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PubMed1997

Phenotypic and evolutionary adaptation of a model bacterial system to stressful thermal environments.

We studied both phenotypic and evolutionary adaptation to various thermal environments using the bacterium Escherichia coli as an experimental model system. We determined that 42 degrees C was stressful to a bacterial clone adapted to 37 degrees C, based on reductions in both absolute and competitive fitness, as well as induction of a heat stress response. This clone was also used to found replicated populations that were propagated for thousands of generations under several different thermal regimes, including 42 degrees C. Evolutionary adaptation of the populations to 42 degrees C resulted in an increase in both absolute and relative fitness at that temperature, measured respectively as an increase in the number of descendants (and their biovolume) and in competitive ability relative to the ancestral clone. The replicated experimental lineages achieved their evolutionary improvement by several distinct pathways, which produced differential preadaptation to a non-stressful nutrient environment. Adaptation to this stressful temperature entailed neither a change in the ancestral thermal niche nor any pronounced trade-offs in fitness within the thermal niche, contrary to a priori predictions. This study system was several important advantages for evaluating hypotheses concerning the effects of stress on phenotypic and evolutionary adaptation, including the ability to obtain lineages that have evolved in controlled and defined environments, to make direct measurements of fitness and to quantify the degree of stress imposed by different environments.

Adaptation, Physiological↗

PubMed1980

Functional morphology and evolutionary biology.

In this study the relationship between functional morpholoy and evolutionary biology is analysed by confronting the main concepts in both disciplines. Rather than only discussing this connection theoretically, the analysis is carried out by introducing important practical and experimental studies, which use aspects from both disciplines. The mentioned investigations are methodologically analysed and the consequences for extensions of the relationship are worked out. It can be shown that both disciplines have a large domain of their own and also share a large common ground. Many disagreements among evolutionary biologists can be reduced to differences in general philosophy (idealism vs. realism), selection of phenomenona (structure vs. function), definition of concepts (natural selection) and the position of the concept theory as an explaining factor (neutralists vs selectionist, random variation, determinate selection, etc.). The significance of functional morphology for evolutionary biology, and vice versa depends on these differences. For a neo-Darwinian evolutionary theory, contributions from functional and ecological morphology are indispensable. Of ultimate importance are the notions of internal selection and constraints in the constructions determining further development. In this context the concepts of random variation and natural selection need more detailed definition. The study ends with a recommendation for future research founded in a system-theoretical or structuralistic conception.

Adaptation, Physiological↗

PubMed1993

Evolutionary genetics of the Drosophila alcohol dehydrogenase gene-enzyme system.

Evolutionary genetics embodies a broad research area that ranges from the DNA level to studies of genetic aspects in populations. In all cases the purpose is to determine the impact of genetic variation on evolutionary change. The broad range of evolutionary genetics requires the involvement of a diverse group of researchers: molecular biologists, (population) geneticists, biochemists, physiologists, ecologists, ethologists and theorists, each of which has its own insights and interests. For example, biochemists are often not concerned with the physiological function of a protein (with respect to pH, substrates, temperature, etc.), while ecologists, in turn, are often not interested in the biochemical-physiological aspects underlying the traits they study. This review deals with several evolutionary aspects of the Drosophila alcohol dehydrogenase gene-enzyme system, and includes my own personal viewpoints. I have tried to condense and integrate the current knowledge in this field as it has developed since the comprehensive review by van Delden (1982). Details on specific issues may be gained from Sofer and Martin (1987), Sullivan, Atkinson and Starmer (1990); Chambers (1988, 1991); Geer, Miller and Heinstra (1991); and Winberg and McKinley-McKee (1992).

Alcohol Dehydrogenase↗

PubMed1990

Evolutionary optimization and neural network models of behavior.

One of the main challenges to the adaptionist program in general and the use of optimization models in behavioral and evolutionary ecology, in particular, is that organisms are so constrained by ontogeny and phylogeny that they may not be able to attain optimal solutions, however those are defined. This paper responds to the challenge through the comparison of optimality and neural network models for the behavior of an individual polychaete worm. The evolutionary optimization model is used to compute behaviors (movement in and out of a tube) that maximize a measure of Darwinian fitness based on individual survival and reproduction. The neural network involves motor, sensory, energetic reserve and clock neuronal groups. Ontogeny of the neural network is the change of connections of a single individual in response to its experiences in the environment. Evolution of the neural network is the natural selection of initial values of connections between groups and learning rules for changing connections. Taken together, these can be viewed as "design parameters". The best neural networks have fitnesses between 85% and 99% of the fitness of the evolutionary optimization model. More complicated models for polychaete worms are discussed. Formulation of a neural network model for host acceptance decisions by tephritid fruit flies leads to predictions about the neurobiology of the flies. The general conclusion is that neural networks appear to be sufficiently rich and plastic that even weak evolution of design parameters may be sufficient for organisms to achieve behaviors that give fitnesses close to the evolutionary optimal fitness, particularly if the behaviors are relatively simple.

Animals↗

PubMed1994

The neocortex. An overview of its evolutionary development, structural organization and synaptology.

By way of introduction, an outline is presented of the origin and evolutionary development of the neocortex. A cortical formation is lacking in amphibians, but a simple three-layered cortex is present throughout the pallium of reptiles. In mammals, two three-layered cortical structures, i.e. the prepiriform cortex and the hippocampus, are separated from each other by a six-layered neocortex. Still small in marsupials and insectivores, this "new" structure attains amazing dimensions in anthropoids and cetaceans. Neocortical neurons can be allocated to one of two basic categories: pyramidal and nonpyramidal cells. The pyramidal neurons form the principal elements in neocortical circuitry, accounting for at least 70% of the total neocortical population. The evolutionary development of the pyramidal neurons can be traced from simple, "extraverted" neurons in the amphibian pallium, via pyramid-like neurons in the reptilian cortex to the fully developed neocortical elements designated by Cajal as "psychic cells". Typical mammalian pyramidal neurons have the following eight features in common: (1) spiny dendrites, (2) a stout radially oriented apical dendrite, forming (3) a terminal bouquet in the most superficial cortical layer, (4) a set of basal dendrites, (5) an axon descending to the subcortical white matter, (6) a number of intracortical axon collaterals, (7) terminals establishing synaptic contacts of the round vesicle/asymmetric variety, and (8) the use of the excitatory aminoacids glutamate and/or aspartate as their neurotransmitter. The pyramidal neurons constitute the sole output and the largest input system of the neocortex. They form the principal targets of the axon collaterals of other pyramidal neurons, as well as of the endings of the main axons of cortico-cortical neurons. Indeed, the pyramidal neurons constitute together a continuous network extending over the entire neocortex, justifying the generalization: the neocortex communicates first and foremost within itself. The typical pyramidal neurons represent the end stage of a progressive evolutionary process. During further development many of these elements have become transformed by reduction into various kinds of atypical or aberrant pyramidal neurons. Interestingly, none of the six morphological characteristics, mentioned above under 1-6, has appeared to be unassailable; pyramidal neurons lacking spines, apical dendrites, long axons and intracortical axon collaterals etc. have all been described. From an evolutionary point of view the typical pyramidal neurons represent not only the principal neocortical elements, but also the source of various excitatory local circuit neurons. The spiny stellate cells, which are abundant in highly specialized primary sensory areas, form a remarkable case in point.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗

PubMed1988

New prospects for deducing the evolutionary history of metabolic pathways in prokaryotes: aromatic biosynthesis as a case-in-point.

Metabolic pathways of prokaryotes are more biochemically diverse than is generally recognized. Distinctive biochemical features are shared by phylogenetic clusters. The hierarchical levels of character-state clustering depends upon evolutionary events which fortuitously became fixed in the genome of a common ancestor. Prokaryotes can now be ordered on a phylogenetic tree. This allows the evolutionary steps that underlie the construction and regulation of appropriately complex biochemical pathways to be traced in an evolutionary progression of prokaryote types that house these pathways. Essentially the approach is to deduce ancestral character states at ever deeper phylogenetic levels, utilizing logical principles of maximum parsimony. The current perspective on the evolution of the biochemical pathway for biosynthesis of aromatic amino acids is developed as a case-in-point model for analyses that should be feasible with many major metabolic systems. Phenylalanine biosynthesis probably arose prior to the addition of branches leading to tyrosine and tryptophan. An evolutionary scenario is developed that begins with non-enzymatic reactions which may have operated in primitive systems, followed by the evolution of an enzymatic system that pre-dated the divergence of major lineages of modern eubacteria (Gram-positive bacteria, Gram-negative purple bacteria, and cyanobacteria).

Amino Acids↗

PubMed1991

An evolutionary model for maximum likelihood alignment of DNA sequences.

Most algorithms for the alignment of biological sequences are not derived from an evolutionary model. Consequently, these alignment algorithms lack a strong statistical basis. A maximum likelihood method for the alignment of two DNA sequences is presented. This method is based upon a statistical model of DNA sequence evolution for which we have obtained explicit transition probabilities. The evolutionary model can also be used as the basis of procedures that estimate the evolutionary parameters relevant to a pair of unaligned DNA sequences. A parameter-estimation approach which takes into account all possible alignments between two sequences is introduced; the danger of estimating evolutionary parameters from a single alignment is discussed.

Algorithms↗

PubMed1983

Transitions and transversions in evolutionary descent: an approach to understanding.

In this paper I lay a quantitative theoretical groundwork for understanding the proportions of the possible types of base substitutions observed between 12 genes sharing a common ancestor and isolated from extant species. The experimentally observed types of base substitution between two sequenced genes do not give a direct measure of the types of base substitutions that occur during evolutionary descent. However, by use of a statistical assemblage of these observations, we can recover, without the assumption of parsimony, the conditional base substitution probabilities that determine this descent. Three methods - direct count, regression, and informational entropy maximization - are described by which these probabilities can be estimated from experimental data. The methods are complementary in that each is most useful for somewhat different types of experimental data. These methods are used to study the ratio of transversions to transitions during gene divergence. Though this ratio is not constant during divergence, it does approach a stable limiting value that in principle can vary from zero, corresponding to 100% transition differences, to infinity, corresponding to 0% transition differences. In practice the limiting ratio tends to hover around a value of two, which is expected on a random basis. However, base substitution pathways that are very nonrandom also may lead to a limiting ratio of exactly two, so that such a value is not diagnostic for random pathways. The limiting ratio can be directly calculated from a knowledge of the twelve conditional probabilities for each type of base substitution, or from a knowledge of the equilibrium base composition of the DNAs compared. An expression is given for this calculation. Fifteen years ago Jean Derancourt, Andrew Lebor and Emile Zuckerkandl (1967), analyzing the amino acid sequence of globin chains coded by nuclear genes, made the original observation that the proportion of transition differences decreases with increasing evolutionary time. Recently Brown et al. (1982) and Brown and Simpson (1982) have reported a decrease in the observed proportion of transition differences in mitochondrial DNA with increasing evolutionary divergence. The conditions that must be satisfied for this type of behavior to occur at stable base composition and with stable base substitution probabilities are defined. Multiple substitutions per se do not lead to a decrease in transition differences with increasing evolutionary divergence.

Animals↗

PubMed1999

Close evolutionary relatedness of alpha-amylases from Archaea and plants.

The amino acid sequences of 22 alpha-amylases from family 13 of glycosyl hydrolases were analyzed with the aim of revealing the evolutionary relationships between the archaeal alpha-amylases and their eubacterial and eukaryotic counterparts. Two evolutionary distance trees were constructed: (i) the first one based on the alignment of extracted best-conserved sequence regions (58 residues) comprising beta2, beta3, beta4, beta5, beta7, and beta8 strand segments of the catalytic (alpha/beta)8-barrel and a short conserved stretch in domain B protruding out of the barrel in the beta3 --> alpha3 loop, and (ii) the second one based on the alignment of the substantial continuous part of the (alpha/beta)8-barrel involving the entire domain B (consensus length: 386 residues). With regard to archaeal alpha-amylases, both trees compared brought, in fact, the same results; i.e., all family 13 alpha-amylases from domain Archaea were clustered with barley pI isozymes, which represent all plant alpha-amylases. The enzymes from Bacillus licheniformis and Escherichia coli, representing liquefying and cytoplasmic alpha-amylases, respectively, seem to be the further closest relatives to archaeal alpha-amylases. This evolutionary relatedness clearly reflects the discussed similarities in the amino acid sequences of these alpha-amylases, especially in the best-conserved sequence regions. Since the results for alpha-amylases belonging to all three domains (Eucarya, Eubacteria, Archaea) offered by both evolutionary trees are very similar, it is proposed that the investigated conserved sequence regions may indeed constitute the "sequence fingerprints" of a given alpha-amylase.

Amino Acid Sequence↗

PubMed2002

The lineage-specific base-pair contents in the stem regions of ribosomal RNAs and their influence on the estimation of evolutionary distances.

The base-pair changes in the stem regions of ribosomal RNAs provide a useful measure for resolving the phylogeny of organisms. In the present study, how the biased base-pair content influences the estimation of evolutionary distances is theoretically investigated. By regarding the biased base-pair content as a result of the difference in selective strength between A:U and G:C base pairs, the evolutionary distance empirically obtained by enumerating base-pair changes is theoretically expressed in terms of selective strength, base-pair change rate, and divergence time. Its application to nuclear-coded large subunit ribosomal RNAs (LSU rRNAs) reveals the followings. LSU rRNAs from most organisms have moderate base-pair contents and the empirical evolutionary distances obtained by the comparison of these LSU rRNAs are approximately proportional to their divergence times. In the comparison of these moderate LSU rRNAs with the GC-rich LSU rRNAs such as those from Mycoplasma, Crenarchaeota, and Giardia, however, the empirically calculated distances are considerably smaller than the true evolutionary distances, while the comparison with AU-rich LSU rRNAs from Microsporidia overestimates their distances. With this result in mind, the relative base-pair change probabilities among three kingdoms are carefully estimated from the statistical distribution of base-pair change ratios enumerated for LSU rRNAs showing almost the same base-pair contents, leading to the result that prokaryotes and eukaryotes first diverged and that archaebacteria and eubacteria diverged on the line of prokaryotes slightly later, by about 0.3 billion years.

Animals↗

PubMed2004

Detection of lateral gene transfer events in the prokaryotic tRNA synthetases by the ratios of evolutionary distances method.

The availability of large numbers of genomic sequences has demonstrated the importance of lateral gene transfer (LGT) in prokaryotic evolution. However, considerable uncertainty remains concerning the frequency of LGT compared to other evolutionary processes. To examine LGTs in ancient lineages of prokaryotes a method was developed that utilizes the ratios of evolutionary distances (RED) to distinguish between alternative evolutionary histories. The advantages of this approach are that the variability inherent in comparing protein sequences is transparent, the direction of LGT and the relative rates of evolution are readily identified, and it is possible to detect other types of evolutionary events. This method was standardized using 35 genes encoding ribosomal proteins that were believed to share a vertical evolution. Using RED-T, an original computer program designed to implement the RED method, the evolution of the genes encoding the 20 aminoacyl-tRNA synthetases was examined. Although LGTs were common in the evolution of the aminoacyl-tRNA synthetases, they were not sufficient to obscure the organismal phylogeny. Moreover, much of the apparent complexity of the gene tree was consistent with the formation of the paralogs in the ancestors to the modern lineages followed by more recent loss of one paralog or the other.

Amino Acid Sequence↗

PubMed2006

Functional and evolutionary inference in gene networks: does topology matter?

The relationship between the topology of a biological network and its functional or evolutionary properties has attracted much recent interest. It has been suggested that most, if not all, biological networks are 'scale free.' That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa. The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein-protein interactions. These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology. One often cited general property is that, when compromised, highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes. For example, more highly connected proteins are more likely to be lethal when knocked out. However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect. Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations. Evolutionary simulations of gene-regulatory networks, presented here, suggest that such weak or non-existent correlations are to be expected, and are likely not due to inadequacy of experimental data. We argue that 'top-down' inferences of biological properties based on simple measures of network topology are of limited utility, and we present simulation results suggesting that much more detailed information about a gene's location in a regulatory network, as well as dynamic gene-expression data, are needed to make more meaningful functional and evolutionary predictions. Specifically, we find in our simulations that: (1) the relationship between a gene's connectivity and its fitness effect upon knockout depends on its equilibrium expression level; (2) correlation between connectivity and genetic variation is virtually non-existent, yet upon independent evolution of networks with identical topologies, some nodes exhibit consistently low or high polymorphism; and (3) certain genes show low polymorphism yet high divergence among independent evolutionary runs. This latter pattern is generally taken as a signature of positive selection, but in our simulations its cause is often neutral coevolution of regulatory inputs to the same gene.

Algorithms↗

PubMed1987

An evolutionary epidemiological mechanism, with applications to type A influenza.

In this paper I develop a model that describes an evolutionary epidemiological mechanism and apply this model to the epidemiology of type A influenza. This evolutionary epidemiological model differs from the classical nonevolutionary epidemiological model which has been applied to diseases like measles, rubella, and whooping cough in having a novel mechanism which causes susceptible individuals to be introduced into the host population. In the nonevolutionary model, susceptibles are continually introduced into the host population by demographic processes: most hosts that die are immune, while newborn hosts are susceptible. In this evolutionary model, the susceptible class is continually replenished because the pathogen changes genetically, and hence immunologically, from one epidemic to the next, causing previously immune hosts to become susceptible. I derive formulae which describe how the equilibrium number of infected hosts, the interepidemic period, and the probability that a host will become reinfected depend on the rate of amino acid substitution in the pathogen, m, a parameter describing the effect of these substitutions on host immunity, gamma, as well as the host population size, N, and the recovery rate, r. To apply the model to influenza, I show how the nondimensional parameter epsilon = m gamma N/r2 may be estimated from four types of data. The methods are applied to several data sets, and I conclude that epsilon much less than 1; sampling variation and inconsistencies between the various data sets do not permit epsilon to be estimated more precisely. The evolutionary epidemiological model has no threshold host population size, in contrast to the nonevolutionary model.

Amino Acids↗

PubMed2004

Finding optimal decision scores by evolutionary strategies.

OBJECTIVE: Severeness of illness is often rated by physicians at admission time. For this purpose, medical scores have been developed as 'objective' rating methods. When considering their classification performance, it is not assumed that such an expert-driven score is an optimal one. Our aim is to design an optimized data-driven score. In particular, we compare classical scores with a new data-driven score for abdominal septic shock patients. METHODS AND MATERIAL: Medical scores are used as ratings for different aspects of a patient's health status. The medical score indicates either a more critical or a healthier condition. For example, physicians rate organ conditions for different organs. We consider four different scores, SOFA, APACHE II, SAPS II, and MODS. Beyond the use of such classical scores, we propose an evolutionary strategy, that is suitable for score design, to find optimized data-driven scores. A database of 282 patients is used to optimize a new score for abdominal septic shock patients. Classification performance is compared by a ROC analysis. RESULTS: We give a general instruction for building optimized scores, i.e. we define individuals and operators for the evolutionary score design task. We apply this instruction to abdominal septic shock patient data. When compared to the SOFA score, it has similar classification performance, but it is more performant than APACHE II, SAPS II, and MODS. It can be used as a daily bedside score. CONCLUSIONS: We argue that evolutionary strategies should be used for optimizing purposes in the medical score design process. Using abdominal septic shock patient data, we show that evolutionary score design is a feasible and performant method that can complement or replace expert knowledge, provided that qualitative data is available.

Abdomen↗

PubMed2006

Analysis of a Schnute postulate-based unified growth model for model selection in evolutionary computations.

In order to evaluate the feasibility of a combined evolutionary algorithm-information theoretic approach to select the best model from a set of candidate invasive species models in ecology, and/or to evolve the most parsimonious model from a suite of competing models by comparing their relative performance, it is prudent to use a unified model that covers a myriad of situations. Using Schnute's postulates as a starting point [Schnute, J., 1981. A versatile growth model with statistically stable parameters, Can. J. Fish Aquat. Sci. 38, 1128-1140], we present a single, unified model for growth that can be successfully utilized for model selection in evolutionary computations. Depending on the parameter settings, the unified equation can describe several growth mechanisms. Such a generalized model mechanism, which encompasses a suite of competing models, can be successfully implemented in evolutionary computational algorithms to evolve the most parsimonious model that best fits ground truth data. We have done exactly this by testing the effectiveness of our reaction-diffusion-advection (RDA) model in an evolutionary computation model selection algorithm. The algorithm was validated (with success) against field data sets of the Zebra mussel invasion of Lake Champlain in the United States.

Algorithms↗

PubMed2006

Evolutionary conflicts of interest between males and females.

Sexual conflict arises from differences in the evolutionary interests of males and females and can occur over traits related to courtship, mating and fertilisation through to parental investment. Theory shows that sexual conflict can lead to sexually antagonistic coevolution (SAC), where adaptation in one sex can lead to counter-adaptation in the other. Thus, sexual conflict can lead to evolutionary change within species. In addition, SAC can--through its effects on traits related to the probability of mating and of zygote formation--potentially lead to reproductive isolation. In this review, I discuss that, although sexual conflict is ubiquitous, the actual expression of sexual conflict leading to SAC is less frequent. The balance between the benefits and costs of the manipulation of one sex by the other, and the availability of mechanisms by which conflict is expressed, determine whether actual sexual conflict is likely to occur. New insights address the relationship between sexual conflict and conflict resolution, adaptation, sexual selection and fitness. I suggest that it will be useful to examine systematically the parallels and contrasts between sexual and other evolutionary conflicts. Understanding why some traits, but not others, are subject to evolutionary change by SAC will require data on the mechanisms of the traits involved and on the relative benefits and costs of manipulation and resistance to manipulation.

Animals↗

PubMed2005

Evolutionary conservation and mutational spectrum of BMPR2 gene.

A variety of mutations in the bone morphogenetic protein receptor type 2 (BMPR2) have been identified in patients with pulmonary arterial hypertension. In this study, using our BMPR2 mutation database and BMPR-II protein sequences from eight distantly related species, we defined the relationship among evolutionary conservation, mutation frequency and mutation distribution. As a whole, BMPR2 is evolving slower than the average for mammalian protein-encoding genes. As expected, the kinase domain is evolving more slowly than the extracellular ligand-binding and C-terminal domains. A detailed map of evolutionary conservation shows that there are repeating peaks and valleys within the C-terminal domain, representing higher and lower evolutionary conservation. We observed a strong correlation between evolutionary conservation and the distribution of mutations along the gene. All except two, of the nineteen missense mutations occur in absolutely conserved amino acids among the vertebrate homologs. In addition, we identified six mutational hotspots (P<0.05) by comparing the observed distribution of mutations to the pattern expected from a random multinomial distribution. Furthermore, analysis of the sequence environment surrounding the mutations revealed a specific pattern of mutagenesis. Over 22% of all single base-paired substitutions and 30% of all deletions and insertions are situated within tandem or non-tandem direct repeats of at least 5-bp and may be explained by slipped-mispairing model of mutagenesis. Also, over 59% of single base-paired substitutions versus 20% of deletions and insertions are located in perfect palindromic sequences that could produce "hairpin-loop" secondary structures with relatively high thermodynamic stability under physiological conditions. In addition, 3.7% of single base-paired substitutions versus 30% of deletions and insertions are located either within or in close proximity to the Krawczak and Cooper consensus sequence (TG A/G A/G G/T A/C). Further study of the mechanism of mutagenesis in BMPR2 may help identify other potentially mutable sites and differentiate between deleterious mutations and harmless polymorphic variants.

Amino Acid Sequence↗

PubMed2006

Different evolutionary fates of recently integrated human and chimpanzee LINE-1 retrotransposons.

The long interspersed element-1 (LINE-1 or L1) is a highly successful retrotransposon in mammals. L1 elements have continued to actively propagate subsequent to the human-chimpanzee divergence, approximately 6 million years ago, resulting in species-specific inserts. Here, we report a detailed characterization of chimpanzee-specific L1 subfamily diversity and a comparison with their human-specific counterparts. Our results indicate that L1 elements have experienced different evolutionary fates in humans and chimpanzees within the past approximately 6 million years. Although the species-specific L1 copy numbers are on the same order in both species (1200-2000 copies), the number of retrotransposition-competent elements appears to be much higher in the human genome than in the chimpanzee genome. Also, while human L1 subfamilies belong to the same lineage, we identified two lineages of recently integrated L1 subfamilies in the chimpanzee genome. The two lineages seem to have coexisted for several million years, but only one shows evidence of expansion within the past three million years. These differential evolutionary paths may be the result of random variation, or the product of competition between L1 subfamily lineages. Our results suggest that the coexistence of several L1 subfamily lineages within a species may be resolved in a very short evolutionary period of time, perhaps in just a few million years. Therefore, the chimpanzee genome constitutes an excellent model in which to analyze the evolutionary dynamics of L1 retrotransposons.

5' Untranslated Regions↗