Understanding the cold acclimation process in plants may help us develop genetically engineered plants that are resistant to cold. The key factor in understanding this process is to study the genes and thus the gene regulatory network that is involved in the cold acclimation process. Most of the existing approaches1-8 in deriving regulatory networks rely only on the gene expression data. Since the expression data is usually noisy and sparse the networks generated by these approaches are usually incoherent and incomplete. Hence a new approach is proposed here that analyzes the promoter regions along with the expression data in inferring the regulatory networks. In this approach genes are grouped into sets if they contain similar over-represented motifs or motif pairs in their promoter regions and if their expression pattern follows the expression pattern of the regulating gene. The network thus derived is evaluated using known literature evidence, functional annotations and from statistical tests.

Knowing the underlying cause of a genetic disorder could not only further our understanding of the disease itself, and the otherwise healthy mechanism that is disrupted. It could potentially improve people’s lives. Even if whole genome sequencing has drastically improved the potential of discovering the cause, a comparison of two non-related individual’s genome will find several million sequence variations. While most variants have no significant impact, it is enough for only one to functionally impact a gene, for it to cause a genetic disorder. This project therefore focused on the filtering of variants, from lists of several million possible causes, to the stage where they could feasible be manually analysed one by one. Single-nucleotide variants, indels and structural variants were filtered, based on a dataset where single-nucleotide variants and indels had already been called. The more difficult process of structural variants discovery was performed, but it required the application of four different tools to minimise the drawback of each separate discovery technique. The same three filtering approaches were applied to all variants; the intersecting of datasets that should contain the same variant, the removal of variants in common with the general population and the selection of variants impacting functionality. Each approach proved to be an efficient filtering step, with their combination reducing each list to only a couple of variants out of the original five million. Due to lower accuracy and sensitivity of the structural variant analysis, this data will likely require more extensive manual analysis.

I följande rapport kommer vi ta upp hur vi löste problemet med att hitta gemensamma sekvenser hos en mängd koagulasnegativa stafylokocker (KNS) för att bl.a. kunna skilja dem ifrån dess släkting Staphylococcus aureus (S. aureus). Problemet har sin grund i att projektbeställaren, Q-linea, vill kunna identifiera infekterande bakterier i fall av blodsjukdomen sepsis. Vi kunde dessvärre inte hitta sekvenser som fungerade för alla våra utvalda stafylokocker. Däremot lyckades vi hitta flera sekvenser som parvis fungerade tillsammans för att urskilja stafylokockgruppen mot S. aureus. För att utföra alla jämförelser konstruerade och implementerade vi en bioinformatisk pipeline med en tredelad optimeringsmetod för att göra de tunga beräkningarna snabbare.

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This project was built upon a previous study (Johansson et al.2020) that tested multiple drugs on glioblastoma cell lines and found a big division between the drug response for proteasome inhibitors. The aim of this project was to try to obtain a better insight into the differences between the two drug response subgroups’ processes by creating and comparing two genome scale metabolic models (GEMs) of the two subgroups. To do this, genomescale metabolic models were made for each cell line and later merged after its proteasome inhibitor response to obtain two general models. After having multiple models for each cell line and two general drug response models, comparisons could be made. Overall, the differences between cell lines were larger than the differences between drug responses, but some differences could still be seen. Some differences in the number of reactions in subsystems were found between the two general GEMs, where the Ureacycle subsystem showed the largest difference between the two models. Another difference was in the metabolic activity of the models, where the sensitive model passed ten tasks which the resistant model could not. The last and the most important comparison was essentiality analysis which gave a multitude of essential genes but only twelve genes that were unique to the twogeneral GEMs. Nine genes for the resistant model and three for the sensitive. Out of these genes CYP51A1 and FDFT1, for the resistant model, and genes RBP1 and CYP27A1, for the sensitive model, had already been in at least one study regarding Glioblastoma or Proteasome Inhibitors. Since some of the found genes already seem to have been found interesting for PIs or glioblastoma treatment the unique genes from the essentiality analysis could be interesting to look more into in the future.

In recent years, the edible biomedicinal products called nutraceuticals have been becoming more popular among the pharmaceutical industries and the consumers. In the process of developing nutraceuticals, in silico approaches play an important role in structural elucidation, receptor-ligand interactions, drug designing etc., that critically help the laboratory experiments to avoid biological and financial risk. In this thesis, three nutraceuticals possessing antimicrobial and anticancer activities have been studied. Firstly, a tertiary structure was elucidated for a coagulant protein (MO2.1) of Moringa oleifera based on homology modeling and also studied its oligomerization that is believed to interfere with its medicinal properties. Secondly, the antimicrobial efficiency of a limonoid from neem tree called ‘azadirachtin’ was studied with a bacterial (Proteus mirabilis) detoxification agent, glutathione S-transferase, to propose it as a potent drug candidate for urinary tract infections. Thirdly, sequence specific binding activity was analyzed for a plant alkaloid called ‘palmatine’ for the purpose of developing intercalators in cancer therapy. Cumulatively, we have used in silico methods to propose the structure of an antimicrobial peptide and also to understand the interactions between protein and nucleic acids with these nutraceuticals. / <p>QC 20130531</p>

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