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Abstract GenX is an environmental contaminant with wide industrial use and found in drinking water. These chemicals have high bioaccumulation and cause adverse health effects in animals and humans. Previous study has shown that pre GenX perfluoroalkyl/polyfluoroalkyl substances (PFAS) and GenX affect immune cells but there is limited study on their impact on innate immune cells (neutrophils) in livestock, such as cows. This study evaluated the effect of GenX exposure on innate and adaptive immune response gene transcription in bovine neutrophils ex vivo. Whole blood was collected using Acid citrate dextrose as a coagulant from cows (n = 5). Neutrophils were isolated using differential centrifugation and hypotonic lysis of red cells. Isolated neutrophils were treated with GenX (100 ng), or untreated (control group) for hour at 37oC, 5%CO2 and 85% relative humidity. After treatment, total RNA was extracted using Trizol reagent, reverse transcribed to cDNA, and quantitative PCR was performed using the innate and adaptive immunity RT2 profiler array (Qiagen) with 84 genes. The qPCR data were analyzed using Livak’s method to calculate fold change (FC) in gene expression between GenX-treated and control neutrophils, and FC >2, (p < 0.05) was considered significant. Normalization of data was performed with GAPDH and Beta Actin as an internal control. Out of the 84 genes tested, 78 were expressed, 25 were upregulated and4 were downregulated, in response to GenX exposure. Exposure to GenX upregulated the expression of TLR8 (FC = 24.75), NOD2 (FC = 7.65), STAT1 (FC = 49.35), HLA-A (FC = 14.07), and NKB1A (FC = 16.52). Treatment with GenX decreased the expression TICAM1 (FC = -2.30), CD86 (FC = -2.27) and RAG1 (FC = -2.50). These results indicate that GenX exposure can activate and modulate the expression of innate and adaptive immune response genes in cow neutrophils. STAT1 is involved in transcriptional regulation and activation of TLR7/8 shifts neutrophil function from phagocytosis to NETosis. Thus, the mechanism of action of GenX on identified gene targets and their function and possible animal health consequences warrants further study.
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An assessment of machine learning methods to quantify blood lactate from neutrophils phagocytic activity
Abstract Phagocytosis is a critical component of innate immunity that helps the body defend itself against infection, foreign particles, and cellular debris. Investigating and quantifying phagocytosis can help understand how the immune system identifies foreign particles and how phagocytosis relates to other biomarkers, e.g., cytokines, cell surface receptors, or blood lactate levels. In particular, increased blood lactate levels can be a potential biomarker to study diseases, e.g., septic shock. Establishing a relationship between phagocytosis and lactate levels can serve as an effective tool to monitor the immune response and may help stratify patients. In this study, we use phagocytosis activity data to classify the patients into two groups of blood lactate levels (High and Low) with machine learning models. The neutrophils extracted from the whole blood samples of 19 patients were used to collect data on phagocytosis, where the neutrophils were allowed to internalize IgG coated fluorescent bioparticles. The data collection process involved collecting whole blood samples, neutrophil isolation, adding fluorescent beads, incubating, and imaging the sample using a fluorescence microscope. The phagocytosis assay images were used to generate a numerical dataset by manually counting the number of particles engulfed by each cell. The study first presents an improved understanding by employing hierarchical clustering and heatmaps to generate the graphical representation of phagocytosis data. By comparing the results of heat maps and clustering techniques, it can be observed that the phagocytosis activity data can be used to differentiate blood lactate levels in two groups (control and high-risk). Later, three machine learning models (Decision Tree, k-nearest Neighbor, and Naïve Bayes) were trained on the original and pruned datasets after the outliers were removed. The AI models classified the data into high-risk and low-risk groups of blood lactate levels. A maximum classification accuracy of 78% and an area under the curve of 0.78 was achieved using the trained models.
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- Award ID(s):
- 2053149
- PAR ID:
- 10573441
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 15
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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