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McMillen P, Levin M. Collective intelligence: A unifying concept for integrating biology across scales and substrates. Commun Biol 2024; 7:378. [PMID: 38548821 PMCID: PMC10978875 DOI: 10.1038/s42003-024-06037-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 03/11/2024] [Indexed: 04/01/2024] Open
Abstract
A defining feature of biology is the use of a multiscale architecture, ranging from molecular networks to cells, tissues, organs, whole bodies, and swarms. Crucially however, biology is not only nested structurally, but also functionally: each level is able to solve problems in distinct problem spaces, such as physiological, morphological, and behavioral state space. Percolating adaptive functionality from one level of competent subunits to a higher functional level of organization requires collective dynamics: multiple components must work together to achieve specific outcomes. Here we overview a number of biological examples at different scales which highlight the ability of cellular material to make decisions that implement cooperation toward specific homeodynamic endpoints, and implement collective intelligence by solving problems at the cell, tissue, and whole-organism levels. We explore the hypothesis that collective intelligence is not only the province of groups of animals, and that an important symmetry exists between the behavioral science of swarms and the competencies of cells and other biological systems at different scales. We then briefly outline the implications of this approach, and the possible impact of tools from the field of diverse intelligence for regenerative medicine and synthetic bioengineering.
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Affiliation(s)
- Patrick McMillen
- Department of Biology, Tufts University, Medford, MA, 02155, USA
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA
| | - Michael Levin
- Department of Biology, Tufts University, Medford, MA, 02155, USA.
- Allen Discovery Center at Tufts University, Medford, MA, 02155, USA.
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, 02115, USA.
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2
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Mathews J, Chang A(J, Devlin L, Levin M. Cellular signaling pathways as plastic, proto-cognitive systems: Implications for biomedicine. PATTERNS (NEW YORK, N.Y.) 2023; 4:100737. [PMID: 37223267 PMCID: PMC10201306 DOI: 10.1016/j.patter.2023.100737] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Many aspects of health and disease are modeled using the abstraction of a "pathway"-a set of protein or other subcellular activities with specified functional linkages between them. This metaphor is a paradigmatic case of a deterministic, mechanistic framework that focuses biomedical intervention strategies on altering the members of this network or the up-/down-regulation links between them-rewiring the molecular hardware. However, protein pathways and transcriptional networks exhibit interesting and unexpected capabilities such as trainability (memory) and information processing in a context-sensitive manner. Specifically, they may be amenable to manipulation via their history of stimuli (equivalent to experiences in behavioral science). If true, this would enable a new class of biomedical interventions that target aspects of the dynamic physiological "software" implemented by pathways and gene-regulatory networks. Here, we briefly review clinical and laboratory data that show how high-level cognitive inputs and mechanistic pathway modulation interact to determine outcomes in vivo. Further, we propose an expanded view of pathways from the perspective of basal cognition and argue that a broader understanding of pathways and how they process contextual information across scales will catalyze progress in many areas of physiology and neurobiology. We argue that this fuller understanding of the functionality and tractability of pathways must go beyond a focus on the mechanistic details of protein and drug structure to encompass their physiological history as well as their embedding within higher levels of organization in the organism, with numerous implications for data science addressing health and disease. Exploiting tools and concepts from behavioral and cognitive sciences to explore a proto-cognitive metaphor for the pathways underlying health and disease is more than a philosophical stance on biochemical processes; at stake is a new roadmap for overcoming the limitations of today's pharmacological strategies and for inferring future therapeutic interventions for a wide range of disease states.
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Affiliation(s)
- Juanita Mathews
- Allen Discovery Center at Tufts University, Medford, MA, USA
| | | | - Liam Devlin
- Allen Discovery Center at Tufts University, Medford, MA, USA
| | - Michael Levin
- Allen Discovery Center at Tufts University, Medford, MA, USA
- Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, MA, USA
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3
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Erenpreisa J, Giuliani A, Yoshikawa K, Falk M, Hildenbrand G, Salmina K, Freivalds T, Vainshelbaum N, Weidner J, Sievers A, Pilarczyk G, Hausmann M. Spatial-Temporal Genome Regulation in Stress-Response and Cell-Fate Change. Int J Mol Sci 2023; 24:2658. [PMID: 36769000 PMCID: PMC9917235 DOI: 10.3390/ijms24032658] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 01/17/2023] [Accepted: 01/22/2023] [Indexed: 02/04/2023] Open
Abstract
Complex functioning of the genome in the cell nucleus is controlled at different levels: (a) the DNA base sequence containing all relevant inherited information; (b) epigenetic pathways consisting of protein interactions and feedback loops; (c) the genome architecture and organization activating or suppressing genetic interactions between different parts of the genome. Most research so far has shed light on the puzzle pieces at these levels. This article, however, attempts an integrative approach to genome expression regulation incorporating these different layers. Under environmental stress or during cell development, differentiation towards specialized cell types, or to dysfunctional tumor, the cell nucleus seems to react as a whole through coordinated changes at all levels of control. This implies the need for a framework in which biological, chemical, and physical manifestations can serve as a basis for a coherent theory of gene self-organization. An international symposium held at the Biomedical Research and Study Center in Riga, Latvia, on 25 July 2022 addressed novel aspects of the abovementioned topic. The present article reviews the most recent results and conclusions of the state-of-the-art research in this multidisciplinary field of science, which were delivered and discussed by scholars at the Riga symposium.
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Affiliation(s)
| | - Alessandro Giuliani
- Istituto Superiore di Sanita Environment and Health Department, 00161 Roma, Italy
| | - Kenichi Yoshikawa
- Faculty of Life and Medical Sciences, Doshisha University, Kyoto 610-0394, Japan
| | - Martin Falk
- Institute of Biophysics, The Czech Academy of Sciences, 612 65 Brno, Czech Republic
- Kirchhoff Institute for Physics, Heidelberg University, 69120 Heidelberg, Germany
| | - Georg Hildenbrand
- Kirchhoff Institute for Physics, Heidelberg University, 69120 Heidelberg, Germany
- Faculty of Engineering, University of Applied Science Aschaffenburg, 63743 Aschaffenburg, Germany
| | - Kristine Salmina
- Latvian Biomedicine Research and Study Centre, LV1067 Riga, Latvia
| | - Talivaldis Freivalds
- Institute of Cardiology and Regenerative Medicine, University of Latvia, LV1004 Riga, Latvia
| | - Ninel Vainshelbaum
- Latvian Biomedicine Research and Study Centre, LV1067 Riga, Latvia
- Doctoral Study Program, University of Latvia, LV1004 Riga, Latvia
| | - Jonas Weidner
- Kirchhoff Institute for Physics, Heidelberg University, 69120 Heidelberg, Germany
| | - Aaron Sievers
- Kirchhoff Institute for Physics, Heidelberg University, 69120 Heidelberg, Germany
- Institute for Human Genetics, University Hospital Heidelberg, 69117 Heidelberg, Germany
| | - Götz Pilarczyk
- Kirchhoff Institute for Physics, Heidelberg University, 69120 Heidelberg, Germany
| | - Michael Hausmann
- Kirchhoff Institute for Physics, Heidelberg University, 69120 Heidelberg, Germany
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Vainshelbaum NM, Giuliani A, Salmina K, Pjanova D, Erenpreisa J. The Transcriptome and Proteome Networks of Malignant Tumours Reveal Atavistic Attractors of Polyploidy-Related Asexual Reproduction. Int J Mol Sci 2022; 23:ijms232314930. [PMID: 36499258 PMCID: PMC9736112 DOI: 10.3390/ijms232314930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 11/18/2022] [Accepted: 11/26/2022] [Indexed: 12/02/2022] Open
Abstract
The expression of gametogenesis-related (GG) genes and proteins, as well as whole genome duplications (WGD), are the hallmarks of cancer related to poor prognosis. Currently, it is not clear if these hallmarks are random processes associated only with genome instability or are programmatically linked. Our goal was to elucidate this via a thorough bioinformatics analysis of 1474 GG genes in the context of WGD. We examined their association in protein-protein interaction and coexpression networks, and their phylostratigraphic profiles from publicly available patient tumour data. The results show that GG genes are upregulated in most WGD-enriched somatic cancers at the transcriptome level and reveal robust GG gene expression at the protein level, as well as the ability to associate into correlation networks and enrich the reproductive modules. GG gene phylostratigraphy displayed in WGD+ cancers an attractor of early eukaryotic origin for DNA recombination and meiosis, and one relative to oocyte maturation and embryogenesis from early multicellular organisms. The upregulation of cancer-testis genes emerging with mammalian placentation was also associated with WGD. In general, the results suggest the role of polyploidy for soma-germ transition accessing latent cancer attractors in the human genome network, which appear as pre-formed along the whole Evolution of Life.
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Affiliation(s)
- Ninel M. Vainshelbaum
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
- Faculty of Biology, The University of Latvia, LV-1586 Riga, Latvia
- Correspondence: (N.M.V.); (J.E.)
| | - Alessandro Giuliani
- Environmen and Health Department, Istituto Superiore di Sanità, 00161 Rome, Italy
| | - Kristine Salmina
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
| | - Dace Pjanova
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
| | - Jekaterina Erenpreisa
- Cancer Research Division, Latvian Biomedicine Research and Study Centre, LV-1067 Riga, Latvia
- Correspondence: (N.M.V.); (J.E.)
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5
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Ma L, Shao Z, Li L, Huang J, Wang S, Lin Q, Li J, Gong M, Nandi AK. Heuristics and metaheuristics for biological network alignment: A review. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2021.08.156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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An integrated network representation of multiple cancer-specific data for graph-based machine learning. NPJ Syst Biol Appl 2022; 8:14. [PMID: 35487924 PMCID: PMC9054771 DOI: 10.1038/s41540-022-00226-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 04/04/2022] [Indexed: 12/20/2022] Open
Abstract
Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https://osf.io/dzx7b/.
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7
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Abstract
Drug resistance and metastasis-the major complications in cancer-both entail adaptation of cancer cells to stress, whether a drug or a lethal new environment. Intriguingly, these adaptive processes share similar features that cannot be explained by a pure Darwinian scheme, including dormancy, increased heterogeneity, and stress-induced plasticity. Here, we propose that learning theory offers a framework to explain these features and may shed light on these two intricate processes. In this framework, learning is performed at the single-cell level, by stress-driven exploratory trial-and-error. Such a process is not contingent on pre-existing pathways but on a random search for a state that diminishes the stress. We review underlying mechanisms that may support this search, and show by using a learning model that such exploratory learning is feasible in a high-dimensional system as the cell. At the population level, we view the tissue as a network of exploring agents that communicate, restraining cancer formation in health. In this view, disease results from the breakdown of homeostasis between cellular exploratory drive and tissue homeostasis.
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Affiliation(s)
- Aseel Shomar
- Department of Chemical Engineering, Israel Institute of Technology, Haifa 32000, Israel
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
| | - Omri Barak
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
- Rappaport Faculty of Medicine Technion, Israel Institute of Technology, Haifa 32000, Israel
| | - Naama Brenner
- Department of Chemical Engineering, Israel Institute of Technology, Haifa 32000, Israel
- Network Biology Research Laboratory, Israel Institute of Technology, Haifa 32000, Israel
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Liu YJ, Zeng SH, Hu YD, Zhang YH, Li JP. Overexpression of NREP Promotes Migration and Invasion in Gastric Cancer Through Facilitating Epithelial-Mesenchymal Transition. Front Cell Dev Biol 2021; 9:746194. [PMID: 34746143 PMCID: PMC8565479 DOI: 10.3389/fcell.2021.746194] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Accepted: 10/01/2021] [Indexed: 12/12/2022] Open
Abstract
The identification of biomarkers and effective therapeutic targets for gastric cancer (GC), the most common cause of cancer-related deaths around the world, is currently a major focus area in research. Here, we examined the utility of Neuronal Regeneration Related Protein (NREP) as a prognostic biomarker and therapeutic target for GC. We assessed the clinical relevance, function, and molecular role of NREP in GC using bioinformatics analysis and experimental validation. Our results showed that in GC, NREP overexpression was significantly associated with a poor prognosis. Our findings also suggested that NREP may be involved in the activation of cancer-associated fibroblasts and the epithelial-mesenchymal transition (EMT), with transforming growth factor β1 mediating both processes. In addition, NREP expression showed a positive correlation with the abundance of M2 macrophages, which are potent immunosuppressors. Together, these results indicate that NREP is overexpressed in GC and affects GC prognosis. Thus, NREP could be a prognostic biomarker and therapeutic target for GC.
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Affiliation(s)
- Yuan-Jie Liu
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, China.,Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.,No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Shu-Hong Zeng
- Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese Medicine, Nanjing, Jiangsu, China.,No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
| | - Yi-Dou Hu
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, China
| | - Yong-Hua Zhang
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, China
| | - Jie-Pin Li
- Department of Oncology, Zhangjiagang TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Zhangjiagang, Jiangsu, China.,No. 1 Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China
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Xu A, Qian C, Lin J, Yu W, Jin J, Liu B, Tao H. Cell Differentiation Trajectory-Associated Molecular Classification of Osteosarcoma. Genes (Basel) 2021; 12:genes12111685. [PMID: 34828292 PMCID: PMC8625454 DOI: 10.3390/genes12111685] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 10/19/2021] [Accepted: 10/21/2021] [Indexed: 01/01/2023] Open
Abstract
This study aims to investigate the differentiation trajectory of osteosarcoma cells and to construct molecular subtypes with their respective characteristics and generate a multi-gene signature for predicting prognosis. Integrated single-cell RNA-sequencing (scRNA-seq) data, bulk RNA-seq data and microarray data from osteosarcoma samples were used for analysis. Via scRNA-seq data, time-related as well as differentiation-related genes were recognized as osteosarcoma tumor stem cell-related genes (OSCGs). In Gene Expression Omnibus (GEO) cohort, osteosarcoma patients were classified into two subtypes based on prognostic OSCGs and it was found that molecular typing successfully predicted overall survival, tumor microenvironment and immune infiltration status. Further, available drugs for influencing osteosarcoma via prognostic OSCGs were revealed. A 3-OSCG-based prognostic risk score signature was generated and by combining other clinic-pathological independent prognostic factor, stage at diagnosis, a nomogram was established to predict individual survival probability. In external independent TARGET cohort, the molecular types, the 3-gene signature as well as nomogram were validated. In conclusion, osteosarcoma cell differentiation occupies a crucial position in many facets, such as tumor prognosis and microenvironment, suggesting promising therapeutic targets for this disease.
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Affiliation(s)
- Ankai Xu
- Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou 310009, China; (A.X.); (C.Q.); (J.L.); (W.Y.); (J.J.); (B.L.)
- Orthopedics Research Institute of Zhejiang University, No. 88, Jiefang Road, Hangzhou 310009, China
| | - Chao Qian
- Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou 310009, China; (A.X.); (C.Q.); (J.L.); (W.Y.); (J.J.); (B.L.)
- Orthopedics Research Institute of Zhejiang University, No. 88, Jiefang Road, Hangzhou 310009, China
| | - Jinti Lin
- Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou 310009, China; (A.X.); (C.Q.); (J.L.); (W.Y.); (J.J.); (B.L.)
- Orthopedics Research Institute of Zhejiang University, No. 88, Jiefang Road, Hangzhou 310009, China
| | - Wei Yu
- Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou 310009, China; (A.X.); (C.Q.); (J.L.); (W.Y.); (J.J.); (B.L.)
- Orthopedics Research Institute of Zhejiang University, No. 88, Jiefang Road, Hangzhou 310009, China
| | - Jiakang Jin
- Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou 310009, China; (A.X.); (C.Q.); (J.L.); (W.Y.); (J.J.); (B.L.)
- Orthopedics Research Institute of Zhejiang University, No. 88, Jiefang Road, Hangzhou 310009, China
| | - Bing Liu
- Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou 310009, China; (A.X.); (C.Q.); (J.L.); (W.Y.); (J.J.); (B.L.)
- Orthopedics Research Institute of Zhejiang University, No. 88, Jiefang Road, Hangzhou 310009, China
| | - Huimin Tao
- Department of Orthopedics Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, No. 88, Jiefang Road, Hangzhou 310009, China; (A.X.); (C.Q.); (J.L.); (W.Y.); (J.J.); (B.L.)
- Orthopedics Research Institute of Zhejiang University, No. 88, Jiefang Road, Hangzhou 310009, China
- Correspondence:
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Using proteomic and transcriptomic data to assess activation of intracellular molecular pathways. ADVANCES IN PROTEIN CHEMISTRY AND STRUCTURAL BIOLOGY 2021; 127:1-53. [PMID: 34340765 DOI: 10.1016/bs.apcsb.2021.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Analysis of molecular pathway activation is the recent instrument that helps to quantize activities of various intracellular signaling, structural, DNA synthesis and repair, and biochemical processes. This may have a deep impact in fundamental research, bioindustry, and medicine. Unlike gene ontology analyses and numerous qualitative methods that can establish whether a pathway is affected in principle, the quantitative approach has the advantage of exactly measuring the extent of a pathway up/downregulation. This results in emergence of a new generation of molecular biomarkers-pathway activation levels, which reflect concentration changes of all measurable pathway components. The input data can be the high-throughput proteomic or transcriptomic profiles, and the output numbers take both positive and negative values and positively reflect overall pathway activation. Due to their nature, the pathway activation levels are more robust biomarkers compared to the individual gene products/protein levels. Here, we review the current knowledge of the quantitative gene expression interrogation methods and their applications for the molecular pathway quantization. We consider enclosed bioinformatic algorithms and their applications for solving real-world problems. Besides a plethora of applications in basic life sciences, the quantitative pathway analysis can improve molecular design and clinical investigations in pharmaceutical industry, can help finding new active biotechnological components and can significantly contribute to the progressive evolution of personalized medicine. In addition to the theoretical principles and concepts, we also propose publicly available software for the use of large-scale protein/RNA expression data to assess the human pathway activation levels.
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11
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Singha M, Pu L, Shawky A, Busch K, Wu H, Ramanujam J, Brylinski M. GraphGR: A graph neural network to predict the effect of pharmacotherapy on the cancer cell growth.. [DOI: 10.1101/2020.05.20.107458] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
Abstract
AbstractGenomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to treatment with drugs. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. High prediction accuracies reported in the literature likely result from significant overlaps among training, validation, and testing sets, making many predictors inapplicable to new data. To address these issues, we developed GraphGR, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, GraphGR integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and genedisease associations, into a unified graph structure. In order to construct diverse and information-rich cancer-specific networks, we devised a novel graph reduction protocol based on not only the topological information, but also the biological knowledge. The performance of GraphGR, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches on the same data. Finally, several new predictions are validated against the biomedical literature demonstrating that GraphGR generalizes well to unseen data, i.e. it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. GraphGR is freely available to the academic community at https://github.com/pulimeng/GraphGR.
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12
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Shabani P, Izadpanah S, Aghebati-Maleki A, Baghbani E, Baghbanzadeh A, Fotouhi A, Bakhshinejad B, Aghebati-Maleki L, Baradaran B. Role of miR-142 in the pathogenesis of osteosarcoma and its potential as therapeutic approach. J Cell Biochem 2018; 120:4783-4793. [PMID: 30450580 DOI: 10.1002/jcb.27857] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Accepted: 09/19/2018] [Indexed: 02/06/2023]
Abstract
Osteosarcoma (OS) is the most common primary malignant tumor of the bone with a strong tendency to early metastasis, and occurs in growing bones more commonly in children and adolescents. Considering the limited therapeutic methods and lack of 100% success of these methods, developing innovative therapies with high efficacy and lower side effects is needed. Meanwhile, miRNAs and the studies indicating the involvement of miRNAs in OS development have attracted attentions as a result of the frequent abnormalities in expression of miRNAs in cancer. miRNAs are noncoding short sequences with lengths ranging from 18 to 25 nucleotides that play a very important role in cellular processes, such as proliferation, differentiation, migration, and apoptosis. MiRNAs can have either oncogenic or tumor suppressive role based on cellular function and targets. This review aimed to have overview on miR-142 as a tumor suppressor in OS. Moreover, the genes involved in the disease, such as RAC1, HMAG1, MMP9, MMP2, and E-cadherin, which have irregularities as a result of change in miR-142 expression, and, thereby, result in increasing the proliferation, invasion, and metastasis of the cells in the tissues and OS cells will be discussed.
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Affiliation(s)
- Parastoo Shabani
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Sama Izadpanah
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Aghebati-Maleki
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran.,Department of Genetics and Molecular Medicine, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran
| | - Elham Baghbani
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Amir Baghbanzadeh
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Ali Fotouhi
- Department of Orthopedic Surgery, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Babak Bakhshinejad
- Okinawa Institute of Science and Technology Graduate University, Okinawa, Japan
| | - Leili Aghebati-Maleki
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behzad Baradaran
- Immunology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.,Department of Immunology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
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13
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Kurz FT, Kembro JM, Flesia AG, Armoundas AA, Cortassa S, Aon MA, Lloyd D. Network dynamics: quantitative analysis of complex behavior in metabolism, organelles, and cells, from experiments to models and back. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2016; 9. [PMID: 27599643 DOI: 10.1002/wsbm.1352] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Revised: 06/20/2016] [Accepted: 06/23/2016] [Indexed: 12/15/2022]
Abstract
Advancing from two core traits of biological systems: multilevel network organization and nonlinearity, we review a host of novel and readily available techniques to explore and analyze their complex dynamic behavior within the framework of experimental-computational synergy. In the context of concrete biological examples, analytical methods such as wavelet, power spectra, and metabolomics-fluxomics analyses, are presented, discussed, and their strengths and limitations highlighted. Further shown is how time series from stationary and nonstationary biological variables and signals, such as membrane potential, high-throughput metabolomics, O2 and CO2 levels, bird locomotion, at the molecular, (sub)cellular, tissue, and whole organ and animal levels, can reveal important information on the properties of the underlying biological networks. Systems biology-inspired computational methods start to pave the way for addressing the integrated functional dynamics of metabolic, organelle and organ networks. As our capacity to unravel the control and regulatory properties of these networks and their dynamics under normal or pathological conditions broadens, so is our ability to address endogenous rhythms and clocks to improve health-span in human aging, and to manage complex metabolic disorders, neurodegeneration, and cancer. WIREs Syst Biol Med 2017, 9:e1352. doi: 10.1002/wsbm.1352 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Felix T Kurz
- Massachusetts General Hospital, Cardiovascular Research Center, Harvard Medical School, Charlestown, MA, USA.,Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Jackelyn M Kembro
- Instituto de Investigaciones Biológicas y Tecnológicas (IIByT-CONICET), and Instituto de Ciencia y Tecnología de los Alimentos, Cátedra de Química Biológica, Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Ana G Flesia
- Centro de Investigaciones y Estudios de Matemática (CIEM-CONICET), and Facultad de Matemática, Astronomía y Física FAMAF, Universidad Nacional de Córdoba, Córdoba, Argentina
| | - Antonis A Armoundas
- Massachusetts General Hospital, Cardiovascular Research Center, Harvard Medical School, Charlestown, MA, USA
| | - Sonia Cortassa
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Miguel A Aon
- Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - David Lloyd
- Cardiff University School of Biosciences, Cardiff, UK
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14
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Abstract
The central nervous system (CNS) underlies memory, perception, decision-making, and behavior in numerous organisms. However, neural networks have no monopoly on the signaling functions that implement these remarkable algorithms. It is often forgotten that neurons optimized cellular signaling modes that existed long before the CNS appeared during evolution, and were used by somatic cellular networks to orchestrate physiology, embryonic development, and behavior. Many of the key dynamics that enable information processing can, in fact, be implemented by different biological hardware. This is widely exploited by organisms throughout the tree of life. Here, we review data on memory, learning, and other aspects of cognition in a range of models, including single celled organisms, plants, and tissues in animal bodies. We discuss current knowledge of the molecular mechanisms at work in these systems, and suggest several hypotheses for future investigation. The study of cognitive processes implemented in aneural contexts is a fascinating, highly interdisciplinary topic that has many implications for evolution, cell biology, regenerative medicine, computer science, and synthetic bioengineering.
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Affiliation(s)
- František Baluška
- Department of Plant Cell Biology, IZMB, University of Bonn Bonn, Germany
| | - Michael Levin
- Biology Department, Tufts Center for Regenerative and Developmental Biology, Tufts University Medford, MA, USA
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15
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Sinha I, Karagoz K, Fogle RL, Hollenbeak CS, Zea AH, Arga KY, Stanley AE, Hawkes WC, Sinha R. “Omics” of Selenium Biology: A Prospective Study of Plasma Proteome Network Before and After Selenized-Yeast Supplementation in Healthy Men. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2016; 20:202-13. [DOI: 10.1089/omi.2015.0187] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Indu Sinha
- Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania, USA
| | - Kubra Karagoz
- Department of Bioengineering, Marmara University, Istanbul, Turkey
| | - Rachel L. Fogle
- Department of Surgery, Penn State University College of Medicine, Hershey, Pennsylvania, USA
| | | | - Arnold H. Zea
- Stanley S, Scott Cancer Center and Department of Microbiology, Immunology, and Parasitology, LSU Health Sciences Center, New Orleans, Louisiana, USA
| | - Kazim Y. Arga
- Department of Bioengineering, Marmara University, Istanbul, Turkey
| | - Anne E. Stanley
- Department of Mass Spectrometry Core, Penn State University College of Medicine, Hershey, Pennsylvania, USA
| | - Wayne C. Hawkes
- United State Department of Agriculture, Agricultural Research Service, Western Human Nutrition Research Center, University of California Davis, California, USA
| | - Raghu Sinha
- Department of Biochemistry and Molecular Biology, Penn State University College of Medicine, Hershey, Pennsylvania, USA
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16
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Schisandrin B suppresses glioma cell metastasis mediated by inhibition of mTOR/MMP-9 signal pathway. Biomed Pharmacother 2015; 74:77-82. [DOI: 10.1016/j.biopha.2015.07.006] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Accepted: 07/09/2015] [Indexed: 12/31/2022] Open
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17
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Taylor S, Lam M, Pararasa C, Brown JE, Carmichael AR, Griffiths HR. Evaluating the evidence for targeting FOXO3a in breast cancer: a systematic review. Cancer Cell Int 2015; 15:1. [PMID: 25678856 PMCID: PMC4325954 DOI: 10.1186/s12935-015-0156-6] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2014] [Accepted: 01/02/2015] [Indexed: 12/21/2022] Open
Abstract
Background Tumour cells show greater dependency on glycolysis so providing a sufficient and rapid energy supply for fast growth. In many breast cancers, estrogen, progesterone and epidermal growth factor receptor-positive cells proliferate in response to growth factors and growth factor antagonists are a mainstay of treatment. However, triple negative breast cancer (TNBC) cells lack receptor expression, are frequently more aggressive and are resistant to growth factor inhibition. Downstream of growth factor receptors, signal transduction proceeds via phosphatidylinositol 3-kinase (PI3k), Akt and FOXO3a inhibition, the latter being partly responsible for coordinated increases in glycolysis and apoptosis resistance. FOXO3a may be an attractive therapeutic target for TNBC. Therefore we have undertaken a systematic review of FOXO3a as a target for breast cancer therapeutics. Methods Articles from NCBI were retrieved systematically when reporting primary data about FOXO3a expression in breast cancer cells after cytotoxic drug treatment. Results Increased FOXO3a expression is common following cytotoxic drug treatment and is associated with apoptosis and cell cycle arrest. There is some evidence that metabolic enzyme expression is also altered and that this effect is also elicited in TNBC cells. FOXO3a expression serves as a positive prognostic marker, especially in estrogen (ER) receptor positive cells. Discussion FOXO3a is upregulated by a number of receptor-dependent and -independent anti-cancer drugs and associates with apoptosis. The identification of microRNA that regulate FOXO3a directly suggest that it offers a tangible therapeutic target that merits wider evaluation.
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Affiliation(s)
- Simon Taylor
- Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET UK
| | - Matthew Lam
- Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET UK
| | - Chathyan Pararasa
- Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET UK
| | - James Ep Brown
- Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET UK
| | | | - Helen R Griffiths
- Life and Health Sciences, Aston University, Aston Triangle, Birmingham, B4 7ET UK
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18
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ZHENG ZHONGHUI, DING MULIANG, NI JIANGDONG, SONG DEYE, HUANG JUN, WANG JUNJIE. miR-142 acts as a tumor suppressor in osteosarcoma cell lines by targeting Rac1. Oncol Rep 2014; 33:1291-9. [DOI: 10.3892/or.2014.3687] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 11/12/2014] [Indexed: 11/05/2022] Open
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19
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Csermely P, Hódsági J, Korcsmáros T, Módos D, Perez-Lopez ÁR, Szalay K, Veres DV, Lenti K, Wu LY, Zhang XS. Cancer stem cells display extremely large evolvability: alternating plastic and rigid networks as a potential Mechanism: network models, novel therapeutic target strategies, and the contributions of hypoxia, inflammation and cellular senescence. Semin Cancer Biol 2014; 30:42-51. [PMID: 24412105 DOI: 10.1016/j.semcancer.2013.12.004] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2013] [Revised: 12/17/2013] [Accepted: 12/22/2013] [Indexed: 12/13/2022]
Abstract
Cancer is increasingly perceived as a systems-level, network phenomenon. The major trend of malignant transformation can be described as a two-phase process, where an initial increase of network plasticity is followed by a decrease of plasticity at late stages of tumor development. The fluctuating intensity of stress factors, like hypoxia, inflammation and the either cooperative or hostile interactions of tumor inter-cellular networks, all increase the adaptation potential of cancer cells. This may lead to the bypass of cellular senescence, and to the development of cancer stem cells. We propose that the central tenet of cancer stem cell definition lies exactly in the indefinability of cancer stem cells. Actual properties of cancer stem cells depend on the individual "stress-history" of the given tumor. Cancer stem cells are characterized by an extremely large evolvability (i.e. a capacity to generate heritable phenotypic variation), which corresponds well with the defining hallmarks of cancer stem cells: the possession of the capacity to self-renew and to repeatedly re-build the heterogeneous lineages of cancer cells that comprise a tumor in new environments. Cancer stem cells represent a cell population, which is adapted to adapt. We argue that the high evolvability of cancer stem cells is helped by their repeated transitions between plastic (proliferative, symmetrically dividing) and rigid (quiescent, asymmetrically dividing, often more invasive) phenotypes having plastic and rigid networks. Thus, cancer stem cells reverse and replay cancer development multiple times. We describe network models potentially explaining cancer stem cell-like behavior. Finally, we propose novel strategies including combination therapies and multi-target drugs to overcome the Nietzschean dilemma of cancer stem cell targeting: "what does not kill me makes me stronger".
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
| | - János Hódsági
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary
| | - Tamás Korcsmáros
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117 Budapest, Hungary
| | - Dezső Módos
- Department of Genetics, Eötvös Loránd University, Pázmány P. s. 1C, H-1117 Budapest, Hungary; Semmelweis University, Department of Morphology and Physiology, Faculty of Health Sciences, Vas u. 17, H-1088 Budapest, Hungary
| | - Áron R Perez-Lopez
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary
| | - Kristóf Szalay
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary
| | - Dániel V Veres
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary
| | - Katalin Lenti
- Semmelweis University, Department of Morphology and Physiology, Faculty of Health Sciences, Vas u. 17, H-1088 Budapest, Hungary
| | - Ling-Yun Wu
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, No. 55, Zhongguancun East Road, Beijing 100190, China
| | - Xiang-Sun Zhang
- Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, No. 55, Zhongguancun East Road, Beijing 100190, China
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