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Data visualization is an essential methodology for bioinformatics studies. Spatial Transcriptomics(ST) is a method that aims at measuring the transcriptome of tissue sections while maintaining its spacial information. Finally, the study of biological pathway focuses on a series of biochemical reactions that take place in organisms. As these studies generate a large number of datasets, this thesis attempts to combine the ST’s data with pathwayinformation and visualize it in an intuitive way to assist user comprehension and insight.In this thesis, Python was used for integrating the dataset and JavaScript libraries wereused for building the visualization. The processing of ST pathway data together with the data visualization interface are the outcomes of this thesis. The data visualization can show the regulation of pathways in the ST data and can be accessed by modern browsers. These outcomes can help users navigate the ST and pathway datasets more effectively. / Datavisualisering är en viktig del av bioinformatik. Spatial transkriptomik (ST) är en metod som mäter transkriptom, samtidigt som den behåller spatial information. Biologiskapathways å andrasidan fokuserar på biokemiska reaktioner som sker inom organismer. Dessa studier genererar mycket data, och denna avhandling försöker att kombinera ST-data med pathway information och få en intuitiv visualisering av det integrerade datat.I avhandlingen användes Python för att integrera datat och JavaScript bibliotek för attbygga visualiseringsverktyget. Avhandlingen resulterade i en metod för att integrera STdata och pathway information, samt ett visualiseringsverktyg för ovan nämnda information.Verktyget kan visa pathway regulationer i ST data och kan användas i moderna webbläsare.Forskningen resulterade i ett verktyg som kan hjälpa forskare att förstå ST och pathwaydata.

Non-alcoholic fatty liver disease (NAFLD) represents the major cause of pediatric chronic liver pathology in the United States. The objective of this study was to investigate the effect of partial substitution of dietary lard by an isocaloric amount of olive or coconut oil on endpoints of NAFLD. Thirty-eight 15-d-old Iberian pigs housed in pens balanced for weight and sex were randomly assigned to receive 1 of 3 hypercaloric high-fructose high-fat (HFF) diets for 10 weeks: 1) lard (LAR; n=5 pens), 2) lard + olive oil (OLI, n=10), and 3) lard + coconut oil (COC; n=10). Additional pigs (BSL, n=4) were fed a eucaloric diet to establish baseline values. Animals were euthanized at 85 d of age after blood sampling. Liver tissue was collected for histology, metabolomics, and transcriptomics. Compared with BSL, OLI decreased high-density lipoproteins, phosphatidylcholines (PC), and total cholesterol in blood, and increased acylcarnitines in liver, whereas COC increased triacylglycerides (TAGs) in liver and blood. All HFF diets increased bile acids in liver, and decreased choline and fibroblast growth factor 19 in liver and blood. OLI and COC increased hepatic steatosis, necrosis, ballooning, and composite lesion score compared with LAR. OLI decreased gene expression of carnitine O-palmitoyltransferase 1, and COC increased expression of fatty acid binding proteins and acyl-CoA synthetase. In conclusion, partial replacement of dietary lard with olive and coconut oil dysregulated acylcarnitine metabolism and lipogenesis in the liver, increasing the severity of NAFLD in juvenile pigs.

Endocrine disrupting chemicals (EDCs) are compounds that can interfere with hormone signaling pathways and are now recognized as pervasive in estuarine and marine waters. One prevalent EDC in California’s coastal waters is the xenoestrogen 4-nonylphenol (4-NP), which has been shown to impair reproduction, development, growth, and in some cases immune function of marine invertebrates. To further investigate effects of 4-NP on marine invertebrate immune function we measured total hemocyte counts (THC), relative transcript abundance of immune-relevant genes, and lysozyme activity in Pacific oysters (Crassostrea gigas) following bacterial infection. To quantify these effects we exposed oysters to dissolved phase 4-NP at high (100 μg l-1), low (2 μg l-1), or control (100 μl ethanol) concentrations for 7 days, and then experimentally infected (via injection into the adductor muscle) the oysters with the marine bacterium Vibrio campbellii. 4-NP significantly altered the effects of bacterial infection had on THC. Oysters exposed to both high and low 4-NP did not experience a bacteria-induced increase in THC, as seen in control oysters. We also determined that V. campbellii infection induced differential expression of a subset of immune-related genes tested (Cg-bigdef2, Cg-bpi1, Cg-lys1, Cg-timp) in some, but not all, tissues; 4-NP exposure altered expression patterns in two of these genes (Cg-bpi1 and Cg-tgase). Exposure to 4-NP alone also caused differential expression in some genes (Cg-bpi1, Cg-galectin1, Cg-clec2). Lastly, low levels of 4-NP significantly increased lysozyme activity 24 h post-infection. These results suggest that exposure to 4-NP can alter both cellular and humoral immune responses to bacterial infection in C. gigas.

Transcriptional programs regulating myogenesis are multi-layered, requiring carefully orchestrated temporal activation of a wide range of myogenic transcription factors for proper muscle formation. The MEF2 transcription factor family is required for muscle differentiation, however the roles of individual mammalian MEF2 isoforms, MEF2A, -B, -C, and -D, in this process has not been thoroughly investigated. Acute knockdown of individual MEF2 isoforms in skeletal myoblasts revealed that MEF2A is required for myogenesis in vitro, whereas MEF2B, -C, and -D are dispensable for this process. Microarray analysis performed on myotubes depleted of each MEF2 isoform revealed that MEF2 factors regulate distinct gene programs in skeletal muscle. Moreover, computational analysis of the upstream regulatory regions of MEF2 isoform-dependent genes uncovered a distinct complement of transcription factor binding sites suggesting potential co-factor interactions in muscle gene regulation. Whereas all four MEF2 family members are expressed in adult skeletal muscle, MEF2A and MEF2D are the major isoforms expressed in the post-natal heart. Previous studies in cardiomyocytes have demonstrated that MEF2A regulates genes encoding proteins localized to the costamere, an essential macromolecular complex required for proper muscle contraction. By contrast, genome-wide expression analysis suggests a role for MEF2D in cardiomyocyte cell cycle regulation. MEF2D- deficient cardiomyocytes up-regulate a subset of positive cell cycle regulators and display activation of the PI3K/AKT signaling pathway. Furthermore, MEF2D-depleted cardiomyocytes have increased levels of cytoplasmic FOXO3a, a cell cycle inhibitor and direct AKT target. Along these lines, MEF2D-depleted cardiomyocytes have decreased levels of the PI3K/AKT repressor PTEN. Analysis of the Pten promoter revealed a highly conserved MEF2 site, which is required for activation of this promoter by MEF2D. Taken together, these findings demonstrate that MEF2D modulates PI3K/AKT activation through transcriptional regulation of the tumor suppressor PTEN. In the absence of MEF2D, aberrant activation of the cell cycle ultimately results in cardiomyocyte cell death. These results demonstrate that MEF2 family members regulate distinct gene programs required for proper skeletal and cardiac muscle function.

<p>The prediction of chromatin accessibility for cancer and neuron cell lines using genomic sequences is quite challenging. Advances in machine learning and deep learning techniques allow such challenges to be addressed. This thesis investigates the use of both the transfer learning and the multi-task learning techniques. In particular, this research demonstrates the potential of transfer learning and multi-task learning in improving the prediction accu?racy for twenty-three cancer types in human and neuron cell lines. Three different network architectures are used: the Basset network, the network, and the DeepSEA network. In addition, two transfer learning techniques are also used. In the first technique data relevant to the desired prediction task is not used during the pre-training stage while the second technique includes limited data about the desired prediction task in the pre-training phase. The preferred performance evaluation metric used to evaluate the performance of the models was the AUPRC due to the numerous negative samples. Our results demonstrate an average improvement of 4% of the DeepSEA network in predicting all twenty-three cancer cell line types when using the first technique, a decrease of 0.42% when using the second technique, and an increase of 0.40% when using multi-task learning. Also, it had an average improvement of 3.09% when using the first technique, 1.16% when using the second technique and 4.60% for the multi-task learning when predicting chromatin accessibility for the 14 neuron cell line types. The DanQ network had an average improvement of 1.18% using the first transfer learning technique, the second transfer learning technique showed an average decrease of 1.93% and also, a decrease of 0.90% for the multi-task learning technique when predicting for the different cancer cell line types. When predicting for the different neuron cell line types the DanQ had an average improvement of 1.56% using the first technique, 3.21% when using the second technique, and 5.35% for the multi-task learning techniques. The Basset network showed an average improvement of 2.93% using the first transfer learning technique and an average decrease of 0.02%, and 0.63% when using the second technique and multi-task learning technique respectively. Using the Basset network for prediction of chromatin accessibility in the different neuron types showed an average increase of 2.47%, 9 3.80% and 5.50% for the first transfer learning technique, second transfer learning technique and the multi-task learning technique respectively. The results show that the best technique for the cancer cell lines prediction is the first transfer learning model as it showed an improvement for all three network types, while the best technique for predicting chromatin accessibility in the neuron cell lines is the multi-task learning technique which showed the highest average improvement among all networks. The DeepSEA network showed the greatest improvement in performance among all techniques when predicting the different cancer cell line types. Also, it showed the greatest improvement when using the first transfer learning technique for predicting chromatin accessibility for neuron cell lines in the brain. The basset network showed the greatest improvement for the multi-task learning technique and the second transfer learning technique when predicting the accessibility for neuron cell lines. </p>

Bovine respiratory disease (BRD) is a highly dynamic disease complex that results from host, microbial agent, and environmental interactions. Despite nearly a century of targeted research, BRD remains the most economically damaging disease in beef cattle production and appears to be increasing in global incidence. While modern modalities for BRD detection exist, clinical diagnosis and management decisions largely depend upon clinical observations and their associated risk of disease. Though these approaches lack precision, they remain in use for many reasons, including fiscal and time constraints within beef production systems. Advancements in high-throughput sequencing have demonstrated the ability to provide insight into complex biological disorders, leading to the development of predictive biomarkers and individualized therapy. Through the use of observational research methods and previously published data, transcriptome analyses were used to capture biological information related to the host-disease or host-pathogen relationship. These studies independently elaborated findings related to host management of inflammation, ultimately being associated with both acquisition and severity of BRD. Through advances in sequencing technology and data analysis methodology, novel components related to host inflammatory mitigation and antimicrobial defense are described for clinical BRD. Factors related to increased alternative complement activation, decreased specialized proresolving lipid mediator biosynthesis, decreased antimicrobial peptide production, and increased type I interferon stimulation were associated with severe clinical BRD. These findings define molecular networks, mechanisms, and pathways that are associated with BRD outcome, and may serve as a foundation for precision medicine in beef cattle.

Multi -omics data analysis and integration facilitates hypothesis building toward an understanding of genes and pathway responses driven by environments. Methods designed to estimate and analyze gene expression, with regard to treatments or conditions, can be leveraged to understand gene-level responses in the cell. However, genes often interact and signal within larger structures such as pathways and networks. Complex studies guided toward describing dynamic genetic pathways and networks require algorithms or methods designed for inference based on gene interactions and related topologies. Classes of algorithms and methods may be integrated into generalized workflows for comparative genomics studies, as multi -omics data can be standardized between contact points in various software applications. Further, network inference or network comparison algorithmic designs may involve interchangeable operations given the structure of their implementations. Network comparison and inference methods can also guide transfer-of-knowledge between model organisms and those with less knowledge base.

Within solid tumours, a microenvironment exists that causes resistance to chemotherapy. New drugs that target cells within this microenvironment are required, the first step in this process being the identification of new targets. The aim of this thesis was to characterise changes in the transcriptome and proteome within specific regions of multicell-tumour spheroids (MCTS), an experimental model that mimics many of the features of the tumour microenvironment. HT29 MCTS were separated by sequential trypsinisation into 3 main regions; the outer surface layer (SL) the peri-necroric region (PN) and the necrotic core (NC). Using an iTRAQ quantitative proteomics approach, the proteome of the different MCTS regions was investigated. A 2 dimensional separation approach using Agilent¿s OffGel system and RP-nano HPLC was incorporated prior to MS analysis. MS analysis was done using both MALDI-TOF-TOF (Bruker Ultraflex II) and ESI-Q-TOF (Agilent 6530 QTOF LC/MS) instruments. Gene expression profiles of the different MCTS were investigated and compared using Agilent¿s one-color oligonucleotide based microarrays. Transcriptomic and proteomic analysis identified several key differences in the proteins involved in cell metabolism between the SL and PN/NC regions. Similar metabolic changes were also noted between autophagic and normal monolayer cells. Many highlighted proteins represented established cancer associated proteins. Interestingly, a number of proteins were highlighted which have no previous association with cancer and may upon further validation, provide attractive leads for therapeutic intervention.

Ökningen av sekvenseringsdata har skapat ett behov av att ta fram nya och flexibla analysmetoder för att kunna analysera datan. Många sekvenseringsteknologier har utvecklats genom åren, med olika syften och de är idag mer specialiserade. Kostnaden för att sekvensera har även sjunkit kraftigt och idag är kostnaden bara en bråkdel av kostnaden för 20 år sedan.   En av dessa heter Spatial Transcriptomics där mRNA kan analyseras med Spatiell upplösning. Experimenten skapar stora mängder data och analysmetoder som ursprungligen var utvecklade för scRNA-seq har nu ocksp blivit mer specialiserade mot spatial data. En analysmetod som använts länge är Seurat som utvecklades av Satija labbet under 2015. Men de senaste åren har även nya metoder utvecklats. Två av dessa, Giotto och Squidpy kommer att jämföras med Seurat som referens för att reda ut hur bra de presterar för Spatial Transcriptomics analyser. Datan som kommer användas kommer från hjärnvävnad från fyra olika möss som testades i NASAs RR3 mission. Två av mössen är av ”flight” skick och kommer jämföras med två stycken ”ground” kontroller. I data analysen kommer Quality Control, Normalization, Integration, Dimensional reduction, Clustering och Differential Expression analysis testas. Förutom de steg som testas i analysen kommer även parametrar som analysmetodernas flexibilitet, duration och prestation att testas och jämföras. Resultaten i detta projekt visade att Seurat presterar bättre än Giotto och Squidpy utifrån de parametrar som testas. / The increase in data received from sequencing has created a need for new and accurate frameworks to analyze the data. There are many sequencing technologies developed for different purposes. They have become more specialized and the cost compared to 20 years ago is just a fraction. One of the technologies is Spatial Transcriptomics, where mRNA can be analyzed with spatial resolution. The experiments has high throughput, and frameworks that was original developed for scRNA-seq has also started to be more specialised towards spatial data. Seurat has been widely used for that purpose for many years and was developed by the Satija Lab. But many more frameworks have been developed. In this project’s scope, two other frameworks, Giotto and Squidpy, will be benchmarked with Seurat as the golden standard and a referece to examine how the frameworks perform with Spatial Transcriptomics data as input. The dataset consists of four mouse brain tissue sections from the NASA RR3 mission. Two of the mouse brains are of ”flight” condition while the two others are used as ”ground” controls. The pipeline used in all three frameworks includes Quality Control, normalization, integration, dimensional reduction, clustering, and differential expression analysis. Except for the pipeline steps other parameters has been tested including: the flexibility of the frameworks, the duration of analysis, and the performance. The results showed that Seurat outperforms Giotto and Squidpy according to the tested parameters. Mainly because of more developed integration features when working with multiple data. But both Squidpy and Giotto shows great potential and has a lot of features that was not useful for this project, but however can for other projects be very promising.