Habitat variability does not generally promote metabolic network modularity in flies and mammals
Introduction
The evolution of species habitat range is an important topic of scientific inquiry at several scales of biological research, from fundamental biological processes to ecology (Bridle and Vines, 2007, Root et al., 2003, Roy et al., 2009), particularly in the context of predictions related to biodiversity and climate change. Therefore, understanding the factors that determine species habitat use is a relevant topic for advancing these research fields. In particular, it is important to identify the molecular (microscopic) mechanisms that contribute to determining a species habitat range, because the behavior of a species (macroscopic) may result from complex biological systems. For example, some previous studies (Barrett and Schluter, 2008, Kellermann et al., 2009) suggested the importance of genetic variation to the ability to adapt and exploit new environments. Moreover, recent studies reported that gene duplication promotes habitat variability in flies (Drosophila species; Makino and Kawata, 2012) and mammals (Tamate et al., 2014). This work was inspired by the proposed importance of gene duplication to increasing biological robustness and evolvability (Wagner, 2008), which are themselves related to habitat variability.
However, it remains unclear how these genetic events affect species habitat variability at a higher level of biological organization. In this context, evaluation of modular organization in biological systems (Hartwell et al., 1999) is useful because it is also generally considered to be related to robustness (Hintze and Adami, 2008) and evolvability (Yang, 2001), despite some opinions to the contrary (Hansen, 2003, Holme, 2011). The evolution of modularity in cellular networks has been specifically intriguing to researchers in the context of network biology (Barabási and Oltvai, 2004, Takemoto, 2012a). In particular, a hypothesis has been proposed that variability in natural habitats promotes network modularity. For example, in a theoretical model, Kashtan and Alon (2005) showed that modular networks spontaneously evolved when a fitness peak determined by the environment changes over time in a manner that preserves the same subgoals but in different permutations. Similarly, Lipson et al. (2002) suggested that changing environments could promote modularity. Hintze and Adami (2008) showed that modularity evolves in biological networks (modeled as metabolic networks) to deal with a multitude of functional goals, with the degree of modularity depending on the extent of environmental variability.
In this context, metabolic networks are particularly interesting because metabolic processes are essential for physiological functions and for maintaining homeostasis in living organisms (Takemoto and Oosawa, 2012, Takemoto, 2012a). Metabolic networks also determine the behavior of organisms, such as the space use (Jetz et al., 2004) and feeding rate (Brown et al., 2004) of animals, which may in turn be related to habitat variability. In addition, analyses can be performed using actual empirical data, because metabolic networks are available for a wide diversity of species in databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG; Kanehisa et al., 2014) and the Encyclopedia of Metabolic Pathways (MetaCyc; Caspi et al., 2012). In fact, using network analysis, Parter et al. (2007) showed that variability in natural habitats promotes the modularity observed in metabolic networks. This result clearly supports the predictions derived from theoretical models, and several researchers have actively investigated the ecological interactions underlying metabolic networks according to habitat variability (Chave, 2013, Levy and Borenstein, 2012).
Despite this recent attention to this relationship between modularity and habitat viability, more comprehensive examinations are required to resolve some outstanding questions. Indeed, recent studies have cast doubt on this relationship. For example, several alternative theories for explaining the origin and evolution of modularity have been proposed, including the neutral theories of protein (Solé and Valverde, 2008) and metabolic networks (Takemoto, 2012b), connection-cost theory (Clune et al., 2013), and multiplicative-mutation theory (Friedlander et al., 2013). Furthermore, a study conducted in archaea, a type of prokaryote distinct from bacteria, did not find a positive correlation between habitat variability and metabolic network modularity (Takemoto and Borjigin, 2011). Similarly, in bacteria, no positive correlation was observed using the latest version of the metabolic database (Takemoto, 2013, Zhou and Nakhleh, 2012). In short, the observed associations between metabolic network modularity and habitat variability (Parter et al., 2007) may be the result of an artifact due to lack of available data on metabolic reactions. More importantly, the studies conducted thus far are limited to lower organisms such as bacteria and archaea.
Therefore, the aim of this study was to investigate the relationship between habitat variability and metabolic network modularity in higher organisms, including data from flies (Makino and Kawata, 2012) and mammals (Tamate et al., 2014). In addition to the potential effect on habitat variability in promoting network modularity, an association between gene duplication and habitat variability has also been observed. Given that gene duplication also influences the metabolic network structure (Barabási and Oltvai, 2004, Díaz-Mejía et al., 2007, Papp et al., 2004, Takemoto, 2012a), it is reasonable to hypothesize that habitat variability may be linked to not only gene duplication but also metabolic network modularity. To investigate these relationships, data related to habitat variability were collected from the published literature; data were collected only from species for which metabolic network data are also available (see Section 2). Using these data, I evaluated whether habitat variability increases metabolic network modularity and how the association between gene duplication and habitat variability might influence the modularity of the metabolic network.
Section snippets
Collection of data related to habitat variability and fraction of duplicated genes
The data on habitat variability and fraction of duplicated genes in flies and mammals were obtained from Makino and Kawata (2012) and Tamate et al. (2014), respectively. Habitat variability was measured based on the Köppen climate classification in habitat areas for living organisms (see Makino and Kawata, 2012, Tamate et al., 2014 for details). In this study, the Brillouin index was used for measuring habitat variability, because such indices tend to follow a normal distribution for species,
Habitat variability promotes compound network modularity in flies and mammals
After the data collection and integration procedures, data on habitat variability, the fraction of duplicated genes, and metabolic networks were obtained for 11 different fly and 14 mammal species (see Section 2.1 and Supplementary Tables S1 and S2).
For these living organisms, compound metabolic networks were constructed, which are represented as directed networks in which the nodes and edges correspond to metabolites and reactions (i.e., substrate–product relationships), respectively (see
Discussion
The results of this study did not confirm an association between metabolic network modularity and habitat variability, thereby rejecting the hypothesis derived from previous analytic results (Parter et al., 2007) and theoretical models (Hintze and Adami, 2008, Kashtan and Alon, 2005, Lipson et al., 2002) that habitat variability should promote network modularity. Although a positive correlation between the undirected version of network modularity and habitat variability was observed in the
Acknowledgements
This study was supported by a Grant-in-Aid for Young Scientists (A) from the Japan Society for the Promotion of Science (no. 25700030). KT thanks J.-B. Mouret for providing an executable file for calculating Qd.
References (74)
- et al.
Adaptation from standing genetic variation
Trends Ecol. Evol.
(2008) - et al.
Limits to evolution at range margins: when and why does adaptation fail?
Trends Ecol. Evol.
(2007) - et al.
Optimality in evolution: new insights from synthetic biology
Curr. Opin. Biotechnol.
(2013) Community detection in graphs
Phys. Rep.
(2010)- et al.
Genome size and longevity in fish
Exp. Gerontol.
(2003) Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability
Biosystems
(2003)Chemical and genomic evolution of enzyme-catalyzed reaction networks
FEBS Lett.
(2013)- et al.
Link communities reveal multiscale complexity in networks
Nature
(2010) - et al.
Size reduction of complex networks preserving modularity
N. J. Phys.
(2007) Comment on “Network Motifs: Simple Building Blocks of Complex Networks” and “Superfamilies of Evolved and Designed Networks”
Science (New York, N.Y.)
(2004)
Network biology: understanding the cell's functional organization
Nat. Rev. Genet.
MEPE evolution in mammals reveals regions and residues of prime functional importance
Cell Mol. Life Sci.
Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks
J. R. Soc. Interface/R. Soc.
Large-scale 13C-flux analysis reveals mechanistic principles of metabolic network robustness to null mutations in yeast
Genome Biol.
Constraint-based models predict metabolic and associated cellular functions
Nat. Rev. Genet.
Toward a metabolic theory of ecology
Ecology
The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases
Nucleic Acids Res.
The problem of pattern and scale in ecology: what have we learned in 20 years?
Ecol. Lett.
Evolution of genes and genomes on the Drosophila phylogeny
Nature
The evolutionary origins of modularity
Proc. R. Soc. B
A network perspective on the evolution of metabolism by gene duplication
Genome Biol.
Phylogenies and the comparative method
Am. Nat.
eQuilibrator – the biochemical thermodynamics calculator
Nucleic Acids Res.
Resolution limit in community detection
Proc. Natl. Acad. Sci. U. S. A.
Mutation rules and the evolution of sparseness and modularity in biological systems
PLoS ONE
Evolution of bow-tie architectures in biology
PLoS Computat. Biol.
Phylogenetic approaches in comparative physiology
J. Exp. Biol.
Procedures for the analysis of comparative data using phylogenetically independent contrasts
Systematic Biol.
Modularity from fluctuations in random graphs and complex networks
Phys. Rev. E
Expanding metabolic networks: scopes of compounds, robustness, and evolution
J. Mol. Evol.
From molecular to modular cell biology
Nature
Evolution of complex modular biological networks
PLoS Comput. Biol.
Metabolic robustness and network modularity: a model study
PLoS ONE
Atmospheric reaction systems as null-models to identify structural traces of evolution in metabolism
PLoS ONE
The scaling of animal space use
Science (New York, N.Y.)
Data, information, knowledge and principle: back to metabolism in KEGG
Nucleic Acids Res.
Spontaneous evolution of modularity and network motifs
Proc. Natl. Acad. Sci. U. S. A.
Cited by (3)
On the role of sparseness in the evolution of modularity in gene regulatory networks
2018, PLoS Computational BiologyExosomes in mammals with greater habitat variability contain more proteins and RNAs
2017, Royal Society Open Science