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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 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.g002: Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of 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 showed that only two commonly used reference genes, Hprt and Pkg1, show M < 0.5, and therefore are acceptable as RG, ranked 8th and 15th respectively; in contrast, expression stability of Ubc, B2m, Polr2a and Tbp are all above the threshold, hence their use is not recommended (Fig 2A). geNorm ranked Il6st and Itgb2 as the most stably expressed genes (Table 2), enough for optimal normalization according to V parameter (Fig 2B). NormFinder analysis also agreed to only rank Hprt and Pkg1 (among the candidate RG) in the top 20 genes, 7th and 14th respectively (Fig 2C), far from the genes whose expression is most stable in the Leishmania-infected mice group, Itgb2 and Il2rg (Table 2). In contrast, Il10rb and Tgfbr1 were the top-ranked genes for normalization of gene expression according to RefFinder algorithm (Fig 2D). The commonly used reference gene Pkg1 only ranked 10th in this ranking.


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 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.g002: Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of 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 showed that only two commonly used reference genes, Hprt and Pkg1, show M < 0.5, and therefore are acceptable as RG, ranked 8th and 15th respectively; in contrast, expression stability of Ubc, B2m, Polr2a and Tbp are all above the threshold, hence their use is not recommended (Fig 2A). geNorm ranked Il6st and Itgb2 as the most stably expressed genes (Table 2), enough for optimal normalization according to V parameter (Fig 2B). NormFinder analysis also agreed to only rank Hprt and Pkg1 (among the candidate RG) in the top 20 genes, 7th and 14th respectively (Fig 2C), far from the genes whose expression is most stable in the Leishmania-infected mice group, Itgb2 and Il2rg (Table 2). In contrast, Il10rb and Tgfbr1 were the top-ranked genes for normalization of gene expression according to RefFinder algorithm (Fig 2D). The commonly used reference gene Pkg1 only ranked 10th in this ranking.

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.