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The Challenge of Stability in High-Throughput Gene Expression Analysis: Comprehensive Selection and Evaluation of Reference Genes for BALB/c Mice Spleen Samples in the Leishmania infantum Infection Model

View Article: PubMed Central - PubMed

ABSTRACT

The interaction of Leishmania with BALB/c mice induces dramatic changes in transcriptome patterns in the parasite, but also in the target organs (spleen, liver…) due to its response against infection. Real-time quantitative PCR (qPCR) is an interesting approach to analyze these changes and understand the immunological pathways that lead to protection or progression of disease. However, qPCR results need to be normalized against one or more reference genes (RG) to correct for non-specific experimental variation. The development of technical platforms for high-throughput qPCR analysis, and powerful software for analysis of qPCR data, have acknowledged the problem that some reference genes widely used due to their known or suspected “housekeeping” roles, should be avoided due to high expression variability across different tissues or experimental conditions. In this paper we evaluated the stability of 112 genes using three different algorithms: geNorm, NormFinder and RefFinder in spleen samples from BALB/c mice under different experimental conditions (control and Leishmania infantum-infected mice). Despite minor discrepancies in the stability ranking shown by the three methods, most genes show very similar performance as RG (either good or poor) across this massive data set. Our results show that some of the genes traditionally used as RG in this model (i.e. B2m, Polr2a and Tbp) are clearly outperformed by others. In particular, the combination of Il2rg + Itgb2 was identified among the best scoring candidate RG for every group of mice and every algorithm used in this experimental model. Finally, we have demonstrated that using “traditional” vs rationally-selected RG for normalization of gene expression data may lead to loss of statistical significance of gene expression changes when using large-scale platforms, and therefore misinterpretation of results. Taken together, our results highlight the need for a comprehensive, high-throughput search for the most stable reference genes in each particular experimental model.

No MeSH data available.


Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of both groups of mice (control and Leishmania-infected BALB/c mice).A) M-stability value according to geNorm; horizontal line marks the threshold stability value M = 0.5. B) Pairwise variation (Vn/n+1) between the normalization factors of the samples according to geNorm. C) Stability ranking according to NormFinder. D) Stability ranking according to RefFinder. Lower values indicate higher stability for all rankings.
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pone.0163219.g003: Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of both groups of mice (control and Leishmania-infected BALB/c mice).A) M-stability value according to geNorm; horizontal line marks the threshold stability value M = 0.5. B) Pairwise variation (Vn/n+1) between the normalization factors of the samples according to geNorm. C) Stability ranking according to NormFinder. D) Stability ranking according to RefFinder. Lower values indicate higher stability for all rankings.

Mentions: geNorm analysis of the whole set of mice revealed that 19 genes showed M < 0.5, only one of them being a traditional RG: Hprt, ranked 7/19 (Fig 3A). According to this ranking, Itgb2 and Stat6 are the most stable genes (Table 3), and enough for an optimal normalization (Fig 3B). It must be pointed out that Il2rg ranks 4th in this analysis. NormFinder analysis revealed Il2rg and Il6ra as the genes with highest expression stability in this ranking (Table 3). The best scoring classical RG is Hprt, as low as 23rd (Fig 3C). RefFinder algorithm was also used to determine the best genes for normalization in the whole data set (Fig 3D). Again, Hprt is the only commonly used RG that ranks among the top-20 (6/20). In this ranking, Itgb2, Stat6 and Il2rg were most stable genes (Table 3).


The Challenge of Stability in High-Throughput Gene Expression Analysis: Comprehensive Selection and Evaluation of Reference Genes for BALB/c Mice Spleen Samples in the Leishmania infantum Infection Model
Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of both groups of mice (control and Leishmania-infected BALB/c mice).A) M-stability value according to geNorm; horizontal line marks the threshold stability value M = 0.5. B) Pairwise variation (Vn/n+1) between the normalization factors of the samples according to geNorm. C) Stability ranking according to NormFinder. D) Stability ranking according to RefFinder. Lower values indicate higher stability for all rankings.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5036817&req=5

pone.0163219.g003: Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of both groups of mice (control and Leishmania-infected BALB/c mice).A) M-stability value according to geNorm; horizontal line marks the threshold stability value M = 0.5. B) Pairwise variation (Vn/n+1) between the normalization factors of the samples according to geNorm. C) Stability ranking according to NormFinder. D) Stability ranking according to RefFinder. Lower values indicate higher stability for all rankings.
Mentions: geNorm analysis of the whole set of mice revealed that 19 genes showed M < 0.5, only one of them being a traditional RG: Hprt, ranked 7/19 (Fig 3A). According to this ranking, Itgb2 and Stat6 are the most stable genes (Table 3), and enough for an optimal normalization (Fig 3B). It must be pointed out that Il2rg ranks 4th in this analysis. NormFinder analysis revealed Il2rg and Il6ra as the genes with highest expression stability in this ranking (Table 3). The best scoring classical RG is Hprt, as low as 23rd (Fig 3C). RefFinder algorithm was also used to determine the best genes for normalization in the whole data set (Fig 3D). Again, Hprt is the only commonly used RG that ranks among the top-20 (6/20). In this ranking, Itgb2, Stat6 and Il2rg were most stable genes (Table 3).

View Article: PubMed Central - PubMed

ABSTRACT

The interaction of Leishmania with BALB/c mice induces dramatic changes in transcriptome patterns in the parasite, but also in the target organs (spleen, liver&hellip;) due to its response against infection. Real-time quantitative PCR (qPCR) is an interesting approach to analyze these changes and understand the immunological pathways that lead to protection or progression of disease. However, qPCR results need to be normalized against one or more reference genes (RG) to correct for non-specific experimental variation. The development of technical platforms for high-throughput qPCR analysis, and powerful software for analysis of qPCR data, have acknowledged the problem that some reference genes widely used due to their known or suspected &ldquo;housekeeping&rdquo; roles, should be avoided due to high expression variability across different tissues or experimental conditions. In this paper we evaluated the stability of 112 genes using three different algorithms: geNorm, NormFinder and RefFinder in spleen samples from BALB/c mice under different experimental conditions (control and Leishmania infantum-infected mice). Despite minor discrepancies in the stability ranking shown by the three methods, most genes show very similar performance as RG (either good or poor) across this massive data set. Our results show that some of the genes traditionally used as RG in this model (i.e. B2m, Polr2a and Tbp) are clearly outperformed by others. In particular, the combination of Il2rg + Itgb2 was identified among the best scoring candidate RG for every group of mice and every algorithm used in this experimental model. Finally, we have demonstrated that using &ldquo;traditional&rdquo; vs rationally-selected RG for normalization of gene expression data may lead to loss of statistical significance of gene expression changes when using large-scale platforms, and therefore misinterpretation of results. Taken together, our results highlight the need for a comprehensive, high-throughput search for the most stable reference genes in each particular experimental model.

No MeSH data available.