The Innate Immune Database (IIDB)

<p>Abstract</p> <p>Background</p> <p>As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site <url>http://www.innateImmunity-systemsbiology.org</url>. Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens.</p> <p>Description</p> <p>We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser.</p> <p>Conclusion</p> <p>We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at <url>http://db.systemsbiology.net/IIDB</url>.</p

Prognostic and Predictive Biomarkers in Patients With Coronavirus Disease 2019 Treated With Tocilizumab in a Randomized Controlled Trial

OBJECTIVES: To explore candidate prognostic and predictive biomarkers identified in retrospective observational studies (interleukin-6, C-reactive protein, lactate dehydrogenase, ferritin, lymphocytes, monocytes, neutrophils, d-dimer, and platelets) in patients with coronavirus disease 2019 pneumonia after treatment with tocilizumab, an anti-interleukin-6 receptor antibody, using data from the COVACTA trial in patients hospitalized with severe coronavirus disease 2019 pneumonia. DESIGN: Exploratory analysis from a multicenter, randomized, double-blind, placebo-controlled, phase 3 trial. SETTING: Hospitals in North America and Europe. PATIENTS: Adults hospitalized with severe coronavirus disease 2019 pneumonia receiving standard care. INTERVENTION: Randomly assigned 2:1 to IV tocilizumab 8 mg/kg or placebo. MEASUREMENTS AND MAIN RESULTS: Candidate biomarkers were measured in 295 patients in the tocilizumab arm and 142 patients in the placebo arm. Efficacy outcomes assessed were clinical status on a seven-category ordinal scale (1, discharge; 7, death), mortality, time to hospital discharge, and mechanical ventilation (if not receiving it at randomization) through day 28. Prognostic and predictive biomarkers were evaluated continuously with proportional odds, binomial or Fine-Gray models, and additional sensitivity analyses. Modeling in the placebo arm showed all candidate biomarkers except lactate dehydrogenase and d-dimer were strongly prognostic for day 28 clinical outcomes of mortality, mechanical ventilation, clinical status, and time to hospital discharge. Modeling in the tocilizumab arm showed a predictive value of ferritin for day 28 clinical outcomes of mortality (predictive interaction, p = 0.03), mechanical ventilation (predictive interaction, p = 0.01), and clinical status (predictive interaction, p = 0.02) compared with placebo. CONCLUSIONS: Multiple biomarkers prognostic for clinical outcomes were confirmed in COVACTA. Ferritin was identified as a predictive biomarker for the effects of tocilizumab in the COVACTA patient population; high ferritin levels were associated with better clinical outcomes for tocilizumab compared with placebo at day 28

Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics

Macrophages are versatile immune cells that can detect a variety of pathogen-associated molecular patterns through their Toll-like receptors (TLRs). In response to microbial challenge, the TLR-stimulated macrophage undergoes an activation program controlled by a dynamically inducible transcriptional regulatory network. Mapping a complex mammalian transcriptional network poses significant challenges and requires the integration of multiple experimental data types. In this work, we inferred a transcriptional network underlying TLR-stimulated murine macrophage activation. Microarray-based expression profiling and transcription factor binding site motif scanning were used to infer a network of associations between transcription factor genes and clusters of co-expressed target genes. The time-lagged correlation was used to analyze temporal expression data in order to identify potential causal influences in the network. A novel statistical test was developed to assess the significance of the time-lagged correlation. Several associations in the resulting inferred network were validated using targeted ChIP-on-chip experiments. The network incorporates known regulators and gives insight into the transcriptional control of macrophage activation. Our analysis identified a novel regulator (TGIF1) that may have a role in macrophage activation

Differential host response, rather than early viral replication efficiency, correlates with pathogenicity caused by influenza viruses.

Influenza viruses exhibit large, strain-dependent differences in pathogenicity in mammalian hosts. Although the characteristics of severe disease, including uncontrolled viral replication, infection of the lower airway, and highly inflammatory cytokine responses have been extensively documented, the specific virulence mechanisms that distinguish highly pathogenic strains remain elusive. In this study, we focused on the early events in influenza infection, measuring the growth rate of three strains of varying pathogenicity in the mouse airway epithelium and simultaneously examining the global host transcriptional response over the first 24 hours. Although all strains replicated equally rapidly over the first viral life-cycle, their growth rates in both lung and tracheal tissue strongly diverged at later times, resulting in nearly 10-fold differences in viral load by 24 hours following infection. We identified separate networks of genes in both the lung and tracheal tissues whose rapid up-regulation at early time points by specific strains correlated with a reduced viral replication rate of those strains. The set of early-induced genes in the lung that led to viral growth restriction is enriched for both NF-κB binding site motifs and members of the TREM1 and IL-17 signaling pathways, suggesting that rapid, NF-κB -mediated activation of these pathways may contribute to control of viral replication. Because influenza infection extending into the lung generally results in severe disease, early activation of these pathways may be one factor distinguishing high- and low-pathogenicity strains

miR-144 attenuates the host response to influenza virus by targeting the TRAF6-IRF7 signaling axis

<div><p>Antiviral responses must rapidly defend against infection while minimizing inflammatory damage, but the mechanisms that regulate the magnitude of response within an infected cell are not well understood. miRNAs are small non-coding RNAs that suppress protein levels by binding target sequences on their cognate mRNA. Here, we identify miR-144 as a negative regulator of the host antiviral response. Ectopic expression of miR-144 resulted in increased replication of three RNA viruses in primary mouse lung epithelial cells: influenza virus, EMCV, and VSV. We identified the transcriptional network regulated by miR-144 and demonstrate that miR-144 post-transcriptionally suppresses TRAF6 levels. <i>In vivo</i> ablation of miR-144 reduced influenza virus replication in the lung and disease severity. These data suggest that miR-144 reduces the antiviral response by attenuating the TRAF6-IRF7 pathway to alter the cellular antiviral transcriptional landscape.</p></div