The aim of this project was to present a cost-efficient quality control routine to ensure thefunctionality of HS-Lenti RT Activity Kit, an ELISA-based kit produced by Cavidi AB, thatscreens for lentiviruses such as HIV. Two main methods for quality control are presented inthis report; acceptance sampling and Statistical Process Control (SPC). Acceptance samplingis a process where only parts of a batch are tested in order to determine whether or not thewhole batch should be accepted or rejected. SPC centers around monitoring an ongoingprocess by using statistical measures and charts which visualize variations in these measuresover time. Initially, using an acceptance sampling plan is recommended as the primaryapproach. SPC charts can then be set up using the data generated from the acceptancesampling, and be used in parallel with the acceptance sampling for some time until they canbe implemented to a wider extent. This report also presents different options forimmunoassay data processing. Bayesian methods of estimating analyte concentrations inunknown samples are highlighted as promising candidates in improving the performance andusability of the kit. The report also includes a customer requirements analysis, based on aconducted survey, that investigates the demands researchers within Uppsala University placeon products similar to HS-Lenti RT Activity Kit. The data which the analysis is based on wasobtained from an online questionnaire and three interviews. An ethical analysis regarding thequality control approach and survey is included as well.

Proteins are essential in all living organisms. They are involved in various critical activities and are also structural components of cells and tissues. Lactose permease a membrane protein has become a prototype for the major facilitator super family and utilises an existing electrochemical proton gradient to shuttle galactoside sugars to the cell. Therefore it exists in two principle states exposing the internal binding site to either side of the membrane. From previous studies it has been suggested that protonation precedes substrate binding but it is still unclear why this has to occur in the event of substrate binding. Therefore this study aimed to bridge this gap and to determine the chemical characteristics of the transport pathway. Molecular dynamics simulation methods and specialised simulation hardware were employed to elucidate the dependency of substrate binding on the protonation nature of Lactose permease. Protein models that differed in their conformation as well as their protonation states were defined from their respective X-ray structures. Targeted molecular dynamics was implemented to drive the substrate to the binding site and umbrella sampling was used to define the free energy of the transport pathway. It was therefore suggested that protonation for sugar binding is due to the switch-like mechanism of Glu325 in the residue-residue interaction (His322 and Glu269) that leads to sugar binding only in the protonated state of LacY. Furthermore, the free energy profile of sugar transport path way was lower only in the protonated state which indicates stability of sugar binding in the protonated state.

Celiac disease (CD) is a chronic autoimmune disease, caused by the consumption of gluten in genetically predisposed individuals. Celiac patients develop many clinical features include; weight loss, diarrhea, and Intestinal damage, and if left untreated, CD patient may face an increased risk of malignancies. Materials and methods403 patient were admitted to the study. These patients were divided into three groups; celiac cases, controls, and latent celiac cases. Gene expression analysis was performed for intestinal biopsies and blood samples (leukocytes) using a quantitative PCR technique. The second section of the study was studying the effect of PRODH enzyme on Drosophila Melanogaster intestines. To achieve that PRODH enzyme and different amino acids were added to the fly food.  One way ANOVA and Wilcoxon tests were applied to find out the significant genes. ResultsMost of the differentially expressed genes in celiac disease are involved in the inflammatory response. However, many genes have significantly altered expression in the latent celiac group but not altered significantly in CD group. These genes are CXCL1, IL15RA, IL2RB, MAPK11, and TGM2. They are involved in the TNF signaling pathway and in inflammatory cytokines. It was noticed that in celiac disease there is a significant alteration in PRODH expression in the intestines, and the addition of PRODH enzyme to glutamine has a similar effect on the intestinal gene expression as gluten does. ConclusionWe can conclude that Non-HLA genes are important in activating the immune system, increasing proline level, and developing the clinical features of celiac disease. Secondly,  Proline metabolism has an important role in tumor suppression and in augmenting tumor growth, which makes it an important therapeutic target in tumor therapy.

No description available.

The cluster orchestration tool Kubernetes enables easy deployment and reproducibility of life science research by utilizing the advantages of the container technology. The container technology allows for easy tool creation, sharing and runs on any Linux system once it has been built. The applicability of Kubernetes as an approach to run bioinformatic workflows was evaluated and resulted in some examples of how Kubernetes and containers could be used within the field of life science and how they should not be used. The resulting examples serves as proof of concepts and the general idea of how implementation is done. Kubernetes allows for easy resource management and includes automatic scheduling of workloads. It scales rapidly and has some interesting components that are beneficial when conducting life science research.

Regardless of scientific field computers have become pivotal tools for data analysis and the field of structural biology is not an exception. Here, computers are the main tools used for tasks including structural calculations of proteins, spectral analysis of nuclear magnetic resonance (NMR) spectroscopy data and fitting mathematical models to data. As results reported in papers heavily rely on software and scripts it is of key importance that the employed computational methods are robust and yield reliable results. However, as many scientific fields are niched and possess a small potential user base the task to develop necessary software often falls on researchers themselves. This can cause divergence when comparing data analyzed by different measures or by using subpar methods. Therein lies the importance of development of accurate computational methods that can be employed by the scientific community. The main theme of this thesis is software development applied to structural biology, with the purpose to aid research in this scientific field by speeding up the process of data analysis as well as to ensure that acquired data is properly analyzed. Among the original results of this thesis are three user-friendly software: COMPASS - a resonance assignment software for NMR spectroscopy data capable of analyzing chemical shifts and providing the user with suggestions to potential resonance assignments, based on a meticulous database comparison. CDpal - a curve fitting software used to fit thermal and chemical denaturation data of proteins acquired by circular dichroism (CD) spectroscopy or fluorescence spectroscopy. PINT - a line shape fitting and downstream analysis software forNMRspectroscopy data, designed with the important purpose to easily and accurately fit peaks in NMR spectra and extract parameters such as relaxation rates, intensities and volumes of peaks. This thesis also describes a study performed on variants of the life essential regulatory protein calmodulin that have been associated with the congenital life threatening heart disease long QT syndrome (LQTS). The study provided novel insights revealing that all variants are distinct from the wild type in regards to structure and dynamics on a detailed level; the presented results are useful for the interpretation of results from protein interaction studies. The underlying research of this paper makes use of all three developed software, which validates that all developed methods fulfil a scientific purpose and are capable of producing solid results.

No description available.

Bioinformatics, as a field, rapidly develops and such development requires the design ofalgorithms and software. RNA-seq provides robust information on RNAs, both alreadyknown and new, hence the increased study of the RNA. Alignment is an important step indownstream analyses and the ability to map reads across splice junctions is a requirement ofan aligner to be suitable for mapping RNA-seq reads. Therefore, the necessity for a standardsplice-aware aligner. STAR, Rsubread and HISAT2 have not been singly studied for thepurpose of benchmarking one of them as a standard aligner for spliced RNA-seq reads. Thisstudy compared these aligners, found to be sensitive to splice sites, with regards to theirsensitivity to splice sites, performance with default parameter settings and the resource usageduring the alignment process. The aligners were matched with featureCounts. The resultsshow that STAR and Rsubread outperform HISAT2 in the aspects of sensitivity and defaultparameter settings. Rsubread was more sensitive to splice junctions than STAR butunderperformed with featureCounts. STAR had a consistent performance, with more demandon the memory and time resource, but showed it could be more sensitive with real data.

Alzheimer’s disease (AD) is an aging-related neurodegenerative disorder with large implications for society and individuals. AD is a multi-factor disorder, with these factors having a direct or indirect correlation with each other. Despite many studies with different aspects on molecular and cellular pathways, there is still no specific treatment for AD. Identification of potential pathogenic factors can be done by transcriptomic studies of differentially expressed genes (DEGs), but the outcomes have been contradictory. Using both bioinformatics and meta-analysis methods can be useful for removing such inconsistencies. A useful and common approach for a better understanding of neurodegenerative disease is to assess its molecular causes, by comparing the gene expression levels in healthy and disease tissues. Next-generation RNA-sequencing is a valuable method for analyzing both coding and non-coding regions of RNA, and it has made it possible to identify differentially expressed genes in large-scale data. The aim of the current study was to get a better understanding of the transcriptional changes in AD models, and identify differentially expressed genes between healthy and AD individuals from the adult mouse brain model as well as detecting AD pathways. In this study, the transcriptomes of purified neuron, astrocyte and microglia cells from mouse brains were analyzed using publicly available RNA-seq datasets. The DEGs were identified for all three mentioned cell types using DESeq2 and EdgeR packages. All statistical analyses were performed by R software and the DEGs detected by DESeq2 and edgeR, respectively, were compared using Venn diagrams. Additionally, analyzing the AD pathway was performed using GOrilla tool for visualizing the enriched gene ontology (GO) terms in the list of ranked genes. From this project, it was found that there were very few significantly DEGs between AD and healthy samples in neuron cells, while there were more DEGs in astrocyte and microglia cells. In conclusion, comparing DESeq2 and egeR packages using Venn diagrams showed a slight advantage of DESeq2 in detection accuracy, since it was able to identify more DEGs than edgeR. Moreover, analyzing AD pathway using GOrilla tool indicated that identified enriched GO terms by each cell type differed from each other. For astrocytes, more enriched GO terms were identified than for microglia cells, while no significant enriched GO terms were detected for neuron cells.

Rapid progress of AI technology in the life science area is observed in recent years. Convolutionalneural network (CNN) models were successfully applied for the localization and classification of cellson microscopic images. Induced pluripotent stem cells are one of the most important innovations inbiomedical research and are widely used, e.g. in regenerative medicine, drug screening, and diseasemodeling. However, assessment of cell cultures’ quality requires trained personnel, is timeconsumingand hence expensive. Fluorescence microscope images of human induced pluripotentstem‐hepatocytes (hiPS‐HEPs) derived from three human induced pluripotent stem cell (hiPSC) lineswere taken daily from day 1 until day 22 of differentiation. The cells from day 1 to 14 were classifiedas ´Early differentiation´, and above day 16 as ´Late differentiation´. In this study, it wasdemonstrated that a CNN‐based model can be trained with simple fluorescence microscope imagesof human induced pluripotent stem‐hepatocytes, and then used to predict with high accuracy(96.4%) the differentiation stage of an independent new set of images.