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What advantage would the initial 'donor' in horizontal gene transfer by conjugation have received?

What advantage would the initial 'donor' in horizontal gene transfer by conjugation have received?


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I am struggling to think why horizontal gene transfer between bacteria would have persisted during the course of evolution as surely it puts the 'donor' at a disadvantage?

For example, consider a hypothetical situation where only one species of bacteria has a gene to resist a new (almost) omnipotent antibiotic. When growing on this medium, this species of bacteria has nil inter-specific competition for the resources it needs. Yet if it passes on the plasmid containing the resistance gene via conjugation to individuals of a different bacterial species, they then too are able to grow on the medium and potentially out-compete the first species - thus the conjugation could possibly be extremely detrimental to the survival of the original population.

I'm obviously missing something as the feature has persisted, so what is the advantage to the donor species of conjugation?


The donor bacterium, viewed as a unit, may well gain no advantage from sharing its genes. The genes that are shared, however, may gain a very substantial reproductive advantage from being able to spread to other strains. If some of the genes that are capable of being shared also promote sharing, they may be selected for by evolution.

This is a good example of the basic principle of the gene-centered view of evolution, famously popularized by Richard Dawkins is his book The Selfish Gene: evolution selects for genes that are good at spreading themselves. This may, but need not always, imply helping the organism containing the gene to survive and reproduce.


Demonstrating plasmid-based horizontal gene transfer in complex environmental matrices: A practical approach for a critical review

We examined three methods for assessing plasmid transfer in environmental matrices.

Isolation of transconjugants by culture is impaired by background antibioresistances.

Detection of plasmid transfer by fluorescence is unsuitable for environmental samples.

Molecular-based methods allow detecting rare successful plasmid transfer events.

Eukaryotic predation both promotes and limits plasmid transfer in natural communities.


Background

The recombination of genetic material that leads to the creation of novel and beneficial traits is achieved by different means in different organisms. In prokaryotes and their communities, it is achieved through bacterial conjugation, viral transduction, and transformation. All of these processes can lead to horizontal gene transfer, a prominent mode of recombination between bacterial genomes [1,2,3]. Such transfer occurs both between closely related and highly divergent species [4], and it can confer novel traits that help microbes adapt to a broad range of environments [5,6,7]. Horizontal gene transfer can involve DNA molecules that are circular or linear, single-stranded or double stranded, self-replicating or not [3, 8,9,10]. These molecules can be integrated into the host genome via homology-based or illegitimate recombination [11], whose by-products may include gene duplications [12] and large-scale structural genomic changes [13, 14]. Homologous recombination is the most common means for genomic integration of horizontally acquired genes [15]. It generally requires the RecBCD enzyme [16], which is highly conserved among bacteria [17]. Although homologous recombination may have originated as a DNA repair mechanism [18], it plays an important role in adaptive evolution, for example by inserting or replacing gene clusters that facilitate local adaptation [19]. In E. coli, recombination is no less frequent than spontaneous mutation [20,21,22], suggesting that recombination contributes substantially to genome evolution.

Horizontal gene transfer helps augment the genetic diversity of a microbial population or community by shuffling genes in the “flexible genome” [23], a part of the genome whose genes are often private to a locally adapted strain. Recent comparative studies of 2000 E. coli genomes indicate that the flexible genome may comprise thousands of different gene families, which may help E. coli to occupy a wide range of ecological niches [24].

Horizontally transferred genes typically have functions different from core housekeeping genes. They are lost and gained more readily than core genes [25,26,27], and can endow their recipient with new traits that facilitate adaptation to a changing environment [28, 29]. For example, the horizontal transfer of such genes has helped marine microbes adapt to a variety of carbon sources [19, 30], it has helped bacteria adapt to extreme environments [31, 32], and it has helped gut microbial communities or pathogens adapt to human hosts [33, 34]. A recent comparative studies of 53 E. coli genomes showed that at least 10% of adaptations to new environments may have been achieved by horizontal gene transfer [35].

Most evidence of recent horizontal gene transfer in prokaryotic communities comes from comparative genomics studies or phylogenetic reconstructions [36,37,38]. These often focus on horizontal transfer among phenotypically differentiated organisms of the same species, such as pathogenic and non-pathogenic strains [36, 39]. Such studies can help identify key horizontally transferred genes that confer novel traits, but they are inconclusive about the immediate fitness benefit (if any) of a horizontal gene transfer event, which may transfer one or few driver genes along with multiple passenger genes that may impose fitness costs on host [40]. Such fitness information can be provided by laboratory evolution experiments [41, 42]. However, there are few such experiments that study horizontal gene transfer [43,44,45,46,47,48,49], and even fewer that do not focus on the transfer of plasmids [46,47,48] but of chromosomal genes [41,42,43]. In one of the latter experiments [43], E. coli K12 adapted to a constant glucose minimum environment while recombining with E. coli B REL606. Although recombination conferred increased genetic diversity, it did not improve growth significantly. In a more recent experiment, replacing ribosomal protein coding genes of Salmonella typhimurium with orthologues from other eubacteria, yeast, or archaea resulted in poor fitnesses due to suboptimal expression of these foreign genes [44]. However, after 40-250 genereations of laboratory evolution, fitness improved through amplification of the affected genes. In a third experiment [45], Salmonella transformed with random chromosomal DNA fragments from Bacteroides fragilis, Proteus mirabilis, and human intestinal phages showed reduced fitness in a constant glucose minmum environment, suggesting that horizontal gene transfer can be costly. Neither of these experiments demonstrated a direct advantage of horizontal gene transfer observed in natural populations [5, 50].

We here conducted laboratory evolution experiments that aimed to address several fundamental questions. Can the advantages of horizontal gene transfer be demonstrated on the short time scales of laboratory evolution? And if so, what is the genetic basis of specific adaptive changes brought about by horizontal gene transfer in evolution experiments? To address these and related questions, we conducted two evolution experiments that expose DNA recipient strains of E.coli to various donor strains that can transfer DNA to them, and that select for the recipient’s viability on a novel carbon and energy source. In the first experiment, we used a carbon source on which the donor could grow, but the recipient could not, such that horizontal transfer and recombination would be required for growth of the recipient. In the second experiment we used a carbon source on which neither donor nor recipient could grow, such that a combination of recombination and point mutations might be needed to ensure the recipient’s viability.

At the end of the evolution experiments, we measured the growth phenotypes of evolving populations, and analysed the complete genomes of 65 clones from these populations. Our observations show that the advantage of horizontal gene transfer depends critically on the growth environment, and less so on the donor strain. Horizontal gene transfer was the key driver for adaptation on HPA, whereas a combination of point mutations and horizontal transfer events may have facilitated adaptation on butyric acid.


3 Results

3.1 Validation of the experimental system – test of model assumptions

The model assumes that the position of cells follows a Poisson process, so that intracellular distances have a distribution function given by Eq. 1 above. Comparisons with experimental distances revealed no significant deviation from this model ( Fig. 1). Experimentally determined cell numbers and colony radius increased exponentially during the 9 h incubation period. Specific growth rates, as well as colony radial specific growth rates, were not significantly different between the donor and recipient strains (P>0.05) and the average values for each growth rate were used as model constants ( Table 1).

Experimental intercellular distances (bars) and density functions predicted by the model (lines) for intensities of (cells μm −2 ): 0.06×10 −3 (A), 0.17×10 −3 (B), 1.92×10 −3 (C) and 1.65×10 −3 (D). n=33, 49, 67 and 84 respectively. Chi-square tests indicated that experimental and model distances did not differ significantly (P>0.05).

Experimental intercellular distances (bars) and density functions predicted by the model (lines) for intensities of (cells μm −2 ): 0.06×10 −3 (A), 0.17×10 −3 (B), 1.92×10 −3 (C) and 1.65×10 −3 (D). n=33, 49, 67 and 84 respectively. Chi-square tests indicated that experimental and model distances did not differ significantly (P>0.05).

The influence of motility on colony expansion and conjugation was assessed by comparing changes in colony diameter and plasmid transfer using the P. fluorescens MON787 system and non-motile mutants of the donor and recipient strains. Rates of colony expansion or changes in donor, recipient or transconjugant numbers were not significantly different (P>0.05, data not shown). Therefore, motility effects were considered to be negligible for our experimental system.

3.2 Model testing 1: comparison of computer simulation experiments and model predictions

Computer simulations of bacterial colonies developing on filters were carried out to test model predictions concerning establishment of contact between colonies (clumping) and conjugation. The approximation defining the probability of clumps of n colonies occurring ( Eq. 2) was good when the number of colonies per unit area was low (low λ), so that the mean clump size was small, i.e. less than five ( Table 2). Simulations gave a larger probability of single colonies because of the finite area (colonies at the edge can have no neighbours on one side). The theoretical mean clump sizes were accurate for low λ, but as λ increased the theoretical mean clump size and the simulation results differed. The approximation worked well for values of λ up to 0.2, when the area of discs is 60% of the area of the surface. In our experimental system, this corresponds to inocula lower than 1.8×10 7 cells per filter, which was generally the case, except for the highest inoculum levels in experiments 1–5 (see Section 3.3).

Comparison of clump size estimates (i.e. number of colonies that are meeting at a certain time) using the model approximate theory against those obtained by computer simulation experiments

Intensity a (λ) Probability of colony in clump of size one Mean clump size
Theory Simulation Theory Simulation
0.01 0.882 0.882 1.07 1.07
0.02 0.778 0.780 1.14 1.13
0.05 0.533 0.537 1.39 1.38
0.1 0.285 0.292 2.00 1.94
0.2 0.0810 0.0966 4.51 3.95
0.5 0.00187 0.00821 85.1 74.5
Intensity a (λ) Probability of colony in clump of size one Mean clump size
Theory Simulation Theory Simulation
0.01 0.882 0.882 1.07 1.07
0.02 0.778 0.780 1.14 1.13
0.05 0.533 0.537 1.39 1.38
0.1 0.285 0.292 2.00 1.94
0.2 0.0810 0.0966 4.51 3.95
0.5 0.00187 0.00821 85.1 74.5

Results are based on hypothetical colonies of unit radius placed on a filter with radius 150. Simulation results are based on 1000 replicates.

a The number of colonies per unit area in a Poisson distribution.

Comparison of clump size estimates (i.e. number of colonies that are meeting at a certain time) using the model approximate theory against those obtained by computer simulation experiments

Intensity a (λ) Probability of colony in clump of size one Mean clump size
Theory Simulation Theory Simulation
0.01 0.882 0.882 1.07 1.07
0.02 0.778 0.780 1.14 1.13
0.05 0.533 0.537 1.39 1.38
0.1 0.285 0.292 2.00 1.94
0.2 0.0810 0.0966 4.51 3.95
0.5 0.00187 0.00821 85.1 74.5
Intensity a (λ) Probability of colony in clump of size one Mean clump size
Theory Simulation Theory Simulation
0.01 0.882 0.882 1.07 1.07
0.02 0.778 0.780 1.14 1.13
0.05 0.533 0.537 1.39 1.38
0.1 0.285 0.292 2.00 1.94
0.2 0.0810 0.0966 4.51 3.95
0.5 0.00187 0.00821 85.1 74.5

Results are based on hypothetical colonies of unit radius placed on a filter with radius 150. Simulation results are based on 1000 replicates.

a The number of colonies per unit area in a Poisson distribution.

When the assumption of instantaneous conjugation was dropped, predictions from the mathematical model agreed with results from computer simulation experiments when the intensity was very low (λ<0.1), and gave qualitatively similar results with λ between 0.1 and 0.2. When λ=0.2 the predicted number of transconjugants overestimated simulated results by about 15%.

3.3 Model testing 2: laboratory experimental testing of model predictions

Model predictions of final donors, recipients and transconjugants were obtained for varying donor and recipient inoculum sizes and initial nutrient concentration, using the model parameter values given in Table 1. We tested these predictions against final donor, recipient and transconjugant numbers in the experimental system, over a range of inoculum concentrations ( Figs. 2–4). Each set of conditions was triplicated and values are presented as means from triplicates with associated 95% confidence intervals. We use the term ‘accurate’ for predictions that fall within the 95% confidence limits of experimental data. When transconjugant cell numbers approached or exceeded those of one of the parental strains, the transconjugants grew on rifampicin- or on kanamycin-supplemented media to levels that masked those of the parental strains. When this was the case, no experimental values for recipient or donor cells were presented in figures and, for simplicity, the respective predicted values were also omitted from figures.

Predicted (pred) and experimental (obs) numbers of donors (D), recipients (R,) and transconjugants (T) following inoculation with similar numbers of donors and recipients in the range log 1.03–7.03 for donors and log 1.24–7.24 for recipients. Triplicate Nuclepore filters were incubated over LB agar, at 30°C, for 9 h. Error bars represent 95% confidence limits for experimental data. The experimental detection limit was log 1.35 (CFU). Model predictions for instantaneous conjugation (A) and for conjugation occurring after 2.25 h of contact (B). Donor predictions remain unchanged and are not presented again in panel B.

Predicted (pred) and experimental (obs) numbers of donors (D), recipients (R,) and transconjugants (T) following inoculation with similar numbers of donors and recipients in the range log 1.03–7.03 for donors and log 1.24–7.24 for recipients. Triplicate Nuclepore filters were incubated over LB agar, at 30°C, for 9 h. Error bars represent 95% confidence limits for experimental data. The experimental detection limit was log 1.35 (CFU). Model predictions for instantaneous conjugation (A) and for conjugation occurring after 2.25 h of contact (B). Donor predictions remain unchanged and are not presented again in panel B.

Predicted and experimental final numbers of donors, recipients and transconjugants for initial donor numbers varied from log 1.77 to 7.77 and initial recipient numbers kept constant at log 1.86, with instantaneous conjugation (A) or conjugation occurring after 2.25 h of contact (C) initial donor numbers varied from log 1.32 to 7.32 and initial recipient numbers kept constant at log 5.2, with instantaneous conjugation (B) or conjugation occurring after 2.25 h of contact (D). Donor predictions remain unchanged and are not presented again in panels B and D. Symbols, error bars, experimental details as described in Fig. 2.

Predicted and experimental final numbers of donors, recipients and transconjugants for initial donor numbers varied from log 1.77 to 7.77 and initial recipient numbers kept constant at log 1.86, with instantaneous conjugation (A) or conjugation occurring after 2.25 h of contact (C) initial donor numbers varied from log 1.32 to 7.32 and initial recipient numbers kept constant at log 5.2, with instantaneous conjugation (B) or conjugation occurring after 2.25 h of contact (D). Donor predictions remain unchanged and are not presented again in panels B and D. Symbols, error bars, experimental details as described in Fig. 2.

Predicted and experimental final numbers of donors, recipients and transconjugants for initial recipient numbers varied from log 1.41 to 7.41 and initial donor numbers kept constant at log 4.47, with instantaneous conjugation (A) or conjugation occurring after 2.25 h of contact (C) and initial recipient numbers varied from log 1.64 to 7.64 and initial donor numbers kept constant at log 5.48, with instantaneous conjugation (B) or conjugation occurring after 2.25 h of contact (D). Donor predictions remain unchanged and are not presented again in panels B and D. Symbols, error bars, experimental details as described in Fig. 2.

Predicted and experimental final numbers of donors, recipients and transconjugants for initial recipient numbers varied from log 1.41 to 7.41 and initial donor numbers kept constant at log 4.47, with instantaneous conjugation (A) or conjugation occurring after 2.25 h of contact (C) and initial recipient numbers varied from log 1.64 to 7.64 and initial donor numbers kept constant at log 5.48, with instantaneous conjugation (B) or conjugation occurring after 2.25 h of contact (D). Donor predictions remain unchanged and are not presented again in panels B and D. Symbols, error bars, experimental details as described in Fig. 2.

In experiment 1 we inoculated similar numbers of donor and recipient cells on filters (range 1.08×10 1 –1.08×10 7 donor and 1.75 ×10 1 –1.75×10 7 recipient cells). Final donor cell numbers were generally predicted accurately, increasing with inoculum size to a maximum value, equivalent to the maximum yield, following complete nutrient utilisation ( Fig. 2A). Predicted final recipient cell numbers were close to experimental numbers until an inoculum of 2.78×10 4 cells, after which the model predicts a decline in recipient numbers due to considering that these are being converted to transconjugants. However, experimental recipient numbers were two orders of magnitude greater than predicted for an inoculum of 2.78×10 5 , indicating an overestimation of conjugation by the model. At the lowest inoculum level (2.78×10 1 ), the predicted number of transconjugants was below the level of detection of the experimental system (2.25×10 1 ). For inoculum levels greater than 2.78×10 3 , predicted transconjugant numbers were greater than those detected experimentally, confirming overestimation of conjugation by the model. Nevertheless, the rate of change in transconjugant numbers with increasing inoculum size followed that of the experimental system closely. Transconjugant numbers increased at a greater rate than donor cells, due to combined effects of plasmid acquisition and growth.

Experiments 2 and 3 investigated increasing donor:recipient inoculum ratio at constant initial recipient cell number. In experiment 2 ( Fig. 3A), donor inocula were varied from 5.87×10 1 to 5.87×10 7 and recipient inocula were 7.28×10 1 cells. Donor predictions were accurate and recipient predictions were generally an underestimation by half a log unit. Predicted transconjugant numbers followed the experimental trends closely, but those for donor inocula of 5.87×10 1 , 5.87×10 4 and 5.87×10 7 were overestimated by half a log unit. As donor inoculum size increased, conjugation increased, due to greater numbers of encounters between donor and recipient colonies within the incubation period and prior to complete nutrient utilisation. Maximum conjugation in the experimental system occurred with a donor inoculum of 5.87×10 5 . At higher inocula, conjugation was reduced due to earlier nutrient exhaustion by the higher number of donor cells. This decline is also predicted by the model.

In experiment 3 initial donors were varied from 2.1×10 1 to 2.1×10 7 and the recipients kept constant at a higher inoculum (1.58×10 5 ) ( Fig. 3B). The model underestimated donor numbers by 0.3–0.9 of a log unit and a decline is predicted for recipient numbers for donor inocula greater than 2.1×10 3 , which is not observed experimentally. The model overestimated transconjugant numbers, the discrepancy between model and experimental results varying between 0.2 and 1.7 of a log unit and being greater for lower donor inocula. Underestimation of recipients results from the overestimation of conjugation. Underestimation of donors seems to indicate a faster growth rate in the experimental system. When the 95% upper confidence limit for the population growth rate (0.924 cells h −1 ) was used, donor predictions matched experimental data (data not shown). Experimental transconjugant numbers peaked at a donor inoculum of 2.1×10 5 –2.1×10 6 followed by a decrease for the highest inoculum, as predicted by the model and explained in the previous paragraph.

Increasing recipient:donor ratios at constant donor inoculum was studied in experiments 4 and 5. In experiment 4 ( Fig. 4A), initial donor numbers were 2.95×10 4 and recipients were varied from 2.6× 10 1 to 2.6×10 7 . Donor numbers were predicted accurately. In the experimental system, final donors could not be distinguished from transconjugants at recipient inocula greater than 2.6×10 5 . Likewise, predicted donor numbers for these initial conditions were lower than predicted transconjugants (not shown). Changes in final recipients were accurately predicted until a recipient inoculum of 2.6×10 4 , above which predicted recipients declined due to predicted extensive conjugation. In the experimental system, however, recipients continued to be detected, indicating lower levels of conjugation. Predicted transconjugant numbers were an overestimation by 0.3–1.8 log units, but followed the general experimental trend, increasing with increasing proportions of recipients.

In experiment 5 ( Fig. 4B), initial donor numbers were 3.07×10 5 and recipients were varied from 4.37×10 1 to 4.37×10 7 . Donor numbers were in general underestimated by the model by 0.5 log unit however, if the 95% upper confidence limit for the population growth rate (0.924 cells h −1 ) was used, donor predictions were accurate (data not shown). Transconjugant numbers were slightly underestimated (by less than 0.5 log unit) by the model until a recipient inoculum of 4.37×10 5 , beyond which they were overestimated by 0.5–1 log unit. Final recipient numbers could not be determined experimentally, except for the highest inoculum (4.37×10 7 ), when they were present at a very high level and the model predicts that all would have become transconjugants. This confirms overestimation of conjugation at high initial cell numbers.

The effects of nutrient concentration on conjugation were examined by floating triplicate filters, inoculated with 3.98×10 5 donor and 5.01×10 5 recipient cells, on LB broth, or different dilutions of LB broth in 0.85% (w/v) NaCl, for 9 h at 30°C (experiment 6, Fig. 5A). No transconjugants were found in the liquid medium beneath floating filters at the end of the experiment, except for 0.6% LB broth, where they constituted 0.065% of the total number of transconjugants. Transconjugants arising from conjugation events in resuspension media and on plates during incubation were less than 2.5% of total numbers. The values for parameters used in the model were given in Table 1. The lowest level of nutrient supported both cell growth and conjugation in the experimental system. Increasing nutrient concentration led to increasing cell numbers as nutrients were exhausted later, increasing total colony expansion and the number of encounters between donor and recipient cells. As with most previous experiments, donor predictions were accurate but transconjugants were overestimated by 0.3–1 log unit. Recipient predictions were less accurate, with underestimation by several orders of magnitude for the higher nutrient levels.

Predicted and experimental final numbers of donors, recipients and transconjugants for initial donor and recipient numbers of log 5.6 and 5.67, respectively, when nutrient concentration was varied experimentally. Model predictions for instantaneous conjugation (A) or conjugation occurring after 2.25 h of contact (B). Donor predictions remain unchanged and are not presented again in panel B. Triplicate Nuclepore filters were floated over different dilutions LB broth in 0.85% (w/v) NaCl or full strength LB broth and incubated at 30°C, for 9 h. Symbols and error bars as described in Fig. 2.

Predicted and experimental final numbers of donors, recipients and transconjugants for initial donor and recipient numbers of log 5.6 and 5.67, respectively, when nutrient concentration was varied experimentally. Model predictions for instantaneous conjugation (A) or conjugation occurring after 2.25 h of contact (B). Donor predictions remain unchanged and are not presented again in panel B. Triplicate Nuclepore filters were floated over different dilutions LB broth in 0.85% (w/v) NaCl or full strength LB broth and incubated at 30°C, for 9 h. Symbols and error bars as described in Fig. 2.

3.4 Consideration of conjugation time by the model

The model predictions presented so far were obtained on the assumption that conjugation occurred instantly when donor or transconjugant colonies met recipient microcolonies. A tentative delay in conjugation was subsequently taken into account by the model, so that all cells in a recipient colony only became transconjugants after contact for a certain length of time. Model predictions for transconjugants and recipients were greatly improved (generally to within half a log unit of experimental data) by a delay of 2–3 h depending on the experiment. A delay of 2.25 h gave the best overall fit and the corresponding predictions were presented in Figs. 2B, 3C, D, 4C, D and 5B. Donor predictions were unaltered by the delay and were not presented again. The most dramatic improvements in predictions were for recipients in experiment 1 ( Fig. 2B), transconjugants and recipients in experiment 3 ( Fig. 3D), transconjugants and recipients corresponding to a recipient inoculum of up to 2.6×10 4 –10 5 in experiment 4 ( Fig. 4C) and recipients in experiment 6 ( Fig. 5). Significant discrepancies between experimental transconjugants and recipients and respective predictions by the model were still observed for conditions where recipient inocula were larger than donor inocula. This occurred for transconjugants and recipients corresponding to a recipient inoculum greater than 2.6×10 5 in experiment 4 ( Fig. 4C) and for transconjugants and recipients arising from recipient inocula greater than 4.37×10 5 ( Fig. 4D).


Phylogenetic Methods

The use of phylogenetic analysis in the detection of HGT was advanced by the availability of many newly sequenced genomes. Phylogenetic methods detect inconsistencies in gene and species evolutionary history in two ways: explicitly, by reconstructing the gene tree and reconciling it with the reference species tree, or implicitly, by examining aspects that correlate with the evolutionary history of the genes in question, e.g., patterns of presence and absence across species, or unexpectedly short or distant pairwise evolutionary distances.

Explicit phylogenetic methods

The aim of explicit phylogenetic methods is to compare gene trees with their associated species trees. While weakly-supported differences between gene and species trees can be due to inference uncertainty, statistically significant differences can be suggestive of HGT events (see Fig 1A). For example, if two genes from different species share the most recent ancestral connecting node in the gene tree, but the respective species are spaced apart in the species tree, an HGT event can be invoked. Such an approach can produce more detailed results than parametric approaches because the involved species, time, and direction of transfer can potentially be identified.

As discussed in more details below, phylogenetic methods range from simple methods merely identifying discordance between gene and species trees to mechanistic models inferring probable sequences of HGT events. An intermediate strategy entails deconstructing the gene tree into smaller parts until each matches the species tree (genome spectral approaches).

Explicit phylogenetic methods rely upon the accuracy of the input rooted gene and species trees, yet these can be challenging to build [41]. Even when there is no doubt in the input trees, the conflicting phylogenies can be the result of evolutionary processes other than HGT, such as duplications and losses, causing these methods to erroneously infer HGT events when paralogy is the correct explanation. Similarly, in the presence of incomplete lineage sorting, explicit phylogeny methods can erroneously infer HGT events [42]. That is why some explicit model-based methods test multiple evolutionary scenarios involving different kinds of events and compare their fit to the data, given parsimonious or probabilistic criteria.

Tests of topologies.

To detect sets of genes that fit poorly to the reference tree, one can use statistical tests of topology, such as the Kishino-Hasegawa (KH) [43], Shimodaira-Hasegawa (SH) [44], and Approximately Unbiased (AU) [45] tests. These tests assess the likelihood of the gene sequence alignment when the reference topology is given as the null hypothesis.

The rejection of the reference topology is an indication that the evolutionary history for that gene family is inconsistent with the reference tree. When these inconsistencies cannot be explained using a small number of nonhorizontal events, such as gene loss and duplication, an HGT event is inferred.

One such analysis checked for HGT in groups of homologs of the γ-Proteobacterial lineage [46]. Six reference trees were reconstructed using either the highly conserved small subunit ribosomal RNA sequences, a consensus of the available gene trees or concatenated alignments of orthologs. The failure to reject the six evaluated topologies, and the rejection of seven alternative topologies, was interpreted as evidence for a small number of HGT events in the selected groups.

Tests of topology identify differences in tree topology taking into account the uncertainty in tree inference, but they make no attempt at inferring how the differences came about. To infer the specifics of particular events, genome spectral or subtree pruning and regraft methods are required.

Genome spectral approaches.

In order to identify the location of HGT events, genome spectral approaches decompose a gene tree into substructures (such as bipartitions or quartets) and identify those that are consistent or inconsistent with the species tree.

Removing one edge from a reference tree produces two unconnected subtrees, each containing a disjoint set of nodes—a bipartition. If a bipartition is present in both the gene and the species trees, it is compatible otherwise, it is conflicting. These conflicts can indicate an HGT event or may be the result of uncertainty in gene tree inference. To reduce uncertainty, bipartition analyses typically focus on strongly supported bipartitions such as those associated with branches with bootstrap values or posterior probabilities above certain thresholds. Any gene family found to have one or several conflicting, but strongly supported, bipartitions is considered as an HGT candidate [47,48].

Alternatively, trees can be decomposed into quartets. Quartets are trees consisting of four leaves. In bifurcating (fully resolved) trees, each internal branch induces a quartet whose leaves are either subtrees of the original tree or actual leaves of the original tree. If the topology of a quartet extracted from the reference species tree is embedded in the gene tree, the quartet is compatible with the gene tree. Conversely, incompatible strongly supported quartets indicate potential HGT events [49]. Quartet mapping methods are much more computationally efficient and naturally handle heterogeneous representation of taxa among gene families, making them a good basis for developing large-scale scans for HGT, looking for highways of gene sharing in databases of hundreds of complete genomes [50,51].

Subtree pruning and regrafting.

A mechanistic way of modelling an HGT event on the reference tree is to first cut an internal branch—i.e., prune the tree—and then regraft it onto another edge, an operation referred to as subtree pruning and regrafting (SPR) [52]. If the gene tree was topologically consistent with the original reference tree, the editing results in an inconsistency. Similarly, when the original gene tree is inconsistent with the reference tree, it is possible to obtain a consistent topology by a series of one or more prune and regraft operations applied to the reference tree. By interpreting the edit path of pruning and regrafting, HGT candidate nodes can be flagged and the host and donor genomes inferred [48,53]. To avoid reporting false positive HGT events due to uncertain gene tree topologies, the optimal "path" of SPR operations can be chosen among multiple possible combinations by considering the branch support in the gene tree. Weakly supported gene tree edges can be ignored a priori [54], or the support can be used to compute an optimality criterion [55,56].

Because conversion of one tree to another by a minimum number of SPR operations is NP-Hard [57], solving the problem becomes considerably more difficult as more nodes are considered. The computational challenge lies in finding the optimal edit path, i.e., the one that requires the fewest steps [58,59], and different strategies are used in solving the problem. For example, the HorizStory algorithm reduces the problem by first eliminating the consistent nodes [60] recursive pruning and regrafting reconciles the reference tree with the gene tree and optimal edits are interpreted as HGT events. The SPR methods included in the supertree reconstruction package SPRSupertrees substantially decrease the time of the search for the optimal set of SPR operations by considering multiple localised subproblems in large trees through a clustering approach [61].

Model-based reconciliation methods.

Reconciliation of gene and species trees entails mapping evolutionary events onto gene trees in a way that makes them concordant with the species tree, given a mechanistic model. Different reconciliation models exist, differing in the types of event they consider to explain the incongruences between gene and species tree topologies. Early methods exclusively modelled horizontal transfers (T) [52,55]. More recent ones also account for duplication (D), loss (L), incomplete lineage sorting (ILS), or homologous recombination (HR) events. The difficulty is that by allowing for multiple types of events, the number of possible reconciliations increases rapidly. For instance, conflicting gene tree topologies might be explained in terms of a single HGT event or multiple duplication and loss events. Both alternatives can be considered plausible reconciliation depending on the frequency of these respective events along the species tree.

Reconciliation methods can rely on a parsimonious or a probabilistic framework to infer the most likely scenario(s), where the relative cost and probability of D, T, and L events can be fixed a priori or estimated from the data [62]. The space of DTL reconciliations and their parsimony costs—which can be extremely vast for large multicopy gene family trees—can be efficiently explored through dynamic programming algorithms [63–65]. In some programs, the gene tree topology can be refined where it was uncertain to fit a better evolutionary scenario as well as the initial sequence alignment [63,66,67]. More refined models account for the biased frequency of HGT between closely related lineages [68], reflecting the loss of efficiency of HR with phylogenetic distance [69], for ILS [70], or for the fact that the actual donor of most HGT belong to extinct or unsampled lineages [71]. Further extensions of DTL models are being developed towards an integrated description of the genome evolution processes. In particular, some of them consider horizontal transfer at multiple scales—modelling independent evolution of gene fragments [72] or recognising coevolution of several genes (e.g., due to cotransfer) within and across genomes [73].

Implicit phylogenetic methods

In contrast to explicit phylogenetic methods, which compare the agreement between gene and species trees, implicit phylogenetic methods compare evolutionary distances or sequence similarity. Here, an unexpectedly short or long distance from a given reference compared to the average can be suggestive of an HGT event (see Fig 1). Because tree construction is not required, implicit approaches tend to be simpler and faster than explicit methods.

However, implicit methods can be limited by disparities between the underlying correct phylogeny and the evolutionary distances considered. For instance, the most similar sequence as obtained by the highest-scoring BLAST hit is not always the evolutionarily closest one [74].

Top sequence match in a distant species.

A simple way of identifying HGT events is by looking for high-scoring sequence matches in distantly related species. For example, an analysis of the top BLAST hits of protein sequences in the bacteria Thermotoga maritima revealed that most hits were in archaea rather than closely-related bacteria, suggesting extensive HGT between the two [37] these predictions were later supported by an analysis of the structural features of the DNA molecule [17].

However, this method is limited to detecting relatively recent HGT events. Indeed, if the HGT occurred in the common ancestor of two or more species included in the database, the closest hit will reside within that clade, and therefore the HGT will not be detected by the method. Thus, the threshold of the minimum number of foreign top BLAST hits to observe to decide a gene was transferred is highly dependent on the taxonomic coverage of sequence databases. Therefore, experimental settings may need to be defined in an ad-hoc way [75].

Discrepancy between gene and species distances.

The molecular clock hypothesis posits that homologous genes evolve at an approximately constant rate across different species [76]. If one only considers homologous genes related through speciation events (referred to as “orthologous" genes), their underlying tree should by definition correspond to the species tree. Therefore, assuming a molecular clock, the evolutionary distance between orthologous genes should be approximately proportional to the evolutionary distances between their respective species. If a putative group of orthologs contains xenologs (pairs of genes related through an HGT), the proportionality of evolutionary distances may only hold among the orthologs, not the xenologs [77].

Simple approaches compare the distribution of similarity scores of particular sequences and their orthologous counterparts in other species HGT are inferred from outliers [78,79]. The more sophisticated DLIGHT (Distance Likelihood-based Inference of Genes Horizontally Transferred) method considers simultaneously the effect of HGT on all sequences within groups of putative orthologs [7]: if a likelihood-ratio test of the HGT hypothesis versus a hypothesis of no HGT is significant, a putative HGT event is inferred. In addition, the method allows inference of potential donor and recipient species and provides an estimation of the time since the HGT event.

Phylogenetic profiles.

A group of orthologous or homologous genes can be analysed in terms of the presence or absence of group members in the reference genomes such patterns are called phylogenetic profiles [80]. To find HGT events, phylogenetic profiles are scanned for an unusual distribution of genes. Isolated occurrence of a gene, i.e., absence of a homolog in other members of a group of closely related species is an indication that the examined gene might have arrived via an HGT event. For example, the three facultatively symbiotic Frankia spp. strains are of strikingly different sizes: 5.43 Mbp, 7.50 Mbp, and 9.04 Mbp, depending on their range of hosts [81]. Marked portions of strain-specific genes were found to have no significant hit in the reference database and were possibly acquired by HGT transfers from other bacteria. Similarly, three phenotypically diverse Escherichia coli strains (uropathogenic, enterohemorrhagic, and benign) shared about 40% of the total combined gene pool, with the other 60% being strain-specific genes and, consequently, HGT candidates [82]. Further evidence for these genes resulting from HGT was their strikingly different codon usage patterns from the core genes and a lack of gene order conservation (order conservation is typical of vertically-evolved genes) [82]. The presence and absence of homologs (or their effective count) can thus be used by programs to reconstruct the most likely evolutionary scenario along the species tree. Just as with reconciliation methods, this can be achieved through parsimonious [83] or probabilistic estimation of the number of gain and loss events [84,85]. Models can be complexified by adding processes, like the truncation of genes [86], but also by modelling the heterogeneity of rates of gain and loss across lineages [87] and/or gene families [85,88].

Clusters of polymorphic sites.

Genes are commonly regarded as the basic units transferred through an HGT event. However, it is also possible for HGT to occur within genes. For example, it has been shown that horizontal transfer between closely related species results in more exchange of ORF fragments [89,90], a type a transfer called gene conversion, mediated by homologous recombination. The analysis of a group of four E. coli and two Shigella flexneri strains revealed that the sequence stretches common to all six strains contain polymorphic sites, consequences of homologous recombination [91]. Clusters of excess of polymorphic sites can thus be used to detect tracks of DNA recombined with a distant relative [92]. This method of detection is, however, restricted to the sites in common with all analysed sequences, limiting the analysis to a group of closely related organisms.


Sensing Environmental Conditions and Host Cell Physiology

Repressor Inactivation Mediated by the SOS Response

Similarly to the temperate bacteriophage lambda, ICEs contain integrases and excisionases for integration and excision (int/xis in Figure 1B). In SXT, an ICE of Vibrio cholerae, a repressor protein with similarity to the lambda CI repressor, SetR, maintains the OFF status of the integrated conjugative element. The repressor can be inactivated by RecA mediated autocleavage through DNA damaging agents which induce the SOS response. Repressor inactivation is followed by expression of SetC and SetD which act as activators of int/xis and tra genes (Beaber et al., 2004). The low transfer frequency observed for SXT transfer and repressor inactivation is presumably maintained by a subpopulation of cells that inherently express SOS genes (McCool et al., 2004), specific inducers of this system, however, are unknown.

Activation by Specific Nutrients and Quorum Sensing

Agrobacteria harboring Ti plasmids are not only capable of transforming plant cells by T-DNA transfer but also contain a specific set of DNA transfer genes for conjugation. Tra genes are not transcribed unless a specific transcriptional activator, TraR is expressed. First of all, traR transcription is dependent on opines, amino sugars specifically produced by transformed plant tumor cells. Opines can be taken up and used as nutrients only by agrobacteria harboring the Ti plasmid. Opines, specifically nopalines, inactivate a Ti plasmid encoded repressor (AccR) that controls several genes on the Ti plasmid. Among the genes controlled by AccR is the gene for the transcriptional activator TraR. Secondly, TraR acts as a receptor for𠅊nd is additionally activated by𠅊n N-acyl-L-homoserine lactone (AHL) quorum signaling molecule. A positive feedback loop is constituted by the fact that production of AHL by TraI is also under the control of TraR. As a consequence, Ti plasmid encoded tra genes are only turned ON inside crown galls (where opines are produced by plant cells) at high cell densities (reviewed in White and Winans, 2007). In the Ti plasmid system, induction of tra genes is therefore dependent on signal molecule mediated repressor inactivation and activator production, which, once initiated, is enhanced by a positive feedback loop (provided by AHL synthesis), presumably resulting in a burst of tra gene expression in individual cells harboring the Ti plasmid. The system can be turned OFF by anti-activators (TraM and TrlR under the control of TraR—negative feedback loop) and may be modulated by lactonases that can specifically hydrolyze the AHL molecule in response to plant signals (Haudecoeur and Faure, 2010). Besides the Ti plasmid and two chromosomes, Agrobacterium tumefaciens C58 also harbors another large conjugative plasmid, pAT. Interestingly, conjugation genes of pAT are activated by opines but are independent of AHL (Lang et al., 2013).

Activator Escape and Environmental Cues in F-Like Plasmids

Notwithstanding the lack of obvious signaling molecules involved in F-conjugation module mediated DNA transfer, sensing environmental conditions in combination with the physiological status of the potential donor cell affects the behavior of the cell through a network of regulatory elements (for a detailed description see Frost and Koraimann, 2010). CPs with F-like conjugation modules are mainly found in the Enterobacteriaceae including pathogenic Escherichia, Salmonella, and Klebsiella species. Typically, the plasmid encoded transcriptional activator of tra genes, TraJ, is under the negative control of two fertility inhibition elements, FinO and FinP. While FinP is a small regulatory RNA that is produced as a countertranscript to the translation initiation region of the TraJ mRNA, FinO is an RNA chaperone that is required for efficient suppression of TraJ expression (Arthur et al., 2003). In populations of donor cells—under optimal conditions promoting growth and cell division—only in few cells (1� out of 1000 potential donor cells) TraJ escapes this negative FinOP mediated control and promotes transcription of tra genes together with the host encoded transcriptional activator ArcA-P (Strohmaier et al., 1998 Frost and Koraimann, 2010 Wagner et al., 2013). Once initiated, a positive feedback-loop leads to a burst of tra gene expression which ensures the transformation into a transfer competent cell (Dempsey, 1989 Pölzleitner et al., 1997). Similarly to other conjugation systems described in this review, a negative feedback-loop exists that mediates shut-off of tra gene expression via the DNA binding protein TraY which has an activating role at low concentrations but can inhibit tra gene expression at higher concentrations. Other factors that contribute to the shut-off of tra gene transcription or modulate and fine tune this system are extracellular and cellular stress response elements, including the CpxAR two component system, proteases, and the chaperone protein GroEL (Zahrl et al., 2006, 2007 Lau-Wong et al., 2008).


Introduction

Horizontal gene transfer mediated by conjugative plasmids and integrative conjugative elements (ICEs) is a major cause of the rapid spread of bacterial antibiotic resistance [1]. The process starts with a “mating” stage, which depends on contact through sexual pili or cell–cell aggregation proteins followed via a type IV secretion system [2]. Importantly, expression of these conjugation determinants bears a significant fitness cost, reducing the host’s growth rate, implying the existence of a trade-off between the horizontal and vertical modes of plasmid transfer [3]. Indeed, across gram-negative and gram-positive bacteria, plasmid donor populations are generally in a constitutive “off-state,” and only after induction by environmental factors or signaling molecules is conjugation activated [4]. Also, a general feature of these systems is that only a few members of the population activate the response [4]. This property suggests that antibiotic-resistant plasmids and ICEs naturally maintain a high proportion of vertical rather than horizontal transfer. On an evolutionary timescale, under constant low availability of plasmid recipients, plasmid variants with lower conjugation rates are expected to gain a fitness advantage by increasing their vertical inheritance through host proliferation. Contrary to that, evolution is expected to favor plasmid variants with increased conjugation rates under conditions of constant high recipient availability [5]. Therefore, the optimal effort that a plasmid invests on horizontal spread depends principally on the social environment under which conjugation control evolved. However, the primary population parameters determining the potential donor–recipient encounter rates, i.e., the densities of donors and recipients, can vary dynamically at faster timescales through growth, dilution, and migration processes. Intuitively, then, plasmid variants able to dynamically monitor mating likelihood and regulate conjugation effort accordingly could evolve.

Antibiotic-resistant plasmids from Enterococcus faecalis are a major threat to public health [6] because of the efficiency of their pheromone-sensitive conjugation systems [7] and the multiplicity of resistances they can transfer, including those against the last-line antibiotic vancomycin [8]. Plasmids from E. faecalis exhibit the capacity to sense recipient densities [9]. In the pCF10 plasmid and its family members, this mate sensing is achieved with unidirectional signaling based on conserved peptide import–export systems [10–13]. This system is also no exception in terms of the cellular cost of conjugation, given the strong protein expression up-regulation that ensues. Indeed, efficient cell–cell aggregation in this system relies on the high abundance of the aggregation substance from pCF10 (Asc10) protein [14,15], as well as on the expression of >20 pheromone-regulated genes, including the ATP-dependent type IV conjugation machinery [16,17]. Donor populations exposed to a given level of inducer also have the capacity to sort into responding and nonresponding cells—in this case, through a pheromone-dependent bistable switch at the transcriptional level [18].

From an evolutionary perspective, sensing the presence of mates through selection for specificity (Fig 1A, compare left and center panels) is straightforward to interpret because it avoids unproductive donor–donor interactions. Intriguingly, however, in the E. faecalis system, two antagonistic plasmid-encoded, pheromone-sensing systems control conjugation. These integrate information about the presence of potential plasmid recipients (mate-sensing) and about plasmid donors (self-sensing) (Fig 1A, right [16]). Such integration is antagonistic, with recipient-produced cCF10 and donor-produced iCF10 pheromones causing activation and repression of conjugation functions, respectively (Fig 1A, right). Such repressive self-sensing could effectively prevent self-aggregation and unproductive homophilic interactions at high donor densities, in which donor-produced leaky cCF10 (Fig 1A) could accumulate to significant levels. Self-sensing is mediated by basal production of iCF10 during the growth of uninduced donors. This pheromone is essential for keeping the conjugation pathway inactive in donor populations, even if leaky cCF10 accumulates due to growth, as shown by saturated constitutive pathway expression in plasmids carrying a nonfunctional version of iCF10 [19]. We rationalized that the combination of self-sensing and mate sensing could serve another function—namely, conferring to donor cells the capacity to perceive mate availability (ratio sensing) rather than mate concentration only (quorum sensing, [20]). This could enable cells to respond specifically to the population composition (Fig 1B). Intuitively, growth in liquid may increase the rate of accumulation of both cCF10 and iCF10 (Fig 1B, right), but the effects produced by each could balance each other out, producing a response that remains insensitive to fluctuations in the degree of cellular crowding.

(A) Possible topological structures of pheromone sensing in pCF10. Plasmids that perceive cCF10 pheromone (blue balls) from either donors or recipients (left) and consequently activate the pathway in proportion to the total population density (QS) are less efficient at mating than variants that minimize the production of endogenous cCF10 (center, MS). The present-day topological structure involves the presence of an antagonistic extracellular pheromone (iCF10, right). Pheromones cCF10 (blue balls) and iCF10 (red triangles) accumulate in proportion to recipients and donors, respectively. (B) The population parameter disentanglement capabilities of pCF10 might provide a function of iCF10 [D] (left), [R] (center), and [R+D] (right) are sensed differently by each one of the signaling schemes. Contrary to QS and MS, only the RS (green) scheme can distinguish specifically meaningful changes in recipient availability from simple fluctuations in crowding. (C) The mating system of E. faecalis. The pCF10 plasmid in donor cells encodes “mating” (preconjugative) functions involved in self-incompatibility (red), which allows avoidance of nonproductive donor–donor interactions in at least three ways. First, by Sec10 (encoded by prgA) activity, which minimizes interactions of the neighboring cell-wall–associated aggregation substance (Asc10, coded by the prgB gene) with LTA in the cell walls of other donors (not shown) by steric hindrance [21] and keeps those interactions specific for the LTA in recipients (shown). Second, by prgY, which restricts production of the cCF10 pheromone (blue) [22], a secreted product of the normal processing of a protein encoded by the ccfA gene, encoded in the genome which serves as the main cue used for activation. Finally, by secreting the iCF10 pheromone, which antagonizes the effect of cCF10 at the signal integration level (yellow) through competitive binding to the PrgX transcription factor. PrgZ is responsible for pheromone binding along with internalization by the native Opp system (gray) [10,11]. In this study, the pathway’s response was quantified by measuring Asc10-dependent phenotypes, such as adherence to surfaces and sexual aggregate formation, and by monitoring the expression of a GFP reporter cotranscribed with the prgB gene [23]. The reporter’s RBS (white box on transcript) is identical to that of prgB. Functions further downstream of prgB (including the conjugation machinery) are not shown. ccfA, cCF10 pheromone gene [D], donor concentration GFP, green fluorescent protein LTA, lipoteichoic acid MS, mate sensing Opp, oligopeptide permease Prg, pheromone responding gene [R], recipient concentration RBS, ribosome binding site [R+D], total population concentration RS, ratio sensing QS, quorum sensing Sec10, surface exclusion from pCF10.

Here, we demonstrate that density-robust ratiometric control over horizontal plasmid transfer allows antibiotic-resistant Enterococcus plasmid donors to estimate the conjugation likelihood in a cost-effective manner, maximizing their fitness. We further suggest that this mechanism robustly stabilizes the population composition in the long term in the face of variation in resource availability.


Horizontal Gene Transfer

The second edition of Horizontal Gene Transfer has been organized to provide a concise and up-to-date coverage of the most important discoveries in this fascinating field. Written by the most prominent gene transfer and genome analytical scientists, this book details experimental evidence for the phenomenon of horizontal gene transfer and discusses further evidence provided by the recent completion of genomic sequences from Archea, Bacteria, and Eucarya members. The relevance of horizontal gene transfer to plant and metazoan taxonomy, GM foods, antibiotic resistance, paleontology, and phylogenetic reconstruction is also explored. Horizontal Gene Transfer is essential for microbiologists, geneticists, biochemists, evolutionary biologists, infectious disease specialists, paleontologists, ecologists, and researchers working in plant/animal systematics and agriculture with an interest in gene transfer. This includes scientific researchers from government and industry concerned with the release of genetically modified organisms.

The second edition of Horizontal Gene Transfer has been organized to provide a concise and up-to-date coverage of the most important discoveries in this fascinating field. Written by the most prominent gene transfer and genome analytical scientists, this book details experimental evidence for the phenomenon of horizontal gene transfer and discusses further evidence provided by the recent completion of genomic sequences from Archea, Bacteria, and Eucarya members. The relevance of horizontal gene transfer to plant and metazoan taxonomy, GM foods, antibiotic resistance, paleontology, and phylogenetic reconstruction is also explored. Horizontal Gene Transfer is essential for microbiologists, geneticists, biochemists, evolutionary biologists, infectious disease specialists, paleontologists, ecologists, and researchers working in plant/animal systematics and agriculture with an interest in gene transfer. This includes scientific researchers from government and industry concerned with the release of genetically modified organisms.


4. What is LUCA?

The LUCA is a moot point in that it is a theoretical construct designed to explain the origin of especially the Bacteria and Archaea domains, collectively called prokaryotes. It is believed that LUCA existed at the time that these two domains became separate entities in their own right. From this it can be surmised that LUCA may have been a complex and almost fully formed living entity which probably even had DNA as a repository for information, as has been shown by various comparative genomic studies. More to the point, Prof. John Allen (Queen Mary, University of London—see summary report) proposed: what does the word “last” in “last universal common ancestor” signify? There are two lines of thought on this question, i.e., whether LUCA means the ancestor of all things alive on Earth today. or of all things that have ever lived on Earth. In the case of latter supposition it is suggested that, perhaps, its correct title should be the 𠇏irst universal common ancestor”. Such reasoning raises another question: what came before LUCA? Since RNA acts both as a repository of information and as a catalyst, perhaps there were evolving entities made purely from RNAs. So, what was the nature of LUCA? Some theoretical biologists think that LUCA was only one of several designs for early life, from which a single entity capable of evolving into prokaryotes arose. Others have questioned its existence altogether that is, some scientists maintain that there may not even have been such an entity as the LUCA at all, and that it should no longer be considered as a relevant part of evolutionary theory—this view being the central tenet of the “metabolism first hypothesis” as opposed to the “genes first hypothesis”.

However a middle ground is held by those scientists who believe that the LUCA was not a single entity, but a consortium of many “LUCA-like” entities (as was proposed by Prof. Armen Mulkidjanian—see summary report). Use of the term “LUCA-like” is deliberate because although similar to the single entity, the components of such a consortium would not have been individually 𠇌omplete”—sort of proto-LUCAs, as it were. In order for these entities to move up to the next level and form a complete LUCA, they would have had to exchange genes with each other and therefore exist in close proximity, which could have been encased within “semi-permeable” bubbles of clay on the sea floor and/or floating about in small pools of water, where their concentration would have been sufficiently high enough for interaction. This would have facilitated exchange of genetic material (i.e., via HGT). On the balance of probability and based on the evidence derived from the mechanisms used in HGT (see below), I believe it is more than likely that there was a sort of “united nations of LUCAs” in operation at the time.

The explanatory evolutionary tool of LUCA does not, however, explain the presence of viruses, in particular RNA ones. There is evidence to suggest that some viruses arose independently from an RNA-world without the intervention of LUCA and are, thus, not directly connected with the emergence of either Bacteria or Archaea. In this respect some scientists working in the RNA-world hypothesis and comparative genomic studies believe that the word 𠇌ommon” in the acronym LUCA be replaced with �llular”, as it is not in common to viruses. However, it should still be noted that there were extensive exchanges of MGEs between RNA viruses and the LUCA with evolving cellular genomes.


Concluding remarks

Our belief is that to understand how virulence mechanisms such as biofilm formation are established, we need to understand social dilemmas raised by microbial interactions. To achieve the above-mentioned goal, it is essential that we gain a better understanding of the molecular mechanisms involved. HGT and MGEs, such as plasmids, are at the very heart of this. In this review, we have argued that the interconnectedness between biofilm formation and plasmid biology may act as a positive loop that promotes both. The perspective extends to an overall interconnectedness between HGT, MGE and social evolution of bacteria.


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