levenson
- Division of Biometrics VII, Office of Biostatistics, Center for Drug Evaluation and Research, U.S. Food and Drug Administration
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3
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Maroufy V, Shah P, Asghari A, Deng N, Le RNU, Ramirez JC, Yaseen A, Zheng WJ, Umetani M, Wu H. Gene expression dynamic analysis reveals co-activation of Sonic Hedgehog and epidermal growth factor followed by dynamic silencing. Oncotarget 2020; 11:1358-1372. [PMID: 32341755 PMCID: PMC7170495 DOI: 10.18632/oncotarget.27547] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2019] [Accepted: 03/14/2020] [Indexed: 12/02/2022] Open
Abstract
Aberrant activation of the Sonic Hedgehog (SHH) gene is observed in various cancers. Previous studies have shown a “cross-talk” effect between the canonical Hedgehog signaling pathway and the Epidermal Growth Factor (EGF) pathway when SHH is active in the presence of EGF. However, the precise mechanism of the cross-talk effect on the entire gene population has not been investigated. Here, we re-analyzed publicly available data to study how SHH and EGF cooperate to affect the dynamic activity of the gene population. We used genome dynamic analysis to explore the expression profiles under different conditions in a human medulloblastoma cell line. Ordinary differential equations, equipped with solid statistical and computational tools, were exploited to extract the information hidden in the dynamic behavior of the gene population. Our results revealed that EGF stimulation plays a dominant role, overshadowing most of the SHH effects. We also identified cross-talk genes that exhibited expression profiles dissimilar to that seen under SHH or EGF stimulation alone. These unique cross-talk patterns were validated in a cell culture model. These cross-talk genes identified here may serve as valuable markers to study or test for EGF co-stimulatory effects in an SHH+ environment. Furthermore, these cross-talk genes may play roles in cancer progression, thus they may be further explored as cancer treatment targets.
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Affiliation(s)
- Vahed Maroufy
- Department of Biostatistics and Data Science, School of Public Heath, University of Texas Health Science Center at Houston, Houston, TX, USA.,These authors contributed equally to this work
| | - Pankil Shah
- Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA.,These authors contributed equally to this work
| | - Arvand Asghari
- Center for Nuclear Receptors and Cell Signaling, Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Nan Deng
- Department of Biostatistics and Data Science, School of Public Heath, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Rosemarie N U Le
- Center for Nuclear Receptors and Cell Signaling, Department of Biology and Biochemistry, University of Houston, Houston, TX, USA
| | - Juan C Ramirez
- Facultad de Ingeniería de Sistemas, Universidad Antonio Nariño, Bogota, Colombia
| | - Ashraf Yaseen
- Department of Biostatistics and Data Science, School of Public Heath, 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
| | - Michihisa Umetani
- Center for Nuclear Receptors and Cell Signaling, Department of Biology and Biochemistry, University of Houston, Houston, TX, USA.,HEALTH Research Institute, University of Houston, Houston, TX, USA
| | - Hulin Wu
- Department of Biostatistics and Data Science, School of Public Heath, University of Texas Health Science Center at Houston, Houston, TX, 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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Carey M, Ramírez JC, Wu S, Wu H. A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection. Stat Methods Med Res 2019; 27:1930-1955. [PMID: 29846143 DOI: 10.1177/0962280217746719] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
A biological host response to an external stimulus or intervention such as a disease or infection is a dynamic process, which is regulated by an intricate network of many genes and their products. Understanding the dynamics of this gene regulatory network allows us to infer the mechanisms involved in a host response to an external stimulus, and hence aids the discovery of biomarkers of phenotype and biological function. In this article, we propose a modeling/analysis pipeline for dynamic gene expression data, called Pipeline4DGEData, which consists of a series of statistical modeling techniques to construct dynamic gene regulatory networks from the large volumes of high-dimensional time-course gene expression data that are freely available in the Gene Expression Omnibus repository. This pipeline has a consistent and scalable structure that allows it to simultaneously analyze a large number of time-course gene expression data sets, and then integrate the results across different studies. We apply the proposed pipeline to influenza infection data from nine studies and demonstrate that interesting biological findings can be discovered with its implementation.
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Affiliation(s)
- Michelle Carey
- 1 School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
| | - Juan Camilo Ramírez
- 2 Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
| | | | - Hulin Wu
- 2 Department of Biostatistics, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA
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6
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O'Donnell MS, Edmunds DR, Aldridge CL, Heinrichs JA, Coates PS, Prochazka BG, Hanser SE. Designing multi‐scale hierarchical monitoring frameworks for wildlife to support management: a sage‐grouse case study. Ecosphere 2019. [DOI: 10.1002/ecs2.2872] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Affiliation(s)
- Michael S. O'Donnell
- U.S. Geological Survey Fort Collins Science Center Fort Collins Colorado 80526 USA
| | - David R. Edmunds
- Natural Resource Ecology Laboratory Colorado State University, in cooperation with the Fort Collins Science Center, U.S. Geological Survey Fort Collins Colorado 80526 USA
| | - Cameron L. Aldridge
- Natural Resource Ecology Laboratory Department of Ecosystem Science and Sustainability Colorado State University, in cooperation with the Fort Collins Science Center, U.S. Geological Survey Fort Collins Colorado 80526 USA
| | - Julie A. Heinrichs
- Natural Resource Ecology Laboratory Colorado State University, in cooperation with the Fort Collins Science Center, U.S. Geological Survey Fort Collins Colorado 80526 USA
| | - Peter S. Coates
- U.S. Geological Survey Western Ecological Research Center Dixon California 95620 USA
| | - Brian G. Prochazka
- U.S. Geological Survey Western Ecological Research Center Dixon California 95620 USA
| | - Steve E. Hanser
- U.S. Geological Survey Ecosystems Mission Area Reston VA 20192 USA
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7
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Deng N, Ramirez JC, Carey M, Miao H, Arias CA, Rice AP, Wu H. Investigation of temporal and spatial heterogeneities of the immune responses to Bordetella pertussis infection in the lung and spleen of mice via analysis and modeling of dynamic microarray gene expression data. Infect Dis Model 2019; 4:215-226. [PMID: 31236525 PMCID: PMC6579965 DOI: 10.1016/j.idm.2019.06.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2019] [Revised: 06/06/2019] [Accepted: 06/06/2019] [Indexed: 12/24/2022] Open
Abstract
Bordetella pertussis (B. pertussis) is the causative agent of pertussis, also referenced as whooping cough. Although pertussis has been appropriately controlled by routine immunization of infants, it has experienced a resurgence since the beginning of the 21st century. Given that elucidating the immune response to pertussis is a crucial factor to improve therapeutic and preventive treatments, we re-analyzed a time course microarray dataset of B. pertussis infection by applying a newly developed dynamic data analysis pipeline. Our results indicate that the immune response to B. pertussis is highly dynamic and heterologous across different organs during infection. Th1 and Th17 cells, which are two critical types of T helper cell populations in the immune response to B. pertussis, and follicular T helper cells (TFHs), which are also essential for generating antibodies, might be generated at different time points and distinct locations after infection. This phenomenon may indicate that different lymphoid organs may have their unique functions during infection. These findings provide a better understanding of the basic immunology of bacterial infection, which may provide valuable insights for the improvement of pertussis vaccine design in the future.
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Affiliation(s)
- Nan Deng
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Juan C Ramirez
- Facultad de Ingeniería de Sistemas, Universidad Antonio Nariño, Bogotá, Colombia
| | - Michelle Carey
- School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
| | - Hongyu Miao
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Cesar A Arias
- Center for Antimicrobial Resistance and Microbial Genomics (CARMiG), UTHealth McGovern Medical School, USA.,Divicon of Infectious Diseases and Department of Microbiology and Molecular Genetics, UTHealth McGovern Medical School, USA.,Center for Infectious Diseases, UTHealth School of Public Health, USA.,Molecular Genetics and Antimicrobial Resistance Unit and International Center for Microbial Genomics, Universidad El Bosque, Bogota, Colombia
| | - Andrew P Rice
- Department of Molecular Virology & Microbiology, Baylor College of Medicine, Houston, TX, USA
| | - Hulin Wu
- Department of Biostatistics and Data Science, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
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8
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Chen G, Ramírez JC, Deng N, Qiu X, Wu C, Zheng WJ, Wu H. Restructured GEO: restructuring Gene Expression Omnibus metadata for genome dynamics analysis. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2019; 2019:5289627. [PMID: 30649296 PMCID: PMC6333964 DOI: 10.1093/database/bay145] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2018] [Accepted: 12/11/2018] [Indexed: 11/14/2022]
Abstract
Motivation Gene Expression Omnibus (GEO) and other publicly available data store their metadata in the format of unstructured English text, which is very difficult for automated reuse. Results We employed text mining techniques to analyze the metadata of GEO and developed Restructured GEO database (ReGEO). ReGEO reorganizes and categorizes GEO series and makes them searchable by two new attributes extracted automatically from each series' metadata. These attributes are the number of time points tested in the experiment and the disease being investigated. ReGEO also makes series searchable by other attributes available in GEO, such as platform organism, experiment type, associated PubMed ID as well as general keywords in the study's description. Our approach greatly expands the usability of GEO data, demonstrating a credible approach to improve the utility of vast amount of publicly available data in the era of Big Data research.
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Affiliation(s)
- Guocai Chen
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | | | - Nan Deng
- Department of Biostatistics & Data Science, School of Public Health, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Xing Qiu
- Department of Biostatistics and Computational Biology, School of Medicine and Dentistry, University of Rochester, Rochester, New York, USA
| | - Canglin Wu
- TechWave International. Inc., Houston, Texas, USA
| | - W Jim Zheng
- School of Biomedical Informatics, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
| | - Hulin Wu
- Department of Biostatistics & Data Science, School of Public Health, University of Texas Health Science Center at Houston (UTHealth), Houston, Texas, USA
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9
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Song J, Carey M, Zhu H, Miao H, Ramírez JC, Wu H. Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations. ACTA ACUST UNITED AC 2018; 11:135-153. [PMID: 34531927 DOI: 10.1504/ijcbdd.2018.10011910] [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
Reactivation of latently infected cells has emerged as an important strategy for eradication of HIV. However, genetic mechanisms of regulation after reactivation remain unclear. We describe a five-step pipeline to study the dynamics of the gene regulatory network following a viral reactivation using high-dimensional ordinary differential equations. Our pipeline implements a combination of five different methods, by detecting temporally differentially expressed genes (step 1), clustering genes with similar temporal expression patterns into a small number of response modules (step2), performing a functional enrichment analysis within each gene response module (step 3), identifying a network structure based on the gene response modules using ordinary differential equations (ODE) and a high-dimensional variable selection technique (step 4), and obtaining a gene regulatory model based on refined parameter estimates using nonlinear least squares (step 5). We applied our pipeline to a time course gene expression data of latently infected T-cells following a latency-reversion.
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Affiliation(s)
- Jaejoon Song
- Department of Biostatistics, The University of Texas MD, Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030, USA
| | - Michelle Carey
- Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, Canada, H3A 0B9
| | - Hongjian Zhu
- Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, 77030, USA
| | - Hongyu Miao
- Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, 77030, USA
| | - Juan Camilo Ramírez
- Faculty of Computer Engineering, Universidad Antonio Nariño, Cl. 58a Bis 3794, Bogotá, Cundinamarca, Colombia
| | - Hulin Wu
- Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, USA, 77030, USA
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