A versatile panel of reference gene assays for the measurement of chicken mRNA by quantitative PCR

Quantitative real-time PCR assays are widely used for the quantification of mRNA within avian experimental samples. Multiple stably-expressed reference genes, selected for the lowest variation in representative samples, can be used to control random technical variation. Reference gene assays must be reliable, have high amplification specificity and efficiency, and not produce signals from contaminating DNA. Whilst recent research papers identify specific genes that are stable in particular tissues and experimental treatments, here we describe a panel of ten avian gene primer and probe sets that can be used to identify suitable reference genes in many experimental contexts. The panel was tested with TaqMan and SYBR Green systems in two experimental scenarios: a tissue collection and virus infection of cultured fibroblasts. GeNorm and NormFinder algorithms were able to select appropriate reference gene sets in each case. We show the effects of using the selected genes on the detection of statistically significant differences in expression. The results are compared with those obtained using 28s ribosomal RNA, the present most widely accepted reference gene in chicken work, identifying circumstances where its use might provide misleading results. Methods for eliminating DNA contamination of RNA reduced, but did not completely remove, detectable DNA. We therefore attached special importance to testing each qPCR assay for absence of signal using DNA template. The assays and analyses developed here provide a useful resource for selecting reference genes for investigations of avian biology

Identification of prostate cancer diagnostic and prognostic biomarkers in urine expression data with a focus on extracellular vesicles

Prostate Cancer (PCa) is a major clinical problem worldwide with considerable variability in clinical outcome of patients. PCa diagnostics and prognostics currently lack specific and sensitive clinical biomarkers and treatment is not well individualised. The PCA3 test, amongst others, highlights the utility of urine in PCa diagnostics and prognostics. Urine contains cells and extracellular vesicles (EV) that originate in the prostate. There are many areas of the PCa clinical process that could be aided with an expression based urine test, including diagnosis, prognosis and response to therapy. NanoString data (167 transcripts) from 485 EV RNA samples were collected from PCa patients and used to build models that would aid in PCa diagnosis and prognosis i.e. i) PCa (low- (L), intermediate-(I), and high-risk(H)) vs CB (Clinically Benign/No evidence for cancer), ii) high-risk PCa vs CB, and iii) trend in expression across CB>L>I>H. These models were validated in 235 samples, with AUCs of i) 0.851 ii) 0.897 and iii) 0.709, respectively. The potential of using urine EVs to predict patient response to treatments was also investigated. In a pilot data set a signature of seven transcripts was identified that could optimally predict progression of patients on hormone therapy (p = 2.3x10-05; HR = 0.04288). Models were also built using NanoString data from 92 cell RNA samples. Intercomparing expression data from matched cell and EV fractions of urine showed that transcripts significantly higher in the EV samples were associated with the prostate, PCa and cancer in general, supporting them as a viable source of biomarkers in the clinical management of PCa. In conclusion my analyses have demonstrated the utility of examining urine RNA for the diagnosis and prognosis of PCa. My studies have formed the basis of the production of a Prostate Urine Risk test that is currently under development at UEA

Establishing a major cause of discrepancy in the calibration of Affymetrix GeneChips

<p>Abstract</p> <p>Background</p> <p>Affymetrix GeneChips are a popular platform for performing whole-genome experiments on the transcriptome. There are a range of different calibration steps, and users are presented with choices of different background subtractions, normalisations and expression measures. We wished to establish which of the calibration steps resulted in the biggest uncertainty in the sets of genes reported to be differentially expressed.</p> <p>Results</p> <p>Our results indicate that the sets of genes identified as being most significantly differentially expressed, as estimated by the z-score of fold change, is relatively insensitive to the choice of background subtraction and normalisation. However, the contents of the gene list are most sensitive to the choice of expression measure. This is irrespective of whether the experiment uses a rat, mouse or human chip and whether the chip definition is made using probe mappings from Unigene, RefSeq, Entrez Gene or the original Affymetrix definitions. It is also irrespective of whether both Present and Absent, or just Present, Calls from the MAS5 algorithm are used to filter genelists, and this conclusion holds for genes of differing intensities. We also reach the same conclusion after assigning genes to be differentially expressed using t-statistics, although this approach results in a large amount of false positives in the sets of genes identified due to the small numbers of replicates typically used in microarray experiments.</p> <p>Conclusion</p> <p>The major calibration uncertainty that biologists need to consider when analysing Affymetrix data is how their multiple probe values are condensed into one expression measure.</p

Whole genome expression analysis within the angiotensin II-apolipoprotein E deficient mouse model of abdominal aortic aneurysm

Abstract\ud Background: An animal model commonly used to investigate pathways and potential therapeutic\ud interventions relevant to abdominal aortic aneurysm (AAA) involves subcutaneous infusion of\ud angiotensin II within the apolipoprotein E deficient mouse. The aim of this study was to investigate\ud genes differentially expressed in aneurysms forming within this mouse model in order to assess the\ud relevance of this model to human AAA.\ud Results: Using microarrays we identified genes relevant to aneurysm formation within\ud apolipoprotein E deficient mice. Firstly we investigated genes differentially expressed in the\ud aneurysm prone segment of the suprarenal aorta in these mice. Secondly we investigated genes that\ud were differentially expressed in the aortas of mice developing aneurysms relative to those that did\ud not develop aneurysms in response to angiotensin II infusion. Our findings suggest that a host of\ud inflammation and extracellular matrix remodelling pathways are upregulated within the aorta in\ud mice developing aneurysms. Kyoto Encyclopedia of Genes and Genome categories enriched in the\ud aortas of mice with aneurysms included cytokine-cytokine receptor interaction, leukocyte\ud transendothelial migration, natural killer cell mediated cytotoxicity and hematopoietic cell lineage.\ud Genes associated with extracellular matrix remodelling, such as a range of matrix\ud metalloproteinases were also differentially expressed in relation to aneurysm formation.\ud Conclusion: This study is the first report describing whole genome expression arrays in the\ud apolipoprotein E deficient mice in relation to aneurysm formation. The findings suggest that the\ud pathways believed to be critical in human AAA are also relevant to aneurysm formation in this\ud mouse model. The findings therefore support the value of this model to investigate interventions\ud and mechanisms of human AAA

A Comprehensive and Universal Method for Assessing the Performance of Differential Gene Expression Analyses

The number of methods for pre-processing and analysis of gene expression data continues to increase, often making it difficult to select the most appropriate approach. We present a simple procedure for comparative estimation of a variety of methods for microarray data pre-processing and analysis. Our approach is based on the use of real microarray data in which controlled fold changes are introduced into 20% of the data to provide a metric for comparison with the unmodified data. The data modifications can be easily applied to raw data measured with any technological platform and retains all the complex structures and statistical characteristics of the real-world data. The power of the method is illustrated by its application to the quantitative comparison of different methods of normalization and analysis of microarray data. Our results demonstrate that the method of controlled modifications of real experimental data provides a simple tool for assessing the performance of data preprocessing and analysis methods

Analysis of gene expression data from non-small celllung carcinoma cell lines reveals distinct sub-classesfrom those identified at the phenotype level

Microarray data from cell lines of Non-Small Cell Lung Carcinoma (NSCLC) can be used to look for differences in gene expression between the cell lines derived from different tumour samples, and to investigate if these differences can be used to cluster the cell lines into distinct groups. Dividing the cell lines into classes can help to improve diagnosis and the development of screens for new drug candidates. The micro-array data is first subjected to quality control analysis and then subsequently normalised using three alternate methods to reduce the chances of differences being artefacts resulting from the normalisation process. The final clustering into sub-classes was carried out in a conservative manner such that subclasses were consistent across all three normalisation methods. If there is structure in the cell line population it was expected that this would agree with histological classifications, but this was not found to be the case. To check the biological consistency of the sub-classes the set of most strongly differentially expressed genes was be identified for each pair of clusters to check if the genes that most strongly define sub-classes have biological functions consistent with NSCLC

Comparison Between Expression Microarrays and RNA-Sequencing Using UKBEC Dataset Identified a trans-eQTL Associated with MPZ Gene in Substantia Nigra

In recent years, the advantages of RNA-sequencing (RNA-Seq) have made it the platform of choice for measuring gene expression over traditional microarrays. However, RNA-Seq comes with bioinformatical challenges and higher computational costs. Therefore, this study set out to assess whether the increased depth of transcriptomic information facilitated by RNA-Seq is worth the increased computation over microarrays, specifically at three levels: absolute expression levels, differentially expressed genes identification, and expression QTL (eQTL) mapping in regions of the human brain. Using the United Kingdom Brain Expression Consortium (UKBEC) dataset, there is high agreement of gene expression levels measured by microarrays and RNA-seq when quantifying absolute expression levels and when identifying differentially expressed genes. These findings suggest that depending on the aims of a study, the relative ease of working with microarray data may outweigh the computational time and costs of RNA-Seq pipelines. On the other, there was low agreement when mapping eQTLs. However, a number of eQTLs associated with genes that play important roles in the brain were found in both platforms. For example, a trans-eQTL was mapped that is associated with the MPZ gene in the substantia nigra. These eQTLs that we have highlighted are extremely promising candidates that merit further investigation

Error estimates for the analysis of differential expression fromRNA-seq count data

Background: A number of algorithms exist for analysing RNA-sequencing data to infer profiles of differential gene expression. Problems inherent in building algorithms around statistical models of over dispersed count data are formidable and frequently lead to non-uniform p-value distributions for null-hypothesis data and to inaccurate estimates of false discovery rates (FDRs). This can lead to an inaccurate measure of significance and loss of power to detect differential expression. Results: We use synthetic and real biological data to assess the ability of several available R packages to accurately estimate FDRs. The packages surveyed are based on statistical models of overdispersed Poisson data and include edgeR, DESeq, DESeq2, PoissonSeq and QuasiSeq. Also tested is an add-on package to edgeR and DESeq which we introduce called Polyfit. Polyfit aims to address the problem of a non-uniform null p-value distribution for two-class datasets by adapting the Storey-Tibshirani procedure. Conclusions: We find the best performing package in the sense that it achieves a low FDR which is accurately estimated over the full range of p-values, albeit with a very slow run time, is the QLSpline implementation of QuasiSeq. This finding holds provided the number of biological replicates in each condition is at least 4. The next best performing packages are edgeR and DESeq2. When the number of biological replicates is sufficiently high, and within a range accessible tomultiplexed experimental designs, the Polyfit extension improves the performance DESeq (for approximately 6 or more replicates per condition), making its performance comparable with that of edgeR and DESeq2 in our tests with synthetic data

Reconstruction of phrenic neuron identity in embryonic stem cell-derived motor neurons

Air breathing is an essential motor function for vertebrates living on land. The rhythm that drives breathing is generated within the central nervous system and relayed via specialised subsets of spinal motor neurons to muscles that regulate lung volume. In mammals, a key respiratory muscle is the diaphragm, which is innervated by motor neurons in the phrenic nucleus. Remarkably, relatively little is known about how this crucial subtype of motor neuron is generated during embryogenesis. Here, we used direct differentiation of motor neurons from mouse embryonic stem cells as a tool to identify genes that direct phrenic neuron identity. We find that three determinants, Pou3f1, Hoxa5 and Notch, act in combination to promote a phrenic neuron molecular identity. We show that Notch signalling induces Pou3f1 in developing motor neurons in vitro and in vivo. This suggests that the phrenic neuron lineage is established through a local source of Notch ligand at mid-cervical levels. Furthermore, we find that the cadherins Pcdh10, which is regulated by Pou3f1 and Hoxa5, and Cdh10, which is controlled by Pou3f1, are both mediators of like-like clustering of motor neuron cell bodies. This specific Pcdh10/Cdh10 activity might provide the means by which phrenic neurons are assembled into a distinct nucleus. Our study provides a framework for understanding how phrenic neuron identity is conferred and will help to generate this rare and inaccessible yet vital neuronal subtype directly from pluripotent stem cells, thus facilitating subsequent functional investigations

Error, reproducibility and sensitivity : a pipeline for data processing of Agilent oligonucleotide expression arrays

Background Expression microarrays are increasingly used to obtain large scale transcriptomic information on a wide range of biological samples. Nevertheless, there is still much debate on the best ways to process data, to design experiments and analyse the output. Furthermore, many of the more sophisticated mathematical approaches to data analysis in the literature remain inaccessible to much of the biological research community. In this study we examine ways of extracting and analysing a large data set obtained using the Agilent long oligonucleotide transcriptomics platform, applied to a set of human macrophage and dendritic cell samples. Results We describe and validate a series of data extraction, transformation and normalisation steps which are implemented via a new R function. Analysis of replicate normalised reference data demonstrate that intrarray variability is small (only around 2% of the mean log signal), while interarray variability from replicate array measurements has a standard deviation (SD) of around 0.5 log2 units ( 6% of mean). The common practise of working with ratios of Cy5/Cy3 signal offers little further improvement in terms of reducing error. Comparison to expression data obtained using Arabidopsis samples demonstrates that the large number of genes in each sample showing a low level of transcription reflect the real complexity of the cellular transcriptome. Multidimensional scaling is used to show that the processed data identifies an underlying structure which reflect some of the key biological variables which define the data set. This structure is robust, allowing reliable comparison of samples collected over a number of years and collected by a variety of operators. Conclusions This study outlines a robust and easily implemented pipeline for extracting, transforming normalising and visualising transcriptomic array data from Agilent expression platform. The analysis is used to obtain quantitative estimates of the SD arising from experimental (non biological) intra- and interarray variability, and for a lower threshold for determining whether an individual gene is expressed. The study provides a reliable basis for further more extensive studies of the systems biology of eukaryotic cells