Comparative genome analysis of Pseudomonas knackmussii B13, the first bacterium known to degrade chloroaromatic compounds.

Pseudomonas knackmussii B13 was the first strain to be isolated in 1974 that could degrade chlorinated aromatic hydrocarbons. This discovery was the prologue for subsequent characterization of numerous bacterial metabolic pathways, for genetic and biochemical studies, and which spurred ideas for pollutant bioremediation. In this study, we determined the complete genome sequence of B13 using next generation sequencing technologies and optical mapping. Genome annotation indicated that B13 has a variety of metabolic pathways for degrading monoaromatic hydrocarbons including chlorobenzoate, aminophenol, anthranilate and hydroxyquinol, but not polyaromatic compounds. Comparative genome analysis revealed that B13 is closest to Pseudomonas denitrificans and Pseudomonas aeruginosa. The B13 genome contains at least eight genomic islands [prophages and integrative conjugative elements (ICEs)], which were absent in closely related pseudomonads. We confirm that two ICEs are identical copies of the 103 kb self-transmissible element ICEclc that carries the genes for chlorocatechol metabolism. Comparison of ICEclc showed that it is composed of a variable and a 'core' region, which is very conserved among proteobacterial genomes, suggesting a widely distributed family of so far uncharacterized ICE. Resequencing of two spontaneous B13 mutants revealed a number of single nucleotide substitutions, as well as excision of a large 220 kb region and a prophage that drastically change the host metabolic capacity and survivability.

Mutational screening of splicing factor genes in cases with autosomal dominant retinitis pigmentosa.

PURPOSE: Mutations in genes encoding proteins from the tri-snRNP complex of the spliceosome account for more than 12% of cases of autosomal dominant retinitis pigmentosa (adRP). Although the exact mechanism by which splicing factor defects trigger photoreceptor death is not completely clear, their role in retinitis pigmentosa has been demonstrated by several genetic and functional studies. To test for possible novel associations between splicing factors and adRP, we screened four tri-snRNP splicing factor genes (EFTUD2, PRPF4, NHP2L1, and AAR2) as candidate disease genes. METHODS: We screened up to 303 patients with adRP from Europe and North America who did not carry known RP mutations. Exon-PCR and Sanger methods were used to sequence the NHP2L1 and AAR2 genes, while the sequences of EFTUD2 and PRPF4 were obtained by using long-range PCRs spanning coding and non-coding regions followed by next-generation sequencing. RESULTS: We detected novel missense changes in individual patients in the sequence of the genes PRPF4 and EFTUD2, but the role of these changes in relationship to disease could not be verified. In one other patient we identified a novel nucleotide substitution in the 5' untranslated region (UTR) of NHP2L1, which did not segregate with the disease in the family. CONCLUSIONS: The absence of clearly pathogenic mutations in the candidate genes screened in our cohort suggests that EFTUD2, PRPF4, NHP2L1, and AAR2 are either not involved in adRP or are associated with the disease in rare instances, at least as observed in this study in patients of European and North American origin.

Comparative genome analysis of Pseudomonas knackmussii B13, the first bacterium known to degrade chloroaromatic compounds.

Pseudomonas knackmussii B13 was the first strain to be isolated in 1974 that could degrade chlorinated aromatic hydrocarbons. This discovery was the prologue for subsequent characterization of numerous bacterial metabolic pathways, for genetic and biochemical studies, and which spurred ideas for pollutant bioremediation. In this study, we determined the complete genome sequence of B13 using next generation sequencing technologies and optical mapping. Genome annotation indicated that B13 has a variety of metabolic pathways for degrading monoaromatic hydrocarbons including chlorobenzoate, aminophenol, anthranilate and hydroxyquinol, but not polyaromatic compounds. Comparative genome analysis revealed that B13 is closest to Pseudomonas denitrificans and Pseudomonas aeruginosa. The B13 genome contains at least eight genomic islands [prophages and integrative conjugative elements (ICEs)], which were absent in closely related pseudomonads. We confirm that two ICEs are identical copies of the 103 kb self-transmissible element ICEclc that carries the genes for chlorocatechol metabolism. Comparison of ICEclc showed that it is composed of a variable and a 'core' region, which is very conserved among proteobacterial genomes, suggesting a widely distributed family of so far uncharacterized ICE. Resequencing of two spontaneous B13 mutants revealed a number of single nucleotide substitutions, as well as excision of a large 220 kb region and a prophage that drastically change the host metabolic capacity and survivability.

The planarian flatworm: an in vivo model for stem cell biology and nervous system regeneration

Planarian flatworms are an exception among bilaterians in that they possess a large pool of adult stem cells that enables them to promptly regenerate any part of their body, including the brain. Although known for two centuries for their remarkable regenerative capabilities, planarians have only recently emerged as an attractive model for studying regeneration and stem cell biology. This revival is due in part to the availability of a sequenced genome and the development of new technologies, such as RNA interference and next-generation sequencing, which facilitate studies of planarian regeneration at the molecular level. Here, we highlight why planarians are an exciting tool in the study of regeneration and its underlying stem cell biology in vivo, and discuss the potential promises and current limitations of this model organism for stem cell research and regenerative medicine.

Planarian (Platyhelminthes, Tricladida) Diversity and Molecular Markers: A New View of an Old Group

Planarians are a group of free-living platyhelminths (triclads) best-known largely due to long-standing regeneration and pattern formation research. However, the group"s diversity and evolutionary history has been mostly overlooked. A few taxonomists have focused on certain groups, resulting in the description of many species and the establishment of higher-level groups within the Tricladida. However, the scarcity of morphological features precludes inference of phylogenetic relationships among these taxa. The incorporation of molecular markers to study their diversity and phylogenetic relationships has facilitated disentangling many conundrums related to planarians and even allowed their use as phylogeographic model organisms. Here, we present some case examples ranging from delimiting species in an integrative style, and barcoding them, to analysing their evolutionary history on a lower scale to infer processes affecting biodiversity origin, or on a higher scale to understand the genus level or even higher relationships. In many cases, these studies have allowed proposing better classifications and resulted in taxonomical changes. We also explain shortcomings resulting in a lack of resolution or power to apply the most up-to-date data analyses. Next-generation sequencing methodologies may help improve this situation and accelerate their use as model organisms.

Diagnosis of endocrine disease: High-yield thyroid fine-needle aspiration cytology: an update focused on ancillary techniques improving its accuracy.

Thyroid fine-needle aspiration (FNA) cytology is a fast growing field. One of the most developing areas is represented by molecular tests applied to cytological material. Patients that could benefit the most from these tests are those that have been diagnosed as 'indeterminate' on FNA. They could be better stratified in terms of malignancy risk and thus oriented with more confidence to the appropriate management. Taking in to consideration the need to improve and keep high the yield of thyroid FNA, professionals from various fields (i.e. molecular biologists, endocrinologists, nuclear medicine physicians and radiologists) are refining and fine-tuning their diagnostic instruments. In particular, all these developments aim at increasing the negative predictive value of FNA to improve the selection of patients for diagnostic surgery. These advances involve terminology, the application of next-generation sequencing to thyroid FNA, the use of immunocyto- and histo-chemistry, the development of new sampling techniques and the increasing use of nuclear medicine as well as molecular imaging in the management of patients with a thyroid nodule. Herein, we review the recent advances in thyroid FNA cytology that could be of interest to the 'thyroid-care' community, with particular focus on the indeterminate diagnostic category.

Targeting the undruggable: immunotherapy meets personalized oncology in the genomic era.

Owing to recent advances in genomic technologies, personalized oncology is poised to fundamentally alter cancer therapy. In this paradigm, the mutational and transcriptional profiles of tumors are assessed, and personalized treatments are designed based on the specific molecular abnormalities relevant to each patient's cancer. To date, such approaches have yielded impressive clinical responses in some patients. However, a major limitation of this strategy has also been revealed: the vast majority of tumor mutations are not targetable by current pharmacological approaches. Immunotherapy offers a promising alternative to exploit tumor mutations as targets for clinical intervention. Mutated proteins can give rise to novel antigens (called neoantigens) that are recognized with high specificity by patient T cells. Indeed, neoantigen-specific T cells have been shown to underlie clinical responses to many standard treatments and immunotherapeutic interventions. Moreover, studies in mouse models targeting neoantigens, and early results from clinical trials, have established proof of concept for personalized immunotherapies targeting next-generation sequencing identified neoantigens. Here, we review basic immunological principles related to T-cell recognition of neoantigens, and we examine recent studies that use genomic data to design personalized immunotherapies. We discuss the opportunities and challenges that lie ahead on the road to improving patient outcomes by incorporating immunotherapy into the paradigm of personalized oncology.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

Scalable Feature Selection Applications for Genome-Wide Association Studies of Complex Diseases

Personalized medicine will revolutionize our capabilities to combat disease. Working toward this goal, a fundamental task is the deciphering of geneticvariants that are predictive of complex diseases. Modern studies, in the formof genome-wide association studies (GWAS) have afforded researchers with the opportunity to reveal new genotype-phenotype relationships through the extensive scanning of genetic variants. These studies typically contain over half a million genetic features for thousands of individuals. Examining this with methods other than univariate statistics is a challenging task requiring advanced algorithms that are scalable to the genome-wide level. In the future, next-generation sequencing studies (NGS) will contain an even larger number of common and rare variants. Machine learning-based feature selection algorithms have been shown to have the ability to effectively create predictive models for various genotype-phenotype relationships. This work explores the problem of selecting genetic variant subsets that are the most predictive of complex disease phenotypes through various feature selection methodologies, including filter, wrapper and embedded algorithms. The examined machine learning algorithms were demonstrated to not only be effective at predicting the disease phenotypes, but also doing so efficiently through the use of computational shortcuts. While much of the work was able to be run on high-end desktops, some work was further extended so that it could be implemented on parallel computers helping to assure that they will also scale to the NGS data sets. Further, these studies analyzed the relationships between various feature selection methods and demonstrated the need for careful testing when selecting an algorithm. It was shown that there is no universally optimal algorithm for variant selection in GWAS, but rather methodologies need to be selected based on the desired outcome, such as the number of features to be included in the prediction model. It was also demonstrated that without proper model validation, for example using nested cross-validation, the models can result in overly-optimistic prediction accuracies and decreased generalization ability. It is through the implementation and application of machine learning methods that one can extract predictive genotype–phenotype relationships and biological insights from genetic data sets.

Characterisation of Cladonia rangiferina transcriptome and genome, and the effects of dehydration and rehydration on its gene expression

Lichens are symbiotic organisms, which consist of the fungal partner and the photosynthetic partner, which can be either an alga or a cyanobacterium. In some lichen species the symbiosis is tripartite, where the relationship includes both an alga and a cyanobacterium alongside the primary symbiont, fungus. The lichen symbiosis is an evolutionarily old adaptation to life on land and many extant fungal species have evolved from lichenised ancestors. Lichens inhabit a wide range of habitats and are capable of living in harsh environments and on nutrient poor substrates, such as bare rocks, often enduring frequent cycles of drying and wetting. Most lichen species are desiccation tolerant, and they can survive long periods of dehydration, but can rapidly resume photosynthesis upon rehydration. The molecular mechanisms behind lichen desiccation tolerance are still largely uncharacterised and little information is available for any lichen species at the genomic or transcriptomic level. The emergence of the high-throughput next generation sequencing (NGS) technologies and the subsequent decrease in the cost of sequencing new genomes and transcriptomes has enabled non-model organism research on the whole genome level. In this doctoral work the transcriptome and genome of the grey reindeer lichen, Cladonia rangiferina, were sequenced, de novo assembled and characterised using NGS and traditional expressed sequence tag (EST) technologies. RNA extraction methods were optimised to improve the yield and quality of RNA extracted from lichen tissue. The effects of rehydration and desiccation on C. rangiferina gene expression on whole transcriptome level were studied and the most differentially expressed genes were identified. The secondary metabolites present in C. rangiferina decreased the quality – integrity, optical characteristics and utility for sensitive molecular biological applications – of the extracted RNA requiring an optimised RNA extraction method for isolating sufficient quantities of high-quality RNA from lichen tissue in a time- and cost-efficient manner. The de novo assembly of the transcriptome of C. rangiferina was used to produce a set of contiguous unigene sequences that were used to investigate the biological functions and pathways active in a hydrated lichen thallus. The de novo assembly of the genome yielded an assembly containing mostly genes derived from the fungal partner. The assembly was of sufficient quality, in size similar to other lichen-forming fungal genomes and included most of the core eukaryotic genes. Differences in gene expression were detected in all studied stages of desiccation and rehydration, but the largest changes occurred during the early stages of rehydration. The most differentially expressed genes did not have any annotations, making them potentially lichen-specific genes, but several genes known to participate in environmental stress tolerance in other organisms were also identified as differentially expressed.

New Contig Creation Algorithm for the de novo DNA Assembly Problem

DNA assembly is among the most fundamental and difficult problems in bioinformatics. Near optimal assembly solutions are available for bacterial and small genomes, however assembling large and complex genomes especially the human genome using Next-Generation-Sequencing (NGS) technologies is shown to be very difficult because of the highly repetitive and complex nature of the human genome, short read lengths, uneven data coverage and tools that are not specifically built for human genomes. Moreover, many algorithms are not even scalable to human genome datasets containing hundreds of millions of short reads. The DNA assembly problem is usually divided into several subproblems including DNA data error detection and correction, contig creation, scaffolding and contigs orientation; each can be seen as a distinct research area. This thesis specifically focuses on creating contigs from the short reads and combining them with outputs from other tools in order to obtain better results. Three different assemblers including SOAPdenovo [Li09], Velvet [ZB08] and Meraculous [CHS+11] are selected for comparative purposes in this thesis. Obtained results show that this thesis’ work produces comparable results to other assemblers and combining our contigs to outputs from other tools, produces the best results outperforming all other investigated assemblers.

La dynamique chromatinienne induite par le pic de LH dans les cellules de granulosa chez la souris

La régulation transcriptionnelle des gènes est un processus indispensable sans lequel la diversité phénotypique des cellules ainsi que l’adaptation à leur environnement serait inexistant. L’identification des éléments de régulation dans le génome est d’une importance capitale afin de comprendre les mécanismes gouvernant l’expression des gènes spécifiques à un type cellulaire donné. Ainsi, suite au pic de LH, le follicule ovarien entre dans un programme intensif de différentiation cellulaire, orchestré par des modifications majeures du profile transcriptionnel des cellules de granulosa, déclenchant ultimement l’ovulation et la lutéinisation, processus indispensables à la fertilité femelle. L’hypothèse supportée par cette étude stipule qu’une réorganisation de la structure chromatinienne survient aux régions régulatrices d’une panoplie de gènes dans les heures suivant le pic de LH et qu’en isolant et identifiant ces régions, il serait possible de retrouver des éléments essentiels aux processus d’ovulation et de lutéinisation. Ainsi, en utilisant un protocole standard de superovulation chez la souris, les éléments de régulation se modifiant 4h suivant l’administration de hCG ont été isolés et identifiés dans les cellules de granulosa en utilisant la méthode FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) combinée à un séquençage haut débit. Cette étude a démontré que suite au stimulus ovulatoire, les cellules de granulosa subissent une reprogrammation majeure des éléments de régulation, qui est corrélée avec une modification drastique de leurs fonctions biologiques. De plus, cette étude a mis en évidence une association majoritaire des éléments de régulation à des régions intergéniques distales et à des introns, indiquant que ces régions ont une importance capitale dans la régulation transcriptionnelle dans les cellules de granulosa. Cette étude a également permis d’identifier une panoplie de régulateurs transcriptionnels reconnus pour être essentiels à la fonction ovarienne, ainsi que leur sites de liaison dans le génome, démontrant que la méthode FAIRE est une méthode assez puissante pour permettre la prédiction d’événements moléculaires précis ayant un sens physiologique réel.

Mutations impliquées dans la progression du cancer épithélial de l'ovaire

Le cancer épithélial de l’ovaire (CEO) est le cancer gynécologique le plus létal. Plus de 70% des patientes diagnostiquées avec une tumeur de stade avancé rechutent suite aux traitements chimiothérapeutiques de première ligne, la survie à cinq ans étant ainsi très faible. Afin de mieux comprendre l’évolution de la maladie, nous avons recherché de nouveaux gènes, responsables de l’initiation et de la progression du CEO. Précédemment, des lignées cellulaires ont été dérivées à partir de la tumeur primaire et récurrente et/ou d’ascites de trois patientes. Le séquençage de l’ARN de ces lignées par la technologie de séquençage de nouvelle génération (TSNG) nous a permis d’identifier des mutations ponctuelles qui pourraient nous indiquer des gènes dérégulés dans le CEO. La TSNG est un bon outil qui permet d’identifier et de cribler à grande échelle des mutations. Nous avons sélectionné PLEC1, SCRIB, NCOR2, SEMA6C, IKBKB, GLCE et ITGAE comme gènes candidats présentant des mutations dans nos lignées et ayant une relation fonctionnelle avérée avec le cancer. Étant donné que la TSNG est une technique à taux de fiabilité limité, nous avons validé ces mutations par séquençage Sanger. Ensuite, nous avons étudié l’effet de ces mutations sur la structure protéique et l’expression de PLEC1, de SCRIB et de SEMA6C. Seules certaines mutations dans les gènes PLEC1, SCRIB et SEMA6C ont pu être confirmées. PLEC1 et SCRIB sont deux protéines d’échafaudage dont la mutation, rapportée dans plusieurs cancers, pourrait induire des changements de leurs conformations et affecter leurs interactions et leurs fonctions. Les conséquences de ces mutations sur la tumorigenèse de l’ovaire devront être étudiées.

Caractérisation moléculaire d’un récent modèle d’étude de la leucémie myéloïde aigüe à caryotype normal :la lignée cellulaire CG-SH

La leucémie myéloïde aigüe (LMA) est la forme de leucémie la plus fréquente chez l’adulte au Canada. Bien que de nombreux réarrangements chromosomiques récurrents aient été identifiés chez les patients LMA, près de la moitié des cas présentent un caryotype normal (LMA-CN). L’étude de la LMA-CN in vitro est rendue difficile par le fait que la survie des cellules primaires de patients est défectueuse sur le long terme et que les lignées cellulaires leucémiques ont un caryotype hautement anormal. En 2009, Munker et son équipe ont établi une nouvelle lignée cellulaire, CG-SH, ayant la particularité d’avoir un caryotype normal. L’objectif principal de ce projet d’étude est de caractériser plus en détail ce nouveau modèle d’étude. Nous avons identifié l’ensemble des variants génétiques présents dans CG-SH grâce au séquençage du génome entier. Les variants susceptibles de participer à la leucémogénèse ont été isolés, tels que des insertions détectées dans EZH2 et GATA2, et de nombreux variants faux-sens détectés dans des gènes pertinents pour la LMA. Nous avons montré que les cellules CG-SH sont sensibles à l’effet prolifératif d’une combinaison de cytokines, qui agissent sur le comportement des cellules en modifiant l’expression des gènes associés à la régulation de la prolifération, de l’apoptose et de la différentiation. De plus, les cytokines diminuent le taux de nécrose des cellules en culture sur le court terme. La présente étude a permis d’approfondir notre connaissance sur les caractéristiques moléculaires de la lignée cellulaire CG-SH, un nouveau modèle d’étude in vitro de la LMA-CN.

Systems biology of the human MHC class I immunopeptidome

Le système de différenciation entre le « soi » et le « non-soi » des vertébrés permet la détection et le rejet de pathogènes et de cellules allogéniques. Il requiert la surveillance de petits peptides présentés à la surface cellulaire par les molécules du complexe majeur d’histocompatibilité de classe I (CMH I). Les molécules du CMH I sont des hétérodimères composés par une chaîne lourde encodée par des gènes du CMH et une chaîne légère encodée par le gène β2-microglobuline. L’ensemble des peptides est appelé l’immunopeptidome du CMH I. Nous avons utilisé des approches en biologie de systèmes pour définir la composition et l’origine cellulaire de l’immunopeptidome du CMH I présenté par des cellules B lymphoblastoïdes dérivés de deux pairs de fratries avec un CMH I identique. Nous avons découvert que l’immunopeptidome du CMH I est spécifique à l’individu et au type cellulaire, qu’il dérive préférentiellement de transcrits abondants, est enrichi en transcrits possédant d’éléments de reconnaissance par les petits ARNs, mais qu’il ne montre aucun biais ni vers les régions génétiques invariables ni vers les régions polymorphiques. Nous avons également développé une nouvelle méthode qui combine la spectrométrie de masse, le séquençage de nouvelle génération et la bioinformatique pour l’identification à grand échelle de peptides du CMH I, dont ceux résultants de polymorphismes nucléotidiques simples non-synonymes (PNS-ns), appelés antigènes mineurs d’histocompatibilité (AMHs), qui sont les cibles de réponses allo-immunitaires. La comparaison de l’origine génomique de l’immunopeptidome de soeurs avec un CMH I identique a révélé que 0,5% des PNS-ns étaient représentés dans l’immunopeptidome et que 0,3% des peptides du CMH I seraient immunogéniques envers une des deux soeurs. En résumé, nous avons découvert des nouveaux facteurs qui modèlent l’immunopeptidome du CMH I et nous présentons une nouvelle stratégie pour l’indentification de ces peptides, laquelle pourrait accélérer énormément le développement d’immunothérapies ciblant les AMHs.