Chemokines triggered by Toll-like receptors (TLRs) are small chemoattractant proteins, which mainly regulate leukocyte trafficking in inflammatory reactions via interaction with G protein-coupled receptors. Forty-two chemokines and 19 cognate receptors have been found in the human genome. Prior to this study, only 11 chicken chemokines and 7 receptors had been reported. The objectives of this study were to identify systematically chicken chemokines and their cognate receptor genes in the chicken genome and to annotate these genes and ligand-receptor binding by a comparative genomics approach. Twenty-three chemokine and 14 chemokine receptor genes were identified in the chicken genome. The number of coding exons in these genes and the syntenies are highly conserved between human, mouse, and chicken although the amino acid sequence homologies are generally low between mammalian and chicken chemokines. Chicken genes were named with the systematic nomenclature used in humans and mice based on phylogeny, synteny, and sequence homology. The independent nomenclature of chicken chemokines and chemokine receptors suggests that the chicken may have ligand-receptor pairings similar to mammals. The TLR family represents evolutionarily conserved components of the patternrecognizing receptors (PRRs) of the innate immune system that recognize specific pathogen-associated molecular patterns (PAMPs) through their ectodomains (ECDs). TLR's ECDs contain 19 to 25 tandem copies of leucine-rich repeat (LRR) motifs. TLRs play important roles in the activation of pro-inflammatory cytokines, chemokines and modulation of antigen-specific adaptive immune responses. To date, nine TLRs have been reported in chicken, along with a non-functional TLR8. Two non-mammalian TLRs, TLR21 and TLR22, have been identified in pufferfish and zebrafish. The objectives of this study were to determine if there is the existence of chicken genes homologous to fish-specific TLRs, and if possible ligands of these receptors exist. After searching the chicken genome sequence and EST database, a novel chicken TLR homologous to fish TLR21 was identified. Phylogenetic analysis indicated that the identified chicken TLR is the orthologue of TLR21 in fish. Bioinformatic analysis of potential PAMP binding sites within LRR insertions showed that CpG DNA is the putative ligand of this receptor.

DNA methylation serves as a major epigenetic modification crucial to the normal organismal development and the onset and progression of complex diseases such as cancer. Computational predictions for DNA methylation profiling serve multiple purposes. First, accurate predictions can contribute valuable information for speeding up genome-wide DNA methylation profiling so that experimental resources can be focused on a few selected while computational procedures are applied to the bulk of the genome. Second, computational predictions can extract functional features and construct useful models of DNA methylation based on existing data, and can therefore be used as an initial step toward quantitative identification of critical factors or pathways controlling DNA methylation patterns. Third, computational prediction of DNA methylation can provide benchmark data to calibrate DNA methylation profiling equipment and to consolidate profiling results from different equipments or techniques. This thesis is written based on our study on the computational analysis of the DNA methylation patterns of the human genome. Particularly, we have established computational models (1) to predict the methylation patterns of the CpG islands in normal conditions, and (2) to detect the CpG islands that are unmethylated in normal conditions but aberrantly methylated in cancer conditions. When evaluated using the CD4 lymphocyte data of Human Epigenome Project (HEP) data set based on bisulfite sequencing, our computational models for predicting the methylation status of CpG islands in the normal conditions can achieve a high accuracy of 93-94%, specificity of 94%, and sensitivity of 92-93%. And, when evaluated using the aberrant methylation data from the MethCancerDB database for aberrantly methylated genes in cancer, our models for detecting the CpG islands that are unmethylated in normal conditions but aberrantly methylated in colon or prostate cancer can achieve an accuracy of 92-93%, specificity of 98-99%, and sensitivity of 92-93%.

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 179-191).

Title from title page of PDF (University of Missouri--St. Louis, viewed March 3, 2010). Includes bibliographical references (p. 26).

Genomic data facilitate opportunities to track complex population histories of divergence and gene flow. We used 47,506 single-nucleotide polymorphisms (SNPs) to investigate cattle population history. Cattle are descendants of two independently domesticated lineages, taurine and indicine, that diverged 200,000 or more years ago. We found that New World cattle breeds, as well as many related breeds of cattle in southern Europe, exhibit ancestry from both the taurine and indicine lineages. Although European cattle are largely descended from the taurine lineage, gene flow from African cattle (partially of indicine origin) contributed substantial genomic components to both southern European cattle breeds and their New World descendants. We extended these analyses to compare timing of admixture in several breeds of taurine-indicine hybrid origin. We developed a metric, scaled block size (SBS), that uses the unrecombined block size of introgressed regions of chromosomes to differentiate between recent and ancient admixture. By comparing test individuals to standards with known recent hybrid ancestry, we were able to differentiate individuals of recent hybrid origin from other admixed individuals using the SBS metric. We genotyped SNP loci using the bovine 50K SNP panel. The selection of sites to include in SNP analyses can influence inferences from the data, especially when particular populations are used to select the array of polymorphic sites. To test the impact of this bias on the inference of population genetic parameters, we used empirical and simulated data representing the three major continental groups of cattle: European, African, and Indian. We compared the inference of population histories for simulated data sets across different ascertainment conditions using F[subscript ST] and principal components analysis (PCA). Ascertainment bias that results in an over-representation of within-group polymorphism decreases estimates of F[subscript ST] between groups. Geographically biased selection of polymorphic SNPs changes the weighting of principal component axes and can bias inferences about proportions of admixture and population histories using PCA. By combining empirical and simulated data, we were able to both test methods for inferring population histories from genomic SNP data and apply these methods to practical problems. / text

Species evolution can often be adequately described with a phylogenetic tree. Interestingly, this is the case also for the evolution of homologous genes; a gene in an ancestral species may – through gene duplication, gene loss, lateral gene transfer (LGT), and speciation events – give rise to a gene family distributed across contemporaneous species. However, molecular sequence evolution and genetic recombination make the history – the gene tree – non-trivial to reconstruct from present-day sequences. This history is of biological interest, e.g., for inferring potential functional equivalences of extant gene pairs. In this thesis, we present biologically sound probabilistic models for gene family evolution guided by species evolution – effectively yielding a gene-species tree reconciliation. Using Bayesian Markov-chain Monte Carlo (MCMC) inference techniques, we show that by taking advantage of the information provided by the species tree, our methods achieve more reliable gene tree estimates than traditional species tree-uninformed approaches. Specifically, we describe a comprehensive model that accounts for gene duplication, gene loss, a relaxed molecular clock, and sequence evolution, and we show that the method performs admirably on synthetic and biological data. Further-more, we present two expansions of the inference procedure, enabling it to pro-vide (i) refined gene tree estimates with timed duplications, and (ii) probabilistic orthology estimates – i.e., that the origin of a pair of extant genes is a speciation. Finally, we present a substantial development of the model to account also for LGT. A sophisticated algorithmic framework of dynamic programming and numerical methods for differential equations is used to resolve the computational hurdles that LGT brings about. We apply the method on two bacterial datasets where LGT is believed to be prominent, in order to estimate genome-wide LGT and duplication rates. We further show that traditional methods – in which gene trees are reconstructed and reconciled with the species tree in separate stages – are prone to yield inferior gene tree estimates that will overestimate the number of LGT events. / Arters evolution kan i många fall beskrivas med ett träd, vilket redan Darwins anteckningsböcker från HMS Beagle vittnar om. Detta gäller också homologa gener; en gen i en ancestral art kan – genom genduplikationer, genförluster, lateral gentransfer (LGT) och artbildningar – ge upphov till en genfamilj spridd över samtida arter. Att från sekvenser från nu levande arter rekonstruera genfamiljens framväxt – genträdet – är icke-trivialt på grund av genetisk rekombination och sekvensevolution. Genträdet är emellertid av biologiskt intresse, i synnerhet för att det möjliggör antaganden om funktionellt släktskap mellan nutida genpar. Denna avhandling behandlar biologiskt välgrundade sannolikhetsmodeller för genfamiljsevolution. Dessa modeller tar hjälp av artevolutionens starka inverkan på genfamiljens historia, och ger väsentligen upphov till en förlikning av genträd och artträd. Genom Bayesiansk inferens baserad på Markov-chain Monte Carlo (MCMC) visar vi att våra metoder presterar bättre genträdsskattningar än traditionella ansatser som inte tar artträdet i beaktning. Mer specifikt beskriver vi en modell som omfattar genduplikationer, genförluster, en relaxerad molekylär klocka, samt sekvensevolution, och visar att metoden ger högkvalitativa skattningar på både syntetiska och biologiska data. Vidare presenterar vi två utvidgningar av detta ramverk som möjliggör (i) genträdsskattningar med tidpunkter för duplikationer, samt (ii) probabilistiska ortologiskattningar – d.v.s. att två nutida gener härstammar från en artbildning. Slutligen presenterar vi en modell som inkluderar LGT utöver ovan nämnda mekanismer. De beräkningsmässiga svårigheter som LGT ger upphov till löses med ett intrikat ramverk av dynamisk programmering och numeriska metoder för differentialekvationer. Vi tillämpar metoden för att skatta LGT- och duplikationsraten hos två bakteriella dataset där LGT förmodas ha spelat en central roll. Vi visar också att traditionella metoder – där genträd skattas och förlikas med artträdet i separata steg – tenderar att ge sämre genträdsskattningar, och därmed överskatta antalet LGT-händelser. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 5: Manuscript.</p>

Prostate cancer is the second leading cause of cancer deaths among men. Targeted analyses of DNA from prostate cancers have identified recurrent somatic alterations that promote tumor growth and survival. Only recently, however, has the comprehensive analysis of cancer genomes become possible due to rapid advances in DNA sequencing technology.

The development of cancer from a population of precancerous cells is a rapid evolutionary process. During progression, cells evolve several new traits for survive and proliferation via a few key `driver' mutations. However, these few driver alterations reside in a cancer genome alongside tens of thousands of additional `passenger' mutations. Passengers are widely believed to have no role in cancer, yet many passengers fall within functional genomic elements that may have potentially deleterious effects on the cancer cells. Here we investigate the potential of moderately deleterious passengers to accumulate and alter neoplastic progression. Evolutionary simulations suggest that moderately-deleterious passengers accumulate during progression and largely evade natural selection. Accumulation is possible because of cancer's unique evolutionary constraints: an initially small population size, an elevated mutation rate, and a need to acquire several driver mutations within a short evolutionary timeframe. Cancer dynamics can be theoretically understood as a tug-of-war between rare, strongly-beneficial drives and frequent mildly-deleterious passengers. In this formalism, passengers present a barrier to cancer progression describable by a critical population size, below which most lesions fail to progress, and a critical mutation rate, above which cancers collapse. In essence, cancer progression can be subverted by its own unique evolutionary constraints. The collective burden of passengers explain several oncological phenomena that are difficult to explain otherwise. Genomics data confirms that many passengers are likely damaging and have largely evaded negative selection, while age-incidence curves and the distribution of mutation totals suggests that drivers and passengers exhibit competing effects. These data also provide estimates of the strength of drivers and passengers. Finally, we use our model to explore cancer treatments. We identify two broad regimes of adaptive evolutionary dynamics and use these regimes to understand outcomes from various treatment strategies. Our theory explains previously paradoxical treatment outcomes and suggest that passengers could serve as a biomarker of response to mutagenic therapies. Deleterious passengers are targetable by either (i) increasing the mutation rate or (ii) exacerbating their deleterious effects. Our results suggest a unique framework for understanding cancer progression as a balance between driver and passenger mutations.

Many insects form symbioses with maternally inherited, intracellular bacteria, which can have major effects on the ecology and evolution of the insect host. Here I investigated the interaction between a global agricultural pest, Bemisia tabaci (the sweetpotato whitefly), and a Rickettsia bacterial symbiont. Rickettsia had previously been tracked sweeping through field populations of B. tabaci in the southwestern USA and had been shown to dramatically increase whitefly fitness under laboratory conditions. In contrast, the Rickettsia present in whiteflies in Israel has few observable fitness effects and is declining in frequency in field populations. I explored the population dynamics of Rickettsia in B. tabaci field populations in the USA and Israel, and assessed the genetic diversity of the Rickettsia in these populations. In laboratory experiments, there was no observable effect of Rickettsia on the heat shock or constant temperature tolerance of USA B. tabaci. Instead, whitefly genetic background appears to influence the effects of Rickettsia. Lastly, analysis of the genome sequence of Rickettsia provided insights into the mechanism of the fitness benefit and evolutionary history of the bacterium. Taken together, these integrated ecological, physiological and genomic studies provide some explanation for the contrasting and wide-ranging phenotypes associated with whitefly Rickettsia, and provide support for the hypothesis that the fitness benefit provided by Rickettsia is context dependent. The Rickettsia symbiosis exhibits geographically distinct population dynamics, is affected by whitefly genotype, and may involve manipulation of host plants and/or defense against pathogens rather than nutritional supplementation. Overall, these results highlight the important role that microbial symbionts may play in the adaptation of invasive species to changing environments.

Σήμερα υπάρχει ελεύθερη πρόσβαση μέσω του internet σε εκατοντάδες δημόσιες βάσεις βιολογικών δεδομένων. Παραταύτα, η προσπάθεια του να εκμεταλλευτεί κάποιος τα αποθηκευμένα δεδομένα ανομοιογενών βάσεων δεδομένων, καταλήγει να αποτελεί μια διαδικασία ιδιαίτερα δύσκολη και χρονοβόρα λόγω ποικίλων αιτιάσεων. Στις αιτίες αυτές συμπεριλαμβάνονται ο χαοτικός όγκος των βιολογικών δεδομένων, ο ολοένα αυξανόμενος αριθμός βιολογικών βάσεων δεδομένων, η υπεραφθονία τύπων και μορφών δεδομένων (format), η ποικιλομορφία βιοπληροφορικών τεχνικών πρόσβασης στα δεδομένα και βέβαια η διαφορετικότητα των βάσεων βιολογικών δεδομένων. Χάρη στις διεθνείς προσπάθειες ολοκλήρωσης αλληλουχιών (sequencing), οι ομάδες γονιδιακών δεδομένων έχουν αυξηθεί γεωμετρικά την τελευταία δεκαετία. Το έτος 2003 για παράδειγμα, η βάση βιολογικών δεδομένων Genbank διπλασιάστηκε σε μέγεθος μέσα σε 15 μήνες. Με τόσο γρήγορη ανάπτυξη, τα γενωμικά δεδομένα και οι συνδεόμενες με αυτά δομές έχουν αποκτήσει τεράστιο μέγεθος για να χωρέσουν στην κεντρική μνήμη ενός υπολογιστή. Το σημαντικότερο πρόβλημα που ανακύπτει έγκειται στο ότι μεγάλο μέρος της πληροφορίας που αναζητείται μέσα στο τεράστιο και ολοένα αυξανόμενο σε μέγεθος ορυχείο των δεδομένων εν τέλει χάνεται. Η ανάγκη κατασκευής των κατάλληλων εργαλείων εξ’ όρυξης της ζητούμενης πληροφορίας από το ορυχείο αυτό είναι μονόδρομος. Η παρούσα διπλωματική εργασία επικεντρώνεται στην διεύρυνση μιας υπάρχουσας βάσης βιολογικών δεδομένων ολοκληρωμένων γονιδιωμάτων, της COGENT. Η COGENT αναπτύχθηκε το 2003 από την Ομάδα Υπολογιστικής Γενωμικής (Computational Genomics Group – CGG), στο Ευρωπαϊκό Ινστιτούτο Βιοπληροφορικής (European Bioinformatics Institute – EBI), και τελικός τεχνικός στόχος της διπλωματικής εργασίας αποτελεί η προσθήκη βιβλιογραφικών δεδομένων καθώς και νουκλεοτιδικών πληροφοριών αλληλουχίας (DNA) στην βάση COGENT. / Today, hundreds of public biological databases are accessible via the Internet However taking advantage of data stored in heterogeneous biological databases can be a difficult, time consuming task for a multitude of reasons. These reasons include the vast volume of biological data, the growing number of biological databases, the rapid rate in the growth of data, the overabundance of data types and formats, the wide Variety of bioinformatics data access techniques, and database heterogeneity. Thanks to international sequencing efforts, genome data sets have been growing exponentially in the past few years. The GenBank database, for example, has doubled every 15 months. With such a rapid growth, genome data and the associated access structures have become too large to fit in the main memory of a computer, leading to a large number of disk accesses (and therefore, slow response times) for homology searches and other queries. Much of the important information in this enormous and exponentially growing gold mine will be wasted if we do not develop proper tools to access and mine them efficiently. The focus of this thesis was to extend an existing biological database for the complete tracking of genomes, the COGENT database, which the Computational Genomics Group at the European Bioinformatics Institute in Cambridge produced in 2003, so that it can incorporate literature and DNA sequence information.