Thesis (Ph. D.)--University of Idaho, 2006. / Abstract. "July 2006." Includes bibliographical references. Also available online in PDF format.

Nesta tese propomos e estudamos um modelo alternativo para investigar a evolução de quase-espécies moleculares, no qual supomos que a população seja uma combinação aleatória das moléculas constituintes em cada geração. Essa aleatoriedade deve-se a inclusão de um procedimento adicional de amostragem da população além dos procedimentos usuais de mutação e reprodução diferenciada. O modelo, denominado modelo de amostragens, é baseado em um algoritmo que mimetiza procedimentos experimentais que usam técnicas de transferências em série para reproduzir o processo de evolução de microorganismos in vitro. Além do modelo reproduzir a solução exata do estado estacionário do modelo de quase-espécies no regime determinístico, ele permite o estudo da evolução da quase-espécie molecular em todo espaço de parâmetros de controle, incluindo o caso em que a população é finita. A generalização dessa formulação alternativa para uma classe geral de relevos de replicação permite-nos realizar um estudo bastante completo do fenômeno do limiar de erro, levando-nos a uma análise crítica sobre a generalidade desse fenômeno / In this thesis we propose and study an alternative model to investigate the evolution of a molecular quasispecies, in which we assume that the population is a random combination of the constituent molecules in each generation. This randomness is due to the inclusion of an additional sampling procedure of the population, besides the usual procedures of mutation and differential reprodution. This model, termed sampling model, is based on an algorithm that mimics experimental procedures using serial transfer techniques to study the microbial evolutionary process in vitro. Besides yielding the exact steady-state solution of the quasispecies model in the deterministic limit, the sampling model allows the study of the molecular evolution in the full space of the control parameter, including the case where of population is finite. The generalization of this alternative formulation to a general class of fitness landscapes, allows us to investigate thoroughly the error threshold phenomenon, leading us to discuss critically the generality of this phenomenon in molecular evolution

Multiple versions of a parent gene can function within molecular systems as gene duplicates (paralogs) and alternatively spliced isoforms. Proteins related in this manner often serve redundant roles, though they can be selectively or randomly prescribed unique functions. The present collection of three manuscripts details the evolution of members of the deubiquitinating enzyme superfamily. The first manuscript delineates the chronology of USP4, USP15 and USP11 emergence and concludes that the presumed ancestor, USP11, is in fact a recent duplicate and that, at minimum, one copy of USP4 or USP15 is required for organismal viability. The second determines that the long and short isoforms of mammalian USP4 are maintained by natural selection to occupy discrete spatial roles. The final manuscript broadens the scope and objectively draws the genealogy of all deubiquitinating enzymes, with emphasis on significant points of functional divergence of paralogs within innate immunity and DNA repair pathways.

We describe the initial realization of behavior in the biosphere, which we term behavioral chemistry. If molecules are complex enough to attain a stochastic element to their structural conformation in such a way as to radically affect their function in a biological (evolvable) setting, then they have the capacity to behave. This circumstance is described here as behavioral chemistry, unique in its definition from the colloquial chemical behavior. This transition between chemical behavior and behavioral chemistry need be explicit when discussing the root cause of behavior, which itself lies squarely at the origins of life and is the foundation of choice. RNA polymers of sufficient length meet the criteria for behavioral chemistry and therefore are capable of making a choice. We test these theoretical findings with a empirical systems; since RNA simultaneously possesses evolvability and catalytic function we set to construct a model system. Firstly, during in vitro transcription of the Chlorella PBCV-1 pdg intron, we observed multiple RNAs of slower electrophoretic mobility along with the expected 98-nucleotide transcript. The preferences observed for TI or linear RNA for the pdg intron are manifestations of alternative phenotypic states and represent evidence of behavior at the chemical level. Secondly, we provide a basis for the marriage between empirical findings and the philosophical studies of biosemiotics. As well, provide a basis for the minimal criteria for an interpretative response and suggest that interpretation is evolvable. The studies hold relevance to the origins of life showing a minimal system capable of carrying out a choice based on the environment. We have shown this phenomenon is not an intrinsic characteristic to all RNAs and that the Tetrahymena ribozyme recognizes the presence of the object through the sensing of a sign.

The emergence of life depended on the ability of the first biopolymer populations to thrive and approach larger population sizes and longer sequences. The evolution of these populations likely occurred under circumstances under which Muller's Ratchet in synergism with random drift could have caused large genetic deterioration of the biopolymers. This deterioration can drive the populations to extinction unless there is a mechanism to counteract it. The effect of the mutation rate and the effective population size on the time to extinction was tested on clonal populations of B16-19 ligase ribozymes, evolved with the continuous evolution in vitro system. Populations of 100, 300, 600 and/or 3000 molecules were evolved with and without the addition of Mn(II). The times to extinction for populations evolved without Mn(II) were found to be directly related to the effective size of the population. The small populations approached extinction at an average of 24.3 cycles; while the large populations did so at an average of 44.5 cycles. Genotypic characterization of the populations showed the presence of deleterious mutations in the small populations, which are the likely cause of their genetic deterioration and extinction via mutational meltdown. These deleterious mutations were not observed in the large populations; in contrast an advantageous mutant was present. Populations of 100 and 3000 molecules were evolved with Mn(II). None of the populations showed signs of genetic deterioration nor did they become extinct. Genotypic characterization of the 100-molecule population indicated the presence of a cloud of mutants closely genetically-related, forming a "quasispecies" structure.. The close connectedness of the mutants facilitates the recovery of one from another in the event of being removed from the population by random genetic drift. Thus, quasispecies shift the target of selection from individual to group. The total fitness of the molecules was measured by identifying the fitness component of the system that effect the ligase replication cycles: ligation, reverse transcription and transcription. It was found that the strength of the three components of fitness varied and that each one has a differential effect in the total absolute fitness of the ligases.

No description available.

Dating of sequence divergence from different species, genes, and strains is now commonplace in biological studies aimed at deciphering micro- and macro-evolutionary temporal patterns. With sequencing becoming increasingly cheaper, molecular datasets are expanding quickly in size. This expansion has necessitated the development of innovative and efficient methods to make the inference of large timetrees feasible from genome-scale datasets that routinely contain hundreds of species. In my dissertation research, I have focused on developing such methods that improved the accuracy, precision, and speed of calculations needed for divergence time inference. I have also conducted large-scale data analyses to reveal fundamental patterns of molecular evolution. The following five related projects were pursued in this dissertation. (1) Development of a machine learning method (CorrTest) for detecting the best-fit model for describing the variation of molecular evolution rates among branches and lineages for large phylogenies. Computer simulations show that the machine learning method outperforms the currently available state-of-the-art methods and is computationally efficient. (2) Development of an analytical method and a new approach to utilize probability densities as calibrations to calculate confidence intervals reliably for RelTime, a non-Bayesian method. Empirical analysis shows that RelTime produces confidence intervals that are comparable to those generated by Bayesian methods, and simulation analysis shows that RelTime confidence intervals often contain the actual values. (3) Application of CorrTest on empirical datasets reveals the extensive autocorrelation in molecular rate in nucleotide and amino acid sequence evolution in diverse taxonomic groups, suggesting that rate autocorrelation is a common phenomenon throughout the tree of life. (4) Investigation of the impact of substitution model complexity on the accuracy and precision of divergence time estimation. Analyses of large-scale empirical data show that the selection of substitution model only has a limited impact on time estimation, as the extremely simple models yield divergence time estimates and credibility intervals remarkably similar to those obtained from very complex models. (5) Inventory of non-Bayesian methods for dating species divergences, including their statistical bases, their performance of estimating divergence times, and the software packages in which they are implemented. A guide has provided for the use of non-Bayesian dating methods to produce reliable divergence times. / Biology

<p> Phylogenetic analysis of gene order data is a developing area, and many questions on gene orders are still unresolved. In this project, we started from the OGRe database, where we obtained the mitochondrial genome information (after some corrections), designed a logarithm correction for breakpoint distance, applied distance matrix methods to both breakpoint distance and the logarithm of breakpoint distance for gene orders, and then focused on Arthropoda phylogeny We tried many phylogenetic methods to infer Arthropod phylogeny; however, no method yielded a satisfying result. We constructed an Arthropod phylogenetic tree based on both molecular and morphological evidences. After we estimated the phylogenetic tree, we used maximum likelihood methods to estimate branch lengths for tRNAs and proteins, calculated the breakpoint numbers and inversion numbers for gene orders, and calculated the correlations among these four measures. We found that: when gene order rearrangements and mutations on sequences are small, the changes are independent, and, when the rearrangements and mutations are large, the changes seem to be correlated. The branch lengths in the tRNA and protein trees are highly correlated in low mutation situations and less correlated when mutation rates are larger. </p> / Thesis / Master of Science (MSc)

A fractal stochastic point process (FSPP) is used to model molecular evolution in agreement with the relationship between the variance and mean numbers of synonymous and nonsynonymous substitutions in mammals. Like other episodic models such as the doubly stochastic Poisson process, this model accounts for the large variances observed in amino acid substitution rates, but unlike other models, it also accounts for the results of Ohta's (1995) analysis of synonymous and nonsynonymous substitutions in mammalian genes. That analysis yields a power-law increase in the index of dispersion and an inverse power-law decrease in the coefficient of variation with the mean number of substitutions, as predicted by the FSPP model but not by the doubly stochastic Poisson model. This result is compatible with the selection theory of evolution and the nearly-neutral theory of evolution.

Microbial communities are recognized as major drivers of global biogeochemical processes. However, the genetic diversity and composition, as well as processes leading to the origin and diversification of these communities in space and time, are poorly understood. Character- ization of microbial communities using high-throughput sequencing of 16S tags shows that Operational Taxonomic Unit (OTU) abundances can be approximated by a gamma distribu- tion, which suggests structuring around small numbers of highly abundant OTUs and a large proportion of rare OTUs. The current methods used to characterize how communities are structured rely on multivariate statistics, which operate on pair-wise distance matrices. My analyses demonstrate that use of these methods, by reducing a highly-dimensional data set (tens of samples, thousands of OTUs), results in a significant loss of information. I demon- strate that, in some cases, up to 80% of the least abundant OTUs may be removed while still recovering the same community relationships; this indicates these metrics are biased toward the highly abundant OTUs. I also demonstrate that the observed patterns of OTU abundance detected from microbial communities can be robustly modeled using techniques similar to those used to model the presence and absence of genes in genome evolution. Using simulation studies, I show that general Markov models in a Bayesian inference framework out- perform traditional, multivariate ecological methods in recovering true community structure. Applying this new methodology to Atlantic Ocean communities uncovered a distance-decay effect which was not revealed by the traditional methods; applying to communities discov- ered on Hog Island point toward mechanisms of thicket establishment. Although the ocean data set operated on a much larger, continental scale, characterization of the sequence data generated from the nutrient-poor soil on Hog Island, a barrier island off the Virginia coast, allows for a better characterization of the processes affecting these communities on a much smaller scale. Finally, using 16S data from the Human Vaginal Microbiome Project, gener- ated here at VCU under the umbrella of the overall NIH HMP initiative, I give examples of the quality control, analysis and visualization pipeline that I developed to support the efforts of this project. In conclusion, my analyses of the metagenomic sequence data from bacterial communities sampled from different environments demonstrate that the proper identification of the biological processes influencing these communities requires the development and im- plementation of new statistical and computational methodologies that take advantage of the extensive amount of information generated in next-generation, high-throughput sequencing projects.