Elsevier

Brain Research Bulletin

Volume 156, March 2020, Pages 43-49
Brain Research Bulletin

Validation of reference genes for gene expression analysis following experimental traumatic brain injury in a pediatric mouse model

https://doi.org/10.1016/j.brainresbull.2019.12.015Get rights and content

Highlights

  • Correct measurement of gene expression requires stable reference genes.

  • Experimental conditions such as age can influence the stability of reference genes.

  • Immature brain and Traumatic brain injury are likely to be influential factors.

  • Hprt and Ppia are the stable reference genes in a model of traumatic brain injury.

Abstract

Quantitative polymerase chain reaction (qPCR) is the gold standard method in targeted analysis of messenger RNA (mRNA) levels in a tissue. To minimize methodological errors, a reference gene (or a combination of reference genes) is routinely used for normalization to account for technical variables such as RNA quality and sample size. While presumed to have stable expression, reference genes in the brain can change during normal development, as well as in response to injury, such as traumatic brain injury (TBI). This study is the first to evaluate the stability of reference genes in a controlled cortical impact (CCI) model in the pediatric mouse brain, using two methods of qPCR normalization for optimal reference gene selection. Three week old mice were subjected to unilateral CCI at two severity of injuries (mild or severe), compared to sham controls. At 1 and 8 weeks post-injury, the ipsilateral hemisphere was analyzed to determine reference gene stability. Five commonly-used reference genes were compared: tyrosine 3 monooxygenase/tryptophan 5 monooxygenase activation protein zeta (Ywhaz), cyclophilin A (Ppia), hypoxanthine phosphoribosyl transferase (Hprt), glyceraldehyde-3-phosphate dehydrogenase (Gapdh) and β-actin (Actb). Ppia and Hprt were chosen as the most stable combination of genes using GeNORM software analysis. These results highlight the instability of several commonly used reference genes after TBI, and provide a selection of validated genes for future gene expression analyses in the injured pediatric mouse brain.

Introduction

With a high prevalence in children and young adults, traumatic brain injury (TBI) is a major health issue in the pediatric population (Thurman, 2016). After the initial injury, a cascade of events follows minutes to years consequential to the initial impact which leads to the spectrum of functional deficits seen in patients (Anderson et al., 2006; Fay et al., 2009; Pentland, 1998). The effects of TBI in pediatric populations (0–4 years of age) is particularly devastating due to an abundance of cellular processes occurring in the brain that are unique to this developmental stage, such as synaptic growth and pruning, network specialization, and ongoing myelination (Xu et al., 2018; Huttenlocher, 1990; Tau and Peterson, 2010; Chechik et al., 1999; Gao et al., 2009). As a consequence, TBI during early life can disrupt not only existing neurological functions but also impair the ability to acquire new skills, which often manifests as the worsening of neurocognitive and psychosocial function over time (Anderson et al., 2005).

Studies in different TBI models have shown that changes in gene expression can lead to pathophysiological changes such as inflammation, cell death and edema (Hua Li et al., 2004; Franz et al., 1999; Raghavendra Rao et al., 2003). Gene expression analysis is therefore an important tool to study neuropathological pathways and signalling molecules. Multiple variables, including injury severity and type, location of injury and time elapsed since injury can all lead to changes in the level of gene expression. Furthermore, as noted above, the development stage of an individual’s brain has influence on the pathogenesis of injury, and hence change in the expression of a wide range of genes (Cho et al., 2016; Xiong et al., 2018; Ritzel et al., 2015; Timaru-Kast et al., 2012). Gene expression analyses can reveal novel insight into the molecular and cellular mechanisms involved in secondary injury processes after TBI. Ultimately, this may lead to the identification of potential biomarkers as well as therapeutic targets for drug development.

Quantitative polymerase chain reaction (qPCR) is a powerful method for targeted quantification of gene expression, due to its sensitivity with small amounts of material, high reproducibility, and quantification of the transcription-level process (Bustin, 2000; Remans et al., 2014; VanGuilder et al., 2008), For increased accuracy and data interpretation, reference genes (also known as ‘house-keeping genes’) are selected as a control measure for internal variability (Dheda et al. (2005)). Reference genes are typically proteins that regulate fundamental and highly conserved cellular functions, which consequently often coincides with relatively stable expression levels independent of environmental changes. Reference genes commonly used for gene expression analysis of brain tissue include proteins from the cytoskeleton family such as beta-actin (Actb); (Chio et al., 2017) glycolytic pathway intermediates such as glyceraldehyde-3-phosphate dehydrogenase (Gapdh); (Taylor et al., 2014) an isomerization of peptide bonds, cyclophilin A (Ppia); (Staib-Lasarzik et al., 2014) transferase proteins, such as hypoxanthine phosphoribosyl transferase (Hprt); (Tanaka et al., 2013; Biervert et al., 2001) and signal transducing mediators, such as tyrosine 3 monooxygenase/tryptophan 5 monooxygenase activation protein zeta (Ywhaz) (Gubern et al., 2009; Salberg et al., 2017).

Selecting the most stable reference gene in the specific context of interest is vital for accurate normalization and interpretation of qPCR results (Suzuki et al. (2000)). Careful consideration and study-specific selection of reference genes should be performed to prevent inaccurate interpretation of the gene (or genes) of interest. Such as in genes of interest, the stability of the reference gene after TBI is also influenced by age, time elapsed post-injury, injury severity and location of injury. Indeed, studies in experimental models of TBI and stroke have shown that many of the reference genes commonly chosen for qPCR may not in fact be the best option, as their expression is regulated by pathophysiological processes associated with secondary brain injury (Yap et al., 2017; Jalloh et al., 2015).

Several studies have sought to optimise choice of reference genes in experimental TBI models, primarily in adult animals. Quantification of reference gene expression levels in adult mice showed Ppia, Hprt and β-microglobulin to be the most stable, while Actb and Gapdh were found to increase following experimental TBI (Thal et al., 2008). In contrast, another study found that after TBI in aged mice (21 month old), Ppia and Hprt were the most stable, while Actb and β-microglobulin had the least stability (Timaru-Kast et al., 2015). Therefore, age alone can influence reference genes stability. Incorporating age factors in addition to the degree of TBI, however, has not yet been conducted in the context of injury to the pediatric brain. In this study, we therefore aimed to identify genes that are constitutively regulated and unaffected by age/time of injury and injury severity. Mild and severe experimental TBI was performed at the pediatric age of postnatal day (p) 21, and gene expression were measured at a sub-acute time point (1 week post-injury) and chronically (8 weeks post-injury; adulthood). We compared the expression of five reference genes commonly used in TBI studies, relative to that of a target gene, glial fibrillary acidic protein (Gfap), a commonly-used marker of astrogliosis which is known to be modulated both during development (Semple et al., 2017) and following brain injury (Biervert et al., 2001).

Section snippets

TBI model and experimental groups

Experiments were conducted at the Florey Institute of Neuroscience and Mental Health according to the Australian Guidelines for the Care and Use of Laboratory Animals, with approval from the local Animal Ethics Committee (#16-100-UM). Mice were housed in well-ventilated cages under a 12 -h light/dark cycle and with continuous access to food and water. A total of 36 male C57Bl/6 J mice, sourced from an in-house breeding colony, were used. All animals were randomly assigned to mild TBI, severe

Efficiency of qPCR amplification

Efficiency of the qPCR reaction was calculated from standard curve slopes with Quantstudio™ qPCR software. An efficiency of close to 100 % indicates a doubling of product in each cycle. The linear correlation coefficient (R2) of all 6 genes ranged from 0.996- 0.999, and the qPCR efficiency (from the formula E = 10 1/slope -1) ranged from 92 % to 101 % (Table 1). This validates the use of further analysis of relative expression of Gfap using the 2−ΔΔCt formula (Livak and Schmittgen, 2001).

Effect of time post-injury and injury severity on reference gene expression

To

Discussion

This study demonstrate changes in mRNA expression of common reference genes in the pediatric mouse brain following an experimental TBI. Between five of the most commonly used reference genes in relative expression analysis studies. Using GeNORM software that uses raw Ct levels to account for the stability of reference genes across all the samples, Hprt and Ppia showed the highest stability value alone, a combination of Hprt, Ppia and Ywhaz was determined to be the optimal reference gene set for

Conclusion

In summary, we tested the stability of five reference genes following experimental TBI in a pediatric mouse model. From our analysis of the level of mRNA transcripts of these genes at 1 and 8 weeks post-injury at two different degrees of injury severity, we conclude that a combination of two genes (Ppia and Hprt) is the most suitable reference gene set for future studies in the context of CCI in mice. The 1 week and 8 weeks post injury were selected as candidate time-points corresponding to

CRediT authorship contribution statement

Akram Zamani: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing - original draft, Writing - review & editing. Kim L. Powell: Funding acquisition, Resources, Supervision, Validation, Writing - review & editing. Ashleigh May: Data curation. Bridgette D. Semple: Conceptualization, Funding acquisition, Investigation, Supervision, Validation, Writing - review & editing.

Declaration of Competing Interest

The authors declare no conflict of interest in the publication of this article.

Acknowledgements

The authors acknowledge funding support from Monash University (BDS and KLP), and the National Health and Medical Research Council of Australia (NHMRC) (Project Grant and Career Development Fellowship to BDS).

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