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1.  miR-451 regulates dendritic cell cytokine responses to influenza infection1 
MicroRNAs are important post-transcriptional regulators in immune cells, but how viral infection regulates microRNA expression to shape dendritic cell responses has not been well characterized. We identified 20 miRNAs that were differentially expressed in primary murine dendritic cells in response to the double-stranded RNA agonist poly(I:C), a subset of which were modestly regulated by influenza infection. miR-451 was unique because it was induced more strongly in primary splenic and lung dendritic cells by live viral infection than by purified agonists of pattern recognition receptors. We determined that miR-451 regulates a subset of pro-inflammatory cytokine responses. Three types of primary dendritic cells treated with anti-sense RNA antagomirs directed against miR-451 secreted elevated levels of IL-6, TNF, CCL5/RANTES, and CCL3/MIP1α, and these results were confirmed using miR-451null cells. miR-451 negatively regulates YWHAZ/14-3-3ζ protein levels in various cell types, and we measured a similar inhibition of YWHAZ levels in dendritic cells. It is known that YWHAZ can control the activity of two negative regulators of cytokine production: FOXO3, which is an inhibitory transcription factor, and ZFP36/Tristetraprolin, which binds to AU-rich elements within 3′-UTRs to destabilize cytokine mRNAs. Inhibition of miR-451 expression correlated with increased YWHAZ protein expression and decreased ZFP36 expression, providing a possible mechanism for the elevated secretion of IL-6, TNF, CCL5/RANTES, and CCL3/MIP1α. miR-451 levels are themselves increased by IL-6 and type I interferon, potentially forming a regulatory loop. These data suggest that viral infection specifically induces a miRNA that directs a negative regulatory cascade to tune dendritic cell cytokine production.
doi:10.4049/jimmunol.1201437
PMCID: PMC3528339  PMID: 23169590
2.  Differential Host Response, Rather Than Early Viral Replication Efficiency, Correlates with Pathogenicity Caused by Influenza Viruses 
PLoS ONE  2013;8(9):e74863.
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.
doi:10.1371/journal.pone.0074863
PMCID: PMC3779241  PMID: 24073225
3.  Genome-wide histone acetylation data improve prediction of mammalian transcription factor binding sites 
Bioinformatics  2010;26(17):2071-2075.
Motivation: Histone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation.
Results: We tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by ∼50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01.
Availability: The data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac.
Contact: aderem@systemsbiology.org; ishmulevich@systemsbiology.org
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq405
PMCID: PMC2922897  PMID: 20663846
6.  Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics 
PLoS Computational Biology  2008;4(3):e1000021.
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.
Author Summary
Macrophages play a vital role in host defense against infection by recognizing pathogens through pattern recognition receptors, such as the Toll-like receptors (TLRs), and mounting an immune response. Stimulation of TLRs initiates a complex transcriptional program in which induced transcription factor genes dynamically regulate downstream genes. Microarray-based transcriptional profiling has proved useful for mapping such transcriptional programs in simpler model organisms; however, mammalian systems present difficulties such as post-translational regulation of transcription factors, combinatorial gene regulation, and a paucity of available gene-knockout expression data. Additional evidence sources, such as DNA sequence-based identification of transcription factor binding sites, are needed. In this work, we computationally inferred a transcriptional network for TLR-stimulated murine macrophages. Our approach combined sequence scanning with time-course expression data in a probabilistic framework. Expression data were analyzed using the time-lagged correlation. A novel, unbiased method was developed to assess the significance of the time-lagged correlation. The inferred network of associations between transcription factor genes and co-expressed gene clusters was validated with targeted ChIP-on-chip experiments, and yielded insights into the macrophage activation program, including a potential novel regulator. Our general approach could be used to analyze other complex mammalian systems for which time-course expression data are available.
doi:10.1371/journal.pcbi.1000021
PMCID: PMC2265556  PMID: 18369420

Results 1-6 (6)