Bronchial responsiveness and inflammation in guinea pigs exposed to acrolein

Bronchial hyperresponsiveness can be produced experimentally after inhalation of numerous nonimmunospecific stimuli; our objective was to determine whether acrolein, a component of cigarette smoke, could increase bronchial reactivity to intravenously administered acetylcholine in guinea pigs. Bronchial responsiveness was assessed twice before and 1, 2, 6, and 24 h after exposures to less than or equal to 0.01 (sham), 0.31, 0.67, 0.94, or 1.26 parts per million for 2 h (5-7 guinea pigs/group). To examine the possible relationships of responsiveness to inflammatory mediator release and cellular infiltration, bronchoalveolar lavage was performed in another 30 guinea pigs before (control) and 0, 1, 2, 6, or 24 h after exposures. Pulmonary resistance was increased immediately after exposure (5 min) and returned to control values within 30-60 min. Increased bronchial responsiveness was evident within 1 h and became maximal 2-4 h after exposure. The acetylcholine dose necessary to double resistance decreased from 104.2 +/- 7.3 to 79.6 +/- 15.9 at 1 h and was 32.5 +/- 7.9 at 2 h and 32.8 +/- 7.6 micrograms.kg-1 at 6 h. Increases in two eicosanoids, thromboxane B2 (from 167 +/- 21 to 314 +/- 77 pg/ml) and prostaglandin F2 alpha (from 98 +/- 20 to 285 +/- 62 pg/ml) occurred immediately after exposure, whereas an influx of neutrophils occurred 24 h later (from 2.2 +/- 1.2 to 11.3 +/- 3.6%). These temporal relationships suggest that neutrophil infiltration may be a sufficient but not a necessary condition for the onset of bronchial hyperresponsiveness and that injury to cells normally present in the lung are responsible for the mediators thought to influence bronchial responsiveness.     

Identifying and annotating human bifunctional RNAs reveals their versatile functions

Bifunctional RNAs that possess both protein-coding and noncoding functional properties were less explored and poorly understood. Here we systematically explored the characteristics and functions of such human bifunctional RNAs by integrating tandem mass spectrometry and RNA-seq data. We first constructed a pipeline to identify and annotate bifunctional RNAs, leading to the characterization of 132 high-confidence bifunctional RNAs. Our analyses indicate that bifunctional RNAs may be involved in human embryonic development and can be functional in diverse tissues. Moreover, bifunctional RNAs could interact with multiple miRNAs and RNA-binding proteins to exert their corresponding roles. Bifunctional RNAs may also function as competing endogenous RNAs to regulate the expression of many genes by competing for common targeting miRNAs. Finally, somatic mutations of diverse carcinomas may generate harmful effect on corresponding bifunctional RNAs. Collectively, our study not only provides the pipeline for identifying and annotating bifunctional RNAs but also reveals their important gene-regulatory functions.     

3D QSAR studies on peroxisome proliferator-activated receptor γ agonists using CoMFA and CoMSIA

The peroxisome proliferator-activated receptors (PPARs) have increasingly become attractive targets for developing novel anti-type 2 diabetic drugs. We employed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to study three-dimensional quantitative structure–activity relationship (3D QSAR) based on existing agonists of PPAR (including five thiazolidinediones and 74 tyrosine-based compounds). Predictive 3D QSAR models with conventional r² and cross-validated coefficient (q²) values up to 0.974 and 0.642 for CoMFA and 0.979 and 0.686 for COMSIA were established using the SYBYL package. These models were validated by a test set containing 18 compounds. The CoMFA and CoMSIA field distributions are in general agreement with the structural characteristics of the binding pockets of PPAR, which demonstrates that the 3D QSAR models built here are very useful in predicting activities of novel compounds for activating PPAR.      

Genome-Wide Screen of Salmonella Genes Expressed during Infection in Pigs, Using In Vivo Expression Technology

Pigs are a food-producing species that readily carry Salmonella but, in the great majority of cases, do not show clinical signs of disease. Little is known about the functions required by Salmonella to be maintained in pigs. We have devised a recombinase-based promoter-trapping strategy to identify genes with elevated expression during pig infection with Salmonella enterica serovar Typhimurium. A total of 55 clones with in vivo-induced promoters were selected from a genomic library of ~10,000 random Salmonella DNA fragments fused to the recombinase cre, and the cloned DNA fragments were analyzed by sequencing. Thirty-one genes encoding proteins involved in bacterial adhesion and colonization (including bcfA, hscA, rffG, and yciR), virulence (metL), heat shock (hscA), and a sensor of a two-component regulator (hydH) were identified. Among the 55 clones, 19 were isolated from both the tonsils and the intestine, while 23 were identified only in the intestine and 13 only in tonsils. High temperature and increased osmolarity were identified as environmental signals that induced in vivo-expressed genes, suggesting possible signals for expression.     

A microarray study of MPP<Superscript>+</Superscript>-treated PC12 Cells: Mechanisms of toxicity (MOT) analysis using bioinformatics tools

Background

This paper describes a microarray study including data quality control, data analysis and the analysis of the mechanism of toxicity (MOT) induced by 1-methyl-4-phenylpyridinium (MPP⁺) in a rat adrenal pheochromocytoma cell line (PC12 cells) using bioinformatics tools. MPP⁺ depletes dopamine content and elicits cell death in PC12 cells. However, the mechanism of MPP⁺-induced neurotoxicity is still unclear.

Results

In this study, Agilent rat oligo 22K microarrays were used to examine alterations in gene expression of PC12 cells after 500 μM MPP⁺ treatment. Relative gene expression of control and treated cells represented by spot intensities on the array chips was analyzed using bioinformatics tools. Raw data from each array were input into the NCTR ArrayTrack database, and normalized using a Lowess normalization method. Data quality was monitored in ArrayTrack. The means of the averaged log ratio of the paired samples were used to identify the fold changes of gene expression in PC12 cells after MPP⁺ treatment. Our data showed that 106 genes and ESTs (Expressed Sequence Tags) were changed 2-fold and above with MPP⁺ treatment; among these, 75 genes had gene symbols and 59 genes had known functions according to the Agilent gene Refguide and ArrayTrack-linked gene library. The mechanism of MPP⁺-induced toxicity in PC12 cells was analyzed based on their genes functions, biological process, pathways and previous published literatures.

Conclusion

Multiple pathways were suggested to be involved in the mechanism of MPP⁺-induced toxicity, including oxidative stress, DNA and protein damage, cell cycling arrest, and apoptosis.

     

Shifting from population-wide to personalized cancer prognosis with microarrays

The era of personalized medicine for cancer therapeutics has taken an important step forward in making accurate prognoses for individual patients with the adoption of high-throughput microarray technology. However, microarray technology in cancer diagnosis or prognosis has been primarily used for the statistical evaluation of patient populations, and thus excludes inter-individual variability and patient-specific predictions. Here we propose a metric called clinical confidence that serves as a measure of prognostic reliability to facilitate the shift from population-wide to personalized cancer prognosis using microarray-based predictive models. The performance of sample-based models predicted with different clinical confidences was evaluated and compared systematically using three large clinical datasets studying the following cancers: breast cancer, multiple myeloma, and neuroblastoma. Survival curves for patients, with different confidences, were also delineated. The results show that the clinical confidence metric separates patients with different prediction accuracies and survival times. Samples with high clinical confidence were likely to have accurate prognoses from predictive models. Moreover, patients with high clinical confidence would be expected to live for a notably longer or shorter time if their prognosis was good or grim based on the models, respectively. We conclude that clinical confidence could serve as a beneficial metric for personalized cancer prognosis prediction utilizing microarrays. Ascribing a confidence level to prognosis with the clinical confidence metric provides the clinician an objective, personalized basis for decisions, such as choosing the severity of the treatment.     

Sim4cc: a cross-species spliced alignment program

Advances in sequencing technologies have accelerated the sequencing of new genomes, far outpacing the generation of gene and protein resources needed to annotate them. Direct comparison and alignment of existing cDNA sequences from a related species is an effective and readily available means to determine genes in the new genomes. Current spliced alignment programs are inadequate for comparing sequences between different species, owing to their low sensitivity and splice junction accuracy. A new spliced alignment tool, sim4cc, overcomes problems in the earlier tools by incorporating three new features: universal spaced seeds, to increase sensitivity and allow comparisons between species at various evolutionary distances, and powerful splice signal models and evolutionarily-aware alignment techniques, to improve the accuracy of gene models. When tested on vertebrate comparisons at diverse evolutionary distances, sim4cc had significantly higher sensitivity compared to existing alignment programs, more than 10% higher than the closest competitor for some comparisons, while being comparable in speed to its predecessor, sim4. Sim4cc can be used in one-to-one or one-to-many comparisons of genomic and cDNA sequences, and can also be effectively incorporated into a high-throughput annotation engine, as demonstrated by the mapping of 64 000 Fagus grandifolia 454 ESTs and unigenes to the poplar genome.     

An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era

Background

Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment?

Results

We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined.

Conclusions

Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-014-0523-y) contains supplementary material, which is available to authorized users.