In this thesis, metabolomics is used to study the role of the host-virus interaction on a metabolic level. A special emphasis is directed on the role of inflammation and oxidative stress on... Show more In this thesis, metabolomics is used to study the role of the host-virus interaction on a metabolic level. A special emphasis is directed on the role of inflammation and oxidative stress on the metabolic level, as part of the innate immune response against viral infection. We chose respiratory syncytial virus (RSV) and hepatitis B virus (HBV) as candidate viruses to metabolically study their role in acute respiratory infection and chronic hepatitis B infection. Secondly we also investigated infant metabolic and immunological consequences of in utero exposure to antiretroviral intervention and human immunodeficiency virus (HIV). Collectively, established targeted metabolomics approaches in conjunction with newly developed metabolomics methodologies and complemented with other “omics” techniques, were used to address pertinent questions related to host metabolic functioning and alterations during viral infection. In vitro RSV studies together with in vivo patient based studies relating to chronic HBV infection and in utero exposure too antiretroviral and HIV were used to address these questions. The work is divided into three research parts containing: i. the analytical methodology development work, ii. in vitro based metabolomics and iii. patient based metabolomics. Show less
The explosive increase in infections by pathogens is a major problem in the clinic today. The theme of this thesis was to find novel antibiotics from actinomycetes. Next-generation... Show more The explosive increase in infections by pathogens is a major problem in the clinic today. The theme of this thesis was to find novel antibiotics from actinomycetes. Next-generation sequencing revealed that the biosynthetic potential of actinomycetes had been grossly underestimated. In this thesis, different antibiotics-eliciting strategies, including microbial cocultivation, streptomycin-resistant mutation, overexpression of pathway-specific activator, variation of culture conditions, were utilized to enforce fluctuations in the production of bioactive compounds in actinomycetes, after which, NMR-based metabolic profiling was used to facilitate uncovering those elicited molecules. This pipeline allowed the discovery of new antibiotics involving various chemical skeletons, such as 7-prenylisatin, methoxylated isocoumarins, endophenazines, and C-glycosylpyranonaphthoquinones. On the other hand, genome-mining methodology enabled the discovery of a group of endophenasides and leucanicidin in Kitasatospora sp. MBT66, whereby the rhamnosylation of both scaffold are executed by a same promiscuous glycosyltransferase. Last but not least, a novel antibiotic termed lugdunomycin with unprecedented chemical scaffold, as well as a number of new angucycline-type antibiotics, were characterized from Streptomyces sp. QL37. The biosynthetic pathway of lugdunomycin was deciphered by genetic knockout and OSMAC (One Strain MAny Compound) strategy. In summary, this thesis explores an interface of genomics and metabolomics to accelerate new antibiotics discovery. Show less
Advances in technology have turned modern biology into a data-intensive enterprise. The advent of high-output technologies like Microarrays and Next-generation sequencing technologies has resulted... Show moreAdvances in technology have turned modern biology into a data-intensive enterprise. The advent of high-output technologies like Microarrays and Next-generation sequencing technologies has resulted in researchers grappling not just with huge volumes but also multiple types of data. While generation and storage of high-quality data are an important research focus, it is increasingly recognized that translating data into actionable information and insight is a critical research challenge. To infer reliable conclusions from the data, it is often necessary to integrate large amounts of heterogeneous data with different formats and semantics. Given the breadth and volume of data involved, this goal is best achieved through automated methods and tools for data integration and workflow management. This thesis presents automated strategies that combine bioinformatics and statistical methods to identify novel biomarkers in high-throughput OMICs datasets pertaining to the metabolic syndrome and to gain mechanistic insight into the underlying biological processes. An underlying theme in this thesis is data-driven approaches that generate plausible hypothesis which is followed by experimental verification. Show less
