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Abstract
The gastrointestinal tract is subject of much research for its role in an organism’s health owing to its role as gatekeeper. A major function of the gut is to act as a barrier to chal- lenges from the external environment. The tissue must keep out harmful substances like pathogens and toxins while absorbing nutrients that arise from the digestion of dietary components in in the lumen. There is a large population of microbiota that plays an important role in the functioning of the gut, digestion and homeostasis of the immune sys- tem. All these sub - systems of the gastrointestinal tract contribute to the normal functioning of the gut. Due to its various functionalities, the gut is able to respond to different types of stimuli and bring the system back to homeostasis after perturbations. This ability is called the functional plasticity of the gut. Chapter 1 provides more information on the morphology and functionality of the gut. The techniques for the measurements of various internal phenotypes (measurements of transcripts, proteins and metabolites, among others) are described. It describes the various methods traditionally used to study the gut and the novel methods used in this thesis to study the gut from a more holistic perspective. It also introduces the two types of data integration methods used in the thesis, vertical and hori- zontal integration. Vertical integration finds connections between different types of data and horizontal data integration combines several datasets of the same type of data. These data integration methods were used in the following five chapters to gain insights into the dynamics of a perturbed gut system over several time points, to find connections between different types of biological measurements (gene expressions, proteins, metabolites and microbes), to improve our understanding of the functional plasticity of the gut under vari- ous types of challenges and to link changes in the diet to changes in the local and systemic immune system.
The work in Chapter 2 aimed to improve our understanding of the dynamics of the gut functionality that contributes to long lasting changes in the intestinal mucosal tissue. The data were obtained from a pig experiment where an antibiotic was administered at a very early age, along with stress that would be common in a farm environment. The small in- testines of these animals were then sampled at three time points over the next six months to obtain the measurements of two internal phenotypes; gene expression of the intestinal mucosal tissue and microbial community data from the luminal content at the same location in the gut. The data analysis in this chapter involves both horizontal data integration (across time) and vertical data integration (integration of gene expression with microbiota). The integration across time was performed along two dimensions by taking into ac- count the time dynamics and the treatment effects simultaneously. This was done using a regression - based method on both the gene expression and microbiota data. Subsequently the results of the time dynamics and treatment effects were integrated with each other. This vertical integration was performed based on correlations to discover potential cross - talk between the host and microbiota that are affected both by treatment and time. The results show that there are several genes with consistent long term changes in their gene expression. These genes could be important regulators of gut functionalities. These regulators seem to cause the persistent changes in the tissue gene expression. The vertical data integration revealed the important roles of a few bacterial groups in maintaining the long - term changes in the gene expression. These results provide insights into the dynamic interactions between the host and its microbes.
Interactions between internal phenotypes were further studied in Chapter 3 , where five measurements of internal phenotypes were obtained from dietary interventions in mice. The mice were given diets with different protein sources and several different internal phenotype measurements were made in the intestine, serum and urine. From the same lo- cation of the small intestine, scrapings were used to obtain gene expression profiles of the mucosal tissue and the content was used to acquire data on microbial community struc- tures. Cytokine levels in the serum were measured along with metabolomic profiles. Metabolomic data was also obtained from urine. Pairwise correlation analysis was per- formed ten times to integrate each dataset to the other four and these results were com- bined to obtain an integrated network with elements from all the five datasets. The corre- lations from the pairwise integration were verified using a permutation approach that checks whether the observed correlations are based on biological information in the data. The connections between internal phenotypes were validated with a literature search for co - occurrence of the connected components. The results of this literature search revealed that several connections uncovered in this Chapter have been found previously in other studies. The integrated network was found to contain several components that were linked to all other four types of data. These components were denoted “connectivity hubs”. The presence of such hubs indicates that there are some components of the internal phenotypes that are more involved in the interactions between internal phenotypes than others. The hypotheses generated in this study can be used to design wet - lab experiments to validate uncovered connections between the internal phenotypes.
Pathways are groups of functionally related genes with information on the interactions between the genes that fulfil a biological function. When integrating several transcriptom- ics datasets together (horizontal integration) the analysis is more comparable between ex- periments and platforms when analysed with pathway expression rather than gene expres- sion. Pathway data from several databases is available in a Resource Description Frame- work format called BioPAX (Biological Pathway eXchange) which is not easy to work with for transcriptomic analysis. In order to use this information in the R environment, an open source programming language and software environment for statistical computing and graphics, the package rBiopaxParser converts the BioPAX object into one that can be utilised in R. One important function in this package is pathway2RegulatoryGraph which converts the information in a pathway to a graph of the regulatory components in the path- way. Chapter 4 is a description of a new function pathway2Graph that can use all infor- mation in the pathway to build a graph of the entire pathway. This function has been in- corporated into the package rBiopaxParser distribution.
The gut is exposed to a variety of challenges and its response to them is of utmost im- portance since the gut is a critical barrier to the external environment. The functional plas- ticity displayed by the gut allows an appropriate response of the gut towards a variety of challenges, perturbations and/or stimulations. In order to study the mechanisms behind functional plasticity, in Chapter 5 a horizontal integration of intestinal mucosal gene ex- pression profiles was performed after exposure to three categories of challenges: dietary, drug - based or immune challenges. These transcriptomics datasets, derived from 14 inde- pendent experiments in mice, were obtained from an online database in order to study the gut under different challenging conditions. The gene expression analysis was performed on a pathway level and combined with information present in the Reactome database ob- tained with the pathway2Graph function developed in Chapter 4 . A high - level data inte- gration was performed where the datasets are analysed separately and only the results are integrated. The results of the high - level data integration demonstrated that there is a con- siderable overlap in the significantly regulated pathways induced by the three categories of challenges. We concluded that these common processes contribute to the functional plasticity of the gut. One of the processes common to the three challenge categories is ‘Regulation of Complement Cascade’, which is an immune process that is regulated in several of the dietary challenge experiments. Since the regulation of this process is not expected in the gut tissue, a validation experiment was designed using mice intestinal organoids to understand the conditions that lead to the regulation of this pathway. The results suggest that an inflammatory condition could be the reason behind the regulation of this pathway. Thus, by performing targeted experiments, based on the results of computational analyses, helps to better understand the mechanisms behind the functional plasticity of the gut. The high - level data integration on different types of challenges, as described in Chapter 5 , showed that the dietary changes induced changes in immune - related process- es. Therefore, in Chapter 6 , a model was built to find connections between dietary chang- es and functional immunity parameters. The data from 9 dietary challenge studies was analysed for differentially expressed genes and differentially regulated pathways. In order to obtain links between genes and functional immunity parameters, information from Genome Wide Association Studies (GWAS) were used that are deposited in the NCBI data- base Genotype and Phenotype. GWAS links traits to Single Nucleotide Polymorphisms (SNP) that are subsequently linked to genes based on the proximity of SNP’s to genes. We retrieved this trait to gene associations for selected functional immunity parameters that can be measured and linked to the immune status of organisms. We formed associations between dietary changes and functional immunity parameters through the genes and path- ways. These connections were validated through a literature search which established that there have been studies linking some dietary changes to the changes in the functional im- munity parameters predicted by the model.
Chapter 7 is a general discussion of all the five research chapters. The ways in which the research objectives were achieved are enumerated along with details of the methods used. Work that can be performed on the current results to improve the understand of the gut is also detailed. I discuss current bottlenecks and possible ways to increase our understanding of gut function. It is concluded that the re - use of existing data and their analysis from a systems wide perspective, provides unique insights into the functioning of biological system. It is also concluded that hidden information, which is present in currently available databases and datasets, can be extracted with methodologies that arise from the objective to understand the functioning of systems as a whole. Finally, it is concluded that the research in this thesis demonstrates that computational methods can be developed and applied to get improved insights into the time - dependent dynamics of a gut systems, to ob- tain information on key links between internal phenotypes of different biological levels, to better understand the functional plasticity of the gut mucosal tissue, and to develop models that allows to make links between internal phenotypes and to immunity parameters functionally related to external immune - based phenotypes.
