Though renewed efforts in tuberculosis (TB) research have facilitated massive strides in treating Mycobacterium tuberculosis (Mtb), TB remains a global health problem with an estimated 10 million infections and 1.5 million deaths in 2018. The ability of the pathogen to sequester itself inside a granuloma, a mass of immune cells whose precise mechanism of regulation is unknown, prevents the simple study of Mtb pathogenesis and subsequent treatment discovery. Current in vivo models have been established to study TB infection using animal models or tissues, limiting biological relevance of human disease while current in vitro models lack components of the complex lung microenvironment during infection. We present the creation of a novel microscale infection model, which uses open and suspended microfluidic principles to enable spatial and temporal manipulation of cultures in suspended hydrogel plugs. Utilizing the ‘stacking’ feature of the device, we demonstrate the ability of a model granuloma consisting of M.bovis BCG (Mycobacterium bovis bacille Calmette-Guérin) and monocyte-derived macrophages to interact with a model vasculature layer consisting of endothelial cells. Analysis of soluble factors for proinflammatory cytokines and characterization of infection-dependent angiogenesis in the vasculature layer are used to verify crosstalk between cultures. In the future, we envision this model expanding to contain multiple immune cell types and to incorporate additional aspects of the lung anatomy to approach a more accurate pathophysiological model as a tool for other researchers’ studies.

Understanding complex population dynamics between species is key for guiding environmental and wildlife management decisions. Accurately identifying the diet of various predator species across northeastern Washington (NEWA) and central Washington (CWA) can provide comprehensive insight into these relationships in terms of predator-predator and predator-prey dynamics. DNA metabarcoding can identify species-specific DNA within a sample and presents an ideal way to perform diet analysis in this context. In a previous NEWA study, the diet profiles of a range of predators were fully resolved, but for the American black bear (Ursus americanus), approximately 80% of its diet composition was undetermined. For increased understanding of the black bear’s diet in Washington, prey species must be identified across a range of geographic areas. This study compares the prey components of the black bear’s diet in both NEWA and CWA in order to provide a comprehensive analysis of its role in the predator-prey community. DNA samples used for analysis were from scat collected by detection dogs during a 2015-2016 NEWA and 2018 CWA field term. Of the 12 bear samples from CWA,9 samples had identified prey and of the 15 bear samples from NEWA, 6 had identified prey. These results add valuable information about prey species composition in a key predator’s diet across a wide geographic region, as well as seasonal shifts in diet composition in relation to other carnivores in the NEWA community. Future research will be conducted on the plant portion of the black bear’s omnivorous seasonal diet. Data collected from this project will provide valuable information that must be considered for further studies on the Washington black bear population and the food groups it consumes.

Over the last decade, wolves have been naturally returning to Washington state. Mapping the population growth and reproductive activity of wolves across Washington is key to understanding their recovery and to assisting wildlife conservation management. Accurate identification of the number of pregnant wolves per pack during the breeding season could help. Progesterone levels excreted in feces provide a reliable index of pregnancy in most mammals; progesterone rises post-ovulation but only remains elevated above a “pregnancy-threshold” among females that become pregnant. Unfortunately, this pregnancy-threshold metric is less reliable in canids because progesterone levels often remain above this threshold during the typical gestation period among all post-ovulatory females, regardless of whether the females become pregnant. Since gut microbiome diversity has also been shown to differ between pregnant and non-pregnant mammals, this study examined whether the combination of progesterone levels and gut microbiome diversity can refine pregnancy diagnosis in free-ranging wolves. Five high progesterone and five low progesterone fecal samples from ten unique female wolves were provided by the Center for Conservation Biology from the 2015-2017 study in Northeast Washington. Gut microbiome profiles were generated by sequencing the V4 16S rRNA gene region in each sample and analyzed using Qiime 2 and R with the Silva reference database for microbial taxonomy classification. Principal coordinates analysis of Bray-Curtis distance between samples at the microbiome phylum level showed separate clusters among high versus low progesterone samples, with one exception. The microbiome community of one high progesterone sample clustered with the low progesterone samples. This sample also had the lowest progesterone concentration among the high progesterone samples and may thus be from a non-pregnant post-ovulatory female. These initial findings suggest that the combination of progesterone levels and microbiome diversity show promise as a pregnancy diagnostic tool that may be able to distinguish pregnant from non-pregnant wolves.