The coupling of ecology and evolution during range expansions enables mutations to establish at expanding range margins and reach high frequencies. This phenomenon, called allele surfing, is thought to have caused revolutions in the gene pool of many species, most evidently in microbial communities. It has remained unclear, however, under which conditions allele surfing promotes or hinders adaptation. Here, using microbial experiments and simulations, we show that, starting with standing adaptive variation, range expansions generate a larger increase in mean fitness than spatially uniform population expansions. The adaptation gain results from ‘soft’ selective sweeps emerging from surfing beneficial mutations. The rate of these surfing events is shown to sensitively depend on the strength of genetic drift, which varies among strains and environmental conditions. More generally, allele surfing promotes the rate of adaptation per biomass produced, which could help developing biofilms and other resource-limited populations to cope with environmental challenges.
Author contributions: OH, MG, FS designed study; MG, FS performed experiments. WM contributed new reagents/analytical tools; MG, OH developed theory; MG, OH developed coarsegrained simulations; FF, BW developed individual-based simulations; MG, FF, BW, OH analyzed data; MG, BW, WM, OH wrote the paper. All authors commented on and edited the manuscript. AbstractThe coupling of ecology and evolution during range expansions enables mutations to establish at expanding range margins and reach high frequencies. This phenomenon, called allele surfing, is thought to have caused revolutions in the gene pool of many species, most evidently in microbial communities. It has remained unclear, however, under which conditions allele surfing promotes or hinders adaptation. Here, using microbial experiments and simulations, we show that, starting with standing adaptive variation, range expansions generate a larger increase in mean fitness than spatially uniform population expansions. The adaptation gain results from 'soft' selective sweeps emerging from surfing beneficial mutations. The rate of these surfing events is shown to sensitively depend on the strength of genetic drift, which varies among strains and environmental conditions. More generally, allele surfing promotes the rate of adaptation per biomass produced, which could help developing biofilms and other resource-limited populations to cope with environmental challenges.Strains. We used S. cerevisiae strains with W303 backgrounds, where selective advantages were adjusted using cycloheximide. For experiments with E. coli, we used both DH5α and MG1655 strains, tuning fitness differences using tetracycline and chloramphenicol, respectively. Additionally, pairs of strains differing only in the fluorescent marker allowed us to perform truly neutral competition experiments (S. cerevisiae, S. pombe, E. coli). S. cerevisiae and E. coli strains with constitutively expressed fluorescent proteins were used to study the dynamics of cells at the front.A detailed description of all strains and growth conditions is found in Appendix C. 6Main Experiment: Adaptation from standing variation during two types of population expansions (see Fig. 1a): For each pair of mutant and wild type, a mixed starting population of size i was prepared that contained an initial frequency i of mutants having a selective advantage s, defined as the relative difference between mutant and wild-type growth rate ). The population was then grown to final size f in two ways, through a range expansion and, for comparison, through uniform growth, and the final mutant frequency f was determined. The associated increase in mean fitness ̅ follows as ∆ ̅ = ( f − i ) . Uniform Growth: Mixtures of cells were grown in well-shaken liquid medium to the desired final population size and the final fraction of mutant cells was determined using flow cytometry. Range Expansion: Colony growth was initiated by placing 2µl of the mixtures onto plates (2% w/v agar) and incubated until the desired final population size was reached. T...
Nine coral survey methods were compared at ten sites in various reef habitats with different levels of coral cover in Kāne‘ohe Bay, O’ahu, Hawaiʻi. Mean estimated coverage at the different sites ranged from less than 10% cover to greater than 90% cover. The methods evaluated include line transects, various visual and photographic belt transects, video transects and visual estimates. At each site 25 m transect lines were laid out and secured. Observers skilled in each method measured coral cover at each site. The time required to run each transect, time required to process data and time to record the results were documented. Cost of hardware and software for each method was also tabulated. Results of this investigation indicate that all of the methods used provide a good first estimate of coral cover on a reef. However, there were differences between the methods in detecting the number of coral species. For example, the classic “quadrat” method allows close examination of small and cryptic coral species that are not detected by other methods such as the “towboard” surveys. The time, effort and cost involved with each method varied widely, and the suitability of each method for answering particular research questions in various environments was evaluated. Results of this study support the finding of three other comparison method studies conducted at various geographic locations throughout the world. Thus, coral cover measured by different methods can be legitimately combined or compared in many situations. The success of a recent modeling effort based on coral cover data consisting of observations taken in Hawai‘i using the different methods supports this conclusion.
The increasing popularity of genome resolved meta genomics -the binning of genomes of potentially uncultured organisms direct from the environmental DNA -has resulted in a deluge of draft genomes. There is a pressing need to develop methods to interpret this data. Here, we used machine learning to predict functional and metabolic traits of microbes from their genomes. We collated an extensive database of 84 phenotypic traits associated with 9407 prokaryotic genomes and trained different machine learning models on this data. We found that a lasso logistic regression based on the frequency of gene orthologs had the best combination of functional prediction performance and interpretability. This model was able to classify 65 phenotypic traits with greater than 90 7 Microbiome Project [3], the Earth Microbiome Project [4] and the Tara Oceans 8 Project [5] have systematically sequenced the microbial communities in a huge variety of 9 environments at great depth. Amplicon sequencing, such as of the 16S rRNA gene, 10 allows detailed study of the taxonomic makeup of these communities, while shotgun 11 metagenomic sequencing allows characterisation of all genes present in an environment. 12 Increasing depth of coverage and improvements in genome binning algorithms for 13 clustering contigs into genomes, in particular the use of differential coverage across 14 different samples [6,7], are allowing more and more full and partial genomes to be 15 assembled from shotgun metagenomic studies. Many of these genomes are novel and 16 belong to uncultured organisms that are never studied in the laboratory. A recent 17 metagenomic study on aquifer systems [8], for example, reconstructed 2540 separate 18 high-quality, near-complete genomes, and claimed to have discovered an astonishing 47 19 new phylum-level lineages among them. 20Converting this exponentially growing sequence data into functional understanding 21 of microbial communities requires us to determine physiological functions from it [9]. 22This would allow the inference of key functions in microbial communities, and how these 23 PLOS1/22 functions change over ecological conditions and with time [9]. In turn, this ability, could 24 allow us to discern ecological adaptations in environmental microbial communities, as 25 well as to achieve functional mechanistic models of stability and function [10]. 26Efforts to achieve phenotype-genotype mapping from environmental sequence data 27 has so far mostly focussed on phylogenetic assignments using the 16S rRNA gene. This 28 highly conserved gene can provide a phylogenetic assignment at the species (or higher) 29 level, which can then be used to infer general functional traits. While this approach has 30 been commonly used to study ecological distribution of microbial functions e.g. [11][12][13], 31 its premise of a direct association of function with phylogenetic assignment (i.e. 32'functional coherence of microbial taxa') is questionable (e.g. [14]). The level of 33 taxonomic coherence of function is not clear even for strains o...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.