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Screening internal controls for expression analyses involving numerous treatments by combining statistical methods with reference gene selection tools.

ABSTRACT: Real-time PCR is always the method of choice for expression analyses involving comparison of a large number of treatments. It is also the favored method for final confirmation of transcript levels followed by high throughput methods such as RNA sequencing and microarray. Our analysis comprised 16 different permutation and combinations of treatments involving four different Agrobacterium strains and three time intervals in the model plant Arabidopsis thaliana. The routinely used reference genes for biotic stress analyses in plants showed variations in expression across some of our treatments. In this report, we describe how we narrowed down to the best reference gene out of 17 candidate genes. Though we initiated our reference gene selection process using common tools such as geNorm, Normfinder and BestKeeper, we faced situations where these software-selected candidate genes did not completely satisfy all the criteria of a stable reference gene. With our novel approach of combining simple statistical methods such as t test, ANOVA and post hoc analyses, along with the routine software-based analyses, we could perform precise evaluation and we identified two genes, UBQ10 and PPR as the best reference genes for normalizing mRNA levels in the context of 16 different conditions of Agrobacterium infection. Our study emphasizes the usefulness of applying statistical analyses along with the reference gene selection software for reference gene identification in experiments involving the comparison of a large number of treatments.

SUBMITTER: Joseph JT

PROVIDER: S-EPMC6352529 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Project description:BackgroundAltered levels of circulating extracellular miRNA in plasma and serum have shown promise as non-invasive biomarkers of disease. However, unlike the assessment of cellular miRNA levels for which there are accepted housekeeping genes, analogous reference controls for normalization of circulating miRNA are lacking. Here, we provide an approach to identify and validate circulating miRNA reference controls on a de novo basis, and demonstrate the advantages of these customized internal controls in different disease settings. Importantly, these internal controls overcome key limitations of external spike-in controls.MethodsUsing a global RT-qPCR screen of 1066 miRNAs in plasma from pulmonary hypertension patients (PAH) and healthy subjects as a case example, we identified a large pool of initial candidate miRNAs that were systematically ranked according to their plasma level stability using a predefined algorithm. The performance of the top candidates was validated against multiple comparators, and in a second independent cohort of PAH and control subjects. The broader utility of this approach was demonstrated in a completely different disease setting with 372 miRNAs screened in plasma from septic shock patients and healthy controls.ResultsNormalization of data with specific internal reference controls significantly reduced the overall variation in circulating miRNA levels between subjects (relative to raw data), provided a more balanced distribution of up- and down-regulated miRNAs, replicated the results obtained by the benchmark geometric averaging of all detected miRNAs, and outperformed the commonly used external spike-in strategy.ConclusionsWe demonstrate the feasibility of identifying circulating reference controls that can reduce extraneous technical variations, and improve the assessment of disease-related changes in plasma miRNA levels. This study provides a novel conceptual framework that addresses a critical and previously unmet need if circulating miRNAs are to advance as reliable diagnostic tools in medicine.

Project description:Real-time reverse transcription PCR (qPCR) normalized to an internal reference gene (RG), is a frequently used method for quantifying gene expression changes in neuroscience. Although RG expression is assumed to be constant independent of physiological or experimental conditions, several studies have shown that commonly used RGs are not expressed stably. The use of unstable RGs has a profound effect on the conclusions drawn from studies on gene expression, and almost universally results in spurious estimation of target gene expression. Approaches aimed at selecting and validating RGs often make use of different statistical methods, which may lead to conflicting results. Based on published RG validation studies involving hypoxia the present study evaluates the expression of 5 candidate RGs (Actb, Pgk1, Sdha, Gapdh, Rnu6b) as a function of hypoxia exposure and hypothermic treatment in the neonatal rat cerebral cortex-in order to identify RGs that are stably expressed under these experimental conditions-using several statistical approaches that have been proposed to validate RGs. In doing so, we first analyzed RG ranking stability proposed by several widely used statistical methods and related tools, i.e. the Coefficient of Variation (CV) analysis, GeNorm, NormFinder, BestKeeper, and the ΔCt method. Using the Geometric mean rank, Pgk1 was identified as the most stable gene. Subsequently, we compared RG expression patterns between the various experimental groups. We found that these statistical methods, next to producing different rankings per se, all ranked RGs displaying significant differences in expression levels between groups as the most stable RG. As a consequence, when assessing the impact of RG selection on target gene expression quantification, substantial differences in target gene expression profiles were observed. Altogether, by assessing mRNA expression profiles within the neonatal rat brain cortex in hypoxia and hypothermia as a showcase, this study underlines the importance of further validating RGs for each individual experimental paradigm, considering the limitations of the statistical methods used for this aim.

Project description:Best known as licorice, Glycyrrhiza Linn., a genus of herbaceous perennial legume, has been used as a traditional herbal medicine in Asia and a flavoring agent for tobacco and food industry in Europe and America. Abiotic stresses and hormonal treatments can significantly impact the development and metabolism of secondary metabolites in Glycyrrhiza. To better understand the biosynthesis of the trace-amount bioactive compounds, we first screened for the suitable reference genes for quantitative real-time reverse transcription PCR (qRT-PCR) analysis in Glycyrrhiza. The expression profiles of 14 candidate reference genes, including Actin1 (ACT), Clathrin complex AP1 (CAC), Cyclophilin (CYP), Heat-shock protein 40 (DNAJ), Dehydration responsive element binding gene (DREB), Translation elongation factor1 (EF1), Ras related protein (RAN), Translation initiation factor (TIF1), β-Tubulin (TUB), Ubiquitin-conjugating enzyme E2 (UBC2), ATP binding-box transpoter 2 (ABCC2), COP9 signal compex subunit 3 (COPS3), Citrate synthase (CS), and R3H domain protein 2 (R3HDM2) from two congeneric species, Glycyrrhiza uralensis F. and Glycyrrhiza inflata B., were examined under abiotic stresses (osmotic and salinity) and hormonal treatments (Abscisic acid (ABA) and methyl jasmonic acid (MeJA)) using a panel of software, including geNorm, NormFinder, BestKeeper, and Delta CT. The overall stability, however, was provided by RefFinder, a comprehensive ranking system integrating inputs from all four algorithms. In G. uralensis, the most stable reference genes under osmotic stress, salt stress, ABA treatment, and MeJA treatment were TIF1, DNAJ, CS, and ABCC2 for leaves and DNAJ, DREB, CAC, and CAC for roots, respectively. In comparison, the top ranked genes were TUB, CAC, UBC2, and RAN for leaves and TIF1, ABCC2, CAC, and UBC2 for roots, respectively, under stress and hormonal treatments in G. inflata. ACT and TIF1, on the other hand, were the least stable genes under the most experimental conditions in the two congeneric species. Finally, our survey of the reference genes in legume shows that EF, ACT, UBC2, and TUB were the top choices for the abiotic stresses while EF, UBC2, CAC, and ABCC2 were recommended for the hormonal treatments in Leguminosae. Our combined results provide reliable normalizers for accurate gene quantifications in Glycyrrhiza species, which will allow us to exploit its medicinal potential in general and antiviral activities in particular.

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Screening internal controls for expression analyses involving numerous treatments by combining statistical methods with reference gene selection tools.

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