1
|
Defoort J, Van de Peer Y, Vermeirssen V. Function, dynamics and evolution of network motif modules in integrated gene regulatory networks of worm and plant. Nucleic Acids Res 2019; 46:6480-6503. [PMID: 29873777 PMCID: PMC6061849 DOI: 10.1093/nar/gky468] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Accepted: 05/14/2018] [Indexed: 12/29/2022] Open
Abstract
Gene regulatory networks (GRNs) consist of different molecular interactions that closely work together to establish proper gene expression in time and space. Especially in higher eukaryotes, many questions remain on how these interactions collectively coordinate gene regulation. We study high quality GRNs consisting of undirected protein–protein, genetic and homologous interactions, and directed protein–DNA, regulatory and miRNA–mRNA interactions in the worm Caenorhabditis elegans and the plant Arabidopsis thaliana. Our data-integration framework integrates interactions in composite network motifs, clusters these in biologically relevant, higher-order topological network motif modules, overlays these with gene expression profiles and discovers novel connections between modules and regulators. Similar modules exist in the integrated GRNs of worm and plant. We show how experimental or computational methodologies underlying a certain data type impact network topology. Through phylogenetic decomposition, we found that proteins of worm and plant tend to functionally interact with proteins of a similar age, while at the regulatory level TFs favor same age, but also older target genes. Despite some influence of the duplication mode difference, we also observe at the motif and module level for both species a preference for age homogeneity for undirected and age heterogeneity for directed interactions. This leads to a model where novel genes are added together to the GRNs in a specific biological functional context, regulated by one or more TFs that also target older genes in the GRNs. Overall, we detected topological, functional and evolutionary properties of GRNs that are potentially universal in all species.
Collapse
Affiliation(s)
- Jonas Defoort
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.,VIB Center for Plant Systems Biology, 9052 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, 9052 Ghent, Belgium
| | - Yves Van de Peer
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.,VIB Center for Plant Systems Biology, 9052 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, 9052 Ghent, Belgium.,Department of Biochemistry, Genetics and Microbiology, University of Pretoria, Pretoria 0028, South Africa
| | - Vanessa Vermeirssen
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium.,VIB Center for Plant Systems Biology, 9052 Ghent, Belgium.,Bioinformatics Institute Ghent, Ghent University, 9052 Ghent, Belgium
| |
Collapse
|
2
|
Ding D, Shu C, Sun X. Transcriptional regulatory module analysis reveals that bridge proteins reconcile multiple signals in extracellular electron transfer pathways. Proteins 2019; 88:196-205. [PMID: 31344265 DOI: 10.1002/prot.25789] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2018] [Revised: 05/01/2019] [Accepted: 07/06/2019] [Indexed: 01/17/2023]
Abstract
Shewanella oneidensis MR-1 shows remarkable respiratory versatility with a large variety of extracellular electron acceptors (termed extracellular electron transfer, EET). To utilize the various electron acceptors, the bacterium must employ complex regulatory mechanisms to elicit the relevant EET pathways. To investigate the relevant mechanisms, we integrated EET genes and related transcriptional factors (TFs) into transcriptional regulatory modules (TRMs) and showed that many bridge proteins in these modules were signal proteins, which generally contained one or more signal processing domains (eg, GGDEF, EAL, PAS, etc.). Since Shewanella has to respond to diverse environmental conditions despite encoding few EET-relevant TFs, the overabundant signal proteins involved in the TRMs can help decipher the mechanism by which these microbes elicit a wide range of condition-specific responses. By combining proteomic data and protein bioinformatic analysis, we demonstrated that diverse signal proteins reconciled the different EET pathways, and we discussed the functional roles of signal proteins involved in the well-known MtrCAB pathway. Additionally, we showed that the signal proteins SO_2145 and SO_1417 played central roles in triggering EET pathways in anaerobic environments. Taken together, our results suggest that signal proteins have a profound impact on the transcriptional regulation of EET genes and thus have potential applications in microbial fuel cells.
Collapse
Affiliation(s)
- Dewu Ding
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, PR China.,School of Mathematics and Computer Science, Yichun University, Yichun, PR China
| | - Chuanjun Shu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, PR China.,Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, PR China
| | - Xiao Sun
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, PR China
| |
Collapse
|
3
|
Ding D, Sun X. A Comparative Study of Network Motifs in the Integrated Transcriptional Regulation and Protein Interaction Networks of Shewanella. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:163-171. [PMID: 29994366 DOI: 10.1109/tcbb.2018.2804393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The Shewanella species shows a remarkable respiratory versatility with a great variety of extracellular electron acceptors (termed Extracellular Electron Transfer, EET). To explore relevant mechanisms from the network motif view, we constructed the integrated networks that combined transcriptional regulation interactions (TRIs) and protein-protein interactions (PPIs) for 13 Shewanella species, identified and compared the network motifs in these integrated networks. We found that the network motifs were evolutionary conserved in these integrated networks. The functional significance of the highly conserved motifs was discussed, especially the important ones that were potentially involved in the Shewanella EET processes. More importantly, we found that: 1) the motif co-regulated PPI took a role in the "standby mode" of protein utilization, which will be helpful for cells to rapidly response to environmental changes; and 2) the type II cofactors, which involved in the motif TRI interacting with a third protein, mainly carried out a signalling role in Shewanella oneidensis MR-1.
Collapse
|
4
|
Takaguchi T, Yoshida Y. Cycle and flow trusses in directed networks. ROYAL SOCIETY OPEN SCIENCE 2016; 3:160270. [PMID: 28018610 PMCID: PMC5180108 DOI: 10.1098/rsos.160270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 10/31/2016] [Indexed: 06/06/2023]
Abstract
When we represent real-world systems as networks, the directions of links often convey valuable information. Finding module structures that respect link directions is one of the most important tasks for analysing directed networks. Although many notions of a directed module have been proposed, no consensus has been reached. This lack of consensus results partly because there might exist distinct types of modules in a single directed network, whereas most previous studies focused on an independent criterion for modules. To address this issue, we propose a generic notion of the so-called truss structures in directed networks. Our definition of truss is able to extract two distinct types of trusses, named the cycle truss and the flow truss, from a unified framework. By applying the method for finding trusses to empirical networks obtained from a wide range of research fields, we find that most real networks contain both cycle and flow trusses. In addition, the abundance of (and the overlap between) the two types of trusses may be useful to characterize module structures in a wide variety of empirical networks. Our findings shed light on the importance of simultaneously considering different types of modules in directed networks.
Collapse
Affiliation(s)
- Taro Takaguchi
- National Institute of Informatics, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430 Tokyo, Japan
- JST, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430 Tokyo, Japan
| | - Yuichi Yoshida
- National Institute of Informatics, ERATO, Kawarabayashi Large Graph Project, 2-1-2 Hitotsubashi, Chiyoda-ku, 101-8430 Tokyo, Japan
- Preferred Infrastructure, 1-6-1 Otemachi, Chiyoda-ku, 100-0004 Tokyo, Japan
| |
Collapse
|
5
|
Benson AR, Gleich DF, Leskovec J. Higher-order organization of complex networks. Science 2016; 353:163-6. [PMID: 27387949 PMCID: PMC5133458 DOI: 10.1126/science.aad9029] [Citation(s) in RCA: 237] [Impact Index Per Article: 29.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Accepted: 05/18/2016] [Indexed: 12/22/2022]
Abstract
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks--at the level of small network subgraphs--remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.
Collapse
Affiliation(s)
- Austin R Benson
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA
| | - David F Gleich
- Department of Computer Science, Purdue University, West Lafayette, IN 47906, USA
| | - Jure Leskovec
- Computer Science Department, Stanford University, Stanford, CA 94305, USA.
| |
Collapse
|
6
|
Bridging topological and functional information in protein interaction networks by short loops profiling. Sci Rep 2015; 5:8540. [PMID: 25703051 PMCID: PMC5224520 DOI: 10.1038/srep08540] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2014] [Accepted: 01/23/2015] [Indexed: 11/09/2022] Open
Abstract
Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.
Collapse
|
7
|
Abstract
The term “transcriptional network” refers to the mechanism(s) that underlies coordinated expression of genes, typically involving transcription factors (TFs) binding to the promoters of multiple genes, and individual genes controlled by multiple TFs. A multitude of studies in the last two decades have aimed to map and characterize transcriptional networks in the yeast Saccharomyces cerevisiae. We review the methodologies and accomplishments of these studies, as well as challenges we now face. For most yeast TFs, data have been collected on their sequence preferences, in vivo promoter occupancy, and gene expression profiles in deletion mutants. These systematic studies have led to the identification of new regulators of numerous cellular functions and shed light on the overall organization of yeast gene regulation. However, many yeast TFs appear to be inactive under standard laboratory growth conditions, and many of the available data were collected using techniques that have since been improved. Perhaps as a consequence, comprehensive and accurate mapping among TF sequence preferences, promoter binding, and gene expression remains an open challenge. We propose that the time is ripe for renewed systematic efforts toward a complete mapping of yeast transcriptional regulatory mechanisms.
Collapse
|
8
|
Demeyer S, Michoel T, Fostier J, Audenaert P, Pickavet M, Demeester P. The index-based subgraph matching algorithm (ISMA): fast subgraph enumeration in large networks using optimized search trees. PLoS One 2013; 8:e61183. [PMID: 23620730 PMCID: PMC3631255 DOI: 10.1371/journal.pone.0061183] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Accepted: 03/05/2013] [Indexed: 11/28/2022] Open
Abstract
Subgraph matching algorithms are designed to find all instances of predefined subgraphs in a large graph or network and play an important role in the discovery and analysis of so-called network motifs, subgraph patterns which occur more often than expected by chance. We present the index-based subgraph matching algorithm (ISMA), a novel tree-based algorithm. ISMA realizes a speedup compared to existing algorithms by carefully selecting the order in which the nodes of a query subgraph are investigated. In order to achieve this, we developed a number of data structures and maximally exploited symmetry characteristics of the subgraph. We compared ISMA to a naive recursive tree-based algorithm and to a number of well-known subgraph matching algorithms. Our algorithm outperforms the other algorithms, especially on large networks and with large query subgraphs. An implementation of ISMA in Java is freely available at http://sourceforge.net/projects/isma/.
Collapse
Affiliation(s)
- Sofie Demeyer
- Department of Information Technology, Ghent University, Ghent, Belgium.
| | | | | | | | | | | |
Collapse
|
9
|
Shellman ER, Burant CF, Schnell S. Network motifs provide signatures that characterize metabolism. MOLECULAR BIOSYSTEMS 2013; 9:352-60. [PMID: 23287894 PMCID: PMC3619197 DOI: 10.1039/c2mb25346a] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Motifs are repeating patterns that determine the local properties of networks. In this work, we characterized all 3-node motifs using enzyme commission numbers of the International Union of Biochemistry and Molecular Biology to show that motif abundance is related to biochemical function. Further, we present a comparative analysis of motif distributions in the metabolic networks of 21 species across six kingdoms of life. We found the distribution of motif abundances to be similar between species, but unique across cellular organelles. Finally, we show that motifs are able to capture inter-species differences in metabolic networks and that molecular differences between some biological species are reflected by the distribution of motif abundances in metabolic networks.
Collapse
Affiliation(s)
- Erin R. Shellman
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, USA
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, USA
| | - Charles F. Burant
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, USA
| | - Santiago Schnell
- Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, USA
- Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, USA
| |
Collapse
|
10
|
Van Landeghem S, De Bodt S, Drebert ZJ, Inzé D, Van de Peer Y. The potential of text mining in data integration and network biology for plant research: a case study on Arabidopsis. THE PLANT CELL 2013; 25:794-807. [PMID: 23532071 PMCID: PMC3634689 DOI: 10.1105/tpc.112.108753] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2012] [Revised: 02/27/2013] [Accepted: 03/08/2013] [Indexed: 05/21/2023]
Abstract
Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies.
Collapse
Affiliation(s)
- Sofie Van Landeghem
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Stefanie De Bodt
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Zuzanna J. Drebert
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Dirk Inzé
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
| | - Yves Van de Peer
- Department of Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Address correspondence to
| |
Collapse
|
11
|
Michoel T, Nachtergaele B. Alignment and integration of complex networks by hypergraph-based spectral clustering. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2012; 86:056111. [PMID: 23214847 DOI: 10.1103/physreve.86.056111] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2012] [Indexed: 06/01/2023]
Abstract
Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.
Collapse
Affiliation(s)
- Tom Michoel
- Freiburg Institute for Advanced Studies (FRIAS), University of Freiburg, Albertstrasse 19, D-79104 Freiburg, Germany.
| | | |
Collapse
|
12
|
Antiqueira L, Janga SC, Costa LDF. Extensive cross-talk and global regulators identified from an analysis of the integrated transcriptional and signaling network in Escherichia coli. MOLECULAR BIOSYSTEMS 2012; 8:3028-35. [PMID: 22960930 DOI: 10.1039/c2mb25279a] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
To understand the regulatory dynamics of transcription factors (TFs) and their interplay with other cellular components we have integrated transcriptional, protein-protein and the allosteric or equivalent interactions which mediate the physiological activity of TFs in Escherichia coli. To study this integrated network we computed a set of network measurements followed by principal component analysis (PCA), investigated the correlations between network structure and dynamics, and carried out a procedure for motif detection. In particular, we show that outliers identified in the integrated network based on their network properties correspond to previously characterized global transcriptional regulators. Furthermore, outliers are highly and widely expressed across conditions, thus supporting their global nature in controlling many genes in the cell. Motifs revealed that TFs not only interact physically with each other but also obtain feedback from signals delivered by signaling proteins supporting the extensive cross-talk between different types of networks. Our analysis can lead to the development of a general framework for detecting and understanding global regulatory factors in regulatory networks and reinforces the importance of integrating multiple types of interactions in underpinning the interrelationships between them.
Collapse
Affiliation(s)
- Lucas Antiqueira
- Institute of Mathematical and Computer Sciences, University of São Paulo, 13560-970, São Carlos, SP, Brazil.
| | | | | |
Collapse
|
13
|
Joshi A, Beck Y, Michoel T. Post-transcriptional regulatory networks play a key role in noise reduction that is conserved from micro-organisms to mammals. FEBS J 2012; 279:3501-12. [PMID: 22436024 DOI: 10.1111/j.1742-4658.2012.08571.x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
RNA-binding proteins (RBPs) are core regulators of mRNA transcript stability and translation in prokaryotes and eukaryotes alike. Genome-wide studies in yeast have shown intriguing relationships between the expression dynamics of RBPs, the structure of post-transcriptional regulatory networks of RBP-mRNA binding interactions and noise reduction in post-transcriptionally regulated expression profiles. In the present study, we assembled and compared the genomic properties of RBPs and integrated transcriptional and post-transcriptional regulatory networks in four species: Escherichia coli, yeast, mouse and human. We found that RBPs are consistently regulated to have minimal levels of protein noise, that known noise-buffering network motifs are enriched in the integrated networks and that post-transcriptional feedback loops act as regulators of other regulators. These results support a general model where RBPs are the key regulators of stochastic noise-buffering in numerous downstream cellular processes. The currently available datasets do not allow clarification of whether post-transcriptional regulation by RBPs and by noncoding RNAs plays a similar or distinct role, although we found evidence that specific combinations of transcription factors, RBPs and micro-RNAs jointly regulate known disease pathways in humans, suggesting complementarity rather than redundancy between both modes of post-transcriptional regulation.
Collapse
Affiliation(s)
- Anagha Joshi
- Department of Haematology, Cambridge Institute for Medical Research, University of Cambridge, UK
| | | | | |
Collapse
|
14
|
Zou J, Ji P, Zhao YL, Li LL, Wei YQ, Chen YZ, Yang SY. Neighbor communities in drug combination networks characterize synergistic effect. MOLECULAR BIOSYSTEMS 2012; 8:3185-96. [DOI: 10.1039/c2mb25267h] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
|