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Burlando B. A general hypothesis of multistable systems in pathophysiology. F1000Res 2022; 11:906. [PMID: 36226044 PMCID: PMC9530619 DOI: 10.12688/f1000research.123183.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/26/2022] [Indexed: 09/19/2023] Open
Abstract
Despite intensive investigations numerous diseases remain etiologically puzzling and recalcitrant to treatments. A hypothesis is proposed here assuming that these difficulties are due to an unsuitable approach to the mechanisms of life, which is subjugated by an apparent complexity and fails to grasp the uniformity that lays behind. The stability of metabolism, despite the enormous complex of chemical reactions, suggests that reciprocal control is a prerequisite of life. Negative feedback loops have been known for a long time to maintain homeostasis, while more recently, different life processes involved in transitions or changes have been modeled by positive loops giving rise to bistable switches, also including various diseases. The present hypothesis makes a generalization, by assuming that any functional element of a biological system is involved in a positive or a negative feedback loop. Consequently, the hypothesis holds that the starting mechanism of any disease that affects a healthy human can be conceptually reduced to a bistable or multistationary loop system, thus providing a unifying model leading to the discovery of critical therapeutic targets.
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Affiliation(s)
- Bruno Burlando
- Department of Pharmacy, University of Genoa, Genoa, 16132, Italy
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2
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Burlando B. A general theory of multistable systems in pathophysiology. F1000Res 2022; 11:906. [PMID: 36226044 PMCID: PMC9530619 DOI: 10.12688/f1000research.123183.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/29/2022] [Indexed: 09/19/2023] Open
Abstract
Despite intensive investigations numerous diseases remain etiologically puzzling and recalcitrant to treatments. A theory is proposed here assuming that these difficulties are due to an unsuitable approach to the mechanisms of life, which is subjugated by an apparent complexity and fails to grasp the uniformity that lays behind. The stability of metabolism, despite the enormous complex of chemical reactions, suggests that reciprocal control is a prerequisite of life. Negative feedback loops have been known for a long time to maintain homeostasis, while more recently, different life processes involved in transitions or changes have been modeled by positive loops giving rise to bistable switches, also including various diseases. The present theory makes a generalization, by assuming that any functional element of a biological system is involved in a positive or a negative feedback loop. Consequently, the theory holds that the starting mechanism of any disease that affects a healthy human can be conceptually reduced to a bistable or multistationary loop system, thus providing a unifying model leading to the discovery of critical therapeutic targets.
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Affiliation(s)
- Bruno Burlando
- Department of Pharmacy, University of Genoa, Genoa, 16132, Italy
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3
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Burlando B. A general hypothesis of multistable systems in pathophysiology. F1000Res 2022; 11:906. [PMID: 36226044 PMCID: PMC9530619 DOI: 10.12688/f1000research.123183.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/22/2022] [Indexed: 01/13/2023] Open
Abstract
Despite intensive investigations numerous diseases remain etiologically puzzling and recalcitrant to treatments. A hypothesis is proposed here assuming that these difficulties are due to an unsuitable approach to the mechanisms of life, which is subjugated by an apparent complexity and fails to grasp the uniformity that lays behind. The stability of metabolism, despite the enormous complex of chemical reactions, suggests that reciprocal control is a prerequisite of life. Negative feedback loops have been known for a long time to maintain homeostasis, while more recently, different life processes involved in transitions or changes have been modeled by positive loops giving rise to bistable switches, also including various diseases. The present hypothesis makes a generalization, by assuming that any functional element of a biological system is involved in a positive or a negative feedback loop. Consequently, the hypothesis holds that the starting mechanism of any disease that affects a healthy human can be conceptually reduced to a bistable or multistationary loop system, thus providing a unifying model leading to the discovery of critical therapeutic targets.
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Affiliation(s)
- Bruno Burlando
- Department of Pharmacy, University of Genoa, Genoa, 16132, Italy
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4
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Ruan X, Li W, Du P, Wang Y. Mechanism of Phellodendron and Anemarrhena Drug Pair on the Treatment of Liver Cancer Based on Network Pharmacology and Bioinformatics. Front Oncol 2022; 12:838152. [PMID: 35463358 PMCID: PMC9021729 DOI: 10.3389/fonc.2022.838152] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 03/09/2022] [Indexed: 11/24/2022] Open
Abstract
Background This study aims to explore the key targets and signaling pathways of the traditional Chinese medicine Phellodendron and Anemarrhena drug pair (PADP) for the treatment of liver cancer. Methods Firstly, bioinformatics technology was used to analyze GSE62232 gene chip to obtain the differential genes of liver cancer. A network pharmacology technology was used to find the active components of PADP and their targets. Secondly, the differential genes were imported into STRING database to draw a PPI network, and network topology structure map combined with Cytoscape software. And the R language was used to identify differential gene targets and pathways through GO and KEGG pathway enrichment analysis. In addition, AutoDock Vina was used for molecular docking of core targets and core compounds. Moreover, GEPIA online analysis tool was used to perform survival analysis of the core target genes. Finally, RT-PCR was used to verify the changes of key target genes. CCK−8 assay was performed to detect cell proliferation. Flow cytometry was performed to detect the cell cycle and apoptotic. Transwell invasion assay was performed to detect cell invasion. Results Firstly, a total of 21,654 genes were obtained. After screening, 1019 differential genes were obtained, including 614 down-regulated genes and 405 up-regulated genes. Furthermore, after screening by ADME standards, 52 active ingredients were obtained, of which 37 were Phellodendron and 15 were Anemarrhena. And a total of 36 differential genes have been identified, including 13 up-regulated genes and 23 down-regulated genes. Moreover, through enrichment analysis, we found that PADP may treat liver cancer through multiple channels and multiple pathways including the p53 signaling pathway, IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway and so on. Secondly, the molecular docking results showed that there was certain affinity between the core compounds and core target genes. In addition, GEPIA online analysis showed that ESR1, AR, CCNB1, CDK1, AKR1C3 and CCNA2 might become potential target genes for the survival and prognosis of PADP for the treatment of liver cancer. Finally, it was found that PADP could up regulate genes ESR1 and AR, down regulate genes CCNB1, CDK1, AKR1C3, and CCNA2. PADP could promote the apoptosis of liver cancer cells, shorten the cell cycle, and inhibit the proliferation and invasion of liver cancer cells. Conclusion PADP may treat liver cancer through multiple targets, multiple channels, and multiple pathways, thereby suppressing cancer cells and improving the living quality of patients.
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Affiliation(s)
- Xiaofeng Ruan
- College of Traditional Chinese Medicine, Hubei University of Traditional Chinese Medicine, Wuhan, China.,Department of Rehabilitation Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Wenyuan Li
- Department of Anesthesiology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Peng Du
- Department of Rehabilitation Medicine, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, China
| | - Yao Wang
- Department of Infectious Diseases, Renmin Hospital of Wuhan University, Wuhan, China
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5
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IndGOterm: a qualitative method for the identification of individually dysregulated GO terms in cancer. Brief Bioinform 2022; 23:6526723. [DOI: 10.1093/bib/bbac012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2021] [Revised: 12/24/2021] [Accepted: 01/08/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Individual pathway analysis can dissect heterogeneities among different cancer patients and provide efficient guidelines for individualized therapy. However, the existence of the batch effect brings extensive limitations for the application of many individual methods for pathway analysis. Previously, researchers proposed that methods based on within-sample relative expression ordering (REO) of the genes are notably insensitive to ‘batch effects’. In this article, we focus on the Gene Ontology (GO) database and propose an individual qualitative GO term analysis method (IndGOterm) based on the REO of genes. Compared with some current widely used single-sample enrichment analysis methods, such as ssGSEA and GSVA, IndGOterm has a predominance of ignoring the batch effects caused by diverse technologies. Through the survival and drug responses analysis, we found IndGOterm could capture more terms connected to cancer than other single-sample enrichment analysis methods. Furthermore, through the application of IndGOterm, we found some terms that present different dysregulation models that manifest heterogenetic in homologous patients. Collectively, these results attested that IndGOterm could capture useful information from patients and be a useful tool to reveal the intrinsic characteristic of cancer. An open-source R statistical analysis package ‘IndGOterm’ is available at https://github.com/robert19960424/IndGOterm.
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6
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Hao J, Hao W, Liu Z, Shi P. The toggle switch model for gene expression change during the prenatal-to-postnatal transition in mammals. Mol Biol Evol 2022; 39:6526405. [PMID: 35143657 PMCID: PMC8892945 DOI: 10.1093/molbev/msac036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
The prenatal-to-postnatal transition is a pivotal process in the life cycle whereby an organism shifts from responding to intrauterine cues to undergoing extrauterine stresses with many physiological adaptations. However, the molecular basis underlying the evolutionarily conserved physiological adaptations remains elusive. Here, we analyze the transcriptomes of seven organs across developmental time points from five mammalian species by constructing computational coexpression networks and report a developmental shift of gene expression at the perinatal stage. The low-to-high and high-to-low expressed genes tightly coalesce in the functional categories and gene regulatory pathways that implicate the physiological adaptions during the prenatal-to-postnatal transition, including lipid metabolism, circadian rhythm, immune response, cell cycle, and cell division. The low-to-high and high-to-low expressed genes around the perinatal stage tend to form the mutually inhibitory toggle switch gene pairs linking the gene regulatory networks in response to the environmental changes. We thus propose the toggle switch model for the developmental shift of gene expression as a mechanic framework to investigate how the physiological adaptations occur during the prenatal-to-postnatal transition.
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Affiliation(s)
- Junjun Hao
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Wuling Hao
- College of Mathematics, Yunnan Normal University, Kunming 650500, China
| | - Zhen Liu
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China
| | - Peng Shi
- State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming 650223, China.,School of Future Technology, University of Chinese Academy of Sciences, Beijing 101408, China.,Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China
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7
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Tomar M, Somvanshi PR, Kareenhalli V. Physiological significance of bistable circuit design in metabolic homeostasis: role of integrated insulin-glucagon signalling network. Mol Biol Rep 2022; 49:5017-5028. [DOI: 10.1007/s11033-022-07175-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/19/2022] [Indexed: 10/19/2022]
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8
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Yang Y, Zhang T, Xiao R, Hao X, Zhang H, Qu H, Xie B, Wang T, Fang X. Platform-independent approach for cancer detection from gene expression profiles of peripheral blood cells. Brief Bioinform 2021; 21:1006-1015. [PMID: 30895303 DOI: 10.1093/bib/bbz027] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2018] [Revised: 02/04/2019] [Accepted: 02/18/2019] [Indexed: 01/08/2023] Open
Abstract
Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning-based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. We showed the diagnostic potential of the method by performing RNA-seq for 37 peripheral blood samples from breast cancer patients and by collecting RNA-seq data from healthy donors in Genotype-Tissue Expression project and microarray mRNA expression datasets in Gene Expression Omnibus. The framework was insensitive to experimental batch effects and data normalization, and it can be simultaneously applied to new sample prediction.
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Affiliation(s)
- Yadong Yang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Tao Zhang
- BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Rudan Xiao
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Xiaopeng Hao
- Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China
| | - Huiqiang Zhang
- Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China
| | - Hongzhu Qu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Bingbing Xie
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Tao Wang
- Breast Oncology Department, Affiliated Hospital, Academy of Military Medical Sciences, Beijing, China
| | - Xiangdong Fang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
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9
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Jeynes-Smith C, Araujo RP. Ultrasensitivity and bistability in covalent-modification cycles with positive autoregulation. Proc Math Phys Eng Sci 2021; 477:20210069. [PMID: 35153570 PMCID: PMC8331239 DOI: 10.1098/rspa.2021.0069] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Accepted: 07/02/2021] [Indexed: 12/17/2022] Open
Abstract
Switch-like behaviours in biochemical networks are of fundamental significance in biological signal processing, and exist as two distinct types: ultra-sensitivity and bistability. Here we propose two new models of a reversible covalent-modification cycle with positive autoregulation (PAR), a motif structure that is thought to be capable of both ultrasensitivity and bistability in different parameter regimes. These new models appeal to a modelling framework that we call complex-complete, which accounts fully for the molecular complexities of the underlying signalling mechanisms. Each of the two new models encodes a specific molecular mechanism for PAR. We demonstrate that the modelling simplifications for PAR models that have been used in previous work, which rely on Michaelian approximations, are unable to accurately recapitulate the qualitative signalling responses supported by our detailed models. Strikingly, we show that complex-complete PAR models are capable of new qualitative responses such as one-way switches and a 'prozone' effect, depending on the specific PAR-encoding mechanism, which are not supported by Michaelian simplifications. Our results highlight the critical importance of accurately representing the molecular details of biochemical signalling mechanisms, and strongly suggest that the Michaelian approximation is inadequate for predictive models of enzyme-mediated chemical reactions with added regulations such as PAR.
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Affiliation(s)
- Cailan Jeynes-Smith
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Institute of Health and Biomedical Innovation (IHBI), Brisbane, Australia
| | - Robyn P. Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
- Institute of Health and Biomedical Innovation (IHBI), Brisbane, Australia
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10
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Terebus A, Manuchehrfar F, Cao Y, Liang J. Exact Probability Landscapes of Stochastic Phenotype Switching in Feed-Forward Loops: Phase Diagrams of Multimodality. Front Genet 2021; 12:645640. [PMID: 34306004 PMCID: PMC8297706 DOI: 10.3389/fgene.2021.645640] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 04/26/2021] [Indexed: 11/13/2022] Open
Abstract
Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the exact steady-state probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter Accurate Chemical Master Equation (ACME) algorithm, and quantified the exact topological features of their high-dimensional probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 probability landscapes, where each landscape resides over 105–106 microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. In addition, we have quantified the topological sensitivity of the multimodality of the landscapes to regulation intensities. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells toward better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.
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Affiliation(s)
- Anna Terebus
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States.,Constellation, Baltimore, MD, United States
| | - Farid Manuchehrfar
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
| | - Youfang Cao
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States.,Merck & Co., Inc., Kenilworth, NJ, United States
| | - Jie Liang
- Center for Bioinformatics and Quantitative Biology, Richard and Loan Hill Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, United States
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11
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Halder S, Ghosh S, Chattopadhyay J, Chatterjee S. Bistability in cell signalling and its significance in identifying potential drug targets. Bioinformatics 2021; 37:4156-4163. [PMID: 34021761 DOI: 10.1093/bioinformatics/btab395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Revised: 04/09/2021] [Accepted: 05/20/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Bistability is one of the salient dynamical features in various all-or-none kinds of decision-making processes. The presence of bistability in a cell signalling network plays a key role in input-output (I/O) relation. Our study is aiming to capture and emphasise the role of motif structure influencing the I/O relation between two nodes in the context of bistability. Here, a model-based analysis is made to investigate the critical conditions responsible for the emergence of different bistable protein-protein interaction (PPI) motifs and their possible applications to find the potential drug targets. RESULTS The global sensitivity analysis is used to identify sensitive parameters and their role in maintaining the bistability. Additionally, the bistable switching through hysteresis is explored to develop an understanding of the underlying mechanisms involved in the cell signalling processes, when significant motifs exhibiting bistability have emerged. Further, we elaborate the application of the results by the implication of the emerged PPI motifs to identify potential drug-targets in three cancer networks, which is validated with existing databases. The influence of stochastic perturbations that could hinder desired functionality of any signalling networks is also described here. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Suvankar Halder
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd milestone, Faridabad-Gurgaon Expressway, Faridabad-121001, India
| | - Sumana Ghosh
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd milestone, Faridabad-Gurgaon Expressway, Faridabad-121001, India
| | - Joydev Chattopadhyay
- Agricultural and Ecological Research Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata-700108, India
| | - Samrat Chatterjee
- Complex Analysis Group, Translational Health Science and Technology Institute, NCR Biotech Science Cluster, 3rd milestone, Faridabad-Gurgaon Expressway, Faridabad-121001, India
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12
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Peterson EJR, Abidi AA, Arrieta-Ortiz ML, Aguilar B, Yurkovich JT, Kaur A, Pan M, Srinivas V, Shmulevich I, Baliga NS. Intricate Genetic Programs Controlling Dormancy in Mycobacterium tuberculosis. Cell Rep 2021; 31:107577. [PMID: 32348771 PMCID: PMC7605849 DOI: 10.1016/j.celrep.2020.107577] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 12/18/2019] [Accepted: 04/06/2020] [Indexed: 11/24/2022] Open
Abstract
Mycobacterium tuberculosis (MTB) displays the remarkable ability to transition in and out of dormancy, a hallmark of the pathogen’s capacity to evade the immune system and exploit susceptible individuals. Uncovering the gene regulatory programs that underlie the phenotypic shifts in MTB during disease latency and reactivation has posed a challenge. We develop an experimental system to precisely control dissolved oxygen levels in MTB cultures in order to capture the transcriptional events that unfold as MTB transitions into and out of hypoxia-induced dormancy. Using a comprehensive genome-wide transcription factor binding map and insights from network topology analysis, we identify regulatory circuits that deterministically drive sequential transitions across six transcriptionally and functionally distinct states encompassing more than three-fifths of the MTB genome. The architecture of the genetic programs explains the transcriptional dynamics underlying synchronous entry of cells into a dormant state that is primed to infect the host upon encountering favorable conditions. Mycobacterium tuberculosis (MTB) persists within the host by counteracting disparate stressors including hypoxia. Peterson et al. report a transcriptional program that coordinates sequential state transitions to drive MTB in and out of hypoxia-induced dormancy. Among varied properties, this program encodes advanced preparedness to infect the host in favorable conditions.
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Affiliation(s)
| | - Abrar A Abidi
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | - Boris Aguilar
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | - Amardeep Kaur
- Institute for Systems Biology, Seattle, WA 98109, USA
| | - Min Pan
- Institute for Systems Biology, Seattle, WA 98109, USA
| | | | | | - Nitin S Baliga
- Institute for Systems Biology, Seattle, WA 98109, USA; Molecular and Cellular Biology Program, Departments of Microbiology and Biology, University of Washington, Seattle, WA; Lawrence Berkeley National Laboratories, Berkeley, CA.
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13
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Chauhan L, Ram U, Hari K, Jolly MK. Topological signatures in regulatory network enable phenotypic heterogeneity in small cell lung cancer. eLife 2021; 10:e64522. [PMID: 33729159 PMCID: PMC8012062 DOI: 10.7554/elife.64522] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Accepted: 03/16/2021] [Indexed: 02/07/2023] Open
Abstract
Phenotypic (non-genetic) heterogeneity has significant implications for the development and evolution of organs, organisms, and populations. Recent observations in multiple cancers have unraveled the role of phenotypic heterogeneity in driving metastasis and therapy recalcitrance. However, the origins of such phenotypic heterogeneity are poorly understood in most cancers. Here, we investigate a regulatory network underlying phenotypic heterogeneity in small cell lung cancer, a devastating disease with no molecular targeted therapy. Discrete and continuous dynamical simulations of this network reveal its multistable behavior that can explain co-existence of four experimentally observed phenotypes. Analysis of the network topology uncovers that multistability emerges from two teams of players that mutually inhibit each other, but members of a team activate one another, forming a 'toggle switch' between the two teams. Deciphering these topological signatures in cancer-related regulatory networks can unravel their 'latent' design principles and offer a rational approach to characterize phenotypic heterogeneity in a tumor.
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Affiliation(s)
- Lakshya Chauhan
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
- Undergraduate Programme, Indian Institute of ScienceBangaloreIndia
| | - Uday Ram
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
- Undergraduate Programme, Indian Institute of ScienceBangaloreIndia
| | - Kishore Hari
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
| | - Mohit Kumar Jolly
- Centre for BioSystems Science and Engineering, Indian Institute of ScienceBangaloreIndia
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14
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Jiang Z, Lu L, Liu Y, Zhang S, Li S, Wang G, Wang P, Chen L. SMAD7 and SERPINE1 as novel dynamic network biomarkers detect and regulate the tipping point of TGF-beta induced EMT. Sci Bull (Beijing) 2020; 65:842-853. [PMID: 36659203 DOI: 10.1016/j.scib.2020.01.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 10/21/2019] [Accepted: 11/18/2019] [Indexed: 01/21/2023]
Abstract
Epithelial-mesenchymal transition (EMT) is a complex nonlinear biological process that plays essential roles in fundamental biological processes such as embryogenesis, wounding healing, tissue regeneration, and cancer metastasis. A hallmark of EMT is the switch-like behavior during state transition, which is characteristic of phase transitions. Hence, detecting the tipping point just before mesenchymal state transition is critical for understanding molecular mechanism of EMT. Through dynamic network biomarkers (DNB) model, a DNB group with 37 genes was identified which can provide the early-warning signals of EMT. Particularly, we found that two DNB genes, i.e., SMAD7 and SERPINE1 promoted EMT by switching their regulatory network which was further validated by biological experiments. Survival analyses revealed that SMAD7 and SERPINE1 as DNB genes further acted as prognostic biomarkers for lung adenocarcinoma.
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Affiliation(s)
- Zhonglin Jiang
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Lina Lu
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Yuwei Liu
- Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai 200241, China; Laboratory of Systems Biology, Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 200031, China
| | - Si Zhang
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shuxian Li
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Guanyu Wang
- Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Guangdong Provincial Key Laboratory of Computational Science and Material Design, Department of Biology, Southern University of Science and Technology, Shenzhen 518055, China.
| | - Peng Wang
- Bio-med Big Data Center, Key Laboratory of Computational Biology, CAS-MPG Partner Institute of Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
| | - Luonan Chen
- Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China; Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China; School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
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15
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Somvanshi PR, Tomar M, Kareenhalli V. Computational Analysis of Insulin-Glucagon Signalling Network: Implications of Bistability to Metabolic Homeostasis and Disease states. Sci Rep 2019; 9:15298. [PMID: 31653897 PMCID: PMC6814820 DOI: 10.1038/s41598-019-50889-4] [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] [Received: 01/14/2019] [Accepted: 09/19/2019] [Indexed: 02/06/2023] Open
Abstract
Insulin and glucagon control plasma macronutrient homeostasis through their signalling network composed of multiple feedback and crosstalk interactions. To understand how these interactions contribute to metabolic homeostasis and disease states, we analysed the steady state response of metabolic regulation (catabolic or anabolic) with respect to structural and input perturbations in the integrated signalling network, for varying levels of plasma glucose. Structural perturbations revealed: the positive feedback of AKT on IRS is responsible for the bistability in anabolic zone (glucose >5.5 mmol); the positive feedback of calcium on cAMP is responsible for ensuring ultrasensitive response in catabolic zone (glucose <4.5 mmol); the crosstalk between AKT and PDE3 is responsible for efficient catabolic response under low glucose condition; the crosstalk between DAG and PKC regulates the span of anabolic bistable region with respect to plasma glucose levels. The macronutrient perturbations revealed: varying plasma amino acids and fatty acids from normal to high levels gradually shifted the bistable response towards higher glucose range, eventually making the response catabolic or unresponsive to increasing glucose levels. The analysis reveals that certain macronutrient composition may be more conducive to homeostasis than others. The network perturbations that may contribute to disease states such as diabetes, obesity and cancer are discussed.
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Affiliation(s)
- Pramod R Somvanshi
- Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai, India.,Bioengineering Division, John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, USA
| | - Manu Tomar
- Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai, India
| | - Venkatesh Kareenhalli
- Department of Chemical Engineering, Indian Institute of Technology, Bombay, Powai, Mumbai, India.
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16
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Yan Y, Li XQ, Duan JL, Bao CJ, Cui YN, Su ZB, Xu JR, Luo Q, Chen M, Xie Y, Lu WL. Nanosized functional miRNA liposomes and application in the treatment of TNBC by silencing Slug gene. Int J Nanomedicine 2019; 14:3645-3667. [PMID: 31190817 PMCID: PMC6529035 DOI: 10.2147/ijn.s207837] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 05/01/2019] [Indexed: 12/31/2022] Open
Abstract
Background: Neo-adjuvant chemotherapy is an effective strategy for improving treatment of breast cancers. However, the efficacy of this treatment strategy is limited for treatment of triple negative breast cancer (TNBC). Gene therapy may be a more effective strategy for improving the prognosis of TNBC. Methods: A novel 25 nucleotide sense strand of miRNA was designed to treat TNBC by silencing the Slug gene, and encapsulated into DSPE-PEG2000-tLyp-1 peptide-modified functional liposomes. The efficacy of miRNA liposomes was evaluated on invasive TNBC cells and TNBC cancer-bearing nude mice. Furthermore, functional vinorelbine liposomes were constructed to investigate the anticancer effects of combined treatment. Results: The functional miRNA liposomes had a round shape and were nanosized (120 nm). Functional miRNA liposomes were effectively captured by TNBC cells in vitro and were target to mitochondria. Treatment with functional liposomes silenced the expression of Slug and Slug protein, inhibited the TGF-β1/Smad pathway, and inhibited invasiveness and growth of TNBC cells. In TNBC cancer-bearing mice, functional miRNA liposomes exerted a stronger anticancer effect than functional vinorelbine liposomes, and combination therapy with these two formulations resulted in nearly complete inhibition of tumor growth. Preliminary safety evaluations indicated that the functional miRNA liposomes did not affect body weight or cause damage to any major organs. Furthermore, the functional liposomes significantly increased the half-life of the drug in the blood of cancer-bearing nude mice, and increased drug accumulation in breast cancer tissues. Conclusion: In this study, we constructed novel functional miRNA liposomes. These liposomes silenced Slug expression and inhibited the TGF-β1/Smad pathway in TNBC cells, and enhanced anticancer efficacy in mice using combined chemotherapy. Hence, the present study demonstrated a promising strategy for gene therapy of invasive breast cancer.
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Affiliation(s)
- Yan Yan
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Xue-Qi Li
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Jia-Lun Duan
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Chun-Jie Bao
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Yi-Nuo Cui
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Zhan-Bo Su
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Jia-Rui Xu
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Qian Luo
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Ming Chen
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Ying Xie
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
| | - Wan-Liang Lu
- State Key Laboratory of Natural and Biomimetic Drugs, Beijing Key Laboratory of Molecular Pharmaceutics and New Drug System, and School of Pharmaceutical Sciences, Peking University, Beijing, People's Republic of China
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17
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Hu B, Guan ZH, Chen G, Lewis FL. Multistability of Delayed Hybrid Impulsive Neural Networks With Application to Associative Memories. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2019; 30:1537-1551. [PMID: 30296243 DOI: 10.1109/tnnls.2018.2870553] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The important topic of multistability of continuous-and discrete-time neural network (NN) models has been investigated rather extensively. Concerning the design of associative memories, multistability of delayed hybrid NNs is studied in this paper with an emphasis on the impulse effects. Arising from the spiking phenomenon in biological networks, impulsive NNs provide an efficient model for synaptic interconnections among neurons. Using state-space decomposition, the coexistence of multiple equilibria of hybrid impulsive NNs is analyzed. Multistability criteria are then established regrading delayed hybrid impulsive neurodynamics, for which both the impulse effects on the convergence rate and the basins of attraction of the equilibria are discussed. Illustrative examples are given to verify the theoretical results and demonstrate an application to the design of associative memories. It is shown by an experimental example that delayed hybrid impulsive NNs have the advantages of high storage capacity and high fault tolerance when used for associative memories.
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18
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Hu B, Guan ZH, Qian TH, Chen G. Dynamic Analysis of Hybrid Impulsive Delayed Neural Networks With Uncertainties. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2018; 29:4370-4384. [PMID: 29990176 DOI: 10.1109/tnnls.2017.2764003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Neural networks (NNs) have emerged as a powerful illustrative diagram for the brain. Unveiling the mechanism of neural-dynamic evolution is one of the crucial steps toward understanding how the brain works and evolves. Inspired by the universal existence of impulses in many real systems, this paper formulates a type of hybrid NNs (HNNs) with impulses, time delays, and interval uncertainties, and studies its global dynamic evolution by a robust interval analysis. The HNNs incorporate both continuous-time implementation and impulsive jump in mutual activations, where time delays and interval uncertainties are represented simultaneously. By constructing a Banach contraction mapping, the existence and uniqueness of the equilibrium of the HNN model are proved and analyzed in detail. Based on nonsmooth Lyapunov functions and delayed impulsive differential equations, new criteria are derived for ensuring the global robust exponential stability of the HNNs. Convergence analysis together with illustrative examples show the effectiveness of the theoretical results.
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19
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Martinez Guimera A, Welsh CM, Proctor CJ, McArdle A, Shanley DP. 'Molecular habituation' as a potential mechanism of gradual homeostatic loss with age. Mech Ageing Dev 2017; 169:53-62. [PMID: 29146308 PMCID: PMC5846846 DOI: 10.1016/j.mad.2017.11.010] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Revised: 10/26/2017] [Accepted: 11/10/2017] [Indexed: 12/17/2022]
Abstract
Constitutive signals indicate homeostatic dysregulation but their effect on signal transduction remains largely unexplored. A theoretical approach is undertaken to examine how oxidative stress may affect redox signal transduction. Constitutive signals can result in a ‘molecular habituation’ effect that interferes with information transmission. The robustness of such a theoretical observation to the underlying methodology hints at the generality of this principle.
The ability of reactive oxygen species (ROS) to cause molecular damage has meant that chronic oxidative stress has been mostly studied from the point of view of being a source of toxicity to the cell. However, the known duality of ROS molecules as both damaging agents and cellular redox signals implies another perspective in the study of sustained oxidative stress. This is a perspective of studying oxidative stress as a constitutive signal within the cell. In this work, we adopt a theoretical perspective as an exploratory and explanatory approach to examine how chronic oxidative stress can interfere with signal processing by redox signalling pathways in the cell. We report that constitutive signals can give rise to a ‘molecular habituation’ effect that can prime for a gradual loss of biological function. This is because a constitutive signal in the environment has the potential to reduce the responsiveness of a signalling pathway through the prolonged activation of negative regulators. Additionally, we demonstrate how this phenomenon is likely to occur in different signalling pathways exposed to persistent signals and furthermore at different levels of biological organisation.
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Affiliation(s)
- Alvaro Martinez Guimera
- Institute for Cell and Molecular Biosciences (ICaMB), Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle Upon Tyne, NE4 5PL,United Kingdom; MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom
| | - Ciaran M Welsh
- Institute for Cell and Molecular Biosciences (ICaMB), Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle Upon Tyne, NE4 5PL,United Kingdom
| | - Carole J Proctor
- Institute of Cellular Medicine, Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle Upon Tyne, NE4 5PL, United Kingdom; MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom
| | - Anne McArdle
- Department of Musculoskeletal Biology, University of Liverpool (University, Not-for-profit), Institute of Ageing and Chronic Disease,William Duncan Building, 6 West Derby Street, Liverpool L7 8TX, United Kingdom; MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom
| | - Daryl P Shanley
- Institute for Cell and Molecular Biosciences (ICaMB), Ageing Research Laboratories, Campus for Ageing and Vitality, Newcastle University, Newcastle Upon Tyne, NE4 5PL,United Kingdom; MRC/Arthritis Research UK Centre for Musculoskeletal Ageing (CIMA), United Kingdom.
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20
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Jia D, Jolly MK, Harrison W, Boareto M, Ben-Jacob E, Levine H. Operating principles of tristable circuits regulating cellular differentiation. Phys Biol 2017; 14:035007. [PMID: 28443829 DOI: 10.1088/1478-3975/aa6f90] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Many cell-fate decisions during embryonic development are governed by a motif comprised of two transcription factors (TFs) A and B that mutually inhibit each other and may self-activate. This motif, called as a self-activating toggle switch (SATS), can typically have three stable states (phenotypes)-two corresponding to differentiated cell fates, each of which has a much higher level of one TF than the other-[Formula: see text] or [Formula: see text]-and the third state corresponding to an 'undecided' stem-like state with similar levels of both A and B-[Formula: see text]. Furthermore, two or more SATSes can be coupled together in various topologies in different contexts, thereby affecting the coordination between multiple cellular decisions. However, two questions remain largely unanswered: (a) what governs the co-existence and relative stability of these three stable states? (b) What orchestrates the decision-making of coupled SATSes? Here, we first demonstrate that the co-existence and relative stability of the three stable states in an individual SATS can be governed by the relative strength of self-activation, external signals activating and/or inhibiting A and B, and mutual degradation between A and B. Simultaneously, we investigate the effects of these factors on the decision-making of two coupled SATSes. Our results offer novel understanding into the operating principles of individual and coupled tristable self-activating toggle switches (SATSes) regulating cellular differentiation and can yield insights into synthesizing three-way genetic circuits and understanding of cellular reprogramming.
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Affiliation(s)
- Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX 77005-1827, United States of America. Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX 77005-1827, United States of America
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21
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Otero-Muras I, Yordanov P, Stelling J. Chemical Reaction Network Theory elucidates sources of multistability in interferon signaling. PLoS Comput Biol 2017; 13:e1005454. [PMID: 28369103 PMCID: PMC5400276 DOI: 10.1371/journal.pcbi.1005454] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2015] [Revised: 04/21/2017] [Accepted: 03/13/2017] [Indexed: 11/29/2022] Open
Abstract
Bistability has important implications in signaling pathways, since it indicates a potential cell decision between alternative outcomes. We present two approaches developed in the framework of the Chemical Reaction Network Theory for easy and efficient search of multiple steady state behavior in signaling networks (both with and without mass conservation), and apply them to search for sources of bistability at different levels of the interferon signaling pathway. Different type I interferon subtypes and/or doses are known to elicit differential bioactivities (ranging from antiviral, antiproliferative to immunomodulatory activities). How different signaling outcomes can be generated through the same receptor and activating the same JAK/STAT pathway is still an open question. Here, we detect bistability at the level of early STAT signaling, showing how two different cell outcomes are achieved under or above a threshold in ligand dose or ligand-receptor affinity. This finding could contribute to explain the differential signaling (antiviral vs apoptotic) depending on interferon dose and subtype (α vs β) observed in type I interferons.
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Affiliation(s)
- Irene Otero-Muras
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Zurich, Switzerland
| | - Pencho Yordanov
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Zurich, Switzerland
| | - Joerg Stelling
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics, ETH Zurich, Zurich, Switzerland
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22
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Chen J, Yang HT, Li Z, Xu N, Yu B, Xu JP, Zhao PG, Wang Y, Zhang XJ, Lin DJ. Construction of protein interaction network involved in lung adenocarcinomas using a novel algorithm. Oncol Lett 2016; 12:1792-1800. [PMID: 27588126 PMCID: PMC4998145 DOI: 10.3892/ol.2016.4822] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2015] [Accepted: 12/01/2015] [Indexed: 12/14/2022] Open
Abstract
Studies that only assess differentially-expressed (DE) genes do not contain the information required to investigate the mechanisms of diseases. A complete knowledge of all the direct and indirect interactions between proteins may act as a significant benchmark in the process of forming a comprehensive description of cellular mechanisms and functions. The results of protein interaction network studies are often inconsistent and are based on various methods. In the present study, a combined network was constructed using selected gene pairs, following the conversion and combination of the scores of gene pairs that were obtained across multiple approaches by a novel algorithm. Samples from patients with and without lung adenocarcinoma were compared, and the RankProd package was used to identify DE genes. The empirical Bayesian (EB) meta-analysis approach, the search tool for the retrieval of interacting genes/proteins database (STRING), the weighted gene coexpression network analysis (WGCNA) package and the differentially-coexpressed genes and links package (DCGL) were used for network construction. A combined network was also constructed with a novel rank-based algorithm using a combined score. The topological features of the 5 networks were analyzed and compared. A total of 941 DE genes were screened. The topological analysis indicated that the gene interaction network constructed using the WGCNA method was more likely to produce a small-world property, which has a small average shortest path length and a large clustering coefficient, whereas the combined network was confirmed to be a scale-free network. Gene pairs that were identified using the novel combined method were mostly enriched in the cell cycle and p53 signaling pathway. The present study provided a novel perspective to the network-based analysis. Each method has advantages and disadvantages. Compared with single methods, the combined algorithm used in the present study may provide a novel method to analyze gene interactions, with increased credibility.
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Affiliation(s)
- Juan Chen
- Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China; Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Hai-Tao Yang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Zhu Li
- Department of Hepatobiliary Surgery, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Ning Xu
- Department of Respiratory Medicine, Weihai Municipal Hospital, Weihai, Shandong 264200, P.R. China
| | - Bo Yu
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Jun-Ping Xu
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Pei-Ge Zhao
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Yan Wang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Xiu-Juan Zhang
- Department of Respiratory Medicine, People's Hospital of Liaocheng, Liaocheng, Shandong 252000, P.R. China
| | - Dian-Jie Lin
- Department of Respiratory Medicine, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250014, P.R. China
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Serra-Musach J, Mateo F, Capdevila-Busquets E, de Garibay GR, Zhang X, Guha R, Thomas CJ, Grueso J, Villanueva A, Jaeger S, Heyn H, Vizoso M, Pérez H, Cordero A, Gonzalez-Suarez E, Esteller M, Moreno-Bueno G, Tjärnberg A, Lázaro C, Serra V, Arribas J, Benson M, Gustafsson M, Ferrer M, Aloy P, Pujana MÀ. Cancer network activity associated with therapeutic response and synergism. Genome Med 2016; 8:88. [PMID: 27553366 PMCID: PMC4995628 DOI: 10.1186/s13073-016-0340-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2016] [Accepted: 08/01/2016] [Indexed: 12/14/2022] Open
Abstract
Background Cancer patients often show no or only modest benefit from a given therapy. This major problem in oncology is generally attributed to the lack of specific predictive biomarkers, yet a global measure of cancer cell activity may support a comprehensive mechanistic understanding of therapy efficacy. We reasoned that network analysis of omic data could help to achieve this goal. Methods A measure of “cancer network activity” (CNA) was implemented based on a previously defined network feature of communicability. The network nodes and edges corresponded to human proteins and experimentally identified interactions, respectively. The edges were weighted proportionally to the expression of the genes encoding for the corresponding proteins and relative to the number of direct interactors. The gene expression data corresponded to the basal conditions of 595 human cancer cell lines. Therapeutic responses corresponded to the impairment of cell viability measured by the half maximal inhibitory concentration (IC50) of 130 drugs approved or under clinical development. Gene ontology, signaling pathway, and transcription factor-binding annotations were taken from public repositories. Predicted synergies were assessed by determining the viability of four breast cancer cell lines and by applying two different analytical methods. Results The effects of drug classes were associated with CNAs formed by different cell lines. CNAs also differentiate target families and effector pathways. Proteins that occupy a central position in the network largely contribute to CNA. Known key cancer-associated biological processes, signaling pathways, and master regulators also contribute to CNA. Moreover, the major cancer drivers frequently mediate CNA and therapeutic differences. Cell-based assays centered on these differences and using uncorrelated drug effects reveals novel synergistic combinations for the treatment of breast cancer dependent on PI3K-mTOR signaling. Conclusions Cancer therapeutic responses can be predicted on the basis of a systems-level analysis of molecular interactions and gene expression. Fundamental cancer processes, pathways, and drivers contribute to this feature, which can also be exploited to predict precise synergistic drug combinations. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0340-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jordi Serra-Musach
- Breast Cancer and Systems Biology Lab, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Francesca Mateo
- Breast Cancer and Systems Biology Lab, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Eva Capdevila-Busquets
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, Barcelona, 08028, Catalonia, Spain
| | - Gorka Ruiz de Garibay
- Breast Cancer and Systems Biology Lab, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Xiaohu Zhang
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Dr. Rockville, Bethesda, MD, 20850, USA
| | - Raj Guha
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Dr. Rockville, Bethesda, MD, 20850, USA
| | - Craig J Thomas
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Dr. Rockville, Bethesda, MD, 20850, USA
| | - Judit Grueso
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology (VHIO), Cellex Center, Natzaret 115-117, Barcelona, 08035, Catalonia, Spain
| | - Alberto Villanueva
- Breast Cancer and Systems Biology Lab, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Samira Jaeger
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, Barcelona, 08028, Catalonia, Spain
| | - Holger Heyn
- Cancer Epigenetics and Biology Program (PEBC), IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Miguel Vizoso
- Cancer Epigenetics and Biology Program (PEBC), IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Hector Pérez
- Cancer Epigenetics and Biology Program (PEBC), IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Alex Cordero
- Cancer Epigenetics and Biology Program (PEBC), IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Eva Gonzalez-Suarez
- Cancer Epigenetics and Biology Program (PEBC), IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Manel Esteller
- Cancer Epigenetics and Biology Program (PEBC), IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain.,Department of Physiological Sciences II, School of Medicine, University of Barcelona, Feixa Llarga s/n, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain.,Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Catalonia, Spain
| | - Gema Moreno-Bueno
- Department of Biochemistry, Autonomous University of Madrid (UAM), Biomedical Research Institute "Alberto Sols" (Spanish National Research Council (CSIC)-UAM), Hospital La Paz Institute for Health Research (IdiPAZ), Arzobispo Morcillo 4, Madrid, 28029, Spain.,MD Anderson International Foundation, Arturo Soria 270, Madrid, 28033, Spain
| | - Andreas Tjärnberg
- The Centre for Individualized Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, 58183, Sweden
| | - Conxi Lázaro
- Hereditary Cancer Program, ICO, IDIBELL, Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain
| | - Violeta Serra
- Experimental Therapeutics Group, Vall d'Hebron Institute of Oncology (VHIO), Cellex Center, Natzaret 115-117, Barcelona, 08035, Catalonia, Spain
| | - Joaquín Arribas
- Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Catalonia, Spain.,Preclinical Research Program, VHIO, Cellex Center, Natzaret 115-117, Barcelona, 08035, Catalonia, Spain.,Department of Biochemistry and Molecular Biology, Medical School Building M, Autonomous University of Barcelona, Bellaterra, 08193, Catalonia, Spain
| | - Mikael Benson
- The Centre for Individualized Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, 58183, Sweden
| | - Mika Gustafsson
- The Centre for Individualized Medicine, Department of Clinical and Experimental Medicine, Linköping University, Linköping, 58183, Sweden
| | - Marc Ferrer
- Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Dr. Rockville, Bethesda, MD, 20850, USA.
| | - Patrick Aloy
- Joint IRB-BSC-CRG Program in Computational Biology, Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10, Barcelona, 08028, Catalonia, Spain. .,Catalan Institution for Research and Advanced Studies (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Catalonia, Spain.
| | - Miquel Àngel Pujana
- Breast Cancer and Systems Biology Lab, Program Against Cancer Therapeutic Resistance (ProCURE), Catalan Institute of Oncology (ICO), Bellvitge Institute for Biomedical Research (IDIBELL), Gran via 199, L'Hospitalet del Llobregat, Barcelona, 08908, Catalonia, Spain.
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Lobikin M, Lobo D, Blackiston DJ, Martyniuk CJ, Tkachenko E, Levin M. Serotonergic regulation of melanocyte conversion: A bioelectrically regulated network for stochastic all-or-none hyperpigmentation. Sci Signal 2015; 8:ra99. [PMID: 26443706 DOI: 10.1126/scisignal.aac6609] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Experimentally induced depolarization of resting membrane potential in "instructor cells" in Xenopus laevis embryos causes hyperpigmentation in an all-or-none fashion in some tadpoles due to excess proliferation and migration of melanocytes. We showed that this stochastic process involved serotonin signaling, adenosine 3',5'-monophosphate (cAMP), and the transcription factors cAMP response element-binding protein (CREB), Sox10, and Slug. Transcriptional microarray analysis of embryos taken at stage 15 (early neurula) and stage 45 (free-swimming tadpole) revealed changes in the abundance of 45 and 517 transcripts, respectively, between control embryos and embryos exposed to the instructor cell-depolarizing agent ivermectin. Bioinformatic analysis revealed that the human homologs of some of the differentially regulated genes were associated with cancer, consistent with the induced arborization and invasive behavior of converted melanocytes. We identified a physiological circuit that uses serotonergic signaling between instructor cells, melanotrope cells of the pituitary, and melanocytes to control the proliferation, cell shape, and migration properties of the pigment cell pool. To understand the stochasticity and properties of this multiscale signaling system, we applied a computational machine-learning method that iteratively explored network models to reverse-engineer a stochastic dynamic model that recapitulated the frequency of the all-or-none hyperpigmentation phenotype produced in response to various pharmacological and molecular genetic manipulations. This computational approach may provide insight into stochastic cellular decision-making that occurs during normal development and pathological conditions, such as cancer.
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Affiliation(s)
- Maria Lobikin
- Biology Department and Center for Regenerative and Developmental Biology, Tufts University, Medford, MA 02155, USA
| | - Daniel Lobo
- Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Douglas J Blackiston
- Biology Department and Center for Regenerative and Developmental Biology, Tufts University, Medford, MA 02155, USA
| | - Christopher J Martyniuk
- Center for Environmental and Human Toxicology and Department of Physiological Sciences, UF Genetics Institute, University of Florida, Gainesville, FL 32611, USA
| | - Elizabeth Tkachenko
- Biology Department and Center for Regenerative and Developmental Biology, Tufts University, Medford, MA 02155, USA
| | - Michael Levin
- Biology Department and Center for Regenerative and Developmental Biology, Tufts University, Medford, MA 02155, USA.
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Yang ZH, Zheng R, Gao Y, Zhang Q. Identification of suitable genes contributes to lung adenocarcinoma clustering by multiple meta-analysis methods. CLINICAL RESPIRATORY JOURNAL 2015; 10:631-46. [PMID: 25619939 DOI: 10.1111/crj.12271] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2014] [Revised: 11/20/2014] [Accepted: 01/20/2015] [Indexed: 11/30/2022]
Abstract
BACKGROUND With the widespread application of high-throughput technology, numerous meta-analysis methods have been proposed for differential expression profiling across multiple studies. OBJECTIVES We identified the suitable differentially expressed (DE) genes that contributed to lung adenocarcinoma (ADC) clustering based on seven popular multiple meta-analysis methods. METHODS Seven microarray expression profiles of ADC and normal controls were extracted from the ArrayExpress database. The Bioconductor was used to perform the data preliminary preprocessing. Then, DE genes across multiple studies were identified. Hierarchical clustering was applied to compare the classification performance for microarray data samples. The classification efficiency was compared based on accuracy, sensitivity and specificity. RESULTS Across seven datasets, 573 ADC cases and 222 normal controls were collected. After filtering out unexpressed and noninformative genes, 3688 genes were remained for further analysis. The classification efficiency analysis showed that DE genes identified by sum of ranks method separated ADC from normal controls with the best accuracy, sensitivity and specificity of 0.953, 0.969 and 0.932, respectively. The gene set with the highest classification accuracy mainly participated in the regulation of response to external stimulus (P = 7.97E-04), cyclic nucleotide-mediated signaling (P = 0.01), regulation of cell morphogenesis (P = 0.01) and regulation of cell proliferation (P = 0.01). CONCLUSIONS Evaluation of DE genes identified by different meta-analysis methods in classification efficiency provided a new perspective to the choice of the suitable method in a given application. Varying meta-analysis methods always present varying abilities, so synthetic consideration should be taken when providing meta-analysis methods for particular research.
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Affiliation(s)
- Ze-Hui Yang
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Rui Zheng
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China.
| | - Yuan Gao
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China
| | - Qiang Zhang
- Department of Respiratory Medicine, Shengjing Hospital of China Medical University, Shenyang, China
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Wang H, Sun Q, Zhao W, Qi L, Gu Y, Li P, Zhang M, Li Y, Liu SL, Guo Z. Individual-level analysis of differential expression of genes and pathways for personalized medicine. ACTA ACUST UNITED AC 2014; 31:62-8. [PMID: 25165092 DOI: 10.1093/bioinformatics/btu522] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
MOTIVATION The differential expression analysis focusing on inter-group comparison can capture only differentially expressed genes (DE genes) at the population level, which may mask the heterogeneity of differential expression in individuals. Thus, to provide patient-specific information for personalized medicine, it is necessary to conduct differential expression analysis at the individual level. RESULTS We proposed a method to detect DE genes in individual disease samples by using the disrupted ordering in individual disease samples. In both simulated data and real paired cancer-normal sample data, this method showed excellent performance. It was found to be insensitive to experimental batch effects and data normalization. The landscape of stable gene pairs in a particular type of normal tissue could be predetermined using previously accumulated data, based on which dysregulated genes and pathways for any disease sample can be readily detected. The usefulness of the RankComp method in clinical settings was exemplified by the identification and application of prognostic markers for lung cancer. AVAILABILITY AND IMPLEMENTATION RankComp is implemented in R script that is freely available from Supplementary Materials.
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Affiliation(s)
- Hongwei Wang
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Qiang Sun
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Wenyuan Zhao
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Lishuang Qi
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Yunyan Gu
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Pengfei Li
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Mengmeng Zhang
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Yang Li
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Shu-Lin Liu
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
| | - Zheng Guo
- College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China College of Bioinformatics Science and Technology, Genomics Research Center, Harbin Medical University, Harbin 150086, China, Department of Microbiology and Infectious Diseases, University of Calgary, Calgary, AB, T2N 4N1, Canada and Bioinformatics Department, Basic Medical College, Fujian Medical University, Fuzhou 350004, China
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Faucon PC, Pardee K, Kumar RM, Li H, Loh YH, Wang X. Gene networks of fully connected triads with complete auto-activation enable multistability and stepwise stochastic transitions. PLoS One 2014; 9:e102873. [PMID: 25057990 PMCID: PMC4109943 DOI: 10.1371/journal.pone.0102873] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2013] [Accepted: 06/24/2014] [Indexed: 02/04/2023] Open
Abstract
Fully-connected triads (FCTs), such as the Oct4-Sox2-Nanog triad, have been implicated as recurring transcriptional motifs embedded within the regulatory networks that specify and maintain cellular states. To explore the possible connections between FCT topologies and cell fate determinations, we employed computational network screening to search all possible FCT topologies for multistability, a dynamic property that allows the rise of alternate regulatory states from the same transcriptional network. The search yielded a hierarchy of FCTs with various potentials for multistability, including several topologies capable of reaching eight distinct stable states. Our analyses suggested that complete auto-activation is an effective indicator for multistability, and, when gene expression noise was incorporated into the model, the networks were able to transit multiple states spontaneously. Different levels of stochasticity were found to either induce or disrupt random state transitioning with some transitions requiring layovers at one or more intermediate states. Using this framework we simulated a simplified model of induced pluripotency by including constitutive overexpression terms. The corresponding FCT showed random state transitioning from a terminal state to the pluripotent state, with the temporal distribution of this transition matching published experimental data. This work establishes a potential theoretical framework for understanding cell fate determinations by connecting conserved regulatory modules with network dynamics. Our results could also be employed experimentally, using established developmental transcription factors as seeds, to locate cell lineage specification networks by using auto-activation as a cipher.
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Affiliation(s)
- Philippe C. Faucon
- School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
| | - Keith Pardee
- Wyss Institute for Biological Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
- Center for BioDynamics and Center for Advanced Biotechnology, Boston University, Boston, Massachusetts, United States of America
| | - Roshan M. Kumar
- Wyss Institute for Biological Inspired Engineering, Harvard University, Boston, Massachusetts, United States of America
- Center for BioDynamics and Center for Advanced Biotechnology, Boston University, Boston, Massachusetts, United States of America
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, United States of America
| | - Yuin-Han Loh
- Epigenetics and Cell Fates Laboratory, A*STAR Institute of Molecular and Cell Biology, Department of Biological Sciences, National University of Singapore, Singapore, Singapore
| | - Xiao Wang
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona, United States of America
- * E-mail:
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Essential functional modules for pathogenic and defensive mechanisms in Candida albicans infections. BIOMED RESEARCH INTERNATIONAL 2014; 2014:136130. [PMID: 24757665 PMCID: PMC3976935 DOI: 10.1155/2014/136130] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2013] [Accepted: 02/10/2014] [Indexed: 12/24/2022]
Abstract
The clinical and biological significance of the study of fungal pathogen Candida albicans (C. albicans) has markedly increased. However, the explicit pathogenic and invasive mechanisms of such host-pathogen interactions have not yet been fully elucidated. Therefore, the essential functional modules involved in C. albicans-zebrafish interactions were investigated in this study. Adopting a systems biology approach, the early-stage and late-stage protein-protein interaction (PPI) networks for both C. albicans and zebrafish were constructed. By comparing PPI networks at the early and late stages of the infection process, several critical functional modules were identified in both pathogenic and defensive mechanisms. Functional modules in C. albicans, like those involved in hyphal morphogenesis, ion and small molecule transport, protein secretion, and shifts in carbon utilization, were seen to play important roles in pathogen invasion and damage caused to host cells. Moreover, the functional modules in zebrafish, such as those involved in immune response, apoptosis mechanisms, ion transport, protein secretion, and hemostasis-related processes, were found to be significant as defensive mechanisms during C. albicans infection. The essential functional modules thus determined could provide insights into the molecular mechanisms of host-pathogen interactions during the infection process and thereby devise potential therapeutic strategies to treat C. albicans infection.
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Brigandt I. Systems biology and the integration of mechanistic explanation and mathematical explanation. STUDIES IN HISTORY AND PHILOSOPHY OF BIOLOGICAL AND BIOMEDICAL SCIENCES 2013; 44:477-492. [PMID: 23863399 DOI: 10.1016/j.shpsc.2013.06.002] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/21/2012] [Revised: 06/12/2013] [Accepted: 06/14/2013] [Indexed: 06/02/2023]
Abstract
The paper discusses how systems biology is working toward complex accounts that integrate explanation in terms of mechanisms and explanation by mathematical models-which some philosophers have viewed as rival models of explanation. Systems biology is an integrative approach, and it strongly relies on mathematical modeling. Philosophical accounts of mechanisms capture integrative in the sense of multilevel and multifield explanations, yet accounts of mechanistic explanation (as the analysis of a whole in terms of its structural parts and their qualitative interactions) have failed to address how a mathematical model could contribute to such explanations. I discuss how mathematical equations can be explanatorily relevant. Several cases from systems biology are discussed to illustrate the interplay between mechanistic research and mathematical modeling, and I point to questions about qualitative phenomena (rather than the explanation of quantitative details), where quantitative models are still indispensable to the explanation. Systems biology shows that a broader philosophical conception of mechanisms is needed, which takes into account functional-dynamical aspects, interaction in complex networks with feedback loops, system-wide functional properties such as distributed functionality and robustness, and a mechanism's ability to respond to perturbations (beyond its actual operation). I offer general conclusions for philosophical accounts of explanation.
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Affiliation(s)
- Ingo Brigandt
- Department of Philosophy, University of Alberta, 2-40 Assiniboia Hall, Edmonton, AB T6G2E7, Canada.
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Bhat R, Bissell MJ. Of plasticity and specificity: dialectics of the microenvironment and macroenvironment and the organ phenotype. WILEY INTERDISCIPLINARY REVIEWS-DEVELOPMENTAL BIOLOGY 2013; 3:147-63. [PMID: 24719287 DOI: 10.1002/wdev.130] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2012] [Revised: 07/30/2013] [Accepted: 08/26/2013] [Indexed: 01/09/2023]
Abstract
The study of biological form and how it arises is the domain of the developmental biologists; but once the form is achieved, the organ poses a fascinating conundrum for all the life scientists: how are form and function maintained in adult organs throughout most of the life of the organism? That they do appears to contradict the inherently plastic nature of organogenesis during development. How do cells with the same genetic information arrive at, and maintain such different architectures and functions, and how do they keep remembering that they are different from each other? It is now clear that narratives based solely on genes and an irreversible regulatory dynamics cannot answer these questions satisfactorily, and the concept of microenvironmental signaling needs to be added to the equation. During development, cells rearrange and differentiate in response to diffusive morphogens, juxtacrine signals, and the extracellular matrix (ECM). These components, which constitute the modular microenvironment, are sensitive to cues from other tissues and organs of the developing embryo as well as from the external macroenvironment. On the other hand, once the organ is formed, these modular constituents integrate and constrain the organ architecture, which ensures structural and functional homeostasis and therefore, organ specificity. We argue here that a corollary of the above is that once the organ architecture is compromised in adults by mutations or by changes in the microenvironment such as aging or inflammation, that organ becomes subjected to the developmental and embryonic circuits in search of a new identity. But since the microenvironment is no longer embryonic, the confusion leads to cancer: hence as we have argued, tumors become new evolutionary organs perhaps in search of an elusive homeostasis.
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Affiliation(s)
- Ramray Bhat
- Department of Cancer & DNA Damage Responses, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
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Wang E, Zou J, Zaman N, Beitel LK, Trifiro M, Paliouras M. Cancer systems biology in the genome sequencing era: part 2, evolutionary dynamics of tumor clonal networks and drug resistance. Semin Cancer Biol 2013; 23:286-92. [PMID: 23792107 DOI: 10.1016/j.semcancer.2013.06.001] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2013] [Accepted: 06/09/2013] [Indexed: 02/08/2023]
Abstract
A tumor often consists of multiple cell subpopulations (clones). Current chemo-treatments often target one clone of a tumor. Although the drug kills that clone, other clones overtake it and the tumor recurs. Genome sequencing and computational analysis allows to computational dissection of clones from tumors, while singe-cell genome sequencing including RNA-Seq allows profiling of these clones. This opens a new window for treating a tumor as a system in which clones are evolving. Future cancer systems biology studies should consider a tumor as an evolving system with multiple clones. Therefore, topics discussed in Part 2 of this review include evolutionary dynamics of clonal networks, early-warning signals (e.g., genome duplication events) for formation of fast-growing clones, dissecting tumor heterogeneity, and modeling of clone-clone-stroma interactions for drug resistance. The ultimate goal of the future systems biology analysis is to obtain a 'whole-system' understanding of a tumor and therefore provides a more efficient and personalized management strategies for cancer patients.
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Affiliation(s)
- Edwin Wang
- National Research Council Canada, Montreal, Canada.
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 506] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Abstract
Allosteric propagation results in communication between distinct sites in the protein structure; it also encodes specific effects on cellular pathways, and in this way it shapes cellular response. One example of long-range effects is binding of morphogens to cell surface receptors, which initiates a cascade of protein interactions that leads to genome activation and specific cellular action. Allosteric propagation results from combinations of multiple factors, takes place through dynamic shifts of conformational ensembles, and affects the equilibria of macromolecular interactions. Here, we (a) emphasize the well-known yet still underappreciated role of allostery in conveying explicit signals across large multimolecular assemblies and distances to specify cellular action; (b) stress the need for quantitation of the allosteric effects; and finally, (c) propose that each specific combination of allosteric effectors along the pathway spells a distinct function. The challenges are colossal; the inspiring reward will be predicting function, misfunction, and outcomes of drug regimes.
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Affiliation(s)
- Ruth Nussinov
- Basic Research Program, SAIC-Frederick, Inc., Center for Cancer Research Nanobiology Program, National Cancer Institute, Frederick, Maryland 21702, USA.
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Rivas AL, Jankowski MD, Piccinini R, Leitner G, Schwarz D, Anderson KL, Fair JM, Hoogesteijn AL, Wolter W, Chaffer M, Blum S, Were T, Konah SN, Kempaiah P, Ong'echa JM, Diesterbeck US, Pilla R, Czerny CP, Hittner JB, Hyman JM, Perkins DJ. Feedback-based, system-level properties of vertebrate-microbial interactions. PLoS One 2013; 8:e53984. [PMID: 23437039 PMCID: PMC3577842 DOI: 10.1371/journal.pone.0053984] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2012] [Accepted: 12/05/2012] [Indexed: 12/22/2022] Open
Abstract
Background Improved characterization of infectious disease dynamics is required. To that end, three-dimensional (3D) data analysis of feedback-like processes may be considered. Methods To detect infectious disease data patterns, a systems biology (SB) and evolutionary biology (EB) approach was evaluated, which utilizes leukocyte data structures designed to diminish data variability and enhance discrimination. Using data collected from one avian and two mammalian (human and bovine) species infected with viral, parasite, or bacterial agents (both sensitive and resistant to antimicrobials), four data structures were explored: (i) counts or percentages of a single leukocyte type, such as lymphocytes, neutrophils, or macrophages (the classic approach), and three levels of the SB/EB approach, which assessed (ii) 2D, (iii) 3D, and (iv) multi-dimensional (rotating 3D) host-microbial interactions. Results In all studies, no classic data structure discriminated disease-positive (D+, or observations in which a microbe was isolated) from disease-negative (D–, or microbial-negative) groups: D+ and D– data distributions overlapped. In contrast, multi-dimensional analysis of indicators designed to possess desirable features, such as a single line of observations, displayed a continuous, circular data structure, whose abrupt inflections facilitated partitioning into subsets statistically significantly different from one another. In all studies, the 3D, SB/EB approach distinguished three (steady, positive, and negative) feedback phases, in which D– data characterized the steady state phase, and D+ data were found in the positive and negative phases. In humans, spatial patterns revealed false-negative observations and three malaria-positive data classes. In both humans and bovines, methicillin-resistant Staphylococcus aureus (MRSA) infections were discriminated from non-MRSA infections. Conclusions More information can be extracted, from the same data, provided that data are structured, their 3D relationships are considered, and well-conserved (feedback-like) functions are estimated. Patterns emerging from such structures may distinguish well-conserved from recently developed host-microbial interactions. Applications include diagnosis, error detection, and modeling.
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Affiliation(s)
- Ariel L Rivas
- Center for Global Health, University of New Mexico, Albuquerque, New Mexico, USA.
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Jung S, Verdicchio M, Kiefer J, Von Hoff D, Berens M, Bittner M, Kim S. Learning contextual gene set interaction networks of cancer with condition specificity. BMC Genomics 2013; 14:110. [PMID: 23418942 PMCID: PMC3644282 DOI: 10.1186/1471-2164-14-110] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2012] [Accepted: 01/29/2013] [Indexed: 12/01/2022] Open
Abstract
Background Identifying similarities and differences in the molecular constitutions of various types of cancer is one of the key challenges in cancer research. The appearances of a cancer depend on complex molecular interactions, including gene regulatory networks and gene-environment interactions. This complexity makes it challenging to decipher the molecular origin of the cancer. In recent years, many studies reported methods to uncover heterogeneous depictions of complex cancers, which are often categorized into different subtypes. The challenge is to identify diverse molecular contexts within a cancer, to relate them to different subtypes, and to learn underlying molecular interactions specific to molecular contexts so that we can recommend context-specific treatment to patients. Results In this study, we describe a novel method to discern molecular interactions specific to certain molecular contexts. Unlike conventional approaches to build modular networks of individual genes, our focus is to identify cancer-generic and subtype-specific interactions between contextual gene sets, of which each gene set share coherent transcriptional patterns across a subset of samples, termed contextual gene set. We then apply a novel formulation for quantitating the effect of the samples from each subtype on the calculated strength of interactions observed. Two cancer data sets were analyzed to support the validity of condition-specificity of identified interactions. When compared to an existing approach, the proposed method was much more sensitive in identifying condition-specific interactions even in heterogeneous data set. The results also revealed that network components specific to different types of cancer are related to different biological functions than cancer-generic network components. We found not only the results that are consistent with previous studies, but also new hypotheses on the biological mechanisms specific to certain cancer types that warrant further investigations. Conclusions The analysis on the contextual gene sets and characterization of networks of interaction composed of these sets discovered distinct functional differences underlying various types of cancer. The results show that our method successfully reveals many subtype-specific regions in the identified maps of biological contexts, which well represent biological functions that can be connected to specific subtypes.
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Affiliation(s)
- Sungwon Jung
- Integrated Cancer Genomics Division, Translational Genomics Research Institute, Phoenix, Arizona, USA
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Clark PM, Dawany N, Dampier W, Byers SW, Pestell RG, Tozeren A. Bioinformatics analysis reveals transcriptome and microRNA signatures and drug repositioning targets for IBD and other autoimmune diseases. Inflamm Bowel Dis 2012; 18:2315-33. [PMID: 22488912 DOI: 10.1002/ibd.22958] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Accepted: 02/27/2012] [Indexed: 12/13/2022]
Abstract
BACKGROUND Inflammatory bowel disease (IBD) is a complex disorder involving pathogen infection, host immune response, and altered enterocyte physiology. Incidences of IBD are increasing at an alarming rate in developed countries, warranting a detailed molecular portrait of IBD. METHODS We used large-scale data, bioinformatics tools, and high-throughput computations to obtain gene and microRNA signatures for Crohn's disease (CD) and ulcerative colitis (UC). These signatures were then integrated with systemic literature review to draw a comprehensive portrait of IBD in relation to autoimmune diseases. RESULTS The top upregulated genes in IBD are associated with diabetogenesis (REG1A, REG1B), bacterial signals (TLRs, NLRs), innate immunity (DEFA6, IDO1, EXOSC1), inflammation (CXCLs), and matrix degradation (MMPs). The downregulated genes coded tight junction proteins (CLDN8), solute transporters (SLCs), and adhesion proteins. Genes highly expressed in UC compared to CD included antiinflammatory ANXA1, transporter ABCA12, T-cell activator HSH2D, and immunoglobulin IGHV4-34. Compromised metabolisms for processing of drugs, nitrogen, androgen and estrogen, and lipids in IBD correlated with an increase in specific microRNA. Highly expressed IBD genes constituted targets of drugs used in gastrointestinal cancers, viral infections, and autoimmunity disorders such as rheumatoid arthritis and asthma. CONCLUSIONS This study presents a clinically relevant gene-level portrait of IBD subtypes and their connectivity to autoimmune diseases. The study identified candidates for repositioning of existing drugs to manage IBD. Integration of mice and human data point to an altered B-cell response as a cause for upregulation of genes in IBD involved in other aspects of immune defense such as interferon-inducible responses.
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Affiliation(s)
- Peter M Clark
- Center for Integrated Bioinformatics, Drexel University, Philadelphia, Pennsylvania 19104, USA
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Crespo I, Roomp K, Jurkowski W, Kitano H, del Sol A. Gene regulatory network analysis supports inflammation as a key neurodegeneration process in prion disease. BMC SYSTEMS BIOLOGY 2012; 6:132. [PMID: 23068602 PMCID: PMC3607922 DOI: 10.1186/1752-0509-6-132] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2012] [Accepted: 09/17/2012] [Indexed: 01/04/2023]
Abstract
Background The activation of immune cells in the brain is believed to be one of the earliest events in prion disease development, where misfolded PrionSc protein deposits are thought to act as irritants leading to a series of events that culminate in neuronal cell dysfunction and death. The role of these events in prion disease though is still a matter of debate. To elucidate the mechanisms leading from abnormal protein deposition to neuronal injury, we have performed a detailed network analysis of genes differentially expressed in several mouse prion models. Results We found a master regulatory core of genes related to immune response controlling other genes involved in prion protein replication and accumulation, and neuronal cell death. This regulatory core determines the existence of two stable states that are consistent with the transcriptome analysis comparing prion infected versus uninfected mouse brain. An in silico perturbation analysis demonstrates that core genes are individually capable of triggering the transition and that the network remains locked once the diseased state is reached. Conclusions We hypothesize that this locking may be the cause of the sustained immune response observed in prion disease. Our analysis supports the hypothesis that sustained brain inflammation is the main pathogenic process leading to neuronal dysfunction and loss, which, in turn, leads to clinical symptoms in prion disease.
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Affiliation(s)
- Isaac Crespo
- Luxembourg Center for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7, avenue des Hauts fourneaux, Luxembourg L-4362, Luxembourg
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Alberghina L, Gaglio D, Gelfi C, Moresco RM, Mauri G, Bertolazzi P, Messa C, Gilardi MC, Chiaradonna F, Vanoni M. Cancer cell growth and survival as a system-level property sustained by enhanced glycolysis and mitochondrial metabolic remodeling. Front Physiol 2012; 3:362. [PMID: 22988443 PMCID: PMC3440026 DOI: 10.3389/fphys.2012.00362] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2012] [Accepted: 08/23/2012] [Indexed: 12/14/2022] Open
Abstract
Systems Biology holds that complex cellular functions are generated as system-level properties endowed with robustness, each involving large networks of molecular determinants, generally identified by “omics” analyses. In this paper we describe four basic cancer cell properties that can easily be investigated in vitro: enhanced proliferation, evasion from apoptosis, genomic instability, and inability to undergo oncogene-induced senescence. Focusing our analysis on a K-ras dependent transformation system, we show that enhanced proliferation and evasion from apoptosis are closely linked, and present findings that indicate how a large metabolic remodeling sustains the enhanced growth ability. Network analysis of transcriptional profiling gives the first indication on this remodeling, further supported by biochemical investigations and metabolic flux analysis (MFA). Enhanced glycolysis, down-regulation of TCA cycle, decoupling of glucose and glutamine utilization, with increased reductive carboxylation of glutamine, so to yield a sustained production of growth building blocks and glutathione, are the hallmarks of enhanced proliferation. Low glucose availability specifically induces cell death in K-ras transformed cells, while PKA activation reverts this effect, possibly through at least two mitochondrial targets. The central role of mitochondria in determining the two investigated cancer cell properties is finally discussed. Taken together the findings reported herein indicate that a system-level property is sustained by a cascade of interconnected biochemical pathways that behave differently in normal and in transformed cells.
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Affiliation(s)
- Lilia Alberghina
- SysBio Centre for Systems Biology Milano and Rome, Italy ; Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza Milano, Italy
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Kuzu G, Keskin O, Gursoy A, Nussinov R. Constructing structural networks of signaling pathways on the proteome scale. Curr Opin Struct Biol 2012; 22:367-77. [PMID: 22575757 DOI: 10.1016/j.sbi.2012.04.004] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2011] [Revised: 03/20/2012] [Accepted: 04/18/2012] [Indexed: 11/30/2022]
Abstract
Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.
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Affiliation(s)
- Guray Kuzu
- Center for Computational Biology and Bioinformatics and College of Engineering, Koc University Rumelifeneri Yolu, 34450 Sariyer Istanbul, Turkey
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Ostaszewski M, Eifes S, del Sol A. Evolutionary conservation and network structure characterize genes of phenotypic relevance for mitosis in human. PLoS One 2012; 7:e36488. [PMID: 22577488 PMCID: PMC3342260 DOI: 10.1371/journal.pone.0036488] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2011] [Accepted: 04/07/2012] [Indexed: 11/19/2022] Open
Abstract
The impact of gene silencing on cellular phenotypes is difficult to establish due to the complexity of interactions in the associated biological processes and pathways. A recent genome-wide RNA knock-down study both identified and phenotypically characterized a set of important genes for the cell cycle in HeLa cells. Here, we combine a molecular interaction network analysis, based on physical and functional protein interactions, in conjunction with evolutionary information, to elucidate the common biological and topological properties of these key genes. Our results show that these genes tend to be conserved with their corresponding protein interactions across several species and are key constituents of the evolutionary conserved molecular interaction network. Moreover, a group of bistable network motifs is found to be conserved within this network, which are likely to influence the network stability and therefore the robustness of cellular functioning. They form a cluster, which displays functional homogeneity and is significantly enriched in genes phenotypically relevant for mitosis. Additional results reveal a relationship between specific cellular processes and the phenotypic outcomes induced by gene silencing. This study introduces new ideas regarding the relationship between genotype and phenotype in the context of the cell cycle. We show that the analysis of molecular interaction networks can result in the identification of genes relevant to cellular processes, which is a promising avenue for future research.
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Affiliation(s)
| | - Serge Eifes
- Luxembourg Centre for Systems Biomedicine, Luxembourg, Luxembourg
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine, Luxembourg, Luxembourg
- * E-mail:
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Abstract
Bistability is a fundamental phenomenon in nature. In biology, a number of fine properties of bistability have been identified. However, these properties are only consequences of bistability at the physiological level, which do not explain why it had to emerge during evolution. Using optimal homeostasis as the first principle, I find that bistability emerges as an indispensable control mechanism. It is the only solution to a dilemma in glucose homeostasis: high insulin efficiency is required to confer rapidness in plasma glucose clearance, whereas an insulin sparing state is required to guarantee the brain's safety during fasting. The optimality consideration renders a clear correspondence between the molecular and physiological levels. This new perspective can illuminate studies on the twin epidemics of obesity and diabetes and the corresponding intervening strategies. For example, overnutrition and sedentary lifestyle may represent sudden environmental changes that cause the lose of optimality, which may contribute to the marked rise of obesity and diabetes in our generation. Because this bistability result is independent of the parameters of the mathematical model (for which the result is quite general), some other biological systems may also use bistability to control homeostasis.
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Affiliation(s)
- Guanyu Wang
- Department of Physics, George Washington University, Washington, DC 20052, USA.
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A novel network integrating a miRNA-203/SNAI1 feedback loop which regulates epithelial to mesenchymal transition. PLoS One 2012; 7:e35440. [PMID: 22514743 PMCID: PMC3325969 DOI: 10.1371/journal.pone.0035440] [Citation(s) in RCA: 132] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2011] [Accepted: 03/16/2012] [Indexed: 12/14/2022] Open
Abstract
Background The majority of human cancer deaths are caused by metastasis. The metastatic dissemination is initiated by the breakdown of epithelial cell homeostasis. During this phenomenon, referred to as epithelial to mesenchymal transition (EMT), cells change their genetic and trancriptomic program leading to phenotypic and functional alterations. The challenge of understanding this dynamic process resides in unraveling regulatory networks involving master transcription factors (e.g. SNAI1/2, ZEB1/2 and TWIST1) and microRNAs. Here we investigated microRNAs regulated by SNAI1 and their potential role in the regulatory networks underlying epithelial plasticity. Results By a large-scale analysis on epithelial plasticity, we highlighted miR-203 and its molecular link with SNAI1 and the miR-200 family, key regulators of epithelial homeostasis. During SNAI1-induced EMT in MCF7 breast cancer cells, miR-203 and miR-200 family members were repressed in a timely correlated manner. Importantly, miR-203 repressed endogenous SNAI1, forming a double negative miR203/SNAI1 feedback loop. We integrated this novel miR203/SNAI1 with the known miR200/ZEB feedback loops to construct an a priori EMT core network. Dynamic simulations revealed stable epithelial and mesenchymal states, and underscored the crucial role of the miR203/SNAI1 feedback loop in state transitions underlying epithelial plasticity. Conclusion By combining computational biology and experimental approaches, we propose a novel EMT core network integrating two fundamental negative feedback loops, miR203/SNAI1 and miR200/ZEB. Altogether our analysis implies that this novel EMT core network could function as a switch controlling epithelial cell plasticity during differentiation and cancer progression.
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Antony PMA, Balling R, Vlassis N. From systems biology to systems biomedicine. Curr Opin Biotechnol 2011; 23:604-8. [PMID: 22119097 DOI: 10.1016/j.copbio.2011.11.009] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2011] [Accepted: 11/06/2011] [Indexed: 12/22/2022]
Abstract
Systems Biology is about combining theory, technology, and targeted experiments in a way that drives not only data accumulation but knowledge as well. The challenge in Systems Biomedicine is to furthermore translate mechanistic insights in biological systems to clinical application, with the central aim of improving patients' quality of life. The challenge is to find theoretically well-chosen models for the contextually correct and intelligible representation of multi-scale biological systems. In this review, we discuss the current state of Systems Biology, highlight the emergence of Systems Biomedicine, and highlight some of the topics and views that we think are important for the efficient application of Systems Theory in Biomedicine.
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Affiliation(s)
- Paul M A Antony
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg.
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Saadatpour A, Wang RS, Liao A, Liu X, Loughran TP, Albert I, Albert R. Dynamical and structural analysis of a T cell survival network identifies novel candidate therapeutic targets for large granular lymphocyte leukemia. PLoS Comput Biol 2011; 7:e1002267. [PMID: 22102804 PMCID: PMC3213185 DOI: 10.1371/journal.pcbi.1002267] [Citation(s) in RCA: 115] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2011] [Accepted: 09/22/2011] [Indexed: 11/18/2022] Open
Abstract
The blood cancer T cell large granular lymphocyte (T-LGL) leukemia is a chronic disease characterized by a clonal proliferation of cytotoxic T cells. As no curative therapy is yet known for this disease, identification of potential therapeutic targets is of immense importance. In this paper, we perform a comprehensive dynamical and structural analysis of a network model of this disease. By employing a network reduction technique, we identify the stationary states (fixed points) of the system, representing normal and diseased (T-LGL) behavior, and analyze their precursor states (basins of attraction) using an asynchronous Boolean dynamic framework. This analysis identifies the T-LGL states of 54 components of the network, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions. We further test and validate one of these newly identified states experimentally. Specifically, we verify the prediction that the node SMAD is over-active in leukemic T-LGL by demonstrating the predominant phosphorylation of the SMAD family members Smad2 and Smad3. Our systematic perturbation analysis using dynamical and structural methods leads to the identification of 19 potential therapeutic targets, 68% of which are corroborated by experimental evidence. The novel therapeutic targets provide valuable guidance for wet-bench experiments. In addition, we successfully identify two new candidates for engineering long-lived T cells necessary for the delivery of virus and cancer vaccines. Overall, this study provides a bird's-eye-view of the avenues available for identification of therapeutic targets for similar diseases through perturbation of the underlying signal transduction network. T-LGL leukemia is a blood cancer characterized by an abnormal increase in the abundance of a type of white blood cell called T cell. Since there is no known curative therapy for this disease, identification of potential therapeutic targets is of utmost importance. Experimental identification of manipulations capable of reversing the disease condition is usually a long, arduous process. Mathematical modeling can aid this process by identifying potential therapeutic interventions. In this work, we carry out a systematic analysis of a network model of T cell survival in T-LGL leukemia to get a deeper insight into the unknown facets of the disease. We identify the T-LGL status of 54 components of the system, out of which 36 (67%) are corroborated by previous experimental evidence and the rest are novel predictions, one of which we validate by follow-up experiments. By deciphering the structure and dynamics of the underlying network, we identify component perturbations that lead to programmed cell death, thereby suggesting several novel candidate therapeutic targets for future experiments.
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Affiliation(s)
- Assieh Saadatpour
- Department of Mathematics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Rui-Sheng Wang
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Aijun Liao
- Penn State Hershey Cancer Institute, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States of America
| | - Xin Liu
- Penn State Hershey Cancer Institute, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States of America
| | - Thomas P. Loughran
- Penn State Hershey Cancer Institute, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, United States of America
| | - István Albert
- Department of Biochemistry and Molecular Biology, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Réka Albert
- Department of Physics, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
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Wu M, Liu L, Chan C. Identification of novel targets for breast cancer by exploring gene switches on a genome scale. BMC Genomics 2011; 12:547. [PMID: 22053771 PMCID: PMC3269833 DOI: 10.1186/1471-2164-12-547] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2011] [Accepted: 11/03/2011] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches. RESULTS We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2. CONCLUSIONS Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.
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Affiliation(s)
- Ming Wu
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824, USA.
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Goltsov A, Faratian D, Langdon SP, Mullen P, Harrison DJ, Bown J. Features of the reversible sensitivity-resistance transition in PI3K/PTEN/AKT signalling network after HER2 inhibition. Cell Signal 2011; 24:493-504. [PMID: 21996585 DOI: 10.1016/j.cellsig.2011.09.030] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2011] [Revised: 09/15/2011] [Accepted: 09/27/2011] [Indexed: 12/19/2022]
Abstract
Systems biology approaches that combine experimental data and theoretical modelling to understand cellular signalling network dynamics offer a useful platform to investigate the mechanisms of resistance to drug interventions and to identify combination drug treatments. Extending our work on modelling the PI3K/PTEN/AKT signalling network (SN), we analyse the sensitivity of the SN output signal, phospho-AKT, to inhibition of HER2 receptor. We model typical aberrations in this SN identified in cancer development and drug resistance: loss of PTEN activity, PI3K and AKT mutations, HER2 overexpression, and overproduction of GSK3β and CK2 kinases controlling PTEN phosphorylation. We show that HER2 inhibition by the monoclonal antibody pertuzumab increases SN sensitivity, both to external signals and to changes in kinetic parameters of the proteins and their expression levels induced by mutations in the SN. This increase in sensitivity arises from the transition of SN functioning from saturation to non-saturation mode in response to HER2 inhibition. PTEN loss or PIK3CA mutation causes resistance to anti-HER2 inhibitor and leads to the restoration of saturation mode in SN functioning with a consequent decrease in SN sensitivity. We suggest that a drug-induced increase in SN sensitivity to internal perturbations, and specifically mutations, causes SN fragility. In particular, the SN is vulnerable to mutations that compensate for drug action and this may result in a sensitivity-to-resistance transition. The combination of HER2 and PI3K inhibition does not sensitise the SN to internal perturbations (mutations) in the PI3K/PTEN/AKT pathway: this combination treatment provides both synergetic inhibition and may prevent the SN from acquired mutations causing drug resistance. Through combination inhibition treatments, we studied the impact of upstream and downstream interventions to suppress resistance to the HER2 inhibitor in the SN with PTEN loss. Comparison of experimental results of PI3K inhibition in the PTEN upstream pathway with PDK1 inhibition in the PTEN downstream pathway shows that upstream inhibition abrogates resistance to pertuzumab more effectively than downstream inhibition. This difference in inhibition effect arises from the compensatory mechanism of an activation loop induced in the downstream pathway by PTEN loss. We highlight that drug target identification for combination anti-cancer therapy needs to account for the mutation effects on the upstream and downstream pathways.
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Affiliation(s)
- Alexey Goltsov
- Centre for Research in Informatics and Systems Pathology (CRISP), University of Abertay Dundee, Dundee, DD1 1HG, United Kingdom.
| | - Dana Faratian
- Edinburgh Breakthrough Research Unit and Division of Pathology, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - Simon P Langdon
- Edinburgh Breakthrough Research Unit and Division of Pathology, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - Peter Mullen
- Edinburgh Breakthrough Research Unit and Division of Pathology, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - David J Harrison
- Edinburgh Breakthrough Research Unit and Division of Pathology, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, EH4 2XU, United Kingdom
| | - James Bown
- Centre for Research in Informatics and Systems Pathology (CRISP), University of Abertay Dundee, Dundee, DD1 1HG, United Kingdom
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Tyson JJ, Baumann WT, Chen C, Verdugo A, Tavassoly I, Wang Y, Weiner LM, Clarke R. Dynamic modelling of oestrogen signalling and cell fate in breast cancer cells. Nat Rev Cancer 2011; 11:523-32. [PMID: 21677677 PMCID: PMC3294292 DOI: 10.1038/nrc3081] [Citation(s) in RCA: 135] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Cancers of the breast and other tissues arise from aberrant decision-making by cells regarding their survival or death, proliferation or quiescence, damage repair or bypass. These decisions are made by molecular signalling networks that process information from outside and from within the breast cancer cell and initiate responses that determine the cell's survival and reproduction. Because the molecular logic of these circuits is difficult to comprehend by intuitive reasoning alone, we present some preliminary mathematical models of the basic decision circuits in breast cancer cells that may aid our understanding of their susceptibility or resistance to endocrine therapy.
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Affiliation(s)
- John J Tyson
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.
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Karrila S, Lee JHE, Tucker-Kellogg G. A comparison of methods for data-driven cancer outlier discovery, and an application scheme to semisupervised predictive biomarker discovery. Cancer Inform 2011; 10:109-20. [PMID: 21584264 PMCID: PMC3091411 DOI: 10.4137/cin.s6868] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
A core component in translational cancer research is biomarker discovery using gene expression profiling for clinical tumors. This is often based on cell line experiments; one population is sampled for inference in another. We disclose a semisupervised workflow focusing on binary (switch-like, bimodal) informative genes that are likely cancer relevant, to mitigate this non-statistical problem. Outlier detection is a key enabling technology of the workflow, and aids in identifying the focus genes. We compare outlier detection techniques MOST, LSOSS, COPA, ORT, OS, and t-test, using a publicly available NSCLC dataset. Removing genes with Gaussian distribution is computationally efficient and matches MOST particularly well, while also COPA and OS pick prognostically relevant genes in their top ranks. Also our stability assessment is in favour of both MOST and COPA; the latter does not pair well with prefiltering for non-Gaussianity, but can handle data sets lacking non-cancer cases. We provide R code for replicating our approach or extending it.
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Affiliation(s)
- Seppo Karrila
- Lilly Singapore Centre for Drug Discovery, Eli Lilly and Company, Singapore
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Vass JK, Higham DJ, Mudaliar MAV, Mao X, Crowther DJ. Discretization provides a conceptually simple tool to build expression networks. PLoS One 2011; 6:e18634. [PMID: 21533165 PMCID: PMC3078920 DOI: 10.1371/journal.pone.0018634] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2010] [Accepted: 03/14/2011] [Indexed: 12/12/2022] Open
Abstract
Biomarker identification, using network methods, depends on finding regular co-expression patterns; the overall connectivity is of greater importance than any single relationship. A second requirement is a simple algorithm for ranking patients on how relevant a gene-set is. For both of these requirements discretized data helps to first identify gene cliques, and then to stratify patients. We explore a biologically intuitive discretization technique which codes genes as up- or down-regulated, with values close to the mean set as unchanged; this allows a richer description of relationships between genes than can be achieved by positive and negative correlation. We find a close agreement between our results and the template gene-interactions used to build synthetic microarray-like data by SynTReN, which synthesizes “microarray” data using known relationships which are successfully identified by our method. We are able to split positive co-regulation into up-together and down-together and negative co-regulation is considered as directed up-down relationships. In some cases these exist in only one direction, with real data, but not with the synthetic data. We illustrate our approach using two studies on white blood cells and derived immortalized cell lines and compare the approach with standard correlation-based computations. No attempt is made to distinguish possible causal links as the search for biomarkers would be crippled by losing highly significant co-expression relationships. This contrasts with approaches like ARACNE and IRIS. The method is illustrated with an analysis of gene-expression for energy metabolism pathways. For each discovered relationship we are able to identify the samples on which this is based in the discretized sample-gene matrix, along with a simplified view of the patterns of gene expression; this helps to dissect the gene-sample relevant to a research topic - identifying sets of co-regulated and anti-regulated genes and the samples or patients in which this relationship occurs.
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Affiliation(s)
- J Keith Vass
- Translational Medicine Research Collaboration Institute, University of Dundee, Ninewells Hospital, Dundee, United Kingdom.
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