Browsing by Subject "transcriptome"

Now showing 1 - 4 of 4
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    A Cell Proliferation and Inflammatory Signature is Induced by Lawsonia intracellularis Infection in Swine
    (2018-07-24) Leite, Fernando, L; Abrahante, Juan, E; Vasquez, Erika; Vannucci, Fabio; Gebhart, Connie, J; Nathan, Winkelman; Mueller, Adam; Jerry, Torrinson; Rambo, Zachary; Isaacson, Richard, E; isaac015@umn.edu; Isaacson, Richard, E
    Lawsonia intracellularis causes porcine proliferative enteropathy. This is an enteric disease characterized by thickening of the wall of the ileum that leads to decreased growth and diarrhea of animals. In this study, we investigated the host response to L. intracellularis infection by performing transcriptomic and pathway analysis of intestinal tissue in groups of infected and non-infected animals at 14, 21 and 28 days post challenge. The data deposited here are BAM files.
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    Immunological selection as a driver of porcine reproductive and respiratory syndrome virus (PRRSV) evolution
    (2016-07) Wang, Xiong
    Porcine reproductive and respiratory syndrome (PRRS) is still one of the most devastating swine infectious diseases worldwide since its initial outbreak in the late 1980s. Its etiologic agent, PRRS virus (PRRSV), is a single strand RNA virus that belongs to the order of Nidovirales, family Arteriviridae and genus Arterivirus. PRRSV is small, viral partial size is about 53nm including a RNA genome with the size of ~ 15kb. PRRSV is highly host restricted to porcine monocyte cells. Currently in the field, biosecurity and passive immunization are the major solutions to reducing the impact of PRRSV endemic. Yet two major factors, the rapidly evolution of PRRSV and the incomplete and highly variable cross-protection induced by passive vaccination, heavily contribute to the penetration of PRRSV to swine herds and result in the emergence and re-emergence of virulent PRRSV. Similar to other RNA virus like human immunodeficiency virus 1 (HIV-1), hepatitis C virus (HCV) and foot-and-mouth disease virus (FMDV), the lack of error prone mechanism during viral replication leads to the production of tremendous mutations in PRRSV progeny. The selection pressure of porcine intrinsic, innate and adaptive immunity on PRRSV population helps shape and drive PRRSV mutation direction. Together, they drive PRRSV’s rapid evolution. Yet there is a limitation in systemic understanding how genetic variation is generated and what selection forces drive PRRSV evolution. The overall objective of this dissertation was to characterize PRRSV evolution in intra-population and field level, as well as exploring the driving forces hidden in the porcine monocyte cells by utilizing high- throughput sequencing and bioinformatics. The findings herein built up an optimized standard protocol to assemble PRRSV whole genome from high-throughput sequencing yields, which can be broadly adapted to other highly-mutated RNA virus. PRRSV infectious clones, similar to field isolates, exist as quasispecies, its population diversity was decreasing under consistent selection pressure of permissive cell intrinsic selection yet retaining significant diversified progeny. In this thesis, an emerging virulent PRRSV in vaccinated herds was identified as a recently evolved member of virulent lineage instead of new virulent strain via built comprehensive and standard analysis pipeline. IFNs and IFN-induced ARFs are highly induced in PAMs after PRRSV inoculation; potential causative key pathways were identified in the PAM age-dependent susceptibility difference scenario. Putting together, all the findings and results provided an improved systematic insight of PRRSV evolution and host innate response, which is a vital immunological selection driver. Ultimately, a better understanding of PRRSV evolution and its driver will lead to a more effective disease prevention, control and elimination.
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    Modeling the dynamics of the plant immune response
    (2022-03) Liu, Xiaotong
    Dynamic modeling is essential for understanding the temporal behavior of a system. Deriving dynamic models from biological omics data can enable effective information reduction by leveraging a few interpretable parameters and capturing the hidden structure in the data. Thanks to the availability of RNA-seq, temporal transcriptomes have been widely profiled as dynamic snapshots of biological responses. My PhD study focuses on dynamic modeling of plant immunity, a plant defense response induced by pathogens. There are two well-defined modes of inducible immunity of plant to overcome pathogen attack, namely pattern triggered immunity (PTI) and effector triggered immunity (ETI). Researchers have generated rich sources of temporal transcriptome data in plants upon challenge of pathogens or pathogen derivatives during both PTI and ETI. My contribution to dynamic modeling of plant immunity comes primarily with two projects. In my main project, I developed a novel computational approach based on an ordinary differential equation system to interpreting the transcriptome dynamics during ETI. The modeling results uncovered intrigue data patterns that direct deep insights into the transcriptional regulation of transcription factors during ETI. In my other project, I developed mechanistic models based on the transcript response of CBP60g, a marker gene of pattern-triggered immunity. The model not only interpreted the dynamics of CBP60g response but also predicted the mechanistic roles of three plant immunity genes in regulating CBP60g transcription. Overall, my efforts on dynamic modeling of plant immunity bring novel mathematical frameworks for transcript/transcriptome data interpretation and derive valuable biological predictions that shed light on transcriptional mechanisms of plant immunity.
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    Transcriptome Meta Data Compilation for Chinese hamster tissues and CHO cell lines
    (2016-06-06) Vishwanathan, Nandita; Yongky, Andrew; Johnson, Kathryn C; Fu, Hsu-Yuan; Jacob, Nithya M; Le, Huong; Bandyopadhyay, Arpan; wshu@umn.edu; Hu, Wei-Shou; University of Minnesota Department of Chemical Engineering and Material Sciences, Hu Group
    Transcriptomics is increasingly being used on Chinese hamster ovary (CHO) cells to unveil physiological insights related to their performance during production processes. The rich transcriptome data can be exploited to provide impetus for systems investigation such as modeling the central carbon metabolism or glycosylation pathways, or even building genome-scale models. To harness the power of transcriptome assays, we assembled and annotated a set of RNA-Seq data from multiple CHO cell lines and Chinese hamster tissues, and constructed a DNA microarray. These tools were used to measure the transcript expression of tissues (liver, brain, ovary), 3 parental cell lines (DG44, DXB11, CHO-K1) and 16 recombinant cell lines. Transcript expression levels for tissues and cell lines have been compiled as an excel spreadsheet to allow for a rapid survey of transcript levels of different genes.