101
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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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102
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Percolation of heterogeneous flows uncovers the bottlenecks of infrastructure networks. Nat Commun 2021; 12:1254. [PMID: 33623037 PMCID: PMC7902621 DOI: 10.1038/s41467-021-21483-y] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2020] [Accepted: 01/13/2021] [Indexed: 11/30/2022] Open
Abstract
Whether it be the passengers’ mobility demand in transportation systems, or the consumers’ energy demand in power grids, the primary purpose of many infrastructure networks is to best serve this flow demand. In reality, the volume of flow demand fluctuates unevenly across complex networks while simultaneously being hindered by some form of congestion or overload. Nevertheless, there is little known about how the heterogeneity of flow demand influences the network flow dynamics under congestion. To explore this, we introduce a percolation-based network analysis framework underpinned by flow heterogeneity. Thereby, we theoretically identify bottleneck links with guaranteed decisive impact on how flows are passed through the network. The effectiveness of the framework is demonstrated on large-scale real transportation networks, where mitigating the congestion on a small fraction of the links identified as bottlenecks results in a significant network improvement. Infrastructure networks are characterized by fluctuations of flow demand between different points and temporal congestion or overload on flow pathways. Hamedmoghadam et al. identify congestion bottlenecks in networks relevant to communication, transportation, water supply, and power distribution.
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103
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Åkesson J, Lubovac-Pilav Z, Magnusson R, Gustafsson M. ComHub: Community predictions of hubs in gene regulatory networks. BMC Bioinformatics 2021; 22:58. [PMID: 33563211 PMCID: PMC7871572 DOI: 10.1186/s12859-021-03987-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/29/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Hub transcription factors, regulating many target genes in gene regulatory networks (GRNs), play important roles as disease regulators and potential drug targets. However, while numerous methods have been developed to predict individual regulator-gene interactions from gene expression data, few methods focus on inferring these hubs. RESULTS We have developed ComHub, a tool to predict hubs in GRNs. ComHub makes a community prediction of hubs by averaging over predictions by a compendium of network inference methods. Benchmarking ComHub against the DREAM5 challenge data and two independent gene expression datasets showed a robust performance of ComHub over all datasets. CONCLUSIONS In contrast to other evaluated methods, ComHub consistently scored among the top performing methods on data from different sources. Lastly, we implemented ComHub to work with both predefined networks and to perform stand-alone network inference, which will make the method generally applicable.
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Affiliation(s)
- Julia Åkesson
- Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden. .,Systems Biology Research Centre, School of bioscience, University of Skövde, 541 28, Skövde, Sweden.
| | - Zelmina Lubovac-Pilav
- Systems Biology Research Centre, School of bioscience, University of Skövde, 541 28, Skövde, Sweden
| | - Rasmus Magnusson
- Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
| | - Mika Gustafsson
- Department of Physics, Chemistry and Biology, Linköping University, 581 83, Linköping, Sweden
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104
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Lim JT, Chen C, Grant AD, Padi M. Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules. Front Genet 2021; 11:603264. [PMID: 33519907 PMCID: PMC7841433 DOI: 10.3389/fgene.2020.603264] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2020] [Accepted: 12/23/2020] [Indexed: 12/24/2022] Open
Abstract
The use of biological networks such as protein-protein interaction and transcriptional regulatory networks is becoming an integral part of genomics research. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new "communities" (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a method for randomizing networks with fixed node strengths. CRANE can be used to generate a null distribution of gene regulatory networks that can in turn be used to rank the most significant changes in candidate disease communities. Compared to other approaches, such as consensus clustering or commonly used generative models, CRANE emulates biologically realistic networks and recovers simulated disease modules with higher accuracy. When applied to breast and ovarian cancer networks, CRANE improves the identification of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes.
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Affiliation(s)
- James T. Lim
- Department of Molecular and Cellular Biology, The University of Arizona, Tucson, AZ, United States
| | - Chen Chen
- Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, The University of Arizona, Tucson, AZ, United States
| | - Adam D. Grant
- University of Arizona Cancer Center, The University of Arizona, Tucson, AZ, United States
| | - Megha Padi
- Department of Molecular and Cellular Biology, The University of Arizona, Tucson, AZ, United States
- University of Arizona Cancer Center, The University of Arizona, Tucson, AZ, United States
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105
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Zheng Y, Han X, Zhao D, Wei K, Yuan Y, Li Y, Liu M, Zhang CS. Exploring Biocontrol Agents From Microbial Keystone Taxa Associated to Suppressive Soil: A New Attempt for a Biocontrol Strategy. FRONTIERS IN PLANT SCIENCE 2021; 12:655673. [PMID: 33959142 PMCID: PMC8095248 DOI: 10.3389/fpls.2021.655673] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 02/22/2021] [Indexed: 05/02/2023]
Abstract
Recent studies have observed differing microbiomes between disease-suppressive and disease-conducive soils. However, it remains unclear whether the microbial keystone taxa in suppressive soil are critical for the suppression of diseases. Bacterial wilt is a common soil-borne disease caused by Ralstonia solanacearum that affects tobacco plants. In this study, two contrasting tobacco fields with bacterial wilt disease incidences of 0% (disease suppressive) and 100% (disease conducive) were observed. Through amplicon sequencing, as expected, a high abundance of Ralstonia was found in the disease-conducive soil, while large amounts of potential beneficial bacteria were found in the disease-suppressive soil. In the fungal community, an abundance of the Fusarium genus, which contains species that cause Fusarium wilt, showed a positive correlation (p < 0.001) with the abundance of Ralstonia. Network analysis revealed that the healthy plants had more complex bacterial networks than the diseased plants. A total of 9 and 13 bacterial keystone taxa were identified from the disease-suppressive soil and healthy root, respectively. Accumulated abundance of these bacterial keystones showed a negative correlation (p < 0.001) with the abundance of Ralstonia. To complement network analysis, culturable strains were isolated, and three species belonging to Pseudomonas showed high 16S rRNA gene similarity (98.4-100%) with keystone taxa. These strains displayed strong inhibition on pathogens and reduced the incidence of bacterial wilt disease in greenhouse condition. This study highlighted the importance of keystone species in the protection of crops against pathogen infection and proposed an approach to obtain beneficial bacteria through identifying keystone species, avoiding large-scale bacterial isolation and cultivation.
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Affiliation(s)
- Yanfen Zheng
- Pest Integrated Management Key Laboratory of China Tobacco, Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Xiaobin Han
- Biological Organic Fertilizer Engineering Technology Center of China Tobacco, Zunyi Branch of Guizhou Tobacco Company, Zunyi, China
| | - Donglin Zhao
- Pest Integrated Management Key Laboratory of China Tobacco, Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Keke Wei
- Pest Integrated Management Key Laboratory of China Tobacco, Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Yuan Yuan
- Pest Integrated Management Key Laboratory of China Tobacco, Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Yiqiang Li
- Pest Integrated Management Key Laboratory of China Tobacco, Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Minghong Liu
- Biological Organic Fertilizer Engineering Technology Center of China Tobacco, Zunyi Branch of Guizhou Tobacco Company, Zunyi, China
- Minghong Liu,
| | - Cheng-Sheng Zhang
- Pest Integrated Management Key Laboratory of China Tobacco, Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
- *Correspondence: Cheng-Sheng Zhang,
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106
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Identifying patient-specific flow of signal transduction perturbed by multiple single-nucleotide alterations. QUANTITATIVE BIOLOGY 2020. [DOI: 10.1007/s40484-020-0227-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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107
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Yin Z, Guo B, Ma S, Sun Y, Mi Z, Zheng Z. DReSS: a method to quantitatively describe the influence of structural perturbations on state spaces of genetic regulatory networks. Brief Bioinform 2020; 22:6032613. [PMID: 33313791 DOI: 10.1093/bib/bbaa315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 09/23/2020] [Accepted: 10/16/2020] [Indexed: 11/14/2022] Open
Abstract
Structures of genetic regulatory networks are not fixed. These structural perturbations can cause changes to the reachability of systems' state spaces. As system structures are related to genotypes and state spaces are related to phenotypes, it is important to study the relationship between structures and state spaces. However, there is still no method can quantitively describe the reachability differences of two state spaces caused by structural perturbations. Therefore, Difference in Reachability between State Spaces (DReSS) is proposed. DReSS index family can quantitively describe differences of reachability, attractor sets between two state spaces and can help find the key structure in a system, which may influence system's state space significantly. First, basic properties of DReSS including non-negativity, symmetry and subadditivity are proved. Then, typical examples are shown to explain the meaning of DReSS and the differences between DReSS and traditional graph distance. Finally, differences of DReSS distribution between real biological regulatory networks and random networks are compared. Results show most structural perturbations in biological networks tend to affect reachability inside and between attractor basins rather than to affect attractor set itself when compared with random networks, which illustrates that most genotype differences tend to influence the proportion of different phenotypes and only a few ones can create new phenotypes. DReSS can provide researchers with a new insight to study the relation between genotypes and phenotypes.
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Affiliation(s)
- Ziqiao Yin
- Shenyuan Honors College and School of Mathematical Sciences, Beihang University, and Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education. He currently works as a visiting scholar at Yale University
| | - Binghui Guo
- Artificial Intelligence Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, LMIB, NLSDE, School of Mathematical Sciences, Beihang University, and Peng Cheng Laboratory
| | - Shuangge Ma
- Department of Biostatistics, Yale University
| | - Yifan Sun
- School of Statistics, Renmin University of China
| | - Zhilong Mi
- Key Laboratory of Mathematics, Informatics and Behavioral Semantics, Ministry of Education, and School of Mathematical Sciences from Beihang University
| | - Zhiming Zheng
- Artificial Intelligence Institute, Beijing Advanced Innovation Center for Big Data and Brain Computing, LMIB, NLSDE, School of Mathematical Sciences, Beihang University, and Peng Cheng Laboratory
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108
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Dotson GA, Ryan CW, Chen C, Muir L, Rajapakse I. Cellular reprogramming: Mathematics meets medicine. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2020; 13:e1515. [PMID: 33289324 PMCID: PMC8867497 DOI: 10.1002/wsbm.1515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 11/05/2020] [Accepted: 11/09/2020] [Indexed: 11/11/2022]
Abstract
Generating needed cell types using cellular reprogramming is a promising strategy for restoring tissue function in injury or disease. A common method for reprogramming is addition of one or more transcription factors that confer a new function or identity. Advancements in transcription factor selection and delivery have culminated in successful grafting of autologous reprogrammed cells, an early demonstration of their clinical utility. Though cellular reprogramming has been successful in a number of settings, identification of appropriate transcription factors for a particular transformation has been challenging. Computational methods enable more sophisticated prediction of relevant transcription factors for reprogramming by leveraging gene expression data of initial and target cell types, and are built on mathematical frameworks ranging from information theory to control theory. This review highlights the utility and impact of these mathematical frameworks in the field of cellular reprogramming. This article is categorized under: Reproductive System Diseases > Reproductive System Diseases>Genetics/Genomics/Epigenetics Reproductive System Diseases > Reproductive System Diseases>Stem Cells and Development Reproductive System Diseases > Reproductive System Diseases>Computational Models.
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Affiliation(s)
- Gabrielle A. Dotson
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Charles W. Ryan
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Program in Cellular and Molecular Biology, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Medical Scientist Training Program, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Can Chen
- Department of Mathematics, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Lindsey Muir
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
| | - Indika Rajapakse
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, 48109, USA
- Department of Mathematics, University of Michigan, Ann Arbor, Michigan, 48109, USA
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109
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Rivas-Barragan D, Mubeen S, Guim Bernat F, Hofmann-Apitius M, Domingo-Fernández D. Drug2ways: Reasoning over causal paths in biological networks for drug discovery. PLoS Comput Biol 2020; 16:e1008464. [PMID: 33264280 PMCID: PMC7735677 DOI: 10.1371/journal.pcbi.1008464] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 12/14/2020] [Accepted: 10/23/2020] [Indexed: 12/24/2022] Open
Abstract
Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.
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Affiliation(s)
- Daniel Rivas-Barragan
- Barcelona Supercomputing Center, Barcelona, Spain
- Computer Architecture Department, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Fraunhofer Center for Machine Learning, Germany
| | | | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Fraunhofer Center for Machine Learning, Germany
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110
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Zhang X, Witthaut D, Timme M. Topological Determinants of Perturbation Spreading in Networks. PHYSICAL REVIEW LETTERS 2020; 125:218301. [PMID: 33274998 DOI: 10.1103/physrevlett.125.218301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2020] [Revised: 08/20/2020] [Accepted: 09/22/2020] [Indexed: 06/12/2023]
Abstract
Spreading phenomena essentially underlie the dynamics of various natural and technological networked systems, yet how spatiotemporal propagation patterns emerge from such networks remains largely unknown. Here we propose a novel approach that reveals universal features determining the spreading dynamics in diffusively coupled networks and disentangles them from factors that are system specific. In particular, we first analytically identify a purely topological factor encoding the interaction structure and strength, and second, numerically estimate a master function characterizing the universal scaling of the perturbation arrival times across topologically different networks. The proposed approach thereby provides intuitive insights into complex propagation patterns as well as accurate predictions for the perturbation arrival times. The approach readily generalizes to a wide range of networked systems with diffusive couplings and may contribute to assess the risks of transient influences of ubiquitous perturbations in real-world systems.
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Affiliation(s)
- Xiaozhu Zhang
- Institute for Theoretical Physics, Center for Advancing Electronics Dresden (cfaed), and Cluster of Excellence Physics of Life, Technical University of Dresden, 01062 Dresden, Germany
| | - Dirk Witthaut
- Institute for Energy and Climate Research-Systems Analysis and Technology Evaluation (IEK-STE), Forschungszentrum Jülich, 52428 Jülich, Germany and Institute for Theoretical Physics, University of Cologne, 50937 Köln, Germany
| | - Marc Timme
- Institute for Theoretical Physics, Center for Advancing Electronics Dresden (cfaed), and Cluster of Excellence Physics of Life, Technical University of Dresden, 01062 Dresden, Germany
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111
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The Rhodamine Isothiocyanate Analogue as a Quorum Sensing Inhibitor Has the Potential to Control Microbially-Induced Biofouling. Mar Drugs 2020; 18:md18090484. [PMID: 32971837 PMCID: PMC7551263 DOI: 10.3390/md18090484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 09/18/2020] [Indexed: 12/02/2022] Open
Abstract
Quorum sensing inhibitors (QSIs) have been proven to be an innovative approach to interfering with biofilm formation, since this process is regulated by QS signals. However, most studies have focused on single-species biofilm formation, whereas studies of the effects of signal interference on the development of multispecies biofilm, especially in the natural environment, are still lacking. Here we develop and evaluate the anti-biofilm capability of a new QSI (rhodamine isothiocyanate analogue, RIA) in natural seawater. During the experiment, biofilm characteristics, microbial communities/functions and network interactions were monitored at 36, 80, and 180 h, respectively. The results showed that the biomass and 3D structure of the biofilm were significantly different in the presence of the QSI. The expression of genes involved in extracellular polysaccharide synthesis was also downregulated in the QSI-treated group. Dramatic differences in microbial composition, β-diversity and functions between the RIA-treated group and the control group were also observed, especially in the early stage of biofilm development. Furthermore, co-occurrence model analysis showed that RIA reduced the complexity of the microbial network. This study demonstrates that rhodamine isothiocyanate analogue is an efficient QS inhibitor and has potential applications in controlling biofouling caused by multispecies biofilm, especially in the early stage of biofouling formation.
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112
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Estrada E. Fractional diffusion on the human proteome as an alternative to the multi-organ damage of SARS-CoV-2. CHAOS (WOODBURY, N.Y.) 2020; 30:081104. [PMID: 32872802 PMCID: PMC7585451 DOI: 10.1063/5.0015626] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 07/15/2020] [Indexed: 05/16/2023]
Abstract
The coronavirus 2019 (COVID-19) respiratory disease is caused by the novel coronavirus SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), which uses the enzyme ACE2 to enter human cells. This disease is characterized by important damage at a multi-organ level, partially due to the abundant expression of ACE2 in practically all human tissues. However, not every organ in which ACE2 is abundant is affected by SARS-CoV-2, which suggests the existence of other multi-organ routes for transmitting the perturbations produced by the virus. We consider here diffusive processes through the protein-protein interaction (PPI) network of proteins targeted by SARS-CoV-2 as an alternative route. We found a subdiffusive regime that allows the propagation of virus perturbations through the PPI network at a significant rate. By following the main subdiffusive routes across the PPI network, we identify proteins mainly expressed in the heart, cerebral cortex, thymus, testis, lymph node, kidney, among others of the organs reported to be affected by COVID-19.
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Affiliation(s)
- Ernesto Estrada
- Instituto Universitario de Matemáticas y Aplicaciones, Universidad de Zaragoza, 50009 Zaragoza, Spain and ARAID Foundation, Government of Aragón, 50018 Zaragoza, Spain
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113
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Lee ED, Katz DM, Bommarito MJ, Ginsparg PH. Sensitivity of collective outcomes identifies pivotal components. J R Soc Interface 2020; 17:20190873. [PMID: 32486948 PMCID: PMC7328396 DOI: 10.1098/rsif.2019.0873] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A social system is susceptible to perturbation when its collective properties depend sensitively on a few pivotal components. Using the information geometry of minimal models from statistical physics, we develop an approach to identify pivotal components to which coarse-grained, or aggregate, properties are sensitive. As an example, we introduce our approach on a reduced toy model with a median voter who always votes in the majority. The sensitivity of majority-minority divisions to changing voter behaviour pinpoints the unique role of the median. More generally, the sensitivity identifies pivotal components that precisely determine collective outcomes generated by a complex network of interactions. Using perturbations to target pivotal components in the models, we analyse datasets from political voting, finance and Twitter. Across these systems, we find remarkable variety, from systems dominated by a median-like component to those whose components behave more equally. In the context of political institutions such as courts or legislatures, our methodology can help describe how changes in voters map to new collective voting outcomes. For economic indices, differing system response reflects varying fiscal conditions across time. Thus, our information-geometric approach provides a principled, quantitative framework that may help assess the robustness of collective outcomes to targeted perturbation and compare social institutions, or even biological networks, with one another and across time.
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Affiliation(s)
- Edward D Lee
- Department of Physics, 142 Sciences Drive, Cornell University, Ithaca, NY 14853, USA.,Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, USA
| | - Daniel M Katz
- Chicago-Kent College of Law, Illinois Institute of Technology, 565 West Adams, Chicago, IL 60661, USA
| | - Michael J Bommarito
- Chicago-Kent College of Law, Illinois Institute of Technology, 565 West Adams, Chicago, IL 60661, USA
| | - Paul H Ginsparg
- Department of Physics, 142 Sciences Drive, Cornell University, Ithaca, NY 14853, USA
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114
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Sumathipala M, Weiss ST. Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data. Sci Rep 2020; 10:8705. [PMID: 32457435 PMCID: PMC7251138 DOI: 10.1038/s41598-020-65633-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2020] [Accepted: 05/07/2020] [Indexed: 12/18/2022] Open
Abstract
With critical roles in regulating gene expression, miRNAs are strongly implicated in the pathophysiology of many complex diseases. Experimental methods to determine disease related miRNAs are time consuming and costly. Computationally predicting miRNA-disease associations has potential applications in finding miRNA therapeutic pathways and in understanding the role of miRNAs in disease-disease relationships. In this study, we propose the MiRNA-disease Association Prediction (MAP) method, an in-silico method to predict and prioritize miRNA-disease associations. The MAP method applies a network diffusion approach, starting from the known disease genes in a heterogenous network constructed from miRNA-gene associations, protein-protein interactions, and gene-disease associations. Validation using experimental data on miRNA-disease associations demonstrated superior performance to two current state-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer. MAP is successfully applied to predict differential miRNA expression in four cancer types. Most strikingly, disease-disease relationships in terms of shared miRNAs revealed hidden disease subtyping comparable to that of previous work on shared genes between diseases, with applications for multi-omics characterization of disease relationships.
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Affiliation(s)
- Marissa Sumathipala
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Harvard College, Cambridge, MA, USA.
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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115
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Vermeulen R, Schymanski EL, Barabási AL, Miller GW. The exposome and health: Where chemistry meets biology. Science 2020; 367:392-396. [PMID: 31974245 DOI: 10.1126/science.aay3164] [Citation(s) in RCA: 434] [Impact Index Per Article: 108.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The "exposome" concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.
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Affiliation(s)
- Roel Vermeulen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands. .,Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.,Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
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116
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Zhou J, Lin ZJ, Cai ZH, Zeng YH, Zhu JM, Du XP. Opportunistic bacteria use quorum sensing to disturb coral symbiotic communities and mediate the occurrence of coral bleaching. Environ Microbiol 2020; 22:1944-1962. [PMID: 32249540 DOI: 10.1111/1462-2920.15009] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 03/28/2020] [Accepted: 03/30/2020] [Indexed: 12/13/2022]
Abstract
Coral associated microorganisms, especially some opportunistic pathogens can utilize quorum-sensing (QS) signals to affect population structure and host health. However, direct evidence about the link between coral bleaching and dysbiotic microbiomes under QS regulation was lacking. Here, using 11 opportunistic bacteria and their QS products (AHLs, acyl-homoserine-lactones), we exposed Pocillopora damicornis to three different treatments: test groups (A and B: mixture of AHLs-producing bacteria and cocktail of AHLs signals respectively); control groups (C and D: group A and B with furanone added respectively); and a blank control (group E: only seawater) for 21 days. The results showed that remarkable bleaching phenomenon was observed in groups A and B. The operational taxonomic units-sequencing analysis shown that the bacterial network interactions and communities composition were significantly changed, becoming especially enhanced in the relative abundances of Vibrio, Edwardsiella, Enterobacter, Pseudomonas, and Aeromonas. Interestingly, the control groups (C and D) were found to have a limited influence upon host microbial composition and reduced bleaching susceptibility of P. damicornis. These results indicate bleaching's initiation and progression may be caused by opportunistic bacteria of resident microbes in a process under regulation by AHLs. These findings add a new dimension to our understanding of the complexity of bleaching mechanisms from a chemoecological perspective.
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Affiliation(s)
- Jin Zhou
- Shenzhen Public Platform for Screening & Application of Marine Microbial Resources, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Zi-Jun Lin
- Shenzhen Public Platform for Screening & Application of Marine Microbial Resources, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.,Department of Earth System Science, Tsinghua University of Education Key Laboratory for Earth System Modeling, Beijing, 100084, People's Republic of China
| | - Zhong-Hua Cai
- Shenzhen Public Platform for Screening & Application of Marine Microbial Resources, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Yan-Hua Zeng
- Shenzhen Public Platform for Screening & Application of Marine Microbial Resources, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
| | - Jian-Ming Zhu
- Shenzhen Public Platform for Screening & Application of Marine Microbial Resources, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China.,School of Environment, Harbin Institute of Technology, Harbin, 150001, People's Republic of China
| | - Xiao-Peng Du
- Shenzhen Public Platform for Screening & Application of Marine Microbial Resources, Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, People's Republic of China
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117
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Magnusson R, Gustafsson M. LiPLike: towards gene regulatory network predictions of high certainty. Bioinformatics 2020; 36:2522-2529. [PMID: 31904818 PMCID: PMC7178405 DOI: 10.1093/bioinformatics/btz950] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 12/05/2019] [Accepted: 01/03/2020] [Indexed: 12/14/2022] Open
Abstract
MOTIVATION High correlation in expression between regulatory elements is a persistent obstacle for the reverse-engineering of gene regulatory networks. If two potential regulators have matching expression patterns, it becomes challenging to differentiate between them, thus increasing the risk of false positive identifications. RESULTS To allow for gene regulation predictions of high confidence, we propose a novel method, the Linear Profile Likelihood (LiPLike), that assumes a regression model and iteratively searches for interactions that cannot be replaced by a linear combination of other predictors. To compare the performance of LiPLike with other available inference methods, we benchmarked LiPLike using three independent datasets from the Dialogue on Reverse Engineering Assessment and Methods 5 (DREAM5) network inference challenge. We found that LiPLike could be used to stratify predictions of other inference tools, and when applied to the predictions of DREAM5 participants, we observed an average improvement in accuracy of >140% compared to individual methods. Furthermore, LiPLike was able to independently predict networks better than all DREAM5 participants when applied to biological data. When predicting the Escherichia coli network, LiPLike had an accuracy of 0.38 for the top-ranked 100 interactions, whereas the corresponding DREAM5 consensus model yielded an accuracy of 0.11. AVAILABILITY AND IMPLEMENTATION We made LiPLike available to the community as a Python toolbox, available at https://gitlab.com/Gustafsson-lab/liplike. We believe that LiPLike will be used for high confidence predictions in studies where individual model interactions are of high importance, and to remove false positive predictions made by other state-of-the-art gene-gene regulation prediction tools. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rasmus Magnusson
- Department of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden
| | - Mika Gustafsson
- Department of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden
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118
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Meyer CT, Wooten DJ, Lopez CF, Quaranta V. Charting the Fragmented Landscape of Drug Synergy. Trends Pharmacol Sci 2020; 41:266-280. [PMID: 32113653 PMCID: PMC7986484 DOI: 10.1016/j.tips.2020.01.011] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 01/16/2020] [Accepted: 01/29/2020] [Indexed: 12/16/2022]
Abstract
Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.
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Affiliation(s)
- Christian T Meyer
- Program in Chemical and Physical Biology, Vanderbilt University, Nashville, TN, USA
| | - David J Wooten
- Department of Physics, Pennsylvania State University, University Park, PA, USA
| | - Carlos F Lopez
- Program in Chemical and Physical Biology, Vanderbilt University, Nashville, TN, USA; Department of Biochemistry, Vanderbilt University, Nashville, TN, USA
| | - Vito Quaranta
- Program in Chemical and Physical Biology, Vanderbilt University, Nashville, TN, USA; Department of Biochemistry, Vanderbilt University, Nashville, TN, USA.
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119
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Rezaei Tavirani M, Zamanian Azodi M, Rostami-Nejad M, Morravej H, Razzaghi Z, Okhovatian F, Rezaei-Tavirani M. Introducing Serine as Cardiovascular Disease Biomarker Candidate via Pathway Analysis. Galen Med J 2020; 9:e1696. [PMID: 34466570 PMCID: PMC8343801 DOI: 10.31661/gmj.v9i0.1696] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 10/01/2019] [Accepted: 12/03/2019] [Indexed: 11/25/2022] Open
Abstract
Background: The rate of death due to cardiovascular disease (CVD) is growing. Investigations about CVD that leading to introduce varieties of metabolites is available. The monitoring of these metabolites to find effective ones in the future of clinic applications is the main aim of this study. Materials and Methods: Numbers of 34 metabolites for the CVD are extracted from literature and designated for interaction determinations by MetScape V 3.1.3. The compound-reaction-enzyme-gene network was constructed and the pathways were analyzed. Based on the presence of metabolites in the pathways the critical compounds were determined. Results: Pathway analysis revealed 18 disturbed pathways related to the CVD. glycerophospholipid metabolism pathway including 27 compounds is related to the 9 queried metabolites. L-Serine which was communed between 5 pathways and also was presented in the largest pathway was identified as the critical compound. Conclusion: It can be concluded that L-Serine is a proper biomarker candidate for CVD diagnosis and also patients follow up approaches.
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Affiliation(s)
- Mostafa Rezaei Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mona Zamanian Azodi
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
- Correspondence to: Mona Zamanian Azodi, Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran Telephone Number: +982122714248 Email Address:
| | - Mohammad Rostami-Nejad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Hamideh Morravej
- Skin Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Zahra Razzaghi
- Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farshad Okhovatian
- Physiotherapy Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Majid Rezaei-Tavirani
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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120
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Koutrouli M, Karatzas E, Paez-Espino D, Pavlopoulos GA. A Guide to Conquer the Biological Network Era Using Graph Theory. Front Bioeng Biotechnol 2020; 8:34. [PMID: 32083072 PMCID: PMC7004966 DOI: 10.3389/fbioe.2020.00034] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 01/15/2020] [Indexed: 12/24/2022] Open
Abstract
Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.
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Affiliation(s)
- Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.,Department of Informatics and Telecommunications, University of Athens, Athens, Greece
| | - David Paez-Espino
- Lawrence Berkeley National Laboratory, Department of Energy, Joint Genome Institute, Walnut Creek, CA, United States
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121
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Vermeulen R, Schymanski EL, Barabási AL, Miller GW. The exposome and health: Where chemistry meets biology. Science 2020. [PMID: 31974245 DOI: 10.1126/science:aay3164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/21/2023]
Abstract
Despite extensive evidence showing that exposure to specific chemicals can lead to disease, current research approaches and regulatory policies fail to address the chemical complexity of our world. To safeguard current and future generations from the increasing number of chemicals polluting our environment, a systematic and agnostic approach is needed. The "exposome" concept strives to capture the diversity and range of exposures to synthetic chemicals, dietary constituents, psychosocial stressors, and physical factors, as well as their corresponding biological responses. Technological advances such as high-resolution mass spectrometry and network science have allowed us to take the first steps toward a comprehensive assessment of the exposome. Given the increased recognition of the dominant role that nongenetic factors play in disease, an effort to characterize the exposome at a scale comparable to that of the human genome is warranted.
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Affiliation(s)
- Roel Vermeulen
- Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht, the Netherlands.
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Albert-László Barabási
- Network Science Institute, Northeastern University, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Network and Data Science, Central European University, Budapest, Hungary
| | - Gary W Miller
- Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, NY, USA.
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122
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Mansouri V, Rezaei-Tavirani M, Zadeh-Esmaeel MM, Rezaei-Tavirani S, Razzaghi M, Okhovatian F, Rostami-Nejad M, Ahmadzade A. Analysis of Laser Therapy Effects on Squamous Cell Carcinoma Patients: A System Biology Study. J Lasers Med Sci 2019; 10:S1-S6. [PMID: 32021665 DOI: 10.15171/jlms.2019.s1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Introduction: The Mechanism of laser therapy and also its safety are 2 important features of the application of different types of lasers in medicine. This study aims to investigate the critically affected genes after the treatment of squamous cell carcinoma patients. Methods: The gene expression profiles of 4 squamous cell carcinoma patients that were treated via chemoradiotherapy (CRT) plus the laser and 3 similar patients without laser exposure from Gene Expression Omnibus (GEO) were downloaded and were screened to find critical genes via network analysis. The STRING database, Cytoscape software, and the Clue GO plug-in of Cytoscape software were used. Results: The genes HSX70 and NCC27 were determined as neighbors and HSPA1B, CLIC1, RAB13, PPIF, and LCE3D as hub genes. The over-expression of LCE3D was interpreted as the side effect of laser therapy. Apoptosis and the cell cycle were the dominant biological processes regulated by the HSP molecules in the laser-treated patients. Conclusion: The laser affected the main biological processes and simultaneously issued side effects.
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Affiliation(s)
- Vahid Mansouri
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mostafa Rezaei-Tavirani
- Proteomics Research Center, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | | | - Sina Rezaei-Tavirani
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Razzaghi
- Laser Application in Medical Sciences Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Farshad Okhovatian
- Physiotherapy Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Mohammad Rostami-Nejad
- Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Alireza Ahmadzade
- Proteomics Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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123
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Aguilar D, Lemonnier N, Koppelman GH, Melén E, Oliva B, Pinart M, Guerra S, Bousquet J, Anto JM. Understanding allergic multimorbidity within the non-eosinophilic interactome. PLoS One 2019; 14:e0224448. [PMID: 31693680 PMCID: PMC6834334 DOI: 10.1371/journal.pone.0224448] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Accepted: 10/14/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND The mechanisms explaining multimorbidity between asthma, dermatitis and rhinitis (allergic multimorbidity) are not well known. We investigated these mechanisms and their specificity in distinct cell types by means of an interactome-based analysis of expression data. METHODS Genes associated to the diseases were identified using data mining approaches, and their multimorbidity mechanisms in distinct cell types were characterized by means of an in silico analysis of the topology of the human interactome. RESULTS We characterized specific pathomechanisms for multimorbidities between asthma, dermatitis and rhinitis for distinct emergent non-eosinophilic cell types. We observed differential roles for cytokine signaling, TLR-mediated signaling and metabolic pathways for multimorbidities across distinct cell types. Furthermore, we also identified individual genes potentially associated to multimorbidity mechanisms. CONCLUSIONS Our results support the existence of differentiated multimorbidity mechanisms between asthma, dermatitis and rhinitis at cell type level, as well as mechanisms common to distinct cell types. These results will help understanding the biology underlying allergic multimorbidity, assisting in the design of new clinical studies.
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MESH Headings
- Asthma/epidemiology
- Asthma/genetics
- Asthma/immunology
- Blood Cells/immunology
- Blood Cells/metabolism
- Cytokines/immunology
- Cytokines/metabolism
- Datasets as Topic
- Dermatitis, Allergic Contact/epidemiology
- Dermatitis, Allergic Contact/genetics
- Dermatitis, Allergic Contact/immunology
- Dermatitis, Atopic/epidemiology
- Dermatitis, Atopic/genetics
- Dermatitis, Atopic/immunology
- Gene Expression Profiling
- Humans
- Immunity, Cellular/genetics
- Multimorbidity
- Protein Interaction Maps/genetics
- Protein Interaction Maps/immunology
- Rhinitis, Allergic/epidemiology
- Rhinitis, Allergic/genetics
- Rhinitis, Allergic/immunology
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Affiliation(s)
- Daniel Aguilar
- Biomedical Research Networking Center in Hepatic and Digestive Diseases (CIBEREHD), Instituto de Salud Carlos III, Barcelona, Spain
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- 6AM Data Mining, Barcelona, Spain
| | - Nathanael Lemonnier
- Institute for Advanced Biosciences, Inserm U 1209 CNRS UMR 5309 Université Grenoble Alpes, Site Santé, Allée des Alpes, La Tronche, France
| | - Gerard H. Koppelman
- University of Groningen, University Medical Center Groningen, Beatrix Children’s Hospital, Department of Pediatric Pulmonology and Pediatric Allergology, Groningen, Netherlands
- University of Groningen, University Medical Center Groningen, GRIAC Research Institute
| | - Erik Melén
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Baldo Oliva
- Structural Bioinformatics Group, Research Programme on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Mariona Pinart
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
| | - Stefano Guerra
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
- Asthma and Airway Disease Research Center, University of Arizona, Tucson, Arizona, United States of America
| | - Jean Bousquet
- Hopital Arnaud de Villeneuve University Hospital, Montpellier, France
- Charité, Universitätsmedizin Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Comprehensive Allergy Center, Department of Dermatology and Allergy, Berlin, Germany
| | - Josep M. Anto
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain
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124
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Salviato E, Djordjilović V, Chiogna M, Romualdi C. SourceSet: A graphical model approach to identify primary genes in perturbed biological pathways. PLoS Comput Biol 2019; 15:e1007357. [PMID: 31652275 PMCID: PMC6834292 DOI: 10.1371/journal.pcbi.1007357] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Revised: 11/06/2019] [Accepted: 08/23/2019] [Indexed: 11/24/2022] Open
Abstract
Topological gene-set analysis has emerged as a powerful means for omic data interpretation. Although numerous methods for identifying dysregulated genes have been proposed, few of them aim to distinguish genes that are the real source of perturbation from those that merely respond to the signal dysregulation. Here, we propose a new method, called SourceSet, able to distinguish between the primary and the secondary dysregulation within a Gaussian graphical model context. The proposed method compares gene expression profiles in the control and in the perturbed condition and detects the differences in both the mean and the covariance parameters with a series of likelihood ratio tests. The resulting evidence is used to infer the primary and the secondary set, i.e. the genes responsible for the primary dysregulation, and the genes affected by the perturbation through network propagation. The proposed method demonstrates high specificity and sensitivity in different simulated scenarios and on several real biological case studies. In order to fit into the more traditional pathway analysis framework, SourceSet R package also extends the analysis from a single to multiple pathways and provides several graphical outputs, including Cytoscape visualization to browse the results. The rapid increase in omic studies has created a need to understand the biological implications of their results. Gene-set analysis has emerged as a powerful means for gaining such understanding, evolving in the last decade from the classical enrichment analysis to the more powerful topological approaches. Although numerous methods for identifying dysregulated genes have been proposed, few of them aim to distinguish genes that are the real source of perturbation from those that merely respond to the signal dysregulation. This distinction is crucial for network medicine, where the prioritization of the effect of biological perturbations may help in the molecular understanding of drug treatments and diseases. Here we propose a new method, called SourceSet, able to distinguish between primary and secondary dysregulation within a graphical model context, demonstrating a high specificity and sensitivity in different simulated scenarios and on real biological case studies.
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Affiliation(s)
- Elisa Salviato
- IFOM - The FIRC Institute of Molecular Oncology, Milan, Italy
- * E-mail: (ES); (CR)
| | | | - Monica Chiogna
- Department of Statistical Sciences, University of Bologna, Bologna, Italy
| | - Chiara Romualdi
- Department of Biology, University of Padova, Padova, Italy
- * E-mail: (ES); (CR)
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125
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A systems approach to clinical oncology uses deep phenotyping to deliver personalized care. Nat Rev Clin Oncol 2019; 17:183-194. [DOI: 10.1038/s41571-019-0273-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/30/2019] [Indexed: 02/06/2023]
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126
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Abstract
Complex disease such as cancer is often caused by genetic mutations that eventually alter the signal flow in the intra-cellular signaling network and result in different cell fate. Therefore, it is crucial to identify control targets that can most effectively block such unwanted signal flow. For this purpose, systems biological analysis provides a useful framework, but mathematical modeling of complicated signaling networks requires massive time-series measurements of signaling protein activity levels for accurate estimation of kinetic parameter values or regulatory logics. Here, we present a novel method, called SFC (Signal Flow Control), for identifying control targets without the information of kinetic parameter values or regulatory logics. Our method requires only the structural information of a signaling network and is based on the topological estimation of signal flow through the network. SFC will be particularly useful for a large-scale signaling network to which parameter estimation or inference of regulatory logics is no longer applicable in practice. The identified control targets have significant implication in drug development as they can be putative drug targets.
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127
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Busby BP, Niktab E, Roberts CA, Sheridan JP, Coorey NV, Senanayake DS, Connor LM, Munkacsi AB, Atkinson PH. Genetic interaction networks mediate individual statin drug response in Saccharomyces cerevisiae. NPJ Syst Biol Appl 2019; 5:35. [PMID: 31602312 PMCID: PMC6776536 DOI: 10.1038/s41540-019-0112-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 08/20/2019] [Indexed: 01/19/2023] Open
Abstract
Eukaryotic genetic interaction networks (GINs) are extensively described in the Saccharomyces cerevisiae S288C model using deletion libraries, yet being limited to this one genetic background, not informative to individual drug response. Here we created deletion libraries in three additional genetic backgrounds. Statin response was probed with five queries against four genetic backgrounds. The 20 resultant GINs representing drug-gene and gene-gene interactions were not conserved by functional enrichment, hierarchical clustering, and topology-based community partitioning. An unfolded protein response (UPR) community exhibited genetic background variation including different betweenness genes that were network bottlenecks, and we experimentally validated this UPR community via measurements of the UPR that were differentially activated and regulated in statin-resistant strains relative to the statin-sensitive S288C background. These network analyses by topology and function provide insight into the complexity of drug response influenced by genetic background.
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Affiliation(s)
- Bede P. Busby
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
- European Molecular Biology Laboratory, Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Eliatan Niktab
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Christina A. Roberts
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Jeffrey P. Sheridan
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Namal V. Coorey
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Dinindu S. Senanayake
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Lisa M. Connor
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Andrew B. Munkacsi
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
| | - Paul H. Atkinson
- Centre for Biodiscovery, School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand
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128
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Du Z, Wu B, Xia Q, Zhao Y, Lin L, Cai Z, Wang S, Li E, Xu L, Li Y, Xu H, Yin D. LCN2-interacting proteins and their expression patterns in brain tumors. Brain Res 2019; 1720:146304. [PMID: 31233712 DOI: 10.1016/j.brainres.2019.146304] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 06/01/2019] [Accepted: 06/20/2019] [Indexed: 02/08/2023]
Abstract
Lipocalin 2 (LCN2) is a member of the lipocalin family. Elevated expression of LCN2 has been observed in many human tumors, suggesting it might be a potential biomarker and/or therapeutic target in malignancies. In this study, we aimed to explore LCN2 interacting proteins through bioinformatics, as well as their biological functions. Protein-protein interaction networks (PPIN) were constructed using LCN2 and its interacting proteins as the core node. These PPINs were scale free biological networks in which LCN2 and its interacting proteins could connect or cross-talk with at least one partner protein. Both functional and KEGG pathway enrichment analyses identified the known and potential biological functions of the PPIN, such as cell migration and cancer-related pathways. Expression levels of the PPIN proteins, as well as their expression correlations, in five types of brain tumor, were analyzed and integrated into the PPIN to illustrate a dynamic change. A significant correlation was found between the survival time of glioblastoma patients and the expression level of 10 genes (LCN2, MMP9, MMP2, PDE4DIP, L2HGDH, HNRNPA1, DDX31, LOXL2, FAM60A and RNF25). Taken together, our results suggest that LCN2 and its interacting proteins are mostly differentially expressed and have a distinguishing co-expression pattern. They might promote proliferation and migration via cell migration signaling and cancer-related pathways. LCN2 and its interacting proteins might be potential biomarkers in glioblastoma.
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Affiliation(s)
- Zepeng Du
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Genes Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, Guangdong, China; Department of Pathology, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, Guangdong, China
| | - Bingli Wu
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Qiaoxi Xia
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Yan Zhao
- Department of Pathology, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, Guangdong, China
| | - Ling Lin
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Zhixiong Cai
- Department of Cardiology, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, Guangdong, China
| | - Shaohong Wang
- Department of Pathology, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, Guangdong, China
| | - Enmin Li
- Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Liyan Xu
- Institute of Oncologic Pathology, Shantou University Medical College, Shantou 515041, Guangdong, China
| | - Yun Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Genes Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, Guangdong, China
| | - Haixiong Xu
- Department of Neurosurgery, Shantou Central Hospital, Affiliated Shantou Hospital of Sun Yat-sen University, Shantou 515041, Guangdong, China.
| | - Dong Yin
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Genes Regulation, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou 510120, Guangdong, China.
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Li B, Rui J, Ding X, Chen Y, Yang X. Deciphering the multicomponent synergy mechanisms of SiNiSan prescription on irritable bowel syndrome using a bioinformatics/network topology based strategy. PHYTOMEDICINE : INTERNATIONAL JOURNAL OF PHYTOTHERAPY AND PHYTOPHARMACOLOGY 2019; 63:152982. [PMID: 31299593 DOI: 10.1016/j.phymed.2019.152982] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 05/31/2019] [Accepted: 06/03/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND SiNiSan (SNS) is a traditional Chinese medicine (TCM) prescription that has been widely used in the clinical treatment of irritable bowel syndrome (IBS). However, the underlying active substances and molecular mechanisms remain obscure. PURPOSE A bioinformatics/topology based strategy was proposed for identification of the drug targets, therapeutic agents and molecular mechanisms of SiNiSan against irritable bowel syndrome. MATERIALS AND METHODS In this work, a bioinformatics/network topology based strategy was employed by integrating ADME filtering, text mining, bioinformatics, network topology, Venn analysis and molecular docking to uncover systematically the multicomponent synergy mechanisms. In vivo experimental validation was executed in a Visceral Hypersensitivity (VHS) rat model. RESULTS 76 protein targets and 109 active components of SNS were identified. Bioinformatics analysis revealed that 116 disease pathways associated with IBS therapy could be classified into the 19 statistically enriched functional sub-groups. The multi-functional co-synergism of SNS against IBS were predicted, including inflammatory reaction regulation, oxidative-stress depression regulation and hormone and immune regulation. The multi-component synergetic effects were also revealed on the herbal combination of SNS. The hub-bottleneck genes of the protein networks including PTGS2, CALM2, NOS2, SLC6A3 and MAOB, MAOA, CREB1 could become potential drug targets and Paeoniflorin, Naringin, Glycyrrhizic acid may be candidate agents. Experimental results showed that the potential mechanisms of SiNiSan treatment involved in the suppression of activation of Dopaminergic synapse and Amphetamine addiction signaling pathways, which are congruent with the prediction by the systematic approach. CONCLUSION The integrative investigation based on bioinformatics/network topology strategy may elaborate the multicomponent synergy mechanisms of SNS against IBS and provide the way out to develop new combination medicines for IBS.
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Affiliation(s)
- Bangjie Li
- College of Life Sciences, Nanjing Normal University, Nanjing 210023, China
| | - Junqian Rui
- College of Life Sciences, Nanjing Normal University, Nanjing 210023, China
| | - Xuejian Ding
- College of Life Sciences, Nanjing Normal University, Nanjing 210023, China
| | - Yifan Chen
- College of Life Sciences, Nanjing Normal University, Nanjing 210023, China
| | - Xinghao Yang
- College of Life Sciences, Nanjing Normal University, Nanjing 210023, China.
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130
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Tran QH, Vo VT, Hasegawa Y. Scale-variant topological information for characterizing the structure of complex networks. Phys Rev E 2019; 100:032308. [PMID: 31640058 DOI: 10.1103/physreve.100.032308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Indexed: 06/10/2023]
Abstract
The structure of real-world networks is usually difficult to characterize owing to the variation of topological scales, the nondyadic complex interactions, and the fluctuations in the network. We aim to address these problems by introducing a general framework using a method based on topological data analysis. By considering the diffusion process at a single specified timescale in a network, we map the network nodes to a finite set of points that contains the topological information of the network at a single scale. Subsequently, we study the shape of these point sets over variable timescales that provide scale-variant topological information, to understand the varying topological scales and the complex interactions in the network. We conduct experiments on synthetic and real-world data to demonstrate the effectiveness of the proposed framework in identifying network models, classifying real-world networks, and detecting transition points in time-evolving networks. Overall, our study presents a unified analysis that can be applied to more complex network structures, as in the case of multilayer and multiplex networks.
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Affiliation(s)
- Quoc Hoan Tran
- Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
| | - Van Tuan Vo
- Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yoshihiko Hasegawa
- Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
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131
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Li D, Gao J. Towards perturbation prediction of biological networks using deep learning. Sci Rep 2019; 9:11941. [PMID: 31420588 PMCID: PMC6697687 DOI: 10.1038/s41598-019-48391-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Accepted: 07/30/2019] [Indexed: 01/18/2023] Open
Abstract
The mapping of the physical interactions between biochemical entities enables quantitative analysis of dynamic biological living systems. While developing a precise dynamical model on biological entity interaction is still challenging due to the limitation of kinetic parameter detection of the underlying biological system. This challenge promotes the needs of topology-based models to predict biochemical perturbation patterns. Pure topology-based model, however, is limited on the scale and heterogeneity of biological networks. Here we propose a learning based model that adopts graph convolutional networks to learn the implicit perturbation pattern factors and thus enhance the perturbation pattern prediction on the basic topology model. Our experimental studies on 87 biological models show an average of 73% accuracy on perturbation pattern prediction and outperforms the best topology-based model by 7%, indicating that the graph-driven neural network model is robust and beneficial for accurate prediction of the perturbation spread modeling and giving an inspiration of the implementation of the deep neural networks on biological network modeling.
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Affiliation(s)
- Diya Li
- Rensselaer Polytechnic Institute, Department of Computer Science, Troy, 12180, USA
| | - Jianxi Gao
- Rensselaer Polytechnic Institute, Department of Computer Science, Troy, 12180, USA.
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132
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Banerjee S, Walder F, Büchi L, Meyer M, Held AY, Gattinger A, Keller T, Charles R, van der Heijden MGA. Agricultural intensification reduces microbial network complexity and the abundance of keystone taxa in roots. THE ISME JOURNAL 2019; 13:1722-1736. [PMID: 30850707 PMCID: PMC6591126 DOI: 10.1038/s41396-019-0383-2] [Citation(s) in RCA: 389] [Impact Index Per Article: 77.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 02/04/2019] [Accepted: 02/17/2019] [Indexed: 01/28/2023]
Abstract
Root-associated microbes play a key role in plant performance and productivity, making them important players in agroecosystems. So far, very few studies have assessed the impact of different farming systems on the root microbiota and it is still unclear whether agricultural intensification influences the structure and complexity of microbial communities. We investigated the impact of conventional, no-till, and organic farming on wheat root fungal communities using PacBio SMRT sequencing on samples collected from 60 farmlands in Switzerland. Organic farming harbored a much more complex fungal network with significantly higher connectivity than conventional and no-till farming systems. The abundance of keystone taxa was the highest under organic farming where agricultural intensification was the lowest. We also found a strong negative association (R2 = 0.366; P < 0.0001) between agricultural intensification and root fungal network connectivity. The occurrence of keystone taxa was best explained by soil phosphorus levels, bulk density, pH, and mycorrhizal colonization. The majority of keystone taxa are known to form arbuscular mycorrhizal associations with plants and belong to the orders Glomerales, Paraglomerales, and Diversisporales. Supporting this, the abundance of mycorrhizal fungi in roots and soils was also significantly higher under organic farming. To our knowledge, this is the first study to report mycorrhizal keystone taxa for agroecosystems, and we demonstrate that agricultural intensification reduces network complexity and the abundance of keystone taxa in the root microbiome.
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Affiliation(s)
- Samiran Banerjee
- Agroscope, Department of Agroecology & Environment, Reckenholzstrasse 191, 8046, Zürich, Switzerland.
| | - Florian Walder
- Agroscope, Department of Agroecology & Environment, Reckenholzstrasse 191, 8046, Zürich, Switzerland.
| | - Lucie Büchi
- Agroscope, Plant Production Systems, Route de Duillier 50, 1260, Nyon, Switzerland
- Natural Resources Institute, University of Greenwich, London, UK
| | - Marcel Meyer
- Agroscope, Department of Agroecology & Environment, Reckenholzstrasse 191, 8046, Zürich, Switzerland
| | - Alain Y Held
- Agroscope, Department of Agroecology & Environment, Reckenholzstrasse 191, 8046, Zürich, Switzerland
| | - Andreas Gattinger
- Research Institute of Organic Agriculture FiBL, 5070, Frick, Switzerland
- Justus-Liebig University Giessen, Organic Farming with focus on Sustainable Soil Use, Karl-Glöckner-Str. 21C, 35394, Giessen, Germany
| | - Thomas Keller
- Agroscope, Department of Agroecology & Environment, Reckenholzstrasse 191, 8046, Zürich, Switzerland
- Swedish University of Agricultural Sciences, Department of Soil & Environment, Box 7014, 75007, Uppsala, Sweden
| | - Raphael Charles
- Agroscope, Plant Production Systems, Route de Duillier 50, 1260, Nyon, Switzerland
- Research Institute of Organic Agriculture FiBL, Jordils 3, 1001, Lausanne, Switzerland
| | - Marcel G A van der Heijden
- Agroscope, Department of Agroecology & Environment, Reckenholzstrasse 191, 8046, Zürich, Switzerland
- Department of Plant and Microbial Biology, University of Zürich, 8008, Zürich, Switzerland
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133
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The role that choice of model plays in predictions for epilepsy surgery. Sci Rep 2019; 9:7351. [PMID: 31089190 PMCID: PMC6517411 DOI: 10.1038/s41598-019-43871-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 05/02/2019] [Indexed: 12/26/2022] Open
Abstract
Mathematical modelling has been widely used to predict the effects of perturbations to brain networks. An important example is epilepsy surgery, where the perturbation in question is the removal of brain tissue in order to render the patient free of seizures. Different dynamical models have been proposed to represent transitions to ictal states in this context. However, our choice of which mathematical model to use to address this question relies on making assumptions regarding the mechanism that defines the transition from background to the seizure state. Since these mechanisms are unknown, it is important to understand how predictions from alternative dynamical descriptions compare. Herein we evaluate to what extent three different dynamical models provide consistent predictions for the effect of removing nodes from networks. We show that for small, directed, connected networks the three considered models provide consistent predictions. For larger networks, predictions are shown to be less consistent. However consistency is higher in networks that have sufficiently large differences in ictogenicity between nodes. We further demonstrate that heterogeneity in ictogenicity across nodes correlates with variability in the number of connections for each node.
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134
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Lee LYH, Loscalzo J. Network Medicine in Pathobiology. THE AMERICAN JOURNAL OF PATHOLOGY 2019; 189:1311-1326. [PMID: 31014954 DOI: 10.1016/j.ajpath.2019.03.009] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Accepted: 03/05/2019] [Indexed: 12/11/2022]
Abstract
The past decade has witnessed exponential growth in the generation of high-throughput human data across almost all known dimensions of biological systems. The discipline of network medicine has rapidly evolved in parallel, providing an unbiased, comprehensive biological framework through which to interrogate and integrate systematically these large-scale, multi-omic data to enhance our understanding of disease mechanisms and to design drugs that reflect a deep knowledge of molecular pathobiology. In this review, we discuss the key principles of network medicine and the human disease network and explore the latest applications of network medicine in this multi-omic era. We also highlight the current conceptual and technological challenges, which serve as exciting opportunities by which to improve and expand the network-based applications beyond the artificial boundaries of the current state of human pathobiology.
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Affiliation(s)
| | - Joseph Loscalzo
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts.
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135
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Analysis of Topological Parameters of Complex Disease Genes Reveals the Importance of Location in a Biomolecular Network. Genes (Basel) 2019; 10:genes10020143. [PMID: 30769902 PMCID: PMC6409865 DOI: 10.3390/genes10020143] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2019] [Revised: 02/09/2019] [Accepted: 02/11/2019] [Indexed: 12/24/2022] Open
Abstract
Network biology and medicine provide unprecedented opportunities and challenges for deciphering disease mechanisms from integrative viewpoints. The disease genes and their products perform their dysfunctions via physical and biochemical interactions in the form of a molecular network. The topological parameters of these disease genes in the interactome are of prominent interest to the understanding of their functionality from a systematic perspective. In this work, we provide a systems biology analysis of the topological features of complex disease genes in an integrated biomolecular network. Firstly, we identify the characteristics of four network parameters in the ten most frequently studied disease genes and identify several specific patterns of their topologies. Then, we confirm our findings in the other disease genes of three complex disorders (i.e., Alzheimer’s disease, diabetes mellitus, and hepatocellular carcinoma). The results reveal that the disease genes tend to have a higher betweenness centrality, a smaller average shortest path length, and a smaller clustering coefficient when compared to normal genes, whereas they have no significant degree prominence. The features highlight the importance of gene location in the integrated functional linkages.
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136
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Abstract
Networks provide a powerful formalism for modeling complex systems by using a model of pairwise interactions. But much of the structure within these systems involves interactions that take place among more than two nodes at once-for example, communication within a group rather than person to person, collaboration among a team rather than a pair of coauthors, or biological interaction between a set of molecules rather than just two. Such higher-order interactions are ubiquitous, but their empirical study has received limited attention, and little is known about possible organizational principles of such structures. Here we study the temporal evolution of 19 datasets with explicit accounting for higher-order interactions. We show that there is a rich variety of structure in our datasets but datasets from the same system types have consistent patterns of higher-order structure. Furthermore, we find that tie strength and edge density are competing positive indicators of higher-order organization, and these trends are consistent across interactions involving differing numbers of nodes. To systematically further the study of theories for such higher-order structures, we propose higher-order link prediction as a benchmark problem to assess models and algorithms that predict higher-order structure. We find a fundamental difference from traditional pairwise link prediction, with a greater role for local rather than long-range information in predicting the appearance of new interactions.
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