Stochastic dynamics modeling of the protein sequence length distribution in genomes: implications for microbial evolution

In this paper, we report an analysis of the protein sequence length distribution for 13 bacteria, four archaea and one eukaryote whose genomes have been completely sequenced, The frequency distribution of protein sequence length for all the 18 organisms are remarkably similar, independent of genome size and can be described in terms of a lognormal probability distribution function. A simple stochastic model based on multiplicative processes has been proposed to explain the sequence length distribution. The stochastic model supports the random-origin hypothesis of protein sequences in genomes. Distributions of large proteins deviate from the overall lognormal behavior. Their cumulative distribution follows a power-law analogous to Pareto's law used to describe the income distribution of the wealthy. The protein sequence length distribution in genomes of organisms has important implications for microbial evolution and applications. (C) 1999 Elsevier Science B.V. All rights reserved.

Intra-tumor heterogeneity: lessons from microbial evolution and clinical implications

Multiple subclonal populations of tumor cells can coexist within the same tumor. This intra-tumor heterogeneity will have clinical implications and it is therefore important to identify factors that drive or suppress such heterogeneous tumor progression. Evolutionary biology can provide important insights into this process. In particular, experimental evolution studies of microbial populations, which exist as clonal populations that can diversify into multiple subclones, have revealed important evolutionary processes driving heterogeneity within a population. There are transferrable lessons that can be learnt from these studies that will help us to understand the process of intra-tumor heterogeneity in the clinical setting. In this review, we summarize drivers of microbial diversity that have been identified, such as mutation rate and environmental influences, and discuss how knowledge gained from microbial experimental evolution studies may guide us to identify and understand important selective factors that promote intra-tumor heterogeneity. Furthermore, we discuss how these factors could be used to direct and optimize research efforts to improve patient care, focusing on therapeutic resistance. Finally, we emphasize the need for longitudinal studies to address the impact of these potential tumor heterogeneity-promoting factors on drug resistance, metastatic potential and clinical outcome.

A phylogenomic approach to microbial evolution

To study the origin and evolution of biochemical pathways in microorganisms, we have developed methods and software for automatic, large-scale reconstructions of phylogenetic relationships. We define the complete set of phylogenetic trees derived from the proteome of an organism as the phylome and introduce the term phylogenetic connection as a concept that describes the relative relationships between taxa in a tree. A query system has been incorporated into the system so as to allow searches for defined categories of trees within the phylome. As a complement, we have developed the pyphy system for visualising the results of complex queries on phylogenetic connections, genomic locations and functional assignments in a graphical format. Our phylogenomics approach, which links phylogenetic information to the flow of biochemical pathways within and among microbial species, has been used to examine more than 8000 phylogenetic trees from seven microbial genomes. The results have revealed a rich web of phylogenetic connections. However, the separation of Bacteria and Archaea into two separate domains remains robust.

Bacterial evolution by genomic island transfer occurs via DNA transformation in planta

Our understanding of the evolution of microbial pathogens has been advanced by the discovery of "islands" of DNA that differ from core genomes and contain determinants of virulence [1, 2]. The acquisition of genomic islands (GIs) by horizontal gene transfer (HGT) is thought to have played a major role in microbial evolution. There are, however, few practical demonstrations of the acquisition of genes that control virulence, and, significantly, all have been achieved outside the animal or plant host. Loss of a GI from the bean pathogen Pseudomonas syringae pv. phaseolicola (Pph) is driven by exposure to the stress imposed by the plant's resistance response [3]. Here, we show that the complete episomal island, which carries pathogenicity genes including the effector avrPphB, transfers between strains of Pph by transformation in planta and inserts at a specific att site in the genome of the recipient. Our results show that the evolution of bacterial pathogens by HGT may be achieved via transformation, the simplest mechanism of DNA exchange. This process is activated by exposure to plant defenses, when the pathogen is in greatest need of acquiring new genetic traits to alleviate the antimicrobial stress imposed by plant innate immunity [4].

Drosophila Adaptation to Viral Infection through Defensive Symbiont Evolution

Microbial symbionts can modulate host interactions with biotic and abiotic factors. Such interactions may affect the evolutionary trajectories of both host and symbiont. Wolbachia protects Drosophila melanogaster against several viral infections and the strength of the protection varies between variants of this endosymbiont. Since Wolbachia is maternally transmitted, its fitness depends on the fitness of its host. Therefore, Wolbachia populations may be under selection when Drosophila is subjected to viral infection. Here we show that in D. melanogaster populations selected for increased survival upon infection with Drosophila C virus there is a strong selection coefficient for specific Wolbachia variants, leading to their fixation. Flies carrying these selected Wolbachia variants have higher survival and fertility upon viral infection when compared to flies with the other variants. These findings demonstrate how the interaction of a host with pathogens shapes the genetic composition of symbiont populations. Furthermore, host adaptation can result from the evolution of its symbionts, with host and symbiont functioning as a single evolutionary unit.

Genetic and bacterial programming for B-Spline neural networks design

The design phase of B-spline neural networks is a highly computationally complex task. Existent heuristics have been found to be highly dependent on the initial conditions employed. Increasing interest in biologically inspired learning algorithms for control techniques such as Artificial Neural Networks and Fuzzy Systems is in progress. In this paper, the Bacterial Programming approach is presented, which is based on the replication of the microbial evolution phenomenon. This technique produces an efficient topology search, obtaining additionally more consistent solutions.

Design of B-spline neural networks using a bacterial programming approach

The design phase of B-spline neural networks represents a very high computational task. For this purpose, heuristics have been developed, but have been shown to be dependent on the initial conditions employed. In this paper a new technique, Bacterial Programming, is proposed, whose principles are based on the replication of the microbial evolution phenomenon. The performance of this approach is illustrated and compared with existing alternatives.

Design of neuro-fuzzy models by evolutionary and gradient-based algorithms

All systems found in nature exhibit, with different degrees, a nonlinear behavior. To emulate this behavior, classical systems identification techniques use, typically, linear models, for mathematical simplicity. Models inspired by biological principles (artificial neural networks) and linguistically motivated (fuzzy systems), due to their universal approximation property, are becoming alternatives to classical mathematical models. In systems identification, the design of this type of models is an iterative process, requiring, among other steps, the need to identify the model structure, as well as the estimation of the model parameters. This thesis addresses the applicability of gradient-basis algorithms for the parameter estimation phase, and the use of evolutionary algorithms for model structure selection, for the design of neuro-fuzzy systems, i.e., models that offer the transparency property found in fuzzy systems, but use, for their design, algorithms introduced in the context of neural networks. A new methodology, based on the minimization of the integral of the error, and exploiting the parameter separability property typically found in neuro-fuzzy systems, is proposed for parameter estimation. A recent evolutionary technique (bacterial algorithms), based on the natural phenomenon of microbial evolution, is combined with genetic programming, and the resulting algorithm, bacterial programming, advocated for structure determination. Different versions of this evolutionary technique are combined with gradient-based algorithms, solving problems found in fuzzy and neuro-fuzzy design, namely incorporation of a-priori knowledge, gradient algorithms initialization and model complexity reduction.

Molecular adaptation of Ammonia Monooxygenase during independent pH specialization in Thaumarchaeota

Acknowledgements. This work was funded by Natural Environment Research Council Fellowship NE/J019151/1 and by institutional funding from within the University of Aberdeen.

Rhizobium gone native: Unexpected plasmid stability of indigenous Rhizobium leguminosarum

Lateral transfer of bacterial plasmids is thought to play an important role in microbial evolution and population dynamics. However, this assumption is based primarily on investigations of medically or agriculturally important bacterial species. To explore the role of lateral transfer in the evolution of bacterial systems not under intensive, human-mediated selection, we examined the association of genotypes at plasmid-encoded and chromosomal loci of native Rhizobium, the nitrogen-fixing symbiont of legumes. To this end, Rhizobium leguminosarum strains nodulating sympatric species of native Trifolium were characterized genetically at plasmid-encoded symbiotic (sym) regions (nodulation AB and nodulation CIJT loci) and a repeated chromosomal locus not involved in the symbiosis with legumes. Restriction fragment length polymorphism analysis was used to distinguish genetic groups at plasmid and chromosomal loci. The correlation between major sym and chromosomal genotypes and the distribution of genotypes across host plant species and sampling location were determined using χ2 analysis. In contrast to findings of previous studies, a strict association existed between major sym plasmid and chromosomal genetic groups, suggesting a lack of successful sym plasmid transfer between major Rhizobium chromosomal types. These data indicate that previous observations of sym plasmid transfer in agricultural settings may seriously overestimate the rates of successful conjugation in systems not impacted by human activities. In addition, a nonrandom distribution of Rhizobium genotypes across host plant species and sampling site demonstrates the importance of both factors in shaping Rhizobium population dynamics.

Seawater carbonate chemistry and growth rate of Emiliania huxleyi in lab experiment

Predicting the impacts of environmental change on marine organisms, food webs, and biogeochemical cycles presently relies almost exclusively on short-term physiological studies, while the possibility of adaptive evolution is often ignored. Here, we assess adaptive evolution in the coccolithophore Emiliania huxleyi, a well-established model species in biological oceanography, in response to ocean acidification. We previously demonstrated that this globally important marine phytoplankton species adapts within 500 generations to elevated CO2. After 750 and 1000 generations, no further fitness increase occurred, and we observed phenotypic convergence between replicate populations. We then exposed adapted populations to two novel environments to investigate whether or not the underlying basis for high CO2-adaptation involves functional genetic divergence, assuming that different novel mutations become apparent via divergent pleiotropic effects. The novel environment "high light" did not reveal such genetic divergence whereas growth in a low-salinity environment revealed strong pleiotropic effects in high CO2 adapted populations, indicating divergent genetic bases for adaptation to high CO2. This suggests that pleiotropy plays an important role in adaptation of natural E. huxleyi populations to ocean acidification. Our study highlights the potential mutual benefits for oceanography and evolutionary biology of using ecologically important marine phytoplankton for microbial evolution experiments.

Diagnosis of vibriosis in the era of genomics: lessons from invertebrates

Global changes linked to increases in temperature and ocean acidification, but also to more direct anthropogenic influences such as aquaculture, have caused a worldwide increase in the reports of Vibrio-associated illnesses affecting humans and also animals such as shrimp and molluscs. Investigation of the emergence of Vibrio pathogenesis events requires the analysis of microbial evolution at the gene, genome and population levels, in order to identify genomic modifications linked to increased virulence, resistance and/or prevalence, or to recent host shift. From a more applied point of view, the elucidation of virulence mechanisms is a prerequisite to devising prophylactic methods to fight infectious agents. In comparison with human pathogens, fairly little is known about the requirements for virulence in vibrios pathogenic to animals. However, the advent of genome sequencing, especially next-generation technologies,the possibility of genetically manipulating most of the Vibrio strains, and the recent availability of standardised animals for experimental infections have now compensated for the considerable delay in advancement of the knowledge of non-model pathogens such as Vibrio and have led to new scientific questions.

A Mutational Hotspot and Strong Selection Contribute to the Order of Mutations Selected for during Escherichia coli Adaptation to the Gut

The relative role of drift versus selection underlying the evolution of bacterial species within the gut microbiota remains poorly understood. The large sizes of bacterial populations in this environment suggest that even adaptive mutations with weak effects, thought to be the most frequently occurring, could substantially contribute to a rapid pace of evolutionary change in the gut. We followed the emergence of intra-species diversity in a commensal Escherichia coli strain that previously acquired an adaptive mutation with strong effect during one week of colonization of the mouse gut. Following this first step, which consisted of inactivating a metabolic operon, one third of the subsequent adaptive mutations were found to have a selective effect as high as the first. Nevertheless, the order of the adaptive steps was strongly affected by a mutational hotspot with an exceptionally high mutation rate of 10-5. The pattern of polymorphism emerging in the populations evolving within different hosts was characterized by periodic selection, which reduced diversity, but also frequency-dependent selection, actively maintaining genetic diversity. Furthermore, the continuous emergence of similar phenotypes due to distinct mutations, known as clonal interference, was pervasive. Evolutionary change within the gut is therefore highly repeatable within and across hosts, with adaptive mutations of selection coefficients as strong as 12% accumulating without strong constraints on genetic background. In vivo competitive assays showed that one of the second steps (focA) exhibited positive epistasis with the first, while another (dcuB) exhibited negative epistasis. The data shows that strong effect adaptive mutations continuously recur in gut commensal bacterial species.