Shane C Burgess
Publications
PMID: 12072527;PMCID: PMC136297;Abstract:
Understanding the interactions between herpesviruses and their host cells and also the interactions between neoplastically transformed cells and the host immune system is fundamental to understanding the mechanisms of herpesvirus oncology. However, this has been difficult as no animal models of herpesvirus-induced oncogenesis in the natural host exist in which neoplastically transformed cells are also definitively identified and may be studied in vivo. Marek's disease (MD) herpesvirus (MDV) of poultry, although a recognized natural oncogenic virus causing T-cell lymphomas, is no exception. In this work, we identify for the first time the neoplastically transformed cells in MD as the CD4(+) major histocompatibility complex (MHC) class I(hi), MHC class II(hi), interleukin-2 receptor alpha-chain-positive, CD28(lo/-), phosphoprotein 38-negative (pp38(-)), glycoprotein B-negative (gB(-)), alphabeta T-cell-receptor-positive (TCR(+)) cells which uniquely overexpress a novel host-encoded extracellular antigen that is also expressed by MDV-transformed cell lines and recognized by the monoclonal antibody (MAb) AV37. Normal uninfected leukocytes and MD lymphoma cells were isolated directly ex vivo and examined by flow cytometry with MAb recognizing AV37, known leukocyte antigens, and MDV antigens pp38 and gB. CD28 mRNA was examined by PCR. Cell cycle distribution and in vitro survival were compared for each lymphoma cell population. We demonstrate for the first time that the antigen recognized by AV37 is expressed at very low levels by small minorities of uninfected leukocytes, whereas particular MD lymphoma cells uniquely express extremely high levels of the AV37 antigen; the AV37(hi) MD lymphoma cells fulfill the accepted criteria for neoplastic transformation in vivo (protection from cell death despite hyperproliferation, presence in all MD lymphomas, and not supportive of MDV production); the lymphoma environment is essential for AV37(+) MD lymphoma cell survival; pp38 is an antigen expressed during MDV-productive infection and is not expressed by neoplastically transformed cells in vivo; AV37(+) MD lymphoma cells have the putative immune evasion mechanism of CD28 down-regulation; AV37(hi) peripheral blood leukocytes appear early after MDV infection in both MD-resistant and -susceptible chickens; and analysis of TCR variable beta chain gene family expression suggests that MD lymphomas have polyclonal origins. Identification of the neoplastically transformed cells in MD facilitates a detailed understanding of MD pathogenesis and also improves the utility of MD as a general model for herpesvirus oncology.
PMID: 22991541;Abstract:
Effects of dietary methionine (Met) on pectoralis muscle development and the effect that Met as a nutritional substrate has on protein expression of skeletal muscle cells of pectoralis muscle of chickens were evaluated in this study. Broiler chickens received a common pretest diet up to 21 d of age and were subsequently fed either a low (LM) or high Met (HM) diet (0.41 vs. 0.51% of diet) from 21 to 42 d of age. Dietary deficiency was shown in vivo judging by the depression in breast meat weight and yield when broilers were fed the LM diet. Global protein expression was analyzed by quantitative high-performance liquid chromatography nanospray ionization tandem mass spectrometry. Up- and downregulated proteins were analyzed via Ingenuity Pathways Analysis to identify the metabolic pathways affected. Four canonical pathways related to muscle development were identified as being differentially regulated between LM- and HM-fed chickens. These pathways included the citrate cycle and calcium, actin cytoskeleton, and clathrin-mediated endocytosis signaling. The HM diet may have allowed for increased muscle growth by an increased availability of nutrients to muscle cells. Although the Met supplementation was associated with enhanced breast muscle growth, contraction fiber concentrations in muscles decreased and were associated with a lower calcium transportation rate and sensitivity and with a lower energy supply. It is further suggested that increased muscle protein deposition, that was induced by Met supplementation, may have been largely due to sarcoplasmic rather myofibrillar hypertrophy. © 2012 Poultry Science Association Inc.
PMID: 22102568;PMCID: PMC3245151;Abstract:
The Gene Ontology (GO) (http://www.geneontology .org) is a community bioinformatics resource that represents gene product function through the use of structured, controlled vocabularies. The number of GO annotations of gene products has increased due to curation efforts among GO Consortium (GOC) groups, including focused literature-based annotation and ortholog-based functional inference. The GO ontologies continue to expand and improve as a result of targeted ontology development, including the introduction of computable logical definitions and development of new tools for the streamlined addition of terms to the ontology. The GOC continues to support its user community through the use of e-mail lists, social media and web-based resources. © The Author(s) 2011. Published by Oxford University Press.
Abstract:
Evaluating the quality of potential new protein-coding genes that have been predicted by directly searching mass spectrometry against genome sequence is a very challenging task. Many machine learning techniques such as neural networks, decision trees, and support vector machines have been applied to this task. All of these techniques learn a model from a training dataset and predict the quality of potential novel protein-coding genes using various evidential features as inputs. The quality and quantity of the training dataset significantly affect the performance of the learned models. In biological research, data collected is often incomplete and with very few data points. It is desirable to have methods that are robust to noisy data and low sample-size. Furthermore, the models learned by these machine learning techniques typically do not reveal the conditional (in)dependence relations among the evidential features. Gaining insight into the relationships among features is important for biological domains .In this paper, we describe methods for learning Bayesian networks for modeling the conditional (in)dependence relations among features of protein-coding genes and calculating confidence scores for potential novel genes based on their evidential features. Bootstrap methods are applied to assess the confidence measure on the arcs of the learned network structures and to identify a set of robust arcs in order to construct a final model for future predictions. We tested the Bayesian network model learned from our method using a training experimental dataset. The results show that the method significantly improved the accuracy of the learned model in predicting potential novel genes.
PMID: 18256827;Abstract:
Introduction: Marek's disease (MD), a herpesvirus-induced lymphoma of chickens is a unique natural model of CD30-overexpressing (CD30hi) lymphoma. We have previously proposed that the CD30hi neoplastically transformed CD4+ T cells in MD lymphomas have a phenotype antagonistic to cell mediated immunity. Here were test the hypothesis that the CD30hi neoplastically transformed MD lymphoma cells have a phenotype more closely resembling T-helper (Th)-2 or regulatory T (T-reg) cells. Materials and methods: We separated ex vivo-derived CD30hi, from the CD30lo/- (non-transformed), MD lymphoma cells and then quantified the relative amounts of mRNA and proteins for cytokines and other genes that define CD4+ Th-1, Th-2 or T-reg phenotypes. Results and discussion: Gene Ontology-based modeling of our data shows that the CD30hi MD lymphoma cells having a phenotype more similar to T-reg. Sequences that could be bound by the MD virus putative oncoprotein Meq in each of these genes' promoters suggests that the MD herpesvirus may play a direct role in maintaining this T-reg-like phenotype. © 2008 Springer-Verlag.