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Bornhofen E, Fè D, Nagy I, Lenk I, Greve M, Didion T, Jensen CS, Asp T, Janss L. Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data. BMC Genomics 2023; 24:213. [PMID: 37095447 PMCID: PMC10127077 DOI: 10.1186/s12864-023-09292-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/02/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUND Understanding the mechanisms underlining forage production and its biomass nutritive quality at the omics level is crucial for boosting the output of high-quality dry matter per unit of land. Despite the advent of multiple omics integration for the study of biological systems in major crops, investigations on forage species are still scarce. RESULTS Our results identified substantial changes in gene co-expression and metabolite-metabolite network topologies as a result of genetic perturbation by hybridizing L. perenne with another species within the genus (L. multiflorum) relative to across genera (F. pratensis). However, conserved hub genes and hub metabolomic features were detected between pedigree classes, some of which were highly heritable and displayed one or more significant edges with agronomic traits in a weighted omics-phenotype network. In spite of tagging relevant biological molecules as, for example, the light-induced rice 1 (LIR1), hub features were not necessarily better explanatory variables for omics-assisted prediction than features stochastically sampled and all available regressors. CONCLUSIONS The utilization of computational techniques for the reconstruction of co-expression networks facilitates the identification of key omic features that serve as central nodes and demonstrate correlation with the manifestation of observed traits. Our results also indicate a robust association between early multi-omic traits measured in a greenhouse setting and phenotypic traits evaluated under field conditions.
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Affiliation(s)
- Elesandro Bornhofen
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
| | - Dario Fè
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Istvan Nagy
- Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark
| | - Ingo Lenk
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Morten Greve
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | - Thomas Didion
- Research Division, DLF Seeds A/S, Store Heddinge, Denmark
| | | | - Torben Asp
- Center for Quantitative Genetics and Genomics, Aarhus University, Slagelse, Denmark
| | - Luc Janss
- Center for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark.
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Watanabe M, Hoefgen R. Sulphur systems biology-making sense of omics data. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:4155-4170. [PMID: 31404467 PMCID: PMC6698701 DOI: 10.1093/jxb/erz260] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 05/24/2019] [Indexed: 05/22/2023]
Abstract
Systems biology approaches have been applied over the last two decades to study plant sulphur metabolism. These 'sulphur-omics' approaches have been developed in parallel with the advancing field of systems biology, which is characterized by permanent improvements of high-throughput methods to obtain system-wide data. The aim is to obtain a holistic view of sulphur metabolism and to generate models that allow predictions of metabolic and physiological responses. Besides known sulphur-responsive genes derived from previous studies, numerous genes have been identified in transcriptomics studies. This has not only increased our knowledge of sulphur metabolism but has also revealed links between metabolic processes, thus indicating a previously unexpected complex interconnectivity. The identification of response and control networks has been supported through metabolomics and proteomics studies. Due to the complex interlacing nature of biological processes, experimental validation using targeted or systems approaches is ongoing. There is still room for improvement in integrating the findings from studies of metabolomes, proteomes, and metabolic fluxes into a single unifying concept and to generate consistent models. We therefore suggest a joint effort of the sulphur research community to standardize data acquisition. Furthermore, focusing on a few different model plant systems would help overcome the problem of fragmented data, and would allow us to provide a standard data set against which future experiments can be designed and compared.
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Affiliation(s)
- Mutsumi Watanabe
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Nara Institute of Science and Technology, Ikoma, Japan
| | - Rainer Hoefgen
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
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Chudasama D, Bo V, Hall M, Anikin V, Jeyaneethi J, Gregory J, Pados G, Tucker A, Harvey A, Pink R, Karteris E. Identification of cancer biomarkers of prognostic value using specific gene regulatory networks (GRN): a novel role of RAD51AP1 for ovarian and lung cancers. Carcinogenesis 2018; 39:407-417. [PMID: 29126163 PMCID: PMC5862298 DOI: 10.1093/carcin/bgx122] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 11/07/2017] [Indexed: 12/11/2022] Open
Abstract
To date, microarray analyses have led to the discovery of numerous individual ‘molecular signatures’ associated with specific cancers. However, there are serious limitations for the adoption of these multi-gene signatures in the clinical environment for diagnostic or prognostic testing as studies with more power need to be carried out. This may involve larger richer cohorts and more advanced analyses. In this study, we conduct analyses—based on gene regulatory network—to reveal distinct and common biomarkers across cancer types. Using microarray data of triple-negative and medullary breast, ovarian and lung cancers applied to a combination of glasso and Bayesian networks (BNs), we derived a unique network-containing genes that are uniquely involved: small proline-rich protein 1A (SPRR1A), follistatin like 1 (FSTL1), collagen type XII alpha 1 (COL12A1) and RAD51 associated protein 1 (RAD51AP1). RAD51AP1 and FSTL1 are significantly overexpressed in ovarian cancer patients but only RAD51AP1 is upregulated in lung cancer patients compared with healthy controls. The upregulation of RAD51AP1 was mirrored in the bloods of both ovarian and lung cancer patients, and Kaplan–Meier (KM) plots predicted poorer overall survival (OS) in patients with high expression of RAD51AP1. Suppression of RAD51AP1 by RNA interference reduced cell proliferation in vitro in ovarian (SKOV3) and lung (A549) cancer cells. This effect appears to be modulated by a decrease in the expression of mTOR-related genes and pro-metastatic candidate genes. Our data describe how an initial in silico approach can generate novel biomarkers that could potentially support current clinical practice and improve long-term outcomes.
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Affiliation(s)
- Dimple Chudasama
- Institute for Environment, Health and Societies, Brunel University London, Uxbridge, UK
| | - Valeria Bo
- Department of Computer Science, Brunel University London, Uxbridge, UK.,Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | | | - Vladimir Anikin
- Department of Cardiothoracic Surgery, Harefield Hospital, Royal Brompton and Harefield Trust, Harefield, UK
| | - Jeyarooban Jeyaneethi
- Institute for Environment, Health and Societies, Brunel University London, Uxbridge, UK
| | - Jane Gregory
- Department of Cardiothoracic Surgery, Harefield Hospital, Royal Brompton and Harefield Trust, Harefield, UK
| | - George Pados
- University of Thessaloniki Medical School, Thessaloniki, Greece
| | - Allan Tucker
- Department of Computer Science, Brunel University London, Uxbridge, UK
| | - Amanda Harvey
- Institute for Environment, Health and Societies, Brunel University London, Uxbridge, UK
| | - Ryan Pink
- Department of Biological and Medical Sciences, Oxford Brookes University, Oxford, UK
| | - Emmanouil Karteris
- Institute for Environment, Health and Societies, Brunel University London, Uxbridge, UK
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van Dam S, Võsa U, van der Graaf A, Franke L, de Magalhães JP. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 2018; 19:575-592. [PMID: 28077403 PMCID: PMC6054162 DOI: 10.1093/bib/bbw139] [Citation(s) in RCA: 430] [Impact Index Per Article: 71.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2016] [Revised: 12/01/2016] [Indexed: 01/06/2023] Open
Abstract
Gene co-expression networks can be used to associate genes of unknown function with biological processes, to prioritize candidate disease genes or to discern transcriptional regulatory programmes. With recent advances in transcriptomics and next-generation sequencing, co-expression networks constructed from RNA sequencing data also enable the inference of functions and disease associations for non-coding genes and splice variants. Although gene co-expression networks typically do not provide information about causality, emerging methods for differential co-expression analysis are enabling the identification of regulatory genes underlying various phenotypes. Here, we introduce and guide researchers through a (differential) co-expression analysis. We provide an overview of methods and tools used to create and analyse co-expression networks constructed from gene expression data, and we explain how these can be used to identify genes with a regulatory role in disease. Furthermore, we discuss the integration of other data types with co-expression networks and offer future perspectives of co-expression analysis.
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Affiliation(s)
- Sipko van Dam
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
| | - Urmo Võsa
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
| | | | - Lude Franke
- Department of Genetics, UMCG HPC CB50, RB Groningen, Netherlands
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Curtis TY, Bo V, Tucker A, Halford NG. Construction of a network describing asparagine metabolism in plants and its application to the identification of genes affecting asparagine metabolism in wheat under drought and nutritional stress. Food Energy Secur 2018; 7:e00126. [PMID: 29938110 PMCID: PMC5993343 DOI: 10.1002/fes3.126] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 01/04/2018] [Accepted: 01/07/2018] [Indexed: 01/01/2023] Open
Abstract
A detailed network describing asparagine metabolism in plants was constructed using published data from Arabidopsis (Arabidopsis thaliana) maize (Zea mays), wheat (Triticum aestivum), pea (Pisum sativum), soybean (Glycine max), lupin (Lupus albus), and other species, including animals. Asparagine synthesis and degradation is a major part of amino acid and nitrogen metabolism in plants. The complexity of its metabolism, including limiting and regulatory factors, was represented in a logical sequence in a pathway diagram built using yED graph editor software. The network was used with a Unique Network Identification Pipeline in the analysis of data from 18 publicly available transcriptomic data studies. This identified links between genes involved in asparagine metabolism in wheat roots under drought stress, wheat leaves under drought stress, and wheat leaves under conditions of sulfur and nitrogen deficiency. The network represents a powerful aid for interpreting the interactions not only between the genes in the pathway but also among enzymes, metabolites and smaller molecules. It provides a concise, clear understanding of the complexity of asparagine metabolism that could aid the interpretation of data relating to wider amino acid metabolism and other metabolic processes.
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Affiliation(s)
- Tanya Y Curtis
- Plant Sciences Department Rothamsted Research Harpenden Hertfordshire UK
| | - Valeria Bo
- College of Engineering, Design and Physical Sciences Brunel University London Uxbridge Middlesex UK.,Present address: Cancer Research UK Cambridge Institute University of Cambridge Li Ka Shing Centre Robinson Way Cambridge UK
| | - Allan Tucker
- College of Engineering, Design and Physical Sciences Brunel University London Uxbridge Middlesex UK
| | - Nigel G Halford
- Plant Sciences Department Rothamsted Research Harpenden Hertfordshire UK
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Henriques R, Madeira SC. BicNET: Flexible module discovery in large-scale biological networks using biclustering. Algorithms Mol Biol 2016; 11:14. [PMID: 27213009 PMCID: PMC4875761 DOI: 10.1186/s13015-016-0074-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 04/22/2016] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Despite the recognized importance of module discovery in biological networks to enhance our understanding of complex biological systems, existing methods generally suffer from two major drawbacks. First, there is a focus on modules where biological entities are strongly connected, leading to the discovery of trivial/well-known modules and to the inaccurate exclusion of biological entities with subtler yet relevant roles. Second, there is a generalized intolerance towards different forms of noise, including uncertainty associated with less-studied biological entities (in the context of literature-driven networks) and experimental noise (in the context of data-driven networks). Although state-of-the-art biclustering algorithms are able to discover modules with varying coherency and robustness to noise, their application for the discovery of non-dense modules in biological networks has been poorly explored and it is further challenged by efficiency bottlenecks. METHODS This work proposes Biclustering NETworks (BicNET), a biclustering algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. Three major contributions are provided. First, we motivate the relevance of discovering network modules given by constant, symmetric, plaid and order-preserving biclustering models. Second, we propose an algorithm to discover these modules and to robustly handle noisy and missing interactions. Finally, we provide new searches to tackle time and memory bottlenecks by effectively exploring the inherent structural sparsity of network data. RESULTS Results in synthetic network data confirm the soundness, efficiency and superiority of BicNET. The application of BicNET on protein interaction and gene interaction networks from yeast, E. coli and Human reveals new modules with heightened biological significance. CONCLUSIONS BicNET is, to our knowledge, the first method enabling the efficient unsupervised analysis of large-scale network data for the discovery of coherent modules with parameterizable homogeneity.
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Affiliation(s)
- Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Sara C. Madeira
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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Bo V, Tucker A. Integrating Gene Regulatory Networks to identify cancer-specific genes. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2015; 2015:21-5. [PMID: 26306224 PMCID: PMC4525222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Consensus approaches have been widely used to identify Gene Regulatory Networks (GRNs) that are common to multiple studies. However, in this research we develop an application that semi-automatically identifies key mechanisms that are specific to a particular set of conditions. We analyse four different types of cancer to identify gene pathways unique to each of them. To support the results reliability we calculate the prediction accuracy of each gene for the specified conditions and compare to predictions on other conditions. The most predictive are validated using the GeneCards encyclopaedia1 coupled with a statistical test for validating clusters. Finally, we implement an interface that allows the user to identify unique subnetworks of any selected combination of studies using AND & NOT logic operators. Results show that unique genes and sub-networks can be reliably identified and that they reflect key mechanisms that are fundamental to the cancer types under study.
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Affiliation(s)
- Valeria Bo
- Department of Computer Science, Brunel University, London, UK
| | - Allan Tucker
- Department of Computer Science, Brunel University, London, UK
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