A standard curve based method for relative real time PCR data processing

BACKGROUND: Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At the moment, the PCR efficiency approach is preferred in relative PCR whilst the standard curve is often used for absolute PCR. However, there are no barriers to employ standard curves for relative PCR. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR. RESULTS: We designed a procedure for data processing in relative real time PCR. The procedure completely avoids PCR efficiency assessment, minimizes operator involvement and provides a statistical assessment of intra-assay variation. The procedure includes the following steps. (I) Noise is filtered from raw fluorescence readings by smoothing, baseline subtraction and amplitude normalization. (II) The optimal threshold is selected automatically from regression parameters of the standard curve. (III) Crossing points (CPs) are derived directly from coordinates of points where the threshold line crosses fluorescence plots obtained after the noise filtering. (IV) The means and their variances are calculated for CPs in PCR replicas. (V) The final results are derived from the CPs' means. The CPs' variances are traced to results by the law of error propagation. A detailed description and analysis of this data processing is provided. The limitations associated with the use of parametric statistical methods and amplitude normalization are specifically analyzed and found fit to the routine laboratory practice. Different options are discussed for aggregation of data obtained from multiple reference genes. CONCLUSION: A standard curve based procedure for PCR data processing has been compiled and validated. It illustrates that standard curve design remains a reliable and simple alternative to the PCR-efficiency based calculations in relative real time PCR

Statistical analysis of real-time PCR data

BACKGROUND: Even though real-time PCR has been broadly applied in biomedical sciences, data processing procedures for the analysis of quantitative real-time PCR are still lacking; specifically in the realm of appropriate statistical treatment. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data. RESULTS: In the first approach, a multiple regression analysis model was developed to derive ΔΔCt from estimation of interaction of gene and treatment effects. In the second approach, an ANCOVA (analysis of covariance) model was proposed, and the ΔΔCt can be derived from analysis of effects of variables. The other two models involve calculation ΔCt followed by a two group t-test and non-parametric analogous Wilcoxon test. SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS. CONCLUSION: Practical statistical solutions with SAS programs were developed for real-time PCR data and a sample dataset was analyzed with the SAS programs. The analysis using the various models and programs yielded similar results. Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR

Profound effect of profiling platform and normalization strategy on detection of differentially expressed microRNAs

Adequate normalization minimizes the effects of systematic technical variations and is a prerequisite for getting meaningful biological changes. However, there is inconsistency about miRNA normalization performances and recommendations. Thus, we investigated the impact of seven different normalization methods (reference gene index, global geometric mean, quantile, invariant selection, loess, loessM, and generalized procrustes analysis) on intra- and inter-platform performance of two distinct and commonly used miRNA profiling platforms. We included data from miRNA profiling analyses derived from a hybridization-based platform (Agilent Technologies) and an RT-qPCR platform (Applied Biosystems). Furthermore, we validated a subset of miRNAs by individual RT-qPCR assays. Our analyses incorporated data from the effect of differentiation and tumor necrosis factor alpha treatment on primary human skeletal muscle cells and a murine skeletal muscle cell line. Distinct normalization methods differed in their impact on (i) standard deviations, (ii) the area under the receiver operating characteristic (ROC) curve, (iii) the similarity of differential expression. Loess, loessM, and quantile analysis were most effective in minimizing standard deviations on the Agilent and TLDA platform. Moreover, loess, loessM, invariant selection and generalized procrustes analysis increased the area under the ROC curve, a measure for the statistical performance of a test. The Jaccard index revealed that inter-platform concordance of differential expression tended to be increased by loess, loessM, quantile, and GPA normalization of AGL and TLDA data as well as RGI normalization of TLDA data. We recommend the application of loess, or loessM, and GPA normalization for miRNA Agilent arrays and qPCR cards as these normalization approaches showed to (i) effectively reduce standard deviations, (ii) increase sensitivity and accuracy of differential miRNA expression detection as well as (iii) increase inter-platform concordance. Results showed the successful adoption of loessM and generalized procrustes analysis to one-color miRNA profiling experiments

Enhanced analysis of real-time PCR data by using a variable efficiency model : FPK-PCR

Current methodology in real-time Polymerase chain reaction (PCR) analysis performs well provided PCR efficiency remains constant over reactions. Yet, small changes in efficiency can lead to large quantification errors. Particularly in biological samples, the possible presence of inhibitors forms a challenge. We present a new approach to single reaction efficiency calculation, called Full Process Kinetics-PCR (FPK-PCR). It combines a kinetically more realistic model with flexible adaptation to the full range of data. By reconstructing the entire chain of cycle efficiencies, rather than restricting the focus on a 'window of application', one extracts additional information and loses a level of arbitrariness. The maximal efficiency estimates returned by the model are comparable in accuracy and precision to both the golden standard of serial dilution and other single reaction efficiency methods. The cycle-to-cycle changes in efficiency, as described by the FPK-PCR procedure, stay considerably closer to the data than those from other S-shaped models. The assessment of individual cycle efficiencies returns more information than other single efficiency methods. It allows in-depth interpretation of real-time PCR data and reconstruction of the fluorescence data, providing quality control. Finally, by implementing a global efficiency model, reproducibility is improved as the selection of a window of application is avoided

Detection of skewed X-chromosome inactivation in Fragile X syndrome and X chromosome aneuploidy using quantitative melt analysis.

Methylation of the fragile X mental retardation 1 (FMR1) exon 1/intron 1 boundary positioned fragile X related epigenetic element 2 (FREE2), reveals skewed X-chromosome inactivation (XCI) in fragile X syndrome full mutation (FM: CGG > 200) females. XCI skewing has been also linked to abnormal X-linked gene expression with the broader clinical impact for sex chromosome aneuploidies (SCAs). In this study, 10 FREE2 CpG sites were targeted using methylation specific quantitative melt analysis (MS-QMA), including 3 sites that could not be analysed with previously used EpiTYPER system. The method was applied for detection of skewed XCI in FM females and in different types of SCA. We tested venous blood and saliva DNA collected from 107 controls (CGG < 40), and 148 FM and 90 SCA individuals. MS-QMA identified: (i) most SCAs if combined with a Y chromosome test; (ii) locus-specific XCI skewing towards the hypomethylated state in FM females; and (iii) skewed XCI towards the hypermethylated state in SCA with 3 or more X chromosomes, and in 5% of the 47,XXY individuals. MS-QMA output also showed significant correlation with the EpiTYPER reference method in FM males and females (P < 0.0001) and SCAs (P < 0.05). In conclusion, we demonstrate use of MS-QMA to quantify skewed XCI in two applications with diagnostic utility

Simulation of between repeat variability in real time PCR reactions

While many decisions rely on real time quantitative PCR (qPCR) analysis few attempts have hitherto been made to quantify bounds of precision accounting for the various sources of variation involved in the measurement process. Besides influences of more obvious factors such as camera noise and pipetting variation, changing efficiencies within and between reactions affect PCR results to a degree which is not fully recognized. Here, we develop a statistical framework that models measurement error and other sources of variation as they contribute to fluorescence observations during the amplification process and to derived parameter estimates. Evaluation of reproducibility is then based on simulations capable of generating realistic variation patterns. To this end, we start from a relatively simple statistical model for the evolution of efficiency in a single PCR reaction and introduce additional error components, one at a time, to arrive at stochastic data generation capable of simulating the variation patterns witnessed in repeated reactions (technical repeats). Most of the variation in C-q values was adequately captured by the statistical model in terms of foreseen components. To recreate the dispersion of the repeats' plateau levels while keeping the other aspects of the PCR curves within realistic bounds, additional sources of reagent consumption (side reactions) enter into the model. Once an adequate data generating model is available, simulations can serve to evaluate various aspects of PCR under the assumptions of the model and beyond

Self-reinoculation with fecal flora changes microbiota density and composition leading to an altered bile-acid profile in the mouse small intestine

Background: The upper gastrointestinal tract plays a prominent role in human physiology as the primary site for enzymatic digestion and nutrient absorption, immune sampling, and drug uptake. Alterations to the small intestine microbiome have been implicated in various human diseases, such as non-alcoholic steatohepatitis and inflammatory bowel conditions. Yet, the physiological and functional roles of the small intestine microbiota in humans remain poorly characterized because of the complexities associated with its sampling. Rodent models are used extensively in microbiome research and enable the spatial, temporal, compositional, and functional interrogation of the gastrointestinal microbiota and its effects on the host physiology and disease phenotype. Classical, culture-based studies have documented that fecal microbial self-reinoculation (via coprophagy) affects the composition and abundance of microbes in the murine proximal gastrointestinal tract. This pervasive self-reinoculation behavior could be a particularly relevant study factor when investigating small intestine microbiota. Modern microbiome studies either do not take self-reinoculation into account, or assume that approaches such as single housing mice or housing on wire mesh floors eliminate it. These assumptions have not been rigorously tested with modern tools. Here, we used quantitative 16S rRNA gene amplicon sequencing, quantitative microbial functional gene content inference, and metabolomic analyses of bile acids to evaluate the effects of self-reinoculation on microbial loads, composition, and function in the murine upper gastrointestinal tract. Results: In coprophagic mice, continuous self-exposure to the fecal flora had substantial quantitative and qualitative effects on the upper gastrointestinal microbiome. These differences in microbial abundance and community composition were associated with an altered profile of the small intestine bile acid pool, and, importantly, could not be inferred from analyzing large intestine or stool samples. Overall, the patterns observed in the small intestine of non-coprophagic mice (reduced total microbial load, low abundance of anaerobic microbiota, and bile acids predominantly in the conjugated form) resemble those typically seen in the human small intestine. Conclusions: Future studies need to take self-reinoculation into account when using mouse models to evaluate gastrointestinal microbial colonization and function in relation to xenobiotic transformation and pharmacokinetics or in the context of physiological states and diseases linked to small intestine microbiome and to small intestine dysbiosis

Species-specific Real Time-PCR primers/probe systems to identify fish parasites of the genera Anisakis, Pseudoterranova and Hysterothylacium (Nematoda: Ascaridoidea)

Ascaridoid nematodes belonging to the genera Anisakis and Pseudoterranova are heteroxenous parasites, involving marine mammals as definitive hosts in their life-cycles, whereas crustaceans (krill), fish and squids acting as intermediate/paratenic hosts. These parasites are considered among the most important biological hazards present in “seafood” products. Indeed, larval stages of the Anisakis and Pseudoterranova have been reported as etiological agents of human infections (anisakidosis). We developed a primers/probe system for the identification of five species of anisakid nematodes belonging to the genera Anisakis (i.e. A. pegreffii and A. simplex (s. s.)), and Pseudoterranova (i.e. P. decipiens (s. s.), P. krabbei and P. bulbosa) to be used in a real time polymerase chain reaction (RT-PCR) with specific primers based on the mtDNA cox2 gene. Because those anisakid species could be also found in co-infection in some fish species with the raphidascarid nematode Hysterothylacium aduncum, a species-specific primer probe system to be used in RT-PCR for this nematode species was also developed. The detection limit and specificity of the primer/probe systems were evaluated for each of the six nematode species. Singleplex and multiplex RT-PCR protocols were defined and tested. The detection limit of the nematode species tissue was lower than 0.0006 ng/μl. Efficiency (E) of primers/probe systems developed was carried out by standard curve; E value varied between 2.015 and 2.11, with respect to a perfect reaction efficiency value of E = 2. Considering the sensibility and quantitative nature of the assays, the new primers/probe system may represent a useful tool for future basic and applied research that focuses on the identification of Anisakis spp., Pseudoterranova spp. and H. aduncum larvae in fish, even in co-infections, with a potential for application in fish farming, fish processing industries, fish markets, and food producers

Expansion of the Parkinson disease-associated SNCA-Rep1 allele upregulates human alpha-synuclein in transgenic mouse brain.

Alpha-synuclein (SNCA) gene has been implicated in the development of rare forms of familial Parkinson disease (PD). Recently, it was shown that an increase in SNCA copy numbers leads to elevated levels of wild-type SNCA-mRNA and protein and is sufficient to cause early-onset, familial PD. A critical question concerning the molecular pathogenesis of PD is what contributory role, if any, is played by the SNCA gene in sporadic PD. The expansion of SNCA-Rep1, an upstream, polymorphic microsatellite of the SNCA gene, is associated with elevated risk for sporadic PD. However, whether SNCA-Rep1 is the causal variant and the underlying mechanism with which its effect is mediated by remained elusive. We report here the effects of three distinct SNCA-Rep1 variants in the brains of 72 mice transgenic for the entire human SNCA locus. Human SNCA-mRNA and protein levels were increased 1.7- and 1.25-fold, respectively, in homozygotes for the expanded, PD risk-conferring allele compared with homozygotes for the shorter, protective allele. When adjusting for the total SNCA-protein concentration (endogenous mouse and transgenic human) expressed in each brain, the expanded risk allele contributed 2.6-fold more to the SNCA steady-state than the shorter allele. Furthermore, targeted deletion of Rep1 resulted in the lowest human SNCA-mRNA and protein concentrations in murine brain. In contrast, the Rep1 effect was not observed in blood lysates from the same mice. These results demonstrate that Rep1 regulates human SNCA expression by enhancing its transcription in the adult nervous system and suggest that homozygosity for the expanded Rep1 allele may mimic locus multiplication, thereby elevating PD risk

DNA Authentication of St John’s Wort (Hypericum perforatum L.) Commercial Products Targeting the ITS Region

open access articleThere is considerable potential for the use of DNA barcoding methods to authenticate raw medicinal plant materials, but their application to testing commercial products has been controversial. A simple PCR test targeting species-specific sequences within the nuclear ribosomal internal transcribed spacer (ITS) region was adapted to screen commercial products for the presence of Hypericum perforatum L. material. DNA differing widely in amount and extent of fragmentation was detected in a number of product types. Two assays were designed to further analyse this DNA using a curated database of selected Hypericum ITS sequences: A qPCR assay based on a species-specific primer pair spanning the ITS1 and ITS2 regions, using synthetic DNA reference standards for DNA quantitation and a Next Generation Sequencing (NGS) assay separately targeting the ITS1 and ITS2 regions. The ability of the assays to detect H. perforatum DNA sequences in processed medicines was investigated. Out of twenty different matrices tested, both assays detected H. perforatum DNA in five samples with more than 103 ITS copies µL−1 DNA extract, whilst the qPCR assay was also able to detect lower levels of DNA in two further samples. The NGS assay confirmed that H. perforatum was the major species in all five positive samples, though trace contaminants were also detected