Dataset Information

Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A.

ABSTRACT: BACKGROUND: An essential part of using real-time RT-PCR is that expression results have to be normalized before any conclusions can be drawn. This can be done by using one or multiple, validated reference genes, depending on the desired accuracy of the results. In the pig however, very little information is available on the expression stability of reference genes. The aim of this study was therefore to develop a new set of reference genes which can be used for normalization of mRNA expression data of genes expressed in porcine backfat and longissimus dorsi muscle, both representing an economically important part of a pig's carcass. Because of its multiple functions in fat metabolism and muscle fibre type composition, peroxisome proliferative activated receptor gamma coactivator 1alpha (PPARGC1A) is a very interesting candidate gene for meat quality, and was an ideal gene to evaluate our developed set of reference genes for normalization of mRNA expression data of both tissue types. RESULTS: The mRNA expression stability of 10 reference genes was determined. The expression of RPL13A and SDHA appeared to be highly unstable. After normalization to the geometric mean of the three most stably expressed reference genes (ACTB, TBP and TOP2B), the results not only showed that the mRNA expression of PPARGC1A was significantly higher in each of the longissimus dorsi muscle samples than in backfat (P < 0.05), but also that the expression was significantly higher in the most cranial of the three muscle samples (P < 0.05). CONCLUSION: This study provides a new set of reference genes (ACTB, TBP and TOP2B) suitable for normalization of real-time RT-PCR data of backfat and longissimus dorsi muscle in the pig. The obtained PPARGC1A expression results, after application of this set of reference genes, are a first step in unravelling the PPARGC1A expression pattern in the pig and provide a basis for possible selection towards improved meat quality while maintaining a lean carcass.

SUBMITTER: Erkens T

PROVIDER: S-EPMC1609116 | biostudies-literature | 2006

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A.

Erkens Tim T Van Poucke Mario M Vandesompele Jo J Goossens Karen K Van Zeveren Alex A Peelman Luc J LJ

BMC biotechnology 20061009

<h4>Background</h4>An essential part of using real-time RT-PCR is that expression results have to be normalized before any conclusions can be drawn. This can be done by using one or multiple, validated reference genes, depending on the desired accuracy of the results. In the pig however, very little information is available on the expression stability of reference genes. The aim of this study was therefore to develop a new set of reference genes which can be used for normalization of mRNA expres ...[more]

PMID: 17026777

Similar Datasets

Project description:BackgroundNormalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.ResultsWe propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.ConclusionsThe proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.

Project description:BACKGROUND: Usually the reference genes used in gene expression analysis have been chosen for their known or suspected housekeeping roles, however the variation observed in most of them hinders their effective use. The assessed lack of validated reference genes emphasizes the importance of a systematic study for their identification. For selecting candidate reference genes we have developed a simple in silico method based on the data publicly available in the wheat databases Unigene and TIGR. RESULTS: The expression stability of 32 genes was assessed by qRT-PCR using a set of cDNAs from 24 different plant samples, which included different tissues, developmental stages and temperature stresses. The selected sequences included 12 well-known HKGs representing different functional classes and 20 genes novel with reference to the normalization issue. The expression stability of the 32 candidate genes was tested by the computer programs geNorm and NormFinder using five different data-sets. Some discrepancies were detected in the ranking of the candidate reference genes, but there was substantial agreement between the groups of genes with the most and least stable expression. Three new identified reference genes appear more effective than the well-known and frequently used HKGs to normalize gene expression in wheat. Finally, the expression study of a gene encoding a PDI-like protein showed that its correct evaluation relies on the adoption of suitable normalization genes and can be negatively affected by the use of traditional HKGs with unstable expression, such as actin and alpha-tubulin. CONCLUSION: The present research represents the first wide screening aimed to the identification of reference genes and of the corresponding primer pairs specifically designed for gene expression studies in wheat, in particular for qRT-PCR analyses. Several of the new identified reference genes outperformed the traditional HKGs in terms of expression stability under all the tested conditions. The new reference genes will enable more accurate normalization and quantification of gene expression in wheat and will be helpful for designing primer pairs targeting orthologous genes in other plant species.

Project description:BackgroundReal-time quantitative reverse transcription PCR (RT-qPCR) data needs to be normalized for its proper interpretation. Housekeeping genes are routinely employed for this purpose, but their expression level cannot be assumed to remain constant under all possible experimental conditions. Thus, a systematic validation of reference genes is required to ensure proper normalization. For soybean, only a small number of validated reference genes are available to date.ResultsA systematic comparison of 14 potential reference genes for soybean is presented. These included seven commonly used (ACT2, ACT11, TUB4, TUA5, CYP, UBQ10, EF1b) and seven new candidates (SKIP16, MTP, PEPKR1, HDC, TIP41, UKN1, UKN2). Expression stability was examined by RT-qPCR across 116 biological samples, representing tissues at various developmental stages, varied photoperiodic treatments, and a range of soybean cultivars. Expression of all 14 genes was variable to some extent, but that of SKIP16, UKN1 and UKN2 was overall the most stable. A combination of ACT11, UKN1 and UKN2 would be appropriate as a reference panel for normalizing gene expression data among different tissues, whereas the combination SKIP16, UKN1 and MTP was most suitable for developmental stages. ACT11, TUA5 and TIP41 were the most stably expressed when the photoperiod was altered, and TIP41, UKN1 and UKN2 when the light quality was changed. For six different cultivars in long day (LD) and short day (SD), their expression stability did not vary significantly with ACT11, UKN2 and TUB4 being the most stable genes. The relative gene expression level of GmFTL3, an ortholog of Arabidopsis FT (FLOWERING LOCUS T) was detected to validate the reference genes selected in this study.ConclusionNone of the candidate reference genes was uniformly expressed across all experimental conditions, and the most suitable reference genes are conditional-, tissue-specific-, developmental-, and cultivar-dependent. Most of the new reference genes performed better than the conventional housekeeping genes. These results should guide the selection of reference genes for gene expression studies in soybean.

Dataset Information

Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A.

Publications

Development of a new set of reference genes for normalization of real-time RT-PCR data of porcine backfat and longissimus dorsi muscle, and evaluation with PPARGC1A.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets