Shane C Burgess
While advances in proteomics have improved proteome coverage and enhanced biological modeling, modeling function in multicellular organisms requires understanding how cells interact. Here we used the chicken bursa of Fabricius, a common experimental system for B cell function, to model organ function from proteomics data. The bursa has two major functional cell types: B cells and the supporting stromal cells. We used differential detergent fractionation-multi- dimensional protein identification technology (DDF-MudPIT) to identify 5198 proteins from all cellular compartments. Of these, 1753 were B cell specific, 1972 were stroma specific and 1473 were shared between the two. By modeling programmed cell death (PCD), cell differentiation and proliferation, and transcriptional activation, we have improved functional annotation of chicken proteins and placed chicken-specific death receptors into the PCD process using phylogenetics. We have identified 114 transcription factors (TFs); 42 of the bursal B cell TFs have not been reported before in any B cells. We have also improved the structural annotation of a newly sequenced genome by confirming the in vivo expression of 4006 "predicted", and 6623 ab initio, ORFs. Finally, we have developed a novel method for facilitating structural annotation, "expressed peptide sequence tags" (ePSTs) and demonstrate its utility by identifying 521 potential novel proteins from the chicken "unassigned chromosome". © 2006 Wiley-VCH Verlag GmbH & Co. KGaA.
PMID: 19028911;PMCID: PMC2620715;Abstract:
Listeria monocytogenes is a gram-positive, food-borne pathogen that causes disease in both humans and animals. There are three major genetic lineages of L. monocytogenes and 13 serovars. To further our understanding of the differences that exist between different genetic lineages/serovars of L. monocytogenes, we analyzed the global protein expression of the serotype 1/2a strain EGD and the serotype 4b strain F2365 during early-stationary-phase growth at 37°C. Using multidimensional protein identification technology with electrospray ionization tandem mass spectrometry, we identified 1,754 proteins from EGD and 1,427 proteins from F2365, of which 1,077 were common to both. Analysis of proteins that had significantly altered expression between strains revealed potential biological differences between these two L. monocytogenes strains. In particular, the strains differed in expression of proteins involved in cell wall physiology and flagellar biosynthesis, as well as DNA repair proteins and stress response proteins. Copyright © 2009, American Society for Microbiology. All Rights Reserved.
Listeria monocytogenes is able to survive and proliferate within macrophages. In the current study, the ability of three L. monocytogenes strains (serovar 1/2a strain EGDe, serovar 4b strain F2365, and serovar 4a strain HCC23) to proliferate in the murine macrophage cell line J774.1 was analyzed. We found that the avirulent strain HCC23 was able to initiate an infection but could not establish prolonged infection within the macrophages. By contrast, strains EGDe and F2365 proliferated within macrophages for at least 7. h. We further analyzed these strains by comparing their protein expression profiles at 0. h, 3. h, and 5. h post-infection using multidimensional protein identification technology coupled with electrospray ionization tandem mass spectrometry. Our results indicated that similar metabolic and cell wall associated proteins were expressed by all three strains at 3. h post-infection. However, increased expression of stress response and DNA repair proteins was associated with the ability to proliferate in macrophages at 5. h post-infection. By comparing the protein expression patterns of these three L. monocytogenes strains during intracellular growth in macrophages, we were able to detect biological differences that may determine the ability of L. monocytogenes to survive in macrophages. © 2011 Elsevier B.V.
Laser-induced breakdown spectroscopy (LIBS) is an on-line, real-time technology that can produce immediate information about the elemental contents of tissue samples. We have previously shown that LIBS may be used to distinguish cancerous from non-cancerous tissue. In this work, we study LIBS spectra produced from chicken brain, lung, spleen, liver, kidney and skeletal muscle. Different data processing techniques were used to study if the information contained in these LIBS spectra is able to differentiate between different types of tissue samples and then identify unknown tissues. We have demonstrated a clear distinguishing between each of the known tissue types with only 21 selected analyte lines from each observed LIBS spectrum. We found that in order to produce an analytical model to work well with new sample we need to have representative training data to cover a wide range of spectral variation due to experimental or environmental changes. © 2009 Elsevier B.V. All rights reserved.
Since the sequencing of the genome and the development of high-throughput tools for the exploration of functional elements of the genome, the chicken has reached model organism status. Functional genomics focuses on understanding the function and regulation of genes and gene products on a global or genome-wide scale. Systems biology attempts to integrate functional information derived from multiple high-content data sets into a holistic view of all biological processes within a cell or organism. Generation of a large collection (∼600K) of chicken expressed sequence tags, representing most tissues and developmental stages, has enabled the construction of high-density microarrays for transcriptional profiling. Comprehensive analysis of this large expressed sequence tag collection and a set of ∼20K full-length cDNA sequences indicate that the transcriptome of the chicken represents approximately 20,000 genes. Furthermore, comparative analyses of these sequences have facilitated functional annotation of the genome and the creation of several bioinformatic resources for the chicken. Recently, about 20 papers have been published on transcriptional profiling with DNA microarrays in chicken tissues under various conditions. Proteomics is another powerful high-throughput tool currently used for examining the dynamics of protein expression in chicken tissues and fluids. Computational analyses of the chicken genome are providing new insight into the evolution of gene families in birds and other organisms. Abundant functional genomic resources now support large-scale analyses in the chicken and will facilitate identification of transcriptional mechanisms, gene networks, and metabolic or regulatory pathways that will ultimately determine the phenotype of the bird. New technologies such as marker-assisted selection, transgenics, and RNA interference offer the opportunity to modify the phenotype of the chicken to fit defined production goals. This review focuses on functional genomics in the chicken and provides a road map for large-scale exploration of the chicken genome. ©2007 Poultry Science Association Inc.