Nucleotide content is one of the most obvious and most easily quantifiable features of a genome. Consequently, all reports of new genome sequences include a report on the nucleotide content, i.e., the frequencies of each of the four nucleotide bases in the genome sequence. Despite the ease with which nucleotide content can be measured, however, it has proved to be much more complicated to explain the evolution of the genomic nucleotide content. A number of different explanations have been proposed. These include the effects of biased mutational patterns (i.e. neutralist models) such as biased DNA repair during recombination, as well as the effect of natural selection on shaping the genomic nucleotide content (i.e. selectionist models). A well known example for the latter group of theories is the effect of temperature in selecting for higher GC content to enhance the thermostability of the DNA. However, many of these theories do not address the problem of variations in nucleotide content between different regions of a single genome, i.e., intragenomic compositional heterogeneity. In this study, I investigate the evolutionary behaviour of the genomic nucleotide content in a group of model organisms and show that the current state of the nucleotide content of a genome by itself cannot always explain the entire evolutionary history of that genome and, in fact, the sequence of events happening to a genome regarding its nucleotide content can be much more complicated than how it looks. For example I show that having an unbiased content doesn't necessary mean a stationary nucleotide substitution model in an organism. In this study I show that the bias in nucleotide content of a genome can change its direction several times and this going back and forth can actually happen very fast; fast enough to trace the results of this ebb and flow within the same genus. I present an example of this evolutionary ebb and flow of genomic nucleotide content within genus Plasmodium. I also discuss that these exceptional behaviours of the nucleotide contents of the genomes can, indeed, affect other features of living organisms such as the substitution rates between different nucleotides as well as their protein content, and consequently they can interfere with phylogenetic studies. In order to explain the heterogeneities within a single genome, I investigate the relationship between gene length and the degree of nucleotide bias, and find that these two parameters show a significant negative correlation. This lead me to propose a mechanism that can explain heterogeneity in the nucleotide content of the genome, without having to invoke variations in mutational patterns between different parts of the same genome. My proposed model resembles Charlesworth's "Background Selection" model. My findings shed light on the importance of the evolutionary behaviour of the genomic nucleotide content to be considered in studying different features of an organism as well as studying the evolutionary relationship between the organism of our interest and other related species.

Every human has about 100 novel mutations that are absent in the genomes of his/her parents. This intense influx of mutations degrades information that is stored in the DNA sequences and, at the same time, provides an opportunity for creation of new genetic messages. Currently, over one hundred million mutations have been characterized in the public databases. The dynamics of mutation have been investigated for decades in both experiments and sophisticated mathematical models, yet our understanding of genome evolution is still ambiguous. In this project, we computationally processed eighty million human mutations to get clear answers to basic questions about DNA evolution. Specifically, how is the non-randomness in nucleotide composition in vast genomic regions maintained? What biological forces preserve sequence non-randomness from being degraded by novel mutations? Our goal was to uncover peculiarities in dynamics of G+C nucleotide content and evaluate the equilibrium of GC-percentage in the human genome. We found that novel mutations that convert G:C pairs into A:T pairs are 1.39 times more frequent than opposite mutations that change A:T → G:C. This effect is more striking if we take into account the fact that the total number of G:C pairs (42%) is significantly less than the number of A:T pairs (58%). Hence, calculating per nucleotide pair, the mutations of G:C → A:T is 1.93 times more frequent than A:T → G:C mutations. Such bias should create fewer and fewer G:C pairs in the genomes from generation to generation, until it reaches equilibrium at 34% of GC-composition. However, the GC-percentage of the human genome is stable at 42%. There are two possible biological processes that may be responsible for preserving GC-composition from degradation: i) natural selection or ii) biased gene conversion. However, estimated parameters for both processes are unable to explain the maintenance of CG-percentage. We re-evaluated the biased gene conversion parameters and rates that might explain GC-composition. The vast majority of our genome is represented by intergenic regions and introns. The effects of mutations inside these two noncoding regions are practically impossible to evaluate. We generally cannot classify these mutations as increasing or decreasing fitness, or measure their effects. In contrast, the effects of some of the mutations in protein-coding regions, that occupy only 1.2% of the human genome, may be quantifiable. Learning to measure the ratio of synonymous to non-synonymous mutations in coding regions was profoundly important, and revealed important rules in population genetics. Human Genomes have about 5% of regions with extreme nucleotide compositions. These include chromosomal segments with A+T-rich, G+C-rich, purine-rich (pyrimidine-rich), G+T-rich (A+C-rich) and alternating purine/pyrimidine sequences (that may form Z-DNA structures). We called such sequence patterns, exhibiting profound biases in nucleotide composition, Genomic MRI (Mid-Range Inhomogeneity). Genomic-MRI regions may form special DNA structures (e.g. H-DNA, Z-DNA) and are non-randomly distributed along the genome. At least some of them have known biological roles. The best understood are the G+C-rich sequences that organize CG-islands in promoters of many genes, and are the targets of DNA methylation. Genomic MRI-regions allow us to quantify the effect of mutations inside them, because mutations may decrease or increase the nucleotide bias in these regions. For example, A→C, A→G, T→C, and T→G mutations increase GC-composition in G+C-rich sequences, G→A, G→T, C→A, and C→T decrease the GC-composition, while A→T, T→A, G→C, and C→G are neutral to G+C-richness. In this project, we examined how mutations change genomic-MRI regions, and explore the biological forces that maintain these genomic-MRI structures during evolution despite the constant mutational pressure to equilibrium and randomness. We found that the point mutations in MRI preferentially degrade the nucleotide inhomogeneity, decreasing the biases in their nucleotide composition. The level of mutational degradation by novel SNPs was observed to be highest for G+C-rich MRIs and least for the A+T-rich MRIs. Older SNPs (those broadly widespread across populations) showed a decrease in the level of degradation compared to the Novel SNPs. Furthermore, we found that re-evaluation of Biased Gene Conversion parameters could explain how the GC content is preserved despite the bias in mutations.

Although debated since the time of Darwin, the evolutionary role of mutation is still controversial. In over 40 chapters from leading authorities in mutation and evolutionary biology, this book takes a new look at both the theoretical and experimental measurement and significance of new mutation. Deleterious, nearly neutral, beneficial, and polygenic mutations are considered in their effects on fitness, life history traits, and the composition of the gene pool. Mutation is a phenomenon that draws attention from many different disciplines. Thus, the extensive reviews of the literature will be valuable both to established researchers and to those just beginning to study this field. Through up-to-date reviews, the authors provide an insightful overview of each topic and then share their newest ideas and explore controversial aspects of mutation and the evolutionary process. From topics like gonadal mosaicism and mutation clusters to adaptive mutagenesis, mutation in cell organelles, and the level and distribution of DNA molecular changes, the foundation is set for continuing the debate about the role of mutation, fitness, and adaptability. It is a debate that will have profound consequences for our understanding of evolution.

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A thought-provoking exploration of deleterious mutations in the human genome and their effects on human health and wellbeing Despite all of the elaborate mechanisms that a cell employs to handle its DNA with the utmost care, a newborn human carries about 100 new mutations, originated in their parents, about 10 of which are deleterious. A mutation replacing just one of the more than three billion nucleotides in the human genome may lead to synthesis of a dysfunctional protein, and this can be inconsistent with life or cause a tragic disease. Several percent of even young people suffer from diseases that are caused, exclusively or primarily, by pre ]existing and new mutations in their genomes, including both a wide variety of genetically simple Mendelian diseases and diverse complex diseases such as birth anomalies, diabetes, and schizophrenia. Milder, but still substantial, negative effects of mutations are even more pervasive. As of now, we possess no means of reducing the rate at which mutations appear spontaneously. However, the recent flood of genomic data made possible by next-generation methods of DNA sequencing, enabled scientists to explore the impacts of deleterious mutations on humans with previously unattainable precision and begin to develop approaches to managing them. Written by a leading researcher in the field of evolutionary genetics, Crumbling Genome reviews the current state of knowledge about deleterious mutations and their effects on humans for those in the biological sciences and medicine, as well as for readers with only a general scientific literacy and an interest in human genetics. Provides an extensive introduction to the fundamentals of evolutionary genetics with an emphasis on mutation and selection Discusses the effects of pre-existing and new mutations on human genotypes and phenotypes Provides a comprehensive review of the current state of knowledge in the field and considers crucial unsolved problems Explores key ethical, scientific, and social issues likely to become relevant in the near future as the modification of human germline genotypes becomes technically feasible Crumbling Genome is must-reading for students and professionals in human genetics, genomics, bioinformatics, evolutionary biology, and biological anthropology. It is certain to have great appeal among all those with an interest in the links between genetics and evolution and how they are likely to influence the future of human health, medicine, and society.

Together with early theoretical work in population genetics, the debate on sources of genetic makeup initiated by proponents of the neutral theory made a solid contribution to the spectacular growth in statistical methodologies for molecular evolution. Evolutionary Genomics: Statistical and Computational Methods is intended to bring together the more recent developments in the statistical methodology and the challenges that followed as a result of rapidly improving sequencing technologies. Presented by top scientists from a variety of disciplines, the collection includes a wide spectrum of articles encompassing theoretical works and hands-on tutorials, as well as many reviews with key biological insight. Volume 2 begins with phylogenomics and continues with in-depth coverage of natural selection, recombination, and genomic innovation. The remaining chapters treat topics of more recent interest, including population genomics, -omics studies, and computational issues related to the handling of large-scale genomic data. Written in the highly successful Methods in Molecular BiologyTM series format, this work provides the kind of advice on methodology and implementation that is crucial for getting ahead in genomic data analyses. Comprehensive and cutting-edge, Evolutionary Genomics: Statistical and Computational Methods is a treasure chest of state-of the-art methods to study genomic and omics data, certain to inspire both young and experienced readers to join the interdisciplinary field of evolutionary genomics.