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Lewitus E, Aristide L, Morlon H. Characterizing and Comparing Phylogenetic Trait Data from Their Normalized Laplacian Spectrum. Syst Biol 2020; 69:234-248. [PMID: 31529071 DOI: 10.1093/sysbio/syz061] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Revised: 09/02/2019] [Accepted: 09/10/2019] [Indexed: 11/13/2022] Open
Abstract
The dissection of the mode and tempo of phenotypic evolution is integral to our understanding of global biodiversity. Our ability to infer patterns of phenotypes across phylogenetic clades is essential to how we infer the macroevolutionary processes governing those patterns. Many methods are already available for fitting models of phenotypic evolution to data. However, there is currently no comprehensive nonparametric framework for characterizing and comparing patterns of phenotypic evolution. Here, we build on a recently introduced approach for using the phylogenetic spectral density profile (SDP) to compare and characterize patterns of phylogenetic diversification, in order to provide a framework for nonparametric analysis of phylogenetic trait data. We show how to construct the SDP of trait data on a phylogenetic tree from the normalized graph Laplacian. We demonstrate on simulated data the utility of the SDP to successfully cluster phylogenetic trait data into meaningful groups and to characterize the phenotypic patterning within those groups. We furthermore demonstrate how the SDP is a powerful tool for visualizing phenotypic space across traits and for assessing whether distinct trait evolution models are distinguishable on a given empirical phylogeny. We illustrate the approach in two empirical data sets: a comprehensive data set of traits involved in song, plumage, and resource-use in tanagers, and a high-dimensional data set of endocranial landmarks in New World monkeys. Considering the proliferation of morphometric and molecular data collected across the tree of life, we expect this approach will benefit big data analyses requiring a comprehensive and intuitive framework.
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Affiliation(s)
- Eric Lewitus
- Ecole Normale Superieure Paris Sciences et Lettres (PSL) Research University, Institut de Biologie de l'Ecole Normale Superieure (IBENS) CNRS UMR 8197 INSERM U1024 46rue d'Ulm,F-75005, Paris, France.,Henry M. Jackson Foundation in support of the US Military HIV Research Program, Walter Reed Army Institute of Research, Silver Spring, MD 20910, USA
| | - Leandro Aristide
- Ecole Normale Superieure Paris Sciences et Lettres (PSL) Research University, Institut de Biologie de l'Ecole Normale Superieure (IBENS) CNRS UMR 8197 INSERM U1024 46rue d'Ulm,F-75005, Paris, France
| | - Hélène Morlon
- Ecole Normale Superieure Paris Sciences et Lettres (PSL) Research University, Institut de Biologie de l'Ecole Normale Superieure (IBENS) CNRS UMR 8197 INSERM U1024 46rue d'Ulm,F-75005, Paris, France
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Gamermann D, Montagud A, Conejero JA, Fernández de Córdoba P, Urchueguía JF. Large scale evaluation of differences between network-based and pairwise sequence-alignment-based methods of dendrogram reconstruction. PLoS One 2019; 14:e0221631. [PMID: 31487289 PMCID: PMC6728023 DOI: 10.1371/journal.pone.0221631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Accepted: 08/12/2019] [Indexed: 11/19/2022] Open
Abstract
Dendrograms are a way to represent relationships between organisms. Nowadays, these are inferred based on the comparison of genes or protein sequences by taking into account their differences and similarities. The genetic material of choice for the sequence alignments (all the genes or sets of genes) results in distinct inferred dendrograms. In this work, we evaluate differences between dendrograms reconstructed with different methodologies and for different sets of organisms chosen at random from a much larger set. A statistical analysis is performed to estimate fluctuations between the results obtained from the different methodologies that allows us to validate a systematic approach, based on the comparison of the organisms' metabolic networks for inferring dendrograms. This has the advantage that it allows the comparison of organisms very far away in the evolutionary tree even if they have no known ortholog gene in common. Our results show that dendrograms built using information from metabolic networks are similar to the standard sequence-based dendrograms and can be a complement to them.
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Affiliation(s)
- Daniel Gamermann
- Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS), Av. Bento Gonçalves 9500, CP 15051, 91501-970 Porto Alegre RS, Brazil
| | - Arnau Montagud
- Institut Curie, PSL Research University, INSERM, U900, 26 rue d’Ulm, F-75005, Paris, France
- Barcelona Supercomputing Centre (BSC), C/ Jordi Girona 29, E-08034, Barcelona, Spain
| | - J. Alberto Conejero
- Instituto Universitario de Matemática Pura y Aplicada - IUMPA, Universidad Politécnica de Valencia, E-46022 Valencia, Spain
| | - Pedro Fernández de Córdoba
- Instituto Universitario de Matemática Pura y Aplicada - IUMPA, Universidad Politécnica de Valencia, E-46022 Valencia, Spain
| | - Javier F. Urchueguía
- Instituto Universitario de las Telecomunicaciones Avanzadas - ITACA, Universidad Politécnica de Valencia, E-46022 Valencia, Spain
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Topological assessment of metabolic networks reveals evolutionary information. Sci Rep 2018; 8:15918. [PMID: 30374088 PMCID: PMC6206017 DOI: 10.1038/s41598-018-34163-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Accepted: 10/07/2018] [Indexed: 12/03/2022] Open
Abstract
Evolutionary information was inferred from the topology of metabolic networks corresponding to 17 plant species belonging to major plant lineages Chlorophytes, Bryophytes, Lycophytes and Angiosperms. The plant metabolic networks were built using the substrate-product network modeling based on the metabolic reactions available on the PlantCyc database (version 9.5), from which their local topological properties such as degree, in-degree, out-degree, clustering coefficient, hub-score, authority-score, local efficiency, betweenness and eigencentrality were measured. The topological measurements corresponding to each metabolite within the networks were considered as a set of metabolic characters to compound a feature vector representing each plant. Our results revealed that some local topological characters are able to discern among plant kinships, since similar phylogenies were found when comparing dendrograms obtained by topological metrics to the one obtained by DNA sequences of chloroplast genes. Furthermore, we also found that even a smaller number of metabolic characters is able to separate among major clades with high bootstrap support (BS > 95), while for some suborders a bigger content has been required.
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Abstract
The genome-scale cellular network has become a necessary tool in the systematic analysis of microbes. In a cell, there are several layers (i.e., types) of the molecular networks, for example, genome-scale metabolic network (GMN), transcriptional regulatory network (TRN), and signal transduction network (STN). It has been realized that the limitation and inaccuracy of the prediction exist just using only a single-layer network. Therefore, the integrated network constructed based on the networks of the three types attracts more interests. The function of a biological process in living cells is usually performed by the interaction of biological components. Therefore, it is necessary to integrate and analyze all the related components at the systems level for the comprehensively and correctly realizing the physiological function in living organisms. In this review, we discussed three representative genome-scale cellular networks: GMN, TRN, and STN, representing different levels (i.e., metabolism, gene regulation, and cellular signaling) of a cell’s activities. Furthermore, we discussed the integration of the networks of the three types. With more understanding on the complexity of microbial cells, the development of integrated network has become an inevitable trend in analyzing genome-scale cellular networks of microorganisms.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China.,Tianjin Bohai Fisheries Research Institute, Tianjin, China
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5
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Morphologies and phenotypes in Bacillus subtilis biofilms. J Microbiol 2017; 55:619-627. [DOI: 10.1007/s12275-017-7041-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2017] [Revised: 05/29/2017] [Accepted: 06/02/2017] [Indexed: 10/19/2022]
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Liu K, Abdullah AA, Huang M, Nishioka T, Altaf-Ul-Amin M, Kanaya S. Novel Approach to Classify Plants Based on Metabolite-Content Similarity. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5296729. [PMID: 28164123 PMCID: PMC5253511 DOI: 10.1155/2017/5296729] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2016] [Revised: 11/14/2016] [Accepted: 11/30/2016] [Indexed: 12/12/2022]
Abstract
Secondary metabolites are bioactive substances with diverse chemical structures. Depending on the ecological environment within which they are living, higher plants use different combinations of secondary metabolites for adaptation (e.g., defense against attacks by herbivores or pathogenic microbes). This suggests that the similarity in metabolite content is applicable to assess phylogenic similarity of higher plants. However, such a chemical taxonomic approach has limitations of incomplete metabolomics data. We propose an approach for successfully classifying 216 plants based on their known incomplete metabolite content. Structurally similar metabolites have been clustered using the network clustering algorithm DPClus. Plants have been represented as binary vectors, implying relations with structurally similar metabolite groups, and classified using Ward's method of hierarchical clustering. Despite incomplete data, the resulting plant clusters are consistent with the known evolutional relations of plants. This finding reveals the significance of metabolite content as a taxonomic marker. We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations.
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Affiliation(s)
- Kang Liu
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Azian Azamimi Abdullah
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Ming Huang
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Takaaki Nishioka
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Md. Altaf-Ul-Amin
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
| | - Shigehiko Kanaya
- Graduate School of Information Science, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara 630-0192, Japan
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