Repeatable phenotypic and molecular changes occur through coevolution.

<p>(A) A repeatable progression of phenotypic coevolution between <i>Escherichia coli B</i> and bacteriophage T3. New phenotypes are highlighted in red. Dashed lines with arrows indicate that a phage type is able to infect a bacterial type. Dashed lines with crosses indicate evolution of resistant bacteria. (B) Structural changes in LPS molecules on the bacterial outer membrane confer first-order resistance to phage. Second-order resistance can evolve through LPS or thioredoxin A (trxA) pathways. (C) Structural changes in the trimeric tail fiber protein enable phage to infect new hosts. Protein images were produced using PyMol and PDB entry 4AOU [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130639#pone.0130639.ref024" target="_blank">24</a>] and 2TRX [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130639#pone.0130639.ref025" target="_blank">25</a>].</p

A complete list of genomic mutations distinguishing derived phenotypes from their ancestors.

<p>The positions of mutations are indicated for regions of the genome in which mutations have been shown to be sufficient to confer the derived phenotype. (A) Mutations distinguishing first-order resistant B₁ bacteria from the sensitive B₀ ancestor. (B) Mutations distinguishing host-range mutant T3₁ phage from the wild-type T3₀ ancestor. (C) Mutations distinguishing second-order resistant B₂ bacteria from the B₀ ancestor. The “waa” prefix has been omitted from LPS biosynthesis genes to conserve space. The white area indicates a deletion that spans several genes. Detailed information about each mutation is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130639#pone.0130639.s001" target="_blank">S1</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0130639#pone.0130639.s003" target="_blank">S3</a> Tables.</p

The Molecular and Genetic Basis of Repeatable Coevolution between <i>Escherichia coli</i> and Bacteriophage T3 in a Laboratory Microcosm

<div><p>The objective of this study was to determine the genomic changes that underlie coevolution between <i>Escherichia coli</i> B and bacteriophage T3 when grown together in a laboratory microcosm. We also sought to evaluate the repeatability of their evolution by studying replicate coevolution experiments inoculated with the same ancestral strains. We performed the coevolution experiments by growing <i>Escherichia coli</i> B and the lytic bacteriophage T3 in seven parallel continuous culture devices (chemostats) for 30 days. In each of the chemostats, we observed three rounds of coevolution. First, bacteria evolved resistance to infection by the ancestral phage. Then, a new phage type evolved that was capable of infecting the resistant bacteria as well as the sensitive bacterial ancestor. Finally, we observed second-order resistant bacteria evolve that were resistant to infection by both phage types. To identify the genetic changes underlying coevolution, we isolated first- and second-order resistant bacteria as well as a host-range mutant phage from each chemostat and sequenced their genomes. We found that first-order resistant bacteria consistently evolved resistance to phage via mutations in the gene, <i>waaG</i>, which codes for a glucosyltransferase required for assembly of the bacterial lipopolysaccharide (LPS). Phage also showed repeatable evolution, with each chemostat producing host-range mutant phage with mutations in the phage tail fiber gene <i>T3p48</i> which binds to the bacterial LPS during adsorption. Two second-order resistant bacteria evolved via mutations in different genes involved in the phage interaction. Although a wide range of mutations occurred in the bacterial <i>waaG</i> gene, mutations in the phage tail fiber were restricted to a single codon, and several phage showed convergent evolution at the nucleotide level. These results are consistent with previous studies in other systems that have documented repeatable evolution in bacteria at the level of pathways or genes and repeatable evolution in viruses at the nucleotide level. Our data are also consistent with the expectation that adaptation via loss-of-function mutations is less constrained than adaptation via gain-of-function mutations.</p></div

Sequence data

MBO_mapping_file_impingers_controls_figshare.txt - A tab-delimited text file with sample information. The columns of the text file include a sample identifier ("SampleID"), barcode sequences ("BarcodeSequence"), forward primer sequence ("LinkerPrimerSequence"), date of sample collection ("Sample_date"), sample type (rDNA or rRNA, "Sample_type"), barcode, and sample description (samples versus controls, "Description").<div><br></div><div>seqs_figshare.fastq - Raw 16S sequence data in FASTQ format, produced by a sequencing run using Illumina MiSeq 300 bp single-end sequencing technology. </div

Image_1_A Mosaic of Geothermal and Marine Features Shapes Microbial Community Structure on Deception Island Volcano, Antarctica.TIF

<p>Active volcanoes in Antarctica contrast with their predominantly cold surroundings, resulting in environmental conditions capable of selecting for versatile and extremely diverse microbial communities. This is especially true on Deception Island, where geothermal, marine, and polar environments combine to create an extraordinary range of environmental conditions. Our main goal in this study was to understand how microbial community structure is shaped by gradients of temperature, salinity, and geochemistry in polar marine volcanoes. Thereby, we collected surface sediment samples associated with fumaroles and glaciers at two sites on Deception, with temperatures ranging from 0 to 98°C. Sequencing of the 16S rRNA gene was performed to assess the composition and diversity of Bacteria and Archaea. Our results revealed that Deception harbors a combination of taxonomic groups commonly found both in cold and geothermal environments of continental Antarctica, and also groups normally identified at deep and shallow-sea hydrothermal vents, such as hyperthermophilic archaea. We observed a clear separation in microbial community structure across environmental gradients, suggesting that microbial community structure is strongly niche driven on Deception. Bacterial community structure was significantly associated with temperature, pH, salinity, and chemical composition; in contrast, archaeal community structure was strongly associated only with temperature. Our work suggests that Deception represents a peculiar “open-air” laboratory to elucidate central questions regarding molecular adaptability, microbial evolution, and biogeography of extremophiles in polar regions.</p

Table_2_A Mosaic of Geothermal and Marine Features Shapes Microbial Community Structure on Deception Island Volcano, Antarctica.XLSX

<p>Active volcanoes in Antarctica contrast with their predominantly cold surroundings, resulting in environmental conditions capable of selecting for versatile and extremely diverse microbial communities. This is especially true on Deception Island, where geothermal, marine, and polar environments combine to create an extraordinary range of environmental conditions. Our main goal in this study was to understand how microbial community structure is shaped by gradients of temperature, salinity, and geochemistry in polar marine volcanoes. Thereby, we collected surface sediment samples associated with fumaroles and glaciers at two sites on Deception, with temperatures ranging from 0 to 98°C. Sequencing of the 16S rRNA gene was performed to assess the composition and diversity of Bacteria and Archaea. Our results revealed that Deception harbors a combination of taxonomic groups commonly found both in cold and geothermal environments of continental Antarctica, and also groups normally identified at deep and shallow-sea hydrothermal vents, such as hyperthermophilic archaea. We observed a clear separation in microbial community structure across environmental gradients, suggesting that microbial community structure is strongly niche driven on Deception. Bacterial community structure was significantly associated with temperature, pH, salinity, and chemical composition; in contrast, archaeal community structure was strongly associated only with temperature. Our work suggests that Deception represents a peculiar “open-air” laboratory to elucidate central questions regarding molecular adaptability, microbial evolution, and biogeography of extremophiles in polar regions.</p

Image_3_A Mosaic of Geothermal and Marine Features Shapes Microbial Community Structure on Deception Island Volcano, Antarctica.TIFF

<p>Active volcanoes in Antarctica contrast with their predominantly cold surroundings, resulting in environmental conditions capable of selecting for versatile and extremely diverse microbial communities. This is especially true on Deception Island, where geothermal, marine, and polar environments combine to create an extraordinary range of environmental conditions. Our main goal in this study was to understand how microbial community structure is shaped by gradients of temperature, salinity, and geochemistry in polar marine volcanoes. Thereby, we collected surface sediment samples associated with fumaroles and glaciers at two sites on Deception, with temperatures ranging from 0 to 98°C. Sequencing of the 16S rRNA gene was performed to assess the composition and diversity of Bacteria and Archaea. Our results revealed that Deception harbors a combination of taxonomic groups commonly found both in cold and geothermal environments of continental Antarctica, and also groups normally identified at deep and shallow-sea hydrothermal vents, such as hyperthermophilic archaea. We observed a clear separation in microbial community structure across environmental gradients, suggesting that microbial community structure is strongly niche driven on Deception. Bacterial community structure was significantly associated with temperature, pH, salinity, and chemical composition; in contrast, archaeal community structure was strongly associated only with temperature. Our work suggests that Deception represents a peculiar “open-air” laboratory to elucidate central questions regarding molecular adaptability, microbial evolution, and biogeography of extremophiles in polar regions.</p

Image_2_A Mosaic of Geothermal and Marine Features Shapes Microbial Community Structure on Deception Island Volcano, Antarctica.TIFF

<p>Active volcanoes in Antarctica contrast with their predominantly cold surroundings, resulting in environmental conditions capable of selecting for versatile and extremely diverse microbial communities. This is especially true on Deception Island, where geothermal, marine, and polar environments combine to create an extraordinary range of environmental conditions. Our main goal in this study was to understand how microbial community structure is shaped by gradients of temperature, salinity, and geochemistry in polar marine volcanoes. Thereby, we collected surface sediment samples associated with fumaroles and glaciers at two sites on Deception, with temperatures ranging from 0 to 98°C. Sequencing of the 16S rRNA gene was performed to assess the composition and diversity of Bacteria and Archaea. Our results revealed that Deception harbors a combination of taxonomic groups commonly found both in cold and geothermal environments of continental Antarctica, and also groups normally identified at deep and shallow-sea hydrothermal vents, such as hyperthermophilic archaea. We observed a clear separation in microbial community structure across environmental gradients, suggesting that microbial community structure is strongly niche driven on Deception. Bacterial community structure was significantly associated with temperature, pH, salinity, and chemical composition; in contrast, archaeal community structure was strongly associated only with temperature. Our work suggests that Deception represents a peculiar “open-air” laboratory to elucidate central questions regarding molecular adaptability, microbial evolution, and biogeography of extremophiles in polar regions.</p

Table_1_A Mosaic of Geothermal and Marine Features Shapes Microbial Community Structure on Deception Island Volcano, Antarctica.XLSX

<p>Active volcanoes in Antarctica contrast with their predominantly cold surroundings, resulting in environmental conditions capable of selecting for versatile and extremely diverse microbial communities. This is especially true on Deception Island, where geothermal, marine, and polar environments combine to create an extraordinary range of environmental conditions. Our main goal in this study was to understand how microbial community structure is shaped by gradients of temperature, salinity, and geochemistry in polar marine volcanoes. Thereby, we collected surface sediment samples associated with fumaroles and glaciers at two sites on Deception, with temperatures ranging from 0 to 98°C. Sequencing of the 16S rRNA gene was performed to assess the composition and diversity of Bacteria and Archaea. Our results revealed that Deception harbors a combination of taxonomic groups commonly found both in cold and geothermal environments of continental Antarctica, and also groups normally identified at deep and shallow-sea hydrothermal vents, such as hyperthermophilic archaea. We observed a clear separation in microbial community structure across environmental gradients, suggesting that microbial community structure is strongly niche driven on Deception. Bacterial community structure was significantly associated with temperature, pH, salinity, and chemical composition; in contrast, archaeal community structure was strongly associated only with temperature. Our work suggests that Deception represents a peculiar “open-air” laboratory to elucidate central questions regarding molecular adaptability, microbial evolution, and biogeography of extremophiles in polar regions.</p

Illustration of the methodological approach used to investigate trait-based biogeography.

<p>A) Given a phylogenetic tree with characterized “reference” members (denoted Ref) and uncharacterized members (denoted Clade or C; “clades” can design sequences, individuals or species depending on the study), B) the traits of uncharacterized members can be estimated. C) After rarefying the samples to standardize sampling intensity, the inferred traits can be used to estimate pseudo abundance values for each trait in each community. If the “clades” design sequences or individuals (such as in the present study), the community abundance matrix is in fact a simple presence-absence matrix. D) These pseudo-abundances can then be used for biogeographic analyses. The approach, illustrated here for discrete characters, can readily be adapted to continuous ones. In B, the discrete suite of probability values representing the probability that clade <i>i</i> codes for each type would then be replaced by a continuous probability distribution <i>ϕ</i>_<i>i</i>(<i>x</i>) representing the probability that clade <i>i</i> has character <i>x</i>. In C, multiplication of the probability distributions corresponding to each clade with the community matrix yields for each community <i>j</i> a continuous distribution <i>ϕ</i>_<i>j</i> representing the estimated number of clades with character x. For community 1 for example, this distribution would be given by <i>ϕ</i>₁(<i>x</i>) = 9<i>ϕ</i>₁(<i>x</i>) + <i>ϕ</i>₂(<i>x</i>) + 5<i>ϕ</i>₃(<i>x</i>).</p