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1.  A quality improvement programme to increase compliance with an anti-infective prescribing policy 
The UK Department of Health has made recommendations on safe and appropriate prescribing of anti-infectives. In response, we reviewed our anti-infective policies to ensure they were in line with best practice. As a result, a new adult anti-infective policy was launched. To help facilitate its implementation, a quality improvement programme was established, with the aim of achieving >90% compliance with the new policy.
Patients under the care of the medical admissions teams who had been prescribed one or more systemic anti-infectives between January and November 2008 were included in the study. Study pharmacists collected data daily on all patients, including the anti-infective(s) prescribed and indication(s) documented on either the patient's drug prescription chart or health records. A definition of compliance was developed, which required documented indication(s) and associated anti-infectives to match the anti-infective policy. A baseline compliance level was established; we then implemented a series of interventions using the plan-do-study-act (‘PDSA’) approach to monitor and improve compliance. Three overlapping intervention phases were retrospectively identified: raising awareness; education; and weekly feedback of results in the form of run charts distributed to medical teams.
Over the 11 month study period, compliance with the policy increased from 30% to 71%. Since 2008, we have seen the average compliance increase year-on-year to over 90% in 2010 using a sustainable once weekly data collection model.
This study shows that it is possible to use quality improvement methodology to support antimicrobial stewardship within existing resources and suggests that an improvement in policy compliance can be both achieved and sustained.
PMCID: PMC3133488  PMID: 21642650
antibiotic stewardship; anti-infectives; QIP; prescribing
2.  miRNA regulation of macrophage fusion into multinucleated giant cells 
Cellular fusion of macrophages into multinucleated giant cells is a distinguishing feature of the granulomatous response to inflammation, infection and foreign bodies (1). We observed a marked increase in fusion of macrophages genetically deficient in Dicer, an enzyme required for canonical miRNA biogenesis. Gene expression profiling of miRNA deficient macrophages revealed an up-regulation of the IL4 responsive fusion protein Tm7sf4, analyses identify miR-7a-1 as a negative regulator of macrophage fusion, functioning by directly targeting Tm7sf4 mRNA. miR-7a-1 is itself an IL4 responsive gene in macrophages, suggesting feedback control of cellular fusion. Collectively these data indicate that miR-7a-1 functions to regulate IL4 directed multinucleated giant cell formation.
PMCID: PMC3381877  PMID: 22661094
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
Supplementary information: Supplementary data are available at Bioinformatics online.
PMCID: PMC2922897  PMID: 20663846
4.  Activating transcription factor 3 is a negative regulator of allergic pulmonary inflammation 
The Journal of Experimental Medicine  2008;205(10):2349-2357.
We recently demonstrated the pivotal role of the transcription factor (TF) activating TF 3 (ATF3) in dampening inflammation. We demonstrate that ATF3 also ameliorates allergen-induced airway inflammation and hyperresponsiveness in a mouse model of human asthma. ATF3 expression was increased in the lungs of mice challenged with ovalbumin allergen, and this was associated with its recruitment to the promoters of genes encoding Th2-associated cytokines. ATF3-deficient mice developed significantly increased airway hyperresponsiveness, pulmonary eosinophilia, and enhanced chemokine and Th2 cytokine responses in lung tissue and in lung-derived CD4+ lymphocytes. Although several TFs have been associated with enhanced inflammatory responses in the lung, ATF3 attenuates the inflammatory responses associated with allergic airway disease.
PMCID: PMC2556774  PMID: 18794337
7.  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.
PMCID: PMC2265556  PMID: 18369420
8.  The Innate Immune Database (IIDB) 
BMC Immunology  2008;9:7.
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 . 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.
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.
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 .
PMCID: PMC2268913  PMID: 18321385

Results 1-8 (8)