Abstracts (first author)

Invited Speaker 

Genomics of adaptation in evolving microbial populations

Author(s): Kassen R

Summary:

The search for the genetic changes responsible for adaptive evolution has been, at least since Mendel, the ‘holy grail’ of adaptation research. With the introduction of cost-effective next generation sequencing (NGS) technology over the past few years the grail is finally within reach. Combining NGS with experimental evolution of microbial populations is particularly promising in this regard as it provides a glimpse into the natural history of evolving genomes under at least one fairly well defined set of parameters: large population sizes, asexual reproduction, and (usually) haploid genomes. I will review the results of studies that have come from combining NGS with experimental evolution for what they tell us about the genomics of adaptation at the genomic level. In many respects the results are reassuring: the bulk of adaptive changes occur in open reading frames and are non-synonymous, for example. But NGS has also provided some surprises: synonymous mutations that are clearly adaptive, for example, and mutations in genes that, at first glance at least, are hard to interpret in an adaptive light. Making sense of the full spectrum of results will require some careful rethinking in terms of experimental design and genomic sampling.



Abstracts (coauthor)

Summary:

Microbial communities are fundamental for ecosystem function, yet only little is known about their in situ ecology and evolution. How are such communities structured in space and time and which environmental factors drive their adaptation? We obtained soil bacterial isolates from three sites in a spatially structured design every month for eight months, representing a complete growth season. In a fully factorial transplant experiment we measured fitness of all isolates in media mirroring the environmental conditions in the soil sample they were isolated from (their “home” soil) and compared it to their growth rates in media representing soil at different spatial and temporal distances, thus not only describing the spatial fitness landscape for each isolate but also how the shape of this landscape changes over time. In comparison to growth under “home” soil conditions, growth rates were steadily declining in media representing future soil conditions, indicating temporal adaption of isolates. Moreover, fitness increased in media representing past conditions, providing evidence for past selection for successful growth. These findings were unaffected by limiting our analysis to isolates with vigorous growth rates or to soil obtained from different geographical sites. Spatial structuring, either at large (kilometer) or small (meter) scales, did not significantly influence bacterial fitness, indicating a large role of dispersal in soil bacterial biogeography at these scales. Lastly, we correlated environmental factors such as nutrient ion availability, mean temperature and pH with the obtained fitness landscapes to deduce key factors influencing bacterial temporal adaptation in nature.

Summary:

Pseudomonas aeruginosa is an opportunistic pathogen of humans and is the most common bacterial species isolated from the respiratory tracts of adult patients with cystic fibrosis (CF). Chronic infection of the CF lung can lead to decades of direct interaction between the host and resident P. aeruginosa population. Longitudinal studies have documented the patterns of adaptation to the CF lung, and evidence to date suggests that a large number of genes are targets for mutation, but most are mutated in only a small fraction of infections. A more comprehensive view therefore requires the comparison of a larger sample of diverse clinical isolates. To this end, we obtained whole genome sequence data from a collection of P. aeruginosa isolated from the airways of CF patients in order to investigate general patterns of adaptation associated with chronic infection. We also focus attention on a transmissible, epidemic strain that was recently reported within North America. We present multiple lines of evidence that the history of selection imposed by the CF lung environment has a major influence on genomic evolution and the genetic characteristics of isolates causing contemporary infection. We identify candidate genes and important functional pathways, and find that the presence of oxidative stressors and antibiotics appear to be key factors that have driven the adaptive evolution of this pathogen within the host.

Summary:

The repeatability of adaptive evolution depends on the ruggedness of the underlying adaptive landscape, how fitness varies as a function of phenotype or genotype. Rugged landscapes are thought to promote divergent adaptation, with genotypes evolving towards distinct genotypic and phenotypic solutions determined by the number of available fitness peaks. By contrast, genotypes evolving on a smooth landscape containing a single adaptive peak are expected to converge on a single genotypic and phenotypic solution. Here we evaluate the genomic consequences of adaptation on rugged and smooth landscapes by quantifying the degree of genic parallelism observed following adaptive evolution by genetically distinct starting genotypes of Pseudomonas fluorescens evolving on two single carbon substrates, xylose and glucose. Previous work showed that these substrates differ in the number of adaptive solutions available to these genotypes, with xylose being a relatively more rugged landscape than glucose. We find that, consistent with expectation, DNA sequence evolution is less parallel in a rugged, compared to a smooth landscape. Our results suggest that the ruggedness of the adaptive landscape has a strong influence on the pattern of genomic evolution.

Contacts

Chairman: Octávio S. Paulo
Tel: 00 351 217500614 direct
Tel: 00 351 217500000 ext22359
Fax: 00 351 217500028
email: mail@eseb2013.com

Address

XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon
Portugal

Website

Computational Biology & Population Genomics Group 
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