Shane C Burgess

PMID: 16046238;Abstract:

The Harderian gland (HG), a sero-mucous secreting organ in the eye orbit, has long been recognized as immunologically important in chickens. During experimentation to characterize immune components of the gland, proteomics analysis revealed the presence of hematopoietic prostaglandin D synthase (H-PGDS). Extraction of total RNA followed by RT-PCR produced cDNA of 597 base pairs. DNA sequencing revealed nucleic acid and predicted amino acid sequences that were 99% aligned with the one published sequence for chicken H-PGDS of the spleen. Alignment with murine, rat, and human H-PGDS were 69, 69, and 66%, respectively. Ocular vaccination of chickens with a Newcastle Disease/Infectious Bronchitis vaccine (Mass.-Ark. Strain) induced an increase in H-PGDS expression determined by real-time PCR. Furthermore, immunohistochemistry of frozen HG sections showed positive stained cells for both H-PGDS and mast cell tryptase in the sub-epithelial cell layers of the HG ducts. Based on the potent vasoactive role of PGD2, it appears that the chicken HG is a site of active mucosal immunity partially mediated by PGD2 synthesized by H-PGDS in the gland. © 2005 Elsevier B.V. All rights reserved.

PMID: 15822906;Abstract:

Differential detergent fractionation (DDF), which relies on detergents to sequentially extract proteins from eukaryotic cells, has been used to increase proteome coverage of 2D-PAGE. Here, we used DDF extraction in conjunction with the nonelectrophoretic proteomics method of liquid chromatography and electrospray ionization tandem mass spectrometry. We demonstrate that DDF can be used with 2D-LC ESI MS ² for comprehensive cellular proteomics, including a large proportion of membrane proteins. Compared to some published methods designed to isolate membrane proteins specifically, DDF extraction yields comprehensive proteomes which include twice as many membrane proteins. Two-thirds of these membrane proteins have more than one trans-membrane domain. Since DDF separates proteins based upon their physicochemistry and subcellular localization, this method also provides data useful for functional genome annotation. As more genome sequences are completed, methods which can aid in functional annotation will become increasingly important. © 2005 American Chemical Society.

PMID: 19811682;PMCID: PMC3226188;Abstract:

Background: Mass spectrometry-based protein identification methods are fundamental to proteomics. Biological experiments are usually performed in replicates and proteomic analyses generate huge datasets which need to be integrated and quantitatively analyzed. The Sequest™ search algorithm is a commonly used algorithm for identifying peptides and proteins from two dimensional liquid chromatography electrospray ionization tandem mass spectrometry (2-D LC ESI MS²) data. A number of proteomic pipelines that facilitate high throughput 'post data acquisition analysis' are described in the literature. However, these pipelines need to be updated to accommodate the rapidly evolving data analysis methods. Here, we describe a proteomic data analysis pipeline that specifically addresses two main issues pertinent to protein identification and differential expression analysis: 1) estimation of the probability of peptide and protein identifications and 2) non-parametric statistics for protein differential expression analysis. Our proteomic analysis workflow analyzes replicate datasets from a single experimental paradigm to generate a list of identified proteins with their probabilities and significant changes in protein expression using parametric and non-parametric statistics. Results: The input for our workflow is Bioworks™ 3.2 Sequest (or a later version, including cluster) output in XML format. We use a decoy database approach to assign probability to peptide identifications. The user has the option to select "quality thresholds" on peptide identifications based on the P value. We also estimate probability for protein identification. Proteins identified with peptides at a user-specified threshold value from biological experiments are grouped as either control or treatment for further analysis in ProtQuant. ProtQuant utilizes a parametric (ANOVA) method, for calculating differences in protein expression based on the quantitative measure ΣXcorr. Alternatively ProtQuant output can be further processed using non-parametric Monte-Carlo resampling statistics to calculate P values for differential expression. Correction for multiple testing of ANOVA and resampling P values is done using Benjamini and Hochberg's method. The results of these statistical analyses are then combined into a single output file containing a comprehensive protein list with probabilities and differential expression analysis, associated P values, and resampling statistics. Conclusion: For biologists carrying out proteomics by mass spectrometry, our workflow facilitates automated, easy to use analyses of Bioworks (3.2 or later versions) data. All the methods used in the workflow are peer-reviewed and as such the results of our workflow are compliant with proteomic data submission guidelines to public proteomic data repositories including PRIDE. Our workflow is a necessary intermediate step that is required to link proteomics data to biological knowledge for generating testable hypotheses. © 2009 Pendarvis et al; licensee BioMed Central Ltd.

PMID: 19811685;PMCID: PMC3226191;Abstract:

Background: Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information. Results: We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (AGOM) tool to help researchers to quickly retrieve corresponding functional information for their dataset. Conclusion: Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using AGOM tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular basis. © 2009 Buza et al; licensee BioMed Central Ltd.

PMID: 21513508;PMCID: PMC3107813;Abstract:

Background: High-throughput mass spectrometry (MS) proteomics data is increasingly being used to complement traditional structural genome annotation methods. To keep pace with the high speed of experimental data generation and to aid in structural genome annotation, experimentally observed peptides need to be mapped back to their source genome location quickly and exactly. Previously, the tools to do this have been limited to custom scripts designed by individual research groups to analyze their own data, are generally not widely available, and do not scale well with large eukaryotic genomes.Results: The Proteogenomic Mapping Tool includes a Java implementation of the Aho-Corasick string searching algorithm which takes as input standardized file types and rapidly searches experimentally observed peptides against a given genome translated in all 6 reading frames for exact matches. The Java implementation allows the application to scale well with larger eukaryotic genomes while providing cross-platform functionality.Conclusions: The Proteogenomic Mapping Tool provides a standalone application for mapping peptides back to their source genome on a number of operating system platforms with standard desktop computer hardware and executes very rapidly for a variety of datasets. Allowing the selection of different genetic codes for different organisms allows researchers to easily customize the tool to their own research interests and is recommended for anyone working to structurally annotate genomes using MS derived proteomics data. © 2011 Sanders et al; licensee BioMed Central Ltd.