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

Peddinti, D., Memili, E., & Burgess, S. C. (2010). Proteomics-based systems biology modeling of bovine germinal vesicle stage oocyte and cumulus cell interaction. PLoS ONE, 5(6).

PMID: 20574525;PMCID: PMC2888582;Abstract:

Background: Oocytes are the female gametes which establish the program of life after fertilization. Interactions between oocyte and the surrounding cumulus cells at germinal vesicle (GV) stage are considered essential for proper maturation or 'programming' of oocytes, which is crucial for normal fertilization and embryonic development. However, despite its importance, little is known about the molecular events and pathways involved in this bidirectional communication. Methodology/Principal Findings: We used differential detergent fractionation multidimensional protein identification technology (DDF-Mud PIT) on bovine GV oocyte and cumulus cells and identified 811 and 1247 proteins in GV oocyte and cumulus cells, respectively; 371 proteins were significantly differentially expressed between each cell type. Systems biology modeling, which included Gene Ontology (GO) and canonical genetic pathway analysis, showed that cumulus cells have higher expression of proteins involved in cell communication, generation of precursor metabolites and energy, as well as transport than GV oocytes. Our data also suggests a hypothesis that oocytes may depend on the presence of cumulus cells to generate specific cellular signals to coordinate their growth and maturation. Conclusions/Significance: Systems biology modeling of bovine oocytes and cumulus cells in the context of GO and protein interaction networks identified the signaling pathways associated with the proteins involved in cell-to-cell signaling biological process that may have implications in oocyte competence and maturation. This first comprehensive systems biology modeling of bovine oocytes and cumulus cell proteomes not only provides a foundation for signaling and cell physiology at the GV stage of oocyte development, but are also valuable for comparative studies of other stages of oocyte development at the molecular level. © 2010 Peddinti et al.

Nanduri, B., Shack, L. A., Burgess, S. C., & Lawrence, M. L. (2009). The transcriptional response of Pasteurella multocida to three classes of antibiotics. BMC Genomics, 10(SUPPL. 2).

PMID: 19607655;PMCID: PMC2966327;Abstract:

Background: Pasteurella multocida is a gram-negative bacterial pathogen that has a broad host range. One of the diseases it causes is fowl cholera in poultry. The availability of the genome sequence of avian P. multocida isolate Pm70 enables the application of functional genomics for observing global gene expression in response to a given stimulus. We studied the effects of three classes of antibiotics on the P. multocida transcriptome using custom oligonucleotide microarrays from NimbleGen Systems. Hybridizations were conducted with RNA isolated from three independent cultures of Pm70 grown in the presence or absence of sub-minimum inhibitory concentration (sub-MIC) of antibiotics. Differentially expressed (DE) genes were identified by ANOVA and Dunnett's test. Biological modeling of the differentially expressed genes (DE) was conducted based on Clusters of Orthologous (COG) groups and network analysis in Pathway Studio. Results: The three antibiotics used in this study, amoxicillin, chlortetracycline, and enrofloxacin, collectively influenced transcription of 25% of the P. multocida Pm70 genome. Some DE genes identified were common to more than one antibiotic. The overall transcription signatures of the three antibiotics differed at the COG level of the analysis. Network analysis identified differences in the SOS response of P. multocida in response to the antibiotics. Conclusion: This is the first report of the transcriptional response of an avian strain of P. multocida to sub-lethal concentrations of three different classes of antibiotics. We identified common adaptive responses of P. multocida to antibiotic stress. The observed changes in gene expression of known and putative P. multocida virulence factors establish the molecular basis for the therapeutic efficacy of sub-MICs. Our network analysis demonstrates the feasibility and limitations of applying systems modeling to high throughput datasets in 'non-model' bacteria. © 2009 Nanduri et al; licensee BioMed Central Ltd.

Sanders, W. S., Bridges, S. M., McCarthy, F. M., Nanduri, B., & Burgess, S. C. (2007). Prediction of peptides observable by mass spectrometry applied at the experimental set level. BMC Bioinformatics, 8(SUPPL. 7).

PMID: 18047723;PMCID: PMC2099492;Abstract:

Background: When proteins are subjected to proteolytic digestion and analyzed by mass spectrometry using a method such as 2D LC MS/MS, only a portion of the proteotypic peptides associated with each protein will be observed. The ability to predict which peptides can and cannot potentially be observed for a particular experimental dataset has several important applications in proteomics research including calculation of peptide coverage in terms of potentially detectable peptides, systems biology analysis of data sets, and protein quantification. Results: We have developed a methodology for constructing artificial neural networks that can be used to predict which peptides are potentially observable for a given set of experimental, instrumental, and analytical conditions for 2D LC MS/MS (a.k.a Multidimensional Protein Identification Technology [MudPIT]) datasets. Neural network classifiers constructed using this procedure for two MudPIT datasets exhibit 10-fold cross validation accuracy of about 80%. We show that a classifier constructed for one dataset has poor predictive performance with the other dataset, thus demonstrating the need for dataset specific classifiers. Classification results with each dataset are used to compute informative percent amino acid coverage statistics for each protein in terms of the predicted detectable peptides in addition to the percent coverage of the complete sequence. We also demonstrate the utility of predicted peptide observability for systems analysis to help determine if proteins that were expected but not observed generate sufficient peptides for detection. Conclusion: Classifiers that accurately predict the likelihood of detecting proteotypic peptides by mass spectrometry provide proteomics researchers with powerful new approaches for data analysis. We demonstrate that the procedure we have developed for building a classifier based on an individual experimental data set results in classifiers with accuracy comparable to those reported in the literature based on large training sets collected from multiple experiments. Our approach allows the researcher to construct a classifier that is specific for the experimental, instrument, and analytical conditions of a single experiment and amenable to local, condition-specific, implementation. The resulting classifiers have application in a number of areas such as determination of peptide coverage for protein identification, pathway analysis, and protein quantification. © 2007 Sanders et al; licensee BioMed Central Ltd.