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Background Integration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses. Findings We developed an R package for electronic health data preparation, “eHDPrep,” demonstrated upon a multimodal colorectal cancer dataset (661 patients, 155 variables; Colo-661); a further demonstrator is taken from The Cancer Genome Atlas (459 patients, 94 variables; TCGA-COAD). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative “meta-variables” according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free text, completeness analysis, and user review of modifications to the dataset. Conclusions eHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to multimodal colorectal cancer datasets resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN (https://cran.r-project.org/package=eHDPrep) and GitHub (https://github.com/overton-group/eHDPrep).

BackgroundIntegration of data from multiple domains can greatly enhance the quality and applicability of knowledge generated in analysis workflows. However, working with health data is challenging, requiring careful preparation in order to support meaningful interpretation and robust results. Ontologies encapsulate relationships between variables that can enrich the semantic content of health datasets to enhance interpretability and inform downstream analyses.FindingsWe developed an R package for electronic Health Data preparation ‘eHDPrep’, demonstrated upon a multi-modal colorectal cancer dataset (n=661 patients, n=155 variables; Colo-661). eHDPrep offers user-friendly methods for quality control, including internal consistency checking and redundancy removal with information-theoretic variable merging. Semantic enrichment functionality is provided, enabling generation of new informative ‘meta-variables’ according to ontological common ancestry between variables, demonstrated with SNOMED CT and the Gene Ontology in the current study. eHDPrep also facilitates numerical encoding, variable extraction from free-text, completeness analysis and user review of modifications to the dataset.ConclusioneHDPrep provides effective tools to assess and enhance data quality, laying the foundation for robust performance and interpretability in downstream analyses. Application to a multi-modal colorectal cancer dataset resulted in improved data quality, structuring, and robust encoding, as well as enhanced semantic information. We make eHDPrep available as an R package from CRAN [[URL will go here]].

peer-reviewed Neonatal infection remains a primary cause of infant morbidity and mortality worldwide and yet our understanding of how human neonates respond to infection remains incomplete. Changes in host gene expression in response to infection may occur in any part of the body, with the continuous interaction between blood and tissues allowing blood cells to act as biosensors for the changes. In this study we have used whole blood transcriptome profiling to systematically identify signatures and the pathway biology underlying the pathogenesis of neonatal infection. Blood samples were collected from neonates at the first clinical signs of suspected sepsis alongside age matched healthy control subjects. Here we report a detailed description of the study design, including clinical data collected, experimental methods used and data analysis workflows and which correspond with data in Gene Expression Omnibus (GEO) data sets (GSE25504). Our data set has allowed identification of a patient invariant 52-gene classifier that predicts bacterial infection with high accuracy and lays the foundation for advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis.

Highlights • 11β-HSD1-deficiency reduces the hippocampal inflammatory response to LPS. • This happens despite similar peripheral inflammation. • 11β-HSD1-deficiency favours a “Warburg” like response to LPS in the hippocampus. • LPS increases hippocampal fumarate levels in 11β-HSD1-deficient mice. • Fumarate accumulation can cause pseudo-hypoxia., Chronically elevated glucocorticoid levels impair cognition and are pro-inflammatory in the brain. Deficiency or inhibition of 11β-hydroxysteroid dehydrogenase type-1 (11β-HSD1), which converts inactive into active glucocorticoids, protects against glucocorticoid-associated chronic stress- or age-related cognitive impairment. Here, we hypothesised that 11β-HSD1 deficiency attenuates the brain cytokine response to inflammation. Because inflammation is associated with altered energy metabolism, we also examined the effects of 11β-HSD1 deficiency upon hippocampal energy metabolism. Inflammation was induced in 11β-HSD1 deficient (Hsd11b1Del/Del) and C57BL/6 control mice by intraperitoneal injection of lipopolysaccharide (LPS). LPS reduced circulating neutrophil and monocyte numbers and increased plasma corticosterone levels equally in C57BL/6 and Hsd11b1Del/Del mice, suggesting a similar peripheral inflammatory response. However, the induction of pro-inflammatory cytokine mRNAs in the hippocampus was attenuated in Hsd11b1Del/Del mice. Principal component analysis of mRNA expression revealed a distinct metabolic response to LPS in hippocampus of Hsd11b1Del/Del mice. Expression of Pfkfb3 and Ldha, key contributors to the Warburg effect, showed greater induction in Hsd11b1Del/Del mice. Consistent with increased glycolytic flux, levels of 3-phosphoglyceraldehyde and dihydroxyacetone phosphate were reduced in hippocampus of LPS injected Hsd11b1Del/Del mice. Expression of Sdha and Sdhb, encoding subunits of succinate dehydrogenase/complex II that determines mitochondrial reserve respiratory capacity, was induced specifically in hippocampus of LPS injected Hsd11b1Del/Del mice, together with increased levels of its product, fumarate. These data suggest 11β-HSD1 deficiency attenuates the hippocampal pro-inflammatory response to LPS, associated with increased capacity for aerobic glycolysis and mitochondrial ATP generation. This may provide better metabolic support and be neuroprotective during systemic inflammation or aging.

Uterine NK cells (uNK) play a role in the regulation of placentation, but their functions in nonpregnant endometrium are not understood. We have previously reported suppression of endometrial bleeding and alteration of spiral artery morphology in women exposed to asoprisnil, a progesterone receptor modulator. We now compare global endometrial gene expression in asoprisnil-treated versus control women, and we demonstrate a statistically significant reduction of genes in the IL-15 pathway, known to play a key role in uNK development and function. Suppression of IL-15 by asoprisnil was also observed at mRNA level (p < 0.05), and immunostaining for NK cell marker CD56 revealed a striking reduction of uNK in asoprisnil-treated endometrium (p < 0.001). IL-15 levels in normal endometrium are progesterone-responsive. Progesterone receptor (PR) positive stromal cells transcribe both IL-15 and IL-15RA. Thus, the response of stromal cells to progesterone will be to increase IL-15 trans-presentation to uNK, supporting their expansion and differentiation. In asoprisnil-treated endometrium, there is a marked downregulation of stromal PR expression and virtual absence of uNK. These novel findings indicate that the IL-15 pathway provides a missing link in the complex interplay among endometrial stromal cells, uNK, and spiral arteries affecting physiologic and pathologic endometrial bleeding.

SummaryBackground: Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need.Objectives: Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multiprocessor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon’s Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance.Methods: The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences.Results: It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of the algorithm. Resource underutilization can further improve the time to result. End-user’s location impacts on costs due to factors such as local taxation.Conclusions: Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds.

Vaccines can have nontargeted heterologous effects that manifest as increased protection against nonvaccine infections, as described for measles vaccine (MV), or increased susceptibility to infections and death, as described following diphtheria-tetanus-whole cell pertussis (DTP) vaccination. The mechanisms are unknown, and high-quality immunological studies are lacking. This study was designed to investigate the heterologous effects of MV and DTP in 302 Gambian infants. The results support a sex-differential immunosuppressive effect of DTP on innate proinflammatory responses and T-cell immunity. Males but not females receiving MV had enhanced proinflammatory innate responses. The results point to modified signaling via Toll-like receptor 4 (TLR4) as a possible mechanism for the effects on innate immunity. When both vaccines were administered together, purified protein derivative responses were enhanced in females but downregulated in males. Collectively, these data indicate immunological effects that could account for heterologous effects of MV and DTP, to take forward into prospective trials.

Systemic inflammation, which results from the massive release of proinflammatory molecules into the circulatory system, is a major risk factor for severe illness, but the precise mechanisms underlying its control are not fully understood. We observed that prostaglandin ESystemic inflammation, which results from the massive release of proinflammatory molecules into the circulatory system, is a major risk factor for severe illness, but the precise mechanisms underlying its control are not fully understood. We observed that prostaglandin E2 (PGE2), through its receptor EP4, is down-regulated in human systemic inflammatory disease. Mice with reduced PGE2 synthesis develop systemic inflammation, associated with translocation of gut bacteria, which can be prevented by treatment with EP4 agonists. Mechanistically, we demonstrate that PGE2-EP4 signaling acts directly on type 3 innate lymphoid cells (ILCs), promoting their homeostasis and driving them to produce interleukin-22 (IL-22). Disruption of the ILC–IL-22 axis impairs PGE2-mediated inhibition of systemic inflammation. Hence, the ILC–IL-22 axis is essential in protecting against gut barrier dysfunction, enabling PGE2-EP4 signaling to impede systemic inflammation.

In invertebrates, small interfering RNAs are at the vanguard of cell-autonomous antiviral immunity. In contrast, antiviral mechanisms initiated by interferon (IFN) signaling predominate in mammals. Whilst mammalian IFN-induced miRNA are known to inhibit specific viruses, it is not known whether host-directed microRNAs, downstream of IFN-signaling, have a role in mediating broad antiviral resistance. By performing an integrative, systematic, global analysis of RNA turnover utilizing 4-thiouridine labeling of newly transcribed RNA and pri/pre-miRNA in IFN-activated macrophages, we identify a new post-transcriptional viral defense mechanism mediated by miR-342-5p. On the basis of ChIP and site-directed promoter mutagenesis experiments, we find the synthesis of miR-342-5p is coupled to the antiviral IFN response via the IFN-induced transcription factor, IRF1. Strikingly, we find miR-342-5p targets mevalonate-sterol biosynthesis using a multihit mechanism suppressing the pathway at different functional levels: transcriptionally via SREBF2, post-transcriptionally via miR-33, and enzymatically via IDI1 and SC4MOL. Mass spectrometry-based lipidomics and enzymatic assays demonstrate the targeting mechanisms reduce intermediate sterol pathway metabolites and total cholesterol in macrophages. These results reveal a previously unrecognized mechanism by which IFN regulates the sterol pathway. The sterol pathway is known to be an integral part of the macrophage IFN antiviral response, and we show that miR-342-5p exerts broad antiviral effects against multiple, unrelated pathogenic viruses such Cytomegalovirus and Influenza A (H1N1). Metabolic rescue experiments confirm the specificity of these effects and demonstrate that unrelated viruses have differential mevalonate and sterol pathway requirements for their replication. This study, therefore, advances the general concept of broad antiviral defense through multihit targeting of a single host pathway., An interferon-induced miRNA suppresses the sterol biosynthesis pathway via multiple targets, thereby helping establish broad cellular resistance to unrelated clinically significant viruses., Author Summary How infected cells respond to a virus during the first minutes to hours after infection can determine whether a disease develops and influences the host’s long-term survival. In mammals, unlike plants and flies that use small RNAs to fight viral infections, virus-induced interferon responses are a critical early event resulting in broad protection against infection. Interferon is a secreted host protein that binds to receptors on the surface of infected and uninfected cells and activates biochemical pathways that profoundly change the expression of hundreds of cellular genes, including those encoding microRNAs. The antiviral functions of only a handful of these genes are understood, and it is not known how the majority contribute to broadly protect against many different viruses. In this study, we uncover an interferon-regulated microRNA (miR-342-5p) that contributes to broad host cell immunity against infection through the cholesterol biosynthesis pathway. We show that miR-342-5p does this through a multihit strategy, turning down the master regulator of sterol biosynthesis as well as several specifically targeted enzymes within the pathway. A wide range of viruses depend on a number of the metabolite side-branches of the sterol biosynthesis pathway for their replication. Notably, our study reveals that by utilising multihit targeting of key branch-points in a single pathway, miR-342-5p is able to inhibit the replication of unrelated, clinically significant pathogens ranging from Herpes to Flu viruses.

The statistical language R is favoured by many biostatisticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming or even not possible at all with the existing software infrastructure. High performance computing HPC systems offer a solution to these problems but at the expense of increased complexity for the end user. The Simple Parallel R Interface is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelised replacements of existing R functions. In this paper we describe parallel implementations of two popular techniques: exploratory clustering analyses using the random forest classifier and feature selection through identification of differentially expressed genes using the rank product method. Copyright © 2012 John Wiley & Sons, Ltd.

The statistical language R and its Bioconductor package are favoured by many biostatisticians for processing microarray data. The amount of data produced by some analyses has reached the limits of many common bioinformatics computing infrastructures. High Performance Computing systems offer a solution to this issue. The Simple Parallel R Interface (SPRINT) is a package that provides biostatisticians with easy access to High Performance Computing systems and allows the addition of parallelized functions to R. Previous work has established that the SPRINT implementation of an R permutation testing function has close to optimal scaling on up to 512 processors on a supercomputer. Access to supercomputers, however, is not always possible, and so the work presented here compares the performance of the SPRINT implementation on a supercomputer with benchmarks on a range of platforms including cloud resources and a common desktop machine with multiprocessing capabilities. Copyright (C) 2011 John Wiley & Sons, Ltd.

The global transcriptional program of murine cytomegalovirus (MCMV), involving coding, noncoding, and antisense transcription, remains unknown. Here we report an oligonucleotide custom microarray platform capable of measuring both coding and noncoding transcription on a genome-wide scale. By profiling MCMV wild-type and immediate-early mutant strains in fibroblasts, we found rapid activation of the transcriptome by 6.5 h postinfection, with absolute dependency on ie3 , but not ie1 or ie2 , for genomic programming of viral gene expression. Evidence is also presented to show, for the first time, genome-wide noncoding and bidirectional transcription at late stages of MCMV infection.

Varicella-Zoster virus (VZV) is a human herpes virus that reactivates from a latent state in human trigeminal and dorsal root ganglia to cause herpes zoster (shingles) which is a painful vesicular dermatomal skin eruption. The major complication of herpes zoster is post-herpetic neuralgia (PHN) which is a serious condition occurring especially in individuals over 50 years. PHN is extremely painful, may be permanent, and is frequently very refractory to all treatment. The ability to identify those patients with herpes zoster who are likely to develop PHN would be highly beneficial as it would allow pre-emptive anti-viral therapy. We have assessed the potential of using long oligonucleotide VZV microarrays to determine whether MeWo cells infected with VZV isolates obtained from 13 patients with zoster who had subsequently developed PHN showed significant transcriptomal differences from MeWo cells infected with viruses isolated from ten zoster patients who had not developed PHN. We found that viral gene expression from sample to sample within a group (PHN patients or non-PHN patients) varied as much, or more, than the viral gene expression between those groups. Quantitative real-time polymerase chain reaction studies carried out on 11 open reading frames on four representative viral infected MeWo cell lines (two from each group) confirmed the transcriptomal heterogeneity between the two groups. Growth curve analyses of ten representative infected cell lines (five from each group) showed that PHN and non-PHN-associated viruses replicated equally efficiently. Taken together, these findings suggest that viral microarray-based transcriptomal measurements are unlikely to prove of clinical utility in predicting the incidence of PHN.

We present the application of a targeted liquid chromatography/mass spectrometry (LC/MS) approach developed on a linear ion trap for the evaluation of the abundance of cytoplasmic proteins from a HeLa cell extract. Using a standard data-dependent approach, we identified some specific peptides from this extract which were also commercially available in their AQUA form (use for absolute quantitation). For some of the peptides, we observed a non-linear response between the intensity and the added quantity which was then fitted using a quadratic fit. All AQUA peptides spiked into a mix of 3 microg of the HeLa cell digest extract were detected down to 16 fmol. We placed an emphasis on peptide detection which, in this study, is performed using a combination of properties such as three specific Q3-like ion signatures (for a given Q1-like selection) and co-elution with the AQUA peptide counterparts. Detecting a peptide without necessarily identifying it using a search engine imposes less constraint in terms of tandem mass (MS/MS) spectra purity. An example is shown where a peptide is detected using those criteria but could not be identified by Mascot due to its lower abundance. To complement this observation, we used a cross-correlation analysis approach in order to separate two populations of MS/MS fragments based on differences in their elution patterns. Such an approach opens the door to new strategies to analyse lower intensity peptide fragments. An in silico analysis of the human trypsinosome allows the evaluation of how unique are the sets of features that we are using for peptide detection.

Varicella-Zoster virus (VZV) causes varicella as a primary infection following which it becomes latent in human ganglia and then reactivates to cause herpes zoster. VZV vaccines are used to prevent primary infection with varicella, and also to reduce the incidence of viral reactivation causing herpes zoster and post-herpetic neuralgia. To gain further insights into the molecular basis of their attenuated virulence, we used long oligonucleotide microarrays to determine the lytic transcriptomal profiles of two vaccine VZV strains (Merck and GSK) compared with the Oka parental (P-Oka) strain. There was a global downregulation of transcription of both vaccines relative to P-Oka, although this downregulation was more extensive in the GSK strain. Open Reading Frames (ORFs) 62 and 14 were the most transcriptionally downregulated on the arrays for both vaccines compared with the parental strain.

All donor blood samples must be tested pre-transfusion to determine the blood type of donor erythrocytes, based on the ABO typing system. Current methods of testing are well characterised, but require a number of processing steps prior to analysis. In addition, standard testing protocols require additional assays such as hepatitis C and HIV testing be performed separately. We describe and evaluate a protein microarray platform for ABO blood typing that has the potential to be a simple reliable high throughput method, with the added capability for the integration of other important pre-transfusion tests. Sixty seven donor blood samples were incubated on microarrays printed with multiple spotted replicates of blood type antigen specific antibodies. We utilised a hold-out cross validation approach, combined with Receiver Operator Characteristic (ROC) curves to define thresholds within which a sample could be defined as being of a particular blood type. The threshold values from the ROC curve analysis demonstrated an excellent ability to accurately separate samples based on ABO blood type. The results obtained when the thresholds from the training sets were applied to test sets were also very encouraging, with misclassified samples being present in only 2 of the training sets and a mean classification error of 4.28%. When the mean thresholds were applied to the 67 donor samples, 95.5% were correctly blood typed (64 of 67 samples). We have demonstrated the ability of our protein microarray platform to successfully and accurately type human whole blood samples. We believe that this flexible platform provides a strong basis for an integrated approach for combined blood typing and pathogen testing in human whole blood.

Glial cells, particularly microglia, are thought to play a pivotal role in initiating and guiding innate immune responses to CNS infections and in perpetuating inflammation and pathology in CNS diseases such as multiple sclerosis and Alzheimer's disease. We describe here the development and use of a new microarray designed to specifically profile transcript expression of innate immunity genes. Microarray analysis validated by quantitative PCR demonstrated an extensive range of pattern recognition receptor gene expression in resting N9 microglia, including Toll-like receptors, scavenger receptors and lectins. Stimulation with LPS or infection with virus modulated pattern recognition receptor, cytokine, chemokine and other innate immune transcripts in a distinct and stimulus-specific manner. This study demonstrates that a single glial cell phenotype has an innate capability to detect infection, determine its form and generate specific responses.

The unprecedented scale and content of genomic and proteomic information now emerging from global sequencing and analysis efforts offer new opportunities in biological research. Technological and computational developments have enabled first phase analytical platforms for genomic and proteomic studies. However, the ultimate goal will be to integrate data from diverse analytical platforms, so that biological systems can be modelled with increasing complexity to achieve understanding at the systems level. The new techniques of microarray technology and high-throughput screening (HTS) proteomics are already providing new insights into cell form and function. These approaches are poised to revolutionize much of biological research methodology, but will require the increasing fusion of biological sciences with mathematics, computing and physical sciences to generate platform technologies and approaches for the specific and HTS analysis of biological processes. This workshop was held to review the central role of bioarrays in genomic and proteomic studies and to examine their potential for future applications. It was also critical to consider the requirement for bioinformatic and computational tools and methods to enable rational handling and interpretation of data. This event was organized as a research workshop under the auspices of the Royal Society of Edinburgh and the Wellcome Trust. The report below considers the three major bioarray-related themes resulting from the meeting: analytical methods; functional genomic applications and the development of new technologies. These key areas will require fusion to provide a fuller understanding of biological systems (Figure 1).

Highlights • IFN-γ leads to the proteasomal degradation of HMGCR. • IFN-γ and 25-HC can transcriptionally and post-translationally alter levels of HMGCR. • The reduction of HMGCR through the action of IFN-γ requires the de novo synthesis of 25-HC by CH25H., Interferons (IFNs) play a central role in immunity and emerging evidence suggests that IFN-signalling coordinately regulates sterol biosynthesis in macrophages, via Sterol Regulatory Element-Binding Protein (SREBP) dependent and independent pathways. However, the precise mechanisms and kinetic steps by which IFN controls sterol biosynthesis are as yet not fully understood. Here, we elucidate the molecular circuitry governing how IFN controls the first regulated step in the mevalonate-sterol pathway, 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), through the synthesis of 25-Hydroxycholesterol (25-HC) from cholesterol by the IFN-inducible Cholesterol-25-Hydroxylase (CH25H). We show for the first 30-min of IFN stimulation of macrophages the rate of de novo synthesis of the Ch25h transcript is markedly increased but by 120-min becomes transcriptionally curtailed, coincident with induction of the Activating Transcription Factor 3 (ATF3) repressor. We demonstrate ATF3 induction by Toll-like receptors is strictly dependent on IFN-signalling. While the SREBP-pathway dependent rates of de novo transcription of Hmgcr are relatively unchanged in the first 90-min of IFN treatment, we find HMGCR enzyme levels undergo a rapid proteasomal-mediated degradation, defining a previously unappreciated SREBP-independent mechanism for IFN-action. These events precede a sustained marked reduction in Hmgcr RNA levels involving SREBP-dependent mechanisms. We demonstrate that HMGCR proteasomal-degradation by IFN strictly requires the synthesis of endogenous 25-HC and functionally couples HMGCR to CH25H to coordinately suppress sterol biosynthesis. In conclusion, we quantitatively delineate proteomic and transcriptional levels of IFN-mediated control of HMGCR, the primary enzymatic step of the mevalonate-sterol biosynthesis pathway, providing a foundational framework for mathematically modelling the therapeutic outcome of immune-metabolic pathways.

Understanding how human neonates respond to infection remains incomplete. Here, a system-level investigation of neonatal systemic responses to infection shows a surprisingly strong but unbalanced homeostatic immune response; developing an elevated set-point of myeloid regulatory signalling and sugar-lipid metabolism with concomitant inhibition of lymphoid responses. Innate immune-negative feedback opposes innate immune activation while suppression of T-cell co-stimulation is coincident with selective upregulation of CD85 co-inhibitory pathways. By deriving modules of co-expressed RNAs, we identify a limited set of networks associated with bacterial infection that exhibit high levels of inter-patient variability. Whereas, by integrating immune and metabolic pathways, we infer a patient-invariant 52-gene-classifier that predicts bacterial infection with high accuracy using a new independent patient population. This is further shown to have predictive value in identifying infection in suspected cases with blood culture-negative tests. Our results lay the foundation for future translation of host pathways in advancing diagnostic, prognostic and therapeutic strategies for neonatal sepsis., Infection remains a leading cause of morbidity and mortality in neonates worldwide. Here the authors report disproportionate immune stimulatory, co-inhibitory and metabolic pathway responses that specifically mark bacterial infection and can be used to predict sepsis in neonatal patients at the first clinical signs of infection.

F. Noho-Konteh1, J. Adetifa1, M. Cox1, T. Forster2, A. Drammeh1, J. Njie-Jobe1, D. Jeffries1, M. Plebanski3, P. Ghazal2, P. Dickinson2, H. Whittle1, S. Rowland-Jones4, J. Sutherland1, K. Flanagan5,∗ 1 MRC Laboratories, Banjul, Gambia 2 University of Edinburgh Medical School, Edinburgh, United Kingdom 3 Monash University, Melbourne, Australia 4 University of Oxford, Oxford, United Kingdom 5 Monash University, Melbourne, TAS, Australia

Summary Recent studies suggest that the sterol metabolic network participates in the interferon (IFN) antiviral response. However, the molecular mechanisms linking IFN with the sterol network and the identity of sterol mediators remain unknown. Here we report a cellular antiviral role for macrophage production of 25-hydroxycholesterol (cholest-5-en-3β,25-diol, 25HC) as a component of the sterol metabolic network linked to the IFN response via Stat1. By utilizing quantitative metabolome profiling of all naturally occurring oxysterols upon infection or IFN-stimulation, we reveal 25HC as the only macrophage-synthesized and -secreted oxysterol. We show that 25HC can act at multiple levels as a potent paracrine inhibitor of viral infection for a broad range of viruses. We also demonstrate, using transcriptional regulatory-network analyses, genetic interventions and chromatin immunoprecipitation experiments that Stat1 directly coupled Ch25h regulation to IFN in macrophages. Our studies describe a physiological role for 25HC as a sterol-lipid effector of an innate immune pathway., Graphical Abstract Highlights ► Macrophage PRR sensing of virus or IFN activation induce 25HC synthesis and secretion ► Stat1 rapidly binds and activates the promoter of cholesterol-25-hydroxylase (Ch25h) ► 25HC exerts multilevel antiviral function for a range of different viruses ► 25HC is an intrinsic paracrine and autocrine effector of the IFN antiviral response

Activated macrophages play a central role in controlling inflammatory responses to infection and are tightly regulated to rapidly mount responses to infectious challenge. Type I interferon (alpha/beta interferon [IFN-α/β]) and type II interferon (IFN-γ) play a crucial role in activating macrophages and subsequently restricting viral infections. Both types of IFNs signal through related but distinct signaling pathways, inducing a vast number of interferon-stimulated genes that are overlapping but distinguishable. The exact mechanism by which IFNs, particularly IFN-γ, inhibit DNA viruses such as cytomegalovirus (CMV) is still not fully understood. Here, we investigate the antiviral state developed in macrophages upon reversible inhibition of murine CMV by IFN-γ. On the basis of molecular profiling of the reversible inhibition, we identify a significant contribution of a restricted type I IFN subnetwork linked with IFN-γ activation. Genetic knockout of the type I-signaling pathway, in the context of IFN-γ stimulation, revealed an essential requirement for a primed type I-signaling process in developing a full refractory state in macrophages. A minimal transient induction of IFN-β upon macrophage activation with IFN-γ is also detectable. In dose and kinetic viral replication inhibition experiments with IFN-γ, the establishment of an antiviral effect is demonstrated to occur within the first hours of infection. We show that the inhibitory mechanisms at these very early times involve a blockade of the viral major immediate-early promoter activity. Altogether our results show that a primed type I IFN subnetwork contributes to an immediate-early antiviral state induced by type II IFN activation of macrophages, with a potential further amplification loop contributed by transient induction of IFN-β.

The statistical language R is favoured by many biostaticians for processing microarray data. In recent times, the quantity of data that can be obtained in experiments has risen significantly, making previously fast analyses time consuming, or even not possible at all with the existing software infrastructure. High Performance Computing (HPC) systems offer a solution to these problems, but at the expense of increased complexity for the end user. The Simple Parallel R Interface (SPRINT) is a library for R that aims to reduce the complexity of using HPC systems by providing biostatisticians with drop-in parallelized replacements of existing R functions. In this paper we describe the implementation of a parallel version of the Random Forest classifier in the SPRINT library.

Dermcidin is a candidate oncogene capable of increasing the number of cultured neuronal, breast cancer and prostate cancer cells and improving the survival of hepatic cells. The dermcidin gene encodes the proteolysis-inducing factor core peptide (PIF-CP) and the skin antimicrobial peptide DCD-1. The peptide responsible for inducing proliferation of cells and the mechanisms involved are unknown. In this study, we confirmed a proliferative effect of dermcidin overexpression of 20% (p < 0.02) in the HuH7 human hepatic cell line. Proliferation was abrogated by prevention of PIF-CP translation or inactivation of its calcineurin-like phosphatase domain by site-directed mutagenesis. Prevention of DCD-1 translation had no effect. Treatment of cells with a 30 amino acid synthetic PIF-CP induced an analogous increase in proliferation of 14%. Microarray analysis of PIF-CP-treated cells revealed low but significant changes in Ill potential mediator genes. Pathway analysis revealed several gene networks involved in the cellular response to the peptide, one with VEGFB as a hub and two other networks converging on FOS and MYC. Quantitative PCR confirmed direct upregulation of VEGFB. These data reveal PIF-CP as the key mediator of dermcidin-induced proliferation and demonstrate induction of key oncogenic pathways.

Although the functional parameters of microRNAs (miRNAs) have been explored in some depth, the roles of these molecules in viral infections remain elusive. Here we report a general method for global analysis of miRNA function that compares the significance of both overexpressing and inhibiting each mouse miRNA on the growth properties of different viruses. Our comparative analysis of representatives of all three herpesvirus subfamilies identified host miRNAs with broad anti- and proviral properties which extend to a single-stranded RNA virus. Specifically, we demonstrate the broad antiviral capacity of miR-199a-3p and illustrate that this individual host-encoded miRNA regulates multiple pathways required and/or activated by viruses, including PI3K/AKT and ERK/MAPK signaling, oxidative stress signaling, and prostaglandin synthesis. Global miRNA expression analysis further demonstrated that the miR-199a/miR-214 cluster is down-regulated in both murine and human cytomegalovirus infection and manifests similar antiviral properties in mouse and human cells. Overall, we report a general strategy for examining the contributions of individual host miRNAs in viral infection and provide evidence that these molecules confer broad inhibitory potential against multiple viruses.

BackgroundInterferons (IFNs) are potent antiviral cytokines capable of reprogramming the macrophage phenotype through the induction of interferon-stimulated genes (ISGs). Here we have used targeted RNA interference to suppress the expression of a number of key genes associated with IFN signalling in murine macrophages prior to stimulation with interferon-gamma. Genome-wide changes in transcript abundance caused by siRNA activity were measured using exon-level microarrays in the presence or absence of IFNγ.ResultsTransfection of murine bone-marrow derived macrophages (BMDMs) with a non-targeting (control) siRNA and 11 sequence-specific siRNAs was performed using a cationic lipid transfection reagent (Lipofectamine2000) prior to stimulation with IFNγ. Total RNA was harvested from cells and gene expression measured on Affymetrix GeneChip Mouse Exon 1.0 ST Arrays. Network-based analysis of these data revealed six siRNAs to cause a marked shift in the macrophage transcriptome in the presence or absence IFNγ. These six siRNAs targeted the Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2 transcripts. The perturbation of the transcriptome by the six siRNAs was highly similar in each case and affected the expression of over 600 downstream transcripts. Regulated transcripts were clustered based on co-expression into five major groups corresponding to transcriptional networks associated with the type I and II IFN response, cell cycle regulation, and NF-KB signalling. In addition we have observed a significant non-specific immune stimulation of cells transfected with siRNA using Lipofectamine2000, suggesting use of this reagent in BMDMs, even at low concentrations, is enough to induce a type I IFN response.ConclusionOur results provide evidence that the type I IFN response in murine BMDMs is dependent on Ifnb1, Irf3, Irf5, Stat1, Stat2 and Nfkb2, and that siRNAs targeted to these genes results in perturbation of key transcriptional networks associated with type I and type II IFN signalling and a suppression of macrophage M1 polarization.

RNA interference (RNAi) has become a powerful technique for reverse genetics and drug discovery, and in both of these areas large-scale high-throughput RNAi screens are commonly performed. The statistical techniques used to analyze these screens are frequently borrowed directly from small-molecule screening; however, small-molecule and RNAi data characteristics differ in meaningful ways. We examine the similarities and differences between RNAi and small-molecule screens, highlighting particular characteristics of RNAi screen data that must be addressed during analysis. Additionally, we provide guidance on selection of analysis techniques in the context of a sample workflow.

Background Microarray analysis allows the simultaneous measurement of thousands to millions of genes or sequences across tens to thousands of different samples. The analysis of the resulting data tests the limits of existing bioinformatics computing infrastructure. A solution to this issue is to use High Performance Computing (HPC) systems, which contain many processors and more memory than desktop computer systems. Many biostatisticians use R to process the data gleaned from microarray analysis and there is even a dedicated group of packages, Bioconductor, for this purpose. However, to exploit HPC systems, R must be able to utilise the multiple processors available on these systems. There are existing modules that enable R to use multiple processors, but these are either difficult to use for the HPC novice or cannot be used to solve certain classes of problems. A method of exploiting HPC systems, using R, but without recourse to mastering parallel programming paradigms is therefore necessary to analyse genomic data to its fullest. Results We have designed and built a prototype framework that allows the addition of parallelised functions to R to enable the easy exploitation of HPC systems. The Simple Parallel R INTerface (SPRINT) is a wrapper around such parallelised functions. Their use requires very little modification to existing sequential R scripts and no expertise in parallel computing. As an example we created a function that carries out the computation of a pairwise calculated correlation matrix. This performs well with SPRINT. When executed using SPRINT on an HPC resource of eight processors this computation reduces by more than three times the time R takes to complete it on one processor. Conclusion SPRINT allows the biostatistician to concentrate on the research problems rather than the computation, while still allowing exploitation of HPC systems. It is easy to use and with further development will become more useful as more functions are added to the framework.

Gonadotropin withdrawal induces changes in gene expression in all 3 major cell types of the testis. Knowledge of the genes affected, in both the presence and absence of additional progestogen, will give insight into the regulation of human testicular function and aid development of novel contraceptive methods. We have undertaken a whole-genome analysis of RNA expression in testicular biopsies from normal men and after 4 weeks of gonadotropin suppression induced by gonadotropin-releasing hormone antagonist plus testosterone administration sufficient to cause marked suppression of spermatogenesis. Microarray analysis shows that interindividual variability is markedly low, and the response to treatment is focused on a small subset of genes particularly related to pathways in steroidogenesis and cholesterol biosynthesis or metabolism, the Leydig cell gene INSL3, and genes involved in early meiosis or Sertoli-germ cell junctions. These changes in expression were confirmed by quantitative reverse transcriptase polymerase chain reaction. No major changes in gene expression were identified in men additionally treated with a progestogen, although FLJ35767, an expressed sequence tag that is expressed in the germ cell compartment, did show a small but significant additional effect of progestogen. Overall, the results of this investigation disclose a remarkably stringent regulation of testicular gene expression, revealing the genes most sensitive to gonadotropin withdrawal, and might reflect the most labile pathways in the regulation of testicular function.

RNA levels in a cell are determined by the relative rates of RNA synthesis and decay. State-of-the-art transcriptional analyses only employ total cellular RNA. Therefore, changes in RNA levels cannot be attributed to RNA synthesis or decay, and temporal resolution is poor. Recently, it was reported that newly transcribed RNA can be biosynthetically labeled for 1–2 h using thiolated nucleosides, purified from total cellular RNA and subjected to microarray analysis. However, in order to study signaling events at molecular level, analysis of changes occurring within minutes is required. We developed an improved approach to separate total cellular RNA into newly transcribed and preexisting RNA following 10–15 min of metabolic labeling. Employing new computational tools for array normalization and half-life determination we simultaneously study short-term RNA synthesis and decay as well as their impact on cellular transcript levels. As an example we studied the response of fibroblasts to type I and II interferons (IFN). Analysis of RNA transcribed within 15–30 min at different times during the first three hours of interferon-receptor activation resulted in a >10-fold increase in microarray sensitivity and provided a comprehensive profile of the kinetics of IFN-mediated changes in gene expression. We identify a previously undisclosed highly connected network of short-lived transcripts selectively down-regulated by IFNγ in between 30 and 60 min after IFN treatment showing strong associations with cell cycle and apoptosis, indicating novel mechanisms by which IFNγ affects these pathways.

Digital images obtained by the laser scanning of spotted microarrays often include saturated pixel values. These arise when the scan settings are sufficiently high and some pixels exceed the limit L=65535 and are instead set to L. Failure to adjust for this censoring leads to biased estimates of gene expression levels. To impute censored values, we propose a linear model based on the principal components of uncensored spots on the same array. This is computationally fast, flexible to adapt to distinctive spot shapes and profiles on different arrays, and is shown to be more effective than the polynomial-hyperbolic model in correcting for the bias. The application to biological data demonstrates the potential for enhancing the dynamic range of detection. Fortran90 subroutines implementing these methods are available at http://www.bioss.ac.uk/~chris.

We propose a modeling approach based on Probabilistic Boolean Networks for the inference of genetic regulatory networks from gene expression time-course data in different biological conditions i.e. making use of the information contained in sets of genes and the interaction between genes rather than single-gene analyses. This model is a collection of traditional Probabilistic Boolean Networks. We also present an approach which is based on constrained prediction and Coefficient of Determination (COD) for the identification of the model from gene expression data. The modeling approach is applied in the context of pathway biology to the analysis of gene interaction networks.

Objective The purpose of this study was to test whether a quantitative high-throughput molecular screen can be used to probe human endometrium and initiate the development of molecular diagnostic tools with potential for identification of therapeutic targets in women with menstrual complaints. Study design Endometrium was collected from 10 patients with complaint of heavy bleeding, classified into mid or late secretory phase of the menstrual cycle by histologic dating and serum progesterone concentration. Total RNA was extracted and gene activity assessed using high-density oligonucleotide arrays. Results Statistical testing identified 83 ‘signature’ genes whose expression levels differentiated the mid and late secretory phases of the menstrual cycle. Conclusion The results show that the endometrium, a complex heterogeneous tissue, is amenable to high-throughput molecular analyses and this work provides further support for the future application of molecular profiling to clinical diagnosis