Shane C Burgess

Shane C Burgess

Dean, Charles-Sander - College of Agriculture and Life Sciences
Vice President, Agriculture - Life and Veterinary Sciences / Cooperative Extension
Professor, Animal and Comparative Biomedical Sciences
Professor, Immunobiology
Professor, BIO5 Institute
Member of the General Faculty
Member of the Graduate Faculty
Primary Department
Department Affiliations
Contact
(520) 621-7621

Research Interest

Shane C. BurgessVice President for Agriculture, Life and Veterinary Sciences, and Cooperative ExtensionDean, College of Agriculture and Life SciencesInterim Dean, School of Veterinary MedicineDirector, Arizona Experiment StationA native of New Zealand, Dr. Burgess has worked around the world as a practicing veterinarian and scientist. His areas of expertise include cancer biology, virology, proteomics, immunology and bioinformatics.Since 1997 he has 186 refereed publications, trained 37 graduate students and has received nearly $55 million in competitive funding.The first in his extended family to complete college, Dr. Burgess graduated with distinction as a veterinarian in 1989 from Massey University, New Zealand. He has worked in, and managed veterinary clinical practices in Australia and the UK, including horses, farm animals, pets, wild and zoo animals, and emergency medicine and surgery. He did a radiology residency at Murdoch University in Perth in Western Australia, where he co-founded Perth's first emergency veterinary clinic concurrently. He has managed aquaculture facilities in Scotland. He did his PhD in virology, immunology and cancer biology, conferred by Bristol University medical school, UK while working full time outside of the academy between 1995 and 1998. Dr. Burgess volunteered to work in the UK World Reference Laboratory for Exotic Diseases during the 2001 UK foot and mouth disease crisis, where he led the diagnosis reporting office, for the Office of the UK Prime Minister Tony Blair. He was awarded the Institute for Animal Health Director's Award for Service.In 2002, Dr. Burgess joined Mississippi State University’s College of Veterinary Medicine as an assistant professor. He was recruited from Mississippi State as a professor, an associate dean of the college and director of the Institute for Genomics, Biocomputing and Biotechnology to lead the UA College of Agriculture and Life Sciences in July 2011. Under Dr. Burgess’ leadership, the college has a total budget of more than $120M with over 3,400 students and more than 1,800 employees.

Publications

Tang, J. D., Perkins, A. D., Sonstegard, T. S., Schroeder, S. G., Burgess, S. C., & Diehl, S. V. (2012). Short-read sequencing for genomic analysis of the brown rot fungus Fibroporia radiculosa. Applied and Environmental Microbiology, 78(7), 2272-2281.

PMID: 22247176;PMCID: PMC3302605;Abstract:

The feasibility of short-read sequencing for genomic analysis was demonstrated for Fibroporia radiculosa, a copper-tolerant fungus that causes brown rot decay of wood. The effect of read quality on genomic assembly was assessed by filtering Illumina GAIIx reads from a single run of a paired-end library (75-nucleotide read length and 300-bp fragment size) at three different stringency levels and then assembling each data set with Velvet. A simple approach was devised to determine which filter stringency was "best." Venn diagrams identified the regions containing reads that were used in an assembly but were of a low-enough quality to be removed by a filter. By plotting base quality histograms of reads in this region, we judged whether a filter was too stringent or not stringent enough. Our best assembly had a genome size of 33.6 Mb, an N50 of 65.8 kb for a k-mer of 51, and a maximum contig length of 347 kb. Using GeneMark, 9,262 genes were predicted. TargetP and SignalP analyses showed that among the 1,213 genes with secreted products, 986 had motifs for signal peptides and 227 had motifs for signal anchors. Blast2GO analysis provided functional annotation for 5,407 genes. We identified 29 genes with putative roles in copper tolerance and 73 genes for lignocellulose degradation. A search for homologs of these 102 genes showed that F. radiculosa exhibited more similarity to Postia placenta than Serpula lacrymans. Notable differences were found, however, and their involvements in copper tolerance and wood decay are discussed. © 2012, American Society for Microbiology.

Burgess, S. C., & Davison, T. F. (2002). Identification of the neoplastically transformed cells in Marek's disease herpesvirus-induced lymphomas: recognition by the monoclonal antibody AV37.. Journal of Virology, 76(14), 7276-7292.

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.

Zhai, W., Araujo, L. F., Burgess, S. C., Cooksey, A. M., Pendarvis, K., Mercier, Y., & Corzo, A. (2012). Protein expression in pectoral skeletal muscle of chickens as influenced by dietary methionine. Poultry Science, 91(10), 2548-2555.

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.

Blake, J. A., Dolan, M., Drabkin, H., Hill, D. P., Ni, L., Sitnikov, D., Burgess, S., Buza, T., Gresham, C., McCarthy, F., Pillai, L., Wang, H., Carbon, S., Lewis, S. E., Mungall, C. J., Gaudet, P., Chisholm, R. L., Fey, P., Kibbe, W. A., , Basu, S., et al. (2012). The Gene Ontology: Enhancements for 2011. Nucleic Acids Research, 40(D1), D559-D564.

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.

Wang, N., Yuan, C., Burgess, S., Nanduri, B., Lawrence, M., & Bridges, S. (2008). Integrating evidence for evaluation of potential novel protein-coding genes using Bayesian networks. Proceedings of the 2008 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2008, 838-843.

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.