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Molecular identification and gene expression profiles of the T cell receptors and co-receptors in developing red-tailed phascogale (Phascogale calura) pouch young. Mol Immunol 2018; 101:268-275. [DOI: 10.1016/j.molimm.2018.07.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 06/16/2018] [Accepted: 07/02/2018] [Indexed: 11/23/2022]
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Sarker N, Fabijan J, Emes RD, Hemmatzadeh F, Meers J, Moreton J, Owen H, Seddon JM, Simmons G, Speight N, Trott D, Woolford L, Tarlinton RE. Identification of stable reference genes for quantitative PCR in koalas. Sci Rep 2018; 8:3364. [PMID: 29463845 PMCID: PMC5820254 DOI: 10.1038/s41598-018-21723-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2017] [Accepted: 02/09/2018] [Indexed: 11/17/2022] Open
Abstract
To better understand host and immune response to diseases, gene expression studies require identification of reference genes with stable expression for accurate normalisation. This study describes the identification and testing of reference genes with stable expression profiles in koala lymph node tissues across two genetically distinct koala populations. From the 25 most stable genes identified in transcriptome analysis, 11 genes were selected for verification using reverse transcription quantitative PCR, in addition to the commonly used ACTB and GAPDH genes. The expression data were analysed using stable genes statistical software - geNorm, BestKeeper, NormFinder, the comparative ΔCt method and RefFinder. All 13 genes showed relative stability in expression in koala lymph node tissues, however Tmem97 and Hmg20a were identified as the most stable genes across the two koala populations.
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Affiliation(s)
- N Sarker
- School of Veterinary Science, The University of Queensland, Gatton, QLD, Australia
| | - J Fabijan
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - R D Emes
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, United Kingdom.,Advanced Data Analysis Centre (ADAC), University of Nottingham, Sutton Bonington, United Kingdom
| | - F Hemmatzadeh
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - J Meers
- School of Veterinary Science, The University of Queensland, Gatton, QLD, Australia
| | - J Moreton
- Advanced Data Analysis Centre (ADAC), University of Nottingham, Sutton Bonington, United Kingdom
| | - H Owen
- School of Veterinary Science, The University of Queensland, Gatton, QLD, Australia
| | - J M Seddon
- School of Veterinary Science, The University of Queensland, Gatton, QLD, Australia
| | - G Simmons
- School of Veterinary Science, The University of Queensland, Gatton, QLD, Australia
| | - N Speight
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - D Trott
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - L Woolford
- School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, SA, Australia
| | - R E Tarlinton
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD, United Kingdom.
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