Integrative “Omics”-Approach Discovers Dynamic and Regulatory Features of Bacterial Stress Responses
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{"title"=>"Integrative \"Omics\"-Approach Discovers Dynamic and Regulatory Features of Bacterial Stress Responses", "type"=>"journal", "authors"=>[{"first_name"=>"Bork A.", "last_name"=>"Berghoff", "scopus_author_id"=>"35763618200"}, {"first_name"=>"Anne", "last_name"=>"Konzer", "scopus_author_id"=>"36604573500"}, {"first_name"=>"Nils N.", "last_name"=>"Mank", "scopus_author_id"=>"55375655500"}, {"first_name"=>"Mario", "last_name"=>"Looso", "scopus_author_id"=>"35503631400"}, {"first_name"=>"Tom", "last_name"=>"Rische", "scopus_author_id"=>"55211407600"}, {"first_name"=>"Konrad U.", "last_name"=>"F??rstner", "scopus_author_id"=>"14032724100"}, {"first_name"=>"Marcus", "last_name"=>"Kr??ger", "scopus_author_id"=>"7102550659"}, {"first_name"=>"Gabriele", "last_name"=>"Klug", "scopus_author_id"=>"16137779500"}], "year"=>2013, "source"=>"PLoS Genetics", "identifiers"=>{"pui"=>"369235361", "issn"=>"15537390", "isbn"=>"1553-7390", "doi"=>"10.1371/journal.pgen.1003576", "pmid"=>"23818867", "scopus"=>"2-s2.0-84879639724", "sgr"=>"84879639724"}, "id"=>"6d3ad6d5-18ee-3f8b-803e-0b9a2a4664f4", "abstract"=>"Bacteria constantly face stress conditions and therefore mount specific responses to ensure adaptation and survival. Stress responses were believed to be predominantly regulated at the transcriptional level. In the phototrophic bacterium Rhodobacter sphaeroides the response to singlet oxygen is initiated by alternative sigma factors. Further adaptive mechanisms include post-transcriptional and post-translational events, which have to be considered to gain a deeper understanding of how sophisticated regulation networks operate. To address this issue, we integrated three layers of regulation: (1) total mRNA levels at different time-points revealed dynamics of the transcriptome, (2) mRNAs in polysome fractions reported on translational regulation (translatome), and (3) SILAC-based mass spectrometry was used to quantify protein abundances (proteome). The singlet oxygen stress response exhibited highly dynamic features regarding short-term effects and late adaptation, which could in part be assigned to the sigma factors RpoE and RpoH2 generating distinct expression kinetics of corresponding regulons. The occurrence of polar expression patterns of genes within stress-inducible operons pointed to an alternative of dynamic fine-tuning upon stress. In addition to transcriptional activation, we observed significant induction of genes at the post-transcriptional level (translatome), which identified new putative regulators and assigned genes of quorum sensing to the singlet oxygen stress response. Intriguingly, the SILAC approach explored the stress-dependent decline of photosynthetic proteins, but also identified 19 new open reading frames, which were partly validated by RNA-seq. We propose that comparative approaches as presented here will help to create multi-layered expression maps on the system level (\"expressome\"). Finally, intense mass spectrometry combined with RNA-seq might be the future tool of choice to re-annotate genomes in various organisms and will help to understand how they adapt to alternating conditions.", "link"=>"http://www.mendeley.com/research/integrative-omicsapproach-discovers-dynamic-regulatory-features-bacterial-stress-responses", "reader_count"=>80, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>23, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>4, "Other"=>3, "Student > Master"=>8, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>5, "Researcher"=>23, "Student > Doctoral Student"=>4, "Student > Ph. D. Student"=>27, "Student > Postgraduate"=>4, "Other"=>3, "Student > Master"=>8, "Student > Bachelor"=>4, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Biochemistry, Genetics and Molecular Biology"=>7, "Agricultural and Biological Sciences"=>64, "Computer Science"=>2, "Immunology and Microbiology"=>4}, "reader_count_by_subdiscipline"=>{"Immunology and Microbiology"=>{"Immunology and Microbiology"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>64}, "Computer Science"=>{"Computer Science"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>7}, "Unspecified"=>{"Unspecified"=>3}}, "reader_count_by_country"=>{"Netherlands"=>1, "Argentina"=>1, "China"=>1, "Italy"=>2, "United Kingdom"=>1, "Mexico"=>1, "Slovenia"=>1, "France"=>2, "India"=>1}, "group_count"=>10}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1096314"], "description"=>"<p>Bacterial cultures are subjected to stress (e.g., singlet oxygen, <sup>1</sup>O<sub>2</sub>) and relative changes monitored for mRNAs in total RNA isolates (transcriptome), mRNAs in polysomes (translatome), and for proteins (proteome). For the proteome approach, a culture has to be fully labeled with the heavy amino acid <sup>13</sup>C<sub>6</sub>-lysine (Lys6; heavy standard). Protein samples from a reference (no stress) or stressed cells are separately mixed in a 1∶1 ratio with heavy standard protein and subsequently applied to protein digest followed by MS analysis. SILAC ratios are calculated by comparing intensities of heavy (red) to light (green) peaks of individual peptides (m, mass and z, charge). Direct ratios, reflecting relative changes, are determined thereof. The translatome is assessed by microarray analysis of RNA from polysome fractions. Polysomes are enriched by sucrose density gradient centrifugation of cells that have been treated with chloramphenicol. For transcriptome data, total RNA is isolated and applied to microarray analysis.</p>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology", "comparative", "bacterial"], "article_id"=>727638, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Konrad U. Förstner", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Design_of_a_comparative_8220_omics_8221_approach_for_bacterial_stress_responses_/727638", "title"=>"Design of a comparative “omics”-approach for bacterial stress responses.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 02:07:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/1096317"], "description"=>"<p>Relative changes of mRNAs upon singlet oxygen (<sup>1</sup>O<sub>2</sub>) stress were monitored by microarray analysis of total RNA at time-points 7, 45, and 90 min and depicted as log<sub>2</sub> ratios. (A) All mRNAs with significant induction (log<sub>2</sub> ratio ≥0.8 and p-value <0.05) at one of the three time-points were applied to clustering using MeV (Multi Experiment Viewer version 4.7.4) from the TM4 Microarray Software Suite <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576-Saeed1\" target=\"_blank\">[67]</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576-Saeed2\" target=\"_blank\">[68]</a>. Clustering was based on k-means (KMC method) according to Euclidean distance with a maximum of 50 iterations. Changes were illustrated as heat-maps with a color code ranging from −0.5 (green) to 1.5 (red) log<sub>2</sub> ratio. Columns represent time-points of the stress response and rows represent individual genes. (B–F) Up-regulated mRNAs shown in (A) were grouped together according to their function. Expression kinetics, representing the mean of log<sub>2</sub> ratios, were calculated for functional groups of cluster 1 (B), cluster 2a (C), cluster 2b (D), cluster 2c (E), and cluster 3 (F). (G) Analysis of stress-induced sRNAs. Total RNA was isolated at the indicated time-points and applied to Northern blot analysis. RSs0019, RSs0680a, and RSs0827 were hybridized to radioactively labeled oligonucleotides and visualized by phosphoimaging. 5S rRNA was probed as a control for equal loading of samples (not shown). (H) Genes with described RpoE- and RpoH2-dependent promoters <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576-Nuss1\" target=\"_blank\">[28]</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576-Nuss2\" target=\"_blank\">[29]</a>, <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576-Ziegelhoffer1\" target=\"_blank\">[72]</a> were analyzed when they exhibited log<sub>2</sub> ratios ≥0.8. The curves represent the mean of log<sub>2</sub> ratios based on 12 mRNAs (RpoE, black line) and 42 mRNAs (RpoH2, red line). See supplementary <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576.s009\" target=\"_blank\">Tables S1</a> and <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576.s010\" target=\"_blank\">S2</a> for further information on regulated genes and their particular functions.</p>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology", "kinetics", "stress-related", "mrnas"], "article_id"=>727641, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Konrad U. Förstner", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Expression_kinetics_of_stress_related_mRNAs_and_sRNAs_/727641", "title"=>"Expression kinetics of stress-related mRNAs and sRNAs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 02:07:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/1096320"], "description"=>"<p>Relative changes in protein abundance after 90 min of singlet oxygen stress were determined by an indirect quantification approach using a heavy standard labeled with <sup>13</sup>C<sub>6</sub>-lysine (Lys6). Protein mixtures were digested and used for MS analysis (see <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#s4\" target=\"_blank\">Materials and Methods</a>). In the central volcano plot the direct ratios (log<sub>2</sub>) of 1214 quantified proteins of quadruplicates were plotted against negative logarithmized p-values (log<sub>10</sub>). The histogram on the top shows log<sub>2</sub> protein ratio distributions (Gaussian distribution). Up- and down-regulated proteins were grouped according to their functions which relate to stress defense (red triangles), proteases (black triangles), redox reactions (dark blue triangles), carbohydrate metabolism (grey triangles), transport processes (light blue triangles), photosynthesis (green triangles), and motility/chemotaxis (purple triangles). See supplementary <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576.s011\" target=\"_blank\">Table S3</a> for further information on regulated proteins and their particular functions.</p>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology", "quantification"], "article_id"=>727644, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Konrad U. Förstner", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Accurate_quantification_of_protein_changes_by_SILAC_/727644", "title"=>"Accurate quantification of protein changes by SILAC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 02:07:24"}
  • {"files"=>["https://ndownloader.figshare.com/files/1096321"], "description"=>"<p>ORFs detected by SILAC-based MS were compared to deep sequencing data (RNA-seq). (A–H) Integrated Genome Browser (Affymetrix) screenshots depict gene loci with nucleotide positions referring to the plus-strand. RNA-seq data are visualized as numbers of cDNA reads per nucleotide (red plots). Grey and blue arrows represent annotated and new ORFs, respectively. For further details on new ORFs see <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576.s012\" target=\"_blank\">Table S4</a>.</p>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology", "validation", "orfs"], "article_id"=>727645, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Konrad U. Förstner", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_RNA_seq_validation_for_ORFs_newly_identified_by_SILAC_/727645", "title"=>"RNA-seq validation for ORFs newly identified by SILAC.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 02:07:25"}
  • {"files"=>["https://ndownloader.figshare.com/files/1096322"], "description"=>"<p>Scatter-plots represent pairwise comparisons of log<sub>2</sub> ratios between (A) transcriptome (Total 90/0 min) and translatome (Poly 90/0 min), (B) proteome (SILAC 90/0 min) and transcriptome, (C) proteome and translatome. Number (n), Pearson correlation (<i>r</i>), and squared correlation (<i>r<sup>2</sup></i>) of shared features are given for every comparison. It is indicated for up-regulated (log<sub>2</sub> ratio ≥0.8) and down-regulated (log<sub>2</sub> ratio ≤−0.8) features whether changes are unidirectional (log<sub>2</sub> ratio difference between approaches <0.4, green spots) or biased (log<sub>2</sub> ratio difference ≥0.4, red spots). (D) Functional grouping of genes that were newly identified to be stress-responsive by the integrative approach. For a complete list of genes and information on their function see <a href=\"http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1003576#pgen.1003576.s013\" target=\"_blank\">Table S5</a>.</p>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology"], "article_id"=>727646, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Konrad U. Förstner", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_between_global_approaches_/727646", "title"=>"Correlation between global approaches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 02:07:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/1096323"], "description"=>"<p>Graphs representing relative changes for selected operons after 90 min of singlet oxygen stress. Log<sub>2</sub> ratios were derived from transcriptomic (Total 90 min, black bars), translatomic (Poly 90 min, red bars), and proteomic (SILAC 90 min, blue bars) data sets. Orientation of genes within operons is depicted from the left to the right, irrespective of their location in the genome, with the leftmost gene representing the first gene in the operon. Gene numbers refer to corresponding RSP-numbers. Operons were selected according to following categories: transport process and iron metabolism (A), RpoE-dependent (B, C, D), stress defense (C), transport process (D), chaperone (E), RpoH2-dependent (F, G), carbohydrate metabolism (F), redox reaction (G), protease (H).</p>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology", "stress-induced"], "article_id"=>727647, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Konrad U. Förstner", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Polarity_of_stress_induced_operons_/727647", "title"=>"Polarity of stress-induced operons.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-06-20 02:07:27"}
  • {"files"=>["https://ndownloader.figshare.com/files/1096324"], "description"=>"1<p>The annotated <i>R. sphaeroides</i> 2.4.1 genome (<a href=\"http://img.jgi.doe.gov/cgi-bin/w/main.cgi\" target=\"_blank\">http://img.jgi.doe.gov/cgi-bin/w/main.cgi</a>) contains 4304 open reading frames. Numbers of mRNAs/proteins that were unambiguously quantified are depicted.</p>2<p>Numbers (#) and percentages (%) for regulated genes are given. Percentages were calculated relative to quantified mRNAs/proteins. Genes were considered to be regulated when log<sub>2</sub> ratios (stress versus reference) were ≥0.8 (up-regulated) or ≤−0.8 (down-regulated) with p-values <0.05.</p>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology", "quantified", "regulated", "genes"], "article_id"=>727648, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Konrad U. Förstner", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_quantified_and_regulated_genes_in_individual_approaches_/727648", "title"=>"Number of quantified and regulated genes in individual approaches.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-06-20 02:07:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/1096325", "https://ndownloader.figshare.com/files/1096327", "https://ndownloader.figshare.com/files/1096328", "https://ndownloader.figshare.com/files/1096331", "https://ndownloader.figshare.com/files/1096335", "https://ndownloader.figshare.com/files/1096338", "https://ndownloader.figshare.com/files/1096340", "https://ndownloader.figshare.com/files/1096349", "https://ndownloader.figshare.com/files/1096353", "https://ndownloader.figshare.com/files/1096355", "https://ndownloader.figshare.com/files/1096356", "https://ndownloader.figshare.com/files/1096357", "https://ndownloader.figshare.com/files/1096360"], "description"=>"<div><p>Bacteria constantly face stress conditions and therefore mount specific responses to ensure adaptation and survival. Stress responses were believed to be predominantly regulated at the transcriptional level. In the phototrophic bacterium <i>Rhodobacter sphaeroides</i> the response to singlet oxygen is initiated by alternative sigma factors. Further adaptive mechanisms include post-transcriptional and post-translational events, which have to be considered to gain a deeper understanding of how sophisticated regulation networks operate. To address this issue, we integrated three layers of regulation: (1) total mRNA levels at different time-points revealed dynamics of the transcriptome, (2) mRNAs in polysome fractions reported on translational regulation (translatome), and (3) SILAC-based mass spectrometry was used to quantify protein abundances (proteome). The singlet oxygen stress response exhibited highly dynamic features regarding short-term effects and late adaptation, which could in part be assigned to the sigma factors RpoE and RpoH2 generating distinct expression kinetics of corresponding regulons. The occurrence of polar expression patterns of genes within stress-inducible operons pointed to an alternative of dynamic fine-tuning upon stress. In addition to transcriptional activation, we observed significant induction of genes at the post-transcriptional level (translatome), which identified new putative regulators and assigned genes of quorum sensing to the singlet oxygen stress response. Intriguingly, the SILAC approach explored the stress-dependent decline of photosynthetic proteins, but also identified 19 new open reading frames, which were partly validated by RNA-seq. We propose that comparative approaches as presented here will help to create multi-layered expression maps on the system level (“expressome”). Finally, intense mass spectrometry combined with RNA-seq might be the future tool of choice to re-annotate genomes in various organisms and will help to understand how they adapt to alternating conditions.</p></div>", "links"=>[], "tags"=>["genetics", "Molecular genetics", "Gene regulation", "gene expression", "genomics", "Genome expression analysis", "microbiology", "Microbial physiology", "Model organisms", "Prokaryotic models", "Molecular cell biology", "Cellular stress responses", "proteomics", "Protein abundance", "Spectrometric identification of proteins", "systems biology", "discovers", "bacterial"], "article_id"=>727649, "categories"=>["Biological Sciences"], "users"=>["Bork A. Berghoff", "Konrad U. Förstner", "Anne Konzer", "Nils N. Mank", "Mario Looso", "Tom Rische", "Marcus Krüger", "Gabriele Klug"], "doi"=>["https://dx.doi.org/10.1371/journal.pgen.1003576.s001", "https://dx.doi.org/10.1371/journal.pgen.1003576.s002", "https://dx.doi.org/10.1371/journal.pgen.1003576.s003", "https://dx.doi.org/10.1371/journal.pgen.1003576.s004", "https://dx.doi.org/10.1371/journal.pgen.1003576.s005", "https://dx.doi.org/10.1371/journal.pgen.1003576.s006", "https://dx.doi.org/10.1371/journal.pgen.1003576.s007", "https://dx.doi.org/10.1371/journal.pgen.1003576.s008", "https://dx.doi.org/10.1371/journal.pgen.1003576.s009", "https://dx.doi.org/10.1371/journal.pgen.1003576.s010", "https://dx.doi.org/10.1371/journal.pgen.1003576.s011", "https://dx.doi.org/10.1371/journal.pgen.1003576.s012", "https://dx.doi.org/10.1371/journal.pgen.1003576.s013"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Integrative_8220_Omics_8221_Approach_Discovers_Dynamic_and_Regulatory_Features_of_Bacterial_Stress_Responses_/727649", "title"=>"Integrative “Omics”-Approach Discovers Dynamic and Regulatory Features of Bacterial Stress Responses", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2013-06-20 02:07:29"}

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  • {"unique-ip"=>"7", "full-text"=>"8", "pdf"=>"1", "abstract"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"7", "supp-data"=>"8", "cited-by"=>"0", "year"=>"2017", "month"=>"6"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences/Computational biology", "average_usage"=>[295, 511, 651, 775, 882, 992, 1100, 1201, 1304, 1400, 1486, 1570, 1650]}]}
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