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Abstract Rationale Human rhinovirus infections cause colds and trigger exacerbations of lower airway diseases. Objectives To define changes in gene expression profiles during in vivo rhinovirus infections. Methods Nasal epithelial scrapings were obtained before and during experimental rhinovirus infection, and gene expression was evaluated by microarray. Naturally acquired rhinovirus infections, cultured human epithelial cells, and short interfering RNA knockdown were used to further evaluate the role of viperin in rhinovirus infections. Measurements and Main Results Symptom scores and viral titers were measured in subjects inoculated with rhinovirus or sham control, and changes in gene expression were assessed 8 and 48 hours after inoculation. Real-time reverse transcription-polymerase chain reaction for viperin and rhinoviruses was used in naturally acquired infections, and viperin mRNA levels and viral titers were measured in cultured cells. Rhinovirus-induced changes in gene expression were not observed 8 hours after viral infection, but 11,887 gene transcripts were significantly altered in scrapings obtained 2 days postinoculation. Major groups of up-regulated genes included chemokines, signaling molecules, interferon-responsive genes, and antivirals. Viperin expression was further examined and also was increased in naturally acquired rhinovirus infections, as well as in cultured human epithelial cells infected with intact, but not replication-deficient, rhinovirus. Knockdown of viperin with short interfering RNA increased rhinovirus replication in infected epithelial cells. Conclusions Rhinovirus infection significantly alters the expression of many genes associated with the immune response, including chemokines and antivirals. The data obtained provide insights into the host response to rhinovirus infection and identify potential novel targets for further evaluation.

Batch effects can lead to incorrect biological conclusions but are not widely considered. The authors show that batch effects are relevant to a range of high-throughput 'omics' data sets and are crucial to address. They also explain how batch effects can be mitigated. High-throughput technologies are widely used, for example to assay genetic variants, gene and protein expression, and epigenetic modifications. One often overlooked complication with such studies is batch effects, which occur because measurements are affected by laboratory conditions, reagent lots and personnel differences. This becomes a major problem when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. Using both published studies and our own analyses, we argue that batch effects (as well as other technical and biological artefacts) are widespread and critical to address. We review experimental and computational approaches for doing so.

Key Points Over the past decade, high-throughput gene expression experiments have generated data from millions of assays. Data sets linked to publications are stored in functional genomics data archives: ArrayExpress at the European Bioinformatics Institute, Gene Expression Omnibus at the US National Center for Biotechnology Information and at the DNA Databank of Japan Omics Archive. Secondary added-value and topical databases process data from the primary archives, adding analysis and annotation to make these data accessible to every biologist by allowing queries such as 'in which tissue is a particular gene expressed?' or 'which genes are differentially expressed between a particular disease and normal samples?' Public gene expression data are commonly reused to study biological questions, both by reanalysis of primary data and by queries to secondary resources. Approximately half of the studies that use public gene expression data rely solely on existing data without adding newly generated data, and half of them use the public data in combination with new data. The reproducibility of published microarray-based studies is limited, mostly owing to insufficient experiment annotation and sometimes to unavailability of the raw or processed data. A stricter enforcement of Minimum Information About a Microarray Experiment (MIAME) requirements and also development of easy-to-use experiment annotation tools are needed to achieve a better reproducibility. Although most of the public gene expression data still are based on microarray experiments, the contribution of high-throughput-sequencing-based expression studies, known as RNA sequencing (RNA-seq), are growing rapidly. Reuse of RNA-seq data can potentially be even more valuable than reuse of microarray data, partly owing to the costs of experiments and data storage but even more importantly because of a more quantitative nature of sequencing-based expression data. Community standards such as Minimum Information about Sequencing Experiments (MINSEQE) should be adopted to make RNA-seq data maximally reusable. The bioinformatics resources that store and manage public data are sensitive to short-term funding changes, complicating the maintenance of important databases. The development of long-term infrastructure in bioinformatics, such as the ELIXIR project in Europe, is needed to ensure the long term availability of public data. A wealth of microarray gene expression data and a growing volume of RNA sequencing data are now available in public databases. The authors look at how these data are being used and discuss considerations for how such data should be analysed and deposited and how data reuse could be improved. Our understanding of gene expression has changed dramatically over the past decade, largely catalysed by technological developments. High-throughput experiments — microarrays and next-generation sequencing — have generated large amounts of genome-wide gene expression data that are collected in public archives. Added-value databases process, analyse and annotate these data further to make them accessible to every biologist. In this Review, we discuss the utility of the gene expression data that are in the public domain and how researchers are making use of these data. Reuse of public data can be very powerful, but there are many obstacles in data preparation and analysis and in the interpretation of the results. We will discuss these challenges and provide recommendations that we believe can improve the utility of such data.

A plethora of diverse programmed cell death (PCD) processes has been described in living organisms. In animals and plants, different forms of PCD play crucial roles in development, immunity, and responses to the environment. While the molecular control of some animal PCD forms such as apoptosis is known in great detail, we still know comparatively little about the regulation of the diverse types of plant PCD. In part, this deficiency in molecular understanding is caused by the lack of reliable reporters to detect PCD processes. Here, we addressed this issue by using a combination of bioinformatics approaches to identify commonly regulated genes during diverse plant PCD processes in Arabidopsis (Arabidopsis thaliana). Our results indicate that the transcriptional signatures of developmentally controlled cell death are largely distinct from the ones associated with environmentally induced cell death. Moreover, different cases of developmental PCD share a set of cell death-associated genes. Most of these genes are evolutionary conserved within the green plant lineage, arguing for an evolutionary conserved core machinery of developmental PCD. Based on this information, we established an array of specific promoter-reporter lines for developmental PCD in Arabidopsis. These PCD indicators represent a powerful resource that can be used in addition to established morphological and biochemical methods to detect and analyze PCD processes in vivo and in planta.

Background There has been a long-standing need in biomedical research for a method that quantifies the normally mixed composition of leukocytes beyond what is possible by simple histological or flow cytometric assessments. The latter is restricted by the labile nature of protein epitopes, requirements for cell processing, and timely cell analysis. In a diverse array of diseases and following numerous immune-toxic exposures, leukocyte composition will critically inform the underlying immuno-biology to most chronic medical conditions. Emerging research demonstrates that DNA methylation is responsible for cellular differentiation, and when measured in whole peripheral blood, serves to distinguish cancer cases from controls. Results Here we present a method, similar to regression calibration, for inferring changes in the distribution of white blood cells between different subpopulations (e.g. cases and controls) using DNA methylation signatures, in combination with a previously obtained external validation set consisting of signatures from purified leukocyte samples. We validate the fundamental idea in a cell mixture reconstruction experiment, then demonstrate our method on DNA methylation data sets from several studies, including data from a Head and Neck Squamous Cell Carcinoma (HNSCC) study and an ovarian cancer study. Our method produces results consistent with prior biological findings, thereby validating the approach. Conclusions Our method, in combination with an appropriate external validation set, promises new opportunities for large-scale immunological studies of both disease states and noxious exposures.

Charles Perou and colleagues apply a panel of 52 published gene expression signatures of human breast tumors to expression data from The Cancer Genome Project to identify new proliferation drivers. They find genomic regions that are uniquely amplified in highly proliferative luminal breast tumors, including some that are correlated with poor prognosis. Elucidating the molecular drivers of human breast cancers requires a strategy that is capable of integrating multiple forms of data and an ability to interpret the functional consequences of a given genetic aberration. Here we present an integrated genomic strategy based on the use of gene expression signatures of oncogenic pathway activity ( n = 52) as a framework to analyze DNA copy number alterations in combination with data from a genome-wide RNA-mediated interference screen. We identify specific DNA amplifications and essential genes within these amplicons representing key genetic drivers, including known and new regulators of oncogenesis. The genes identified include eight that are essential for cell proliferation ( FGD5 , METTL6 , CPT1A , DTX3 , MRPS23 , EIF2S2 , EIF6 and SLC2A10 ) and are uniquely amplified in patients with highly proliferative luminal breast tumors, a clinical subset of patients for which few therapeutic options are effective. This general strategy has the potential to identify therapeutic targets within amplicons through an integrated use of genomic data sets.

Complementary techniques that deepen information content and minimize reagent costs are required to realize the full potential of massively parallel sequencing. Here, we describe a resequencing approach that directs focus to genomic regions of high interest by combining hybridization-based purification of multi-megabase regions with sequencing on the Illumina Genome Analyzer (GA). The capture matrix is created by a microarray on which probes can be programmed as desired to target any non-repeat portion of the genome, while the method requires only a basic familiarity with microarray hybridization. We present a detailed protocol suitable for 1–2 μg of input genomic DNA and highlight key design tips in which high specificity (>65% of reads stem from enriched exons) and high sensitivity (98% targeted base pair coverage) can be achieved. We have successfully applied this to the enrichment of coding regions, in both human and mouse, ranging from 0.5 to 4 Mb in length. From genomic DNA library production to base-called sequences, this procedure takes approximately 9–10 d inclusive of array captures and one Illumina flow cell run.

Gram-negative bacteria such as Escherichia coli (E. coli) are assumed to be among the main agents that cause severe mastitis disease with clinical signs in dairy cattle. Rapid detection of this disease is so important in order to prevent transmission to other cows and helps to reduce inappropriate use of antibiotics. With the rapid progress in high-throughput technologies, and accumulation of various kinds of '-omics' data in public repositories, there is an opportunity to retrieve, integrate, and reanalyze these resources to improve the diagnosis and treatment of different diseases and to provide mechanistic insights into host resistance in an efficient way. Meta-analysis is a relatively inexpensive option with good potential to increase the statistical power and generalizability of single-study analysis. In the current meta-analysis research, six microarray-based studies that investigate the transcriptome profile of mammary gland tissue after induced mastitis by E. coli infection were used. This meta-analysis not only reinforced the findings in individual studies, but also several novel terms including responses to hypoxia, response to drug, anti-apoptosis and positive regulation of transcription from RNA polymerase II promoter enriched by up-regulated genes. Finally, in order to identify the small sets of genes that are sufficiently informative in E. coli mastitis, the differentially expressed gene introduced by meta-analysis were prioritized by using ten different attribute weighting algorithms. Twelve meta-genes were detected by the majority of attribute weighting algorithms (with weight above 0.7) as most informative genes including CXCL8 (IL8), NFKBIZ, HP, ZC3H12A, PDE4B, CASP4, CXCL2, CCL20, GRO1(CXCL1), CFB, S100A9, and S100A8. Interestingly, the results have been demonstrated that all of these genes are the key genes in the immune response, inflammation or mastitis. The Decision tree models efficiently discovered the best combination of the meta-genes as bio-signature and confirmed that some of the top-ranked genes -ZC3H12A, CXCL2, GRO, CFB- as biomarkers for E. coli mastitis (with the accuracy 83% in average). This research properly indicated that by combination of two novel data mining tools, meta-analysis and machine learning, increased power to detect most informative genes that can help to improve the diagnosis and treatment strategies for E. coli associated with mastitis in cattle.

Background With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem, but retrieving and consistently integrating all this data before delivering it to the wide variety of existing analysis tools becomes the new bottleneck. Results We present the newly released inSilicoMerging R/Bioconductor package which, together with the earlier released inSilicoDb R/Bioconductor package, allows consistent retrieval, integration and analysis of publicly available microarray gene expression data sets. Inside the inSilicoMerging package a set of five visual and six quantitative validation measures are available as well. Conclusions By providing (i) access to uniformly curated and preprocessed data, (ii) a collection of techniques to remove the batch effects between data sets from different sources, and (iii) several validation tools enabling the inspection of the integration process, these packages enable researchers to fully explore the potential of combining gene expression data for downstream analysis. The power of using both packages is demonstrated by programmatically retrieving and integrating gene expression studies from the InSilico DB repository [ https://insilicodb.org/app/ ].

Over 15% of the data sets catalogued in the Gene Expression Omnibus Database involve RNA samples that have been pooled before hybridization. Pooling affects data quality and inference, but the exact effects are not yet known because pooling has not been systematically studied in the context of microarray experiments. Here we report on the results of an experiment designed to evaluate the utility of pooling and the impact on identifying differentially expressed genes. We find that inference for most genes is not adversely affected by pooling, and we recommend that pooling be done when fewer than three arrays are used in each condition. For larger designs, pooling does not significantly improve inferences if few subjects are pooled. The realized benefits in this case do not outweigh the price paid for loss of individual specific information. Pooling is beneficial when many subjects are pooled, provided that independent samples contribute to multiple pools.