Zebrafish Kidney Phagocytes Utilize Macropinocytosis and Ca2+-Dependent Endocytic Mechanisms

Background: The innate immune response constitutes the first line of defense against invading pathogens and consists of a variety of immune defense mechanisms including active endocytosis by macrophages and granulocytes. Endocytosis can be used as a reliable measure of selective and non-selective mechanisms of antigen uptake in the early phase of an immune response. Numerous assays have been developed to measure this response in a variety of mammalian and fish species. The small size of the zebrafish has prevented the large-scale collection of monocytes/macrophages and granulocytes for these endocytic assays. Methodology/Principal Findings: Pooled zebrafish kidney hematopoietic tissues were used as a source of phagocytic cells for flow-cytometry based endocytic assays. FITC-Dextran, Lucifer Yellow and FITC-Edwardsiella ictaluri were used to evaluate selective and non-selective mechanisms of uptake in zebrafish phagocytes. Conclusions/Significance: Zebrafish kidney phagocytes characterized as monocytes/macrophages, neutrophils and lymphocytes utilize macropinocytosis and Ca 2+-dependant endocytosis mechanisms of antigen uptake. These cells do not appear to utilize a mannose receptor. Heat-killed Edwardsiella ictaluri induces cytoskeletal interactions for internalization in zebrafish kidney monocytes/macrophages and granulocytes. The proposed method is easy to implement and should prove especially useful in immunological, toxicological and epidemiological research

An Immunohistochemical Algorithm for Ovarian Carcinoma Typing

There are 5 major histotypes of ovarian carcinomas. Diagnostic typing criteria have evolved over time, and past cohorts may be misclassified by current standards. Our objective was to reclassify the recently assembled Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts using immunohistochemical (IHC) biomarkers and to develop an IHC algorithm for ovarian carcinoma histotyping. A total of 1626 ovarian carcinoma samples from the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type were subjected to a reclassification by comparing the original with the predicted histotype. Histotype prediction was derived from a nominal logistic regression modeling using a previously reclassified cohort (N=784) with the binary input of 8 IHC markers. Cases with discordant original or predicted histotypes were subjected to arbitration. After reclassification, 1762 cases from all cohorts were subjected to prediction models (χ 2 Automatic Interaction Detection, recursive partitioning, and nominal logistic regression) with a variable IHC marker input. The histologic type was confirmed in 1521/1626 (93.5%) cases of the Canadian Ovarian Experimental Unified Resource and the Alberta Ovarian Tumor Type cohorts. The highest misclassification occurred in the endometrioid type, where most of the changes involved reclassification from endometrioid to high-grade serous carcinoma, which was additionally supported by mutational data and outcome. Using the reclassified histotype as the endpoint, a 4-marker prediction model correctly classified 88%, a 6-marker 91%, and an 8-marker 93% of the 1762 cases. This study provides statistically validated, inexpensive IHC algorithms, which have versatile applications in research, clinical practice, and clinical trials