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ABSTRACT: Iron is a critical cofactor for many enzymes and is known to regulate gene expression in many bacterial pathogens. Streptococcus pneumoniae normally inhabits the upper respiratory mucosa but can also invade and replicate in lungs and blood. These anatomic sites vary considerably in both the quantity and form of available iron. The genome of serotype 4 pneumococcal strain TIGR4 encodes a putative iron-dependent transcriptional regulator (IDTR). A mutant deleted at idtr (Δidtr) exhibited growth kinetics similar to parent strain TIGR4 in vitro and in mouse blood for up to 48 hours following infection. However, Δidtr was significantly attenuated in a murine model of sepsis. IDTR down-regulates the expression of ten characterized and putative virulence genes in nasopharyngeal colonization and pneumonia. The host cytokine response was significantly suppressed in sepsis with Δidtr. Since an exaggerated inflammatory response is associated with a poor prognosis in sepsis, the decreased inflammatory response could explain the increased survival with Δidtr. Our results suggest that IDTR, which is dispensable for pneumococcal growth in vitro, is associated with regulation of pneumococcal virulence in specific host environments. Additionally, IDTR ultimately modulates the host cytokine response and systemic inflammation that contributes to morbidity and mortality of invasive pneumococcal disease.
PLoS ONE 01/2013; 8(2):e55157. · 4.09 Impact Factor
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ABSTRACT: BACKGROUND: The events leading to sepsis starts with an invasive infection of a primary organ of the body followed by an overwhelming systemic response. Intra-abdominal infections are the second most common cause of sepsis. Peritoneal fluid is the primary site of infection in these cases. A microarray-based approach was used to study the temporal changes in cells from the peritoneal cavity of septic mice and to identify potential biomarkers and therapeutic targets for this subset of sepsis patients. RESULTS: We conducted microarray analysis of the peritoneal cells of mice infected with a non-pathogenic strain of Escherichia coli. Differentially expressed genes were identified at two early (1 h, 2 h) and one late time point (18 h). A multiplexed bead array analysis was used to confirm protein expression for several cytokines which showed differential expression at different time points based on the microarray data. Gene Ontology based hypothesis testing identified a positive bias of differentially expressed genes associated with cellular development and cell death at 2 h and 18 h respectively. Most differentially expressed genes common to all 3 time points had an immune response related function, consistent with the observation that a few bacteria are still present at 18 h. CONCLUSIONS: Transcriptional regulators like PLAGL2, EBF1, TCF7, KLF10 and SBNO2, previously not described in sepsis, are differentially expressed at early and late time points. Expression pattern for key biomarkers in this study is similar to that reported in human sepsis, indicating the suitability of this model for future studies of sepsis, and the observed differences in gene expression suggest species differences or differences in the response of blood leukocytes and peritoneal leukocytes.
BMC Genomics 09/2012; 13(1):509. · 4.07 Impact Factor
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ABSTRACT: Listeria monocytogenes is a food-borne pathogen responsible for the disease listeriosis. The infectious process depends upon survival in high bile salt conditions encountered throughout the gastrointestinal tract, including the gallbladder. However, it is not clear how bile salt resistance mechanisms are induced, especially under physiologically relevant conditions. This study sought to determine how the L. monocytogenes strains EGDe (serovar 1/2a), F2365 (serovar 4a), and HCC23 (serovar 4b) respond to bile salts under anaerobic conditions. Changes in the expressed proteome were analyzed using multidimensional protein identification technology coupled with electrospray ionization tandem mass spectrometry. In general, the response to bile salts among the strains tested involved significant alterations in the presence of cell wall associated proteins, DNA repair proteins, protein folding chaperones and oxidative stress response proteins. Strain viability correlated with an initial osmotic stress response, yet continued survival for EGDe and F2365 involved different mechanisms. Specifically, proteins associated with biofilm formation in EGDe and transmembrane efflux pumps in F2365 were expressed, suggesting variations exist in how virulent strains respond and adapt to high bile salt environments. These results indicate that the bile salt response varies among these serovars and that further research is needed to elucidate how the response to bile salts correlates with colonization potential in vivo.
Journal of Medical Microbiology 09/2012; · 2.50 Impact Factor
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ABSTRACT: Computational methods for structural gene annotation have propelled gene discovery but face certain drawbacks with regards to prokaryotic genome annotation. Identification of transcriptional start sites, demarcating overlapping gene boundaries, and identifying regulatory elements such as small RNA are not accurate using these approaches. In this study, we re-visit the structural annotation of Mannheimia haemolytica PHL213, a bovine respiratory disease pathogen. M. haemolytica is one of the causative agents of bovine respiratory disease that results in about $3 billion annual losses to the cattle industry. We used RNA-Seq and analyzed the data using freely-available computational methods and resources. The aim was to identify previously unannotated regions of the genome using RNA-Seq based expression profile to complement the existing annotation of this pathogen.
Using the Illumina Genome Analyzer, we generated 9,055,826 reads (average length ~76 bp) and aligned them to the reference genome using Bowtie. The transcribed regions were analyzed using SAMTOOLS and custom Perl scripts in conjunction with BLAST searches and available gene annotation information. The single nucleotide resolution map enabled the identification of 14 novel protein coding regions as well as 44 potential novel sRNA. The basal transcription profile revealed that 2,506 of the 2,837 annotated regions were expressed in vitro, at 95.25% coverage, representing all broad functional gene categories in the genome. The expression profile also helped identify 518 potential operon structures involving 1,086 co-expressed pairs. We also identified 11 proteins with mutated/alternate start codons.
The application of RNA-Seq based transcriptome profiling to structural gene annotation helped correct existing annotation errors and identify potential novel protein coding regions and sRNA. We used computational tools to predict regulatory elements such as promoters and terminators associated with the novel expressed regions for further characterization of these novel functional elements. Our study complements the existing structural annotation of Mannheimia haemolytica PHL213 based on experimental evidence. Given the role of sRNA in virulence gene regulation and stress response, potential novel sRNA described in this study can form the framework for future studies to determine the role of sRNA, if any, in M. haemolytica pathogenesis.
BMC Bioinformatics 09/2012; 13 Suppl 15:S4. · 2.75 Impact Factor
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01/2012; , ISBN: 978-953-307-613-3
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ABSTRACT: Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify "novel" genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method.The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (∼30%) were located in genomic region unique to strain 2336 (∼18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.
PLoS ONE 01/2012; 7(1):e29435. · 4.09 Impact Factor
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ABSTRACT: Alcohol is the most frequently abused substance in the world. Both acute and chronic alcohol consumption have diverse and well-documented effects on the human immune system, leading to increased susceptibility to infections like bacterial pneumonia. Streptococcus pneumoniae is the most common bacterial etiology of community-acquired pneumonia worldwide. The frequency and severity of pneumococcal infections in individuals with a history of alcohol abuse is much higher than the general population. Despite this obvious epidemiological relevance, very few experimental studies have focused on the interaction of pneumococci with the immune system of a host acutely or chronically exposed to alcohol. Understanding these host-pathogen interactions is imperative for designing effective prophylactic and therapeutic interventions for such populations. Recent advances in pneumococcal research have greatly improved our understanding of pneumococcal pathogenesis and virulence mechanisms. Additionally, a large body of data is available on the effect of alcohol on the physiology of the lungs and the innate and adaptive immune system of the host. The purpose of this review is to integrate the available knowledge in these diverse areas of for a better understanding of the how the compromised immune system derived from alcohol exposure responds to pneumococcal infections.
Alcohol (Fayetteville, N.Y.) 09/2011; 45(6):523-39. · 2.41 Impact Factor
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ABSTRACT: Sepsis is a major cause of death worldwide. The associated risks and mortality are known to significantly increase on exposure to alcohol (chronic or acute). The underlying mechanisms of the association of acute ethanol ingestion and poor prognosis of sepsis are largely unknown. The study described here was designed to determine in detail the role of ethanol and TLR4 in the pathogenesis of the sepsis syndrome. The effects of acute ethanol exposure and TLR4 on bacterial clearance, spleen cell numbers, peritoneal macrophage numbers, and cytokine production were evaluated using wild-type and TLR4 hyporesponsive mice treated with ethanol and then challenged with a nonpathogenic strain of Escherichia coli. Ethanol-treated mice exhibited a decreased clearance of bacteria and produced lesser amounts of most pro-inflammatory cytokines in both strains of mice at 2h after challenge. Neither ethanol treatment nor a hyporesponsive TLR4 had significant effects on the cell numbers in the peritoneal cavity and spleen 2h postinfection. The suppressive effect of acute ethanol exposure on cytokine and chemokine production was more pronounced in the wild-type mice, but the untreated hyporesponsive mice produced less of most cytokines than untreated wild-type mice. The major conclusion of this study is that acute ethanol exposure suppresses pro-inflammatory cytokine production and that a hyporesponsive TLR4 (in C3H/HeJ mice) decreases pro-inflammatory cytokine levels, but the cytokines and other mediators induced through other receptors are sufficient to ultimately clear the infection but not enough to induce lethal septic shock. In addition, results reported here demonstrate previously unknown effects of acute ethanol exposure on leukemia inhibitory factor and eotaxin, and provide the first evidence that interleukin (IL)-9 is induced through TLR4 in vivo.
Alcohol (Fayetteville, N.Y.) 08/2011; 45(8):795-803. · 2.41 Impact Factor
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ABSTRACT: 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.
Journal of proteomics 05/2011; 74(10):1906-17. · 5.07 Impact Factor
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ABSTRACT: High-density tiling arrays provide closer view of transcription than regular microarrays and can also be used for annotating functional elements in genomes. The identified transcripts usually have a complex overlapping architecture when compared to the existing genome annotation. Therefore, there is a need for customized tiling array data analysis tools. Since most of the initial tiling arrays were conducted in eukaryotes, data analysis methods are well suited for eukaryotic genomes. For using whole-genome tiling arrays to identify previously unknown transcriptional elements like small RNA and antisense RNA in prokaryotes, existing data analysis tools need to be tailored for prokaryotic genome architecture. Furthermore, automation of such custom data analysis workflow is necessary for biologists to apply this powerful platform for knowledge discovery. Here we describe TAAPP, a web-based package that consists of two modules for prokaryotic tiling array data analysis. The transcript generation module works on normalized data to generate transcriptionally active regions (TARs). The feature extraction and annotation module then maps TARs to existing genome annotation. This module further categorizes the transcription profile into potential novel non-coding RNA, antisense RNA, gene expression and operon structures. The implemented workflow is microarray platform independent and is presented as a web-based service. The web interface is freely available for academic use at http://lims.lsbi.mafes.msstate.edu/TAAPP-HTML/.
Genomics Proteomics & Bioinformatics 04/2011; 9(1-2):56-62.
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ABSTRACT: The study aimed to identify mechanosensitive pathways and gene networks that are stimulated by elevated cyclic pressure in aortic valve interstitial cells (VICs) and lead to detrimental tissue remodeling and/or pathogenesis. Porcine aortic valve leaflets were exposed to cyclic pressures of 80 or 120 mmHg, corresponding to diastolic transvalvular pressure in normal and hypertensive conditions, respectively. Linear, two-cycle amplification of total RNA, followed by microarray was performed for transcriptome analysis (with qRT-PCR validation). A combination of systems biology modeling and pathway analysis identified novel genes and molecular mechanisms underlying the biological response of VICs to elevated pressure. 56 gene transcripts related to inflammatory response mechanisms were differentially expressed. TNF-α, IL-1α, and IL-1β were key cytokines identified from the gene network model. Also of interest was the discovery that pentraxin 3 (PTX3) was significantly upregulated under elevated pressure conditions (41-fold change). In conclusion, a gene network model showing differentially expressed inflammatory genes and their interactions in VICs exposed to elevated pressure has been developed. This system overview has detected key molecules that could be targeted for pharmacotherapy of aortic stenosis in hypertensive patients.
International journal of inflammation. 01/2011; 2011:176412.
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ABSTRACT: Oligotropha carboxidovorans OM5 T. (DSM 1227, ATCC 49405) is a chemolithoautotrophic bacterium able to utilize CO and H(2) to derive energy for fixation of CO(2). Thus, it is capable of growth using syngas, which is a mixture of varying amounts of CO and H(2) generated by organic waste gasification. O. carboxidovorans is capable also of heterotrophic growth in standard bacteriologic media. Here we characterize how the O. carboxidovorans proteome adapts to different lifestyles of chemolithoautotrophy and heterotrophy. Fatty acid methyl ester (FAME) analysis of O. carboxidovorans grown with acetate or with syngas showed that the bacterium changes membrane fatty acid composition. Quantitative shotgun proteomic analysis of O. carboxidovorans grown in the presence of acetate and syngas showed production of proteins encoded on the megaplasmid for assimilating CO and H(2) as well as proteins encoded on the chromosome that might have contributed to fatty acid and acetate metabolism. We found that adaptation to chemolithoautotrophic growth involved adaptations in cell envelope, oxidative homeostasis, and metabolic pathways such as glyoxylate shunt and amino acid/cofactor biosynthetic enzymes.
PLoS ONE 01/2011; 6(2):e17111. · 4.09 Impact Factor
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ABSTRACT: 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.
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.
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.
BMC Bioinformatics 01/2011; 12:115. · 2.75 Impact Factor
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ABSTRACT: Polyamines such as cadaverine, putrescine and spermidine are polycationic molecules that have pleiotropic effects on cells via their interaction with nucleic acids. Streptococcus pneumoniae (the pneumococcus) is a Gram-positive pathogen capable of causing pneumonia, septicaemia, otitis media and meningitis. Pneumococci have a polyamine transport operon (potABCD) responsible for the binding and transport of putrescine and spermidine, and can synthesize cadaverine and spermidine using their lysine decarboxylase (cad) and spermidine synthase (speE) enzymes. Previous studies from our laboratory have shown that an increase in PotD expression is seen following exposure to various stresses, while during infection, potD inactivation significantly attenuates pneumococcal virulence, and anti-PotD immune responses are protective in mice. In spite of their relative importance, not much is known about the global contribution of polyamine biosynthesis and transport pathways to pneumococcal disease. Mutants deficient in polyamine biosynthesis (ΔspeE or Δcad) or transport genes (ΔpotABCD) were constructed and were found to be attenuated in murine models of pneumococcal colonization and pneumonia, either alone or in competition with the wild-type strain. The ΔspeE mutant was also attenuated during invasive disease, while the potABCD and cad genes seemed to be dispensable. HPLC analyses showed reduced intracellular polyamine levels in all mutant strains compared with wild-type bacteria. High-throughput proteomic analyses indicated reduced expression of growth, replication and virulence factors in mutant strains. Thus, polyamine biosynthesis and transport mechanisms are intricately linked to the fitness, survival and pathogenesis of the pneumococcus in host microenvironments, and may represent important targets for prophylactic and therapeutic interventions.
Microbiology 10/2010; 157(Pt 2):504-15. · 3.06 Impact Factor
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ABSTRACT: Abstract
Background
The identification of non-coding transcripts in human, mouse, and Escherichia coli has revealed their widespread occurrence and functional importance in both eukaryotic and prokaryotic life. In prokaryotes, studies have shown that non-coding transcripts participate in a broad range of cellular functions like gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Streptococcus pneumoniae (pneumococcus), an obligate human respiratory pathogen responsible for significant worldwide morbidity and mortality. Tiling microarrays enable genome wide mRNA profiling as well as identification of novel transcripts at a high-resolution.
Results
Here, we describe a high-resolution transcription map of the S. pneumoniae clinical isolate TIGR4 using genomic tiling arrays. Our results indicate that approximately 66% of the genome is expressed under our experimental conditions. We identified a total of 50 non-coding small RNAs (sRNAs) from the intergenic regions, of which 36 had no predicted function. Half of the identified sRNA sequences were found to be unique to S. pneumoniae genome. We identified eight overrepresented sequence motifs among sRNA sequences that correspond to sRNAs in different functional categories. Tiling arrays also identified approximately 202 operon structures in the genome.
Conclusions
In summary, the pneumococcal operon structures and novel sRNAs identified in this study enhance our understanding of the complexity and extent of the pneumococcal 'expressed' genome. Furthermore, the results of this study open up new avenues of research for understanding the complex RNA regulatory network governing S. pneumoniae physiology and virulence.
BMC Genomics. 01/2010;
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ABSTRACT: The identification of non-coding transcripts in human, mouse, and Escherichia coli has revealed their widespread occurrence and functional importance in both eukaryotic and prokaryotic life. In prokaryotes, studies have shown that non-coding transcripts participate in a broad range of cellular functions like gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Streptococcus pneumoniae (pneumococcus), an obligate human respiratory pathogen responsible for significant worldwide morbidity and mortality. Tiling microarrays enable genome wide mRNA profiling as well as identification of novel transcripts at a high-resolution.
Here, we describe a high-resolution transcription map of the S. pneumoniae clinical isolate TIGR4 using genomic tiling arrays. Our results indicate that approximately 66% of the genome is expressed under our experimental conditions. We identified a total of 50 non-coding small RNAs (sRNAs) from the intergenic regions, of which 36 had no predicted function. Half of the identified sRNA sequences were found to be unique to S. pneumoniae genome. We identified eight overrepresented sequence motifs among sRNA sequences that correspond to sRNAs in different functional categories. Tiling arrays also identified approximately 202 operon structures in the genome.
In summary, the pneumococcal operon structures and novel sRNAs identified in this study enhance our understanding of the complexity and extent of the pneumococcal 'expressed' genome. Furthermore, the results of this study open up new avenues of research for understanding the complex RNA regulatory network governing S. pneumoniae physiology and virulence.
BMC Genomics 01/2010; 11:350. · 4.07 Impact Factor
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ABSTRACT: 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.
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).
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.
BMC Bioinformatics 01/2010; 11 Suppl 6:S29. · 2.75 Impact Factor
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ABSTRACT: The utility of a genome sequence in biological research depends entirely on the comprehensive description of all of its functional elements. Analysis of genome sequences is still predominantly gene-centric (i.e., identifying gene models/open reading frames). In this article, we describe a proteomics-based method for identifying open reading frames that are missed by computational algorithms. Mass spectrometry-based identification of peptides and proteins from biological samples provide evidence for the expression of the genome sequence at the protein level. This proteogenomic annotation method combines computationally predicted ORFs and the genome sequence with proteomics to identify novel gene models. We also describe our proteogenomic mapping pipeline - a set of computational tools that automate the proteogenomic annotation work flow. This pipeline is available for download at www.agbase.msstate.edu/tools/ .
Methods in molecular biology (Clifton, N.J.) 01/2010; 604:137-44.
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ABSTRACT: Bovine Viral Diarrhea Virus (BVDV) infection is widespread in cattle worldwide, causing important economic losses. Pathogenesis of the disease caused by BVDV is complex, as each BVDV strain has two biotypes: non-cytopathic (ncp) and cytopathic (cp). BVDV can cause a persistent latent infection and immune suppression if animals are infected with an ncp biotype during early gestation, followed by a subsequent infection of the cp biotype. The molecular mechanisms that underscore the complex disease etiology leading to immune suppression in cattle caused by BVDV are not well understood.
Using proteomics, we evaluated the effect of cp and ncp BVDV infection of bovine monocytes to determine their role in viral immune suppression and uncontrolled inflammation. Proteins were isolated by differential detergent fractionation and identified by 2D-LC ESI MS/MS. We identified 137 and 228 significantly altered bovine proteins due to ncp and cp BVDV infection, respectively. Functional analysis of these proteins using the Gene Ontology (GO) showed multiple under- and over- represented GO functions in molecular function, biological process and cellular component between the two BVDV biotypes. Analysis of the top immunological pathways affected by BVDV infection revealed that pathways representing macropinocytosis signalling, virus entry via endocytic pathway, integrin signalling and primary immunodeficiency signalling were identified only in ncp BVDV-infected monocytes. In contrast, pathways like actin cytoskeleton signalling, RhoA signalling, clathrin-mediated endocytosis signalling and interferon signalling were identified only in cp BDVD-infected cells. Of the six common pathways involved in cp and ncp BVDV infection, acute phase response signalling was the most significant for both BVDV biotypes. Although, most shared altered host proteins between both BVDV biotypes showed the same type of change, integrin alpha 2b (ITGA2B) and integrin beta 3 (ITGB3) were down- regulated by ncp BVDV and up- regulated by cp BVDV infection.
This study shows that, as we expected, there are significant functional differences in the host proteins that respond to cp or ncp BVDV infection. The combined use of GO and systems biology network modelling facilitated a better understanding of host-pathogen interactions.
BMC Bioinformatics 01/2010; 11 Suppl 6:S9. · 2.75 Impact Factor
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ABSTRACT: 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.
BMC Bioinformatics. 01/2010;
