1
|
Al Musawi AF, Roy S, Ghosh P. Examining indicators of complex network vulnerability across diverse attack scenarios. Sci Rep 2023; 13:18208. [PMID: 37875564 PMCID: PMC10598276 DOI: 10.1038/s41598-023-45218-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 10/17/2023] [Indexed: 10/26/2023] Open
Abstract
Complex networks capture the structure, dynamics, and relationships among entities in real-world networked systems, encompassing domains like communications, society, chemistry, biology, ecology, politics, etc. Analysis of complex networks lends insight into the critical nodes, key pathways, and potential points of failure that may impact the connectivity and operational integrity of the underlying system. In this work, we investigate the topological properties or indicators, such as shortest path length, modularity, efficiency, graph density, diameter, assortativity, and clustering coefficient, that determine the vulnerability to (or robustness against) diverse attack scenarios. Specifically, we examine how node- and link-based network growth or depletion based on specific attack criteria affect their robustness gauged in terms of the largest connected component (LCC) size and diameter. We employ partial least squares discriminant analysis to quantify the individual contribution of the indicators on LCC preservation while accounting for the collinearity stemming from the possible correlation between indicators. Our analysis of 14 complex network datasets and 5 attack models invariably reveals high modularity and disassortativity to be prime indicators of vulnerability, corroborating prior works that report disassortative modular networks to be particularly susceptible to targeted attacks. We conclude with a discussion as well as an illustrative example of the application of this work in fending off strategic attacks on critical infrastructures through models that adaptively and distributively achieve network robustness.
Collapse
Affiliation(s)
- Ahmad F Al Musawi
- Department of Information Technology, University of Thi Qar, Thi Qar, Iraq.
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA.
| | - Satyaki Roy
- Department of Mathematical Sciences, The University of Alabama in Huntsville, Huntsville, AL, USA
| | - Preetam Ghosh
- Department of Computer Science, Virginia Commonwealth University, Richmond, VA, USA
| |
Collapse
|
2
|
Chagas MDS, Medeiros F, dos Santos MT, de Menezes MA, Carvalho-Assef APD, da Silva FAB. An updated gene regulatory network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851. Mem Inst Oswaldo Cruz 2022; 117:e220111. [PMID: 36259790 PMCID: PMC9565603 DOI: 10.1590/0074-02760220111] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 09/09/2022] [Indexed: 11/22/2022] Open
Abstract
BACKGROUND Healthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behaviour and provide novel ways to identify potential therapeutic targets and develop new drugs. Gene regulatory networks (GRN) are examples of in silico representation of interaction between regulatory genes and their targets. OBJECTIVES In this work, we update the MDR P. aeruginosa CCBH4851 GRN reconstruction and analyse and discuss its structural properties. METHODS We based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGS Our result is a GRN with a greater number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONS Here, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.
Collapse
Affiliation(s)
- Márcia da Silva Chagas
- Fundação Oswaldo Cruz-Fiocruz, Programa de Computação Científica, Rio de Janeiro, RJ, Brasil,+ Corresponding authors: /
| | - Fernando Medeiros
- Fundação Oswaldo Cruz-Fiocruz, Instituto Nacional de Infectologia, Laboratório de Pesquisa Clínica em Doenças Febris Agudas, Rio de Janeiro, RJ, Brasil
| | | | | | | | | |
Collapse
|
3
|
Chen G, Liu ZP. Inferring causal gene regulatory network via GreyNet: From dynamic grey association to causation. Front Bioeng Biotechnol 2022; 10:954610. [PMID: 36237217 PMCID: PMC9551017 DOI: 10.3389/fbioe.2022.954610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 08/15/2022] [Indexed: 11/23/2022] Open
Abstract
Gene regulatory network (GRN) provides abundant information on gene interactions, which contributes to demonstrating pathology, predicting clinical outcomes, and identifying drug targets. Existing high-throughput experiments provide rich time-series gene expression data to reconstruct the GRN to further gain insights into the mechanism of organisms responding to external stimuli. Numerous machine-learning methods have been proposed to infer gene regulatory networks. Nevertheless, machine learning, especially deep learning, is generally a “black box,” which lacks interpretability. The causality has not been well recognized in GRN inference procedures. In this article, we introduce grey theory integrated with the adaptive sliding window technique to flexibly capture instant gene–gene interactions in the uncertain regulatory system. Then, we incorporate generalized multivariate Granger causality regression methods to transform the dynamic grey association into causation to generate directional regulatory links. We evaluate our model on the DREAM4 in silico benchmark dataset and real-world hepatocellular carcinoma (HCC) time-series data. We achieved competitive results on the DREAM4 compared with other state-of-the-art algorithms and gained meaningful GRN structure on HCC data respectively.
Collapse
Affiliation(s)
- Guangyi Chen
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
| | - Zhi-Ping Liu
- Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan, Shandong, China
- Center for Intelligent Medicine, Shandong University, Jinan, Shandong, China
- *Correspondence: Zhi-Ping Liu,
| |
Collapse
|
4
|
Zhivkoplias EK, Vavulov O, Hillerton T, Sonnhammer ELL. Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops. Front Genet 2022; 13:815692. [PMID: 35222536 PMCID: PMC8872634 DOI: 10.3389/fgene.2022.815692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/13/2022] [Indexed: 11/13/2022] Open
Abstract
The regulatory relationships between genes and proteins in a cell form a gene regulatory network (GRN) that controls the cellular response to changes in the environment. A number of inference methods to reverse engineer the original GRN from large-scale expression data have recently been developed. However, the absence of ground-truth GRNs when evaluating the performance makes realistic simulations of GRNs necessary. One aspect of this is that local network motif analysis of real GRNs indicates that the feed-forward loop (FFL) is significantly enriched. To simulate this properly, we developed a novel motif-based preferential attachment algorithm, FFLatt, which outperformed the popular GeneNetWeaver network generation tool in reproducing the FFL motif occurrence observed in literature-based biological GRNs. It also preserves important topological properties such as scale-free topology, sparsity, and average in/out-degree per node. We conclude that FFLatt is well-suited as a network generation module for a benchmarking framework with the aim to provide fair and robust performance evaluation of GRN inference methods.
Collapse
Affiliation(s)
- Erik K. Zhivkoplias
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden
| | - Oleg Vavulov
- Bioinformatics Institute, St. Petersburg, Russia
| | - Thomas Hillerton
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden
| | - Erik L. L. Sonnhammer
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden
- *Correspondence: Erik L. L. Sonnhammer,
| |
Collapse
|
5
|
Transcription factors linked to the molecular signatures in the development of hepatocellular carcinoma on a cirrhotic background. Med Oncol 2021; 38:121. [PMID: 34468893 DOI: 10.1007/s12032-021-01567-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 08/19/2021] [Indexed: 02/06/2023]
Abstract
Mechanisms underlying the regulation of gene expression in cancer have been surveyed for decades to find novel prognostic factors and new targets for molecular targeted therapies in cancer. Because most cases of liver cancer are associated with liver cirrhosis, we aimed to analyze the gene expression signatures and the gene regulatory mechanism in hepatocellular carcinoma (HCC) on a cirrhotic background using high-throughput data analysis. In the present study, three valid array-based datasets containing HCC and liver cirrhosis samples were obtained to identify common differentially expressed genes (DEGs). Moreover, a comprehensive data analysis was conducted based on RNA-Seq data and using Kaplan-Meier curve analysis to find molecular signatures that reduce patients' survival rate. Furthermore, we proposed a gene regulatory network (GRN) to explore the possible regulatory mechanism of these molecular signatures by transcription factors in HCC progression from cirrhosis. Besides, we analyzed protein-protein interactions, gene ontology (GO), and pathway enrichment to elucidate the cellular and molecular function of the GRN elements in HCC. In this way, we found a list of 231 molecular signatures in HCC derived from cirrhosis. We also found the importance of TCF4, RUNX1, HINFP, KDM2B, MAF, JUN, NR5A2, NFYA, and AR as key differentially expressed transcription factors (DETFs) in the progression of HCC from cirrhosis. In conclusion, the identified molecular signatures and their transcription factors propose candidate prognostic markers and possible molecular targets in the progression of HCC.
Collapse
|
6
|
Motifs enable communication efficiency and fault-tolerance in transcriptional networks. Sci Rep 2020; 10:9628. [PMID: 32541819 PMCID: PMC7296022 DOI: 10.1038/s41598-020-66573-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 05/22/2020] [Indexed: 11/23/2022] Open
Abstract
Analysis of the topology of transcriptional regulatory networks (TRNs) is an effective way to study the regulatory interactions between the transcription factors (TFs) and the target genes. TRNs are characterized by the abundance of motifs such as feed forward loops (FFLs), which contribute to their structural and functional properties. In this paper, we focus on the role of motifs (specifically, FFLs) in signal propagation in TRNs and the organization of the TRN topology with FFLs as building blocks. To this end, we classify nodes participating in FFLs (termed motif central nodes) into three distinct roles (namely, roles A, B and C), and contrast them with TRN nodes having high connectivity on the basis of their potential for information dissemination, using metrics such as network efficiency, path enumeration, epidemic models and standard graph centrality measures. We also present the notion of a three tier architecture and how it can help study the structural properties of TRN based on connectivity and clustering tendency of motif central nodes. Finally, we motivate the potential implication of the structural properties of motif centrality in design of efficient protocols of information routing in communication networks as well as their functional properties in global regulation and stress response to study specific disease conditions and identification of drug targets.
Collapse
|
7
|
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
|
8
|
Latorre M, Burkhead JL, Hodar C, Arredondo M, González M, Araya M. Chronic copper treatment prevents the liver critical balance transcription response induced by acetaminophen. J Trace Elem Med Biol 2019; 53:113-119. [PMID: 30910193 DOI: 10.1016/j.jtemb.2019.02.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 01/31/2019] [Accepted: 02/17/2019] [Indexed: 02/01/2023]
Abstract
The independent toxic effects of copper and acetaminophen are among the most studied topics in liver toxicity. Here, in an animal model of Cebus capucinus chronically exposed to high dietary copper, we assessed clinical and global transcriptional adaptations of the liver induced by a single high dose of acetaminophen. The experiment conditions were chosen to resemble a close to human real-life situation of exposure to both toxic stimuli. The clinical parameters and histological analyses indicated that chronic copper administration does not induce liver damage and may have a protective effect in acetaminophen challenge. Acetaminophen administration in previously non-exposed animals induced down-regulation of a complex network of gene regulators, highlighting the putative participation of the families of gene regulators HNF, FOX, PPAR and NRF controlling this process. This gene response was not observed in animals that previously received chronic oral copper, suggesting that this metal induces a transcriptional adaptation that may protect against acetaminophen toxicity, a classical adaptation response termed preconditioning of the liver.
Collapse
Affiliation(s)
- Mauricio Latorre
- Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, El Líbano 5524, Macul, Santiago, Chile; Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile; Mathomics, Center for Mathematical Modeling, Universidad de Chile, Beauchef 851, 7th Floor, Santiago, Chile; Instituto de Ciencias de la Ingeniería, Universidad de O'Higgins, Av. Viel 1497, Rancagua, Chile.
| | - Jason L Burkhead
- Department of Biological Sciences Anchorage, University of Alaska Anchorage, Anchorage, Alaska, United States
| | - Christian Hodar
- Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, El Líbano 5524, Macul, Santiago, Chile; Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Miguel Arredondo
- Micronutrients Laboratory, INTA, Universidad de Chile, El Líbano 5524, Macul, Santiago, Chile
| | - Mauricio González
- Laboratorio de Bioinformática y Expresión Génica, INTA, Universidad de Chile, El Líbano 5524, Macul, Santiago, Chile; Center for Genome Regulation (Fondap 15090007), Universidad de Chile, Blanco Encalada 2085, Santiago, Chile
| | - Magdalena Araya
- Gastroenterología y Nutrición, INTA, Universidad de Chile, El Líbano 5524, Macul, Santiago, Chile.
| |
Collapse
|