Shane C Burgess

PMID: 20946613;PMCID: PMC3026376;Abstract:

Background: Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available 'discovery-based' computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing.Results: GOModeler tests the relevance of the hypothesis terms chosen by the user for the input gene dataset by providing the individual effects of the genes on the hypothesis terms and the overall effect of the entire dataset on each of the hypothesis terms. It matches the GO identifiers (ids) of the genes with the GO ids of the hypothesis terms and parses the names of those ids that match to assign effects. We demonstrate the capabilities of GOModeler with a dataset of nine differentially expressed cytokine genes and compare the results to those obtained through manual analysis of the dataset by an immunologist. The direction of overall effects on all hypothesis terms except one was consistent with the results obtained by manual analysis. The tool's editing capability enables the user to augment the information extracted. GOModeler is available as a part of the AgBase tool suite (http://www.agbase.msstate.edu).Conclusions: GOModeler allows hypothesis driven analysis of high throughput datasets using the GO. Using this tool, researchers can quickly evaluate the overall effect of quantitative expression changes of gene set on specific biological processes of interest. The results are provided in both tabular and graphical formats. © 2010 Bridges et al; licensee BioMed Central Ltd.

PMID: 24058457;PMCID: PMC3776814;Abstract:

As the use of laparoscopic surgery has become more widespread in recent years, the need has increased for minimally-invasive surgical devices that effectively cut and coagulate tissue with reduced tissue trauma. Although electrosurgery (ES) has been used for many generations, newly-developed ultrasonic devices (HARMONIC® Blade, HB) have been shown at a macroscopic level to offer better coagulation with less thermally-induced tissue damage. We sought to understand the differences between ES and HB at a microscopic level by comparing mRNA transcript and protein responses at the 3-day timepoint to incisions made by the devices in subcutaneous fat tissue in a porcine model. Samples were also assessed via histological examination. ES-incised tissue had more than twice as many differentially-expressed genes as HB (2,548 vs 1,264 respectively), and more differentially-expressed proteins (508 vs 432) compared to control (untreated) tissue. Evaluation of molecular functions using Gene Ontology showed that gene expression changes for the energized devices reflected the start of wound healing, including immune response and inflammation, while protein expression showed a slightly earlier stage, with some remnants of hemostasis. For both transcripts and proteins, ES exhibited a greater response than HB, especially in inflammatory mediators. These findings were in qualitative agreement with histological results. This study has shown that transcriptomics and proteomics can monitor the wound healing response following surgery and can differentiate between surgical devices. In agreement with clinical observations, electrosurgery was shown to incur a greater inflammatory immune response than an ultrasonic device during initial iatrogenic wound healing. © 2013 Nanduri et al.

PMID: 21749422;Abstract:

The equine genome sequence enables the use of high-throughput genomic technologies in equine research, but accurate identification of expressed gene products and interpreting their biological relevance require additional structural and functional genome annotation. Here, we employ the equine genome sequence to identify predicted and known proteins using proteomics and model these proteins into biological pathways, identifying 582 proteins in normal cell-free equine bronchoalveolar lavage fluid (BALF). We improved structural and functional annotation by directly confirming the in vivo expression of 558 (96%) proteins, which were computationally predicted previously, and adding Gene Ontology (GO) annotations for 174 proteins, 108 of which lacked functional annotation. Bronchoalveolar lavage is commonly used to investigate equine respiratory disease, leading us to model the associated proteome and its biological functions. Modelling of protein functions using Ingenuity Pathway Analysis identified carbohydrate metabolism, cell-to-cell signalling, cellular function, inflammatory response, organ morphology, lipid metabolism and cellular movement as key biological processes in normal equine BALF. Comparative modelling of protein functions in normal cell-free bronchoalveolar lavage proteomes from horse, human, and mouse, performed by grouping GO terms sharing common ancestor terms, confirms conservation of functions across species. Ninety-one of 92 human GO categories and 105 of 109 mouse GO categories were conserved in the horse. Our approach confirms the utility of the equine genome sequence to characterize protein networks without antibodies or mRNA quantification, highlights the need for continued structural and functional annotation of the equine genome and provides a framework for equine researchers to aid in the annotation effort. © 2011 The Authors, Animal Genetics.

PMID: 20863402;PMCID: PMC3091675;Abstract:

Background: Oligotropha carboxidovorans OM5 T. (DSM 1227, ATCC 49405) is a chemolithoautotrophic bacterium capable of utilizing CO (carbon monoxide) and fixing CO2 (carbon dioxide). We previously published the draft genome of this organism and recently submitted the complete genome sequence to GenBank.Results: The genome sequence of the chemolithoautotrophic bacterium Oligotropha carboxidovorans OM5 consists of a 3.74-Mb chromosome and a 133-kb megaplasmid that contains the genes responsible for utilization of carbon monoxide, carbon dioxide, and hydrogen. To our knowledge, this strain is the first one to be sequenced in the genus Oligotropha, the closest fully sequenced relatives being Bradyrhizobium sp. BTAi and USDA110 and Nitrobacter hamburgiensis X14. Analysis of the O. carboxidovorans genome reveals potential links between plasmid-encoded chemolithoautotrophy and chromosomally-encoded lipid metabolism. Comparative analysis of O. carboxidovorans with closely related species revealed differences in metabolic pathways, particularly in carbohydrate and lipid metabolism, as well as transport pathways.Conclusion: Oligotropha, Bradyrhizobium sp and Nitrobacter hamburgiensis X14 are phylogenetically proximal. Although there is significant conservation of genome organization between the species, there are major differences in many metabolic pathways that reflect the adaptive strategies unique to each species. © 2010 Paul et al; licensee BioMed Central Ltd.

PMID: 20729368;PMCID: PMC2950491;Abstract:

Clostridium carboxidivorans strain P7^T is a strictly anaerobic acetogenic bacterium that produces acetate, ethanol, butanol, and butyrate. The C. carboxidivorans genome contains all the genes for the carbonyl branch of the Wood-Ljungdahl pathway for CO2 fixation, and it encodes enzymes for conversion of acetyl coenzyme A into butanol and butyrate. Copyright © 2010, American Society for Microbiology. All Rights Reserved.