Recombination resolves the cost of horizontal gene transfer in experimental populations of Helicobacter pylori.

Horizontal gene transfer (HGT) is important for microbial evolution, yet we know little about the fitness effects and dynamics of horizontally transferred genetic variants. In this study, we evolve laboratory populations of Helicobacter pylori, which take up DNA from their environment by natural transformation, and measure the fitness effects of thousands of transferred genetic variants. We find that natural transformation increases the rate of adaptation but comes at the cost of significant genetic load. We show that this cost is circumvented by recombination, which increases the efficiency of selection by decoupling deleterious and beneficial genetic variants. Our results show that adaptation with HGT, pervasive in natural microbial populations, is shaped by a combination of selection, recombination, and genetic drift not accounted for in existing models of evolution.

Environmental dimensions of antibiotic resistance: assessment of basic science gaps.

Antibiotic resistance is one of the major problems facing medical practice in the 21st century. Historical approaches to managing antibiotic resistance have often focused on individual patients, specific pathogens and particular resistance phenotypes. However, it is increasingly recognized that antibiotic resistance is a complex ecological and evolutionary problem. As such, understanding the dynamics of antibiotic resistance requires integration of data on the diverse mobile genetic elements often associated with antibiotic resistance genes, and their dissemination by various mechanisms of horizontal gene transfer between bacterial cells and environments. Most important is understanding the fate and effects of antibiotics at sub-inhibitory concentrations, and co-selection. This opinion paper identifies key knowledge gaps in our understanding of resistance phenomena, and outlines research needs that should be addressed to help us manage resistance into the future.

Costs and benefits of natural transformation in Acinetobacter baylyi.

BACKGROUND: Natural transformation enables acquisition of adaptive traits and drives genome evolution in prokaryotes. Yet, the selective forces responsible for the evolution and maintenance of natural transformation remain elusive since taken-up DNA has also been hypothesized to provide benefits such as nutrients or templates for DNA repair to individual cells. RESULTS: We investigated the immediate effects of DNA uptake and recombination on the naturally competent bacterium Acinetobacter baylyi in both benign and genotoxic conditions. In head-to-head competition experiments between DNA uptake-proficient and -deficient strains, we observed a fitness benefit of DNA uptake independent of UV stress. This benefit was found with both homologous and heterologous DNA and was independent of recombination. Recombination with taken-up DNA reduced survival of transformed cells with increasing levels of UV-stress through interference with nucleotide excision repair, suggesting that DNA strand breaks occur during recombination attempts with taken-up DNA. Consistent with this, we show that absence of RecBCD and RecFOR recombinational DNA repair pathways strongly decrease natural transformation. CONCLUSIONS: Our data show a physiological benefit of DNA uptake unrelated to recombination. In contrast, recombination during transformation is a strand break inducing process that represents a previously unrecognized cost of natural transformation.

Bioinformatic analysis reveals high diversity of bacterial genes for laccase-like enzymes.

Fungal laccases have been used in various fields ranging from processes in wood and paper industries to environmental applications. Although a few bacterial laccases have been characterized in recent years, prokaryotes have largely been neglected as a source of novel enzymes, in part due to the lack of knowledge about the diversity and distribution of laccases within Bacteria. In this work genes for laccase-like enzymes were searched for in over 2,200 complete and draft bacterial genomes and four metagenomic datasets, using the custom profile Hidden Markov Models for two- and three-domain laccases. More than 1,200 putative genes for laccase-like enzymes were retrieved from chromosomes and plasmids of diverse bacteria. In 76% of the genes, signal peptides were predicted, indicating that these bacterial laccases may be exported from the cytoplasm, which contrasts with the current belief. Moreover, several examples of putatively horizontally transferred bacterial laccase genes were described. Many metagenomic sequences encoding fragments of laccase-like enzymes could not be phylogenetically assigned, indicating considerable novelty. Laccase-like genes were also found in anaerobic bacteria, autotrophs and alkaliphiles, thus opening new hypotheses regarding their ecological functions. Bacteria identified as carrying laccase genes represent potential sources for future biotechnological applications.

Unifying vertical and nonvertical evolution: a stochastic ARG-based framework.

Evolutionary biologists have introduced numerous statistical approaches to explore nonvertical evolution, such as horizontal gene transfer, recombination, and genomic reassortment, through collections of Markov-dependent gene trees. These tree collections allow for inference of nonvertical evolution, but only indirectly, making findings difficult to interpret and models difficult to generalize. An alternative approach to explore nonvertical evolution relies on phylogenetic networks. These networks provide a framework to model nonvertical evolution but leave unanswered questions such as the statistical significance of specific nonvertical events. In this paper, we begin to correct the shortcomings of both approaches by introducing the "stochastic model for reassortment and transfer events" (SMARTIE) drawing upon ancestral recombination graphs (ARGs). ARGs are directed graphs that allow for formal probabilistic inference on vertical speciation events and nonvertical evolutionary events. We apply SMARTIE to phylogenetic data. Because of this, we can typically infer a single most probable ARG, avoiding coarse population dynamic summary statistics. In addition, a focus on phylogenetic data suggests novel probability distributions on ARGs. To make inference with our model, we develop a reversible jump Markov chain Monte Carlo sampler to approximate the posterior distribution of SMARTIE. Using the BEAST phylogenetic software as a foundation, the sampler employs a parallel computing approach that allows for inference on large-scale data sets. To demonstrate SMARTIE, we explore 2 separate phylogenetic applications, one involving pathogenic Leptospirochete and the other Saccharomyces.

Evolution in regulatory regions rapidly compensates the cost of nonoptimal codon usage.

The redundant genetic code contains synonymous codons, whose relative frequencies vary among species. Nonoptimal codon usage lowers gene translation efficiency, potentially leading to a fitness cost. This is particularly relevant for horizontal gene transfer, common among bacteria and a key player in antibiotic resistance propagation. By mimicking the horizontal transfer of an antibiotic resistance gene, we established that a nonoptimal codon usage renders Escherichia coli 10-20 times more sensitive to the antibiotic. After 350 generations of experimental evolution under antibiotic selection pressure, this cost was compensated through both in cis changes in the gene promoter and in trans changes in the host bacterial genome, without introducing mutations in the coding sequence of the resistance gene. Further, we have found experimental evidence for convergent molecular adaptive evolution. The high fitness cost of nonoptimal codon usage remains a minor obstacle to gene fixation upon horizontal transfer. Our results highlight the importance of rapid evolution of regulatory mechanisms in the adaptation to new environmental and genetic situations.

A rapid-mutation approximation for cell population dynamics.

Carcinogenesis and cancer progression are often modeled using population dynamics equations for a diverse somatic cell population undergoing mutations or other alterations that alter the fitness of a cell and its progeny. Usually it is then assumed, paralleling standard mathematical approaches to evolution, that such alterations are slow compared to selection, i.e., compared to subpopulation frequency changes induced by unequal subpopulation proliferation rates. However, the alterations can be rapid in some cases. For example, results in our lab on in vitro analogues of transformation and progression in carcinogenesis suggest there could be periods where rapid alterations triggered by horizontal intercellular transfer of genetic material occur and quickly result in marked changes of cell population structure.We here initiate a mathematical study of situations where alterations are rapid compared to selection. A classic selection-mutation formalism is generalized to obtain a "proliferation-alteration" system of ordinary differential equations, which we analyze using a rapid-alteration approximation. A system-theoretical estimate of the total-population net growth rate emerges. This rate characterizes the diverse, interacting cell population acting as a single system; it is a weighted average of subpopulation rates, the weights being components of the Perron-Frobenius eigenvector for an ergodic Markov-process matrix that describes alterations by themselves. We give a detailed numerical example to illustrate the rapid-alteration approximation, suggest a possible interpretation of the fact that average aneuploidy during cancer progression often appears to be comparatively stable in time, and briefly discuss possible generalizations as well as weaknesses of our approach.

The NET-HMM approach: phylogenetic network inference by combining maximum likelihood and Hidden Markov Models.

Horizontal gene transfer (HGT) is the event of transferring genetic material from one lineage in the evolutionary tree to a different lineage. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Although the prevailing assumption is of complete HGT, cases of partial HGT (which are also named chimeric HGT) where only part of a gene is horizontally transferred, have also been reported, albeit less frequently. In this work we suggest a new probabilistic model, the NET-HMM, for analyzing and modeling phylogenetic networks. This new model captures the biologically realistic assumption that neighboring sites of DNA or amino acid sequences are not independent, which increases the accuracy of the inference. The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network's trees. One of the advantages of the NET-HMM is its ability to infer partial HGT as well as complete HGT. We describe the properties of the NET-HMM, devise efficient algorithms for solving a set of problems related to it, and implement them in software. We also provide a novel complementary significance test for evaluating the fitness of a model (NET-HMM) to a given dataset. Using NET-HMM, we are able to answer interesting biological questions, such as inferring the length of partial HGT's and the affected nucleotides in the genomic sequences, as well as inferring the exact location of HGT events along the tree branches. These advantages are demonstrated through the analysis of synthetical inputs and three different biological inputs.

Evolution of four gene families with patchy phylogenetic distributions: influx of genes into protist genomes.

BACKGROUND: Lateral gene transfer (LGT) in eukaryotes from non-organellar sources is a controversial subject in need of further study. Here we present gene distribution and phylogenetic analyses of the genes encoding the hybrid-cluster protein, A-type flavoprotein, glucosamine-6-phosphate isomerase, and alcohol dehydrogenase E. These four genes have a limited distribution among sequenced prokaryotic and eukaryotic genomes and were previously implicated in gene transfer events affecting eukaryotes. If our previous contention that these genes were introduced by LGT independently into the diplomonad and Entamoeba lineages were true, we expect that the number of putative transfers and the phylogenetic signal supporting LGT should be stable or increase, rather than decrease, when novel eukaryotic and prokaryotic homologs are added to the analyses. RESULTS: The addition of homologs from phagotrophic protists, including several Entamoeba species, the pelobiont Mastigamoeba balamuthi, and the parabasalid Trichomonas vaginalis, and a large quantity of sequences from genome projects resulted in an apparent increase in the number of putative transfer events affecting all three domains of life. Some of the eukaryotic transfers affect a wide range of protists, such as three divergent lineages of Amoebozoa, represented by Entamoeba, Mastigamoeba, and Dictyostelium, while other transfers only affect a limited diversity, for example only the Entamoeba lineage. These observations are consistent with a model where these genes have been introduced into protist genomes independently from various sources over a long evolutionary time. CONCLUSION: Phylogenetic analyses of the updated datasets using more sophisticated phylogenetic methods, in combination with the gene distribution analyses, strengthened, rather than weakened, the support for LGT as an important mechanism affecting the evolution of these gene families. Thus, gene transfer seems to be an on-going evolutionary mechanism by which genes are spread between unrelated lineages of all three domains of life, further indicating the importance of LGT from non-organellar sources into eukaryotic genomes.

Bayesian classifiers for detecting HGT using fixed and variable order markov models of genomic signatures.

MOTIVATION: Analyses of genomic signatures are gaining attention as they allow studies of species-specific relationships without involving alignments of homologous sequences. A naïve Bayesian classifier was built to discriminate between different bacterial compositions of short oligomers, also known as DNA words. The classifier has proven successful in identifying foreign genes in Neisseria meningitis. In this study we extend the classifier approach using either a fixed higher order Markov model (Mk) or a variable length Markov model (VLMk). RESULTS: We propose a simple algorithm to lock a variable length Markov model to a certain number of parameters and show that the use of Markov models greatly increases the flexibility and accuracy in prediction to that of a naïve model. We also test the integrity of classifiers in terms of false-negatives and give estimates of the minimal sizes of training data. We end the report by proposing a method to reject a false hypothesis of horizontal gene transfer. AVAILABILITY: Software and Supplementary information available at www.cs.chalmers.se/~dalevi/genetic_sign_classifiers/.

Do different surrogate methods detect lateral genetic transfer events of different relative ages?

Non-tree-based ("surrogate") methods have been used to identify instances of lateral genetic transfer in microbial genomes but agreement among predictions of different methods can be poor. It has been proposed that this disagreement arises because different surrogate methods are biased towards the detection of certain types of transfer events. This conjecture is supported by a rigorous phylogenetic analysis of 3776 proteins in Escherichia coli K12 MG1655 to map the ages of transfer events relative to one another.