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Niarakis A, Thakar J, Barberis M, Rodríguez Martínez M, Helikar T, Birtwistle M, Chaouiya C, Calzone L, Dräger A. Computational modelling in health and disease: highlights of the 6th annual SysMod meeting. Bioinformatics 2022. [DOI: 10.1093/bioinformatics/btac609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Abstract
Summary
The Community of Special Interest (COSI) in Computational Modelling of Biological Systems (SysMod) brings together interdisciplinary scientists interested in combining data-driven computational modelling, multi-scale mechanistic frameworks, large-scale -omics data and bioinformatics. SysMod’s main activity is an annual meeting at the Intelligent Systems for Molecular Biology (ISMB) conference, a meeting for computer scientists, biologists, mathematicians, engineers and computational and systems biologists. The 2021 SysMod meeting was conducted virtually due to the ongoing COVID-19 pandemic (coronavirus disease 2019). During the 2-day meeting, the development of computational tools, approaches and predictive models was discussed, along with their application to biological systems, emphasizing disease mechanisms. This report summarizes the meeting.
Availability and implementation
All resources and further information are freely accessible at https://sysmod.info.
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Affiliation(s)
- Anna Niarakis
- GenHotel, Department of Biology, Univ Évry, University of Paris-Saclay, Genopole , 91025 Évry, France
- Lifeware Group, Inria Saclay-île de France , Palaiseau 91120, France
| | - Juilee Thakar
- Department of Microbiology and Immunology, University of Rochester School of Medicine and Dentistry , Rochester, NY, USA
- Department of Biostatistics and Computational Biology, University of Rochester School of Medicine and Dentistry , Rochester, NY, USA
| | - Matteo Barberis
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey , GU2 7XH Guildford, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey , GU2 7XH Guildford, Surrey, UK
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences University of Amsterdam , 1098 XH Amsterdam, The Netherlands
| | | | - Tomáš Helikar
- Department of Biochemistry, University of Nebraska-Lincoln , Lincoln, NE68588-0664, USA
| | - Marc Birtwistle
- Department of Chemical and Biomolecular Engineering, Clemson University , Clemson, SC 29634, USA
- Department of Bioengineering, Clemson University , Clemson, SC 29634, USA
| | | | - Laurence Calzone
- Institut Curie, PSL Research University , Paris, France
- INSERM, U900 , Paris, France
- MINES ParisTech , Paris, France
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), Eberhard Karl University of Tübingen , 72076 Tübingen, Germany
- Department of Computer Science, Eberhard Karl University of Tübingen , 72076 Tübingen, Germany
- German Center for Infection Research (DZIF), Partner Site Tübingen , Tübingen 72076, Germany
- Cluster of Excellence ‘Controlling Microbes to Fight Infections,’ Eberhard Karl University of Tübingen , Tübingen 72076, Germany
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Unbiased analysis of peripheral blood mononuclear cells reveals CD4 T cell response to RSV matrix protein. Vaccine X 2020; 5:100065. [PMID: 32529184 PMCID: PMC7280769 DOI: 10.1016/j.jvacx.2020.100065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/13/2020] [Accepted: 04/20/2020] [Indexed: 12/02/2022] Open
Abstract
Respiratory syncytial virus (RSV) is the most important cause of respiratory tract illness especially in young infants that develop severe disease requiring hospitalization, and accounting for 74,000–126,000 admissions in the United States (Rezaee et al., 2017; Resch, 2017). Observations of neonatal and infant T cells suggest that they may express different immune markers compared to T-cells from older children. Flow cytometry analysis of cellular responses using “conventional” anti-viral markers (IL2, IFN-γ, TNF, IL10 and IL4) upon RSV-peptide stimulation detected an overall low RSV response in peripheral blood. Therefore we sought an unbiased approach to identify RSV-specific immune markers using RNA-sequencing upon stimulation of infant PBMCs with overlapping peptides representing RSV antigens. To understand the cellular response using transcriptional signatures, transcription factors and cell-type specific signatures were used to investigate breadth of response across peptides. Unexpected from the ICS data, M peptide induced a response equivalent to the F-peptide and was characterized by activation of GATA2, 3, STAT3 and IRF1. This along with upregulation of several unconventional T cell signatures was only observed upon M-peptide stimulation. Moreover, signatures of natural RSV infections were identified from the data available in the public domain to investigate similarities between transcriptional signatures from PBMCs and upon peptide stimulation. This analysis also suggested activation of T cell response upon M-peptide stimulation. Hence, based on transcriptional response, markers were chosen to validate the role of M-peptide in activation of T cells. Indeed, CD4+CXCL9+ cells were identified upon M-peptide stimulation by flow cytometry. Future work using additional markers identified in this study could reveal additional unconventional T cells responding to RSV infections in infants. In conclusion, T cell responses to RSV in infants may not follow the canonical Th1/Th2 patterns of effector responses but include additional functions that may be unique to the neonatal period and correlate with clinical outcomes.
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Benoodt L, Thakar J. Network Analysis of Large-Scale Data and Its Application to Immunology. Methods Mol Biol 2020; 2131:199-211. [PMID: 32162255 DOI: 10.1007/978-1-0716-0389-5_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Diseases and infections elicit a multilayered immune response which consists of molecular and cellular interaction cascades. Recent advances in high-throughput technologies have facilitated multiparameter investigation of immune cells involved in human immune responses. These multiparameter investigations generate large-scale datasets and advanced computational techniques are required to gain useful information from them. Networks or graphs offer a practical way to represent complex information and develop advanced algorithms to unveil the underlying mechanisms. Here we discuss ways to assemble and analyze networks using genome-wide transcriptional profiles. Additionally, we discuss ways to integrate information available in primary literature and databases with the networks assembled using large-scale datasets. Finally, we describe ways in which network analysis offers insights into human immune responses.
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Affiliation(s)
- Lauren Benoodt
- Department of Biochemistry and Biophysics, University of Rochester Medical Center, Rochester, NY, USA
| | - Juilee Thakar
- Department of Microbiology and Immunology, University of Rochester Medical Center, Rochester, NY, USA. .,Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
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Meng Y, Cai XH, Wang L. Potential Genes and Pathways of Neonatal Sepsis Based on Functional Gene Set Enrichment Analyses. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2018; 2018:6708520. [PMID: 30154914 PMCID: PMC6091373 DOI: 10.1155/2018/6708520] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 06/04/2018] [Accepted: 06/27/2018] [Indexed: 12/16/2022]
Abstract
BACKGROUND Neonatal sepsis (NS) is considered as the most common cause of neonatal deaths that newborns suffer from. Although numerous studies focus on gene biomarkers of NS, the predictive value of the gene biomarkers is low. NS pathogenesis is still needed to be investigated. METHODS After data preprocessing, we used KEGG enrichment method to identify the differentially expressed pathways between NS and normal controls. Then, functional principal component analysis (FPCA) was adopted to calculate gene values in NS. In order to further study the key signaling pathway of the NS, elastic-net regression model, Mann-Whitney U test, and coexpression network were used to estimate the weights of signaling pathway and hub genes. RESULTS A total of 115 different pathways between NS and controls were first identified. FPCA made full use of time-series gene expression information and estimated F values of genes in the different pathways. The top 1000 genes were considered as the different genes and were further analyzed by elastic-net regression and MWU test. There were 7 key signaling pathways between the NS and controls, according to different sources. Among those genes involved in key pathways, 7 hub genes, PIK3CA, TGFBR2, CDKN1B, KRAS, E2F3, TRAF6, and CHUK, were determined based on the coexpression network. Most of them were cancer-related genes. PIK3CA was considered as the common marker, which is highly expressed in the lymphocyte group. Little was known about the correlation of PIK3CA with NS, which gives us a new enlightenment for NS study. CONCLUSION This research might provide the perspective information to explore the potential novel genes and pathways as NS therapy targets.
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Affiliation(s)
- YuXiu Meng
- Department of Neonatology, First People's Hospital of Jining, Jining, Shandong 272000, China
| | - Xue Hong Cai
- Department of Pediatrics, Traditional Chinese Medicine Hospital of Yanzhou, Jining, Shandong 272100, China
| | - LiPei Wang
- Department of Neonatology, First People's Hospital of Jining, Jining, Shandong 272000, China
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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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Palli R, Thakar J. Developing Network Models of Multiscale Host Responses Involved in Infections and Diseases. Methods Mol Biol 2018; 1819:385-402. [PMID: 30421414 DOI: 10.1007/978-1-4939-8618-7_18] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Complex interactions involved in host response to infections and diseases require advanced analytical tools to infer drivers of the response in order to develop strategies for intervention. This chapter discusses approaches to assemble interactions ranging from molecular to cellular levels and their analysis to investigate the cross talk between immune pathways. Particularly, construction of immune networks by either data-driven or literature-driven methods is explained. Next, graph theoretic approaches for probing static network properties as well as visualization of networks are discussed. Finally, development of Boolean models for simulation of network dynamics to investigate cross talk and emergent properties are considered along with Boolean-like models that may compensate for some of the limitations encountered in Boolean simulations. In conclusion, the chapter will allow readers to construct and analyze multiscale networks involved in immune responses.
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Affiliation(s)
- Rohith Palli
- Medical Scientist Training Program and Biophysics, Structural & Computational Biology graduate program, Rochester, NY, USA
| | - Juilee Thakar
- Departments of Microbiology and Immunology, Rochester, NY, USA.
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Khan A, Katanic D, Thakar J. Meta-analysis of cell- specific transcriptomic data using fuzzy c-means clustering discovers versatile viral responsive genes. BMC Bioinformatics 2017; 18:295. [PMID: 28587632 PMCID: PMC5461682 DOI: 10.1186/s12859-017-1669-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Accepted: 05/03/2017] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Despite advances in the gene-set enrichment analysis methods; inadequate definitions of gene-sets cause a major limitation in the discovery of novel biological processes from the transcriptomic datasets. Typically, gene-sets are obtained from publicly available pathway databases, which contain generalized definitions frequently derived by manual curation. Recently unsupervised clustering algorithms have been proposed to identify gene-sets from transcriptomics datasets deposited in public domain. These data-driven definitions of the gene-sets can be context-specific revealing novel biological mechanisms. However, the previously proposed algorithms for identification of data-driven gene-sets are based on hard clustering which do not allow overlap across clusters, a characteristic that is predominantly observed across biological pathways. RESULTS We developed a pipeline using fuzzy-C-means (FCM) soft clustering approach to identify gene-sets which recapitulates topological characteristics of biological pathways. Specifically, we apply our pipeline to derive gene-sets from transcriptomic data measuring response of monocyte derived dendritic cells and A549 epithelial cells to influenza infections. Our approach apply Ward's method for the selection of initial conditions, optimize parameters of FCM algorithm for human cell-specific transcriptomic data and identify robust gene-sets along with versatile viral responsive genes. CONCLUSION We validate our gene-sets and demonstrate that by identifying genes associated with multiple gene-sets, FCM clustering algorithm significantly improves interpretation of transcriptomic data facilitating investigation of novel biological processes by leveraging on transcriptomic data available in the public domain. We develop an interactive 'Fuzzy Inference of Gene-sets (FIGS)' package (GitHub: https://github.com/Thakar-Lab/FIGS ) to facilitate use of of pipeline. Future extension of FIGS across different immune cell-types will improve mechanistic investigation followed by high-throughput omics studies.
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Affiliation(s)
- Atif Khan
- Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA
| | - Dejan Katanic
- Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA
| | - Juilee Thakar
- Department of Microbiology and Immunology, University of Rochester, Rochester, NY, 14642, USA.
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, 14642, USA.
- , 601 Elmwood Avenue, Rochester, NY, 14618, USA.
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