Distinct Peripheral Blood RNA Responses to Salmonella in Pigs Differing in Salmonella Shedding Levels: Intersection of IFNG, TLR and miRNA Pathways

Transcriptomic analysis of the response to bacterial pathogens has been reported for several species, yet few studies have investigated the transcriptional differences in whole blood in subjects that differ in their disease response phenotypes. Salmonella species infect many vertebrate species, and pigs colonized with Salmonella enterica serovar Typhimurium (ST) are usually asymptomatic, making detection of these Salmonella-carrier pigs difficult. The variable fecal shedding of Salmonella is an important cause of foodborne illness and zoonotic disease. To investigate gene pathways and biomarkers associated with the variance in Salmonella shedding following experimental inoculation, we initiated the first analysis of the whole blood transcriptional response induced by Salmonella. A population of pigs (n = 40) was inoculated with ST and peripheral blood and fecal Salmonella counts were collected between 2 and 20 days post-inoculation (dpi). Two groups of pigs with either low shedding (LS) or persistent shedding (PS) phenotypes were identified. Global transcriptional changes in response to ST inoculation were identified by Affymetrix Genechip® analysis of peripheral blood RNA at day 0 and 2 dpi. ST inoculation triggered substantial gene expression changes in the pigs and there was differential expression of many genes between LS and PS pigs. Analysis of the differential profiles of gene expression within and between PS and LS phenotypic classes identified distinct regulatory pathways mediated by IFN-γ, TNF, NF-κB, or one of several miRNAs. We confirmed the activation of two regulatory factors, SPI1 and CEBPB, and demonstrated that expression of miR-155 was decreased specifically in the PS animals. These data provide insight into specific pathways associated with extremes in Salmonella fecal shedding that can be targeted for further exploration on why some animals develop a carrier state. This knowledge can also be used to develop rational manipulations of genetics, pharmaceuticals, nutrition or husbandry methods to decrease Salmonella colonization, shedding and spread

Late Multiple Organ Surge in Interferon-Regulated Target Genes Characterizes Staphylococcal Enterotoxin B Lethality

<div><p>Background</p><p>Bacterial superantigens are virulence factors that cause toxic shock syndrome. Here, the genome-wide, temporal response of mice to lethal intranasal staphylococcal enterotoxin B (SEB) challenge was investigated in six tissues.</p><p>Results</p><p>The earliest responses and largest number of affected genes occurred in peripheral blood mononuclear cells (PBMC), spleen, and lung tissues with the highest content of both T-cells and monocyte/macrophages, the direct cellular targets of SEB. In contrast, the response of liver, kidney, and heart was delayed and involved fewer genes, but revealed a dominant genetic program that was seen in all 6 tissues. Many of the 85 uniquely annotated transcripts participating in this shared genomic response have not been previously linked to SEB. Nine of the 85 genes were subsequently confirmed by RT-PCR in every tissue/organ at 24 h. These 85 transcripts, up-regulated in all tissues, annotated to the interferon (IFN)/antiviral-response and included genes belonging to the DNA/RNA sensing system, DNA damage repair, the immunoproteasome, and the ER/metabolic stress-response and apoptosis pathways. Overall, this shared program was identified as a type I and II interferon (IFN)-response and the promoters of these genes were highly enriched for IFN regulatory matrices. Several genes whose secreted products induce the IFN pathway were up-regulated at early time points in PBMCs, spleen, and/or lung. Furthermore, IFN regulatory factors including Irf1, Irf7 and Irf8, and Zbp1, a DNA sensor/transcription factor that can directly elicit an IFN innate immune response, participated in this host-wide SEB signature.</p><p>Conclusion</p><p>Global gene-expression changes across multiple organs implicated a host-wide IFN-response in SEB-induced death. Therapies aimed at IFN-associated innate immunity may improve outcome in toxic shock syndromes.</p></div

Top eleven promoter regulatory matrices identified using the F-match module in ExPlain 3.1 (BioBase Knowledge Library) (see Methods).

a<p>Ratio of the abundance of each promoter matrix in genes differentially regulated across all six tissues compared to 492 mouse housekeeping genes (see Methods).</p>b<p>Prdm1 (Blimp1), a transcriptional repressor essential for B- and T-cell differentiation and homeostasis, is regulated by Irf4. Prdm1 and interferon regulatory factors bind to similar DNA sequences. Some promoters contain overlapping motifs where Prdm1 and Irf family members may competitively interact.</p

Pulmonary pathology: hematoxylin and eosin (H&E) stain, TUNEL assay and immunohistochemistry staining for nitrotyrosine and polyADP-ribose.

<p>Compared to control animals at 24 h, staphylococcal enterotoxin B (SEB) challenge caused a multifocal, minimal to mild perivascular, peribronchiolar, interstitial and subpleural lymphohistiocytic inflammatory infiltrate. At 48 h a coalescing, neutrophil-predominant infiltrate was seen in SEB exposed animals that now extended into alveoli. Multiple vessel walls 48 h after SEB exposure contained neutrophilic fragments (arrows) consistent with vasculitis (H&E inset, SEB 48 h). Terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assay demonstrated an increase in bronchiolar apoptotic cells (arrows) after SEB challenge compared to control that was significant at 24 h post-exposure (2.93±0.12 <i>versus</i> 0.06±0.06 cells/HPF; <i>p<0.001</i>). Immunohistochemistry for nitrotyrosine was not different comparing SEB to control with all specimens showing faint staining (arrows) of alveolar epithelium, small vessel endothelium and alveolar macrophages. In contrast, immunohistochemistry for polyADP-ribose (PAR) showed increased staining associated with SEB exposure that was mostly proportional to the increase in inflammatory cellularity. At 48 h, hypertrophied alveolar epithelial cells (arrows) stained prominently for PAR.</p

Thematic analysis, interferon (IFN) response subtype classification, and promoter analysis for binding matrices responsive to IFN.

<p>(A) Canonical pathways significantly associated with the all-tissue response to staphylococcal enterotoxin B (SEB) challenge. Seventy-nine unique genes were recognized by the Ingenuity Pathway Analysis® (IPA®) database and mapped to IFN signaling, antigen presentation, and activation of IFN regulatory factor (IRF) by cytosolic pattern recognition receptors, among the other canonical pathways shown. (B) Classification of genes significantly up-regulated across all tissues by IFN response subtype. Note that for <i>Mus musculus</i>, the Interferome v2.01 database contained 1655 Type I genes, 1413 Type II genes, and no Type III genes. (C) IFN pathway-driven regulatory binding sites identified in the promoters of genes regulated across all tissues. Of 81 promoter regions analyzed (from +500 to −1500 bp), 68 were found to contain IFN-driven regulatory matrices as shown. Results generated by Interferome v2.01 using TRANSFAC® Professional (2012) matrices and the MATCH™ algorithm.</p

Functional network of selected upstream-regulators and differentially expressed genes across all tissues.

<p>From among the significant nodes identified using the Ingenuity Pathway Analysis® (IPA®) Upstream Regulator tool, the following were selected for inclusion in the displayed network: 1) the T-cell receptor (TCR), as this is the primary target of staphylococcal enterotoxin B (SEB)-mediated cell activation (colored orange at the network center); 2) TNF, IL-1β, IL-2, IFNγ and IL-12B, as these are known interferon (IFN) pathway initiators that were expressed early in the peripheral blood mononuclear cells and/or spleens of the SEB challenged mice (colored blue and positioned as the inner most ring of the network); and 3) any upstream regulator that was also present on our all-tissue list of differentially expressed genes (colored in shades of red proportional to fold-change) and positioned as the next ring moving outward. The resulting network connected 70 of 79 genes recognized by IPA®. The remaining 9 genes (outside of the outermost ring) were connected manually (see text) using PubMed and STRING (<a href="http://string-db.org/newstring_cgi/" target="_blank">http://string-db.org/newstring_cgi/</a>) version 9.05, a database of known and predicted protein-protein interactions. A key defining colors, shapes, and relationships is shown. In addition, changes in gene symbols from those in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088756#pone-0088756-g001" target="_blank">Figure 1</a> and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088756#pone-0088756-t003" target="_blank">Table 3</a> are provided for clarity. Also note that IPA® frequently defaults to all-capital gene symbols that denote human genes, while elsewhere the mouse format is followed of only capitalizing the first letter.</p

Tissue-specific parallel plots of the 103 probesets that met selection criteria.

<p>Expression levels were normalized to the time 0 h control condition to emphasize change over time from baseline. Probesets with peak expression before 24 h in any tissue are displayed in red. Notably, 12 probesets representing 11 unique genes (Cxcl9, Cxcl10, Cxcl11, Cd274, Fam26f, Irf1, Irf8, Irgm2, Parp14, Serpina3g, and Stat1) peaked at 5 h post-staphylococcal enterotoxin B (SEB) challenge in PBMCs and/or spleen as indicated. Gene symbols (in red) are displayed vertically from highest to lowest fold-change at 5 h.</p

Eighty-five annotated genes were up-regulated in all six tissues.

<p>Genes are ordered by maximum fold change from baseline. For two or more probesets annotated to the exact same gene and Entrez ID, results are only shown for the probeset with the largest fold-change. Three probable duplicate entries, with tentative annotations and more than one Entrez ID number, are designated (a, b, c, respectively), leaving a total of 82 unique genes. Tissue and time in hours of maximum fold changes are shown. Multiple probesets for the same gene occasionally showed more than two fold differences in maximum fold-change, but did not differ by tissue or time point of peak effects.</p

Heatmap of 103 probesets differentially regulated in all tissues.

<p>Probesets (≤5% FDR; ≥1.5-fold-change compared to control; and ≥50% present call within at least one condition/time point, across all tissues) are displayed on the vertical axis and designated, when available, by the symbol of the gene to which each is annotated, including duplicates. Tissue and time points are denoted on the horizontal axis. Each probeset has been normalized to its mean value across all times and tissues within one row. Red signifies expression above and green below the mean value within an individual row. As shown, baseline expression of these differentially expressed transcripts tends to decrease from PMBC > Spleen > Lung > Liver > Kidney, Heart. In contrast, all of these genes are induced by staphylococcal enterotoxin B (SEB) challenge with most reaching their highest levels of expression at 24 h across all tissues. ^aThree unannotated probesets, identified only by Affymetrix® probeset IDs; ^bPredicted gene Gm9706 of unknown function; second probeset annotated to Gm9706 is also annotated to the gene symbol Isg15; while these probesets do not cluster together, peak expression for both were seen in PBMCs at 24 h, suggesting that they may interrogate the same gene, but with different efficiencies; ^cProbably detecting Gbp6 with which it clusters, but this probeset retains its annotation to both Gbp10 and Gbp6 as shown; ^dProbably detecting Ifi202b with which it clusters, but this probeset retains its annotation to both LOC100044068 and Ifi202b as shown; ^eProbably detecting Gbp1, but this probeset retains its annotation to both LOC100047734 and Gbp1 as shown.</p

Expression quantitative trait methylation analysis elucidates gene regulatory effects of DNA methylation: the Framingham Heart Study

Abstract Expression quantitative trait methylation (eQTM) analysis identifies DNA CpG sites at which methylation is associated with gene expression. The present study describes an eQTM resource of CpG-transcript pairs derived from whole blood DNA methylation and RNA sequencing gene expression data in 2115 Framingham Heart Study participants. We identified 70,047 significant cis CpG-transcript pairs at p < 1E−7 where the top most significant eGenes (i.e., gene transcripts associated with a CpG) were enriched in biological pathways related to cell signaling, and for 1208 clinical traits (enrichment false discovery rate [FDR] ≤ 0.05). We also identified 246,667 significant trans CpG-transcript pairs at p < 1E−14 where the top most significant eGenes were enriched in biological pathways related to activation of the immune response, and for 1191 clinical traits (enrichment FDR ≤ 0.05). Independent and external replication of the top 1000 significant cis and trans CpG-transcript pairs was completed in the Women’s Health Initiative and Jackson Heart Study cohorts. Using significant cis CpG-transcript pairs, we identified significant mediation of the association between CpG sites and cardiometabolic traits through gene expression and identified shared genetic regulation between CpGs and transcripts associated with cardiometabolic traits. In conclusion, we developed a robust and powerful resource of whole blood eQTM CpG-transcript pairs that can help inform future functional studies that seek to understand the molecular basis of disease