Genetic architecture associations after generation of <i>de novo</i> evolution of cooperation, at (A) high, c = and (B) low, c = secretion cost.

<p>In both cases, we quantified the presence of operons or overlaps between secretion genes and metabolic genes (S vs M) as well as between metabolic genes and other metabolic genes (M vs M). Genes were partitioned in four categories, labeled as in the <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003339#pcbi-1003339-g004" target="_blank">Fig. 4</a>: sharing a promoter (an operon) without overlapping with a metabolic gene (dark blue), overlapping and sharing an operon with a metabolic gene (light blue), overlapping without sharing an operon with a metabolic gene (green), not sharing an operon nor overlapping with any metabolic gene (red). Error bars represent plus and minus one standard error of the mean for the fifty replicate populations and their color corresponds to the genetic architecture category they relate to.</p

Aevol genetic code.

<p>Here we use an example of a functioning gene from an Aevol individual to explain the transcription and the translation processes. The gene is flanked by a promoter and terminator regions and preceded by a ribosome binding site (RBS). The codons for mean position, the width, and the height of the protein are identified, transformed into Gray code using the Genetic code table (box on the right), and finally scaled and normalized, as we summarize in the box on the left and describe in more detail in the Methods. Note that a gene with re-shuffled codons, for example H1 H1 M1 M1 M0 W1 W0 W1, would encode exactly the same protein. START codon may occasionally be found inside a gene, in which case it is interpreted as H0. The promoter differs from the consensus sequence by base out of the maximal differences allowed, giving it a efficiency.</p

Cheater invasion dynamics in phenotypically similar starting populations.

<p>In the main figure each line represents the average secretion, over time, of replicate populations started from the same ancestor. For clarity, we did not include the standard error of each group of replicate populations. The insert figure shows all replicate populations for the two most extreme population groups, zoomed in on the time period of interest for our analyses. Specifically, of all the population groups sharing a common ancestor, the blue populations had the highest and red the lowest average secretion between generation and generation . Note that average secretion in the inset represents the average secretion within each population, whilst in the main figure it is the average of the secretion of all the individuals from the replicate populations that share the same ancestor.</p

Secretion genes at generation and at generation partitioned between four categories:

<p>(a) sharing a promoter ( being on the same operon) without overlapping with a metabolic gene (dark blue), (b) overlapping and sharing an operon with a metabolic gene (light blue), (c) overlapping without sharing an operon with a metabolic gene (green), (d) neither sharing an operon nor overlapping with a metabolic gene (red). Error bars represent one standard error of the mean (fifty original cooperators). The color of the error bars corresponds to the genetic architecture category which they relate to.</p

Examples of constrained genetic architecture in Aevol.

<p>(A) As is often the case in natural systems, here the two digital genes belong to the same operon. They share a promoter and a terminator sequence and are thus being expressed at the same level. These hypothetical genes would belong to the “operon only” category from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003339#pcbi-1003339-g004" target="_blank">Fig. 4</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003339#pcbi-1003339-g005" target="_blank">Fig. 5</a>. (B) These two genes also share the same operon but additionally their sequences overlap, putting them in the “operon and overlap” category. In this case, the genes are in different reading frames and do not share a STOP codon, although such configuration is also possible. As the black gene boxes indicate, the left STOP codon corresponds to the left START codon, while the right STOP codon corresponds to the right START codon. (C, D) Two examples of genes from the “overlap only” category which are encoded on different strands. This is not an exhaustive list of possible genetic constraints, as a gene may, for example, share an operon with a gene on the same strand while simultaneously overlapping with a gene on an opposite strand.</p

Example of the preferential maintenance of certain secretion genes of a single cooperator from our bank.

<p>The bottom row of the graph (ancestor) shows the location on the phenotypic axis of the genes coding for secretion proteins in one cooperator organism from our bank. Above, the secretion genes from the best individual after generations of evolution in each of the replicate populations descending from this ancestor are shown. Colors represent the height of the proteins encoded by the genes (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003339#s4" target="_blank">Materials and Methods</a> for detailed explanation of protein properties in Aevol).</p

Outcomes of competitions between lineages with optimal (<i>U_opt</i> = 0.24) versus suboptimal (<i>U_subopt</i>) mutation rates in the explicit fitness landscape with a valley size of 3.

<p>A total of 250 runs were performed for each treatment shown below. The two lineages started with equal numbers in all cases. The entries show the number of times that each lineage was fixed (i.e., reached 100% of the total population) or that neither lineage was fixed within 300 generations. With waiting time: all individuals started at the lower fitness peak (asterisk in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000187#pcbi-1000187-g003" target="_blank">Figure 3</a>). Without waiting time: one individual belonging to the lineage with <i>U_opt</i> started on the other side of the fitness valley (triangle in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000187#pcbi-1000187-g003" target="_blank">Figure 3</a>). Three different values of <i>U_subopt</i> were examined. <i>P</i> values are based on χ² tests (with 2 degrees of freedom) that measured the effect of waiting time.</p

Indirect Fitness Benefits Enable the Spread of Host Genes Promoting Costly Transfer of Beneficial Plasmids

<div><p>Bacterial genes that confer crucial phenotypes, such as antibiotic resistance, can spread horizontally by residing on mobile genetic elements (MGEs). Although many mobile genes provide strong benefits to their hosts, the fitness consequences of the process of transfer itself are less clear. In previous studies, transfer has been interpreted as a parasitic trait of the MGEs because of its costs to the host but also as a trait benefiting host populations through the sharing of a common gene pool. Here, we show that costly donation is an altruistic act when it spreads beneficial MGEs favoured when it increases the inclusive fitness of donor ability alleles. We show mathematically that donor ability can be selected when relatedness at the locus modulating transfer is sufficiently high between donor and recipients, ensuring high frequency of transfer between cells sharing donor alleles. We further experimentally demonstrate that either population structure or discrimination in transfer can increase relatedness to a level selecting for chromosomal transfer alleles. Both mechanisms are likely to occur in natural environments. The simple process of strong dilution can create sufficient population structure to select for donor ability. Another mechanism observed in natural isolates, discrimination in transfer, can emerge through coselection of transfer and discrimination alleles. Our work shows that horizontal gene transfer in bacteria can be promoted by bacterial hosts themselves and not only by MGEs. In the longer term, the success of cells bearing beneficial MGEs combined with biased transfer leads to an association between high donor ability, discrimination, and mobile beneficial genes. However, in conditions that do not select for altruism, host bacteria promoting transfer are outcompeted by hosts with lower transfer rate, an aspect that could be relevant in the fight against the spread of antibiotic resistance.</p></div

Evolutionarily stable mutation rate does not depend on the frequency with which the mutation rate changes (Π).

<p>The evolution of mutation rates in the explicit fitness landscape with a valley size of three is shown for several values of Π, as indicated by the colored key. Each curve shows the average of 20 runs; the adjacent bands represent±1 s.e.m. The value of <i>U_opt</i> was determined in previous experiments (see text). The rate of approach toward the evolutionarily stable mutation rate depends on Π, but the equilibrium value itself does not.</p

Selection of donor ability in structured populations.

<p><b>A: Experimental setup.</b> D⁺ (good donor, red) and D⁻ (nondonor, blue) strains are competed. 2.5% of D⁺ and D⁻ cells initially carry C plasmids (bright colours), while 97.5% do not (pale colours). The population <i>m</i> is a single well-mixed population; metapopulation <i>s</i> consists of two subpopulations, <i>s</i>_<i>1</i> and <i>s</i>_<i>2</i>, with initial D⁺/D⁻ ratios of 1/9 and 9/1. After growth and transfer (t₀ to t₁), subpopulations from <i>s</i> are pooled and cells are grown to saturation with or without antibiotic (Cm) selection (t₁ to t₂). The proportions of different cell types are represented schematically and do not correspond to actual numbers. <b>B: Selection of D</b>^<b>+</b> <b>strain.</b> The frequency of the good donor D⁺ is shown for <i>s</i> (black) and <i>m</i> (green) populations, with (plain lines) or without (dashed lines) Cm antibiotic during the selection phase. Good donors are only selected for in the <i>s</i> metapopulation, in the presence of antibiotic. <b>C: Plasmid dynamics.</b> Plasmid frequency in each population is shown for the transfer phase (from t₀ to t₁)_, in each of <i>m</i>, <i>s</i>_<i>1</i>, and <i>s</i>_<i>2</i> populations. Plasmids spread mostly in the s₂ subpopulation, enriched in the better donor, D⁺. <b>D: Transfer bias.</b> The proportion of C plasmids present in D⁺ strain, is shown as a function of time for <i>s</i> and <i>m</i> populations (same colour scheme as in B panel). C plasmids get enriched in the better donor D⁺ strain during the transfer phase, for the structured population <i>s</i>. All results are shown as means ± SEM. (<i>N</i> ≥ 6). Data are available from FigShare at <a href="http://dx.doi.org/10.6084/m9.figshare.3199252" target="_blank">http://dx.doi.org/10.6084/m9.figshare.3199252</a>.</p