Using systems biology approaches to elucidate gene regulatory networks controlling the plant defence response

Transcriptional regulation controlling pathogen-responsive gene expression in Arabidopsis is believed to underlie the plant defence response, which confers partial immunity of Arabidopsis to infection by Botrytis cinerea. In this thesis networks of transcriptional regulation mediating the defence response are studied in various ways. First transcriptional regulation was predicted for all genes differentially expressed during B. cinerea infection by development of a novel clustering approach, Temporal Clustering by Affinity Propagation (TCAP). This approach finds groups of genes whose expression profile time series have strong time-delayed correlation, a measure that is demonstrated to be more predictive of transcriptional regulation than conventionally used similarity measures. TCAP predicts the known regulation of GI by LHY, and co-clusters ORA59 and some of its downstream targets. Predicted novel regulators of pathogen-responsive gene expression were then studied in a reverse genetics screen, which discovered several novel but weakly altered susceptibility phenotypes. Comparison of predicted targets to known targets was complicated by the sparsity of mutant versus wildtype gene expression experiments performed during B. cinerea infections in the literature. To explore the context-dependence of transcriptional regulation, evidence of transcriptional regulation in different contexts was collected. This was compiled to generate a qualitative model of transcriptional regulation during the defence response. This model was validated and extended by experimental analysis of transcription factor-promoter binding in Yeast and transcriptional activation in planta. Comparative transcriptomics showed that downstream genes of some of these regulators | TGA3, ARF2, ERF1 and ANAC072 | are over-represented in the list of genes differentially expressed during B. cinerea infection, which is consistent with these targets being regulated by them during B. cinerea infection. Finally this qualitative model was used as prior information and was used along with gene expression time series to infer quantitative models of the gene regulatory network mediating the defence response. Some known regulation was predicted, and additionally ANAC055 was predicted to be a central regulator of pathogenresponsive gene expression

Data integration in inflammatory bowel disease

[eng] INTRODUCTION: Inflammatory bowel disease is a complex intestinal disease with several genetic and environmental factors that can influence its course. The ethiology and pathophysiology of the disease is not fully understood. There is some evidence that microbiome can play a role. Finding relationships between microbiome and host’s mucosa could help advance prevention, diagnosis or treatment. METHODS: We based our analysis on intestinal bacterial 16S rRNA and human transcriptome data from biopsies from multiple timepoints and intestine segments. We extended regularized generalized canonical correlation analysis to find models that are coherent with previous knowledge on the disease taking into account the samples’ information. Multiple inflammatory bowel disease datasets on different treatments and conditions were analysed and the models defining those dataset were compared. The results were compared with multiple co-inertia analysis. RESULTS: Splitting sample variables into different blocks results in models of these relationships that show differences on the genes and microorganisms selected. The models generated using our new method inteRmodel outperformed multiple coinertia analysis to classify the samples according to their location. Despite being used on datasets of different sources the resulting models show similar relationships between variables. DISCUSSION: Comparing multiple models helps find out the relationships within datasets. Our method finds how strong are the relationships between the microbiome, transcriptome and environmental variables. On different datasets genes selected were common. This approach is robust and flexible to different datasets and settings. CONCLUSION: With inteRmodel we found that the microbiome relates more closely to the sample location than to disease, but the transcriptome is highly related to the location of the sample on the intestine. There is a common transcriptome between datasets while microorganisms depend of the dataset. We can improve sample classification by taking into account both bacterial 16S rRNA and host transcriptome.[cat] INTRODUCCIÓ: La malaltia inflamatòria intestinal és una malaltia intestinal complexa amb diversos factors genètics i ambientals que poden influir en el seu curs. L'etiologia i fisiopatologia de la malaltia no es con eix del tot. Hi ha evidències que el microbioma pot tenir un paper rellevant. Trobar relacions entre el microbioma i la mucosa de l'hoste podria ajudar a avançar en la prevenció, el diagnòstic o el tractament. MÈTODES: Vam basar la nostra anàlisi en dades d'ARNr 16S bacteriana intestinal i de transcriptoma humà de biòpsies de múltiples punts de temps i segments intestinals. Hem ampliat l'anàlisi de correlació canònica generalitzada regularitzada per trobar models coherents amb el coneixement previ sobre la malaltia tenint en compte la informació de les mostres. Es van analitzar diversos conjunts de dades de malaltia inflamatòria intestinal sobre diferents tractaments i condicions i es van comparar els models que defineixen aquest conjunt de dades. Els resultats es van comparar amb l'anàlisi de coinèrcia múltiple. RESULTATS: Dividir les variables de la mostra en diferents blocs dona com a resultat models d'aquestes relacions que mostren diferències en els gens i els microorganismes seleccionats. Els models generats mitjançant el nostre nou mètode intermodel van superar l'anàlisi de coinèrcia múltiple per classificar les mostres segons la seva ubicació. Tot i utilitzar-se en conjunts de dades de diferents fonts, els models resultants mostren relacions similars entre variables. DISCUSSIÓ: La comparació de diversos models ajuda a esbrinar les relacions dins dels conjunts de dades. El nostre mètode troba com de fortes són les relacions entre el microbioma, el transcriptoma i les variables ambientals. En diferents conjunts de dades, els gens seleccionats eren comuns. Aquest enfocament és robust i flexible per a diferents conjunts de dades i configuracions. CONCLUSIÓ: Amb inteRmodel vam trobar que el microbioma es relaciona més estretament amb la ubicació de la mostra que amb la malaltia, però el transcriptoma està molt relacionat amb la ubicació de la mostra a l'intestí. Hi ha un transcriptoma comú entre conjunts de dades, mentre que els microorganismes depenen del conjunt de dades. Podem millorar la classificació de les mostres tenint en compte tant l'ARNr 16S bacterià com el transcriptoma hoste.[spa] INTRODUCCIÓN: La enfermedad inflamatoria intestinal es una enfermedad intestinal compleja con factores genéticos y ambientales que pueden influir en su curso. La etiología y la fisiopatología de la enfermedad no se conocen por completo. Existen evidencias que el microbioma puede desempeijar un papel relevante. Encontrar relaciones entre el microbioma y la mucosa del huésped podría ayudar a avanzar en la prevención, el diagnóstico o el tratamiento. MÉTODOS: Basamos nuestro análisis en el ARNr 16S bacteriano intestinal y en datos de transcriptomas humanos de biopsias de múltiples puntos temporales y segmentos intestinales. Extendimos el análisis de correlación canónica generalizada regularizado para encontrar modelos coherentes con el conocimiento previo sobre la enfermedad teniendo en cuenta la información de las muestras. Se analizaron múltiples conjuntos de datos de enfermedad inflamatoria intestinal en diferentes tratamientos y condiciones y se compararon los modelos que definen esos conjuntos de datos. Los resultados se compararon con análisis de coinercia múltiple. RESULTADOS: Dividir las variables de la muestra en diferentes bloques resulta en modelos de estas relaciones que muestran diferencias en los genes y microorganismos seleccionados. Los modelos generados con nuestro nuevo método, inter-Rmodel, superaron el análisis de múltiples coinercias para clasificar las muestras según su ubicación. A pesar de ser utilizados en conjuntos de datos de diferentes fuentes, los modelos resultantes muestran unas relaciones similares entre las variables. DISCUSIÓN: La comparación de varios modelos ayuda a descubrir las relaciones dentro de los conjuntos de datos. Nuestro método encuentra cuán fuertes son las relaciones entre el microbioma, el transcriptoma y las variables ambientales. En diferentes conjuntos de datos, los genes seleccionados eran comunes. Este enfoque es robusto y flexible para diferentes conjuntos de datos y configuraciones. CONCLUSIÓN: Con inteRmodel descubrimos que el microbioma se relaciona más estrechamente con la ubicación de la muestra que con la enfermedad, pero el transcriptoma está muy relacionado con la ubicación de la muestra en el intestino. Hay un transcriptoma común entre los conjuntos de datos, mientras que los microorganismos dependen del conjunto de datos. Podemos mejorar la clasificación de las muestras teniendo en cuenta tanto el ARNr 16S bacteriano como el transcriptoma del huésped

Bioinformatics

This book is divided into different research areas relevant in Bioinformatics such as biological networks, next generation sequencing, high performance computing, molecular modeling, structural bioinformatics, molecular modeling and intelligent data analysis. Each book section introduces the basic concepts and then explains its application to problems of great relevance, so both novice and expert readers can benefit from the information and research works presented here

Gene expression analysis of head and neck cancer development

Microarray analysis was performed on 32 head and neck keratinocytes cultures using Affymetrix U133A/B genechips. The panel of cultures included normal cells, mortal and immortal cultures of dysplastic keratinocytes and mortal and immortal cultures from carcinomas, all grown to a standard protocol. The overall GEP revealed that many of the well-established HNSCC molecular markers associated with motility and invasion were up-regulated in the mortal cells, particularly in the mortal carcinomas. Immortal NHSCC cells showed elevated expression of cell-cycle markers and loss of differentiation markers. In addition, a small number of common changes in gene expression in all the carcinomas, regardless of replicative fate, were identified. This included several transcription factors. A series of 49 novel gene expression changes consistently associated with immortality in dysplastic keratinocytes and SCCs were identified. The list included genes involves in cell cycle control, signalling, cellular metabolism and maintenance of cellular structure. Validation of the expression of these genes by western blot demonstrated that, in general, the protein expression of genes agreed with the RNA expression level from the microarray data. However, some heterogeneity was evident. The mortal and immortal gene expression signatures were validated by IHC in the tumours from which the cultures were derived. The tumours that gave rise to immortal cell cultures demonstrated a relatively uniform pattern of staining in relation to the novel markers of immortality. However, those tumours which gave rise to mortal cultures exhibited significant heterogeneity of gene expression pattern, with areas characteristic of both the mortal and immortal phenotype present. These novel markers give us further insight into the mechanisms and importance of keratinocytes immortalization. Surrogate markers of immortality could therefore be valuable for assessment of prognosis and therapy if confirmed in larger in vivo studies