Masters Theses
Date of Award
8-2023
Degree Type
Thesis
Degree Name
Master of Science
Major
Animal Science
Major Professor
Kyle McLean
Committee Members
Justin D. Rhinehart, Phillip R. Myer
Abstract
The growing world population has continued to pressure the agricultural industry in order to feed the exponentially expanding number of people. One way producers are working towards this goal is the ability to better control breeding seasons. Specifically, the need to diagnose pregnancy earlier after artificial insemination to prevent extended periods of feeding non-pregnant cows. Pregnancy diagnosis can be conducted as early as d 28 via blood constituent testing while other methods such as rectal palpation and ultrasonography are only accurate later in gestation. Pregnancy is an immunological paradox where the maternal immune system allows a semi-allogenic fetus to develop without being attacked as a foreign entity. The maternal immune system must modulate to prevent the rejection of the developing embryo almost immediately. Cytokines are critical components of immune function that control inflammation, host defense, and the maintenance of homeostasis. Current studies have focused on how cytokine profiles change throughout fertilization, peri-implantation, and pregnancy in human, bovine, and murine models. We hypothesized that vaginal cytokine profiles will differ based on pregnancy status and day of gestation in lactating cows. While there were no interactions between the day of pregnancy and pregnancy status cytokines, we did note differences in pro-inflammatory cytokine profiles dependent upon either factor. These results demonstrate a definitive change in the immune profile based on pregnancy status and a possible default immune state favoring pregnancy; as well as, progress toward identifying specific immune factors that may be potential biomarkers for pregnancy diagnosis.
Recommended Citation
Dalton, Christian Michael, "The Effects of Pregnancy Status on Vaginal Cytokine Profiles in Lactating Dairy Cows. " Master's Thesis, University of Tennessee, 2023.
https://trace.tennessee.edu/utk_gradthes/12819