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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

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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.

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Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of control 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.g001: Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of control 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: Cq data was loaded into qBasePLUS software and geNorm was implemented. According to geNorm algorithm, only Hprt, from the typical RG group, showed an M-value below the threshold (M< 0.5), unlike Ubc, B2m, Polr2a, Pkg1 and Tbp, whose M values were higher than 0.5 (Fig 1A). This result indicates that all these typical RG (Ubc, B2m, Polr2a, Pkg1 and Tbp) do not meet the criteria to be used as reference genes in gene expression assays. Moreover, 24 genes related to different immune mechanisms showed better stability than classical RG, reflected as M-values below 0.5 (Fig 1A). geNorm algorithm ranked Il18bp and Il10rb as the most stable genes (Table 1), enough for optimal normalization according to V parameter (Fig 1B). NormFinder analysis of this set of mice ranked Myd88 and Il2rg as the most stably expressed genes (Table 1). Again, the ranking of stability values for the collection of classical RG (Fig 1C), showed that only Hprt was ranked among the top-20 genes (7/20), and two of them (Polr2a and Tbp) were among the 10-worst scoring genes (S2 Table). This same analysis performed with RefFinder algorithm ranked Hprt and Stat4 as the most stable candidate reference genes (Table 1). Only Hprt and Ubc were ranked among the top 20 genes, in first and thirteenth position respectively (Fig 1D).


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 control 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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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5036817&req=5

pone.0163219.g001: Stability values of the best candidate reference genes (light gray) and 6 six classical reference genes (black bars) in spleen samples of control 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: Cq data was loaded into qBasePLUS software and geNorm was implemented. According to geNorm algorithm, only Hprt, from the typical RG group, showed an M-value below the threshold (M< 0.5), unlike Ubc, B2m, Polr2a, Pkg1 and Tbp, whose M values were higher than 0.5 (Fig 1A). This result indicates that all these typical RG (Ubc, B2m, Polr2a, Pkg1 and Tbp) do not meet the criteria to be used as reference genes in gene expression assays. Moreover, 24 genes related to different immune mechanisms showed better stability than classical RG, reflected as M-values below 0.5 (Fig 1A). geNorm algorithm ranked Il18bp and Il10rb as the most stable genes (Table 1), enough for optimal normalization according to V parameter (Fig 1B). NormFinder analysis of this set of mice ranked Myd88 and Il2rg as the most stably expressed genes (Table 1). Again, the ranking of stability values for the collection of classical RG (Fig 1C), showed that only Hprt was ranked among the top-20 genes (7/20), and two of them (Polr2a and Tbp) were among the 10-worst scoring genes (S2 Table). This same analysis performed with RefFinder algorithm ranked Hprt and Stat4 as the most stable candidate reference genes (Table 1). Only Hprt and Ubc were ranked among the top 20 genes, in first and thirteenth position respectively (Fig 1D).

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.