Current Research Projects

The main research theme of our group is the evolution of translation in bacteria. We currently have projects on the evolution of tRNA gene sets and mature tRNA pools. We are also interested in the long-term survival of bacteria under starvation conditions. You can also find us on Twitter: @gallie_jenna

The evolution of bacterial tRNA sets

Transfer RNAs (tRNAs) are adapter molecules that match codons with amino acids during translation (Figure 1). There are 61 theoretically possible types of tRNAs – one matching each of 61 codons that code for amino acids. In practice, organisms use 61 codons but carry far fewer corresponding tRNA types. This is possible because some tRNAs translate more than one codon (through wobble base pairing). Intriguingly, which tRNA types are present in an organism differs considerably across the tree of life. We are interested in exploring the evolutionary bases of tRNA set composition.

Recently, we have published a paper investigating how tRNA gene sets can evolve using the bacterial model system Pseudomonas fluorescens SBW25: Ayan et al., 2020 ( Firstly, we constructed a sub-optimal tRNA gene set in SBW25 by deleting single-copy tRNA gene serCGA (Fig 2). Next, we used a serial transfer experiment to improve the sub-optimal tRNA gene set of the serCGA deletion mutant. The genetic basis of compensation is large-scale (45-290 kb), tandem duplications in the SBW25 chromosome (Fig 2). Each duplication contains a single, compensatory tRNA gene: serTGA, encoding tRNA-Ser(UGA).

Next, we used YAMAT-seq (a method of deep sequencing mature tRNA pools originally developed for human cell lines (Shigematsu et al., 2020)) to determine the effects of serCGA deletion, and subsequent serTGA duplication, on the pool of mature tRNA molecules. serCGA deletion leads to elimination of tRNA-Ser(CGA), and serTGA duplication generates an increase in tRNA-Ser(UGA) (Fig 3).

Finally, we developed a molecular and mathematical model for how tRNA-Ser(CGA) elimination may reduce translation speed and growth rate, and how an increase in tRNA-Ser(UGA) may compensate. We hypothesize that tRNA-Ser(CGA) elimination increases the time needed to translate its cognate codon, UCG (a relatively high use codon in SBW25). Notably, in the absence of tRNA-Ser(CGA), UCG is predicted to be translated by tRNA-Ser(UGA) through wobble base pairing (see Figure 1). Hence, a serCGA deletion mutant is expected to be rescued from death by tRNA-Ser(UGA), but this will put considerable translational pressure on tRNA-Ser(UGA). The pressure is relieved by increasing the level of tRNA-Ser(UGA) via serTGA copy number.

In these experiments we have directly observed the evolution of a bacterial tRNA gene set by within-genome, tRNA gene duplication events. Our current research focusses on related questions: What is the long-term evolutionary fate of large-scale duplications/tRNA genes? Can other tRNA genes evolve by similar large-scale duplication events?

Ayan, Park, Gallie. 2020. eLife 9:e57947.
Shigematsu et al. 2017. Nucl. Acids. Res. 45.


Long-term survival: the evolution of bacteria in starved batch culture

In addition to our research on the evolution of translation, we have a growing interest in mechanisms of long-term survival in bacteria. In a typical laboratory experiment, bacteria are grown under conditions that – at least at first – support rapid and plentiful growth. However, survival in many natural environments is likely to require persistence through substantial periods of hardship. A number of laboratory-based experiments mimicking long term persistence have been performed (starved batch cultures), resulting in the identification of a variety of mutational classes that can provide a growth advantage in stationary phase (GASP mutants; reviewed in Zinser and Kolter, 2004). These include point mutations, small deletions, and larger genomic rearrangements.

We are interested in the long-term survival of our favourite model organism, Pseudomonas fluorescens SBW25 compared with E. coli. We are performing evolution experiments in starved batch culture with these species (Fig 4), with the aim of elucidating the population genetics underpinning survival (or extinction) in each lineage.

Zinser and Kolter, 2004. Res Microbiol: 155.

Long term survival: the evolution of a bacterial bet hedging strategy

This recently-completed projected was peformed in collaboration with Paul Rainey (Department of Microbial Population Dynamics, MPI), Frederic Bertels (Microbial Molecular Evoluiton, MPI), Philippe Remigi (INRA-CNRS, France) and Gayle Ferguson (Massey University, NZ).

Bet hedging – stochastic switching between phenotypes – is a widespread evolutionary adaptation that facilitates survival in unpredictable environments. There are many examples of bet hedging behaviour in nature, ranging from microbes to humans. However, very little is known about how bet hedging strategies emerge. An experiment in Paul Rainey’s laboratory saw the evolution of a bet hedging strategy in two parallel, independent evolutionary lineages of the bacterium P. fluorescens SBW25 (Beaumont et al., 2009). In both cases, the evolved bet hedging type produces colonies of two distinct phenotypes, with each type rapidly giving rise to both itself and the opposite type. This phenotype switching is mirrored at the cellular level by the production of capsulated and non-capsulated cells (Fig 5).

The first of these two bet hedging types (Line 1), including the phenotype, genotype and evolutionary history has been extensively characterized; bet hedging emerges as a consequence of a non-synonymous point mutation in the central metabolic gene carB (Beaumont et al., 2009; Gallie et al., 2015; Remigi et al., 2019). In the second lineage (Line 6), bet hedging is caused by a non-synonymous mutation in the housekeeping transcription factor, rpoD. In our recent paper, we show how mutations in genes as seemingly distantly related as carB and rpoD can reconcile at the molecular level to generate a very similar phenoytpe (Gallie et al., 2019).

Gallie et al. (2019). Mol Biol Evol
Remigi et al. (2019). Mol Biol Evol
Gallie et al. (2015). PLoS Biol
Rainey et al. (2010). Microbial Cell Fact
Beaumont et al. (2009). Nature


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