scholarly journals Oxidative Stress in Canine Histiocytic Sarcoma Cells Induced by an Infection with Canine Distemper Virus Led to a Dysregulation of HIF-1α Downstream Pathway Resulting in a Reduced Expression of VEGF-B In Vitro

Viruses ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 200 ◽  
Author(s):  
Federico Armando ◽  
Matteo Gambini ◽  
Attilio Corradi ◽  
Chiara Giudice ◽  
Vanessa Maria Pfankuche ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Data Analysis ◽  
Microarray Data ◽  
Canine Distemper Virus ◽  
Histiocytic Sarcoma ◽  
Microarray Data Analysis ◽  
Canine Distemper ◽  
The Impact ◽  
Dh82 Cells

Histiocytic sarcomas represent malignant tumors which require new treatment strategies. Canine distemper virus (CDV) is a promising candidate due to its oncolytic features reported in a canine histiocytic sarcoma cell line (DH82 cells). Interestingly, the underlying mechanism might include a dysregulation of angiogenesis. Based on these findings, the aim of the present study was to investigate the impact of a persistent CDV-infection on oxidative stress mediated changes in the expression of hypoxia-inducible factor (HIF)-1α and its angiogenic downstream pathway in DH82 cells in vitro. Microarray data analysis, immunofluorescence for 8-hydroxyguanosine, superoxide dismutase 2 and catalase, and flow cytometry for oxidative burst displayed an increased oxidative stress in persistently CDV-infected DH82 cells (DH82Ond pi) compared to controls. The HIF-1α expression in DH82Ond pi increased, as demonstrated by Western blot, and showed an unexpected, often sub-membranous distribution, as shown by immunofluorescence and immunoelectron microscopy. Furthermore, microarray data analysis and immunofluorescence confirmed a reduced expression of VEGF-B in DH82Ond pi compared to controls. In summary, these results suggest a reduced activation of the HIF-1α angiogenic downstream pathway in DH82Ond pi cells in vitro, most likely due to an excessive, unusually localized, and non-functional expression of HIF-1α triggered by a CDV-induced increased oxidative stress.

scholarly journals Intratumoral Canine Distemper Virus Infection Inhibits Tumor Growth by Modulation of the Tumor Microenvironment in a Murine Xenograft Model of Canine Histiocytic Sarcoma

2021 ◽  
Vol 22 (7) ◽  
pp. 3578
Author(s):  
Federico Armando ◽  
Adnan Fayyad ◽  
Stefanie Arms ◽  
Yvonne Barthel ◽  
Dirk Schaudien ◽  
...  
Keyword(s):  
Tumor Microenvironment ◽  
Virus Infection ◽  
Tumor Growth ◽  
Canine Distemper Virus ◽  
Xenograft Model ◽  
Histiocytic Sarcoma ◽  
Oncolytic Viruses ◽  
Canine Distemper ◽  
Murine Xenograft Model ◽  
The Impact

Histiocytic sarcomas refer to highly aggressive tumors with a poor prognosis that respond poorly to conventional treatment approaches. Oncolytic viruses, which have gained significant traction as a cancer therapy in recent decades, represent a promising option for treating histiocytic sarcomas through their replication and/or by modulating the tumor microenvironment. The live attenuated canine distemper virus (CDV) vaccine strain Onderstepoort represents an attractive candidate for oncolytic viral therapy. In the present study, oncolytic virotherapy with CDV was used to investigate the impact of this virus infection on tumor cell growth through direct oncolytic effects or by virus-mediated modulation of the tumor microenvironment with special emphasis on angiogenesis, expression of selected MMPs and TIMP-1 and tumor-associated macrophages in a murine xenograft model of canine histiocytic sarcoma. Treatment of mice with xenotransplanted canine histiocytic sarcomas using CDV induced overt retardation in tumor progression accompanied by necrosis of neoplastic cells, increased numbers of intratumoral macrophages, reduced angiogenesis and modulation of the expression of MMPs and TIMP-1. The present data suggest that CDV inhibits tumor growth in a multifactorial way, including direct cell lysis and reduction of angiogenesis and modulation of MMPs and their inhibitor TIMP-1, providing further support for the concept of its role in oncolytic therapies.

A microarray data analysis method to evaluate the impact of contaminants on wild animals

2010 ◽  
Vol 408 (23) ◽  
pp. 5824-5827
Author(s):  
Kei Nakayama ◽  
Itsuki C. Handoh ◽  
Shin-Ichi Kitamura ◽  
Eun-Young Kim ◽  
Hisato Iwata ◽  
...  
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Microarray Data Analysis ◽  
Wild Animals ◽  
Analysis Method ◽  
Data Analysis Method ◽  
The Impact

scholarly journals Enhancing Interdisciplinary Mathematics and Biology Education: A Microarray Data Analysis Course Bridging These Disciplines

2010 ◽  
Vol 9 (3) ◽  
pp. 217-226 ◽  
Author(s):  
Yolande V. Tra ◽  
Irene M. Evans
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Biology Education ◽  
Microarray Experiment ◽  
Educational Background ◽  
Microarray Data Analysis ◽  
Data Set ◽  
Set Up ◽  
Interdisciplinary Course ◽  
The Impact

BIO2010 put forth the goal of improving the mathematical educational background of biology students. The analysis and interpretation of microarray high-dimensional data can be very challenging and is best done by a statistician and a biologist working and teaching in a collaborative manner. We set up such a collaboration and designed a course on microarray data analysis. We started using Genome Consortium for Active Teaching (GCAT) materials and Microarray Genome and Clustering Tool software and added R statistical software along with Bioconductor packages. In response to student feedback, one microarray data set was fully analyzed in class, starting from preprocessing to gene discovery to pathway analysis using the latter software. A class project was to conduct a similar analysis where students analyzed their own data or data from a published journal paper. This exercise showed the impact that filtering, preprocessing, and different normalization methods had on gene inclusion in the final data set. We conclude that this course achieved its goals to equip students with skills to analyze data from a microarray experiment. We offer our insight about collaborative teaching as well as how other faculty might design and implement a similar interdisciplinary course.

scholarly journals Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods.

2011 ◽  
Vol 58 (4) ◽  
Author(s):  
Marcin T Schmidt ◽  
Luiza Handschuh ◽  
Joanna Zyprych ◽  
Alicja Szabelska ◽  
Agnieszka K Olejnik-Schmidt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Analysis ◽  
Dna Microarray ◽  
Microarray Data ◽  
Normalization Method ◽  
Differentially Expressed ◽  
Microarray Data Analysis ◽  
Data Set ◽  
Normalization Methods ◽  
The Impact

Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.

scholarly journals Oxidative Stress Compromises Lymphocyte Function in Neonatal Dairy Calves

Antioxidants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 255
Author(s):  
Wilmer Cuervo ◽  
Lorraine M. Sordillo ◽  
Angel Abuelo
Keyword(s):  
Oxidative Stress ◽  
Immune Responses ◽  
Immune Cell ◽  
Lymphocyte Activation ◽  
Dairy Calves ◽  
Lymphocyte Function ◽  
Newborn Calves ◽  
Ifn Γ ◽  
The Impact

Dairy calves are unable to mount an effective immune response during their first weeks of life, which contributes to increased disease susceptibility during this period. Oxidative stress (OS) diminishes the immune cell capabilities of humans and adult cows, and dairy calves also experience OS during their first month of life. However, the impact that OS may have on neonatal calf immunity remains unexplored. Thus, we aimed to evaluate the impact of OS on newborn calf lymphocyte functions. For this, we conducted two experiments. First, we assessed the association of OS status throughout the first month of age and the circulating concentrations of the cytokines interferon-gamma (IFN-γ) and interleukin (IL) 4, as well as the expression of cytokine-encoding genes IFNG, IL2, IL4, and IL10 in peripheral mononuclear blood cells (PBMCs) of 12 calves. Subsequently, we isolated PBMCs from another 6 neonatal calves to investigate in vitro the effect of OS on immune responses in terms of activation of lymphocytes, cytokine expression, and antibody production following stimulation with phorbol 12-myristate 13-acetate or bovine herpesvirus-1. The results were compared statistically through mixed models. Calves exposed to high OS status in their first month of age showed higher concentrations of IL-4 and expression of IL4 and IL10 and lower concentrations of IFN-γ and expression of IFNG and IL2 than calves exposed to lower OS. In vitro, OS reduced lymphocyte activation, production of antibodies, and protein and gene expression of key cytokines. Collectively, our results demonstrate that OS can compromise some immune responses of newborn calves. Hence, further studies are needed to explore the mechanisms of how OS affects the different lymphocyte subsets and the potential of ameliorating OS in newborn calves as a strategy to augment the functional capacity of calf immune cells, as well as enhance calves’ resistance to infections.

SEMIPARAMETRIC CLUSTERING METHOD FOR MICROARRAY DATA ANALYSIS

2008 ◽  
Vol 06 (02) ◽  
pp. 261-282 ◽  
Author(s):  
AO YUAN ◽  
WENQING HE
Keyword(s):  
Data Analysis ◽  
Microarray Data ◽  
Mixture Distribution ◽  
Information Criterion ◽  
Optimal Number ◽  
Microarray Data Analysis ◽  
Parametric Methods ◽  
Clustering Methods ◽  
Microarray Gene Expression ◽  
Data Set

Clustering is a major tool for microarray gene expression data analysis. The existing clustering methods fall mainly into two categories: parametric and nonparametric. The parametric methods generally assume a mixture of parametric subdistributions. When the mixture distribution approximately fits the true data generating mechanism, the parametric methods perform well, but not so when there is nonnegligible deviation between them. On the other hand, the nonparametric methods, which usually do not make distributional assumptions, are robust but pay the price for efficiency loss. In an attempt to utilize the known mixture form to increase efficiency, and to free assumptions about the unknown subdistributions to enhance robustness, we propose a semiparametric method for clustering. The proposed approach possesses the form of parametric mixture, with no assumptions to the subdistributions. The subdistributions are estimated nonparametrically, with constraints just being imposed on the modes. An expectation-maximization (EM) algorithm along with a classification step is invoked to cluster the data, and a modified Bayesian information criterion (BIC) is employed to guide the determination of the optimal number of clusters. Simulation studies are conducted to assess the performance and the robustness of the proposed method. The results show that the proposed method yields reasonable partition of the data. As an illustration, the proposed method is applied to a real microarray data set to cluster genes.

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