1. Evolutionary Biology
Download icon

Metabolism: Evolution retraces its steps to advance

  1. Daniel J Kliebenstein  Is a corresponding author
  1. University of California,Davis, United States
Insight
  • Cited 0
  • Views 1,585
  • Annotations
Cite this article as: eLife 2015;4:e12386 doi: 10.7554/eLife.12386

Abstract

Bacteria in a long-term evolution experiment evolved a new metabolic trait via two separate mutations with opposite effects.

Main text

Selection can increase the fitness of a species in a stable environment by acting on random mutations. The same process can also create new traits if there is a change in the environment. Metabolism may evolve largely via the creation of new traits that either allow the organism to make use of new energy sources or provide new defense mechanisms in a complex environment (Blount et al. 2012; Prasad et al. 2012). However, we do not fully understand how new metabolic traits evolve or how they are integrated into existing metabolic networks.

Studying the creation of new traits is greatly complicated because evolution usually occurs over relatively long timescales. However, the Lenski long-term evolution experiment was designed to alleviate this problem and has been running at Michigan State University since 1988 (Fox and Lenski, 2015). Now, in eLife, Jeffrey Barrick and colleagues – including Erik Quandt as first author – make use of this resource to describe the molecular evolution of a new metabolic trait in E. coli (Quandt et al. 2015).

The long-term evolution experiment started with twelve identical populations of E. coli. These bacteria were forced to grow on culture medium that contained an excess of citrate, but very little glucose. Thus, for tens of thousands of generations of E. coli, the bacteria have been selected to evolve to use citrate as their main carbon source. This is something that E. coli would not normally do if they had access to oxygen. However, one of the populations did indeed evolve this exact ability (Blount et al. 2008; 2012). Sequencing the genome of this unique population throughout the long-term experiment identified the molecular changes that had generated this new trait. The new trait required two separate mutations within the gene that encodes an enzyme called citrate synthase (Quandt et al. 2015).

Barrick and colleagues – who are based at the University of Texas at Austin and Michigan State – now show that these two mutations have opposing effects (Quandt et al. 2015). The first mutation, called gltA1, abolished feedback inhibition in the enzyme and allowed the bacteria to use citrate, albeit weakly. This initial mutation was then followed by evolutionary shifts in genes that transcriptionally regulate primary metabolism (Leiby and Marx, 2014). Critically, this new transcriptional environment made the initial gltA1 mutation detrimental to fitness which, in turn, led to the rapid selection of variants of the citrate synthase gene that made the enzyme less active. Thus, while two opposing mutations within a single gene were required, they had to occur in a specific order and this order caused the mutations to be positive in both instances.

These new results show that the apparently unwavering march of evolution towards a new trait hides a meandering process underneath. In particular, they show that mutations that were at one time beneficial can consequently become a drag on fitness, and that mutations within existing genes can allow the creation of a new metabolic trait. This is in contrast to the standard view that the creation of new genes, often by gene duplication, is essential to the evolution of new metabolic traits (Chae et al. 2014; Wisecaver et al. 2014).

The use of the long-term evolution experiment has illuminated the complex mechanisms that allow adaptation to a consistent selective pressure in a single direction. However, it is possible that fluctuating and unpredictable stresses in the environment are more important drivers of evolution in nature (Kerwin et al. 2015), so there is a need for long-term experiments that include such stresses. The work of Quandt et al. represents, I hope, only the beginning of our ability to empirically study evolution in action.

References

Article and author information

Author details

  1. Daniel J Kliebenstein, Reviewing editor

    Department of Plant Sciences, University of California,Davis, Davis, United States
    For correspondence
    kliebenstein@ucdavis.edu
    Competing interests
    The author declares that no competing interests exist.

Publication history

  1. Version of Record published: December 15, 2015 (version 1)

Copyright

© 2015, Kliebenstein

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 1,585
    Page views
  • 155
    Downloads
  • 0
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Chromosomes and Gene Expression
    2. Evolutionary Biology
    Mathias Scharmann et al.
    Research Article Updated

    Differences between males and females are usually more subtle in dioecious plants than animals, but strong sexual dimorphism has evolved convergently in the South African Cape plant genus Leucadendron. Such sexual dimorphism in leaf size is expected largely to be due to differential gene expression between the sexes. We compared patterns of gene expression in leaves among 10 Leucadendron species across the genus. Surprisingly, we found no positive association between sexual dimorphism in morphology and the number or the percentage of sex-biased genes (SBGs). Sex bias in most SBGs evolved recently and was species specific. We compared rates of evolutionary change in expression for genes that were sex biased in one species but unbiased in others and found that SBGs evolved faster in expression than unbiased genes. This greater rate of expression evolution of SBGs, also documented in animals, might suggest the possible role of sexual selection in the evolution of gene expression. However, our comparative analysis clearly indicates that the more rapid rate of expression evolution of SBGs predated the origin of bias, and shifts towards bias were depleted in signatures of adaptation. Our results are thus more consistent with the view that sex bias is simply freer to evolve in genes less subject to constraints in expression level.

    1. Evolutionary Biology
    2. Neuroscience
    Jan Clemens et al.
    Research Article Updated

    How neural networks evolved to generate the diversity of species-specific communication signals is unknown. For receivers of the signals, one hypothesis is that novel recognition phenotypes arise from parameter variation in computationally flexible feature detection networks. We test this hypothesis in crickets, where males generate and females recognize the mating songs with a species-specific pulse pattern, by investigating whether the song recognition network in the cricket brain has the computational flexibility to recognize different temporal features. Using electrophysiological recordings from the network that recognizes crucial properties of the pulse pattern on the short timescale in the cricket Gryllus bimaculatus, we built a computational model that reproduces the neuronal and behavioral tuning of that species. An analysis of the model’s parameter space reveals that the network can provide all recognition phenotypes for pulse duration and pause known in crickets and even other insects. Phenotypic diversity in the model is consistent with known preference types in crickets and other insects, and arises from computations that likely evolved to increase energy efficiency and robustness of pattern recognition. The model’s parameter to phenotype mapping is degenerate – different network parameters can create similar changes in the phenotype – which likely supports evolutionary plasticity. Our study suggests that computationally flexible networks underlie the diverse pattern recognition phenotypes, and we reveal network properties that constrain and support behavioral diversity.