Network Architecture and Mutational Sensitivity of theC. elegansMetabolome
Authors: Johnson Lindsay M. , Chandler Luke M. , Davies Sarah K. , Baer Charles F. 
Published: August 2017

A fundamental issue in evolutionary systems biology is understanding the relationship between the topological architecture of a biological network, such as a metabolic network, and the evolution of the network. The rate at which an element in a metabolic network accumulates genetic variation via new mutations depends on both the size of the mutational target it presents and its robustness to mutational perturbation. Quantifying the relationship between topological properties of network elements and the mutability of those elements will facilitate understanding the variation in and evolution of networks at the level of populations and higher taxa.

We report an investigation into the relationship between two topological properties of 29 metabolites in theC. elegansmetabolic network and the sensitivity of those metabolites to the cumulative effects of spontaneous mutation. The correlations between measures of network centrality and mutability are not statistically significant, but several trends point toward a weak positive association between network centrality and mutational sensitivity. There is a small but significant negative association between the mutational correlation of a pair of metabolites (rM) and the shortest path length between those metabolites.

Positive association between the centrality of a metabolite and its mutational heritability is consistent with centrally-positioned metabolites presenting a larger mutational target than peripheral ones, and is inconsistent with centrality conferring mutational robustness, at leastin toto. The weakness of the correlation betweenrMand the shortest path length between pairs of metabolites suggests that network locality is an important but not overwhelming factor governing mutational pleiotropy. These findings provide necessary background against which the effects of other evolutionary forces, most importantly natural selection, can be interpreted.

Declarations

Ethics approval and consent to participate: Not applicable

Consent for publication: Not applicable

Availability of data and material:

Metabolomics data (normalized metabolite concentrations) are archived in Dryad (http://dx.doi.org/10.5061/dryad.2dn09/1).

Data used to reconstruct the metabolic networks are included in Supplementary Appendix A1.

Competing interests: The authors declare no competing interests.

Funding: Funding was provided by NIH grant R01GM107227 to CFB and E. C. Andersen. The funding agency had no role in the design of the study and the collection, analysis, and interpretation of the data or in the writing of the manuscript.

Authors’ contributions. LMJ and LMC collected and analyzed data in the network reconstruction and contributed to writing the manuscript. SKD collected and analyzed the GC-MS data. CFB analyzed data and wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements: This work was initially conceived by Armand Leroi and Jake Bundy. We thank Art Edison, Dan Hahn, Tom Hladish, Marta Wayne, Michael Witting, and several anonymous reviewers for their generosity and helpful advice. We especially thank Hongwu Ma for leading us to and through his metabolite database and Reviewer #3 for his/her many insightful comments and suggestions. Support was provided by NIH grant R01GM107227 to CFB and E. C. Andersen.