Prognostic clinical and histopathological features of canine cutaneous epitheliotropic T-cell lymphoma.

Canine cutaneous epitheliotropic T-cell lymphoma is a neoplasm with heterogeneous clinical and histopathological presentations. Survival times and responses to therapy are variable, and indicators to predict outcomes are lacking. Clinical and histopathological parameters from 176 archival cases from the University of Pennsylvania and University of Bern (2012-2018) were investigated for associations with clinical outcomes. Histopathological evaluation used digitized whole slide images and QuPath software. Cases included 107 female and 69 male dogs from 48 breeds, with a mean age of 10.4 years. Most common clinical signs were erythema (n = 131), crusting (n = 108), and scaling (n = 102). Affected sites were haired skin (n = 159), lip (n = 74), nasal planum (n = 49), and paw pads (n = 48). The median survival time (MST) was 95 days (1-850). Dogs had 4.26-fold and 2.82-fold longer MST when treated with chemotherapy and prednisone, respectively, than when receiving supportive care. Haired skin involvement (hazard ratio [HR]: 2.039, 95% confidence interval [CI]: 1.180-3.523), erosions/ulcers (HR: 1.871, 95% CI: 1.373-2.548), nodules (HR: 1.496, 95% CI: 1.056-2.118), and crusting (HR: 1.454, 95% CI: 1.061-1.994) were clinical parameters predicting poor outcomes, whereas complete posttherapeutic clinical remission (HR: 0.469, 95% CI: 0.324-0.680) and a stable disease (HR: 0.323, 95% CI: 0.229-0.456) were associated with longer survival. Histopathological features associated with the increased risk of death were extensive infiltration of the panniculus (HR: 2.865, 95% CI: 1.565-4.809), mitotic count ≥7/high-power field (HR: 3.027, 95% CI: 2.065-4.439), cell diameter ≥10.0 µm (HR: 2.078, 95% CI: 1.281-3.372), and nuclear diameter ≥8.3 µm (HR: 3.787, 95% CI: 1.647-8.707).

Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach.

BACKGROUND: We present results from a computational analysis developed to integrate transcriptome and metabolomic data in order to explore the heat stress response in the liver of the modern broiler chicken. Heat stress is a significant cause of productivity loss in the poultry industry, both in terms of increased livestock morbidity and its negative influence on average feed efficiency. This study focuses on the liver because it is an important regulator of metabolism, controlling many of the physiological processes impacted by prolonged heat stress. Using statistical learning methods, we identify genes and metabolites that may regulate the heat stress response in the liver and adaptations required to acclimate to prolonged heat stress. RESULTS: We describe how disparate systems such as sugar, lipid and amino acid metabolism, are coordinated during the heat stress response. CONCLUSIONS: Our findings provide more detailed context for genomic studies and generates hypotheses about dietary interventions that can mitigate the negative influence of heat stress on the poultry industry.

Identifying mechanisms of regulation to model carbon flux during heat stress and generate testable hypotheses.

Understanding biological response to stimuli requires identifying mechanisms that coordinate changes across pathways. One of the promises of multi-omics studies is achieving this level of insight by simultaneously identifying different levels of regulation. However, computational approaches to integrate multiple types of data are lacking. An effective systems biology approach would be one that uses statistical methods to detect signatures of relevant network motifs and then builds metabolic circuits from these components to model shifting regulatory dynamics. For example, transcriptome and metabolome data complement one another in terms of their ability to describe shifts in physiology. Here, we extend a previously described linear-modeling based method used to identify single nucleotide polymorphisms (SNPs) associated with metabolic changes. We apply this strategy to link changes in sulfur, amino acid and lipid production under heat stress by relating ratios of compounds to potential precursors and regulators. This approach provides integration of multi-omics data to link previously described, discrete units of regulation into functional pathways and identifies novel biology relevant to the heat stress response, in addition to generating hypotheses.

Chicken hepatic response to chronic heat stress using integrated transcriptome and metabolome analysis.

The liver plays a central role in metabolism and is important in maintaining homeostasis throughout the body. This study integrated transcriptomic and metabolomic data to understand how the liver responds under chronic heat stress. Chickens from a rapidly growing broiler line were heat stressed for 8 hours per day for one week and liver samples were collected at 28 days post hatch. Transcriptome analysis reveals changes in genes responsible for cell cycle regulation, DNA replication, and DNA repair along with immune function. Integrating the metabolome and transcriptome data highlighted multiple pathways affected by heat stress including glucose, amino acid, and lipid metabolism along with glutathione production and beta-oxidation.