Staphylococcus aureus kann bei etwa 25% der gesunden Normalbevölkerung nachgewiesen werden, ohne Symptome zu verursachen. Dieser Keim ist jedoch auch einer der wichtigsten Erreger bei Osteomyelitis und Infektionen von orthopädischen Implantaten wie z. B. von künstlichen Knie- oder Hüftgelenken. Diese Infektionen führen meist zu aufwendigen und risikobehafteten operativen Eingriffen sowie zu einer langfristigen Antibiotikagabe. In der vorliegenden Arbeit sollten S. aureus-Isolate charakterisiert werden, die aus Osteomyelitisherden oder infizierten orthopädischen Implantaten gewonnen wurden. Ziel war es, die Isolate daraufhin zu untersuchen, ob bestimmte Stämme dominieren und ob das Vorhandensein bestimmter Virulenzfaktoren mit einem besonderen Risiko für solche Infektionen korreliert. Für diese Untersuchungen wurden DNA-Arrays eingesetzt, welche es ermöglichen, alle relevanten Virulenzfaktoren in einem Experiment nachzuweisen, einen „genetischen Fingerabdruck“ zu erheben und die Isolate so Verwandtschaftsgruppen (klonalen Komplexen, CC) zuzuordnen. Insgesamt wurden 119 klinische Isolate charakterisiert. Sie gehörten zu 20 verschiedenen klonalen Komplexen. CC8 (19,3%), CC45 (17,7%) und CC30 (12,6%) dominierten. MRSA waren selten nachweisbar. Die sieben MRSA-Isolate gehörten zu den lokal dominierenden Stämmen (Rhein-Hessen, Süddeutscher, Barnimer und Berliner Epidemiestamm sowie Europäischer caMRSA-Klon). Die Populationsstruktur der klinischen Isolate und die Häufigkeiten der untersuchten Virulenz- und Adhäsionsfaktoren entsprachen weitestgehend Isolaten von asymptomatischen Trägern, die in einer früheren Studie bestimmt wurden (Molecular epidemiology of Staphylococcus aureus in asymptomatic carriers; Monecke, Lüdicke, Slickers, Ehricht; Eur J Clin Microbiol Infect Dis. 2009). Es konnte kein molekularer Marker identifiziert werden, der allein für eine Risikostratifizierung eingesetzt werden kann. Das Gen für Staphylokinase (sak) war jedoch bei den klinischen Isolaten (90,8%) häufiger nachzuweisen als in Isolaten von asymptomatischen Trägern (71,6%). Einige andere Gene traten ebenfalls bei Patienten häufiger auf, aber waren insgesamt zu selten, um bei Osteomyelitis und Implantatinfektionen eine signifikante Rolle zu spielen. Ein Beispiel dafür war das Panton-Valentine Leukozidin, das in 0,7% der Isolate von asymptomatischen Trägern und in 3,4% der Patientenisolate gefunden wurde. CC15 war bei Isolaten von asymptomatischen Trägern häufiger vertreten (16.8%) als bei Patientenisolaten (5.9%). Da alle CC15-Isolate sak-negativ waren, könnte auch diese Beobachtung als Indiz für einen Zusammenhang zwischen dem Vorhandensein von Staphylokinase und Invasivität gewertet werden. CC45 war bei Patientenisolaten (17,7%) häufiger als bei den asymptomatischen Trägern (9,0%) vorhanden. Es konnte jedoch kein CC45-spezifischer Faktor identifiziert werden, der mit einer höheren Virulenz im Zusammenhang stehen könnte. Des Weiteren sollte untersucht werden, ob S. aureus in infizierten orthopädischen Implantaten endogenen Ursprungs ist. Bei 23 Patienten mit Infektionen von Knie- oder Hüfttotalendoprothesen konnten parallel Nasenabstriche genommen und untersucht werden. Fünfzehn von ihnen (65,2%) waren Träger von S. aureus und bei neun (39,1%) waren die Isolate aus Nasenabstrichen und den infizierten Endoprothesen identisch. Dies weist darauf hin, daß Träger von S. aureus ein erhöhtes Risiko haben, Infektionen von Knie- oder Hüfttotalendoprothesen zu erleiden und daß ein großer Teil dieser Infektionen endogenen Ursprungs ist. Deshalb sollten Patienten vor Implantation von Knie- oder Hüfttotalendoprothesen auf Trägerschaft von S. aureus untersucht werden. Falls S. aureus nachgewiesen wird, sollte dieser Keim generell präoperativ eradiziert werden, um das Risiko endogener Infektionen zu verringern. Eine prospektive Studie zu diesem Thema wird empfohlen. / Staphylococcus aureus is asymptomatically carried by approximately 25% of a normal population. It is also one of the most important causes of osteomyelitis and infections of orthopedic implants such as total hip or knee replacements. Such infections usually lead to complicated and risky surgical procedures as well as to long-term antibiotic treatment. In the present work, S. aureus isolates from osteomyelitis or implant infections were to be characterised. The aim of the study was to prove whether certain strains were overrepresented among patient isolates, and whether the presence of certain virulence factors might correlate with these infections. DNA arrays where used which facilitate to screen for all relevant virulence factors within a single experiment and which allow typing by obtaining a genetic fingerprint of the examined isolate. By this method, it was also possible to assign isolates to phylogenetic clusters, so-called clonal complexes. 119 clinical isolates were characterised in this way. They belonged to 20 different clonal complexes (CC). CC8 (19.3%), CC45 (17.7%) and CC30 (12.6%) dominated. MRSA were rarely detected. The seven MRSA isolates belonged to locally predominant epidemic strains (ST5-MRSA-II, ST228-MRSA-I, ST22-MRSA-IV, ST45-MRSA-IV and ST80-MRSA-IV). The population structure of the clinical isolates and the relative abundances of the examined virulence and adhesion factors corresponded largely to isolates from asymptomatic carriers, which has been examined in an earlier study (Molecular epidemiology of Staphylococcus aureus in asymptomatic carriers; Monecke, Lüdicke, Slickers, Ehricht; Eur J Clin Microbiol Infect Dis. 2009). No molecular maker was detected which could alone be used for risk assessment. The gene for staphylokinase (sak) was clearly more common among clinical isolates (90.8%) than in isolates from asymptomatic carriers (71.6%). Some other genes were also found to be more common in patient isolates, but were very rare so that a significant role in bone and implant infection appeared to be unlikely. An example is Panton-Valentine leukocidin, which was detected in 3.4% of patient isolates and 0.7% of carrier isolates. CC15 was more commonly detected among healthy carriers (16.8%), than among patients (5.9%). Since all CC15 isolates were negative for sak, this also might be related to a possible role of staphylokinase in pathogenesis of invasive disease. CC45 was more abundant in patient samples (17.7%) than in swabs of healthy carriers (9.0%). However, it was not possible to identify a CC45-specific factor which might have been related to a higher virulence. Another aim of the study was to investigate whether S. aureus from orthopaedic implant infections were of endogenous origin. For 23 patients with S. aureus infections of total knee or hip prosthetics, it was possible to obtain nasal swabs in order to detect and type possible S. aureus carriage strains. Fifteen of them (65.2%) carried S. aureus. In nine patients (39.1%), isolates from nasal swabs and foci of infection were identical. This indicates that carriers of S. aureus are at risk of developing infections of total knee or hip prosthetics, and that a considerable proportion of these infections are of endogenous origin. Therefore, patients should generally be screened for S. aureus carriage prior to joint replacement. In case of detection, S. aureus should be eradicated in order to decrease the risk of endogenous infection. A prospective study is recommended.

Seven years into the postgenomic era, we sit atop a mountain of data whose generation was enabled by gene sequencing. The creation, integration, and analysis of these large scale data sets allow us to move forward toward the complementary goals of determining the individual roles of the thousands of uncharacterized mammalian genes and understanding how they work together to produce a healthy human being -- or, perhaps more importantly, how their malfunction results in disease. Collapsing the results of large-scale assays into gene networks provides a useful framework from which we can glean information that advances both of these goals. However, the utility of networks is limited by the quality of the data that goes into them. This study offers seeks to shed some light on the quality and breadth of protein interaction networks, describes a new experimental technique for functional genetic assays in mammalian cell lines, and ultimately suggests a strategy for how to improve the overall utility of the output generated by the systems biology community. / text

Escherichia coli O157:H7 is an enteric pathogen that can cause severe gastrointestinal disease, sometimes leading to hospitalization and death. These bacteria have a variety of virulence factors that can be encoded for on pathogenicity islands (PAIs). The goal of this study was to characterize specific E. coli O157:H7 PAI deletion mutants using three methods: Phentotype Microarrays (PM), growth curves and survival curves were used to elucidate possible roles for the PAIs. Results from the PM study suggest that PAIs have a role in carbon substrate utilization; i.e., four of the O-island (OI) deletion mutants (OI-87, 98, 102 and 172) and an S-Loop (SL-72) deletion mutant exhibited differences in substrate utilization (gains and losses in utilization) compared to parental O157:H7 strains EDL933 (OI) and Sakai (SL), respectively. All of the mutants with the exception of the OI-135 mutant exhibited differences in level of substrate utilization for substrates shown to have important roles in the bacterium. Cell growth results showed that three OI deletion mutants (OI-55, 87 and 102) and the SL (SL-72) mutant exhibited a difference in rate of growth compared to the parental strains. Cell viability results showed that seven of the OI deletion mutants (OI-51, 55, 98, 108, 135, 172 and 176) exhibited different rates of decline in cell number when transferred to sterile water compared to the parental strain. The results show that removal of PAIs from E. coli O157:H7 can affect carbon utilization, growth and survival demonstrating the importance of PAIs in the ecology of these bacteria. / xx, 208 leaves : ill. (some col.) ; 29 cm

La technologie des microarrays demeure à ce jour un outil important pour la mesure de l'expression génique. Au-delà de la technologie elle-même, l'analyse des données provenant des microarrays constitue un problème statistique complexe, ce qui explique la myriade de méthodes proposées pour le pré-traitement et en particulier, l'analyse de l'expression différentielle. Toutefois, l'absence de données de calibration ou de méthodologie de comparaison appropriée a empêché l'émergence d'un consensus quant aux méthodes d'analyse optimales. En conséquence, la décision de l'analyste de choisir telle méthode plutôt qu'une autre se fera la plupart du temps de façon subjective, en se basant par exemple sur la facilité d'utilisation, l'accès au logiciel ou la popularité. Ce mémoire présente une approche nouvelle au problème de la comparaison des méthodes d'analyse de l'expression différentielle. Plus de 800 pipelines d'analyse sont appliqués à plus d'une centaine d'expériences sur deux plateformes Affymetrix différentes. La performance de chacun des pipelines est évaluée en calculant le niveau moyen de co-régulation par l'entremise de scores d'enrichissements pour différentes collections de signatures moléculaires. L'approche comparative proposée repose donc sur un ensemble varié de données biologiques pertinentes, ne confond pas la reproductibilité avec l'exactitude et peut facilement être appliquée à de nouvelles méthodes. Parmi les méthodes testées, la supériorité de la sommarisation FARMS et de la statistique de l'expression différentielle TREAT est sans équivoque. De plus, les résultats obtenus quant à la statistique d'expression différentielle corroborent les conclusions d'autres études récentes à propos de l'importance de prendre en compte la grandeur du changement en plus de sa significativité statistique. / Microarrays remain an important tool for the measurement of gene expression, and a myriad of methods for their pre-processing or statistical testing of differential expression has been proposed in the past. However, insufficient and sometimes contradictory evidence has prevented the emergence of a strong consensus over a preferred methodology. This leaves microarray practitioners to somewhat arbitrarily decide which method should be used to analyze their data. Here we present a novel approach to the problem of comparing methods for the identification of differentially expressed genes. Over eight hundred analytic pipelines were applied to more than a hundred independent microarray experiments. The accuracy of each analytic pipeline was assessed by measuring the average level of co-regulation uncovered across all data sets. This analysis thus relies on a varied set of biologically relevant data, does not confound reproducibility for accuracy and can easily be extended to future analytic pipelines. This procedure identified FARMS summarization and the TREAT gene ordering statistic as algorithms significantly more accurate than other alternatives. Most interestingly, our results corroborate recent findings about the importance of taking the magnitude of change into account along with an assessment of statistical significance.

We explored the combination of metagenomic library and DNA microarray technologies into a single platform as a novel way to rapidly screen metagenomic libraries for genetic targets. In the "metagenomic microarray" system, metagenomic library clone DNA is printed on a microarray surface, and clones of interest are detected by hybridization to single-gene probes. This study represents the initial steps in the development of this technology. We constructed two 5,000-clone large-insert metagenomic libraries from two diesel-contaminated Arctic soil samples. We developed and optimized an automated fosmid purification protocol to rapidly-extract clone DNA in a high-throughput 96-well format. We then created a series of small prototype arrays to optimize various parameters of microarray printing and hybridization, to identify and resolve technical challenges, and to provide proof-of-principle of this novel application. Our results suggest that this method shows promise, but more experimentation must be done to establish the feasibility of this approach.

Thesis (Ph. D.)--Carleton University, 2004. / Includes bibliographical references (p. 200-217). Also available in electronic format on the Internet.

In the present thesis, a novel gridding technique, as well as, two new segmentation methods applied to complementary DNA (cDNA) microarray images is proposed. More precise, a new gridding method based on continuous wavelet transform (CWT) was performed. Line profiles of x and y axis were calculated, resulting to 2 different signals. These signals were independently processed by means of CWT at 15 different levels, using daubechies 4 mother wavelet. A summation, point by point, was performed on the processed signals, in order to suppress noise and enhance spot’s differences. Additionally, a wavelet based hard thresholding filter was applied to each signal for the task of alleviating the noise of the signals. 10 real microarray images were used in order to visually assess the performance of our gridding method. Each microarray image contained 4 sub-arrays, each sub-array 40x40 spots, thus, 6400 spots totally. According to our results, the accuracy of our algorithm was 98% in all 10 images and in all spots. Additionally, processing time was less than 3 sec on a 1024×1024×16 microarray image, rendering the method a promising technique for an efficient and fully automatic gridding processing. Following the gridding process, the Gaussian Mixture Model (GMM) and the Fuzzy GMM algorithms were applied to each cell, with the purpose of discriminating foreground from background. In addition, markov random field (MRF), as well as, a proposed wavelet based MRF model (SMRF) were implemented. The segmentation abilities of all the algorithms were evaluated by means of the segmentation matching factor (SMF), the Coefficient of Determination (r2), and the concordance correlation (pc). Indirect accuracy performances were also tested on the experimental images by means of the Mean Absolute Error (MAE) and the Coefficient of Variation (CV). In the latter case, SPOT and SCANALYZE software results were also tested. In the former case, SMRF attained the best SMF, r2, and pc (92.66%, 0.923, and 0.88, respectively) scores, whereas, in the latter case scored MAE and CV, 497 and 0.88, respectively. The results and support the performance superiority of the SMRF algorithm in segmenting cDNA images. / Τα τελευταία χρόνια παρατηρείται ραγδαία ανάπτυξη της τεχνολογίας των μικροσυστοιχιών (microarrays) με αποτέλεσμα την ποιοτική και ποσοτική μέτρηση της έκφρασης χιλιάδων γονιδίων ταυτοχρόνως σ’ ένα και μόνο πείραμα. Εικόνες μικροσυστοιχιών, στις οποίες έχει λάβει χώρα υβριδοποίηση δείγματος DNA, χρησιμοποιούνται ευρέως για την εξαγωγή αξιόπιστων αποτελεσμάτων γονιδιακής έκφρασης και προσδιορισμό των μηχανισμών που ελέγχουν την ενεργοποίηση των γονιδίων σ’ έναν οργανισμό. Συνεπώς, η δημιουργία κατάλληλων υπολογιστικών τεχνικών για την επεξεργασία των εικόνων αυτών συντελεί καθοριστικά στην εξαγωγή ορθών και έγκυρων αποτελεσμάτων. Στη παρούσα Διδακτορική Διατριβή αναπτύχθηκε στο πρώτο στάδια μια νέα πλήρως αυτοματοποιημένη τεχνική διευθυνσιοδότησης και στο δεύτερο στάδιο δύο νέες τεχνικές τμηματοποίησης. Πιο συγκεκριμένα, αναπτύχθηκε μια νέα μέθοδος διευθυνσιοδότησης η οποία βασίζεται στο συνεχή μετασχηματισμό κυματιδίου (Continuous Wavelet Transform CWT) για την αυτόματη εύρεση των κέντρων των κηλίδων, καθώς και των ορίων μεταξύ δύο διαδοχικών κηλίδων. Στη συνέχεια αναπτύχθηκαν δύο νέες μέθοδοι κατάτμησης της εικόνας για τον διαχωρισμό των κηλίδων από το φόντο, οι οποίες βασίζονται στη τεχνική μίξης ασαφών μοντέλων Γκάους (Fuzzy Gaussian Mixture Models FGMM) καθώς και στη τεχνική συνδυασμού τυχαίων πεδίων Μαρκόφ (Markov Random Field MRF) και μετασχηματισμού κυματιδίου (Wavelet Transform WT) (SMRF). Με σκοπό την αξιολόγηση (validation) των προτεινόμενων μεθόδων της παρούσας Διδακτορικής Διατριβής, δημιουργήθηκαν και χρησιμοποιήθηκαν τόσο πραγματικές εικόνες μικροσυστοιχιών, καθώς και απομιμούμενες (simulated) σύμφωνα με μεθοδολογία η οποία προτείνεται απο τη διεθνή βιβλιογραφία. Όσον αφορά την διευθυνσιοδότηση, χρησιμοποιώντας οπτική ανασκόπηση για κάθε κηλίδα χωριστά σε όλες τις πραγματικές εικόνες, δημιουργήθηκαν δύο κατηγορίες, ανάλογα με το αν οι γραμμές του πλέγματος εφάπτονταν πάνω σε κάποια κηλίδα ή όχι. Η προτεινόμενη μεθοδολογία ήταν ακριβής σε ποσοστό 98% στον ακριβή εντοπισμό των κηλίδων σε όλες τις εικόνες. Σύγκριση ανάμεσα στην απόδοση των GMM, FGMM, MRF και SMRF στις απομιμούμενες εικόνες σε διαφορετικά επίπεδα θορύβου πραγματοποιήθηκε και τα αποτελέσματα σε όλα τα μετρικά, segmentation matching factor (SMF), coefficient of variation ( ), και coefficient of determination ( ), μας έδειξαν ότι η μέθοδος SMRF είναι πιο αξιόπιστη στο να μπορέσει να αναδείξει την πραγματική περιφέρεια της κηλίδας, τόσο σε εικόνες με μεγάλο λόγο σήματος προς θόρυβο, όσο και σε μικρό λόγο. Ενδεικτικά αποτελέσματα σε 1 db SNR για την περίπτωση του SMRF είναι SMF = 92.66, =0.923, και = 0.88, ακολουθούμενο από το MRF ( SMF = 92.15, =0.91, και = 0.85), FGMM ( SMF = 91.07, =0.92, και = 0.86)και GMM (SMF = 90.73, =0.89, και = 0.83). Στη συνέχεια πάρθηκαν αποτελέσματα τα οποία προέκυψαν από τη χρήση πραγματικών εικόνων μικροσυστοιχιών. Και σε αυτή τη περίπτωση, αναδείχθηκε η υπεροχή του WMRF, έναντι των άλλων αλγορίθμων ταξινόμησης μέση τιμή MAE = 497 και CV = 0.88. Τέλος, θα πρέπει να τονιστεί ότι τα παραπάνω μετρικά υπολογίστηκαν και σε αποτελέσματα από δύο ευρέως χρησιμοποιούμενα πακέτα επεξεργασίας εικόνων μικροσυστοιχιών, τα οποία χρησιμοποιούνται και είναι διαθέσιμα. Πιο συγκεκριμένα, χρησιμοποιήθηκαν το SCANALYSE και το SPOT, τα οποία χρησιμοποιούν τις τεχνικές τμηματοποίησης Fixed Circle και Seeded Region Growing, αντίστοιχα. Στη περίπτωση αυτή η τεχνική SMRF κατάφερε να υπολογίσει καλύτερα αποτελέσματα από τα δύο αυτά πακέτα. Πιο συγκεκριμένα η τεχνική GMM πέτυχε MAE = 1470 και CV = 1.29, η τεχνική FGMM πέτυχε MAE = 1430 και CV = 1.21, η τεχνική MRF πέτυχε MAE = 1215 και CV = 1.15, η τεχνική WMRF πέτυχε MAE = 497 και CV = 0.88, η τεχνική FC του λογισμικού πακέτου SCANALYZE πέτυχε MAE = 503 και CV = 0.90, και τέλος η τεχνική SRG του λογισμικού πακέτου SPOT πέτυχε MAE = 1180 και CV = 0.93.

Η τεχνολογία των μικροσυστοιχιών επιτρέπει τη μέτρηση των επιπέδων έκφρασης χιλιάδων γονιδίων ταυτόχρονα σε ένα μόνο πείραμα δημιουργώντας έτσι ένα τεράστιο σύνολο δεδομένων για ανάλυση. Για να είναι δυνατή η εξαγωγή σημαντικής πληροφορίας για το υπό μελέτη βιολογικό σύστημα, έχουν χρησιμοποιηθεί διάφορες μέθοδοι προεπεξεργασίας και ανάλυσης των δεδομένων. Στις μεθόδους προεπεξεργασίας των δεδομένων συμπεριλαμβάνονται και οι μέθοδοι κανονικοποίησης. Σκοπός της κανονικοποίησης είναι η ελαχιστοποίηση των συστηματικών σφαλμάτων που εντοπίζονται στα εκτιμώμενα επίπεδα έκφρασης των γονιδίων, έτσι ώστε οι εμφανιζόμενες διαφορές τους να οφείλονται κυρίως σε βιολογικούς παράγοντες. Επίσης, η κανονικοποίηση καθιστά εφικτή τη σύγκριση των επιπέδων έκφρασης δεδομένων από περισσότερες της μίας μικροσυστοιχίες. Στην παρούσα διπλωματική εργασία παρατίθεται μια ανασκόπηση των μεθόδων κανονικοποίησης για δεδομένα γονιδιακής έκφρασης cDNA μικροσυστοιχιών καθώς και μια σύγκριση των αναλυόμενων μεθόδων κανονικοποίησης. / Microarray technology allows the measurement of gene expression levels of thousands of genes simultaneously in a single experiment, therefore creating a vast set of data for analysis. In order to be able to extract the most essential information for the biological system under examination in a specific microarray, various methods are used for data pre-processing and analysis. These data pre-processing methods also include normalization methods. The purpose of normalization is the minimization of the systematic errors that are found in the estimated gene expression levels, so as the observed biological differences be due mainly to biological factors. Furthermore, the normalization makes possible the comparison of gene expression levels of data from more than one microarrays. In the present thesis a review of the normalization methods for gene expression microarray data is presented, as well as a comparison between the analysed normalization methods.

Uma das principais áreas de pesquisa em Biologia de Sistemas refere-se à descoberta de redes biológicas a partir de conjuntos de dados de microarrays. Estas redes consistem de um grande número de genes cujos níveis de expressão afetam os outros genes de vários modos. Nesta tese, apresenta-se uma nova maneira de analisar os conjuntos de dados de microarrays, com base nos diferentes tipos de ciclos encontrados entre os genes das redes de co-expressão construídas com dados quantificados obtidos a partir dos microarrays. A entrada do método de análise é formada pelos dados brutos, um conjunto de genes de interesse (por exemplo, genes de uma via conhecida) e uma função (ativador ou inibidor) destes genes. A saída do método é um conjunto de ciclos. Um ciclo é um caminho fechado com todos os vértices (exceto o primeiro e o último) distintos. Graças à nova forma de encontrar relações entre os genes, é possível uma interpretação mais robusta das correlações dos genes, porque os ciclos estão associados a mecanismos de feedback, que são muito comuns em redes biológicas. A hipótese é que feedbacks negativos permitem encontrar relações entre os genes que podem ajudar a explicar a estabilidade do processo regulatório dentro da célula. Ciclos de feedback positivo, por outro lado, podem mostrar a quantidade de desequilíbrio de uma determinada célula em um determinado momento. A análise baseada em ciclos permite identificar a relação estequiométrica entre os genes da rede. Esta metodologia proporciona uma melhor compreensão da biologia do tumor. Portanto, as principais contribuições desta tese são: (i) um novo método de análise baseada em ciclos; (ii) um novo método de classificação; (iii) e, finalmente, aplicação dos métodos e a obtenção de resultados práticos. A metodologia proposta foi utilizada para analisar os genes de quatro redes fortemente relacionadas com o câncer - apoptose, glicólise, ciclo celular e NF B - em tecidos do tipo mais agressivo de tumor cerebral (Gliobastoma multiforme - GBM) e em tecidos cerebrais saudáveis. A maioria dos pacientes com GBM morrem em menos de um ano, essencialmente nenhum paciente tem sobrevivência a longo prazo, por isso estes tumores têm atraído atenção significativa. Os principais resultados nesta tese mostram que a relação estequiométrica entre genes envolvidos na apoptose, glicólise, ciclo celular e NF B está desequilibrada em amostras de GBM em comparação as amostras de controle. Este desequilíbrio pode ser medido e explicado pela identificação de um percentual maior de ciclos positivos nas redes das primeiras amostras. Esta conclusão ajuda a entender mais sobre a biologia deste tipo de tumor. O método de classificação baseado no ciclo proposto obteve as mesmas métricas de desempenho como uma rede neural, um método clássico de classificação. No entanto, o método proposto tem uma vantagem significativa em relação às redes neurais. O método de classificação proposto não só classifica as amostras, fornecendo diagnóstico, mas também explica porque as amostras foram classificadas de uma certa maneira em termos dos mecanismos de feedback que estão presentes/ausentes. Desta forma, o método fornece dicas para bioquímicos sobre possíveis experiências laboratoriais, bem como sobre potenciais genes alvo de terapias. / One of the main research areas in Systems Biology concerns the discovery of biological networks from microarray datasets. These networks consist of a great number of genes whose expression levels affect each other in various ways. We present a new way of analyzing microarray datasets, based on the different kind of cycles found among genes of the co-expression networks constructed using quantized data obtained from the microarrays. The input of the analysis method is formed by raw data, a set of interest genes (for example, genes from a known pathway) and a function (activator or inhibitor) of these genes. The output of the method is a set of cycles. A cycle is a closed walk, in which all vertices (except the first and last) are distinct. Thanks to the new way of finding relations among genes, a more robust interpretation of gene correlations is possible, because cycles are associated with feedback mechanisms that are very common in biological networks. Our hypothesis is that negative feedbacks allow finding relations among genes that may help explaining the stability of the regulatory process within the cell. Positive feedback cycles, on the other hand, may show the amount of imbalance of a certain cell in a given time. The cycle-based analysis allows identifying the stoichiometric relationship between the genes of the network. This methodology provides a better understanding of the biology of tumors. As a consequence, it may enable the development of more effective treatment therapies. Furthermore, cycles help differentiate, measure and explain the phenomena identified in healthy and diseased tissues. Cycles may also be used as a new method for classification of samples of a microarray (cancer diagnosis). Compared to other classification methods, cycle-based classification provides a richer explanation of the proposed classification, that can give hints on the possible therapies. Therefore, the main contributions of this thesis are: (i) a new cycle-based analysis method; (ii) a new microarray samples classification method; (iii) and, finally, application and achievement of practical results. We use the proposed methodology to analyze the genes of four networks closely related with cancer - apoptosis, glucolysis, cell cycle and NF B - in tissues of the most aggressive type of brain tumor (Gliobastoma multiforme – GBM) and in healthy tissues. Because most patients with GBMs die in less than a year, and essentially no patient has long-term survival, these tumors have drawn significant attention. Our main results show that the stoichiometric relationship between genes involved in apoptosis, glucolysis, cell cycle and NF B pathways is unbalanced in GBM samples versus control samples. This dysregulation can be measured and explained by the identification of a higher percentage of positive cycles in these networks. This conclusion helps to understand more about the biology of this tumor type. The proposed cycle-based classification method achieved the same performance metrics as a neural network, a classical classification method. However, our method has a significant advantage with respect to neural networks. The proposed classification method not only classifies samples, providing diagnosis, but also explains why samples were classified in a certain way in terms of the feedback mechanisms that are present/absent. This way, the method provides hints to biochemists about possible laboratory experiments, as well as on potential drug target genes.

Organisms are continuously exposed to heterogeneous micro- and macro-parasitic species, hence simultaneous infections often occur in wild and farm environments. This joint project aimed to develop a co-infection model between chronic and acute infections, evaluating their impact on the fish immune system. Proliferative Kidney Disease was studied on farmed rainbow and brown trout during natural seasonal outbreaks, using a parasite gene (Tetracapsuloides bryosalmonae RPL18) as a proxy for assessment of parasite burden. In hosts with elevated susceptibility PKD pathogenesis was shaped by an anti-inflammatory phenotype, a profound B cell/antibody response and dysregulated TH cell-like activity. Pathogen-free brown trout were exposed to Viral Haemorrhagic Septicaemia Virus (comparatively using European VHSV-Ia and North American VHSV-IVb strains) or to the bacterium Yersinia ruckeri. This European native species was highly resistant to the VHSV-IVb strain, which was undetectable in internal organs despite raising a strong antiviral and mucosal immune response. Following VHS and Yersiniosis infection, haemo-lymphopoietic organs were screened by RT-qPCR to assess the specific pathogen burdens and characterise the immune responses elicited. Transcription patterns were analysed for Interferons, CXC chemokines, SOCS (potential disease resistance biomarkers) and genes of the PACAP system. Lastly, PKD-infected brown trout were co-infected with VHSV-Ia, resulting in typical lesions while showing reduced and delayed mortality. PKD+/VHS+ fish were identified by RT-qPCR and histopathology screening. Pro-inflammatory and antimicrobial peptide genes were modulated following virus co-infection when compared to fish with single infection, with an earlier activation of cellular and humoral responses, and a stronger up-regulation of TH1 and antiviral genes. Oligonucleotide microarrays were used to assess the broader immune gene transcription modulation between single- and co-infected fish. Overall, the results suggest that the immune response of brown trout might be enhanced during the PKD/VHS co-infection.