Doctor of Philosophy / Division of Biology / Ari M. Jumpponen / Microorganisms are critically important for establishing and maintaining ecosystem properties and processes that fuel and sustain higher-trophic levels. Despite the universal importance of microbes, we know relatively little about the rules and processes that dictate how microbial communities establish and assemble. Largely, we rely on assumptions that microbial community establishment follow similar trajectories as plants, but on a smaller scale. However, these assumptions have been rarely validated and when validation has been attempted, the plant-based theoretical models apply poorly to microbial communities. Here, I utilized genomics-inspired tools to interrogate microbial communities at levels near community saturation to elucidate the rules and patterns of microbial community assembly. I relied on a community filtering model as a framework: potential members of the microbial community are filtered through environmental and/or biotic filters that control which taxa can establish, persist, and coexist. Additionally, I addressed whether two different microbial groups (fungi and bacteria) share similar assembly patterns. Similar dispersal capabilities and mechanisms are thought to result in similar community assembly rules for fungi and bacteria. I queried fungal and bacterial communities along a deglaciated primary successional chronosequence to determine microbial successional dynamics and to determine if fungal and bacterial assemblies are similar or follow trajectories similar to plants. These experiments demonstrate that not only do microbial community assembly dynamics not follow plant-based models of succession, but also that fungal and bacterial community assembly dynamics are distinct. We can no longer assume that because fungi and bacteria share small propagule sizes they follow similar trends. Further, additional studies targeting biotic filters (here, snow algae) suggest strong controls during community assembly, possibly because of fungal predation of the algae or because of fungal utilization of algal exudates. Finally, I examined various technical aspects of sequence-based ecological investigations. These studies aimed to improve microbial community data reliability and analyses.

Doctor of Philosophy / Department of Computing and Information Sciences / Doina Caragea / Recent advancements in biological sciences have resulted in the availability of large amounts of sequence data (DNA and protein sequences). Biological sequence data can be annotated using machine learning techniques, but most learning algorithms require data to be represented by a vector of features. In the absence of biologically informative features, k-mers generated using a sliding window-based approach are commonly used to represent biological sequences. A larger k value typically results in better features; however, the number of k-mer features is exponential in k, and many k-mers are not informative. Feature selection is widely used to reduce the dimensionality of the input feature space. Most feature selection techniques use feature-class dependency scores to rank the features. However, when the amount of available labeled data is small, feature selection techniques may not accurately capture feature-class dependency scores. Therefore, instead of working with all k-mers, this dissertation proposes the construction of a reduced set of informative k-mers that can be used to represent biological sequences. This work resulted in three novel unsupervised approaches to construct features: 1. Burrows Wheeler Transform-based approach, that uses the sorted permutations of a given sequence to construct sequential features (subsequences) that occur multiple times in a given sequence. 2. Community detection-based approach, that uses a community detection algorithm to group similar subsequences into communities and refines the communities to form motifs (group of similar subsequences). Motifs obtained using the community detection-based approach satisfy the ZOMOPS constraint (Zero, One or Multiple Occurrences of a Motif Per Sequence). All possible unique subsequences of the obtained motifs are then used as features to represent the sequences. 3. Hybrid-based approach, that combines the Burrows Wheeler Transform-based approach and the community detection-based approach to allow certain mismatches to the features constructed using the Burrows Wheeler Transform-based approach. To evaluate the predictive power of the features constructed using the proposed approaches, experiments were conducted in three learning scenarios: supervised, semi-supervised, and domain adaptation for both nucleotide and protein sequence classification problems. The performance of classifiers learned using features generated with the proposed approaches was compared with the performance of the classifiers learned using k-mers (with feature selection) and feature hashing (another unsupervised dimensionality reduction technique). Experimental results from the three learning scenarios showed that features constructed with the proposed approaches were typically more informative than k-mers and feature hashing.

Antibiotic resistance is a worsening threat to human health. Increasing our understanding of the mechanisms causing this resistance will be of great benefit in designing methods to evade resistance and in developing new classes of antibiotics. In this thesis, I have used the TetR Family Transcriptional Regulators (TFRs), which constitute one of the largest antibiotic resistance regulator families, as a model system to study the structure, function and evolution of antibiotic resistance determinants. I performed a thorough examination of the variation and conservation seen in TFR sequences and structures using computational approaches. Through structure comparison, I have identified the most conserved features shared by the TFR family that are crucial for their stability and function. Based on my findings on conserved TFR structural features, a quantitative assay of binding affinity determination was developed. Through sequence comparison and a residue contact map method, I discovered the existence of a conserved residue network that correlates well with the known allostery pathway of TetR. This predicted allosteric communication network was experimentally tested in TtgR. I have also developed methods to identify TFR operator sequences through genomic comparisons and validated my prediction through experiments. In addition, I have developed an in vivo system that can be used to identify and characterize proteins that mediate resistance to almost any antibiotic. This system is simple, fast, and scalable for high-throughput applications, and could be used to discover a wide range of novel antibiotic resistance mechanisms. The principles that I applied to the TFR family could also be applied to other protein families.

Les introns sont des portions de gènes transcrites dans l’ARN messager, mais retirées pendant l’épissage avant la synthèse des produits du gène. Chez les eucaryotes, on rencontre les introns splicéosomaux, qui sont retirés de l’ARN messager par des splicéosomes. Les introns permettent plusieurs processus importants, tels que l'épissage alternatif, la dégradation des ARNs messagers non-sens, et l'encodage d'ARNs fonctionnels. Leurs rôles nous interrogent sur l'influence de la sélection naturelle sur leur évolution. Nous nous intéressons aux mutations qui peuvent modifier les produits d'un gène en changeant les sites d'épissage des introns. Ces mutations peuvent influencer le fonctionnement d'un organisme, et constituent donc un sujet d'étude intéressant, mais il n'existe actuellement pas de logiciels permettant de les étudier convenablement. Le but de notre projet était donc de concevoir une méthode pour détecter et analyser les changements des sites d'épissage des introns splicéosomaux. Nous avons finalement développé une méthode qui repère les évènements évolutifs qui affectent les introns splicéosomaux dans un jeu d'espèces données. La méthode a été exécutée sur un ensemble d'espèces d'oomycètes. Plusieurs évènements détectés ont changé les sites d’épissage et les protéines, mais de nombreux évènements trouvés ont modifié les introns sans affecter les produits des gènes. Il manque à notre méthode une étape finale d'analyse approfondie des données récoltées. Cependant, la méthode actuelle est facilement reproductible et automatise l'analyse des génomes pour la détection des évènements. Les fichiers produits peuvent ensuite être analysés dans chaque étude pour répondre à des questions spécifiques. / Introns are portions of genes transcribed into messenger RNA, but removed during RNA splicing. In eukaryotes, they are called spliceosomal introns as they are removed by spliceosomes. Introns allow many important processes such as alternative splicing, nonsense-mediated decay and functional-RNA coding. These roles leads to the question of the influence of natural selection on evolution of introns. We focus on mutations that are able to change gene products by modifing introns splice sites. These mutations seems to be an interesting topic as they can affect proteins, but there is currently no software to study them properly. The aim of our project was to design a method to detect and analyze changes in splice sites of spliceosomal introns. We finally developed a method that locates the evolutionary events on splice sites of spliceosomal introns in a given species set. The method was performed on a set of oomycetes. Several detected events change splice sites and proteins, but there is also many events that seems to modify introns without affecting gene products. Our method lacks a final step for thorough analysis of the collected events. However, the current method is easily reusable and automates genome analysis for the detection of events. The resulting files can then be analyzed in each study to answer specific questions.

L'acide désoxyribonucléique (ADN) et l'acide ribonucléique (ARN) sont des polymères de nucléotides essentiels à la cellule. À l'inverse de l'ADN qui sert principalement à stocker l'information génétique, les ARN sont impliqués dans plusieurs processus métaboliques. Par exemple, ils transmettent l’information génétique codée dans l’ADN. Ils sont essentiels pour la maturation des autres ARN, la régulation de l’expression génétique, la prévention de la dégradation des chromosomes et le ciblage des protéines dans la cellule. La polyvalence fonctionnelle de l'ARN résulte de sa plus grande diversité structurale. Notre laboratoire a développé MC-Fold, un algorithme pour prédire la structure des ARN qu'on représente avec des graphes d'interactions inter-nucléotidiques. Les sommets de ces graphes représentent les nucléotides et les arêtes leurs interactions. Notre laboratoire a aussi observé qu'un petit ensemble de cycles d'interactions à lui seul définit la structure de n'importe quel motif d'ARN. La formation de ces cycles dépend de la séquence de nucléotides et MC-Fold détermine les cycles les plus probables étant donnée cette séquence. Mon projet de maîtrise a été, dans un premier temps, de définir une base de données des motifs structuraux et fonctionnels d'ARN, bdMotifs, en terme de ces cycles. Par la suite, j’ai implanté un algorithme, MC-Motifs, qui recherche ces motifs dans des graphes d'interactions et, entre autres, ceux générés par MC-Fold. Finalement, j’ai validé mon algorithme sur des ARN dont la structure est connue, tels que les ARN ribosomaux (ARNr) 5S, 16S et 23S, et l'ARN utilisé pour prédire la structure des riborégulateurs. Le mémoire est divisé en cinq chapitres. Le premier chapitre présente la structure chimique, les fonctions cellulaires de l'ARN et le repliement structural du polymère. Dans le deuxième chapitre, je décris la base de données bdMotifs. Dans le troisième chapitre, l’algorithme de recherche MC-Motifs est introduit. Le quatrième chapitre présente les résultats de la validation et des prédictions. Finalement, le dernier chapitre porte sur la discussion des résultats suivis d’une conclusion sur le travail. / Deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) are polymers of nucleotides essential for the survival of the cell. Contrary to DNA, whose main role is to store genetic information, RNA is involved in multiple metabolic processes. For example, RNA is involved in the transfer of information from DNA to protein, the processing and modification of other RNAs, the regulation of gene expression, the end-maintenance of chromosomes, and the sorting of proteins within the cell. This functional versatility of RNA comes from its structural diversity. Our laboratory developed MC-Fold, an algorithm that predicts RNA structures by representing them with nucleotide interaction graphs. The nodes in these graphs represent the nucleotides, and the edges the interactions between them. Our laboratory also observed that a limited number of interaction cycles can define the structure of any RNA motif. The formation of these cycles is determined by the nucleotide sequence and MC-Fold determines the most likely cycles based on that sequence. In this Master Degree project, I first built a database of structural and functional RNA motifs, bdMotifs, based on their constituent cycles. Then, I implemented an algorithm, MC-Motifs, which detects motifs within interaction graphs generated either by MC-Fold or by any other method. Finally, I validated my algorithm on known RNA structures such as the 5S, 16S and 23S ribosomal RNA (rRNA) and predicted structure of riboswitches. The Master thesis is divided into five chapters. The first chapter presents the chemical structure of RNA, its cellular functions and the structural folding of the polymer. In the second chapter, the database bdMotifs is described. In the third chapter, the MC-Motifs algorithm is introduced. In the fourth chapter, I present the results of MC-Motifs. Finally, in the last chapter, I discuss theses results and I give a conclusion on the project.

Master of Science / Department of Statistics / Susan J. Brown / Haiyan Wang / DNA hybridization microarray technologies have made it possible to gain an unbiased perspective of whole genome transcriptional activity on such a scale that is increasing more and more rapidly by the day. However, due to biologically irrelevant bias introduced by the experimental process and the machinery involved, correction methods are needed to restore the data to its true biologically meaningful state. Therefore, it is important that the algorithms developed to remove any sort of technical biases are accurate and robust. This report explores the concept of background correction in microarrays by using a real data set of five replicates of whole genome tiling arrays hybridized with genetic material from Tribolium castaneum. It reviews the literature surrounding such correction techniques and explores some of the more traditional methods through implementation on the data set. Finally, it introduces an alternative approach, implements it, and compares it to the traditional approaches for the correction of such errors.

The freshwater planarian Schmidtea mediterranea has been used in research for over 100 years, and is an emerging stem cell model. Exteriorly, planarians are covered in mucous secretions of unknown composition. While the planarian genome has been sequenced, it remains mostly unannotated. The goal my master’s research was to annotate the planarian proteome and mucous sub-proteome. Using a proteogenomics approach, I elucidated the proteome and mucous subproteome via mass spectrometry together with an in silico translated transcript database. I identified 1604 proteins, which were annotated using the Swiss-Prot BLAST algorithm and Gene Ontology analysis. The S. mediterranea proteome is highly similar to that predicted for the trematode Schistosoma mansoni associated with schistosomiasis. Remarkably, orthologs of 119 planarian mucous proteins are present in human mucosal secretions and tear fluid. I suggest planarians have potential to be a model system for parasitic worms and diseases underlined by mucous aberrancies.

The cellular prion protein (PrPC) is unique amongst mammalian proteins in that it not only has the capacity to aggregate (in the form of scrapie PrP; PrPSc) and cause neuronal degeneration, but can also act as an independent vector for the transmission of disease from one individual to another of the same or, in some instances, other species. Since the discovery of PrPC nearly thirty years ago, two salient questions have remained largely unanswered, namely, (i) what is the normal function of the cellular protein in the central nervous system, and (ii) what is/are the factor(s) involved in the misfolding of PrPC into PrPSc? To shed light on aspects of these questions, we undertook a discovery-based interactome investigation of PrPC in mouse neuroblastoma cells (Chapter 2), and among the candidate interactors, identified two members of the ZIP family of zinc transporters (ZIP6 and ZIP10) as possessing a PrP-like domain. Detailed analyses revealed that the LIV-1 subfamily of ZIP transporters (to which ZIPs 6 and 10 belong) are in fact the evolutionary ancestors of prions (Chapter 3). We were further able to demonstrate that PrPC likely emerged from a ZIP ancestor molecule nearly half-a-billion years ago via a retrotransposition event (Chapter 4). Moreover, biochemical investigations on ZIP10, as a model LIV-1 ZIP transporter, demonstrated that the ectodomain shedding of ZIP10 observed in prion-infected mice resembles a cellular response to transition metal starvation and suggested that prion disease in mice might phenocopy a transition metal starvation status (Chapter 5). These studies have opened a new angle to study prion biology in health and disease. Biochemical investigations on other LIV-1 ZIPs and attempts at the structural elucidation of the PrP-like domain of LIV-1 ZIP proteins are ongoing and have not been included in this thesis.

The objective of this study was to clarify the regulation of production and signal transduction of juvenile hormone (JH) in insects by experimentally examining the function and evolution of a putative receptor (USP/RXR) and a neuropeptide inhibitor (FGLamide allatostatin). To examine the role of USP/RXR, the cDNA sequence of the receptor was obtained from the cockroach Diploptera punctata. Transcript levels during developmentally critical periods for JH sensitivity may suggest USP/RXR is JH responsive. Comparative sequence analysis of evolutionary rates in the Mecopterida support current hypotheses which suggest some gain in function along this lineage, although this acquisition may have occurred more gradually than previously assumed. To examine allatostatin evolution within insects, ancestral peptides inferred using maximum likelihood ancestral reconstruction methods were assayed for in vitro inhibition of JH production in two cockroach species. Shifts in peptide potency in some ancestral peptides reconstructed may be related to peptide copy number evolution.

In order to identify expressologs (orthologs exhibiting the highest expression profile ranking) among a variety of plant species, bioinformatic methods were used in order to first identify sequence orthologs and subsequently to rank these orthologs based on expression profile similarity. Analyses conducted on these data suggested that expressologs exhibited greater functional equivalency. A comparison of drought response in A. thaliana and Populus showed that expressologs exhibited a higher correlation when computed using stress data as opposed to developmental data. This suggested that the use of condition-specific data sets is more appropriate when examining specific conditions. Analysis was conducted in order to investigate the hypothesis that neutral evolution was a predominant factor in gene expression divergence. Some evidence was found for selection acting on expression pattern maintenance. Further analysis will be required in order to confirm the type of selection acting to maintain expression patterns across species.