Statistical significance of quantitative PCR

BACKGROUND: PCR has the potential to detect and precisely quantify specific DNA sequences, but it is not yet often used as a fully quantitative method. A number of data collection and processing strategies have been described for the implementation of quantitative PCR. However, they can be experimentally cumbersome, their relative performances have not been evaluated systematically, and they often remain poorly validated statistically and/or experimentally. In this study, we evaluated the performance of known methods, and compared them with newly developed data processing strategies in terms of resolution, precision and robustness. RESULTS: Our results indicate that simple methods that do not rely on the estimation of the efficiency of the PCR amplification may provide reproducible and sensitive data, but that they do not quantify DNA with precision. Other evaluated methods based on sigmoidal or exponential curve fitting were generally of both poor resolution and precision. A statistical analysis of the parameters that influence efficiency indicated that it depends mostly on the selected amplicon and to a lesser extent on the particular biological sample analyzed. Thus, we devised various strategies based on individual or averaged efficiency values, which were used to assess the regulated expression of several genes in response to a growth factor. CONCLUSION: Overall, qPCR data analysis methods differ significantly in their performance, and this analysis identifies methods that provide DNA quantification estimates of high precision, robustness and reliability. These methods allow reliable estimations of relative expression ratio of two-fold or higher, and our analysis provides an estimation of the number of biological samples that have to be analyzed to achieve a given precision

Statistical modelling of transcript profiles of differentially regulated genes

Background: The vast quantities of gene expression profiling data produced in microarray studies, and the more precise quantitative PCR, are often not statistically analysed to their full potential. Previous studies have summarised gene expression profiles using simple descriptive statistics, basic analysis of variance (ANOVA) and the clustering of genes based on simple models fitted to their expression profiles over time. We report the novel application of statistical non-linear regression modelling techniques to describe the shapes of expression profiles for the fungus Agaricus bisporus, quantified by PCR, and for E. coli and Rattus norvegicus, using microarray technology. The use of parametric non-linear regression models provides a more precise description of expression profiles, reducing the "noise" of the raw data to produce a clear "signal" given by the fitted curve, and describing each profile with a small number of biologically interpretable parameters. This approach then allows the direct comparison and clustering of the shapes of response patterns between genes and potentially enables a greater exploration and interpretation of the biological processes driving gene expression. Results: Quantitative reverse transcriptase PCR-derived time-course data of genes were modelled. "Splitline" or "broken-stick" regression identified the initial time of gene up-regulation, enabling the classification of genes into those with primary and secondary responses. Five-day profiles were modelled using the biologically-oriented, critical exponential curve, y(t) = A + (B + Ct)Rt + ε. This non-linear regression approach allowed the expression patterns for different genes to be compared in terms of curve shape, time of maximal transcript level and the decline and asymptotic response levels. Three distinct regulatory patterns were identified for the five genes studied. Applying the regression modelling approach to microarray-derived time course data allowed 11% of the Escherichia coli features to be fitted by an exponential function, and 25% of the Rattus norvegicus features could be described by the critical exponential model, all with statistical significance of p < 0.05. Conclusion: The statistical non-linear regression approaches presented in this study provide detailed biologically oriented descriptions of individual gene expression profiles, using biologically variable data to generate a set of defining parameters. These approaches have application to the modelling and greater interpretation of profiles obtained across a wide range of platforms, such as microarrays. Through careful choice of appropriate model forms, such statistical regression approaches allow an improved comparison of gene expression profiles, and may provide an approach for the greater understanding of common regulatory mechanisms between genes

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

Position-dependent effects of locked nucleic acid (LNA) on DNA sequencing and PCR primers

Genomes are becoming heavily annotated with important features. Analysis of these features often employs oligonucleotides that hybridize at defined locations. When the defined location lies in a poor sequence context, traditional design strategies may fail. Locked Nucleic Acid (LNA) can enhance oligonucleotide affinity and specificity. Though LNA has been used in many applications, formal design rules are still being defined. To further this effort we have investigated the effect of LNA on the performance of sequencing and PCR primers in AT-rich regions, where short primers yield poor sequencing reads or PCR yields. LNA was used in three positional patterns: near the 5′ end (LNA-5′), near the 3′ end (LNA-3′) and distributed throughout (LNA-Even). Quantitative measures of sequencing read length (Phred Q30 count) and real-time PCR signal (cycle threshold, C(T)) were characterized using two-way ANOVA. LNA-5′ increased the average Phred Q30 score by 60% and it was never observed to decrease performance. LNA-5′ generated cycle thresholds in quantitative PCR that were comparable to high-yielding conventional primers. In contrast, LNA-3′ and LNA-Even did not improve read lengths or C(T). ANOVA demonstrated the statistical significance of these results and identified significant interaction between the positional design rule and primer sequence

Menopause leads to elevated expression of macrophage-associated genes in the aging frontal cortex: rat and human studies identify strikingly similar changes.

BACKGROUND The intricate interactions between the immune, endocrine and central nervous systems shape the innate immune response of the brain. We have previously shown that estradiol suppresses expression of immune genes in the frontal cortex of middle-aged ovariectomized rats, but not in young ones reflecting elevated expression of these genes in middle-aged, ovarian hormone deficient animals. Here, we explored the impact of menopause on the microglia phenotype capitalizing on the differential expression of macrophage-associated genes in quiescent and activated microglia. METHODS We selected twenty-three genes encoding phagocytic and recognition receptors expressed primarily in microglia, and eleven proinflammatory genes and followed their expression in the rat frontal cortex by real-time PCR. We used young, middle-aged and middle-aged ovariectomized rats to reveal age- and ovariectomy-related alterations. We analyzed the expression of the same set of genes in the postcentral and superior frontal gyrus of pre- and postmenopausal women using raw microarray data from our previous study. RESULTS Ovariectomy caused up-regulation of four classic microglia reactivity marker genes including Cd11b, Cd18, Cd45 and Cd86. The change was reversible since estradiol attenuated transcriptional activation of the four marker genes. Expression of genes encoding phagocytic and toll-like receptors such as Cd11b, Cd18, C3, Cd32, Msr2 and Tlr4 increased, whereas scavenger receptor Cd36 decreased following ovariectomy. Ovarian hormone deprivation altered the expression of major components of estrogen and neuronal inhibitory signaling which are involved in the control of microglia reactivity. Strikingly similar changes took place in the postcentral and superior frontal gyrus of postmenopausal women. CONCLUSIONS Based on the overlapping results of rat and human studies we propose that the microglia phenotype shifts from the resting toward the reactive state which can be characterized by up-regulation of CD11b, CD14, CD18, CD45, CD74, CD86, TLR4, down-regulation of CD36 and unchanged CD40 expression. As a result of this shift, microglial cells have lower threshold for subsequent activation in the forebrain of postmenopausal women

Digital PCR methods improve detection sensitivity and measurement precision of low abundance mtDNA deletions

Mitochondrial DNA (mtDNA) mutations are a common cause of primary mitochondrial disorders, and have also been implicated in a broad collection of conditions, including aging, neurodegeneration, and cancer. Prevalent among these pathogenic variants are mtDNA deletions, which show a strong bias for the loss of sequence in the major arc between, but not including, the heavy and light strand origins of replication. Because individual mtDNA deletions can accumulate focally, occur with multiple mixed breakpoints, and in the presence of normal mtDNA sequences, methods that detect broad-spectrum mutations with enhanced sensitivity and limited costs have both research and clinical applications. In this study, we evaluated semi-quantitative and digital PCR-based methods of mtDNA deletion detection using double-stranded reference templates or biological samples. Our aim was to describe key experimental assay parameters that will enable the analysis of low levels or small differences in mtDNA deletion load during disease progression, with limited false-positive detection. We determined that the digital PCR method significantly improved mtDNA deletion detection sensitivity through absolute quantitation, improved precision and reduced assay standard error

A petunia ethylene-responsive element binding factor, PhERF2, plays an important role in antiviral RNA silencing.

Virus-induced RNA silencing is involved in plant antiviral defense and requires key enzyme components, including RNA-dependent RNA polymerases (RDRs), Dicer-like RNase III enzymes (DCLs), and Argonaute proteins (AGOs). However, the transcriptional regulation of these critical components is largely unknown. In petunia (Petunia hybrida), an ethylene-responsive element binding factor, PhERF2, is induced by Tobacco rattle virus (TRV) infection. Inclusion of a PhERF2 fragment in a TRV silencing construct containing reporter fragments of phytoene desaturase (PDS) or chalcone synthase (CHS) substantially impaired silencing efficiency of both the PDS and CHS reporters. Silencing was also impaired in PhERF2- RNAi lines, where TRV-PhPDS infection did not show the expected silencing phenotype (photobleaching). In contrast, photobleaching in response to infiltration with the TRV-PhPDS construct was enhanced in plants overexpressing PhERF2 Transcript abundance of the RNA silencing-related genes RDR2, RDR6, DCL2, and AGO2 was lower in PhERF2-silenced plants but higher in PhERF2-overexpressing plants. Moreover, PhERF2-silenced lines showed higher susceptibility to Cucumber mosaic virus (CMV) than wild-type (WT) plants, while plants overexpressing PhERF2 exhibited increased resistance. Interestingly, growth and development of PhERF2-RNAi lines were substantially slower, whereas the overexpressing lines were more vigorous than the controls. Taken together, our results indicate that PhERF2 functions as a positive regulator in antiviral RNA silencing