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Marques I, Hu H. Molecular Insight of Plants Response to Drought Stress: Perspectives and New Insights towards Food Security. Int J Mol Sci 2024; 25:4988. [PMID: 38732211 PMCID: PMC11084910 DOI: 10.3390/ijms25094988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024] Open
Abstract
In the face of climate-induced challenges, understanding the intricate molecular mechanisms underlying drought tolerance in plants has become imperative [...].
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Affiliation(s)
- Isabel Marques
- Forest Research Centre, Associate Laboratory TERRA, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal
| | - Honghong Hu
- Key Laboratory of Crop Genetic Improvement, College of Life Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
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2
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Martini L, Baek SH, Lo I, Raby BA, Silverman E, Weiss S, Glass K, Halu A. Detecting and dissecting signaling crosstalk via the multilayer network integration of signaling and regulatory interactions. Nucleic Acids Res 2024; 52:e5. [PMID: 37953325 PMCID: PMC10783515 DOI: 10.1093/nar/gkad1035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 06/27/2023] [Accepted: 10/23/2023] [Indexed: 11/14/2023] Open
Abstract
The versatility of cellular response arises from the communication, or crosstalk, of signaling pathways in a complex network of signaling and transcriptional regulatory interactions. Understanding the various mechanisms underlying crosstalk on a global scale requires untargeted computational approaches. We present a network-based statistical approach, MuXTalk, that uses high-dimensional edges called multilinks to model the unique ways in which signaling and regulatory interactions can interface. We demonstrate that the signaling-regulatory interface is located primarily in the intermediary region between signaling pathways where crosstalk occurs, and that multilinks can differentiate between distinct signaling-transcriptional mechanisms. Using statistically over-represented multilinks as proxies of crosstalk, we infer crosstalk among 60 signaling pathways, expanding currently available crosstalk databases by more than five-fold. MuXTalk surpasses existing methods in terms of model performance metrics, identifies additions to manual curation efforts, and pinpoints potential mediators of crosstalk. Moreover, it accommodates the inherent context-dependence of crosstalk, allowing future applications to cell type- and disease-specific crosstalk.
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Affiliation(s)
- Leonardo Martini
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Computer, Control, and Management Engineering, Sapienza University of Rome, Rome, 00185, Italy
| | - Seung Han Baek
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Ian Lo
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Benjamin A Raby
- Division of Pulmonary Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Edwin K Silverman
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Kimberly Glass
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Arda Halu
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
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3
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Yin L, Zander M, Huang SSC, Xie M, Song L, Saldierna Guzmán JP, Hann E, Shanbhag BK, Ng S, Jain S, Janssen BJ, Clark NM, Walley JW, Beddoe T, Bar-Joseph Z, Lewsey MG, Ecker JR. Transcription Factor Dynamics in Cross-Regulation of Plant Hormone Signaling Pathways. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.531630. [PMID: 36945593 PMCID: PMC10028877 DOI: 10.1101/2023.03.07.531630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/11/2023]
Abstract
Cross-regulation between hormone signaling pathways is indispensable for plant growth and development. However, the molecular mechanisms by which multiple hormones interact and co-ordinate activity need to be understood. Here, we generated a cross-regulation network explaining how hormone signals are integrated from multiple pathways in etiolated Arabidopsis (Arabidopsis thaliana) seedlings. To do so we comprehensively characterized transcription factor activity during plant hormone responses and reconstructed dynamic transcriptional regulatory models for six hormones; abscisic acid, brassinosteroid, ethylene, jasmonic acid, salicylic acid and strigolactone/karrikin. These models incorporated target data for hundreds of transcription factors and thousands of protein-protein interactions. Each hormone recruited different combinations of transcription factors, a subset of which were shared between hormones. Hub target genes existed within hormone transcriptional networks, exhibiting transcription factor activity themselves. In addition, a group of MITOGEN-ACTIVATED PROTEIN KINASES (MPKs) were identified as potential key points of cross-regulation between multiple hormones. Accordingly, the loss of function of one of these (MPK6) disrupted the global proteome, phosphoproteome and transcriptome during hormone responses. Lastly, we determined that all hormones drive substantial alternative splicing that has distinct effects on the transcriptome compared with differential gene expression, acting in early hormone responses. These results provide a comprehensive understanding of the common features of plant transcriptional regulatory pathways and how cross-regulation between hormones acts upon gene expression.
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Affiliation(s)
- Lingling Yin
- La Trobe Institute for Agriculture and Food, Department of Animal, Plant and Soil Sciences, School of Agriculture Biomedicine and Environment, AgriBio Building, La Trobe University, Melbourne, VIC 3086, Australia
- Australian Research Council Industrial Transformation Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Mark Zander
- Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Present address: Waksman Institute of Microbiology, Department of Plant Biology, Rutgers, The State University of New Jersey, NJ 08854, USA
| | - Shao-shan Carol Huang
- Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Present address: Department of Biology, New York University, New York, NY 10003, USA
| | - Mingtang Xie
- Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Present address: Cibus, San Diego, CA 92121, USA
| | - Liang Song
- Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Present address: Department of Botany, The University of British Columbia, Vancouver, British Columbia, Canada
| | - J. Paola Saldierna Guzmán
- Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Present address: Department of Soil and Crop Sciences, Colorado State University, Fort Collins, Colorado, USA
| | - Elizabeth Hann
- Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Present address: Department of Chemical and Environmental Engineering, Department of Botany and Plant Sciences, University of California, Riverside, CA 92521, USA
| | - Bhuvana K. Shanbhag
- La Trobe Institute for Agriculture and Food, Department of Animal, Plant and Soil Sciences, School of Agriculture Biomedicine and Environment, AgriBio Building, La Trobe University, Melbourne, VIC 3086, Australia
- Australian Research Council Industrial Transformation Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Sophia Ng
- La Trobe Institute for Agriculture and Food, Department of Animal, Plant and Soil Sciences, School of Agriculture Biomedicine and Environment, AgriBio Building, La Trobe University, Melbourne, VIC 3086, Australia
- Australian Research Council Industrial Transformation Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Siddhartha Jain
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Bart J. Janssen
- The New Zealand Institute for Plant & Food Research Limited, Auckland, New Zealand
| | - Natalie M. Clark
- Proteomics Platform, Broad Institute of MIT and Harvard, Cambridge, MA, 02142 USA
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA, 50011 USA
| | - Justin W. Walley
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA, 50011 USA
| | - Travis Beddoe
- La Trobe Institute for Agriculture and Food, Department of Animal, Plant and Soil Sciences, School of Agriculture Biomedicine and Environment, AgriBio Building, La Trobe University, Melbourne, VIC 3086, Australia
- Australian Research Council Industrial Transformation Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Mathew G. Lewsey
- La Trobe Institute for Agriculture and Food, Department of Animal, Plant and Soil Sciences, School of Agriculture Biomedicine and Environment, AgriBio Building, La Trobe University, Melbourne, VIC 3086, Australia
- Australian Research Council Industrial Transformation Research Hub for Medicinal Agriculture, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
- Australian Research Council Centre of Excellence in Plants For Space, AgriBio Building, La Trobe University, Bundoora, VIC 3086, Australia
| | - Joseph R. Ecker
- Plant Biology Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Genomic Analysis Laboratory, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, Salk Institute for Biological Studies, La Jolla, CA 92037, USA
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4
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Singh A, Kandi AR, Jayaprakashappa D, Thuery G, Purohit DJ, Huelsmeier J, Singh R, Pothapragada SS, Ramaswami M, Bakthavachalu B. The transcriptional response to oxidative stress is independent of stress-granule formation. Mol Biol Cell 2022; 33:ar25. [PMID: 34985933 PMCID: PMC9250384 DOI: 10.1091/mbc.e21-08-0418] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/14/2021] [Accepted: 12/23/2021] [Indexed: 11/11/2022] Open
Abstract
Cells respond to stress with translational arrest, robust transcriptional changes, and transcription-independent formation of mRNP assemblies termed stress granules (SGs). Despite considerable interest in the role of SGs in oxidative, unfolded protein and viral stress responses, whether and how SGs contribute to stress-induced transcription have not been rigorously examined. To address this, we characterized transcriptional changes in Drosophila S2 cells induced by acute oxidative-stress and assessed how these were altered under conditions that disrupted SG assembly. Oxidative stress for 3 h predominantly resulted in induction or up-regulation of stress-responsive mRNAs whose levels peaked during recovery after stress cessation. The stress transcriptome is enriched in mRNAs coding for chaperones including HSP70s, small heat shock proteins, glutathione transferases, and several noncoding RNAs. Oxidative stress also induced cytoplasmic SGs that disassembled 3 h after stress cessation. As expected, RNAi-mediated knockdown of the conserved G3BP1/Rasputin protein inhibited SG assembly. However, this disruption had no significant effect on the stress-induced transcriptional response or stress-induced translational arrest. Thus SG assembly and stress-induced gene expression alterations appear to be driven by distinctive signaling processes. We suggest that while SG assembly represents a fast, transient mechanism, the transcriptional response enables a slower, longer-lasting mechanism for adaptation to and recovery from cell stress.
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Affiliation(s)
- Amanjot Singh
- National Centre for Biological Sciences, TIFR, Bangalore 560065, India
| | - Arvind Reddy Kandi
- Tata Institute for Genetics and Society Centre at inStem, Bellary Road, Bangalore 560065, India
| | | | - Guillaume Thuery
- Trinity College Institute of Neuroscience, School of Genetics and Microbiology, Smurfit Institute of Genetics and School of Natural Sciences, Trinity College Dublin, Dublin-2, Ireland
| | - Devam J Purohit
- National Centre for Biological Sciences, TIFR, Bangalore 560065, India
| | - Joern Huelsmeier
- Trinity College Institute of Neuroscience, School of Genetics and Microbiology, Smurfit Institute of Genetics and School of Natural Sciences, Trinity College Dublin, Dublin-2, Ireland
| | - Rashi Singh
- National Centre for Biological Sciences, TIFR, Bangalore 560065, India
| | | | - Mani Ramaswami
- National Centre for Biological Sciences, TIFR, Bangalore 560065, India
- Trinity College Institute of Neuroscience, School of Genetics and Microbiology, Smurfit Institute of Genetics and School of Natural Sciences, Trinity College Dublin, Dublin-2, Ireland
| | - Baskar Bakthavachalu
- Tata Institute for Genetics and Society Centre at inStem, Bellary Road, Bangalore 560065, India
- School of Basic Sciences, Indian Institute of Technology, Mandi 175005, India
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5
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Ding J, Sharon N, Bar-Joseph Z. Temporal modelling using single-cell transcriptomics. Nat Rev Genet 2022; 23:355-368. [PMID: 35102309 DOI: 10.1038/s41576-021-00444-7] [Citation(s) in RCA: 84] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2021] [Indexed: 12/16/2022]
Abstract
Methods for profiling genes at the single-cell level have revolutionized our ability to study several biological processes and systems including development, differentiation, response programmes and disease progression. In many of these studies, cells are profiled over time in order to infer dynamic changes in cell states and types, sets of expressed genes, active pathways and key regulators. However, time-series single-cell RNA sequencing (scRNA-seq) also raises several new analysis and modelling issues. These issues range from determining when and how deep to profile cells, linking cells within and between time points, learning continuous trajectories, and integrating bulk and single-cell data for reconstructing models of dynamic networks. In this Review, we discuss several approaches for the analysis and modelling of time-series scRNA-seq, highlighting their steps, key assumptions, and the types of data and biological questions they are most appropriate for.
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Ding J, Lugo-Martinez J, Yuan Y, Huang J, Hume AJ, Suder EL, Mühlberger E, Kotton DN, Bar-Joseph Z. Reconstructed signaling and regulatory networks identify potential drugs for SARS-CoV-2 infection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2020.06.01.127589. [PMID: 33083801 PMCID: PMC7574259 DOI: 10.1101/2020.06.01.127589] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Several molecular datasets have been recently compiled to characterize the activity of SARS-CoV-2 within human cells. Here we extend computational methods to integrate several different types of sequence, functional and interaction data to reconstruct networks and pathways activated by the virus in host cells. We identify key proteins in these networks and further intersect them with genes differentially expressed at conditions that are known to impact viral activity. Several of the top ranked genes do not directly interact with virus proteins. We experimentally tested treatments for a number of the predicted targets. We show that blocking one of the predicted indirect targets significantly reduces viral loads in stem cell-derived alveolar epithelial type II cells (iAT2s).
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Affiliation(s)
- Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, Montreal, Quebec, H4A 3J1, Canada
| | - Jose Lugo-Martinez
- Department of Computer Science, University of Puerto Rico, San Juan, Puerto Rico, 00925, USA
| | - Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240, China
| | - Jessie Huang
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Adam J. Hume
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Ellen L. Suder
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Elke Mühlberger
- National Emerging Infectious Diseases Laboratory (NEIDL), Boston University, Boston, MA 02118, USA
- Department of Microbiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Darrell N. Kotton
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, 15213, USA
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7
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Han M, Liu X, Zhang W, Wang M, Bu W, Chang C, Yu M, Li Y, Tian C, Yang X, Zhu Y, He F. TSMiner: a novel framework for generating time-specific gene regulatory networks from time-series expression profiles. Nucleic Acids Res 2021; 49:e108. [PMID: 34313778 PMCID: PMC8502000 DOI: 10.1093/nar/gkab629] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 06/30/2021] [Accepted: 07/09/2021] [Indexed: 12/03/2022] Open
Abstract
Time-series gene expression profiles are the primary source of information on complicated biological processes; however, capturing dynamic regulatory events from such data is challenging. Herein, we present a novel analytic tool, time-series miner (TSMiner), that can construct time-specific regulatory networks from time-series expression profiles using two groups of genes: (i) genes encoding transcription factors (TFs) that are activated or repressed at a specific time and (ii) genes associated with biological pathways showing significant mutual interactions with these TFs. Compared with existing methods, TSMiner demonstrated superior sensitivity and accuracy. Additionally, the application of TSMiner to a time-course RNA-seq dataset associated with mouse liver regeneration (LR) identified 389 transcriptional activators and 49 transcriptional repressors that were either activated or repressed across the LR process. TSMiner also predicted 109 and 47 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways significantly interacting with the transcriptional activators and repressors, respectively. These findings revealed the temporal dynamics of multiple critical LR-related biological processes, including cell proliferation, metabolism and the immune response. The series of evaluations and experiments demonstrated that TSMiner provides highly reliable predictions and increases the understanding of rapidly accumulating time-series omics data.
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Affiliation(s)
- Mingfei Han
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Xian Liu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Wen Zhang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China.,Tianjin Key Laboratory of Food Science and Biotechnology, School of Biotechnology and Food Science, Tianjin University of Commerce, Tianjin 300134, China
| | - Mengnan Wang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Wenjing Bu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Miao Yu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Yingxing Li
- Central Research Laboratory, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China
| | - Chunyan Tian
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Xiaoming Yang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Yunping Zhu
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
| | - Fuchu He
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, National Center for Protein Sciences (Beijing), Beijing 102206, P.R. China
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Ma CZ, Brent MR. Inferring TF activities and activity regulators from gene expression data with constraints from TF perturbation data. Bioinformatics 2021; 37:1234-1245. [PMID: 33135076 PMCID: PMC8189679 DOI: 10.1093/bioinformatics/btaa947] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 09/26/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022] Open
Abstract
Motivation The activity of a transcription factor (TF) in a sample of cells is the extent to which it is exerting its regulatory potential. Many methods of inferring TF activity from gene expression data have been described, but due to the lack of appropriate large-scale datasets, systematic and objective validation has not been possible until now. Results We systematically evaluate and optimize the approach to TF activity inference in which a gene expression matrix is factored into a condition-independent matrix of control strengths and a condition-dependent matrix of TF activity levels. We find that expression data in which the activities of individual TFs have been perturbed are both necessary and sufficient for obtaining good performance. To a considerable extent, control strengths inferred using expression data from one growth condition carry over to other conditions, so the control strength matrices derived here can be used by others. Finally, we apply these methods to gain insight into the upstream factors that regulate the activities of yeast TFs Gcr2, Gln3, Gcn4 and Msn2. Availability and implementation Evaluation code and data are available at https://doi.org/10.5281/zenodo.4050573. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Cynthia Z Ma
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA
| | - Michael R Brent
- Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO 63110, USA.,Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA.,Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
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9
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Treveil A, Bohar B, Sudhakar P, Gul L, Csabai L, Olbei M, Poletti M, Madgwick M, Andrighetti T, Hautefort I, Modos D, Korcsmaros T. ViralLink: An integrated workflow to investigate the effect of SARS-CoV-2 on intracellular signalling and regulatory pathways. PLoS Comput Biol 2021; 17:e1008685. [PMID: 33534793 PMCID: PMC7886129 DOI: 10.1371/journal.pcbi.1008685] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 02/16/2021] [Accepted: 01/10/2021] [Indexed: 12/21/2022] Open
Abstract
The SARS-CoV-2 pandemic of 2020 has mobilised scientists around the globe to research all aspects of the coronavirus virus and its infection. For fruitful and rapid investigation of viral pathomechanisms, a collaborative and interdisciplinary approach is required. Therefore, we have developed ViralLink: a systems biology workflow which reconstructs and analyses networks representing the effect of viruses on intracellular signalling. These networks trace the flow of signal from intracellular viral proteins through their human binding proteins and downstream signalling pathways, ending with transcription factors regulating genes differentially expressed upon viral exposure. In this way, the workflow provides a mechanistic insight from previously identified knowledge of virally infected cells. By default, the workflow is set up to analyse the intracellular effects of SARS-CoV-2, requiring only transcriptomics counts data as input from the user: thus, encouraging and enabling rapid multidisciplinary research. However, the wide-ranging applicability and modularity of the workflow facilitates customisation of viral context, a priori interactions and analysis methods. Through a case study of SARS-CoV-2 infected bronchial/tracheal epithelial cells, we evidence the functionality of the workflow and its ability to identify key pathways and proteins in the cellular response to infection. The application of ViralLink to different viral infections in a context specific manner using different available transcriptomics datasets will uncover key mechanisms in viral pathogenesis.
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Affiliation(s)
- Agatha Treveil
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Balazs Bohar
- Earlham Institute, Norwich, United Kingdom
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Padhmanand Sudhakar
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
- Department of Chronic Diseases, Metabolism and Ageing, Translational Research Center for Gastrointestinal Disorders, Katholieke Universiteit Leuven, Leuven, Belgium
| | - Lejla Gul
- Earlham Institute, Norwich, United Kingdom
| | - Luca Csabai
- Earlham Institute, Norwich, United Kingdom
- Department of Genetics, Eotvos Lorand University, Budapest, Hungary
| | - Marton Olbei
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Martina Poletti
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Matthew Madgwick
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Tahila Andrighetti
- Earlham Institute, Norwich, United Kingdom
- Institute of Biosciences, São Paulo University, Botucatu, Brazil
| | | | - Dezso Modos
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
| | - Tamas Korcsmaros
- Earlham Institute, Norwich, United Kingdom
- Quadram Institute Bioscience, Norwich, United Kingdom
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10
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Comparative Transcriptomics and Co-Expression Networks Reveal Tissue- and Genotype-Specific Responses of qDTYs to Reproductive-Stage Drought Stress in Rice ( Oryza sativa L.). Genes (Basel) 2020; 11:genes11101124. [PMID: 32987927 PMCID: PMC7650634 DOI: 10.3390/genes11101124] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/22/2020] [Accepted: 09/22/2020] [Indexed: 12/23/2022] Open
Abstract
Rice (Oryza sativa L.) is more sensitive to drought stress than other cereals. To dissect molecular mechanisms underlying drought-tolerant yield in rice, we applied differential expression and co-expression network approaches to transcriptomes from flag-leaf and emerging panicle tissues of a drought-tolerant yield introgression line, DTY-IL, and the recurrent parent Swarna, under moderate reproductive-stage drought stress. Protein turnover and efficient reactive oxygen species scavenging were found to be the driving factors in both tissues. In the flag-leaf, the responses further included maintenance of photosynthesis and cell wall reorganization, while in the panicle biosynthesis of secondary metabolites was found to play additional roles. Hub genes of importance in differential drought responses included an expansin in the flag-leaf and two peroxidases in the panicle. Overlaying differential expression data with allelic variation in DTY-IL quantitative trait loci allowed for the prioritization of candidate genes. They included a differentially regulated auxin-responsive protein, with DTY-IL-specific amino acid changes in conserved domains, as well as a protein kinase with a DTY-IL-specific frameshift in the C-terminal region. The approach highlights how the integration of differential expression and allelic variation can aid in the discovery of mechanism and putative causal contribution underlying quantitative trait loci for drought-tolerant yield.
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11
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Jiang Y, Liang Y, Wang D, Xu D, Joshi T. A dynamic programing approach to integrate gene expression data and network information for pathway model generation. Bioinformatics 2020; 36:169-176. [PMID: 31168616 DOI: 10.1093/bioinformatics/btz467] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 05/15/2019] [Accepted: 05/31/2019] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION As large amounts of biological data continue to be rapidly generated, a major focus of bioinformatics research has been aimed toward integrating these data to identify active pathways or modules under certain experimental conditions or phenotypes. Although biologically significant modules can often be detected globally by many existing methods, it is often hard to interpret or make use of the results toward pathway model generation and testing. RESULTS To address this gap, we have developed the IMPRes algorithm, a new step-wise active pathway detection method using a dynamic programing approach. IMPRes takes advantage of the existing pathway interaction knowledge in Kyoto Encyclopedia of Genes and Genomes. Omics data are then used to assign penalties to genes, interactions and pathways. Finally, starting from one or multiple seed genes, a shortest path algorithm is applied to detect downstream pathways that best explain the gene expression data. Since dynamic programing enables the detection one step at a time, it is easy for researchers to trace the pathways, which may lead to more accurate drug design and more effective treatment strategies. The evaluation experiments conducted on three yeast datasets have shown that IMPRes can achieve competitive or better performance than other state-of-the-art methods. Furthermore, a case study on human lung cancer dataset was performed and we provided several insights on genes and mechanisms involved in lung cancer, which had not been discovered before. AVAILABILITY AND IMPLEMENTATION IMPRes visualization tool is available via web server at http://digbio.missouri.edu/impres. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yuexu Jiang
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China.,Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA
| | - Yanchun Liang
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China
| | - Duolin Wang
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China.,Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA
| | - Dong Xu
- Department of Computer Science and Technology, Jilin University, Changchun 130012, China.,Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA.,Informatics Institute and Christopher S. Bond Life Sciences Center, Columbia, MO 65211, USA
| | - Trupti Joshi
- Department of Electrical Engineering and Computer Science, Columbia, MO 65211, USA.,Informatics Institute and Christopher S. Bond Life Sciences Center, Columbia, MO 65211, USA.,Department of Health Management and Informatics, University of Missouri, Columbia, MO 65211, USA
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12
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Cardner M, Meyer-Schaller N, Christofori G, Beerenwinkel N. Inferring signalling dynamics by integrating interventional with observational data. Bioinformatics 2020; 35:i577-i585. [PMID: 31510686 PMCID: PMC6612850 DOI: 10.1093/bioinformatics/btz325] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Motivation In order to infer a cell signalling network, we generally need interventional data from perturbation experiments. If the perturbation experiments are time-resolved, then signal progression through the network can be inferred. However, such designs are infeasible for large signalling networks, where it is more common to have steady-state perturbation data on the one hand, and a non-interventional time series on the other. Such was the design in a recent experiment investigating the coordination of epithelial–mesenchymal transition (EMT) in murine mammary gland cells. We aimed to infer the underlying signalling network of transcription factors and microRNAs coordinating EMT, as well as the signal progression during EMT. Results In the context of nested effects models, we developed a method for integrating perturbation data with a non-interventional time series. We applied the model to RNA sequencing data obtained from an EMT experiment. Part of the network inferred from RNA interference was validated experimentally using luciferase reporter assays. Our model extension is formulated as an integer linear programme, which can be solved efficiently using heuristic algorithms. This extension allowed us to infer the signal progression through the network during an EMT time course, and thereby assess when each regulator is necessary for EMT to advance. Availability and implementation R package at https://github.com/cbg-ethz/timeseriesNEM. The RNA sequencing data and microscopy images can be explored through a Shiny app at https://emt.bsse.ethz.ch. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Mathias Cardner
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
| | | | | | - Niko Beerenwinkel
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.,SIB Swiss Institute of Bioinformatics, Basel, Switzerland
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13
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Nagpal S, Baksi KD, Kuntal BK, Mande SS. NetConfer: a web application for comparative analysis of multiple biological networks. BMC Biol 2020; 18:53. [PMID: 32430035 PMCID: PMC7236966 DOI: 10.1186/s12915-020-00781-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2020] [Accepted: 04/14/2020] [Indexed: 12/29/2022] Open
Abstract
Background Most biological experiments are inherently designed to compare changes or transitions of state between conditions of interest. The advancements in data intensive research have in particular elevated the need for resources and tools enabling comparative analysis of biological data. The complexity of biological systems and the interactions of their various components, such as genes, proteins, taxa, and metabolites, have been inferred, represented, and visualized via graph theory-based networks. Comparisons of multiple networks can help in identifying variations across different biological systems, thereby providing additional insights. However, while a number of online and stand-alone tools exist for generating, analyzing, and visualizing individual biological networks, the utility to batch process and comprehensively compare multiple networks is limited. Results Here, we present a graphical user interface (GUI)-based web application which implements multiple network comparison methodologies and presents them in the form of organized analysis workflows. Dedicated comparative visualization modules are provided to the end-users for obtaining easy to comprehend, insightful, and meaningful comparisons of various biological networks. We demonstrate the utility and power of our tool using publicly available microbial and gene expression data. Conclusion NetConfer tool is developed keeping in mind the requirements of researchers working in the field of biological data analysis with limited programming expertise. It is also expected to be useful for advanced users from biological as well as other domains (working with association networks), benefiting from provided ready-made workflows, as they allow to focus directly on the results without worrying about the implementation. While the web version allows using this application without installation and dependency requirements, a stand-alone version has also been supplemented to accommodate the offline requirement of processing large networks.
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Affiliation(s)
- Sunil Nagpal
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B Hadapsar Industrial Estate, Pune, 411 013, India
| | - Krishanu Das Baksi
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B Hadapsar Industrial Estate, Pune, 411 013, India
| | - Bhusan K Kuntal
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B Hadapsar Industrial Estate, Pune, 411 013, India. .,Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhabha Road, Pune, 411 008, India. .,Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory Campus, Pune, 411 008, India.
| | - Sharmila S Mande
- Bio-Sciences R&D Division, TCS Research, Tata Consultancy Services Ltd., 54-B Hadapsar Industrial Estate, Pune, 411 013, India.
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14
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Chiang S, Shinohara H, Huang JH, Tsai HK, Okada M. Inferring the transcriptional regulatory mechanism of signal-dependent gene expression via an integrative computational approach. FEBS Lett 2020; 594:1477-1496. [PMID: 32052437 DOI: 10.1002/1873-3468.13757] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 12/26/2019] [Accepted: 01/20/2020] [Indexed: 11/10/2022]
Abstract
Eukaryotic transcription factors (TFs) coordinate different upstream signals to regulate the expression of their target genes. To unveil this regulatory network in B-cell receptor signaling, we developed a computational pipeline to systematically analyze the extracellular signal-regulated kinase (ERK)- and IκB kinase (IKK)-dependent transcriptome responses. We combined a bilinear regression method and kinetic modeling to identify the signal-to-TF and TF-to-gene dynamics, respectively. We input a set of time-course experimental data for B cells and concentrated on transcriptional activators. The results show that the combination of TFs differentially controlled by ERK and IKK could contribute divergent expression dynamics in orchestrating the B-cell response. Our findings provide insights into the regulatory mechanisms underlying signal-dependent gene expression in eukaryotic cells.
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Affiliation(s)
- Sufeng Chiang
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | | | - Jia-Hsin Huang
- Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Huai-Kuang Tsai
- Genome and Systems Biology Degree Program, National Taiwan University and Academia Sinica, Taipei, Taiwan.,Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Mariko Okada
- RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.,Laboratory of Cell Systems, Institute for Protein Research, Osaka University, Suita, Japan
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15
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IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation. Methods 2020; 173:16-23. [DOI: 10.1016/j.ymeth.2019.06.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 06/08/2019] [Accepted: 06/13/2019] [Indexed: 01/18/2023] Open
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16
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Deep learning for inferring gene relationships from single-cell expression data. Proc Natl Acad Sci U S A 2019; 116:27151-27158. [PMID: 31822622 DOI: 10.1073/pnas.1911536116] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Several methods were developed to mine gene-gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC's encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance.
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17
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Marshall-Colón A, Kliebenstein DJ. Plant Networks as Traits and Hypotheses: Moving Beyond Description. TRENDS IN PLANT SCIENCE 2019; 24:840-852. [PMID: 31300195 DOI: 10.1016/j.tplants.2019.06.003] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 05/31/2019] [Accepted: 06/04/2019] [Indexed: 05/04/2023]
Abstract
Biology relies on the central thesis that the genes in an organism encode molecular mechanisms that combine with stimuli and raw materials from the environment to create a final phenotypic expression representative of the genomic programming. While conceptually simple, the genotype-to-phenotype linkage in a eukaryotic organism relies on the interactions of thousands of genes and an environment with a potentially unknowable level of complexity. Modern biology has moved to the use of networks in systems biology to try to simplify this complexity to decode how an organism's genome works. Previously, biological networks were basic ways to organize, simplify, and analyze data. However, recent advances are allowing networks to move beyond description and become phenotypes or hypotheses in their own right. This review discusses these efforts, like mapping responses across biological scales, including relationships among cellular entities, and the direct use of networks as traits or hypotheses.
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Affiliation(s)
- Amy Marshall-Colón
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
| | - Daniel J Kliebenstein
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA 95616, USA; DynaMo Center of Excellence, University of Copenhagen, Thorvaldsensvej 40, DK-1871 Frederiksberg C, Denmark.
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18
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Ruffalo M, Bar-Joseph Z. Protein interaction disruption in cancer. BMC Cancer 2019; 19:370. [PMID: 31014259 PMCID: PMC6823625 DOI: 10.1186/s12885-019-5532-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 03/27/2019] [Indexed: 12/18/2022] Open
Abstract
Background Most methods that integrate network and mutation data to study cancer focus on the effects of genes/proteins, quantifying the effect of mutations or differential expression of a gene and its neighbors, or identifying groups of genes that are significantly up- or down-regulated. However, several mutations are known to disrupt specific protein-protein interactions, and network dynamics are often ignored by such methods. Here we introduce a method that allows for predicting the disruption of specific interactions in cancer patients using somatic mutation data and protein interaction networks. Methods We extend standard network smoothing techniques to assign scores to the edges in a protein interaction network in addition to nodes. We use somatic mutations as input to our modified network smoothing method, producing scores that quantify the proximity of each edge to somatic mutations in individual samples. Results Using breast cancer mutation data, we show that predicted edges are significantly associated with patient survival and known ligand binding site mutations. In-silico analysis of protein binding further supports the ability of the method to infer novel disrupted interactions and provides a mechanistic explanation for the impact of mutations on key pathways. Conclusions Our results show the utility of our method both in identifying disruptions of protein interactions from known ligand binding site mutations, and in selecting novel clinically significant interactions.Supporting website with software and data: https://www.cs.cmu.edu/~mruffalo/mut-edge-disrupt/. Electronic supplementary material The online version of this article (10.1186/s12885-019-5532-5) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Matthew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA. .,Machine Learning Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA.
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19
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Del Sol A, Okawa S, Ravichandran S. Computational Strategies for Niche-Dependent Cell Conversion to Assist Stem Cell Therapy. Trends Biotechnol 2019; 37:687-696. [PMID: 30782480 DOI: 10.1016/j.tibtech.2019.01.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 12/28/2022]
Abstract
The field of regenerative medicine has blossomed in recent decades. However, the ultimate goal of tissue regeneration - replacing damaged or aged cells with healthy functioning cells - still faces a number of challenges. In particular, better understanding of the role of the cellular niche in shaping stem cell phenotype and conversion would aid in improving current protocols for stem cell therapies. In this regard, the implementation of novel computational approaches that consider the niche effect on stem cells would be valuable. Here we discuss current problems in stem cell transplantation and rejuvenation, and we propose computational strategies to control niche-dependent cell conversion to overcome them.
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Affiliation(s)
- Antonio Del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg,7 Avenue des Hauts-Fourneaux, Esch-sur-Alzette, L-4362 Luxembourg City, Luxembourg; CIC bioGUNE,Bizkaia Technology Park, 801 Building, 48160 Derio, Spain; IKERBASQUE, Basque Foundation for Science, Bilbao 48013, Spain; Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Russia.
| | - Satoshi Okawa
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg,7 Avenue des Hauts-Fourneaux, Esch-sur-Alzette, L-4362 Luxembourg City, Luxembourg
| | - Srikanth Ravichandran
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg,7 Avenue des Hauts-Fourneaux, Esch-sur-Alzette, L-4362 Luxembourg City, Luxembourg
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20
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Siahpirani AF, Chasman D, Roy S. Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks. Methods Mol Biol 2019; 1883:161-194. [PMID: 30547400 DOI: 10.1007/978-1-4939-8882-2_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.
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Affiliation(s)
- Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.,Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
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21
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Dash M, Yordanov YS, Georgieva T, Wei H, Busov V. Gene network analysis of poplar root transcriptome in response to drought stress identifies a PtaJAZ3PtaRAP2.6-centered hierarchical network. PLoS One 2018; 13:e0208560. [PMID: 30540849 PMCID: PMC6291141 DOI: 10.1371/journal.pone.0208560] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Accepted: 11/19/2018] [Indexed: 12/02/2022] Open
Abstract
Using time-series transcriptomic data from poplar roots undergoing polyethylene glycol (PEG)-induced drought stress, we built a genetic network model of the involved putative molecular responses. We found that the network resembled a hierarchical structure. The highest hierarchical level in this structure is occupied by 9 genes, which we called superhubs because they were primarily connected to 18 hub genes, which are then connected to 2,934 terminal genes. We were only able to regenerate transgenic plants overexpressing two of the superhubs, suggesting that the majority of the superhubs might interfere with the regeneration process and did not allow recovery of transgenic plants. The two superhubs encode proteins with closest homology to JAZ3 and RAP2.6 genes of Arabidopsis and were consequently named PtaJAZ3 and PtaRAP2.6. PtaJAZ3 and PtaRAP2.6 overexpressing transgenic lines showed a significant increase in both root elongation and lateral root proliferation and these responses were specific for the drought stress conditions and were highly correlated with the levels of overexpression of the transgenes. Several lines of evidence suggest of regulatory interactions between the two superhubs. Both superhubs were significantly induced by methyl jasmonate (MeJA). Because jasmonate signaling involves ubiquitin-mediated proteasome degradation, treatment with proteasome inhibitor abolished the MeJA induction for both genes. PtaRAP2.6 was upregulated in PtaJAZ3 transgenics but PtaJAZ3 expression was not affected in the PtaRAP2.6 overexpressors. The discovery of the two genes and further future insights into the associated mechanisms can lead to improved understanding and novel approaches to regulate root architecture in relation to drought stress.
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Affiliation(s)
- Madhumita Dash
- Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, United States of America
| | - Yordan S. Yordanov
- Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, United States of America
| | - Tatyana Georgieva
- Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, United States of America
| | - Hairong Wei
- Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, United States of America
| | - Victor Busov
- Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI, United States of America
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22
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Franks AM, Markowetz F, Airoldi EM. REFINING CELLULAR PATHWAY MODELS USING AN ENSEMBLE OF HETEROGENEOUS DATA SOURCES. Ann Appl Stat 2018; 12:1361-1384. [PMID: 36506698 PMCID: PMC9733905 DOI: 10.1214/16-aoas915] [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] [Indexed: 12/15/2022]
Abstract
Improving current models and hypotheses of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of new high-throughput studies. Moreover, the available sources of data are heterogeneous, and the data need to be integrated in different ways depending on which part of the pathway they are most informative for. In this paper, we introduce a compartment specific strategy to integrate edge, node and path data for refining a given network hypothesis. To carry out inference, we use a local-move Gibbs sampler for updating the pathway hypothesis from a compendium of heterogeneous data sources, and a new network regression idea for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.
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Affiliation(s)
- Alexander M Franks
- Department of Statistics and, Applied Probability, University of California, Santa Barbara, South Hall, Santa Barbara, California 93106, USA
| | - Florian Markowetz
- Cancer Research UK, Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, United Kingdom
| | - Edoardo M Airoldi
- Fox School of Business, Department of Statistical Science, Temple University, Center for Data Science, 1810 Liacouras Walk, Philadelphia, Pennsylvania 19122, USA
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23
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Talavera D, Kershaw CJ, Costello JL, Castelli LM, Rowe W, Sims PFG, Ashe MP, Grant CM, Pavitt GD, Hubbard SJ. Archetypal transcriptional blocks underpin yeast gene regulation in response to changes in growth conditions. Sci Rep 2018; 8:7949. [PMID: 29785040 PMCID: PMC5962585 DOI: 10.1038/s41598-018-26170-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Accepted: 05/01/2018] [Indexed: 01/30/2023] Open
Abstract
The transcriptional responses of yeast cells to diverse stresses typically include gene activation and repression. Specific stress defense, citric acid cycle and oxidative phosphorylation genes are activated, whereas protein synthesis genes are coordinately repressed. This view was achieved from comparative transcriptomic experiments delineating sets of genes whose expression greatly changed with specific stresses. Less attention has been paid to the biological significance of 1) consistent, albeit modest, changes in RNA levels across multiple conditions, and 2) the global gene expression correlations observed when comparing numerous genome-wide studies. To address this, we performed a meta-analysis of 1379 microarray-based experiments in yeast, and identified 1388 blocks of RNAs whose expression changes correlate across multiple and diverse conditions. Many of these blocks represent sets of functionally-related RNAs that act in a coordinated fashion under normal and stress conditions, and map to global cell defense and growth responses. Subsequently, we used the blocks to analyze novel RNA-seq experiments, demonstrating their utility and confirming the conclusions drawn from the meta-analysis. Our results provide a new framework for understanding the biological significance of changes in gene expression: 'archetypal' transcriptional blocks that are regulated in a concerted fashion in response to external stimuli.
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Affiliation(s)
- David Talavera
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
| | - Christopher J Kershaw
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Joseph L Costello
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.,Department of Biosciences, College of Life and Environmental Sciences, University of Exeter, Exeter, United Kingdom
| | - Lydia M Castelli
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.,Sheffield Institute for Translational Neuroscience, The University of Sheffield, Sheffield, United Kingdom
| | - William Rowe
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.,Department of Chemistry, Loughborough University, Loughborough, United Kingdom
| | - Paul F G Sims
- Manchester Institute of Biotechnology (MIB), The University of Manchester, Manchester, United Kingdom
| | - Mark P Ashe
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Chris M Grant
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom
| | - Graham D Pavitt
- Division of Molecular and Cellular Function, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
| | - Simon J Hubbard
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, United Kingdom.
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24
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Mertins P, Przybylski D, Yosef N, Qiao J, Clauser K, Raychowdhury R, Eisenhaure TM, Maritzen T, Haucke V, Satoh T, Akira S, Carr SA, Regev A, Hacohen N, Chevrier N. An Integrative Framework Reveals Signaling-to-Transcription Events in Toll-like Receptor Signaling. Cell Rep 2018; 19:2853-2866. [PMID: 28658630 DOI: 10.1016/j.celrep.2017.06.016] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 04/11/2017] [Accepted: 06/01/2017] [Indexed: 10/19/2022] Open
Abstract
Building an integrated view of cellular responses to environmental cues remains a fundamental challenge due to the complexity of intracellular networks in mammalian cells. Here, we introduce an integrative biochemical and genetic framework to dissect signal transduction events using multiple data types and, in particular, to unify signaling and transcriptional networks. Using the Toll-like receptor (TLR) system as a model cellular response, we generate multifaceted datasets on physical, enzymatic, and functional interactions and integrate these data to reveal biochemical paths that connect TLR4 signaling to transcription. We define the roles of proximal TLR4 kinases, identify and functionally test two dozen candidate regulators, and demonstrate a role for Ap1ar (encoding the Gadkin protein) and its binding partner, Picalm, potentially linking vesicle transport with pro-inflammatory responses. Our study thus demonstrates how deciphering dynamic cellular responses by integrating datasets on various regulatory layers defines key components and higher-order logic underlying signaling-to-transcription pathways.
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Affiliation(s)
- Philipp Mertins
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Dariusz Przybylski
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Nir Yosef
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Department of Electrical Engineering and Computer Science and Center for Computational Biology, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jana Qiao
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Karl Clauser
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | | | - Thomas M Eisenhaure
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Tanja Maritzen
- Molecular Physiology and Cell Biology Section, Leibniz-Institute for Molecular Pharmacology (FMP), 13125 Berlin, Germany
| | - Volker Haucke
- Molecular Physiology and Cell Biology Section, Leibniz-Institute for Molecular Pharmacology (FMP), 13125 Berlin, Germany
| | - Takashi Satoh
- WPI Immunology Frontier Research Center, Osaka University, 3-1 Yamada-oka, Suita, Osaka 565-0871, Japan
| | - Shizuo Akira
- WPI Immunology Frontier Research Center, Osaka University, 3-1 Yamada-oka, Suita, Osaka 565-0871, Japan
| | - Steven A Carr
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA
| | - Aviv Regev
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Howard Hughes Medical Institute, Department of Biology, MIT, Cambridge, MA 02142, USA.
| | - Nir Hacohen
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA; Center for Immunology and Inflammatory Diseases and Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA 02129, USA.
| | - Nicolas Chevrier
- Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA 02142, USA.
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25
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Ding J, Hagood JS, Ambalavanan N, Kaminski N, Bar-Joseph Z. iDREM: Interactive visualization of dynamic regulatory networks. PLoS Comput Biol 2018. [PMID: 29538379 PMCID: PMC5868853 DOI: 10.1371/journal.pcbi.1006019] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The Dynamic Regulatory Events Miner (DREM) software reconstructs dynamic regulatory networks by integrating static protein-DNA interaction data with time series gene expression data. In recent years, several additional types of high-throughput time series data have been profiled when studying biological processes including time series miRNA expression, proteomics, epigenomics and single cell RNA-Seq. Combining all available time series and static datasets in a unified model remains an important challenge and goal. To address this challenge we have developed a new version of DREM termed interactive DREM (iDREM). iDREM provides support for all data types mentioned above and combines them with existing interaction data to reconstruct networks that can lead to novel hypotheses on the function and timing of regulators. Users can interactively visualize and query the resulting model. We showcase the functionality of the new tool by applying it to microglia developmental data from multiple labs.
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Affiliation(s)
- Jun Ding
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - James S. Hagood
- Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego and Rady Children’s Hospital, La Jolla, California, United States of America
| | - Namasivayam Ambalavanan
- Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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26
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Ruffalo M, Stojanov P, Pillutla VK, Varma R, Bar-Joseph Z. Reconstructing cancer drug response networks using multitask learning. BMC SYSTEMS BIOLOGY 2017; 11:96. [PMID: 29017547 PMCID: PMC5635550 DOI: 10.1186/s12918-017-0471-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 10/02/2017] [Indexed: 01/03/2023]
Abstract
BACKGROUND Translating in vitro results to clinical tests is a major challenge in systems biology. Here we present a new Multi-Task learning framework which integrates thousands of cell line expression experiments to reconstruct drug specific response networks in cancer. RESULTS The reconstructed networks correctly identify several shared key proteins and pathways while simultaneously highlighting many cell type specific proteins. We used top proteins from each drug network to predict survival for patients prescribed the drug. CONCLUSIONS Predictions based on proteins from the in-vitro derived networks significantly outperformed predictions based on known cancer genes indicating that Multi-Task learning can indeed identify accurate drug response networks.
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Affiliation(s)
- Matthew Ruffalo
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Petar Stojanov
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Venkata Krishna Pillutla
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Rohan Varma
- Electrical and Computer Engineering, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA. .,Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
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27
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Jain S, Arrais J, Venkatachari NJ, Ayyavoo V, Bar-Joseph Z. Reconstructing the temporal progression of HIV-1 immune response pathways. Bioinformatics 2017; 32:i253-i261. [PMID: 27307624 PMCID: PMC4908338 DOI: 10.1093/bioinformatics/btw254] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
Motivation: Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages. Results: We present TimePath, a new method that integrates time series and static datasets to reconstruct dynamic models of host response to stimulus. TimePath uses an Integer Programming formulation to select a subset of pathways that, together, explain the observed dynamic responses. Applying TimePath to study human response to HIV-1 led to accurate reconstruction of several known regulatory and signaling pathways and to novel mechanistic insights. We experimentally validated several of TimePaths’ predictions highlighting the usefulness of temporal models. Availability and Implementation: Data, Supplementary text and the TimePath software are available from http://sb.cs.cmu.edu/timepath Contact:zivbj@cs.cmu.edu Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Siddhartha Jain
- Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Joel Arrais
- Department of Computer Science, University of Coimbra, Coimbra, Portugal
| | | | - Velpandi Ayyavoo
- Department of Infectious Diseases, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ziv Bar-Joseph
- Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
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28
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Ahn H, Jung I, Shin SJ, Park J, Rhee S, Kim JK, Jung W, Kwon HB, Kim S. Transcriptional Network Analysis Reveals Drought Resistance Mechanisms of AP2/ERF Transgenic Rice. FRONTIERS IN PLANT SCIENCE 2017; 8:1044. [PMID: 28663756 PMCID: PMC5471331 DOI: 10.3389/fpls.2017.01044] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2017] [Accepted: 05/30/2017] [Indexed: 05/18/2023]
Abstract
This study was designed to investigate at the molecular level how a transgenic version of rice "Nipponbare" obtained a drought-resistant phenotype. Using multi-omics sequencing data, we compared wild-type rice (WT) and a transgenic version (erf71) that had obtained a drought-resistant phenotype by overexpressing OsERF71, a member of the AP2/ERF transcription factor (TF) family. A comprehensive bioinformatics analysis pipeline, including TF networks and a cascade tree, was developed for the analysis of multi-omics data. The results of the analysis showed that the presence of OsERF71 at the source of the network controlled global gene expression levels in a specific manner to make erf71 survive longer than WT. Our analysis of the time-series transcriptome data suggests that erf71 diverted more energy to survival-critical mechanisms related to translation, oxidative response, and DNA replication, while further suppressing energy-consuming mechanisms, such as photosynthesis. To support this hypothesis further, we measured the net photosynthesis level under physiological conditions, which confirmed the further suppression of photosynthesis in erf71. In summary, our work presents a comprehensive snapshot of transcriptional modification in transgenic rice and shows how this induced the plants to acquire a drought-resistant phenotype.
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Affiliation(s)
- Hongryul Ahn
- Department of Computer Science and Engineering, Seoul National UniversitySeoul, South Korea
| | - Inuk Jung
- Interdisciplinary Program in Bioinformatics, Seoul National UniversitySeoul, South Korea
| | - Seon-Ju Shin
- Department of Biomedical Sciences, Sunmoon UniversityAsan, South Korea
| | - Jinwoo Park
- Department of Computer Science and Engineering, Seoul National UniversitySeoul, South Korea
| | - Sungmin Rhee
- Department of Computer Science and Engineering, Seoul National UniversitySeoul, South Korea
| | - Ju-Kon Kim
- Graduate School of International Agricultural Technology and Crop Biotechnology Institute/GreenBio Science and Technology, Seoul National UniversitySeoul, South Korea
| | - Woosuk Jung
- Department of Applied Bioscience, Konkuk UniversitySeoul, South Korea
| | - Hawk-Bin Kwon
- Department of Biomedical Sciences, Sunmoon UniversityAsan, South Korea
| | - Sun Kim
- Department of Computer Science and Engineering, Seoul National UniversitySeoul, South Korea
- Interdisciplinary Program in Bioinformatics, Seoul National UniversitySeoul, South Korea
- Bioinformatics Institute, Seoul National UniversitySeoul, South Korea
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29
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Sychev ZE, Hu A, DiMaio TA, Gitter A, Camp ND, Noble WS, Wolf-Yadlin A, Lagunoff M. Integrated systems biology analysis of KSHV latent infection reveals viral induction and reliance on peroxisome mediated lipid metabolism. PLoS Pathog 2017; 13:e1006256. [PMID: 28257516 PMCID: PMC5352148 DOI: 10.1371/journal.ppat.1006256] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2016] [Revised: 03/15/2017] [Accepted: 02/22/2017] [Indexed: 12/22/2022] Open
Abstract
Kaposi’s Sarcoma associated Herpesvirus (KSHV), an oncogenic, human gamma-herpesvirus, is the etiological agent of Kaposi’s Sarcoma the most common tumor of AIDS patients world-wide. KSHV is predominantly latent in the main KS tumor cell, the spindle cell, a cell of endothelial origin. KSHV modulates numerous host cell-signaling pathways to activate endothelial cells including major metabolic pathways involved in lipid metabolism. To identify the underlying cellular mechanisms of KSHV alteration of host signaling and endothelial cell activation, we identified changes in the host proteome, phosphoproteome and transcriptome landscape following KSHV infection of endothelial cells. A Steiner forest algorithm was used to integrate the global data sets and, together with transcriptome based predicted transcription factor activity, cellular networks altered by latent KSHV were predicted. Several interesting pathways were identified, including peroxisome biogenesis. To validate the predictions, we showed that KSHV latent infection increases the number of peroxisomes per cell. Additionally, proteins involved in peroxisomal lipid metabolism of very long chain fatty acids, including ABCD3 and ACOX1, are required for the survival of latently infected cells. In summary, novel cellular pathways altered during herpesvirus latency that could not be predicted by a single systems biology platform, were identified by integrated proteomics and transcriptomics data analysis and when correlated with our metabolomics data revealed that peroxisome lipid metabolism is essential for KSHV latent infection of endothelial cells. Kaposi’s Sarcoma herpesvirus (KSHV) is the etiologic agent of Kaposi’s Sarcoma, the most common tumor of AIDS patients. KSHV modulates host cell signaling and metabolism to maintain a life-long latent infection. To unravel the underlying cellular mechanisms modulated by KSHV, we used multiple global systems biology platforms to identify and integrate changes in both cellular protein expression and transcription following KSHV infection of endothelial cells, the relevant cell type for KS tumors. The analysis identified several interesting pathways including peroxisome biogenesis. Peroxisomes are small cytoplasmic organelles involved in redox reactions and lipid metabolism. KSHV latent infection increases the number of peroxisomes per cell and proteins involved in peroxisomal lipid metabolism are required for the survival of latently infected cells. In summary, through integration of multiple global systems biology analyses we were able to identify novel pathways that could not be predicted by one platform alone and found that lipid metabolism in a small cytoplasmic organelle is necessary for the survival of latent infection with a herpesvirus.
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Affiliation(s)
- Zoi E. Sychev
- Molecular and Cellular Biology Program, University of Washington, Seattle, Washington, United States of America
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Alex Hu
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
| | - Terri A. DiMaio
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
| | - Anthony Gitter
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison and Morgridge Institute for Research, Madison, Wisconsin, USA
| | - Nathan D. Camp
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
| | - William S. Noble
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
| | - Alejandro Wolf-Yadlin
- Department of Genome Science, University of Washington, Seattle, Washington, United States of America
- * E-mail: (ML); (AWY)
| | - Michael Lagunoff
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
- * E-mail: (ML); (AWY)
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30
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Korla K, Chandra N. A Systems Perspective of Signalling Networks in Host–Pathogen Interactions. J Indian Inst Sci 2017. [DOI: 10.1007/s41745-016-0017-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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31
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Kleyman M, Sefer E, Nicola T, Espinoza C, Chhabra D, Hagood JS, Kaminski N, Ambalavanan N, Bar-Joseph Z. Selecting the most appropriate time points to profile in high-throughput studies. eLife 2017; 6:e18541. [PMID: 28124972 PMCID: PMC5319842 DOI: 10.7554/elife.18541] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2016] [Accepted: 01/23/2017] [Indexed: 12/25/2022] Open
Abstract
Biological systems are increasingly being studied by high throughput profiling of molecular data over time. Determining the set of time points to sample in studies that profile several different types of molecular data is still challenging. Here we present the Time Point Selection (TPS) method that solves this combinatorial problem in a principled and practical way. TPS utilizes expression data from a small set of genes sampled at a high rate. As we show by applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points. Further, even though the selection is only based on gene expression, these points are also appropriate for representing a much larger set of protein, miRNA and DNA methylation changes over time. TPS can thus serve as a key design strategy for high throughput time series experiments. Supporting Website: www.sb.cs.cmu.edu/TPS.
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Affiliation(s)
- Michael Kleyman
- Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
| | - Emre Sefer
- Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
| | - Teodora Nicola
- Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, United States
| | - Celia Espinoza
- Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, United States.,CARady Children's Hospital San Diego, San Diego, United States
| | - Divya Chhabra
- Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, United States.,CARady Children's Hospital San Diego, San Diego, United States
| | - James S Hagood
- Division of Respiratory Medicine, Department of Pediatrics, University of California, San Diego, United States.,CARady Children's Hospital San Diego, San Diego, United States
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, School of Medicine, Yale University, New Haven, United States
| | - Namasivayam Ambalavanan
- Division of Neonatology, Department of Pediatrics, University of Alabama at Birmingham, Birmingham, United States
| | - Ziv Bar-Joseph
- Machine Learning and Computational Biology, School of Computer Science, Carnegie Mellon University, Pittsburgh, United States
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32
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Abstract
PathLinker is a graph-theoretic algorithm for reconstructing the interactions in a signaling pathway of interest. It efficiently computes multiple short paths within a background protein interaction network from the receptors to transcription factors (TFs) in a pathway. We originally developed PathLinker to complement manual curation of signaling pathways, which is slow and painstaking. The method can be used in general to connect any set of sources to any set of targets in an interaction network. The app presented here makes the PathLinker functionality available to Cytoscape users. We present an example where we used PathLinker to compute and analyze the network of interactions connecting proteins that are perturbed by the drug lovastatin.
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Affiliation(s)
- Daniel P Gil
- Department of Computer Science, Virginia Tech, Blacksburg, USA
| | - Jeffrey N Law
- Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, USA
| | - T M Murali
- Department of Computer Science, Virginia Tech, Blacksburg, USA.,ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, USA
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33
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He FQ, Ollert M. Network-Guided Key Gene Discovery for a Given Cellular Process. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2016. [PMID: 27783134 DOI: 10.1007/10_2016_39] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Identification of key genes for a given physiological or pathological process is an essential but still very challenging task for the entire biomedical research community. Statistics-based approaches, such as genome-wide association study (GWAS)- or quantitative trait locus (QTL)-related analysis have already made enormous contributions to identifying key genes associated with a given disease or phenotype, the success of which is however very much dependent on a huge number of samples. Recent advances in network biology, especially network inference directly from genome-scale data and the following-up network analysis, opens up new avenues to predict key genes driving a given biological process or cellular function. Here we review and compare the current approaches in predicting key genes, which have no chances to stand out by classic differential expression analysis, from gene-regulatory, protein-protein interaction, or gene expression correlation networks. We elaborate these network-based approaches mainly in the context of immunology and infection, and urge more usage of correlation network-based predictions. Such network-based key gene discovery approaches driven by information-enriched 'omics' data should be very useful for systematic key gene discoveries for any given biochemical process or cellular function, and also valuable for novel drug target discovery and novel diagnostic, prognostic and therapeutic-efficiency marker prediction for a specific disease or disorder.
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Affiliation(s)
- Feng Q He
- Department of Infection and Immunity, Group of Immune Systems Biology, Luxembourg Institute of Health, 29, rue Henri Koch, 4354, Esch-sur-Alzette, Luxembourg.
| | - Markus Ollert
- Department of Infection and Immunity, Group of Allergy and Clinical Immunology, Luxembourg Institute of Health, 29, rue Henri Koch, 4354, Esch-sur-Alzette, Luxembourg
- Odense Research Center for Anaphylaxis, Department of Dermatology and Allergy Center, Odense University Hospital, University of Southern Denmark, 5000, Odense C, Denmark
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34
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Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens. PLoS Comput Biol 2016; 12:e1005013. [PMID: 27403523 PMCID: PMC4942116 DOI: 10.1371/journal.pcbi.1005013] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Accepted: 06/06/2016] [Indexed: 12/17/2022] Open
Abstract
Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. An important challenge in infectious disease research is to understand how the human immune system responds to different types of pathogenic infections. An important component of mounting proper response is the transcriptional regulatory network that specifies the context-specific gene expression program in the host cell. However, our understanding of this regulatory network and how it drives context-specific transcriptional programs is incomplete. To address this gap, we performed a network-based analysis of host response to influenza viruses that integrated high-throughput mRNA- and protein measurements and protein-protein interaction networks to identify virus and pathogenicity-specific modules and their upstream physical regulatory programs. We inferred regulatory networks for human cell line and mouse host systems, which recapitulated several known regulators and pathways of the immune response and viral life cycle. We used the networks to study time point and strain-specific subnetworks and to prioritize important regulators of host response. We predicted several novel regulators, both at the mRNA and protein levels, and experimentally verified their role in the virus life cycle based on their ability to significantly impact viral replication.
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35
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Lee S, Mardinoglu A, Zhang C, Lee D, Nielsen J. Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis. Nucleic Acids Res 2016; 44:5529-39. [PMID: 27216817 PMCID: PMC4937331 DOI: 10.1093/nar/gkw462] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Accepted: 05/16/2016] [Indexed: 12/22/2022] Open
Abstract
Hepatocellular carcinoma (HCC) has a high mortality rate and early detection of HCC is crucial for the application of effective treatment strategies. HCC is typically caused by either viral hepatitis infection or by fatty liver disease. To diagnose and treat HCC it is necessary to elucidate the underlying molecular mechanisms. As a major cause for development of HCC is fatty liver disease, we here investigated anomalies in regulation of lipid metabolism in the liver. We applied a tailored network-based approach to identify signaling hubs associated with regulation of this part of metabolism. Using transcriptomics data of HCC patients, we identified significant dysregulated expressions of lipid-regulated genes, across many different lipid metabolic pathways. Our findings, however, show that viral hepatitis causes HCC by a distinct mechanism, less likely involving lipid anomalies. Based on our analysis we suggest signaling hub genes governing overall catabolic or anabolic pathways, as novel drug targets for treatment of HCC that involves lipid anomalies.
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Affiliation(s)
- Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305 338, Republic of Korea
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Doheon Lee
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305 338, Republic of Korea
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
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36
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Genome-Wide Mapping of Binding Sites Reveals Multiple Biological Functions of the Transcription Factor Cst6p in Saccharomyces cerevisiae. mBio 2016; 7:mBio.00559-16. [PMID: 27143390 PMCID: PMC4959655 DOI: 10.1128/mbio.00559-16] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
In the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to play important roles in several biological processes. However, the genome-wide targets of Cst6p and its physiological functions remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. Cst6p binds to the promoter regions of 59 genes with various biological functions when cells are grown on ethanol but hardly binds to the promoter at any gene when cells are grown on glucose. The retarded growth of the CST6 deletion mutant on ethanol is attributed to the markedly decreased expression of NCE103, encoding a carbonic anhydrase, which is a direct target of Cst6p. The target genes of Cst6p have a large overlap with those of stress-responsive transcription factors, such as Sko1p and Skn7p. In addition, a CST6 deletion mutant growing on ethanol shows hypersensitivity to oxidative stress and ethanol stress, assigning Cst6p as a new member of the stress-responsive transcriptional regulatory network. These results show that mapping of genome-wide binding sites can provide new insights into the function of transcription factors and highlight the highly connected and condition-dependent nature of the transcriptional regulatory network in S. cerevisiae. Transcription factors regulate the activity of various biological processes through binding to specific DNA sequences. Therefore, the determination of binding positions is important for the understanding of the regulatory effects of transcription factors. In the model eukaryote Saccharomyces cerevisiae, the transcription factor Cst6p has been reported to regulate several biological processes, while its genome-wide targets remain unknown. Here, we mapped the genome-wide binding sites of Cst6p at high resolution. We show that the binding of Cst6p to its target promoters is condition dependent and explain the mechanism for the retarded growth of the CST6 deletion mutant on ethanol. Furthermore, we demonstrate that Cst6p is a new member of a stress-responsive transcriptional regulatory network. These results provide deeper understanding of the function of the dynamic transcriptional regulatory network in S. cerevisiae.
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37
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Tuncbag N, Gosline SJC, Kedaigle A, Soltis AR, Gitter A, Fraenkel E. Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package. PLoS Comput Biol 2016; 12:e1004879. [PMID: 27096930 PMCID: PMC4838263 DOI: 10.1371/journal.pcbi.1004879] [Citation(s) in RCA: 113] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2015] [Accepted: 03/23/2016] [Indexed: 02/07/2023] Open
Abstract
High-throughput, ‘omic’ methods provide sensitive measures of biological responses to perturbations. However, inherent biases in high-throughput assays make it difficult to interpret experiments in which more than one type of data is collected. In this work, we introduce Omics Integrator, a software package that takes a variety of ‘omic’ data as input and identifies putative underlying molecular pathways. The approach applies advanced network optimization algorithms to a network of thousands of molecular interactions to find high-confidence, interpretable subnetworks that best explain the data. These subnetworks connect changes observed in gene expression, protein abundance or other global assays to proteins that may not have been measured in the screens due to inherent bias or noise in measurement. This approach reveals unannotated molecular pathways that would not be detectable by searching pathway databases. Omics Integrator also provides an elegant framework to incorporate not only positive data, but also negative evidence. Incorporating negative evidence allows Omics Integrator to avoid unexpressed genes and avoid being biased toward highly-studied hub proteins, except when they are strongly implicated by the data. The software is comprised of two individual tools, Garnet and Forest, that can be run together or independently to allow a user to perform advanced integration of multiple types of high-throughput data as well as create condition-specific subnetworks of protein interactions that best connect the observed changes in various datasets. It is available at http://fraenkel.mit.edu/omicsintegrator and on GitHub at https://github.com/fraenkel-lab/OmicsIntegrator.
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Affiliation(s)
- Nurcan Tuncbag
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Sara J. C. Gosline
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Amanda Kedaigle
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Anthony R. Soltis
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Anthony Gitter
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
- * E-mail:
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38
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Horowitz M. Epigenetics and cytoprotection with heat acclimation. J Appl Physiol (1985) 2016; 120:702-10. [DOI: 10.1152/japplphysiol.00552.2015] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 10/05/2015] [Indexed: 01/19/2023] Open
Abstract
Studying “phenotypic plasticity” involves comparison of traits expressed in response to environmental fluctuations and aims to understand tolerance and survival in new settings. Reversible phenotypic changes that enable individuals to match their phenotype to environmental demands throughout life can be artificially induced, i.e., acclimation or occur naturally, i.e., acclimatization. The onset and achievement of acclimatory homeostasis are determined by molecular programs that induce the acclimated transcriptome. In heat acclimation, much evidence suggests that epigenetic mechanisms are powerful players in these processes. Epigenetic mechanisms affect the accessibility of the DNA to transcription factors, thereby regulating gene expression and controlling the phenotype. The heat-acclimated phenotype confers cytoprotection against novel stressors via cross-tolerance mechanisms, by attenuation of the initial damage and/or by accelerating spontaneous recovery through the release of help signals. This indispensable acclimatory feature has a memory and can be rapidly reestablished after the loss of acclimation and the return to the physiological preacclimated phenotype. The transcriptional landscape of the deacclimated phenotype includes constitutive transcriptional activation of epigenetic bookmarks. Heat shock protein (HSP) 70/HSP90/heat shock factor 1 memory protocol demonstrated constitutive histone H4 acetylation on hsp70 and hsp90 promotors. Novel players in the heat acclimation setup are poly(ADP-ribose)ribose polymerase 1 affecting chromatin condensation, DNA linker histones from the histone H1 cluster, and transcription factors associated with the P38 pathway. We suggest that these orchestrated responses maintain euchromatin and proteostasis during deacclimation and predispose to rapid reacclimation and cytoprotection. These mechanisms represent within-life epigenetic adaptations and cytoprotective memory.
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Affiliation(s)
- Michal Horowitz
- Laboratory of Environmental Physiology, Faculty of Dental Medicine, The Hebrew University, Jerusalem, Israel
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Abstract
Genes are often combinatorially regulated by multiple transcription factors (TFs). Such combinatorial regulation plays an important role in development and facilitates the ability of cells to respond to different stresses. While a number of approaches have utilized sequence and ChIP-based datasets to study combinational regulation, these have often ignored the combinational logic and the dynamics associated with such regulation. Here we present cDREM, a new method for reconstructing dynamic models of combinatorial regulation. cDREM integrates time series gene expression data with (static) protein interaction data. The method is based on a hidden Markov model and utilizes the sparse group Lasso to identify small subsets of combinatorially active TFs, their time of activation, and the logical function they implement. We tested cDREM on yeast and human data sets. Using yeast we show that the predicted combinatorial sets agree with other high throughput genomic datasets and improve upon prior methods developed to infer combinatorial regulation. Applying cDREM to study human response to flu, we were able to identify several combinatorial TF sets, some of which were known to regulate immune response while others represent novel combinations of important TFs.
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Affiliation(s)
- Aaron Wise
- Lane Center for Computational Biology, Carnegie Mellon University , Pittsburgh, Pennsylvania
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Castrillo JI, Oliver SG. Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks. Methods Mol Biol 2016; 1303:3-48. [PMID: 26235058 DOI: 10.1007/978-1-4939-2627-5_1] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.
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Affiliation(s)
- Juan I Castrillo
- Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge, CB2 1GA, UK,
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41
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Gitter A, Bar-Joseph Z. The SDREM Method for Reconstructing Signaling and Regulatory Response Networks: Applications for Studying Disease Progression. Methods Mol Biol 2016; 1303:493-506. [PMID: 26235087 DOI: 10.1007/978-1-4939-2627-5_30] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The Signaling and Dynamic Regulatory Events Miner (SDREM) is a powerful computational approach for identifying which signaling pathways and transcription factors control the temporal cellular response to a stimulus. SDREM builds end-to-end response models by combining condition-independent protein-protein interactions and transcription factor binding data with two types of condition-specific data: source proteins that detect the stimulus and changes in gene expression over time. Here we describe how to apply SDREM to study human diseases, using epidermal growth factor (EGF) response impacting neurogenesis and Alzheimer's disease as an example.
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Acharya L, Reynolds R, Zhu D. Network inference through synergistic subnetwork evolution. EURASIP JOURNAL ON BIOINFORMATICS & SYSTEMS BIOLOGY 2015; 2015:12. [PMID: 26640480 PMCID: PMC4662719 DOI: 10.1186/s13637-015-0027-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2015] [Accepted: 08/21/2015] [Indexed: 12/02/2022]
Abstract
Study of signaling networks is important for a better understanding of cell behaviors e.g., growth, differentiation, metabolism, proptosis, and gaining deeper insights into the molecular mechanisms of complex diseases. While there have been many successes in developing computational approaches for identifying potential genes and proteins involved in cell signaling, new methods are needed for identifying network structures that depict underlying signal cascading mechanisms. In this paper, we propose a new computational approach for inferring signaling network structures from overlapping gene sets related to the networks. In the proposed approach, a signaling network is represented as a directed graph and is viewed as a union of many active paths representing linear and overlapping chains of signal cascading activities in the network. Gene sets represent the sets of genes participating in active paths without prior knowledge of the order in which genes occur within each path. From a compendium of unordered gene sets, the proposed algorithm reconstructs the underlying network structure through evolution of synergistic active paths. In our context, the extent of edge overlapping among active paths is used to define the synergy present in a network. We evaluated the performance of the proposed algorithm in terms of its convergence and recovering true active paths by utilizing four gene set compendiums derived from the KEGG database. Evaluation of results demonstrate the ability of the algorithm in reconstructing the underlying networks with high accuracy and precision.
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Affiliation(s)
- Lipi Acharya
- Dow AgroSciences, 9330 Zionsville Road, Indianapolis, IN 46268 USA
| | - Robert Reynolds
- Department of Computer Science, Wayne State University, 5057 Woodward Avenue, Detroit, MI 48202 USA
| | - Dongxiao Zhu
- Department of Computer Science, Wayne State University, 5057 Woodward Avenue, Detroit, MI 48202 USA
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Venkatachari NJ, Zerbato JM, Jain S, Mancini AE, Chattopadhyay A, Sluis-Cremer N, Bar-Joseph Z, Ayyavoo V. Temporal transcriptional response to latency reversing agents identifies specific factors regulating HIV-1 viral transcriptional switch. Retrovirology 2015; 12:85. [PMID: 26438393 PMCID: PMC4594640 DOI: 10.1186/s12977-015-0211-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Accepted: 09/25/2015] [Indexed: 12/27/2022] Open
Abstract
Background Latent HIV-1 reservoirs are identified as one of the major challenges to achieve HIV-1 cure. Currently available strategies are associated with wide variability in outcomes both in patients and CD4+ T cell models. This underlines the critical need to develop innovative strategies to predict and recognize ways that could result in better reactivation and eventual elimination of latent HIV-1 reservoirs. Results and discussion In this study, we combined genome wide transcriptome datasets post activation with Systems Biology approach (Signaling and Dynamic Regulatory Events Miner, SDREM analyses) to reconstruct a dynamic signaling and regulatory network involved in reactivation mediated by specific activators using a latent cell line. This approach identified several critical regulators for each treatment, which were confirmed in follow-up validation studies using small molecule inhibitors. Results indicate that signaling pathways involving JNK and related factors as predicted by SDREM are essential for virus reactivation by suberoylanilide hydroxamic acid. ERK1/2 and NF-κB pathways have the foremost role in reactivation with prostratin and TNF-α, respectively. JAK-STAT pathway has a central role in HIV-1 transcription. Additional evaluation, using other latent J-Lat cell clones and primary T cell model, also confirmed that many of the cellular factors associated with latency reversing agents are similar, though minor differences are identified. JAK-STAT and NF-κB related pathways are critical for reversal of HIV-1 latency in primary resting T cells. Conclusion These results validate our combinatorial approach to predict the regulatory cellular factors and pathways responsible for HIV-1 reactivation in latent HIV-1 harboring cell line models. JAK-STAT have a role in reversal of latency in all the HIV-1 latency models tested, including primary CD4+ T cells, with additional cellular pathways such as NF-κB, JNK and ERK 1/2 that may have complementary role in reversal of HIV-1 latency. Electronic supplementary material The online version of this article (doi:10.1186/s12977-015-0211-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Narasimhan J Venkatachari
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh/GSPH, Room A435, Crabtree Hall, 130 DeSoto Street, Pittsburgh, PA, 15261, USA.
| | - Jennifer M Zerbato
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Siddhartha Jain
- Lane Center for Computational Biology, Carnegie Mellon University, Pittsburgh, PA, 15217, USA.
| | - Allison E Mancini
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh/GSPH, Room A435, Crabtree Hall, 130 DeSoto Street, Pittsburgh, PA, 15261, USA.
| | - Ansuman Chattopadhyay
- Molecular Biology Information Service, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Nicolas Sluis-Cremer
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA.
| | - Ziv Bar-Joseph
- Computer Science Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15217, USA.
| | - Velpandi Ayyavoo
- Department of Infectious Diseases and Microbiology, Graduate School of Public Health, University of Pittsburgh/GSPH, Room A435, Crabtree Hall, 130 DeSoto Street, Pittsburgh, PA, 15261, USA.
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Terfve CDA, Wilkes EH, Casado P, Cutillas PR, Saez-Rodriguez J. Large-scale models of signal propagation in human cells derived from discovery phosphoproteomic data. Nat Commun 2015; 6:8033. [PMID: 26354681 PMCID: PMC4579397 DOI: 10.1038/ncomms9033] [Citation(s) in RCA: 67] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Accepted: 07/09/2015] [Indexed: 12/27/2022] Open
Abstract
Mass spectrometry is widely used to probe the proteome and its modifications in an untargeted manner, with unrivalled coverage. Applied to phosphoproteomics, it has tremendous potential to interrogate phospho-signalling and its therapeutic implications. However, this task is complicated by issues of undersampling of the phosphoproteome and challenges stemming from its high-content but low-sample-throughput nature. Hence, methods using such data to reconstruct signalling networks have been limited to restricted data sets and insights (for example, groups of kinases likely to be active in a sample). We propose a new method to handle high-content discovery phosphoproteomics data on perturbation by putting it in the context of kinase/phosphatase-substrate knowledge, from which we derive and train logic models. We show, on a data set obtained through perturbations of cancer cells with small-molecule inhibitors, that this method can study the targets and effects of kinase inhibitors, and reconcile insights obtained from multiple data sets, a common issue with these data. Phosphoproteomics can offer significant insight into cell signalling and how signalling is modified in response to perturbations. Here the authors develop a new tool for the analysis of high-content phosphoproteomics in the context of kinase/phosphatase-substrate knowledge, which is used to train logic models.
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Affiliation(s)
- Camille D A Terfve
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Edmund H Wilkes
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK
| | - Pedro Casado
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK
| | - Pedro R Cutillas
- Integrative Cell Signalling and Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, John Vane Science Centre, Charterhouse Square, London EC1M 6BQ, UK
| | - Julio Saez-Rodriguez
- European Molecular Biology Laboratory-European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
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Tripathi S, Flobak Å, Chawla K, Baudot A, Bruland T, Thommesen L, Kuiper M, Lægreid A. The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms. BMC SYSTEMS BIOLOGY 2015. [PMID: 26205660 PMCID: PMC4513977 DOI: 10.1186/s12918-015-0181-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders. Results We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO (http://sblab.celldesigner.org:18080/Payao11/bin/). Conclusion We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of cellular processes. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0181-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Sushil Tripathi
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | - Åsmund Flobak
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | - Konika Chawla
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
| | - Anaïs Baudot
- I2M, Marseilles Institute of Mathematics CNRS - AMU, Case 907, 13288, Marseille, Cedex 9, France.
| | - Torunn Bruland
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | - Liv Thommesen
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. .,Department of Technology, Sør-Trøndelag University College, N-7004, Trondheim, Norway.
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
| | - Astrid Lægreid
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. .,Institute of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
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Li Y, Varala K, Coruzzi GM. From milliseconds to lifetimes: tracking the dynamic behavior of transcription factors in gene networks. Trends Genet 2015; 31:509-15. [PMID: 26072453 DOI: 10.1016/j.tig.2015.05.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Revised: 05/08/2015] [Accepted: 05/12/2015] [Indexed: 12/17/2022]
Abstract
Modeling dynamic gene regulatory networks (GRNs) is a new frontier in systems biology. It has special implications for plants, whose survival requires rapid deployment of GRNs in response to environmental changes. However, capturing and dissecting transient interactions of transcription factors (TFs) and their targets in GRNs remains a considerable experimental challenge. Here we review recent progress in understanding GRNs as a function of time and discuss the relevance of these findings in plants to studies in other eukaryotes. We cover progress in profiling and modeling time-course transcriptome changes across plant species and the insights they have provided into the regulatory mechanisms underlying these temporal transcriptional responses, with a focus on the dynamic behavior of TFs. Lastly, we review state-of-the-art techniques to monitor the single-molecule dynamics of TFs in vivo. Together, these advances have helped develop new models for dynamic transcriptional control with relevance across eukaryotes.
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Affiliation(s)
- Ying Li
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Kranthi Varala
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA
| | - Gloria M Coruzzi
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA.
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Ho YH, Gasch AP. Exploiting the yeast stress-activated signaling network to inform on stress biology and disease signaling. Curr Genet 2015; 61:503-11. [PMID: 25957506 DOI: 10.1007/s00294-015-0491-0] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2015] [Revised: 04/23/2015] [Accepted: 04/24/2015] [Indexed: 12/24/2022]
Abstract
Healthy cells utilize intricate systems to monitor their environment and mount robust responses in the event of cellular stress. Whether stress arises from external insults or defects due to mutation and disease, cells must be able to respond precisely to mount the appropriate defenses. Multi-faceted stress responses are generally coupled with arrest of growth and cell-cycle progression, which both limits the transmission of damaged materials and serves to reallocate limited cellular resources toward defense. Therefore, stress defense versus rapid growth represent competing interests in the cell. How eukaryotic cells set the balance between defense versus proliferation, and in particular knowledge of the regulatory networks that control this decision, are poorly understood. In this perspective, we expand upon our recent work inferring the stress-activated signaling network in budding yeast, which captures pathways controlling stress defense and regulators of growth and cell-cycle progression. We highlight similarities between the yeast and mammalian stress responses and explore how stress-activated signaling networks in yeast can inform on signaling defects in human cancers.
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Affiliation(s)
- Yi-Hsuan Ho
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Audrey P Gasch
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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48
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Jain S, Gitter A, Bar-Joseph Z. Multitask learning of signaling and regulatory networks with application to studying human response to flu. PLoS Comput Biol 2014; 10:e1003943. [PMID: 25522349 PMCID: PMC4270428 DOI: 10.1371/journal.pcbi.1003943] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2014] [Accepted: 09/28/2014] [Indexed: 01/04/2023] Open
Abstract
Reconstructing regulatory and signaling response networks is one of the major goals of systems biology. While several successful methods have been suggested for this task, some integrating large and diverse datasets, these methods have so far been applied to reconstruct a single response network at a time, even when studying and modeling related conditions. To improve network reconstruction we developed MT-SDREM, a multi-task learning method which jointly models networks for several related conditions. In MT-SDREM, parameters are jointly constrained across the networks while still allowing for condition-specific pathways and regulation. We formulate the multi-task learning problem and discuss methods for optimizing the joint target function. We applied MT-SDREM to reconstruct dynamic human response networks for three flu strains: H1N1, H5N1 and H3N2. Our multi-task learning method was able to identify known and novel factors and genes, improving upon prior methods that model each condition independently. The MT-SDREM networks were also better at identifying proteins whose removal affects viral load indicating that joint learning can still lead to accurate, condition-specific, networks. Supporting website with MT-SDREM implementation: http://sb.cs.cmu.edu/mtsdrem To understand why some flu strains are more virulent than others, researchers attempt to profile and model the molecular human response to these strains and identify similarities and differences between the resulting models. So far, the modeling and analysis part has been done independently for each strain and the results contrasted in a post-processing step. Here we present a new method, termed MT-SDREM, that simultaneously models the response to all strains allowing us to identify both, the core response elements that are shared among the strains, and factors that are uniquely activated or repressed by individual strains. We applied this method to study the human response to three flu strains: H1N1, H3N2 and H5N1. As we show, the method was able to correctly identify several common and known factors regulating immune response to such strains and also identified unique factors for each of the strains. The models reconstructed by the simultaneous analysis method improved upon those generated by methods that model each strain response separately. Our joint models can be used to identify strain specific treatments as well as treatments that are likely to be effective against all three strains.
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Affiliation(s)
- Siddhartha Jain
- Computer Science Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Anthony Gitter
- Microsoft Research, Cambridge, Massachusetts, United States of America
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Ziv Bar-Joseph
- Lane Center for Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- * E-mail:
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TimeXNet: identifying active gene sub-networks using time-course gene expression profiles. BMC SYSTEMS BIOLOGY 2014; 8 Suppl 4:S2. [PMID: 25522063 PMCID: PMC4290689 DOI: 10.1186/1752-0509-8-s4-s2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Background Time-course gene expression profiles are frequently used to provide insight into the changes in cellular state over time and to infer the molecular pathways involved. When combined with large-scale molecular interaction networks, such data can provide information about the dynamics of cellular response to stimulus. However, few tools are currently available to predict a single active gene sub-network from time-course gene expression profiles. Results We introduce a tool, TimeXNet, which identifies active gene sub-networks with temporal paths using time-course gene expression profiles in the context of a weighted gene regulatory and protein-protein interaction network. TimeXNet uses a specialized form of the network flow optimization approach to identify the most probable paths connecting the genes with significant changes in expression at consecutive time intervals. TimeXNet has been extensively evaluated for its ability to predict novel regulators and their associated pathways within active gene sub-networks in the mouse innate immune response and the yeast osmotic stress response. Compared to other similar methods, TimeXNet identified up to 50% more novel regulators from independent experimental datasets. It predicted paths within a greater number of known pathways with longer overlaps (up to 7 consecutive edges) within these pathways. TimeXNet was also shown to be robust in the presence of varying amounts of noise in the molecular interaction network. Conclusions TimeXNet is a reliable tool that can be used to study cellular response to stimuli through the identification of time-dependent active gene sub-networks in diverse biological systems. It is significantly better than other similar tools. TimeXNet is implemented in Java as a stand-alone application and supported on Linux, MS Windows and Macintosh. The output of TimeXNet can be directly viewed in Cytoscape. TimeXNet is freely available for non-commercial users.
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Wise A, Bar-Joseph Z. SMARTS: reconstructing disease response networks from multiple individuals using time series gene expression data. ACTA ACUST UNITED AC 2014; 31:1250-7. [PMID: 25480376 DOI: 10.1093/bioinformatics/btu800] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2014] [Accepted: 11/26/2014] [Indexed: 02/02/2023]
Abstract
MOTIVATION Current methods for reconstructing dynamic regulatory networks are focused on modeling a single response network using model organisms or cell lines. Unlike these models or cell lines, humans differ in their background expression profiles due to age, genetics and life factors. In addition, there are often differences in start and end times for time series human data and in the rate of progress based on the specific individual. Thus, new methods are required to integrate time series data from multiple individuals when modeling and constructing disease response networks. RESULTS We developed Scalable Models for the Analysis of Regulation from Time Series (SMARTS), a method integrating static and time series data from multiple individuals to reconstruct condition-specific response networks in an unsupervised way. Using probabilistic graphical models, SMARTS iterates between reconstructing different regulatory networks and assigning individuals to these networks, taking into account varying individual start times and response rates. These models can be used to group different sets of patients and to identify transcription factors that differentiate the observed responses between these groups. We applied SMARTS to analyze human response to influenza and mouse brain development. In both cases, it was able to greatly improve baseline groupings while identifying key relevant TFs that differ between the groups. Several of these groupings and TFs are known to regulate the relevant processes while others represent novel hypotheses regarding immune response and development. AVAILABILITY AND IMPLEMENTATION Software and Supplementary information are available at http://sb.cs.cmu.edu/smarts/. CONTACT zivbj@cs.cmu.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Aaron Wise
- Lane Center for Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Ziv Bar-Joseph
- Lane Center for Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA Lane Center for Computational Biology and Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA
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