Shane C Burgess

PMID: 22979947;PMCID: PMC3472249;Abstract:

Background: Marek's Disease (MD) is a hyperproliferative, lymphomatous, neoplastic disease of chickens caused by the oncogenic Gallid herpesvirus type 2 (GaHV-2; MDV). Like several human lymphomas the neoplastic MD lymphoma cells overexpress the CD30 antigen (CD30 ^hi) and are in minority, while the non-neoplastic cells (CD30 ^lo) form the majority of population. MD is a unique natural in-vivo model of human CD30 ^hi lymphomas with both natural CD30 ^hi lymphomagenesis and spontaneous regression. The exact mechanism of neoplastic transformation from CD30 ^lo expressing phenotype to CD30 ^hi expressing neoplastic phenotype is unknown. Here, using microarray, proteomics and Systems Biology modeling; we compare the global gene expression of CD30 ^lo and CD30 ^hi cells to identify key pathways of neoplastic transformation. We propose and test a specific mechanism of neoplastic transformation, and genetic resistance, involving the MDV oncogene Meq, host gene products of the Nuclear Factor Kappa B (NF-κB) family and CD30; we also identify a novel Meq protein interactome.Results: Our results show that a) CD30 ^lo lymphocytes are pre-neoplastic precursors and not merely reactive lymphocytes; b) multiple transformation mechanisms exist and are potentially controlled by Meq; c) Meq can drive a feed-forward cycle that induces CD30 transcription, increases CD30 signaling which activates NF-κB, and, in turn, increases Meq transcription; d) Meq transcriptional repression or activation of the CD30 promoter generally correlates with polymorphisms in the CD30 promoter distinguishing MD-lymphoma resistant and susceptible chicken genotypes e) MDV oncoprotein Meq interacts with proteins involved in physiological processes central to lymphomagenesis.Conclusions: In the context of the MD lymphoma microenvironment (and potentially in other CD30 ^hi lymphomas as well), our results show that the neoplastic transformation is a continuum and the non-neoplastic cells are actually pre-neoplastic precursor cells and not merely immune bystanders. We also show that NF-κB is a central player in MDV induced neoplastic transformation of CD30-expressing lymphocytes in vivo. Our results provide insights into molecular mechanisms of neoplastic transformation in MD specifically and also herpesvirus induced lymphoma in general. © 2012 Kumar et al.; licensee BioMed Central Ltd.

PMID: 23161678;PMCID: PMC3531070;Abstract:

The Gene Ontology (GO) Consortium (GOC, http://www.geneontology.org) is a community-based bio-informatics resource that classifies gene product function through the use of structured, controlled vocabularies. Over the past year, the GOC has implemented several processes to increase the quantity, quality and specificity of GO annotations. First, the number of manual, literature-based annotations has grown at an increasing rate. Second, as a result of a new 'phylogenetic annotation' process, manually reviewed, homology-based annotations are becoming available for a broad range of species. Third, the quality of GO annotations has been improved through a streamlined process for, and automated quality checks of, GO annotations deposited by different annotation groups. Fourth, the consistency and correctness of the ontology itself has increased by using automated reasoning tools. Finally, the GO has been expanded not only to cover new areas of biology through focused interaction with experts, but also to capture greater specificity in all areas of the ontology using tools for adding new combinatorial terms. The GOC works closely with other ontology developers to support integrated use of terminologies. The GOC supports its user community through the use of e-mail lists, social media and web-based resources. © The Author(s) 2012.

PMID: 18047724;PMCID: PMC2099493;Abstract:

Background: Effective and economical methods for quantitative analysis of high throughput mass spectrometry data are essential to meet the goals of directly identifying, characterizing, and quantifying proteins from a particular cell state. Multidimensional Protein Identification Technology (MudPIT) is a common approach used in protein identification. Two types of methods are used to detect differential protein expression in MudPIT experiments: those involving stable isotope labelling and the so-called label-free methods. Label-free methods are based on the relationship between protein abundance and sampling statistics such as peptide count, spectral count, probabilistic peptide identification scores, and sum of peptide Sequest XCorr scores (ΣXCorr). Although a number of label-free methods for protein quantification have been described in the literature, there are few publicly available tools that implement these methods. We describe ProtQuant, a Java-based tool for label-free protein quantification that uses the previously published ΣXCorr method for quantification and includes an improved method for handling missing data. Results: ProtQuant was designed for ease of use and portability for the bench scientist. It implements the ΣXCorr method for label free protein quantification from MudPIT datasets. ProtQuant has a graphical user interface, accepts multiple file formats, is not limited by the size of the input files, and can process any number of replicates and any number of treatments. In addition,ProtQuant implements a new method for dealing with missing values for peptide scores used for quantification. The new algorithm, called ΣXCorr, uses "below threshold" peptide scores to provide meaningful non-zero values for missing data points. We demonstrate that ΣXCorr produces an average reduction in false positive identifications of differential expression of 25% compared to ΣXCorr. Conclusion: ProtQuant is a tool for protein quantification built for multi-platform use with an intuitive user interface. ProtQuant efficiently and uniquely performs label-free quantification of protein datasets produced with Sequest and provides the user with facilities for data management and analysis. Importantly, ProtQuant is available as a self-installing executable for the Windows environment used by many bench scientists. © 2007 Bridges et al; licensee BioMed Central Ltd.

PMID: 19811692;PMCID: PMC3226197;Abstract:

Background: The horse genome is sequenced, allowing equine researchers to use high-throughput functional genomics platforms such as microarrays; next-generation sequencing for gene expression and proteomics. However, for researchers to derive value from these functional genomics datasets, they must be able to model this data in biologically relevant ways; to do so requires that the equine genome be more fully annotated. There are two interrelated types of genomic annotation: structural and functional. Structural annotation is delineating and demarcating the genomic elements (such as genes, promoters, and regulatory elements). Functional annotation is assigning function to structural elements. The Gene Ontology (GO) is the de facto standard for functional annotation, and is routinely used as a basis for modelling and hypothesis testing, large functional genomics datasets. Results: An Equine Whole Genome Oligonucleotide (EWGO) array with 21,351 elements was developed at Texas A&M University. This 70-mer oligoarray was designed using the approximately 7× assembled and annotated sequence of the equine genome to be one of the most comprehensive arrays available for expressed equine sequences. To assist researchers in determining the biological meaning of data derived from this array, we have structurally annotated it by mapping the elements to multiple database accessions, including UniProtKB, Entrez Gene, NRPD (Non-Redundant Protein Database) and UniGene. We next provided GO functional annotations for the gene transcripts represented on this array. Overall, we GO annotated 14,531 gene products (68.1% of the gene products represented on the EWGO array) with 57,912 annotations. GAQ (GO Annotation Quality) scores were calculated for this array both before and after we added GO annotation. The additional annotations improved the meanGAQ score 16-fold. This data is publicly available at AgBase http://www.agbase.msstate.edu/. Conclusion: Providing additional information about the public databases which link to the gene products represented on the array allows users more flexibility when using gene expression modelling and hypothesis-testing computational tools. Moreover, since different databases provide different types of information, users have access to multiple data sources. In addition, our GO annotation underpins functional modelling for most gene expression analysis tools and enables equine researchers to model large lists of differentially expressed transcripts in biologically relevant ways. © 2009 Bright et al; licensee BioMed Central Ltd.

Abstract:

Structural annotation of genomes is one of major goals of genomics research. Most popular tools for structural annotation of genomes are determined by computational pipelines. It is well-known that these computational methods have a number of shortcomings including false identifications and incorrect identification of gene boundaries. Proteomic data can used to confirm the identification of genes identified by computational methods and correct mistakes. A Proteogenomic mapping method has been developed, which uses peptides identified from mass spectrometry for structural annotation of genomes. Spectra are matched against both a protein database and the genome database translated in all six reading frames. Those peptides that match the genome but not the protein database potentially represent novel protein coding genes, annotation errors. These short experimentally derived peptides are used to discover potential novel protein coding genes called expressed Protein Sequence Tags (ePSTs) by aligning the peptides to the genomic DNA and extending the translation in the 3' and 5' direction. In the paper, an enhanced pipeline, has been designed and developed for discovering and evaluating of potential novel protein coding genes: 1) a distance-based outlier detection method for validating peptides identified from MS/MS, 2) a proteogenomic mapping for discovery of potential novel protein coding genes, 3) collection of evidence from a number of sources and automatically evaluate potential novel protein coding genes by using machine learning techniques, such as Neural Network, Support Vector Machine, Naïve Bayes etc.