The Landscape of Host Transcriptional Response Programs Commonly Perturbed by Bacterial Pathogens: Towards Host-Oriented Broad-Spectrum Drug Targets
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{"title"=>"The Landscape of Host Transcriptional Response Programs Commonly Perturbed by Bacterial Pathogens: Towards Host-Oriented Broad-Spectrum Drug Targets", "type"=>"journal", "authors"=>[{"first_name"=>"Yared H.", "last_name"=>"Kidane", "scopus_author_id"=>"55872402100"}, {"first_name"=>"Christopher", "last_name"=>"Lawrence", "scopus_author_id"=>"7402265726"}, {"first_name"=>"T. M.", "last_name"=>"Murali", "scopus_author_id"=>"7004135100"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0058553", "pui"=>"368518444", "sgr"=>"84874910098", "scopus"=>"2-s2.0-84874910098", "pmid"=>"23516507", "issn"=>"19326203"}, "id"=>"4d94a748-fc7f-385b-9831-9877d7dbc7b1", "abstract"=>"BACKGROUND: The emergence of drug-resistant pathogen strains and new infectious agents pose major challenges to public health. A promising approach to combat these problems is to target the host's genes or proteins, especially to discover targets that are effective against multiple pathogens, i.e., host-oriented broad-spectrum (HOBS) drug targets. An important first step in the discovery of such drug targets is the identification of host responses that are commonly perturbed by multiple pathogens.\\n\\nRESULTS: In this paper, we present a methodology to identify common host responses elicited by multiple pathogens. First, we identified host responses perturbed by each pathogen using a gene set enrichment analysis of publicly available genome-wide transcriptional datasets. Then, we used biclustering to identify groups of host pathways and biological processes that were perturbed only by a subset of the analyzed pathogens. Finally, we tested the enrichment of each bicluster in human genes that are known drug targets, on the basis of which we elicited putative HOBS targets for specific groups of bacterial pathogens. We identified 84 up-regulated and three down-regulated statistically significant biclusters. Each bicluster contained a group of pathogens that commonly dysregulated a group of biological processes. We validated our approach by checking whether these biclusters correspond to known hallmarks of bacterial infection. Indeed, these biclusters contained biological process such as inflammation, activation of dendritic cells, pro- and anti- apoptotic responses and other innate immune responses. Next, we identified biclusters containing pathogens that infected the same tissue. After a literature-based analysis of the drug targets contained in these biclusters, we suggested new uses of the drugs Anakinra, Etanercept, and Infliximab for gastrointestinal pathogens Yersinia enterocolitica, Helicobacter pylori kx2 strain, and enterohemorrhagic Escherichia coli and the drug Simvastatin for hematopoietic pathogen Ehrlichia chaffeensis.\\n\\nCONCLUSIONS: Using a combination of automated analysis of host-response gene expression data and manual study of the literature, we have been able to suggest host-oriented treatments for specific bacterial infections. The analyses and suggestions made in this study may be utilized to generate concrete hypothesis on which gene sets to probe further in the quest for HOBS drug targets for bacterial infections. All our results are available at the following supplementary website: http://bioinformatics.cs.vt.edu/ murali/supplements/2013-kidane-plos-one.", "link"=>"http://www.mendeley.com/research/landscape-host-transcriptional-response-programs-commonly-perturbed-bacterial-pathogens-towards-host", "reader_count"=>18, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>3, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>2, "Student > Doctoral Student"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>8, "Student > Ph. D. Student"=>3, "Other"=>1, "Student > Master"=>1, "Student > Bachelor"=>2, "Student > Doctoral Student"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>4, "Agricultural and Biological Sciences"=>6, "Medicine and Dentistry"=>2, "Chemistry"=>2, "Immunology and Microbiology"=>2}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>6}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Mexico"=>1}, "group_count"=>1}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/986286"], "description"=>"<p>Overview of our computational system to compute host-oriented broad-spectrum drug targets. (A) Obtaining relevant collection of taxonomic names for human bacterial pathogens. Querying the GEO metadatabase in search of relevant transcriptional datasets. (B) Gene Set Enrichment Analysis of the transcriptional datasets collected in Step A. (C) Identification of pathogen-gene set biclusters and estimation of statistical significance of biclusters (D) Testing bicluster enrichment for known drug targets. (E) Literature analysis of putative HOBS drug targets contained in biclusters.</p>", "links"=>[], "tags"=>["genetics and genomics", "immunology", "Infectious diseases", "Computational biology", "pharmacology"], "article_id"=>651408, "categories"=>["Pharmacology", "Biological Sciences", "Genetics", "Infectious Diseases", "Immunology"], "users"=>["Yared H. Kidane", "Christopher Lawrence", "T. M. Murali"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058553.g001", "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Overview_of_our_system_/651408", "title"=>"Overview of our system.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-14 10:28:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/1006974"], "description"=>"<p>For each gene set, the table shows the number of pathogens that perturb it and the number of biclusters it appears in.</p>", "links"=>[], "tags"=>["sets", "perturbed"], "article_id"=>667602, "categories"=>["Pharmacology", "Biological Sciences", "Genetics", "Infectious Diseases", "Immunology"], "users"=>["Yared H. Kidane", "Christopher Lawrence", "T. M. Murali"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058553.t001", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gene_sets_perturbed_in_many_pathogens_/667602", "title"=>"Gene sets perturbed in many pathogens.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-13 02:06:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/1006992"], "description"=>"<p>The table shows top three GO biological processes that have the highest overlap with each of the ten most frequently perturbed gene sets (in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058553#pone-0058553-t001\" target=\"_blank\">Table 1</a>). The -value indicates the statistical significance of the overlap, based on Fisher's exact test.</p>", "links"=>[], "tags"=>["sets"], "article_id"=>667619, "categories"=>["Pharmacology", "Biological Sciences", "Genetics", "Infectious Diseases", "Immunology"], "users"=>["Yared H. Kidane", "Christopher Lawrence", "T. M. Murali"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058553.t002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Mapping_of_Gene_Sets_to_GO_Biological_Processes_/667619", "title"=>"Mapping of Gene Sets to GO Biological Processes.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-13 02:06:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/986289"], "description"=>"<p>Pathogens that perturb the “Seki Inflammatory Response LPS up” gene set. The second column contains the -values as well as a color indicating the magnitude of the -value. <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058553#pone-0058553-g003\" target=\"_blank\">Figure 3</a> contains the legend mapping -values to colors. All pathogens up-regulate this gene set, except <i>Streptococcus gordonii</i>, which down-regulates it.</p>", "links"=>[], "tags"=>["perturb", "inflammatory", "lps"], "article_id"=>651411, "categories"=>["Pharmacology", "Biological Sciences", "Genetics", "Infectious Diseases", "Immunology"], "users"=>["Yared H. Kidane", "Christopher Lawrence", "T. M. Murali"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058553.g002", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pathogens_that_perturb_the_8220_Seki_Inflammatory_Response_LPS_up_8221_gene_set_/651411", "title"=>"Pathogens that perturb the “Seki Inflammatory Response LPS up” gene set.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-14 10:28:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/986291"], "description"=>"<p>Dendrogram of hierarchical clustering of gene sets for three tissue-specific biclusters. (A) <i>Yersinia enterocolitica</i> wap and p60 strains, <i>Helicobacter pylori</i> kx2 strain, and enterohemorrhagic <i>Escherichia coli</i>. (B) <i>Pseudomonas aeruginosa</i> and <i>Mycobacterium tuberculosis</i>. (C) <i>E.chaffeensis</i> Arkansa and Wakulla strains. The figure only shows gene sets that contain one or more known human drug targets.</p>", "links"=>[], "tags"=>["hierarchical", "clustering", "sets", "tissue-specific"], "article_id"=>651413, "categories"=>["Pharmacology", "Biological Sciences", "Genetics", "Infectious Diseases", "Immunology"], "users"=>["Yared H. Kidane", "Christopher Lawrence", "T. M. Murali"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058553.g003", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dendrogram_of_hierarchical_clustering_of_gene_sets_for_three_tissue_specific_biclusters_/651413", "title"=>"Dendrogram of hierarchical clustering of gene sets for three tissue-specific biclusters.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-14 10:28:51"}
  • {"files"=>["https://ndownloader.figshare.com/files/986294", "https://ndownloader.figshare.com/files/986296", "https://ndownloader.figshare.com/files/986297", "https://ndownloader.figshare.com/files/986298", "https://ndownloader.figshare.com/files/986299", "https://ndownloader.figshare.com/files/986300"], "description"=>"<div><p>Background</p><p>The emergence of drug-resistant pathogen strains and new infectious agents pose major challenges to public health. A promising approach to combat these problems is to target the host’s genes or proteins, especially to discover targets that are effective against multiple pathogens, i.e., host-oriented broad-spectrum (HOBS) drug targets. An important first step in the discovery of such drug targets is the identification of host responses that are commonly perturbed by multiple pathogens.</p> <p>Results</p><p>In this paper, we present a methodology to identify common host responses elicited by multiple pathogens. First, we identified host responses perturbed by each pathogen using a gene set enrichment analysis of publicly available genome-wide transcriptional datasets. Then, we used biclustering to identify groups of host pathways and biological processes that were perturbed only by a subset of the analyzed pathogens. Finally, we tested the enrichment of each bicluster in human genes that are known drug targets, on the basis of which we elicited putative HOBS targets for specific groups of bacterial pathogens. We identified 84 up-regulated and three down-regulated statistically significant biclusters. Each bicluster contained a group of pathogens that commonly dysregulated a group of biological processes. We validated our approach by checking whether these biclusters correspond to known hallmarks of bacterial infection. Indeed, these biclusters contained biological process such as inflammation, activation of dendritic cells, pro- and anti- apoptotic responses and other innate immune responses. Next, we identified biclusters containing pathogens that infected the same tissue. After a literature-based analysis of the drug targets contained in these biclusters, we suggested new uses of the drugs Anakinra, Etanercept, and Infliximab for gastrointestinal pathogens <i>Yersinia enterocolitica</i>, <i>Helicobacter pylori</i> kx2 strain, and enterohemorrhagic <i>Escherichia coli</i> and the drug Simvastatin for hematopoietic pathogen <i>Ehrlichia chaffeensis</i>.</p> <p>Conclusions</p><p>Using a combination of automated analysis of host-response gene expression data and manual study of the literature, we have been able to suggest host-oriented treatments for specific bacterial infections. The analyses and suggestions made in this study may be utilized to generate concrete hypothesis on which gene sets to probe further in the quest for HOBS drug targets for bacterial infections. All our results are available at the following supplementary website: <a href=\"http://bioinformatics.cs.vt.edu/\" target=\"_blank\">http://bioinformatics.cs.vt.edu/</a> murali/supplements/2013-kidane-plos-one</p> </div>", "links"=>[], "tags"=>["transcriptional", "programs", "perturbed", "bacterial", "host-oriented", "broad-spectrum", "targets"], "article_id"=>651416, "categories"=>["Pharmacology", "Biological Sciences", "Genetics", "Infectious Diseases", "Immunology"], "users"=>["Yared H. Kidane", "Christopher Lawrence", "T. M. Murali"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0058553.s001", "https://dx.doi.org/10.1371/journal.pone.0058553.s002", "https://dx.doi.org/10.1371/journal.pone.0058553.s003", "https://dx.doi.org/10.1371/journal.pone.0058553.s004", "https://dx.doi.org/10.1371/journal.pone.0058553.s005", "https://dx.doi.org/10.1371/journal.pone.0058553.s006"], "stats"=>{"downloads"=>13, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Landscape_of_Host_Transcriptional_Response_Programs_Commonly_Perturbed_by_Bacterial_Pathogens_Towards_Host_Oriented_Broad_Spectrum_Drug_Targets__/651416", "title"=>"The Landscape of Host Transcriptional Response Programs Commonly Perturbed by Bacterial Pathogens: Towards Host-Oriented Broad-Spectrum Drug Targets", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-03-14 10:29:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1007001"], "description"=>"<p>The table shows the biclusters that contained pathogens that cause an infection in a single type of tissue. The columns from left to right are: (i) list of pathogens contained in a bicluster, (ii) a -value indicating the statistical significance of the bicluster, (iii) the number of gene sets in the bicluster, (iv) the number of known human drug target genes/proteins in the bicluster, and (v) -value indicating the enrichment of the bicluster in know human drug-target genes/proteins.</p>", "links"=>[], "tags"=>["divided"], "article_id"=>667635, "categories"=>["Pharmacology", "Biological Sciences", "Genetics", "Infectious Diseases", "Immunology"], "users"=>["Yared H. Kidane", "Christopher Lawrence", "T. M. Murali"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0058553.t003", "stats"=>{"downloads"=>0, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Biclusters_divided_by_kind_of_infection_/667635", "title"=>"Biclusters divided by kind of infection.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-13 02:07:15"}

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Relative Metric

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