Novel statistical approaches for non-normal censored immunological data: analysis of cytokine and gene expression data

Background: For several immune-mediated diseases, immunological analysis will become more complex in the future with datasets in which cytokine and gene expression data play a major role. These data have certain characteristics that require sophisticated statistical analysis such as strategies for non-normal distribution and censoring. Additionally, complex and multiple immunological relationships need to be adjusted for potential confounding and interaction effects. Objective: We aimed to introduce and apply different methods for statistical analysis of non-normal censored cytokine and gene expression data. Furthermore, we assessed the performance and accuracy of a novel regression approach in order to allow adjusting for covariates and potential confounding. Methods: For non-normally distributed censored data traditional means such as the Kaplan-Meier method or the generalized Wilcoxon test are described. In order to adjust for covariates the novel approach named Tobit regression on ranks was introduced. Its performance and accuracy for analysis of non-normal censored cytokine/gene expression data was evaluated by a simulation study and a statistical experiment applying permutation and bootstrapping. Results: If adjustment for covariates is not necessary traditional statistical methods are adequate for non-normal censored data. Comparable with these and appropriate if additional adjustment is required, Tobit regression on ranks is a valid method. Its power, type-I error rate and accuracy were comparable to the classical Tobit regression. Conclusion: Non-normally distributed censored immunological data require appropriate statistical methods. Tobit regression on ranks meets these requirements and can be used for adjustment for covariates and potential confounding in large and complex immunological datasets

Absolute estimation of initial concentrations of amplicon in a real-time RT-PCR process

<p>Abstract</p> <p>Background</p> <p>Since real time PCR was first developed, several approaches to estimating the initial quantity of template in an RT-PCR reaction have been tried. While initially only the early thermal cycles corresponding to exponential duplication were used, lately there has been an effort to use all of the cycles in a PCR. The efforts have included both fitting empirical sigmoid curves and more elaborate mechanistic models that explore the chemical reactions taking place during each cycle. The more elaborate mechanistic models require many more parameters than can be fit from a single amplification, while the empirical models provide little insight and are difficult to tailor to specific reactants.</p> <p>Results</p> <p>We directly estimate the initial amount of amplicon using a simplified mechanistic model based on chemical reactions in the annealing step of the PCR. The basic model includes the duplication of DNA with the digestion of Taqman probe and the re-annealing between previously synthesized DNA strands of opposite orientation. By modelling the amount of Taqman probe digested and matching that with the observed fluorescence, the conversion factor between the number of fluorescing dye molecules and observed fluorescent emission can be estimated, along with the absolute initial amount of amplicon and the rate parameter for re-annealing. The model is applied to several PCR reactions with known amounts of amplicon and is shown to work reasonably well. An expanded version of the model allows duplication of amplicon without release of fluorescent dye, by adding 1 more parameter to the model. The additional process is helpful in most cases where the initial primer concentration exceeds the initial probe concentration. Software for applying the algorithm to data may be downloaded at <url>http://www.niehs.nih.gov/research/resources/software/pcranalyzer/</url></p> <p>Conclusion</p> <p>We present proof of the principle that a mechanistically based model can be fit to observations from a single PCR amplification. Initial amounts of amplicon are well estimated without using a standard solution. Using the ratio of the predicted initial amounts of amplicon from 2 PCRs is shown to work well even when the absolute amounts of amplicon are underestimated in the individual PCRs.</p

Digital PCR provides sensitive and absolute calibration for high throughput sequencing

<p>Abstract</p> <p>Background</p> <p>Next-generation DNA sequencing on the 454, Solexa, and SOLiD platforms requires absolute calibration of the number of molecules to be sequenced. This requirement has two unfavorable consequences. First, large amounts of sample-typically micrograms-are needed for library preparation, thereby limiting the scope of samples which can be sequenced. For many applications, including metagenomics and the sequencing of ancient, forensic, and clinical samples, the quantity of input DNA can be critically limiting. Second, each library requires a titration sequencing run, thereby increasing the cost and lowering the throughput of sequencing.</p> <p>Results</p> <p>We demonstrate the use of digital PCR to accurately quantify 454 and Solexa sequencing libraries, enabling the preparation of sequencing libraries from nanogram quantities of input material while eliminating costly and time-consuming titration runs of the sequencer. We successfully sequenced low-nanogram scale bacterial and mammalian DNA samples on the 454 FLX and Solexa DNA sequencing platforms. This study is the first to definitively demonstrate the successful sequencing of picogram quantities of input DNA on the 454 platform, reducing the sample requirement more than 1000-fold without pre-amplification and the associated bias and reduction in library depth.</p> <p>Conclusion</p> <p>The digital PCR assay allows absolute quantification of sequencing libraries, eliminates uncertainties associated with the construction and application of standard curves to PCR-based quantification, and with a coefficient of variation close to 10%, is sufficiently precise to enable direct sequencing without titration runs.</p

The Use of Antisense Oligonucleotides in Evaluating Survivin as a Therapeutic Target for Radiation Sensitization in Lung Cancer

Elucidating the mechanism of over and under expression of proteins is critical in developing a better understanding of cancer. Multiple techniques are used to examine differential expression of proteins in cells and assess changes in protein expression in response to therapies such as radiation. Reduced expression can be caused by protein inactivation, mRNA instability, or reduced transcription. The following protocol was used to determine the mechanism for the reduced expression of an antiapoptotic factor, survivin, in normal tissues in response to radiation and the defect in cancer cells that prevents this reduction. We also examined ways to overcome survivin over expression in cancer cells in order to sensitize them to radiation. We will focus on the use of antisense oligonucleotides, cell cycle analysis, and luciferase reporter genes

Unique reporter-based sensor platforms to monitor signalling in cells

Introduction: In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level. &lt;p/&gt;Methods: Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis. &lt;p/&gt;Findings: We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation. &lt;p/&gt;Conclusions: We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters

Quantification Bias Caused by Plasmid DNA Conformation in Quantitative Real-Time PCR Assay

Quantitative real-time PCR (qPCR) is the gold standard for the quantification of specific nucleic acid sequences. However, a serious concern has been revealed in a recent report: supercoiled plasmid standards cause significant over-estimation in qPCR quantification. In this study, we investigated the effect of plasmid DNA conformation on the quantification of DNA and the efficiency of qPCR. Our results suggest that plasmid DNA conformation has significant impact on the accuracy of absolute quantification by qPCR. DNA standard curves shifted significantly among plasmid standards with different DNA conformations. Moreover, the choice of DNA measurement method and plasmid DNA conformation may also contribute to the measurement error of DNA standard curves. Due to the multiple effects of plasmid DNA conformation on the accuracy of qPCR, efforts should be made to assure the highest consistency of plasmid standards for qPCR. Thus, we suggest that the conformation, preparation, quantification, purification, handling, and storage of standard plasmid DNA should be described and defined in the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to assure the reproducibility and accuracy of qPCR absolute quantification

Internal control genes for quantitative RT-PCR expression analysis in mouse osteoblasts, osteoclasts and macrophages

<p>Abstract</p> <p>Background</p> <p>Real-time quantitative RT-PCR (qPCR) is a powerful technique capable of accurately quantitating mRNA expression levels over a large dynamic range. This makes qPCR the most widely used method for studying quantitative gene expression. An important aspect of qPCR is selecting appropriate controls or normalization factors to account for any differences in starting cDNA quantities between samples during expression studies. Here, we report on the selection of a concise set of housekeeper genes for the accurate normalization of quantitative gene expression data in differentiating osteoblasts, osteoclasts and macrophages. We implemented the use of geNorm, an algorithm that determines the suitability of genes to function as housekeepers by assessing expression stabilities. We evaluated the expression stabilities of 18S, ACTB, B2M, GAPDH, HMBS and HPRT1 genes.</p> <p>Findings</p> <p>Our analyses revealed that 18S and GAPDH were regulated during osteoblast differentiation and are not suitable for use as reference genes. The most stably expressed genes in osteoblasts were ACTB, HMBS and HPRT1 and their geometric average constitutes a suitable normalization factor upon which gene expression data can be normalized. In macrophages, 18S and GAPDH were the most variable genes while HMBS and B2M were the most stably expressed genes. The geometric average of HMBS and B2M expression levels forms a suitable normalization factor to account for potential differences in starting cDNA quantities during gene expression analysis in macrophages. The expression stabilities of the six candidate reference genes in osteoclasts were, on average, more variable than that observed in macrophages but slightly less variable than those seen in osteoblasts. The two most stably expressed genes in osteoclasts were HMBS and B2M and the genes displaying the greatest levels of variability were 18S and GAPDH. Notably, 18S and GAPDH were the two most variably expressed control genes in all three cell types. The geometric average of HMBS, B2M and ACTB creates an appropriate normalization factor for gene expression studies in osteoclasts.</p> <p>Conclusion</p> <p>We have identified concise sets of genes suitable to use as normalization factors for quantitative real-time RT-PCR gene expression studies in osteoblasts, osteoclasts and macrophages.</p

Effect of various normalization methods on Applied Biosystems expression array system data

BACKGROUND: DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays. RESULTS: We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan(® )assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists. CONCLUSION: Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan(® )assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System

Inverse correlation between PDGFC expression and lymphocyte infiltration in human papillary thyroid carcinomas

<p>Abstract</p> <p>Background</p> <p>Members of the PDGF family have been suggested as potential biomarkers for papillary thyroid carcinomas (PTC). However, it is known that both expression and stimulatory effect of PDGF ligands can be affected by inflammatory cytokines. We have performed a microarray study in a collection of PTCs, of which about half the biopsies contained tumour-infiltrating lymphocytes or thyroiditis. To investigate the expression level of PDGF ligands and receptors in PTC we measured the relative mRNA expression of all members of the PDGF family by qRT-PCR in 10 classical PTC, eight clinically aggressive PTC, and five non-neoplastic thyroid specimens, and integrated qRT-PCR data with microarray data to enable us to link PDGF-associated gene expression profiles into networks based on recognized interactions. Finally, we investigated potential influence on PDGF mRNA levels by the presence of tumour-infiltrating lymphocytes.</p> <p>Methods</p> <p>qRT-PCR was performed on <it>PDGFA</it>, <it>PDGFB</it>, <it>PDGFC</it>, <it>PDGFD</it>, <it>PDGFRA PDGFRB </it>and a selection of lymphocyte specific mRNA transcripts. Semiquantitative assessment of tumour-infiltrating lymphocytes was performed on the adjacent part of the biopsy used for RNA extraction for all biopsies, while direct quantitation by qRT-PCR of lymphocyte-specific mRNA transcripts were performed on RNA also subjected to expression analysis. Relative expression values of PDGF family members were combined with a cDNA microarray dataset and analyzed based on clinical findings and PDGF expression patterns. Ingenuity Pathway Analysis (IPA) was used to elucidate potential molecular interactions and networks.</p> <p>Results</p> <p>PDGF family members were differentially regulated at the mRNA level in PTC as compared to normal thyroid specimens. Expression of <it>PDGFA </it>(p = 0.003), <it>PDGFB </it>(p = 0.01) and <it>PDGFC </it>(p = 0.006) were significantly up-regulated in PTCs compared to non-neoplastic thyroid tissue. In addition, expression of <it>PDGFC </it>was significantly up-regulated in classical PTCs as compared to clinically aggressive PTCs (p = 0.006), and <it>PDGFRB </it>were significantly up-regulated in clinically aggressive PTCs (p = 0.01) as compared to non-neoplastic tissue. Semiquantitative assessment of lymphocytes correlated well with quantitation of lymphocyte-specific gene expression. Further more, by combining TaqMan and microarray data we found a strong inverse correlation between <it>PDGFC </it>expression and the expression of lymphocyte specific mRNAs.</p> <p>Conclusion</p> <p>At the mRNA level, several members of the PDGF family are differentially expressed in PTCs as compared to normal thyroid tissue. Of these, only the <it>PDGFC </it>mRNA expression level initially seemed to distinguish classical PTCs from the more aggressive PTCs. However, further investigation showed that <it>PDGFC </it>expression level correlated inversely to the expression of several lymphocyte specific genes, and to the presence of lymphocytes in the biopsies. Thus, we find that <it>PDGFC </it>mRNA expression were down-regulated in biopsies containing infiltrated lymphocytes or thyroiditis. No other PDGF family member could be linked to lymphocyte specific gene expression in our collection of PTCs biopsies.</p