101
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Li Q, Dai W, Liu J, Sang Q, Li YX, Li YY. Gene dysregulation analysis builds a mechanistic signature for prognosis and therapeutic benefit in colorectal cancer. J Mol Cell Biol 2020; 12:881-893. [PMID: 32717065 PMCID: PMC7883816 DOI: 10.1093/jmcb/mjaa041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 06/21/2020] [Accepted: 07/01/2020] [Indexed: 12/09/2022] Open
Abstract
The implementation of cancer precision medicine requires biomarkers or signatures for predicting prognosis and therapeutic benefits. Most of current efforts in this field are paying much more attention to predictive accuracy than to molecular mechanistic interpretability. Mechanism-driven strategy has recently emerged, aiming to build signatures with both predictive power and explanatory power. Driven by this strategy, we developed a robust gene dysregulation analysis framework with machine learning algorithms, which is capable of exploring gene dysregulations underlying carcinogenesis from high-dimensional data with cooperativity and synergy between regulators and several other transcriptional regulation rules taken into consideration. We then applied the framework to a colorectal cancer (CRC) cohort from The Cancer Genome Atlas. The identified CRC-related dysregulations significantly covered known carcinogenic processes and exhibited good prognostic effect. By choosing dysregulations with greedy strategy, we built a four-dysregulation (4-DysReg) signature, which has the capability of predicting prognosis and adjuvant chemotherapy benefit. 4-DysReg has the potential to explain carcinogenesis in terms of dysfunctional transcriptional regulation. These results demonstrate that our gene dysregulation analysis framework could be used to develop predictive signature with mechanistic interpretability for cancer precision medicine, and furthermore, elucidate the mechanisms of carcinogenesis.
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Affiliation(s)
- Quanxue Li
- School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China.,Shanghai Center for Bioinformation Technology, Shanghai 201203, China
| | - Wentao Dai
- Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
| | - Jixiang Liu
- Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
| | - Qingqing Sang
- Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yi-Xue Li
- School of Biotechnology, East China University of Science and Technology, Shanghai 200237, China.,Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,CAS Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
| | - Yuan-Yuan Li
- Shanghai Center for Bioinformation Technology, Shanghai 201203, China.,Shanghai Engineering Research Center of Pharmaceutical Translation and Shanghai Industrial Technology Institute, Shanghai 201203, China
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102
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CODC: a Copula-based model to identify differential coexpression. NPJ Syst Biol Appl 2020; 6:20. [PMID: 32561750 PMCID: PMC7305108 DOI: 10.1038/s41540-020-0137-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 03/18/2020] [Indexed: 11/21/2022] Open
Abstract
Differential coexpression has recently emerged as a new way to establish a fundamental difference in expression pattern among a group of genes between two populations. Earlier methods used some scoring techniques to detect changes in correlation patterns of a gene pair in two conditions. However, modeling differential coexpression by means of finding differences in the dependence structure of the gene pair has hitherto not been carried out. We exploit a copula-based framework to model differential coexpression between gene pairs in two different conditions. The Copula is used to model the dependency between expression profiles of a gene pair. For a gene pair, the distance between two joint distributions produced by copula is served as differential coexpression. We used five pan-cancer TCGA RNA-Seq data to evaluate the model that outperforms the existing state of the art. Moreover, the proposed model can detect a mild change in the coexpression pattern across two conditions. For noisy expression data, the proposed method performs well because of the popular scale-invariant property of copula. In addition, we have identified differentially coexpressed modules by applying hierarchical clustering on the distance matrix. The identified modules are analyzed through Gene Ontology terms and KEGG pathway enrichment analysis.
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103
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Kerr CH, Skinnider MA, Andrews DDT, Madero AM, Chan QWT, Stacey RG, Stoynov N, Jan E, Foster LJ. Dynamic rewiring of the human interactome by interferon signaling. Genome Biol 2020; 21:140. [PMID: 32539747 PMCID: PMC7294662 DOI: 10.1186/s13059-020-02050-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 05/20/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The type I interferon (IFN) response is an ancient pathway that protects cells against viral pathogens by inducing the transcription of hundreds of IFN-stimulated genes. Comprehensive catalogs of IFN-stimulated genes have been established across species and cell types by transcriptomic and biochemical approaches, but their antiviral mechanisms remain incompletely characterized. Here, we apply a combination of quantitative proteomic approaches to describe the effects of IFN signaling on the human proteome, and apply protein correlation profiling to map IFN-induced rearrangements in the human protein-protein interaction network. RESULTS We identify > 26,000 protein interactions in IFN-stimulated and unstimulated cells, many of which involve proteins associated with human disease and are observed exclusively within the IFN-stimulated network. Differential network analysis reveals interaction rewiring across a surprisingly broad spectrum of cellular pathways in the antiviral response. We identify IFN-dependent protein-protein interactions mediating novel regulatory mechanisms at the transcriptional and translational levels, with one such interaction modulating the transcriptional activity of STAT1. Moreover, we reveal IFN-dependent changes in ribosomal composition that act to buffer IFN-stimulated gene protein synthesis. CONCLUSIONS Our map of the IFN interactome provides a global view of the complex cellular networks activated during the antiviral response, placing IFN-stimulated genes in a functional context, and serves as a framework to understand how these networks are dysregulated in autoimmune or inflammatory disease.
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Affiliation(s)
- Craig H Kerr
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
- Current Address: Department of Genetics, Stanford University, Stanford, CA, 94305, USA
| | - Michael A Skinnider
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Daniel D T Andrews
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Angel M Madero
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Queenie W T Chan
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - R Greg Stacey
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Nikolay Stoynov
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - Eric Jan
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada
| | - Leonard J Foster
- Michael Smith Laboratories, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada.
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC, V6T 1Z3, Canada.
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104
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Eckhardt M, Hultquist JF, Kaake RM, Hüttenhain R, Krogan NJ. A systems approach to infectious disease. Nat Rev Genet 2020; 21:339-354. [PMID: 32060427 PMCID: PMC7839161 DOI: 10.1038/s41576-020-0212-5] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/08/2020] [Indexed: 01/01/2023]
Abstract
Ongoing social, political and ecological changes in the 21st century have placed more people at risk of life-threatening acute and chronic infections than ever before. The development of new diagnostic, prophylactic, therapeutic and curative strategies is critical to address this burden but is predicated on a detailed understanding of the immensely complex relationship between pathogens and their hosts. Traditional, reductionist approaches to investigate this dynamic often lack the scale and/or scope to faithfully model the dual and co-dependent nature of this relationship, limiting the success of translational efforts. With recent advances in large-scale, quantitative omics methods as well as in integrative analytical strategies, systems biology approaches for the study of infectious disease are quickly forming a new paradigm for how we understand and model host-pathogen relationships for translational applications. Here, we delineate a framework for a systems biology approach to infectious disease in three parts: discovery - the design, collection and analysis of omics data; representation - the iterative modelling, integration and visualization of complex data sets; and application - the interpretation and hypothesis-based inquiry towards translational outcomes.
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Affiliation(s)
- Manon Eckhardt
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Judd F Hultquist
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- J. David Gladstone Institutes, San Francisco, CA, USA.
- Division of Infectious Diseases, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
| | - Robyn M Kaake
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Ruth Hüttenhain
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA
- J. David Gladstone Institutes, San Francisco, CA, USA
| | - Nevan J Krogan
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA, USA.
- Quantitative Biosciences Institute, University of California, San Francisco, San Francisco, CA, USA.
- J. David Gladstone Institutes, San Francisco, CA, USA.
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105
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Gov E. Co-expressed functional module-related genes in ovarian cancer stem cells represent novel prognostic biomarkers in ovarian cancer. Syst Biol Reprod Med 2020; 66:255-266. [DOI: 10.1080/19396368.2020.1759730] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Esra Gov
- Department of Bioengineering, Faculty of Engineering, Adana Alparslan Türkeş Science and Technology University, Adana, Turkey
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106
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Foroughi Pour A, Pietrzak M, Dalton LA, Rempała GA. High dimensional model representation of log-likelihood ratio: binary classification with expression data. BMC Bioinformatics 2020; 21:156. [PMID: 32334509 PMCID: PMC7183128 DOI: 10.1186/s12859-020-3486-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 04/08/2020] [Indexed: 08/19/2023] Open
Abstract
Background Binary classification rules based on a small-sample of high-dimensional data (for instance, gene expression data) are ubiquitous in modern bioinformatics. Constructing such classifiers is challenging due to (a) the complex nature of underlying biological traits, such as gene interactions, and (b) the need for highly interpretable glass-box models. We use the theory of high dimensional model representation (HDMR) to build interpretable low dimensional approximations of the log-likelihood ratio accounting for the effects of each individual gene as well as gene-gene interactions. We propose two algorithms approximating the second order HDMR expansion, and a hypothesis test based on the HDMR formulation to identify significantly dysregulated pairwise interactions. The theory is seen as flexible and requiring only a mild set of assumptions. Results We apply our approach to gene expression data from both synthetic and real (breast and lung cancer) datasets comparing it also against several popular state-of-the-art methods. The analyses suggest the proposed algorithms can be used to obtain interpretable prediction rules with high prediction accuracies and to successfully extract significantly dysregulated gene-gene interactions from the data. They also compare favorably against their competitors across multiple synthetic data scenarios. Conclusion The proposed HDMR-based approach appears to produce a reliable classifier that additionally allows one to describe how individual genes or gene-gene interactions affect classification decisions. Both real and synthetic data analyses suggest that our methods can be used to identify gene networks with dysregulated pairwise interactions, and are therefore appropriate for differential networks analysis.
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Affiliation(s)
- Ali Foroughi Pour
- Department of Electrical and Computer Engineering, The Ohio State University, 205 Dreese laboratories, 2015 Neil Ave., Columbus, 43210, USA.,Department of Mathematics, The Ohio State University, 100 Math Tower, 31 West 18th Ave., Columbus, 43210, USA
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, 1585 Neil Ave, Columbus, 43210, USA
| | - Lori A Dalton
- Department of Electrical and Computer Engineering, The Ohio State University, 205 Dreese laboratories, 2015 Neil Ave., Columbus, 43210, USA
| | - Grzegorz A Rempała
- Department of Mathematics, The Ohio State University, 100 Math Tower, 31 West 18th Ave., Columbus, 43210, USA. .,College of Public Health, 250 Cunz Hall, 1841 Neil Ave., Columbus, 43210, USA.
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107
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Wekesa JS, Luan Y, Meng J. Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis. J Comput Biol 2020; 28:1-18. [PMID: 32302512 DOI: 10.1089/cmb.2019.0120] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023] Open
Abstract
Proteins are polypeptides essential in biological processes. Protein physical interactions are complemented by other types of functional relationship data including genetic interactions, knowledge about co-expression, and evolutionary pathways. Existing algorithms integrate protein interaction and gene expression data to retrieve context-specific subnetworks composed of genes/proteins with known and unknown functions. However, most protein function prediction algorithms fail to exploit diverse intrinsic information in feature and label spaces. We develop a novel integrative method based on differential Co-expression analysis and Neighbor-voting algorithm for Protein Function Prediction, namely CNPFP. The method integrates heterogeneous data and exploits intrinsic and latent linkages via global iterative approach and genomic features. CNPFP performs three tasks: clustering, differential co-expression analysis, and predicts protein functions. Our aim is to identify yeast cell cycle-specific proteins linked to differentially expressed proteins in the protein-protein interaction network. To capture intrinsic information, CNPFP selects the most relevant feature subset based on global iterative neighbor-voting algorithm. We identify eight condition-specific modules. The most relevant subnetwork has 87 genes highly enriched with cyclin-dependent kinases, a protein kinase relevant for cell cycle regulation. We present comprehensive annotations for 3538 Saccharomyces cerevisiae proteins. Our method achieves an AUROC of 0.9862, accuracy of 0.9710, and F-score of 0.9691. From the results, we can summarize that exploiting intrinsic nature of protein relationships improves the quality of function prediction. Thus, the proposed method is useful in functional genomics studies.
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Affiliation(s)
- Jael Sanyanda Wekesa
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
- School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
| | - Yushi Luan
- School of Life Science and Biotechnology, Dalian University of Technology, Dalian, China
| | - Jun Meng
- School of Computer Science and Technology, Dalian University of Technology, Dalian, China
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108
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Liu W, Gan C, Wang W, Liao L, Li C, Xu L, Li E. Identification of lncRNA-associated differential subnetworks in oesophageal squamous cell carcinoma by differential co-expression analysis. J Cell Mol Med 2020; 24:4804-4818. [PMID: 32164040 PMCID: PMC7176870 DOI: 10.1111/jcmm.15159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2019] [Revised: 02/21/2020] [Accepted: 02/25/2020] [Indexed: 02/06/2023] Open
Abstract
Differential expression analysis has led to the identification of important biomarkers in oesophageal squamous cell carcinoma (ESCC). Despite enormous contributions, it has not harnessed the full potential of gene expression data, such as interactions among genes. Differential co-expression analysis has emerged as an effective tool that complements differential expression analysis to provide better insight of dysregulated mechanisms and indicate key driver genes. Here, we analysed the differential co-expression of lncRNAs and protein-coding genes (PCGs) between normal oesophageal tissue and ESCC tissues, and constructed a lncRNA-PCG differential co-expression network (DCN). DCN was characterized as a scale-free, small-world network with modular organization. Focusing on lncRNAs, a total of 107 differential lncRNA-PCG subnetworks were identified from the DCN by integrating both differential expression and differential co-expression. These differential subnetworks provide a valuable source for revealing lncRNA functions and the associated dysfunctional regulatory networks in ESCC. Their consistent discrimination suggests that they may have important roles in ESCC and could serve as robust subnetwork biomarkers. In addition, two tumour suppressor genes (AL121899.1 and ELMO2), identified in the core modules, were validated by functional experiments. The proposed method can be easily used to investigate differential subnetworks of other molecules in other cancers.
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Affiliation(s)
- Wei Liu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Department of Biochemistry and Molecular BiologyShantou University Medical CollegeShantouChina
- Department of MathematicsHeilongjiang Institute of TechnologyHarbinChina
| | - Cai‐Yan Gan
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Department of Biochemistry and Molecular BiologyShantou University Medical CollegeShantouChina
| | - Wei Wang
- Department of MathematicsHeilongjiang Institute of TechnologyHarbinChina
| | - Lian‐Di Liao
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Institute of Oncologic PathologyShantou University Medical CollegeShantouChina
| | - Chun‐Quan Li
- Department of Medical InformaticsHarbin Medical University‐DaqingDaqingChina
| | - Li‐Yan Xu
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Institute of Oncologic PathologyShantou University Medical CollegeShantouChina
| | - En‐Min Li
- The Key Laboratory of Molecular Biology for High Cancer Incidence Coastal Chaoshan AreaShantou University Medical CollegeShantouChina
- Department of Biochemistry and Molecular BiologyShantou University Medical CollegeShantouChina
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109
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Tiwary BK. Computational medicine: quantitative modeling of complex diseases. Brief Bioinform 2020; 21:429-440. [PMID: 30698665 DOI: 10.1093/bib/bbz005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 12/21/2018] [Accepted: 12/26/2018] [Indexed: 12/18/2022] Open
Abstract
Biological complex systems are composed of numerous components that interact within and across different scales. The ever-increasing generation of high-throughput biomedical data has given us an opportunity to develop a quantitative model of nonlinear biological systems having implications in health and diseases. Multidimensional molecular data can be modeled using various statistical methods at different scales of biological organization, such as genome, transcriptome and proteome. I will discuss recent advances in the application of computational medicine in complex diseases such as network-based studies, genome-scale metabolic modeling, kinetic modeling and support vector machines with specific examples in the field of cancer, psychiatric disorders and type 2 diabetes. The recent advances in translating these computational models in diagnosis and identification of drug targets of complex diseases are discussed, as well as the challenges researchers and clinicians are facing in taking computational medicine from the bench to bedside.
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Affiliation(s)
- Basant K Tiwary
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Pondicherry, India
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110
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Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction. Sci Rep 2020; 10:3612. [PMID: 32107391 PMCID: PMC7046773 DOI: 10.1038/s41598-020-60235-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 11/05/2019] [Indexed: 12/15/2022] Open
Abstract
Methods for phenotype and outcome prediction are largely based on inductive supervised models that use selected biomarkers to make predictions, without explicitly considering the functional relationships between individuals. We introduce a novel network-based approach named Patient-Net (P-Net) in which biomolecular profiles of patients are modeled in a graph-structured space that represents gene expression relationships between patients. Then a kernel-based semi-supervised transductive algorithm is applied to the graph to explore the overall topology of the graph and to predict the phenotype/clinical outcome of patients. Experimental tests involving several publicly available datasets of patients afflicted with pancreatic, breast, colon and colorectal cancer show that our proposed method is competitive with state-of-the-art supervised and semi-supervised predictive systems. Importantly, P-Net also provides interpretable models that can be easily visualized to gain clues about the relationships between patients, and to formulate hypotheses about their stratification.
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111
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Kozell LB, Lockwood D, Darakjian P, Edmunds S, Shepherdson K, Buck KJ, Hitzemann R. RNA-Seq Analysis of Genetic and Transcriptome Network Effects of Dual-Trait Selection for Ethanol Preference and Withdrawal Using SOT and NOT Genetic Models. Alcohol Clin Exp Res 2020; 44:820-830. [PMID: 32090358 PMCID: PMC7169974 DOI: 10.1111/acer.14312] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2019] [Accepted: 02/13/2020] [Indexed: 12/16/2022]
Abstract
BACKGROUND Genetic factors significantly affect alcohol consumption and vulnerability to withdrawal. Furthermore, some genetic models showing predisposition to severe withdrawal are also predisposed to low ethanol (EtOH) consumption and vice versa, even when tested independently in naïve animals. METHODS Beginning with a C57BL/6J × DBA/2J F2 intercross founder population, animals were simultaneously selectively bred for both high alcohol consumption and low acute withdrawal (SOT line), or vice versa (NOT line). Using randomly chosen fourth selected generation (S4) mice (N = 18-22/sex/line), RNA-Seq was employed to assess genome-wide gene expression in ventral striatum. The MegaMUGA array was used to detect genome-wide genotypic differences. Differential gene expression and the weighted gene co-expression network analysis were implemented as described elsewhere (Genes Brain Behav 16, 2017, 462). RESULTS The new selection of the SOT and NOT lines was similar to that reported previously (Alcohol Clin Exp Res 38, 2014, 2915). One thousand eight hundred and sixteen transcripts were detected as differentially expressed between the lines. For genes more highly expressed in the SOT line, there was enrichment in genes associated with cell adhesion, synapse organization, and postsynaptic membrane. The genes with a cell adhesion annotation included 23 protocadherins, Mpdz and Dlg2. Genes with a postsynaptic membrane annotation included Gabrb3, Gphn, Grid1, Grin2b, Grin2c, and Grm3. The genes more highly expressed in the NOT line were enriched in a network module (red) with annotations associated with mitochondrial function. Several of these genes were module hub nodes, and these included Nedd8, Guk1, Elof1, Ndufa8, and Atp6v1f. CONCLUSIONS Marked effects of selection on gene expression were detected. The NOT line was characterized by higher expression of hub nodes associated with mitochondrial function. Genes more highly expressed in the SOT aligned with previous findings, for example, Colville and colleagues (Genes Brain Behav 16, 2017, 462) that both high EtOH preference and consumption are associated with effects on cell adhesion and glutamate synaptic plasticity.
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Affiliation(s)
- Laura B Kozell
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Denesa Lockwood
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Priscila Darakjian
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Stephanie Edmunds
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Karen Shepherdson
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Kari J Buck
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
| | - Robert Hitzemann
- From the, Department of Behavioral Neuroscience, VA Portland Health Care System, Oregon Health & Science University, Portland, Oregon
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112
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Kernel Differential Subgraph Analysis to Reveal the Key Period Affecting Glioblastoma. Biomolecules 2020; 10:biom10020318. [PMID: 32079293 PMCID: PMC7072688 DOI: 10.3390/biom10020318] [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: 10/23/2019] [Revised: 02/05/2020] [Accepted: 02/10/2020] [Indexed: 12/26/2022] Open
Abstract
Glioblastoma (GBM) is a fast-growing type of malignant primary brain tumor. To explore the mechanisms in GBM, complex biological networks are used to reveal crucial changes among different biological states, which reflect on the development of living organisms. It is critical to discover the kernel differential subgraph (KDS) that leads to drastic changes. However, identifying the KDS is similar to the Steiner Tree problem that is an NP-hard problem. In this paper, we developed a criterion to explore the KDS (CKDS), which considered the connectivity and scale of KDS, the topological difference of nodes and function relevance between genes in the KDS. The CKDS algorithm was applied to simulated datasets and three single-cell RNA sequencing (scRNA-seq) datasets including GBM, fetal human cortical neurons (FHCN) and neural differentiation. Then we performed the network topology and functional enrichment analyses on the extracted KDSs. Compared with the state-of-art methods, the CKDS algorithm outperformed on simulated datasets to discover the KDSs. In the GBM and FHCN, seventeen genes (one biomarker, nine regulatory genes, one driver genes, six therapeutic targets) and KEGG pathways in KDSs were strongly supported by literature mining that they were highly interrelated with GBM. Moreover, focused on GBM, there were fifteen genes (including ten regulatory genes, three driver genes, one biomarkers, one therapeutic target) and KEGG pathways found in the KDS of neural differentiation process from activated neural stem cells (aNSC) to neural progenitor cells (NPC), while few genes and no pathway were found in the period from NPC to astrocytes (Ast). These experiments indicated that the process from aNSC to NPC is a key differentiation period affecting the development of GBM. Therefore, the CKDS algorithm provides a unique perspective in identifying cell-type-specific genes and KDSs.
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113
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Basha O, Argov CM, Artzy R, Zoabi Y, Hekselman I, Alfandari L, Chalifa-Caspi V, Yeger-Lotem E. Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes. Bioinformatics 2020; 36:2821-2828. [DOI: 10.1093/bioinformatics/btaa034] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 01/07/2020] [Accepted: 01/16/2020] [Indexed: 01/19/2023] Open
Abstract
Abstract
Motivation
Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking.
Results
Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82–0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases.
Summary
Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact.
Availability and implementation
Datasets are available as part of the Supplementary data.
Supplementary information
Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Omer Basha
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Chanan M Argov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Raviv Artzy
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Yazeed Zoabi
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Liad Alfandari
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
| | - Vered Chalifa-Caspi
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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114
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Abstract
Macromolecular complexes play a key role in cellular function. Predicting the structure and dynamics of these complexes is one of the key challenges in structural biology. Docking applications have traditionally been used to predict pairwise interactions between proteins. However, few methods exist for modeling multi-protein assemblies. Here we present two methods, CombDock and DockStar, that can predict multi-protein assemblies starting from subunit structural models. CombDock can assemble subunits without any assumptions about the pairwise interactions between subunits, while DockStar relies on the interaction graph or, alternatively, a homology model or a cryo-electron microscopy (EM) density map of the entire complex. We demonstrate the two methods using RNA polymerase II with 12 subunits and TRiC/CCT chaperonin with 16 subunits.
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Affiliation(s)
- Dina Schneidman-Duhovny
- School of Computer Science and Engineering and the Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Haim J Wolfson
- Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel.
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115
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Zhou X, Cai X. Inference of differential gene regulatory networks based on gene expression and genetic perturbation data. Bioinformatics 2020; 36:197-204. [PMID: 31263873 PMCID: PMC6956787 DOI: 10.1093/bioinformatics/btz529] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 06/09/2019] [Accepted: 06/28/2019] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. RESULTS In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. AVAILABILITY AND IMPLEMENTATION The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xin Zhou
- Department of Electrical and Computer Engineering, University of Miami, FL 33146, USA
| | - Xiaodong Cai
- Department of Electrical and Computer Engineering, University of Miami, FL 33146, USA
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116
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Synergistic Effect of Network-Based Multicomponent Drugs: An Investigation on the Treatment of Non-Small-Cell Lung Cancer with Compound Liuju Formula. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2019; 2019:9854047. [PMID: 31949474 PMCID: PMC6948348 DOI: 10.1155/2019/9854047] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 09/25/2019] [Accepted: 11/12/2019] [Indexed: 11/22/2022]
Abstract
Lung cancer is the most common cause of cancer death with high morbidity and mortality, which non-small-cell lung cancer (NSCLC) accounting for the majority. Traditional Chinese Medicine (TCM) is effective in the treatment of complex diseases, especially cancer. However, TCM is still in the conceptual stage. The interaction between different components remains unknown due to its multicomponent and multitarget characteristics. In this study, compound Liuju formula was taken as an example to isolate compounds with synergistic biological activity through systems pharmacology strategy. Through pharmacokinetic evaluation, 37 potentially active compounds were screened out. Meanwhile, 116 targets of these compounds were obtained by combing with the target prediction model. Through network analysis, we found that multicomponent drugs can present a synergistic effect through regulating inflammatory signaling pathway, invasion pathway, proliferation, and apoptosis pathway. Finally, it was confirmed that the bioactive compounds of compound Liuju formula have not only a killing effect on NSCLC tumor cells but also a synergistic effect on inhibiting the secretion of correlative inflammatory mediators, including TNF-α and IL-1β. The systems pharmacology method was applied in this study, which provides a new direction for analyzing the mechanism of TCM.
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117
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Lu J, Lu Y, Ding Y, Xiao Q, Liu L, Cai Q, Kong Y, Bai Y, Yu T. DNLC: differential network local consistency analysis. BMC Bioinformatics 2019; 20:489. [PMID: 31874600 PMCID: PMC6929334 DOI: 10.1186/s12859-019-3046-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Accepted: 08/21/2019] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. RESULTS In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. CONCLUSIONS The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.
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Affiliation(s)
- Jianwei Lu
- School of Software Engineering, Tongji University, Shanghai, China
- Institute of Advanced Translational Medicine, Tongji University, Shanghai, China
| | - Yao Lu
- School of Software Engineering, Tongji University, Shanghai, China
| | - Yusheng Ding
- School of Software Engineering, Tongji University, Shanghai, China
| | - Qingyang Xiao
- Department of Environmental Health, Emory University, Atlanta, GA USA
| | - Linqing Liu
- School of Software Engineering, Tongji University, Shanghai, China
| | - Qingpo Cai
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA
| | - Yunchuan Kong
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA
| | - Yun Bai
- Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Georgia Campus, Suwanee, GA USA
| | - Tianwei Yu
- Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA USA
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118
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Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T, Boekelheide K, Wambaugh JF, Jones D, Whelan M, Thomas R, Yauk C, Barton-Maclaren T, Cote I. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch Toxicol 2019; 94:1-58. [DOI: 10.1007/s00204-019-02613-4] [Citation(s) in RCA: 124] [Impact Index Per Article: 24.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 11/05/2019] [Indexed: 12/19/2022]
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119
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Zhang J, Ju S. Identifying genuine protein-protein interactions within communities of gene co-expression networks using a deconvolution method. IET Syst Biol 2019; 13:290-296. [PMID: 31778125 PMCID: PMC8687158 DOI: 10.1049/iet-syb.2019.0060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2019] [Revised: 06/24/2019] [Accepted: 07/09/2019] [Indexed: 11/20/2022] Open
Abstract
Direct relationships between biological molecules connected in a gene co-expression network tend to reflect real biological activities such as gene regulation, protein-protein interactions (PPIs), and metabolisation. As correlation-based networks contain numerous indirect connections, those direct relationships are always 'hidden' in them. Compared with the global network, network communities imply more biological significance on predicting protein function, detecting protein complexes and studying network evolution. Therefore, identifying direct relationships in communities is a pervasive and important topic in the biological sciences. Unfortunately, this field has not been well studied. A major thrust of this study is to apply a deconvolution algorithm on communities stemming from different gene co-expression networks, which are constructed by fixing different thresholds for robustness analysis. Using the fifth Dialogue on Reverse Engineering Assessment and Methods challenge (DREAM5) framework, the authors demonstrate that nearly all new communities extracted from a 'deconvolution filter' contain more genuine PPIs than before deconvolution.
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Affiliation(s)
- Jin Zhang
- School of Information Science and Engineering, University of Jinan, Jinan 250022, People's Republic of China.
| | - Shan Ju
- School of International Trade and Economics, Shandong University of Finance and Economics, Jinan 250014, People's Republic of China
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120
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Bhuva DD, Cursons J, Smyth GK, Davis MJ. Differential co-expression-based detection of conditional relationships in transcriptional data: comparative analysis and application to breast cancer. Genome Biol 2019; 20:236. [PMID: 31727119 PMCID: PMC6857226 DOI: 10.1186/s13059-019-1851-8] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 10/02/2019] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Elucidation of regulatory networks, including identification of regulatory mechanisms specific to a given biological context, is a key aim in systems biology. This has motivated the move from co-expression to differential co-expression analysis and numerous methods have been developed subsequently to address this task; however, evaluation of methods and interpretation of the resulting networks has been hindered by the lack of known context-specific regulatory interactions. RESULTS In this study, we develop a simulator based on dynamical systems modelling capable of simulating differential co-expression patterns. With the simulator and an evaluation framework, we benchmark and characterise the performance of inference methods. Defining three different levels of "true" networks for each simulation, we show that accurate inference of causation is difficult for all methods, compared to inference of associations. We show that a z-score-based method has the best general performance. Further, analysis of simulation parameters reveals five network and simulation properties that explained the performance of methods. The evaluation framework and inference methods used in this study are available in the dcanr R/Bioconductor package. CONCLUSIONS Our analysis of networks inferred from simulated data show that hub nodes are more likely to be differentially regulated targets than transcription factors. Based on this observation, we propose an interpretation of the inferred differential network that can reconstruct a putative causal network.
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Affiliation(s)
- Dharmesh D Bhuva
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Joseph Cursons
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Gordon K Smyth
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia.,School of Mathematics and Statistics, Faculty of Science, University of Melbourne, Melbourne, VIC, 3010, Australia
| | - Melissa J Davis
- Bioinformatics Division, Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, 3052, Australia. .,Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia. .,Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, 3010, Australia.
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121
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Jiang X, Zhang H, Zhang Z, Quan X. Flexible Non-Negative Matrix Factorization to Unravel Disease-Related Genes. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2019; 16:1948-1957. [PMID: 29993985 DOI: 10.1109/tcbb.2018.2823746] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Recently, non-negative matrix factorization (NMF) has been shown to perform well in the analysis of omics data. NMF assumes that the expression level of one gene is a linear additive composition of metagenes. The elements in metagene matrix represent the regulation effects and are restricted to non-negativity. However, according to the real biological meaning, there are two kinds of regulation effects, i.e., up-regulation and down-regulation. Few methods based on NMF have considered this biological meaning. Therefore, we designed a flexible non-negative matrix factorization (FNMF) algorithm by further considering the biological meaning of gene expression data. It allows negative numbers in the metagene matrix, and negative numbers represent down-regulation effects. We separated gene expression data into disease-driven gene expression and background gene expression. Subsequently, we computed disease-driven gene relative expression, and a ranked list of genes was obtained. The top ranked genes are considered to be involved in some disease-related biological processes. Experimental results on two real-world gene expression data demonstrate the feasibility and effectiveness of FNMF. Compared with conventional disease-related gene identification algorithms, FNMF has superior performance in analyzing gene expression data of diseases with complex pathology.
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122
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Gemovic B, Sumonja N, Davidovic R, Perovic V, Veljkovic N. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. Curr Med Chem 2019; 26:3890-3910. [PMID: 29446725 DOI: 10.2174/0929867325666180214113704] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 09/14/2017] [Accepted: 01/29/2018] [Indexed: 01/04/2023]
Abstract
BACKGROUND The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology. OBJECTIVE Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions. METHODS We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions. RESULTS We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs. CONCLUSION PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein-protein complexes for experimental studies.
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Affiliation(s)
- Branislava Gemovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Neven Sumonja
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Radoslav Davidovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Vladimir Perovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
| | - Nevena Veljkovic
- Center for Multidisciplinary Research, Institute of Nuclear Sciences Vinca, University of Belgrade, Belgrade, Serbia
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123
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Gazestani VH, Pramparo T, Nalabolu S, Kellman BP, Murray S, Lopez L, Pierce K, Courchesne E, Lewis NE. A perturbed gene network containing PI3K-AKT, RAS-ERK and WNT-β-catenin pathways in leukocytes is linked to ASD genetics and symptom severity. Nat Neurosci 2019; 22:1624-1634. [PMID: 31551593 PMCID: PMC6764590 DOI: 10.1038/s41593-019-0489-x] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 08/07/2019] [Indexed: 12/14/2022]
Abstract
Hundreds of genes are implicated in autism spectrum disorder (ASD) but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here, we analyzed leukocyte transcriptomics from 1–4 year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes genes that are highly expressed during fetal brain development and which is dysregulated in hiPSC-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS/ERK, PI3K/AKT, and WNT/β-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.
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Affiliation(s)
- Vahid H Gazestani
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA.,Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Novo Nordisk Foundation Center for Biosustainability, University of California San Diego, La Jolla, CA, USA
| | - Tiziano Pramparo
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Srinivasa Nalabolu
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA.,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
| | - Sarah Murray
- Department of Pathology, University of California San Diego, La Jolla, CA, USA
| | - Linda Lopez
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Karen Pierce
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA
| | - Eric Courchesne
- Autism Center of Excellence, Department of Neuroscience, University of California San Diego, La Jolla, CA, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA. .,Novo Nordisk Foundation Center for Biosustainability, University of California San Diego, La Jolla, CA, USA. .,Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA. .,Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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124
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Mitchell HD, Eisfeld AJ, Stratton KG, Heller NC, Bramer LM, Wen J, McDermott JE, Gralinski LE, Sims AC, Le MQ, Baric RS, Kawaoka Y, Waters KM. The Role of EGFR in Influenza Pathogenicity: Multiple Network-Based Approaches to Identify a Key Regulator of Non-lethal Infections. Front Cell Dev Biol 2019; 7:200. [PMID: 31616667 PMCID: PMC6763731 DOI: 10.3389/fcell.2019.00200] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 09/05/2019] [Indexed: 12/14/2022] Open
Abstract
Despite high sequence similarity between pandemic and seasonal influenza viruses, there is extreme variation in host pathogenicity from one viral strain to the next. Identifying the underlying mechanisms of variability in pathogenicity is a critical task for understanding influenza virus infection and effective management of highly pathogenic influenza virus disease. We applied a network-based modeling approach to identify critical functions related to influenza virus pathogenicity using large transcriptomic and proteomic datasets from mice infected with six influenza virus strains or mutants. Our analysis revealed two pathogenicity-related gene expression clusters; these results were corroborated by matching proteomics data. We also identified parallel downstream processes that were altered during influenza pathogenesis. We found that network bottlenecks (nodes that bridge different network regions) were highly enriched in pathogenicity-related genes, while network hubs (highly connected network nodes) were significantly depleted in these genes. We confirmed that this trend persisted in a distinct virus: Severe Acute Respiratory Syndrome Coronavirus (SARS). The role of epidermal growth factor receptor (EGFR) in influenza pathogenesis, one of the bottleneck regulators with corroborating signals across transcript and protein expression data, was tested and validated in additional mouse infection experiments. We demonstrate that EGFR is important during influenza infection, but the role it plays changes for lethal versus non-lethal infections. Our results show that by using association networks, bottleneck genes that lack hub characteristics can be used to predict a gene's involvement in influenza virus pathogenicity. We also demonstrate the utility of employing multiple network approaches for analyzing host response data from viral infections.
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Affiliation(s)
- Hugh D Mitchell
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Amie J Eisfeld
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, United States
| | - Kelly G Stratton
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Natalie C Heller
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Lisa M Bramer
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Ji Wen
- Pacific Northwest National Laboratory, Richland, WA, United States
| | | | - Lisa E Gralinski
- Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Amy C Sims
- Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Mai Q Le
- National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
| | - Ralph S Baric
- Department of Microbiology and Epidemiology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Yoshihiro Kawaoka
- Department of Pathobiological Sciences, University of Wisconsin-Madison, Madison, WI, United States.,Division of Virology, Department of Microbiology and Immunology, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan.,International Research Center for Infectious Diseases, Institute of Medical Sciences, The University of Tokyo, Tokyo, Japan
| | - Katrina M Waters
- Pacific Northwest National Laboratory, Richland, WA, United States
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125
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Ma J, Karnovsky A, Afshinnia F, Wigginton J, Rader DJ, Natarajan L, Sharma K, Porter AC, Rahman M, He J, Hamm L, Shafi T, Gipson D, Gadegbeku C, Feldman H, Michailidis G, Pennathur S. Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease. Bioinformatics 2019; 35:3441-3452. [PMID: 30887029 PMCID: PMC6748777 DOI: 10.1093/bioinformatics/btz114] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 01/31/2019] [Accepted: 02/12/2019] [Indexed: 12/28/2022] Open
Abstract
MOTIVATION Functional enrichment testing methods can reduce data comprising hundreds of altered biomolecules to smaller sets of altered biological 'concepts' that help generate testable hypotheses. This study leveraged differential network enrichment analysis methodology to identify and validate lipid subnetworks that potentially differentiate chronic kidney disease (CKD) by severity or progression. RESULTS We built a partial correlation interaction network, identified highly connected network components, applied network-based gene-set analysis to identify differentially enriched subnetworks, and compared the subnetworks in patients with early-stage versus late-stage CKD. We identified two subnetworks 'triacylglycerols' and 'cardiolipins-phosphatidylethanolamines (CL-PE)' characterized by lower connectivity, and a higher abundance of longer polyunsaturated triacylglycerols in patients with severe CKD (stage ≥4) from the Clinical Phenotyping Resource and Biobank Core. These finding were replicated in an independent cohort, the Chronic Renal Insufficiency Cohort. Using an innovative method for elucidating biological alterations in lipid networks, we demonstrated alterations in triacylglycerols and cardiolipins-phosphatidylethanolamines that precede the clinical outcome of end-stage kidney disease by several years. AVAILABILITY AND IMPLEMENTATION A complete list of NetGSA results in HTML format can be found at http://metscape.ncibi.org/netgsa/12345-022118/cric_cprobe/022118/results_cric_cprobe/main.html. The DNEA is freely available at https://github.com/wiggie/DNEA. Java wrapper leveraging the cytoscape.js framework is available at http://js.cytoscape.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jing Ma
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Alla Karnovsky
- Department of Computational Medicine & Bioinformatics, Ann Arbor, MI, USA
- Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI, USA
| | - Farsad Afshinnia
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Janis Wigginton
- Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI, USA
| | - Daniel J Rader
- Department of Medicine, Translational-Clinical Research, University of Pennsylvania, Philadelphia, PA, USA
| | - Loki Natarajan
- Department of Family Medicine and Public Health, University of California at San Diego, San Diego, CA, USA
| | - Kumar Sharma
- Department of Internal Medicine, University of Texas Health at San Antonio, San Antonio, TX, USA
| | - Anna C Porter
- Department of Internal Medicine, University of Illinois at Chicago, Chicago, IL, USA
| | - Mahboob Rahman
- Department of Internal Medicine, Case-Western Reserve University, Cleveland, OH, USA
| | - Jiang He
- Department of Epidemiology, Tulane University School of Medicine, Tulane University, New Orleans, LA, USA
| | - Lee Hamm
- School of Medicine, Division of Nephrology and Hypertension, Tulane University, New Orleans, LA, USA
| | - Tariq Shafi
- Department of Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Debbie Gipson
- Department of Pediatrics, University of Michigan, Ann Arbor, MI, USA
| | - Crystal Gadegbeku
- Department of Internal Medicine, Temple University, Philadelphia, PA, USA
| | - Harold Feldman
- Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania, Philadelphia, PA, USA
| | - George Michailidis
- Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI, USA
- Department of Statistics and the Informatics Institute, University of Florida, Gainesville, FL, USA
| | - Subramaniam Pennathur
- Michigan Regional Comprehensive Metabolomics Resource Core, Ann Arbor, MI, USA
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, USA
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126
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De Bastiani MA, Klamt F. Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates. Cancer Med 2019; 8:6717-6729. [PMID: 31503425 PMCID: PMC6825976 DOI: 10.1002/cam4.2493] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Revised: 07/18/2019] [Accepted: 07/31/2019] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Lung adenocarcinoma is the major cause of cancer-related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment. METHODS We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case-control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning. RESULTS The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival. CONCLUSION Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.
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Affiliation(s)
- Marco Antônio De Bastiani
- Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.,National Institute of Science and Technology for Translational Medicine (INCT-TM), Porto Alegre, RS, Brazil
| | - Fábio Klamt
- Laboratory of Cellular Biochemistry, Department of Biochemistry, Federal University of Rio Grande do Sul (UFRGS), Porto Alegre, RS, Brazil.,National Institute of Science and Technology for Translational Medicine (INCT-TM), Porto Alegre, RS, Brazil
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127
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Moutaoufik MT, Malty R, Amin S, Zhang Q, Phanse S, Gagarinova A, Zilocchi M, Hoell L, Minic Z, Gagarinova M, Aoki H, Stockwell J, Jessulat M, Goebels F, Broderick K, Scott NE, Vlasblom J, Musso G, Prasad B, Lamantea E, Garavaglia B, Rajput A, Murayama K, Okazaki Y, Foster LJ, Bader GD, Cayabyab FS, Babu M. Rewiring of the Human Mitochondrial Interactome during Neuronal Reprogramming Reveals Regulators of the Respirasome and Neurogenesis. iScience 2019; 19:1114-1132. [PMID: 31536960 PMCID: PMC6831851 DOI: 10.1016/j.isci.2019.08.057] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Revised: 06/28/2019] [Accepted: 08/29/2019] [Indexed: 12/13/2022] Open
Abstract
Mitochondrial protein (MP) assemblies undergo alterations during neurogenesis, a complex process vital in brain homeostasis and disease. Yet which MP assemblies remodel during differentiation remains unclear. Here, using mass spectrometry-based co-fractionation profiles and phosphoproteomics, we generated mitochondrial interaction maps of human pluripotent embryonal carcinoma stem cells and differentiated neuronal-like cells, which presented as two discrete cell populations by single-cell RNA sequencing. The resulting networks, encompassing 6,442 high-quality associations among 600 MPs, revealed widespread changes in mitochondrial interactions and site-specific phosphorylation during neuronal differentiation. By leveraging the networks, we show the orphan C20orf24 as a respirasome assembly factor whose disruption markedly reduces respiratory chain activity in patients deficient in complex IV. We also find that a heme-containing neurotrophic factor, neuron-derived neurotrophic factor [NENF], couples with Parkinson disease-related proteins to promote neurotrophic activity. Our results provide insights into the dynamic reorganization of mitochondrial networks during neuronal differentiation and highlights mechanisms for MPs in respirasome, neuronal function, and mitochondrial diseases. Rewiring of mitochondrial (mt) protein interaction network in distinct cell states Dramatic changes in site-specific phosphorylation during neuronal differentiation C20orf24 is a respirasome assembly factor depleted in patients deficient in CIV NENF binding with DJ-1/PINK1 promotes neurotrophic activity and neuronal survival
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Affiliation(s)
| | - Ramy Malty
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Shahreen Amin
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Qingzhou Zhang
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Sadhna Phanse
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Alla Gagarinova
- Department of Biochemistry, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Mara Zilocchi
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Larissa Hoell
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Zoran Minic
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Maria Gagarinova
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Hiroyuki Aoki
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Jocelyn Stockwell
- Department of Surgery, Neuroscience Research Group, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Matthew Jessulat
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Florian Goebels
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Kirsten Broderick
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Nichollas E Scott
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - James Vlasblom
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada
| | - Gabriel Musso
- Department of Medicine, Harvard Medical School and Cardiovascular Division, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Bhanu Prasad
- Department of Medicine, Regina Qu'Appelle Health Region, Regina, SK S4P 0W5, Canada
| | - Eleonora Lamantea
- Medical Genetics and Neurogenetics Unit, Fondazione IRCCS Instituto Neurologico Carlo Besta, via L. Temolo, 4, 20126 Milan, Italy
| | - Barbara Garavaglia
- Medical Genetics and Neurogenetics Unit, Fondazione IRCCS Instituto Neurologico Carlo Besta, via L. Temolo, 4, 20126 Milan, Italy
| | - Alex Rajput
- Department of Medicine, Division of Neurology, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Kei Murayama
- Department of Metabolism, Chiba Children's Hospital, 579-1 Heta-cho, Midori, Chiba 266-0007, Japan
| | - Yasushi Okazaki
- Graduate School of Medicine, Intractable Disease Research Center, Juntendo University, Hongo 2-1-1, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Leonard J Foster
- Department of Biochemistry and Molecular Biology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Francisco S Cayabyab
- Department of Surgery, Neuroscience Research Group, College of Medicine, University of Saskatchewan, Saskatoon, SK S7N 5E5, Canada
| | - Mohan Babu
- Department of Biochemistry, University of Regina, Regina, SK S4S 0A2, Canada.
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128
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Tang Z, Yu Z, Wang C. A fast iterative algorithm for high-dimensional differential network. Comput Stat 2019. [DOI: 10.1007/s00180-019-00915-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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129
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Hjaltelin JX, Izarzugaza JMG, Jensen LJ, Russo F, Westergaard D, Brunak S. Identification of hyper-rewired genomic stress non-oncogene addiction genes across 15 cancer types. NPJ Syst Biol Appl 2019; 5:27. [PMID: 31396397 PMCID: PMC6685999 DOI: 10.1038/s41540-019-0104-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Accepted: 06/26/2019] [Indexed: 12/24/2022] Open
Abstract
Non-oncogene addiction (NOA) genes are essential for supporting the stress-burdened phenotype of tumours and thus vital for their survival. Although NOA genes are acknowledged to be potential drug targets, there has been no large-scale attempt to identify and characterise them as a group across cancer types. Here we provide the first method for the identification of conditional NOA genes and their rewired neighbours using a systems approach. Using copy number data and expression profiles from The Cancer Genome Atlas (TCGA) we performed comparative analyses between high and low genomic stress tumours for 15 cancer types. We identified 101 condition-specific differential coexpression modules, mapped to a high-confidence human interactome, comprising 133 candidate NOA rewiring hub genes. We observe that most modules lose coexpression in the high-stress state and that activated stress modules and hubs take part in homoeostasis maintenance processes such as chromosome segregation, oxireductase activity, mitotic checkpoint (PLK1 signalling), DNA replication initiation and synaptic signalling. We furthermore show that candidate NOA rewiring hubs are unique for each cancer type, but that their respective rewired neighbour genes largely are shared across cancer types.
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Affiliation(s)
- Jessica Xin Hjaltelin
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Jose M. G. Izarzugaza
- Department of Bio and Health Informatics, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Francesco Russo
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - David Westergaard
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2100 Copenhagen, Denmark
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130
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Hu LZ, Goebels F, Tan JH, Wolf E, Kuzmanov U, Wan C, Phanse S, Xu C, Schertzberg M, Fraser AG, Bader GD, Emili A. EPIC: software toolkit for elution profile-based inference of protein complexes. Nat Methods 2019; 16:737-742. [PMID: 31308550 PMCID: PMC7995176 DOI: 10.1038/s41592-019-0461-4] [Citation(s) in RCA: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 05/15/2019] [Indexed: 11/08/2022]
Abstract
Protein complexes are key macromolecular machines of the cell, but their description remains incomplete. We and others previously reported an experimental strategy for global characterization of native protein assemblies based on chromatographic fractionation of biological extracts coupled to precision mass spectrometry analysis (chromatographic fractionation-mass spectrometry, CF-MS), but the resulting data are challenging to process and interpret. Here, we describe EPIC (elution profile-based inference of complexes), a software toolkit for automated scoring of large-scale CF-MS data to define high-confidence multi-component macromolecules from diverse biological specimens. As a case study, we used EPIC to map the global interactome of Caenorhabditis elegans, defining 612 putative worm protein complexes linked to diverse biological processes. These included novel subunits and assemblies unique to nematodes that we validated using orthogonal methods. The open source EPIC software is freely available as a Jupyter notebook packaged in a Docker container (https://hub.docker.com/r/baderlab/bio-epic/).
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Affiliation(s)
- Lucas ZhongMing Hu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Florian Goebels
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - June H Tan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Eric Wolf
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Uros Kuzmanov
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Cuihong Wan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- School of Life Science, Central China Normal University, Wuhan, China
| | - Sadhna Phanse
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Changjiang Xu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Mike Schertzberg
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
| | - Andrew G Fraser
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Gary D Bader
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
| | - Andrew Emili
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario, Canada.
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada.
- Departments of Biochemistry and Biology, Boston University, Boston, MA, USA.
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131
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Tan A, Huang H, Zhang P, Li S. Network-based cancer precision medicine: A new emerging paradigm. Cancer Lett 2019; 458:39-45. [DOI: 10.1016/j.canlet.2019.05.015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2019] [Revised: 04/29/2019] [Accepted: 05/15/2019] [Indexed: 12/20/2022]
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132
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Abstract
Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.
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Affiliation(s)
- Michael Prummer
- NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland.,Swiss Institute of Bioinformatics, Zurich, Switzerland
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133
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A Statistical Test for Differential Network Analysis Based on Inference of Gaussian Graphical Model. Sci Rep 2019; 9:10863. [PMID: 31350445 PMCID: PMC6659630 DOI: 10.1038/s41598-019-47362-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2018] [Accepted: 07/15/2019] [Indexed: 11/09/2022] Open
Abstract
Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases. Based on the asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional setting, we developed a computationally efficient test for differential network analysis through testing the equality of two precision matrices, which summarize the conditional dependence network structures of the genes. Additionally, we applied a multiple testing procedure to infer the differential network structure with false discovery rate (FDR) control. Through extensive simulation studies with different combinations of parameters including sample size, number of vertices, level of heterogeneity and graph structure, we demonstrated that our method performed much better than the current available methods in terms of accuracy and computational time. In real data analysis on lung adenocarcinoma, we revealed a differential network with 3503 nodes and 2550 edges, which consisted of 50 clusters with an FDR threshold at 0.05. Many of the top gene pairs in the differential network have been reported relevant to human cancers. Our method represents a powerful tool of network analysis for high-dimensional biological data.
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134
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Wu N, Huang J, Zhang XF, Ou-Yang L, He S, Zhu Z, Xie W. Weighted Fused Pathway Graphical Lasso for Joint Estimation of Multiple Gene Networks. Front Genet 2019; 10:623. [PMID: 31396259 PMCID: PMC6662592 DOI: 10.3389/fgene.2019.00623] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 06/13/2019] [Indexed: 01/17/2023] Open
Abstract
Gene regulatory networks (GRNs) are often inferred based on Gaussian graphical models that could identify the conditional dependence among genes by estimating the corresponding precision matrix. Classical Gaussian graphical models are usually designed for single network estimation and ignore existing knowledge such as pathway information. Therefore, they can neither make use of the common information shared by multiple networks, nor can they utilize useful prior information to guide the estimation. In this paper, we propose a new weighted fused pathway graphical lasso (WFPGL) to jointly estimate multiple networks by incorporating prior knowledge derived from known pathways and gene interactions. Based on the assumption that two genes are less likely to be connected if they do not participate together in any pathways, a pathway-based constraint is considered in our model. Moreover, we introduce a weighted fused lasso penalty in our model to take into account prior gene interaction data and common information shared by multiple networks. Our model is optimized based on the alternating direction method of multipliers (ADMM). Experiments on synthetic data demonstrate that our method outperforms other five state-of-the-art graphical models. We then apply our model to two real datasets. Hub genes in our identified state-specific networks show some shared and specific patterns, which indicates the efficiency of our model in revealing the underlying mechanisms of complex diseases.
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Affiliation(s)
- Nuosi Wu
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
| | - Jiang Huang
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Xiao-Fei Zhang
- School of Mathematics and Statistics, Central China Normal University, Wuhan, China
| | - Le Ou-Yang
- Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
- Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, China
| | - Shan He
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Zexuan Zhu
- College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China
| | - Weixin Xie
- College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China
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135
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Bouhaddou M, Eckhardt M, Chi Naing ZZ, Kim M, Ideker T, Krogan NJ. Mapping the protein-protein and genetic interactions of cancer to guide precision medicine. Curr Opin Genet Dev 2019; 54:110-117. [PMID: 31288129 DOI: 10.1016/j.gde.2019.04.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2019] [Revised: 04/06/2019] [Accepted: 04/09/2019] [Indexed: 01/05/2023]
Abstract
Massive efforts to sequence cancer genomes have compiled an impressive catalogue of cancer mutations, revealing the recurrent exploitation of a handful of 'hallmark cancer pathways'. However, unraveling how sets of mutated proteins in these and other pathways hijack pro-proliferative signaling networks and dictate therapeutic responsiveness remains challenging. Here, we show that cancer driver protein-protein interactions are enriched for additional cancer drivers, highlighting the power of physical interaction maps to explain known, as well as uncover new, disease-promoting pathway interrelationships. We hypothesize that by systematically mapping the protein-protein and genetic interactions in cancer-thereby creating Cancer Cell Maps-we will create resources against which to contextualize a patient's mutations into perturbed pathways/complexes and thereby specify a matching targeted therapeutic cocktail.
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Affiliation(s)
- Mehdi Bouhaddou
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States
| | - Manon Eckhardt
- Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States
| | - Zun Zar Chi Naing
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States
| | - Minkyu Kim
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
| | - Trey Ideker
- Department of Medicine, University of California, San Diego, California, United States.
| | - Nevan J Krogan
- Cellular and Molecular Pharmacology, University of California, San Francisco, CA, United States; Gladstone Institute of Data Science and Biotechnology, San Francisco, CA, United States; Quantitative Biosciences Institute, University of California, San Francisco, CA, United States.
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136
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Clarke R, Tyson JJ, Tan M, Baumann WT, Jin L, Xuan J, Wang Y. Systems biology: perspectives on multiscale modeling in research on endocrine-related cancers. Endocr Relat Cancer 2019; 26:R345-R368. [PMID: 30965282 PMCID: PMC7045974 DOI: 10.1530/erc-18-0309] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Accepted: 04/08/2019] [Indexed: 12/12/2022]
Abstract
Drawing on concepts from experimental biology, computer science, informatics, mathematics and statistics, systems biologists integrate data across diverse platforms and scales of time and space to create computational and mathematical models of the integrative, holistic functions of living systems. Endocrine-related cancers are well suited to study from a systems perspective because of the signaling complexities arising from the roles of growth factors, hormones and their receptors as critical regulators of cancer cell biology and from the interactions among cancer cells, normal cells and signaling molecules in the tumor microenvironment. Moreover, growth factors, hormones and their receptors are often effective targets for therapeutic intervention, such as estrogen biosynthesis, estrogen receptors or HER2 in breast cancer and androgen receptors in prostate cancer. Given the complexity underlying the molecular control networks in these cancers, a simple, intuitive understanding of how endocrine-related cancers respond to therapeutic protocols has proved incomplete and unsatisfactory. Systems biology offers an alternative paradigm for understanding these cancers and their treatment. To correctly interpret the results of systems-based studies requires some knowledge of how in silico models are built, and how they are used to describe a system and to predict the effects of perturbations on system function. In this review, we provide a general perspective on the field of cancer systems biology, and we explore some of the advantages, limitations and pitfalls associated with using predictive multiscale modeling to study endocrine-related cancers.
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Affiliation(s)
- Robert Clarke
- Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - John J Tyson
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Ming Tan
- Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - William T Baumann
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | - Lu Jin
- Department of Oncology, Georgetown University Medical Center, Washington, District of Columbia, USA
| | - Jianhua Xuan
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia, USA
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137
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A hypergraph-based method for large-scale dynamic correlation study at the transcriptomic scale. BMC Genomics 2019; 20:397. [PMID: 31117943 PMCID: PMC6530038 DOI: 10.1186/s12864-019-5787-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 05/09/2019] [Indexed: 12/22/2022] Open
Abstract
Background The biological regulatory system is highly dynamic. Correlations between functionally related genes change over different biological conditions, which are often unobserved in the data. At the gene level, the dynamic correlations result in three-way gene interactions involving a pair of genes that change correlation, and a third gene that reflects the underlying cellular conditions. This type of ternary relation can be quantified by the Liquid Association statistic. Studying these three-way interactions at the gene triplet level have revealed important regulatory mechanisms in the biological system. Currently, due to the extremely large amount of possible combinations of triplets within a high-throughput gene expression dataset, no method is available to examine the ternary relationship at the biological system level and formally address the false discovery issue. Results Here we propose a new method, Hypergraph for Dynamic Correlation (HDC), to construct module-level three-way interaction networks. The method is able to present integrative uniform hypergraphs to reflect the global dynamic correlation pattern in the biological system, providing guidance to down-stream gene triplet-level analyses. To validate the method’s ability, we conducted two real data experiments using a melanoma RNA-seq dataset from The Cancer Genome Atlas (TCGA) and a yeast cell cycle dataset. The resulting hypergraphs are clearly biologically plausible, and suggest novel relations relevant to the biological conditions in the data. Conclusions We believe the new approach provides a valuable alternative method to analyze omics data that can extract higher order structures. The software is at https://github.com/yunchuankong/HypergraphDynamicCorrelation. Electronic supplementary material The online version of this article (10.1186/s12864-019-5787-x) contains supplementary material, which is available to authorized users.
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138
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Wang C, Gao F, Giannakis GB, D'Urso G, Cai X. Efficient proximal gradient algorithm for inference of differential gene networks. BMC Bioinformatics 2019; 20:224. [PMID: 31046666 PMCID: PMC6498668 DOI: 10.1186/s12859-019-2749-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2018] [Accepted: 03/18/2019] [Indexed: 02/07/2023] Open
Abstract
Background Gene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes. Results In this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1. Conclusion With its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions. Electronic supplementary material The online version of this article (10.1186/s12859-019-2749-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chen Wang
- Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA
| | - Feng Gao
- Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA
| | - Georgios B Giannakis
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, 55455, MN, USA
| | - Gennaro D'Urso
- Department of Molecular and Cellular Pharmacology, University of Miami, Miami, 33136, FL, USA
| | - Xiaodong Cai
- Department of Electrical and Computer Engineering, University of Miami, 1251 Memorial Drive, Coral Gables, 33146, FL, USA. .,Sylvester Comprehensive Cancer Center, University of Miami, Miami, 33136, FL, USA.
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139
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Grimbs A, Klosik DF, Bornholdt S, Hütt MT. A system-wide network reconstruction of gene regulation and metabolism in Escherichia coli. PLoS Comput Biol 2019; 15:e1006962. [PMID: 31050661 PMCID: PMC6519848 DOI: 10.1371/journal.pcbi.1006962] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2018] [Revised: 05/15/2019] [Accepted: 03/18/2019] [Indexed: 11/19/2022] Open
Abstract
Genome-scale metabolic models have become a fundamental tool for examining metabolic principles. However, metabolism is not solely characterized by the underlying biochemical reactions and catalyzing enzymes, but also affected by regulatory events. Since the pioneering work of Covert and co-workers as well as Shlomi and co-workers it is debated, how regulation and metabolism synergistically characterize a coherent cellular state. The first approaches started from metabolic models, which were extended by the regulation of the encoding genes of the catalyzing enzymes. By now, bioinformatics databases in principle allow addressing the challenge of integrating regulation and metabolism on a system-wide level. Collecting information from several databases we provide a network representation of the integrated gene regulatory and metabolic system for Escherichia coli, including major cellular processes, from metabolic processes via protein modification to a variety of regulatory events. Besides transcriptional regulation, we also take into account regulation of translation, enzyme activities and reactions. Our network model provides novel topological characterizations of system components based on their positions in the network. We show that network characteristics suggest a representation of the integrated system as three network domains (regulatory, metabolic and interface networks) instead of two. This new three-domain representation reveals the structural centrality of components with known high functional relevance. This integrated network can serve as a platform for understanding coherent cellular states as active subnetworks and to elucidate crossover effects between metabolism and gene regulation.
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Affiliation(s)
- Anne Grimbs
- Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany
| | - David F. Klosik
- Institute for Theoretical Physics, University of Bremen, Bremen, Germany
| | - Stefan Bornholdt
- Institute for Theoretical Physics, University of Bremen, Bremen, Germany
| | - Marc-Thorsten Hütt
- Computational Systems Biology, Department of Life Sciences & Chemistry, Jacobs University, Bremen, Germany
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140
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De Ollas C, Morillón R, Fotopoulos V, Puértolas J, Ollitrault P, Gómez-Cadenas A, Arbona V. Facing Climate Change: Biotechnology of Iconic Mediterranean Woody Crops. FRONTIERS IN PLANT SCIENCE 2019; 10:427. [PMID: 31057569 PMCID: PMC6477659 DOI: 10.3389/fpls.2019.00427] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2018] [Accepted: 03/21/2019] [Indexed: 05/03/2023]
Abstract
The Mediterranean basin is especially sensitive to the adverse outcomes of climate change and especially to variations in rainfall patterns and the incidence of extremely high temperatures. These two concurring adverse environmental conditions will surely have a detrimental effect on crop performance and productivity that will be particularly severe on woody crops such as citrus, olive and grapevine that define the backbone of traditional Mediterranean agriculture. These woody species have been traditionally selected for traits such as improved fruit yield and quality or alteration in harvesting periods, leaving out traits related to plant field performance. This is currently a crucial aspect due to the progressive and imminent effects of global climate change. Although complete genome sequence exists for sweet orange (Citrus sinensis) and clementine (Citrus clementina), olive tree (Olea europaea) and grapevine (Vitis vinifera), the development of biotechnological tools to improve stress tolerance still relies on the study of the available genetic resources including interspecific hybrids, naturally occurring (or induced) polyploids and wild relatives under field conditions. To this respect, post-genomic era studies including transcriptomics, metabolomics and proteomics provide a wide and unbiased view of plant physiology and biochemistry under adverse environmental conditions that, along with high-throughput phenotyping, could contribute to the characterization of plant genotypes exhibiting physiological and/or genetic traits that are correlated to abiotic stress tolerance. The ultimate goal of precision agriculture is to improve crop productivity, in terms of yield and quality, making a sustainable use of land and water resources under adverse environmental conditions using all available biotechnological tools and high-throughput phenotyping. This review focuses on the current state-of-the-art of biotechnological tools such as high throughput -omics and phenotyping on grapevine, citrus and olive and their contribution to plant breeding programs.
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Affiliation(s)
- Carlos De Ollas
- Departament de Ciències Agràries i del Medi Natural, Universitat Jaume I, Castellón de la Plana, Spain
| | - Raphaël Morillón
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Petit-Bourg, France
| | - Vasileios Fotopoulos
- Department of Agricultural Sciences, Biotechnology and Food Science, Cyprus University of Technology, Limassol, Cyprus
| | - Jaime Puértolas
- Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom
| | - Patrick Ollitrault
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), San-Giuliano, France
| | - Aurelio Gómez-Cadenas
- Departament de Ciències Agràries i del Medi Natural, Universitat Jaume I, Castellón de la Plana, Spain
| | - Vicent Arbona
- Departament de Ciències Agràries i del Medi Natural, Universitat Jaume I, Castellón de la Plana, Spain
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141
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Sonawane AR, Weiss ST, Glass K, Sharma A. Network Medicine in the Age of Biomedical Big Data. Front Genet 2019; 10:294. [PMID: 31031797 PMCID: PMC6470635 DOI: 10.3389/fgene.2019.00294] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 03/19/2019] [Indexed: 12/13/2022] Open
Abstract
Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.
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Affiliation(s)
- Abhijeet R. Sonawane
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Scott T. Weiss
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Kimberly Glass
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
| | - Amitabh Sharma
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Boston, MA, United States
- Department of Medicine, Harvard Medical School, Boston, MA, United States
- Center for Interdisciplinary Cardiovascular Sciences, Cardiovascular Division, Brigham and Women’s Hospital, Boston, MA, United States
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142
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Liang Y, Kelemen A. Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications. Brief Bioinform 2019; 19:1051-1068. [PMID: 28430854 DOI: 10.1093/bib/bbx036] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2016] [Indexed: 12/23/2022] Open
Abstract
Inferring networks and dynamics of genes, proteins, cells and other biological entities from high-throughput biological omics data is a central and challenging issue in computational and systems biology. This is essential for understanding the complexity of human health, disease susceptibility and pathogenesis for Predictive, Preventive, Personalized and Participatory (P4) system and precision medicine. The delineation of the possible interactions of all genes/proteins in a genome/proteome is a task for which conventional experimental techniques are ill suited. Urgently needed are rapid and inexpensive computational and statistical methods that can identify interacting candidate disease genes or drug targets out of thousands that can be further investigated or validated by experimentations. Moreover, identifying biological dynamic systems, and simultaneously estimating the important kinetic structural and functional parameters, which may not be experimentally accessible could be important directions for drug-disease-gene network studies. In this article, we present an overview and comparison of recent developments of dynamic modeling and network approaches for time-course omics data, and their applications to various biological systems, health conditions and disease statuses. Moreover, various data reduction and analytical schemes ranging from mathematical to computational to statistical methods are compared including their merits, drawbacks and limitations. The most recent software, associated web resources and other potentials for the compared methods are also presented and discussed in detail.
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Affiliation(s)
- Yulan Liang
- Department of Family and Community Health, University of Maryland, Baltimore, MD, USA
| | - Arpad Kelemen
- Department of Family and Community Health, University of Maryland, Baltimore, MD, USA
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143
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Abstract
Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines.
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Affiliation(s)
- Michael Prummer
- NEXUS Personalized Health Technologies, ETH Zurich, Zurich, Switzerland
- Swiss Institute of Bioinformatics, Zurich, Switzerland
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144
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Siahpirani AF, Chasman D, Roy S. Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks. Methods Mol Biol 2019; 1883:161-194. [PMID: 30547400 DOI: 10.1007/978-1-4939-8882-2_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.
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Affiliation(s)
- Alireza Fotuhi Siahpirani
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA.,Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
| | - Deborah Chasman
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
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145
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Dondelinger F, Mukherjee S. Statistical Network Inference for Time-Varying Molecular Data with Dynamic Bayesian Networks. Methods Mol Biol 2019; 1883:25-48. [PMID: 30547395 DOI: 10.1007/978-1-4939-8882-2_2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/24/2023]
Abstract
In this chapter, we review the problem of network inference from time-course data, focusing on a class of graphical models known as dynamic Bayesian networks (DBNs). We discuss the relationship of DBNs to models based on ordinary differential equations, and consider extensions to nonlinear time dynamics. We provide an introduction to time-varying DBN models, which allow for changes to the network structure and parameters over time. We also discuss causal perspectives on network inference, including issues around model semantics that can arise due to missing variables. We present a case study of applying time-varying DBNs to gene expression measurements over the life cycle of Drosophila melanogaster. We finish with a discussion of future perspectives, including possible applications of time-varying network inference to single-cell gene expression data.
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Affiliation(s)
| | - Sach Mukherjee
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
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146
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Erola P, Bonnet E, Michoel T. Learning Differential Module Networks Across Multiple Experimental Conditions. Methods Mol Biol 2019; 1883:303-321. [PMID: 30547406 DOI: 10.1007/978-1-4939-8882-2_13] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.
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Affiliation(s)
- Pau Erola
- Division of Genetics and Genomics, Roslin Institute, University of Edinburgh, Midlothian, Scotland, UK
| | - Eric Bonnet
- Centre National de Recherche en Génomique Humaine, Institut de Biologie François Jacob, Direction de la Recherche Fondamentale, CEA, Evry, France
| | - Tom Michoel
- Division of Genetics and Genomics, The Roslin Institute, University of Edinburgh, Midlothian, Scotland, UK.
- Current Address: Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway.
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147
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Todorov H, Cannoodt R, Saelens W, Saeys Y. Network Inference from Single-Cell Transcriptomic Data. Methods Mol Biol 2019; 1883:235-249. [PMID: 30547403 DOI: 10.1007/978-1-4939-8882-2_10] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Recent technological breakthroughs in single-cell RNA sequencing are revolutionizing modern experimental design in biology. The increasing size of the single-cell expression data from which networks can be inferred allows identifying more complex, non-linear dependencies between genes. Moreover, the inter-cellular variability that is observed in single-cell expression data can be used to infer not only one global network representing all the cells, but also numerous regulatory networks that are more specific to certain conditions. By experimentally perturbing certain genes, the deconvolution of the true contribution of these genes can also be greatly facilitated. In this chapter, we will therefore tackle the advantages of single-cell transcriptomic data and show how new methods exploit this novel data type to enhance the inference of gene regulatory networks.
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Affiliation(s)
- Helena Todorov
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium. .,Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium. .,Centre International de Recherche en Infectiologie, Inserm, U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, École Normale Supérieure de Lyon, Univ Lyon, Lyon, France.
| | - Robrecht Cannoodt
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.,Center for Medical Genetics, Ghent University Hospital, Ghent, Belgium
| | - Wouter Saelens
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium.,Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
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148
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Sondhi A, Shojaie A. The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks. JOURNAL OF MACHINE LEARNING RESEARCH : JMLR 2019; 20:https://jmlr.org/papers/v20/17-601.html. [PMID: 37799538 PMCID: PMC10552884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 10/07/2023]
Abstract
We consider the task of estimating a high-dimensional directed acyclic graph, given observations from a linear structural equation model with arbitrary noise distribution. By exploiting properties of common random graphs, we develop a new algorithm that requires conditioning only on small sets of variables. The proposed algorithm, which is essentially a modified version of the PC-Algorithm, offers significant gains in both computational complexity and estimation accuracy. In particular, it results in more efficient and accurate estimation in large networks containing hub nodes, which are common in biological systems. We prove the consistency of the proposed algorithm, and show that it also requires a less stringent faithfulness assumption than the PC-Algorithm. Simulations in low and high-dimensional settings are used to illustrate these findings. An application to gene expression data suggests that the proposed algorithm can identify a greater number of clinically relevant genes than current methods.
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Affiliation(s)
- Arjun Sondhi
- Department of Biostatistics, University of Washington
| | - Ali Shojaie
- Department of Biostatistics, University of Washington
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149
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Heigwer F, Scheeder C, Miersch T, Schmitt B, Blass C, Pour Jamnani MV, Boutros M. Time-resolved mapping of genetic interactions to model rewiring of signaling pathways. eLife 2018; 7:40174. [PMID: 30592458 PMCID: PMC6319608 DOI: 10.7554/elife.40174] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 11/21/2018] [Indexed: 12/23/2022] Open
Abstract
Context-dependent changes in genetic interactions are an important feature of cellular pathways and their varying responses under different environmental conditions. However, methodological frameworks to investigate the plasticity of genetic interaction networks over time or in response to external stresses are largely lacking. To analyze the plasticity of genetic interactions, we performed a combinatorial RNAi screen in Drosophila cells at multiple time points and after pharmacological inhibition of Ras signaling activity. Using an image-based morphology assay to capture a broad range of phenotypes, we assessed the effect of 12768 pairwise RNAi perturbations in six different conditions. We found that genetic interactions form in different trajectories and developed an algorithm, termed MODIFI, to analyze how genetic interactions rewire over time. Using this framework, we identified more statistically significant interactions compared to end-point assays and further observed several examples of context-dependent crosstalk between signaling pathways such as an interaction between Ras and Rel which is dependent on MEK activity. Editorial note This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
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Affiliation(s)
- Florian Heigwer
- Division Signaling and Functional Genomics, German Cancer Research Center, Heidelberg, Germany.,HBIGS Graduate School, Heidelberg University, Heidelberg, Germany.,Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Christian Scheeder
- Division Signaling and Functional Genomics, German Cancer Research Center, Heidelberg, Germany.,Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.,HBIGS Graduate School, Heidelberg University, Heidelberg, Germany
| | - Thilo Miersch
- Division Signaling and Functional Genomics, German Cancer Research Center, Heidelberg, Germany.,Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Barbara Schmitt
- Division Signaling and Functional Genomics, German Cancer Research Center, Heidelberg, Germany.,Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Claudia Blass
- Division Signaling and Functional Genomics, German Cancer Research Center, Heidelberg, Germany.,Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Mischan Vali Pour Jamnani
- Division Signaling and Functional Genomics, German Cancer Research Center, Heidelberg, Germany.,Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Michael Boutros
- Division Signaling and Functional Genomics, German Cancer Research Center, Heidelberg, Germany.,Department of Cell and Molecular Biology, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
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150
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He Y, Ji J, Xie L, Zhang X, Xue F. A new insight into underlying disease mechanism through semi-parametric latent differential network model. BMC Bioinformatics 2018; 19:493. [PMID: 30591011 PMCID: PMC6309076 DOI: 10.1186/s12859-018-2461-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND In genomic studies, to investigate how the structure of a genetic network differs between two experiment conditions is a very interesting but challenging problem, especially in high-dimensional setting. Existing literatures mostly focus on differential network modelling for continuous data. However, in real application, we may encounter discrete data or mixed data, which urges us to propose a unified differential network modelling for various data types. RESULTS We propose a unified latent Gaussian copula differential network model which provides deeper understanding of the unknown mechanism than that among the observed variables. Adaptive rank-based estimation approaches are proposed with the assumption that the true differential network is sparse. The adaptive estimation approaches do not require precision matrices to be sparse, and thus can allow the individual networks to contain hub nodes. Theoretical analysis shows that the proposed methods achieve the same parametric convergence rate for both the difference of the precision matrices estimation and differential structure recovery, which means that the extra modeling flexibility comes at almost no cost of statistical efficiency. Besides theoretical analysis, thorough numerical simulations are conducted to compare the empirical performance of the proposed methods with some other state-of-the-art methods. The result shows that the proposed methods work quite well for various data types. The proposed method is then applied on gene expression data associated with lung cancer to illustrate its empirical usefulness. CONCLUSIONS The proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended.
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Affiliation(s)
- Yong He
- School of Statistics, Shandong University of Finance and Economics, Jinan, 250014 China
| | - Jiadong Ji
- School of Statistics, Shandong University of Finance and Economics, Jinan, 250014 China
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, 10065 USA
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, 10016 USA
| | - Xinsheng Zhang
- School of Management, Fudan University, Shanghai, 200433 China
| | - Fuzhong Xue
- School of Public Health, Shandong University, Jinan, 250012 China
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