Selection and optimisation reference genes for normalisation of mitochondrial gene-expression by qRT-PCR in different potato tissues and during anther development

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K. Jones This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6373075/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Plant Biology → Version 1 posted 4 You are reading this latest preprint version Abstract Background Potato is the most widely grown tuber crop worldwide and a staple food in many countries: it has become the focus of many molecular breeding studies. One topical area is breeding potato seeds, especially advancing male sterile plants, focusing on developing cytoplasmic male sterility (CMS) as a breeding tool. A major obstacle has been the identification of mitochondrial genes for CMS. Quantifying the expression of candidate CMS genes is a critical aspect needed for the validation of gene expression levels for all organisms, and quantitative real-time polymerase chain reaction (qRT-PCR) is a powerful tool for this purpose. However, selecting appropriate internal control genes for normalisation of mitochondrial gene expression presents specific challenges. The aim of this study was to identify suitable reference genes best suited for analysis of mitochondrial gene expression in different tissues and developmental stages of potato, particularly in developing anthers. Results We assessed the expression of eighteen candidate internal control genes, including four previously studied nuclear reference genes and fourteen mitochondrial candidate reference genes. By studying gene expression in a range of tissues, the genes nad1 and nad2 were the most stable reference genes, since they were expressed most consistently using four different analytical tools, GeNorm, Delta Ct, Bestkeeper and NormFinder. In contrast, expression levels of the conventional nuclear reference genes were more variable. The comprehensively ranked first candidate gene, nad2 is proposed as the preferred choice as a reference gene, especially when studying different stages of anther development. Notably, actin , the most widely used marker expression gene, worked well in some cases, but there was significant variation in its rankings, for example, using the Bestkeeper tool it was ranked sixth. Conclusions The results indicate that nad1 and nad2 respectively were the most stably expressed marker genes in 8 different tissues and stages of anther development. This study provides valuable support for future research on mitochondrial gene expression in potato, specifically for identifying patterns of expression of CMS genes, and can be a valuable tool to quantify gene expression for other Solanaceae species. Expression reference genes qRT-PCR mitochondria tissue expression anther potato nad1 nad2 Figures Figure 1 Figure 2 Background With the increasing world population, demand for food is also increasing. Hybrid breeding is a strategy that can help increase crop yields, and has been used successfully to increase the yields of crops such as maise and rice [ 1 , 2 ]. The potato ( Solanum tuberosum L.) is a vital tuber crop, and is a staple food for approximately 1.3 billion people worldwide [ 3 , 4 ]. Unlike most seed crops, cultivated potato is normally propagated clonally through tubers [ 5 ]. However, tuber propagation has the disadvantage of exposing plants to various diseases, especially viruses, during the bulking up of seed potatoes. Additionally, most cultivated potato species exhibit tetrasomic inheritance, complicating the breeding process. To address these challenges, several research groups advocate re-inventing potato as an inbred-line-based diploid crop. This approach aims to simplify the genome and promote propagation by seeds rather than tubers, so reducing the opportunity to accumulate diseases [ 6 – 8 ]. In this context, Zhang et al. [ 3 ] used genome design to develop pure, fertile diploid potato lines, resulting in uniform and vigorous F1 hybrids. This innovation can transform potato breeding from a slow, non-accumulative process into a rapid, iterative one. Importantly, this research provides inbred-lines, suitable materials for potato breeding, heralding a ‘green revolution’ in the potato industry and facilitating the advance of hybrid potato breeding through seed propagation [ 9 ]. Hybrid potato breeding involves significant work, particularly the emasculation of female parental lines for cross-pollination. Manual emasculation, mechanical emasculation or chemical treatments used in hybrid breeding programs are costly, inefficient, and chemical emasculation agents may be environmentally damaging [ 10 ]. These methods may also allow some self-pollination, reducing hybrid seed purity and so reducing seed hybrid quality. The availability of male-sterile plants addresses these issues. In addition, male sterility is crucial for studying heterosis in hybrid crops, and to enable large-scale production of hybrid seeds [ 11 ]. Cytoplasmic male sterility (CMS), a maternally inherited inability to produce functional pollen, occurs in over 200 species of higher plants. CMS lines lack functional ‘restorer-of-fertility’ genes ( Rf ) in the nucleus [ 12 , 13 ]. At least 29 CMS genes have been identified in 13 crop species [ 10 , 13 , 14 ]: these genes are often chimeric, resulting from the recombination of mitochondrial genomes [ 15 – 17 ]. CMS has been studied extensively in monocotyledonous plants, particularly rice. The major CMS types in rice include the wild abortive type (CMS-WA), the Boro II type (CMS-BT), and the Honglian type (CMS-HL). The BT-type was the first CMS system identified in rice [ 19 , 20 ]. Genetic transformation experiments later confirmed orf79 as the CMS-causing gene in BT-type rice, and the restorer genes Rf1a and Rf1b were found to restore fertility by suppressing orf79 expression [ 21 ]. In the HL-type system, the CMS-associated gene is orfH79 , which shares a high sequence similarity with orf79 , differing only by five SNPs in the coding region, leading to five amino acid substitutions in its translated protein [ 22 , 23 ]. For the WA-type, the sterility-inducing gene is WA352 , is composed of four DNA fragments (284s, cs3, cs2, and cs1) and encodes a 352-amino acid protein [ 18 ]. Although some CMS genes have been identified in other plants, they have rarely been reported in potato. Quantitative real-time PCR (qRT-PCR) is still one of the most widely used methods to assess gene expression. A crucial aspect of this method is normalisation, which mitigates technical variability arising from factors such as differences in sample size, pipetting inaccuracies, and sample quality [ 24 ]. Current evidence suggests that no gene can be used universally as a reference gene, emphasising the need for systematic validation of reference genes [ 25 , 26 ]. For instance, GAPDH (glyceraldehyde-3-phosphate dehydrogenase), a commonly used reference gene and is referred to as “classical.” While it provides good results in many studies, it is not recommended in others due to variability of expression caused by varying experimental factors [ 26 ]. In addition, reference genes chosen for normalisation in expression studies should take into account the origin of the mRNA type to minimise bias resulting from differences in extraction efficiency, reverse transcription, or PCR amplification [ 27 ]. Historically, most reference genes have been nuclear genes, which are unlikely to be useful for mitochondrial gene expression studies. Alternatively, reference genes which target microRNAs (miRNAs) have also been studied [ 27 ]. In the tea plant ( Camellia sinensis ), miR159a was found to be the best single reference gene from the bud to the fifth leaf, 5S rRNA was the most suitable gene in different organs, and miR6149 was the most stable gene when leaves were attacked by Ectropis oblique [ 28 ]. In sweet potato ( Ipomoea batatas L.), a combination of miRn60 and miR482 was used as reliable reference genes across four tissues and two cultivars under drought and salt stress treatments [ 29 ]. Investigating expression patterns of mitochondrial genes could provide clues to identify CMS genes. Because of the relative cost of sequencing, assembly and annotation of mitochondrial genomes, qRT-PCR is still used widely as a sensitive technique for quantifying levels of mitochondrial gene expression. Its accuracy depends on the reference genes used for data normalisation. It is essential to use a reference gene with stable expression and wide applicability for measuring relative patterns of CMS gene expression. However, no mitochondrial reference genes have been reported so far for potato. In this study, we aimed to identify reliable mitochondrial reference genes for normalisation of qRT-PCR data in potato, focusing on the characterisation of CMS genes. Fourteen mitochondrial genes, including four types of adenosine triphosphate ( ATP1 , ATP4 , ATP6 , ATP9 ), apocytochrome b gene ( cob ), subunit 2 of cytochrome c oxidase ( cox2 ), five types of NADH dehydrogenase subunit ( nad1, nad2, nad3, nad5, nad6) , genes for three cytoplasmic ribosomal protein (rps3, rps4, rps19 ), and four previously used nuclear reference genes - Elongation factor 1-alpha ( EF1-α ), Actin, exocyst complex component ( sec3 ), and tubulin, were selected as candidate reference genes. The stability of their expression was determined in eight different tissues - anthers, roots, stems, leaves, tubers, petals, stigmas, stolons, including anthers at different developmental stages, and systematically evaluated using the programs GeNorm, NormFinder and BestKeeper, and Delta Ct. A comprehensive analysis of reference gene stability resulted in identification of the most stable reference gene(s) for corresponding experiments. Materials and methods Plant materials A diploid potato line derived from code number BS 278 provided by the United States Department of Agriculture (USDA) was used in this study. The plants were grown in a growth chamber under long-day conditions (16 h light/8 h dark at 25°C) to produce seedlings and flowers. Plant tissues were collected and divided into two groups. The first combined eight different fresh tissues, including petal, stigma, stem, root, tuber, leaf, stolon, and anthers. To study reference genes during anther development, six anther developmental stages were collected. All these tissues were frozen immediately in liquid nitrogen and stored at − 80°C until RNA extraction and analysis of target gene expression. RNA extraction and cDNA synthesis RNA extraction was done using TRIzol™ Reagent (Code No 15596018, Thermo Fisher Scientific, USA) following the manufacturer’s guidelines. The quality and quantity of extracted RNA was assessed using a Nanodrop Spectrophotometer. To generate the first-strand cDNA, a HiScript III 1st Strand cDNA Synthesis Kit (+ gDNA wiper) (Code No R312-02, Vazyme, Nanjing, China) was used with 1 ug of total RNA. Both oligo (dT) primers and random primers were used in each PCR reaction mixture. Before obtaining first-strand cDNA, genomic DNA contamination was removed from isolated RNA using DNase provided in the kit. The cDNA was stored at − 20°C until use. Reference gene selection Based on previous studies [ 30 ], the four nuclear reference genes and 14 mitochondrial genes selected were: sec3 , EF1-α , Actin , tubulin , and ATP1 , ATP4 , ATP6 , ATP9 , cob , cox2 , nad1 , nad2 , nad3 , nad5 , nad6 , rps19 , rps3 and rps4 . From previous research on reference genes for potato under abiotic stress [ 30 ], four nuclear genes were also studied. Target-specific primers of 14 mitochondrial genes were designed using Primer-BLAST [ 31 ] with a melting temperature (Tm) between 57°C and 63°C, primer length of 19–21 nucleotides, and amplicon size between 78 and 126 bp. The names and sequences of primers of candidate reference genes are provided in Table 1 . Primer specificity was confirmed through melting curve analysis of qRT-PCR reactions. Real-time polymerase chain reaction Real-time PCR was performed using SYBR Green Premix Pro Taq HS qPCR Kit III (High Rox Plus) (Code No AG11738, Accurate Biotechnology, Hunan, China). To assess the specificity of the primers, melt curve analysis was undertaken, initially using mixed samples containing all the tissues at varying concentrations of cDNA (5 0 , 5 1 , 5 2 , 5 3 , 5 4 , 5 5 ). A negative control using distilled water instead of cDNA was included at every stage of the experiment. The respective qRT-PCR efficiencies ( E ) (Table 1 ) for each gene were calculated based on the slope. Each reaction consisted of a 10 µL volume, and two-step PCR was used. Each sample included three biological replicates, and each biological replicate was done in triplicate for technical replication. Data analysis To identify the most stable gene expression values, GeNorm [ 32 ], BestKeeper [ 33 ], NormFinder [ 34 ] and Delta Ct [ 35 ] tools were used. BestKeeper and Delta Ct directly employ the Ct value for stability analysis, avoiding an additional conversion step. These two tools estimate the standard deviation values (SD) and variation coefficient (CV) of each reference gene based on their Ct values. The reference genes with the lowest SD and CV are considered the most stably expressed. In contrast, GeNorm and Normfinder tools transform raw Ct values into relative quantities through the formula 2 −ΔCt (ΔCt = each corresponding Ct value − lowest Ct value). GeNorm introduces expression stability values ( M ) to investigate the most stable reference gene. As for GeNorm, the stability value (SV) is applied to NormFinder to estimate expression variation among the tested candidate reference genes. Results Tissues from eight stages of potato growth and development were analysed, together with six stages of anther development, to identify the most stable reference genes for study of expression patterns of mitochondrial genes, especially CMS genes. PCR efficiency and correlation coefficient data for the candidate reference genes are summarised in Table 1 . The results indicate that PCR efficiency for the candidate genes tested ranged from 92.3–111.4%, with an R 2 between 0.992 and 0.999. According to the information for publication of quantitative real-time PCR experiments (MIQE) guidelines, PCR efficiency should be in the order of 80% ≤ E ≤ 120% to ensure the reliability of experiments [ 36 ]. The additional test of melt curve analysis is usually done to confirm the specificity of primer annealing. A single peak for a melt curve analysis, as shown in Fig. 1 -a, indicates a single PCR product for each of the candidate genes - that is, that PCR amplification was specific for each of the genes studied. Table 1 Candidate reference genes and primer sequences Gene Primer sequence (5'-3') Length (bp) R 2 E (%) Resources ATP1 F: TGGTCTCAGTTGGGGATGG R: ACACCGCTGGCAAATTCAAC 86 0.999 111.4 This study ATP4 F: CGGTGTAGCTCGAAAGCAGA R: AAGAGAATCCCCCACCCGAA 84 0.999 101.6 This study ATP6 F: TTCGTGCTGAACCCGGTAAA R: AAAGTGACCGAGATGCGAGG 86 0.999 104.3 This study ATP9 F: CTTCAGCGGGAGCTGCTATC R: CCAATGATGGATTTCGCGCC 78 0.997 103.7 This study cob F: TGGGTTCTCCGTGGACAATG R: GCGGCCAGATGAAGAAGACT 99 0.999 100.5 This study cox2 F: CTCGTCCCATACCTTCTGCC R: TCTCACCCAGCCCTACCTAC 100 0.992 104.8 This study nad1 F: TATGGGTCCGTGCAGCATTT R: CACCAGAAACGGGGACTACC 108 0.998 98.0 This study nad2 F: GGCTAACGGGGGTATTCCTG R: TAGCATTACGGCAAACCCGT 125 0.999 100.0 This study nad3 F: AGTGATCAGCCCGCTAGTTTC R: GCATCACCGGAAGGATCGAA 123 0.992 105.0 This study nad5 F: AAAGGGAACGAGGAGGCAAG R: ATTCCTGAGTGCAGGTTCGG 87 0.994 96.7 This study nad6 F: ACGGTTTATGCCGGAAAGGT R: AGCCCCAATCATGGCTACTA 126 0.997 98.9 This study rps19 F: CGGAATTCGTTGATTGCTCCG R: TCGAAGGTCTTCGTTTCCGTG 126 0.998 92.2 This study rps3 F: GTGCTTCTCCGATTGCTCAAG R: CCCCTCCACCCCCTTTTTC 122 0.992 101.2 This study rps4 F: TCAAGCAAGGCAGCCGATAA R: GCGGGTTCTCGCATCATTTT 124 0.996 103.1 This study Actin F: AGGAGCATCCTGTCCTCCTAA R: CACCATCACCAGAGTCCAACA 180 0.998 96.9 [ 30 ] EF1-α F: GATGGTCAGACCCGTGAACA R: CCTTGGAGTACTTCGGGGTG 106 0.997 106.2 [ 30 ] sec3 F: GCTTGCACACGCCATATCAAT R: TGGATTTTACCACCTTCCGCA 160 0.960 107.0 [ 30 ] tubulin F: GGGAATAACTGGGCGAAAGGT R: CCTCCACCAAGTGAGTGACAA 134 0.994 92.3 [ 30 ] F forward primer, R reverse primer, E PCR efficiency, R 2 regression coefficient. Analysis of expression stability of selected reference genes Absolute Ct values in qRT-PCR reflect transcript abundance, and the results obtained show the stability of expression of the candidate reference genes. As shown in Fig. 1 -b and Table 2 , overall, the mean Ct values of the reference genes ranged from 17.96 to 24.90 in all groups. This analysis shows that the most highly expressed candidate reference gene was nad1 , which is involved cellular energy metabolism [ 37 ], with a mean Ct of 17.96 in 8 different tissues, and 18.27 in anther developmental stages. The three nuclear reference genes, actin , sec3 and tubulin , had a relatively lower expression level: sec3 had the highest mean Ct value in all eight stages of potato development, and in the stages of anther development studied. The Coefficient of Variance (CV) as anther standard was used to assess the variability of experimental replicates and further visualise the fluctuation in the expression of reference genes. As shown in Table. 1, in group1, nad3 had the lowest expression, CV of 2.96%, closely followed by ATP4 at 3.53%, nad5 at 3.54% and ATP1 at 3.57%. Interestingly, the two conventional nuclear reference genes had fairly high CVs ( tubulin at 6.40% and EF1-α at 5.68%). However, in group 2, the nuclear candidate reference genes displayed better stability ( actin at 1.02%, sec3 at 1.38% and EF1-α at 5.68% CV), with tubulin the least stable at 5.00% CV. Four reliable statistical analytic tools, NormFinder, Detal Ct, GeNorm and BestKeeper were applied to evaluate the stability of gene expression. These tools were employed to validate the results and gain a deeper understanding of the dataset. By applying these statistical tools, we evaluated the stability of mitochondrial genes as reference genes during anther development, providing crucial insights for the relatively quantitative analysis of CMS genes, thereby enhancing the identification of CMS-associated genes. Table 2 Statistical analysis of candidate reference gene Ct value Gene Group1: Different tissue types Group 2: Different anther developmental stages Mean SD CV (%) Mean SD CV (%) ATP1 19.48 0.696 3.57 19.02 0.358 1.88 ATP4 20.91 0.738 3.53 20.84 0.380 1.83 ATP6 18.51 0.871 4.70 20.59 0.563 2.73 ATP9 18.22 0.548 3.01 18.79 0.282 1.50 cob 19.24 0.700 3.64 18.99 0.384 2.02 cox2 22.33 0.959 4.29 22.28 0.372 1.67 nad1 17.96 0.874 4.87 18.27 0.455 2.49 nad2 19.32 0.804 4.16 19.60 0.309 1.58 nad3 21.93 0.648 2.96 19.71 0.620 3.15 nad5 20.11 0.713 3.54 18.96 0.618 3.26 nad6 19.50 0.752 3.86 19.36 0.428 2.21 rps19 20.22 0.865 4.28 19.94 0.369 1.85 rps3 20.38 0.932 4.57 20.71 0.662 3.19 rps4 20.84 1.358 6.51 19.55 0.411 2.10 Actin 22.29 1.075 4.82 22.22 0.233 1.02 EF1-α 20.30 1.154 5.68 19.31 0.267 1.38 Sec3 24.72 0.981 3.97 24.90 0.294 1.18 tubulin 23.23 1.486 6.40 23.20 1.161 5.00 SD measures the dispersion of data points from the mean Ct and was calculated using the STDEV.S function in Excel, CV was calculated using the formula: CV (%)= [Standard Deviation / Mean (Average Ct)]×100% GeNorm analysis GeNorm analysis determines the normalisation value based on the geometric mean of various candidate reference genes and mean pairwise variation of each gene from all the reference genes in a given set of samples. To identify the most stable reference genes, the internal control gene-stability measure stability value (M) was defined as the average pairwise-to-decreasing expression stability. A lower M value indicates higher stability, with a recommended cut-off of less than 1.5 [ 30 , 32 , 36 ]. We analysed our data and found that all 18 candidate reference genes exhibited high expression stability with low (< 0.9) M values among the two experimental sets, and these were much lower than the default threshold of 1.5 (Table 3 ). More specifically, in the first group of sample tissues, the results indicate that the combination of two mitochondrial genes nad5 and nad6 were the most stable reference genes with M values of 0.276, followed by nad2, at nearly 0.4, and the third, cob, with M values 0.462. Interestingly, the M values of four nuclear candidate genes were either the highest M (tubulin at 0.836) or a significantly higher M (Actin at 0.64, sec3 at 0.675, EF1-α at 0.709). In the group of samples from anthers, cob and nad1 exhibited the lowest M value, and tubulin had the highest M value, indicating that cob and nad1 were most stably expressed and tubulin the least. NormFinder analysis NormFinder calculates the SV using an ANOVA-based model to estimate expression variation among the tested candidate genes. A higher SV indicates lower stability. By considering both intra-group and inter-group variations, NormFinder ranks the candidate genes based on their stability [ 34 , 38 ]. The outcome of the NormFinder analysis for the 18 candidate reference genes is provided in Table 3 . There was a significant gap between the top and bottom of SV in the first group of different tissues. The results highlight nad1 as the most stable gene, with a stability value of 0.216, surpassing the others. ATP1 and rps19 had close stability values, 0.241 and 0.249, respectively. In contrast, the four nuclear candidate reference genes exhibited poorer expression stability in all of these samples. However, in the second group, the overall SV for each candidate gene was relatively low, with the lowest SV of 0.282 for tubulin. The distinct difference from the first group is that the nuclear gene Actin displayed the minimum SV with 0.074, indicating that Actin was most stably expressed. Delta Ct (ΔCt) The ΔCt method is used to identify useful reference genes, by comparing the relative expression of ‘pairs-of-genes’ in each sample. Genes remaining constant in distinct samples are regarded as expressed stably. However, fluctuations in ΔCt values suggest that one or both genes may have variable expression. Introducing a third, fourth, or even fifth gene into the comparisons allows a deeper analysis, revealing which pairs demonstrate less variability. In turn, this helps identify the gene(s) with stable expression across the tested samples, enabling a process of ranking or discarding based on the results obtained. Expression stabilities are determined from the mean SDs [ 35 ]. Data analysis using the ΔCt method suggested that the five most stable genes in the eight tissue samples under normal conditions were nad1 > nad2 and rps19 > ATP1 > cob, which were all mitochondrial genes (Table 3 ). The rank of all the nuclear candidate reference genes was lower. However, for the different anther development stages, Actin, a potential nuclear candidate reference gene, exhibited the smallest mean SD, together with nad2 and ATP9. Because of their similar expression stability, the ΔCt method these three genes shared the same ranking. To ensure reliability, we further validated the selection using additional statistical tools. However, tubulin was ranked last. These results are similar to those from GeNorm and NormFinder - tubulin exhibited the poorest stability among the two experiment sets. Table 3 Stability values of candidate reference genes calculated by four algorithms in two groups Gene Group 1: Different tissue types Group 2: Different anther developmental stages GeNorm NormFinder ΔCt BestKeeper GeNorm NormFinder ΔCt BestKeeper M SV SD r SD CV (%) M SV SD r SD CV (%) ATP1 0.570 0.241 0.71 0.813 0.51 2.60 0.285 0.103 0.46 0.790 0.29 1.53 ATP4 0.658 0.434 0.86 - - - 0.385 0.169 0.57 - - - ATP6 0.586 0.339 0.76 0.874 0.68 3.70 0.498 0.227 0.72 - - - ATP9 0.740 0.577 1.02 - - - 0.299 0.099 0.44 0.684 0.24 1.25 cob 0.462 0.290 0.72 0.847 0.50 2.61 cob | nad1 0.178 0.105 0.45 0.909 0.33 1.74 cox2 0.601 0.364 0.78 0.875 0.70 3.11 0.347 0.162 0.54 - - - nad1 0.505 0.216 0.68 0.952 0.64 3.54 cob | nad1 0.178 0.115 0.48 0.924 0.39 2.11 nad2 0.408 0.253 0.70 0.897 0.55 2.86 0.213 0.096 0.44 0.836 0.25 1.26 nad3 0.532 0.314 0.75 0.800 0.50 2.28 0.411 0.198 0.67 - - - nad5 nad5 | nad6 0.276 0.350 0.77 0.776 0.50 2.49 0.443 0.209 0.73 - - - nad6 nad5 | nad6 0.276 0.324 0.74 0.849 0.56 2.89 0.312 0.133 0.50 0.769 0.37 1.89 rps19 0.559 0.249 0.7 0.894 0.68 3.34 0.323 0.129 0.48 0.700 0.28 1.42 rps3 0.617 0.395 0.81 - - - 0.473 0.194 0.72 - - - rps4 0.783 0.647 1.11 - - - 0.367 0.155 0.54 - - - Actin 0.640 0.380 0.82 - - - 0.258 0.074 0.44 0.703 0.20 0.88 EF1-α 0.709 0.537 0.97 - - - 0.334 0.134 0.50 0.449 0.20 1.05 sec3 0.675 0.467 0.89 - - - 0.269 0.097 0.46 0.655 0.22 0.89 tubulin 0.836 0.766 1.25 - - - 0.572 0.282 1.17 - - - BestKeeper analysis BestKeeper evaluates the stability of candidate genes by analysing several parameters, including SD, CV, and Pearson correlation coefficient ( r ). Genes exhibiting the highest r value and an SD > 1 are considered to be the most stably expressed. The number of candidate genes that can be analysed at one time is limited to ten candidate genes by BestKeeper. Hence, in the first group, eight candidate genes, including all four nuclear candidate genes and rps4 , rps3 , ATP4 and ATP9 , were eliminated by the results from GeNorm, NormFinder and Delta Ct, as they consistently ranked the lowest in all three software tools (Table 3 ). nad1 was considered the most stable reference gene in the eight different tissues, with the highest r value at 0.952 and SD at 0.64. nad2 was placed second, demonstrating strong stability in all evaluations with rps19 third, followed by cox2 . ATP6 occupied the fifth position in terms of stability across the tools. Using the same analytical approach for the second group of samples, the eight candidate genes - cox2 , ATP4 , ATP6 , nad3 , nad5 , rps3 , rps4 , and tubulin were removed from the BestKeeper analysis. nad1 was also the most stably expressed in the different anther tissues/ developmental stages, with the highest r value at 0.924 and SD at 0.39, followed by cob and nad2 . Notably, the ranking of Actin decreased, and it was placed sixth using the BestKeeper tool. Ordering of reference genes from the results of the four analytical tools A comprehensive comparative ranking of the effectiveness and stability of candidate reference genes is provided in Fig. 2 , which combines the ranking results from the four tools, and the average rank for each tool. Despite some differences in the results obtained from these tools, three genes nad1 , nad2 and rps19 emerged as stably genes expressed in different tissues, with a consistent ranking from 1 to 3 based on NormFinder, BestKeeper and Detal Ct analysis. In particular, the mitochondrial gene, nad1 , was ranked first using three tools. In contrast, four previously reported nuclear reference genes from the first group were less stable as shown by NormFinder, GeNorm and Detal Ct analysis. Similarly, there was a fluctuation in their rank in tissues of different stages of anther development. In contrast, the rank of nad2 was consistent and was always in the top three for all analytical tools. It also was ranked at the top in the second group, although for this group, Actin could also be considered to be useful, since in different anther developmental stages, it ranked second in NormFinder and Detal Ct analyses. Discussion qRT-PCR is a widely used method to examine gene expression patterns [ 39 ]. Ensuring the precision of experiments relies on the qRT-PCR assay itself, including primer design and length, and the PCR conditions, but most importantly, on selecting reliable internal controls. It requires an appropriate reference gene to normalise the target transcript levels. While reference genes generally exhibit stable expression under standard conditions, they are involved in essential cellular processes, and their expression can fluctuate under specific circumstances, such as environmental stress or during different developmental stages [ 26 , 40 ]. Additionally, the availability of suitable reference genes for accurate normalisation in mitochondrial genes remains limited. In eukaryotes, poly(A) tails are present on almost every mRNA [ 41 ], whereas in plant mitochondria, polyadenylated mitochondrial transcripts are rare and unstable because polyadenylation of mRNAs in chloroplasts serves as an RNA degradation signal [ 42 , 43 ]. Reverse transcriptase PCR (RT-PCR), a step before qRT-PCR is used to synthesise cDNA, but RT-PCR is usually undertaken with Oligo (dT) primers which target mRNAs with poly(A) tails. The synthesis of mitochondrial mRNAs and their patterns of expression have rarely been studied. Thus, selecting reference genes tailored for measuring mitochondrial gene expression, based on their stability and consistency of expression under different experimental conditions, is required to ensure accurate and dependable analysis of gene expression. Mitochondrial gene expression analysis is clearly important for studying mitochondrial gene function. Many CMS genes originate from mitochondrial gene rearrangements during evolution. When substoichiometric shifting (SSS) occurs, the copy number of specific mitochondrial molecules can increase, leading to the preferential expression of CMS-associated genes previously present at low levels [ 10 , 13 ]. The application of a three-line CMS breeding system to development the hybrid population has proved to be a successful approach for exploiting heterosis to improve yield in rice ( Oryza sativa ) [ 44 ]. However, there is limited knowledge of CMS genes in potato, which has slowed the development of hybrid potato breeding. qRT-PCR analysis is widely used to study gene-expression patterns, and it an be used to identify CMS genes in potato. However, accurate qRT-PCR analysis of mitochondrial genes is challenging because of limited flexibility of primer design and the lack of appropriate reference genes to standardise gene expression levels. If a common internal reference gene is selected without screening, it is likely to reduce the accuracy of quantitative analyses, and could result in incorrect conclusions. To improve the accuracy of gene-expression analyses, we studied and selected reliable internal control genes systematically for normalisation of mitochondrial gene expression in a range of potato tissue and anther developmental stages. The E values of the eighteen candidate reference gene primer pairs ranged from 92.3–111.4%, with R 2 values were 0.960–0.999 (Table 1 ). These results demonstrate the high accuracy, efficiency, and sensitivity of the primer pairs used for reference gene selection. In addition, the mean Ct values of the candidate reference genes ranged from 17.964 ( nad1 ) to 24.717 ( sec3 ) (Table 2 ). Notably, the mean Ct values for four nuclear candidate reference genes were not consistent in potato under abiotic stress [ 26 ], suggesting that no reference gene is equally expressed under different conditions. Therefore, it is evident that selecting appropriate reference genes for normalisation of mitochondrial genes under specific experimental conditions is required. Four well-established algorithms (Delta Ct, GeNorm, NormFinder and BestKeeper) were used to identify stably expressed reference genes [ 45 ]. The operating principle of NormFinder is similar to that of the GeNorm program; however, GeNorm has the added capability to identify optimal reference gene combinations and determine the ideal number of reference genes. In contrast to GeNorm and NormFinder, BestKeeper and Delta Ct software directly make calculations using Ct values [ 46 ]. The results of NormFinder and Delta Ct were generally similar in this study (Fig. 2 ; Table 3 ) as the top two candidate reference genes ( nad1 and nad2) selected by these two algorithms remained consistent in all tissues studied, whereas four nuclear candidate reference genes did not meet the required criteria in eight different tissue types of potato. Overall, nad2 was identified as the best candidate reference gene for different anther developmental stages, although Actin also performed well, albeit with more variation. These result show that selection of reliable reference genes requires analysis of expression under different conditions or treatments. Conclusions This is the first detailed study to select the best candidate mitochondrial reference genes which can be used in qRT-PCR expression studies in different tissues and developmental stages of potato to normalise gene expression. The results show that the most suitable reference gene is nad1 for expression studies in eight potato tissues, whereas nad2 is the most appropriate gene for normalising expression in developing anthers. Overall expression of nad2 was most stable in all of the tissues studied. Abbreviations Cytoplasmic male sterility (CMS) quantitative real-time polymerase chain reaction (qRT-PCR) minimum information for publication of quantitative real-time PCR experiments (MIQE) adenosine triphosphate (ATP) apocytochrome b gene ( cob ) subunit 2 of cytochrome c oxidase ( cox2 ) NADH dehydrogenase subunit ( nad ) cytoplasmic ribosomal protein ( rps ) Elongation factor 1-alpha ( EF1-α ) exocyst complex component ( sec3 ) melting temperature (Tm) PCR efficiency ( E ) Coefficient of variance (CV) Delta Ct (ΔCt) standard deviation (SD) Declarations Acknowledgements We thank Professor Chunzhi Zhang and her team at the Agricultural Genomics Institute at Shenzhen for providing plant materials and glasshouse facilities used in this study. We also thank Professor Yonglin Ren, Murdoch University, for liaising between these two groups. Funding This work was funded by a grant to Li Yuan from the China Postdoctoral Science Foundation ID: 2024M753583, and a Murdoch University International PhD Scholarship to QL. Authors and Affiliations Authors: *Qing Li, School of Agricultural Sciences, Western Australian State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Western Australia 6150, Australia and Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China *Jing Xu, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China and College of Agronomy, Qingdao Agricultural University, Qingdao, China. Li Yuan, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120. Michael G. K. Jones, School of Agricultural Sciences, Western Australian State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Perth, Western Australia 6150, Australia. Contributions QL and LY contributed equally to this article. Study conception and design: LY, QL, LY; data collection: JX, LY; analysis and interpretation of results: QL, JX, MJ; draft manuscript preparation: MJ, QL. All authors reviewed the results and approved the final version of the manuscript. Corresponding author Michael G. K. Jones ( [email protected] ) Ethics declarations Ethics declaration and consent to participate Not applicable Consent for publication Not applicable Competing interests The authors declare no competing interests Clinical trial number Not applicable References Hochholdinger F, Baldauf JA. Heterosis in plants. Curr Biol [Internet]. 2018;28:R1089–92. Available from: http://dx.doi.org/10.1016/j.cub.2018.06.041 Cheng S-H, Zhuang J-Y, Fan Y-Y, Du J-H, Cao L-Y. Progress in Research and Development on Hybrid Rice: A Super-domesticate in China. Ann Bot [Internet]. 2007;100:959–66. Available from: https://academic.oup.com/aob/article-lookup/doi/10.1093/aob/mcm121 Zhang C, Yang Z, Tang D, Zhu Y, Wang P, Li D, et al. Genome design of hybrid potato. Cell [Internet]. 2021;184:3873-3883.e12. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0092867421007078 Stokstad E. The new potato. Science (80- ). 2019;363:574–7. Xu X, Pan S, Cheng S, Zhang B, Mu D, Ni P, et al. 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Cite Share Download PDF Status: Published Journal Publication published 08 Oct, 2025 Read the published version in BMC Plant Biology → Version 1 posted Editorial decision: Revision requested 09 Apr, 2025 Editor assigned by journal 09 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 03 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6373075","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440845168,"identity":"ea71a4de-d1cf-4c4f-a37b-9fb5a44a00a4","order_by":0,"name":"Qing Li","email":"","orcid":"","institution":"Western Australian State Agricultural Biotechnology Centre, Murdoch University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Li","suffix":""},{"id":440845169,"identity":"fa58c285-5a05-4fe1-a6f0-f934f644e430","order_by":1,"name":"Jing Xu","email":"","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Xu","suffix":""},{"id":440845170,"identity":"93a88e7f-b8e3-425f-a82c-383f74775e7e","order_by":2,"name":"Li Yuan","email":"","orcid":"","institution":"Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Yuan","suffix":""},{"id":440845171,"identity":"68c96e2d-9170-4e86-ab08-61427795c9a7","order_by":3,"name":"Michael G. K. Jones","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHCCBBAhByIkGBgOwAQIazEmSQsYJDYQrUW3geHh44KKuvS1M3If3vhQc4eBnz3HgOFnG24tZgcYko1nnDmcu+1GurHljGPPGCR73hgw9uLXkibN23YAqCWNTZq34TCDwQ2gLbwEtfyrSzeDabEHamH8S1BLA3MCXIuBRI4BM15bDgP9wnPssOG2M8+YgX45zCNx5lnBYZlzeLQc70l8zFNTJ292PI0RGGKH5fjbkzc+fFOGWwsDM08CCp8HRBzAowEI2AnIj4JRMApGwSgAACmkUOo3xC03AAAAAElFTkSuQmCC","orcid":"","institution":"Western Australian State Agricultural Biotechnology Centre, Murdoch University","correspondingAuthor":true,"prefix":"","firstName":"Michael","middleName":"G. K.","lastName":"Jones","suffix":""}],"badges":[],"createdAt":"2025-04-04 04:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6373075/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6373075/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12870-025-07301-3","type":"published","date":"2025-10-08T15:57:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80297543,"identity":"2c7d3a95-0dbd-4ba5-9653-106d7ca30437","added_by":"auto","created_at":"2025-04-10 08:44:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":637195,"visible":true,"origin":"","legend":"\u003cp\u003eMelting curve analysis and expression levels of candidate reference genes in experimental samples. a, Melting curve analysis of 18 candidate reference genes. b, Expression levels of candidate reference genes in two experimental sets. Expression data are displayed as Ct values for each reference gene in all samples. The line across each individual box indicates the median value, and the box limits indicate the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles. ‘×’ represents the maximum and minimum values. Points represent the average.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6373075/v1/7e209aec01e8d5aa5b266a4b.png"},{"id":80297550,"identity":"8c568ebf-ca38-463b-8b7e-707b559624b0","added_by":"auto","created_at":"2025-04-10 08:44:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159186,"visible":true,"origin":"","legend":"\u003cp\u003eComprehensive ranking of candidate reference genes using the GeNorm, NormFinder, Delta Ct, and BestKeeper tools. a, Comprehensive ranking for the group including different potato tissues. b, Comprehensive ranking for the group of different anther developmental stages. ‘-’ represent not available to rank. The mean is calculated by averaging the ranks after sorting.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6373075/v1/d598e81adeeb11585cf59e10.png"},{"id":93419831,"identity":"6576a6d2-6f9a-4b51-8574-4727738498d3","added_by":"auto","created_at":"2025-10-13 16:08:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2034259,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6373075/v1/e895528b-e84e-486d-9f24-6b831bcf5bcc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Selection and optimisation reference genes for normalisation of mitochondrial gene-expression by qRT-PCR in different potato tissues and during anther development","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the increasing world population, demand for food is also increasing. Hybrid breeding is a strategy that can help increase crop yields, and has been used successfully to increase the yields of crops such as maise and rice [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe potato (\u003cem\u003eSolanum tuberosum\u003c/em\u003e L.) is a vital tuber crop, and is a staple food for approximately 1.3\u0026nbsp;billion people worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Unlike most seed crops, cultivated potato is normally propagated clonally through tubers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, tuber propagation has the disadvantage of exposing plants to various diseases, especially viruses, during the bulking up of seed potatoes. Additionally, most cultivated potato species exhibit tetrasomic inheritance, complicating the breeding process. To address these challenges, several research groups advocate re-inventing potato as an inbred-line-based diploid crop. This approach aims to simplify the genome and promote propagation by seeds rather than tubers, so reducing the opportunity to accumulate diseases [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In this context, Zhang et al. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] used genome design to develop pure, fertile diploid potato lines, resulting in uniform and vigorous F1 hybrids. This innovation can transform potato breeding from a slow, non-accumulative process into a rapid, iterative one. Importantly, this research provides inbred-lines, suitable materials for potato breeding, heralding a \u0026lsquo;green revolution\u0026rsquo; in the potato industry and facilitating the advance of hybrid potato breeding through seed propagation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHybrid potato breeding involves significant work, particularly the emasculation of female parental lines for cross-pollination. Manual emasculation, mechanical emasculation or chemical treatments used in hybrid breeding programs are costly, inefficient, and chemical emasculation agents may be environmentally damaging [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. These methods may also allow some self-pollination, reducing hybrid seed purity and so reducing seed hybrid quality. The availability of male-sterile plants addresses these issues. In addition, male sterility is crucial for studying heterosis in hybrid crops, and to enable large-scale production of hybrid seeds [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Cytoplasmic male sterility (CMS), a maternally inherited inability to produce functional pollen, occurs in over 200 species of higher plants. CMS lines lack functional \u0026lsquo;restorer-of-fertility\u0026rsquo; genes (\u003cem\u003eRf\u003c/em\u003e) in the nucleus [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. At least 29 CMS genes have been identified in 13 crop species [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]: these genes are often chimeric, resulting from the recombination of mitochondrial genomes [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCMS has been studied extensively in monocotyledonous plants, particularly rice. The major CMS types in rice include the wild abortive type (CMS-WA), the Boro II type (CMS-BT), and the Honglian type (CMS-HL). The BT-type was the first CMS system identified in rice [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Genetic transformation experiments later confirmed \u003cem\u003eorf79\u003c/em\u003e as the CMS-causing gene in BT-type rice, and the restorer genes \u003cem\u003eRf1a\u003c/em\u003e and \u003cem\u003eRf1b\u003c/em\u003e were found to restore fertility by suppressing \u003cem\u003eorf79\u003c/em\u003e expression [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In the HL-type system, the CMS-associated gene is \u003cem\u003eorfH79\u003c/em\u003e, which shares a high sequence similarity with \u003cem\u003eorf79\u003c/em\u003e, differing only by five SNPs in the coding region, leading to five amino acid substitutions in its translated protein [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. For the WA-type, the sterility-inducing gene is \u003cem\u003eWA352\u003c/em\u003e, is composed of four DNA fragments (284s, cs3, cs2, and cs1) and encodes a 352-amino acid protein [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Although some CMS genes have been identified in other plants, they have rarely been reported in potato.\u003c/p\u003e \u003cp\u003eQuantitative real-time PCR (qRT-PCR) is still one of the most widely used methods to assess gene expression. A crucial aspect of this method is normalisation, which mitigates technical variability arising from factors such as differences in sample size, pipetting inaccuracies, and sample quality [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Current evidence suggests that no gene can be used universally as a reference gene, emphasising the need for systematic validation of reference genes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. For instance, GAPDH (glyceraldehyde-3-phosphate dehydrogenase), a commonly used reference gene and is referred to as \u0026ldquo;classical.\u0026rdquo; While it provides good results in many studies, it is not recommended in others due to variability of expression caused by varying experimental factors [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition, reference genes chosen for normalisation in expression studies should take into account the origin of the mRNA type to minimise bias resulting from differences in extraction efficiency, reverse transcription, or PCR amplification [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Historically, most reference genes have been nuclear genes, which are unlikely to be useful for mitochondrial gene expression studies. Alternatively, reference genes which target microRNAs (miRNAs) have also been studied [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In the tea plant (\u003cem\u003eCamellia sinensis\u003c/em\u003e), \u003cem\u003emiR159a\u003c/em\u003e was found to be the best single reference gene from the bud to the fifth leaf, 5S rRNA was the most suitable gene in different organs, and \u003cem\u003emiR6149\u003c/em\u003e was the most stable gene when leaves were attacked by \u003cem\u003eEctropis oblique\u003c/em\u003e [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In sweet potato (\u003cem\u003eIpomoea batatas\u003c/em\u003e L.), a combination of \u003cem\u003emiRn60\u003c/em\u003e and \u003cem\u003emiR482\u003c/em\u003e was used as reliable reference genes across four tissues and two cultivars under drought and salt stress treatments [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInvestigating expression patterns of mitochondrial genes could provide clues to identify CMS genes. Because of the relative cost of sequencing, assembly and annotation of mitochondrial genomes, qRT-PCR is still used widely as a sensitive technique for quantifying levels of mitochondrial gene expression. Its accuracy depends on the reference genes used for data normalisation. It is essential to use a reference gene with stable expression and wide applicability for measuring relative patterns of CMS gene expression. However, no mitochondrial reference genes have been reported so far for potato.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to identify reliable mitochondrial reference genes for normalisation of qRT-PCR data in potato, focusing on the characterisation of CMS genes. Fourteen mitochondrial genes, including four types of adenosine triphosphate (\u003cem\u003eATP1\u003c/em\u003e, \u003cem\u003eATP4\u003c/em\u003e, \u003cem\u003eATP6\u003c/em\u003e, \u003cem\u003eATP9\u003c/em\u003e), \u003cem\u003eapocytochrome b\u003c/em\u003e gene (\u003cem\u003ecob\u003c/em\u003e), subunit 2 of cytochrome c oxidase (\u003cem\u003ecox2\u003c/em\u003e), five types of NADH dehydrogenase subunit (\u003cem\u003enad1, nad2, nad3, nad5, nad6)\u003c/em\u003e, genes for three cytoplasmic ribosomal protein \u003cem\u003e(rps3, rps4, rps19\u003c/em\u003e), and four previously used nuclear reference genes - Elongation factor 1-alpha (\u003cem\u003eEF1-α\u003c/em\u003e), Actin, exocyst complex component (\u003cem\u003esec3\u003c/em\u003e), and tubulin, were selected as candidate reference genes. The stability of their expression was determined in eight different tissues - anthers, roots, stems, leaves, tubers, petals, stigmas, stolons, including anthers at different developmental stages, and systematically evaluated using the programs GeNorm, NormFinder and BestKeeper, and Delta Ct. A comprehensive analysis of reference gene stability resulted in identification of the most stable reference gene(s) for corresponding experiments.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant materials\u003c/h2\u003e \u003cp\u003eA diploid potato line derived from code number BS 278 provided by the United States Department of Agriculture (USDA) was used in this study. The plants were grown in a growth chamber under long-day conditions (16 h light/8 h dark at 25\u0026deg;C) to produce seedlings and flowers. Plant tissues were collected and divided into two groups. The first combined eight different fresh tissues, including petal, stigma, stem, root, tuber, leaf, stolon, and anthers. To study reference genes during anther development, six anther developmental stages were collected. All these tissues were frozen immediately in liquid nitrogen and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until RNA extraction and analysis of target gene expression.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA extraction and cDNA synthesis\u003c/h3\u003e\n\u003cp\u003eRNA extraction was done using TRIzol\u0026trade; Reagent (Code No 15596018, Thermo Fisher Scientific, USA) following the manufacturer\u0026rsquo;s guidelines. The quality and quantity of extracted RNA was assessed using a Nanodrop Spectrophotometer. To generate the first-strand cDNA, a HiScript III 1st Strand cDNA Synthesis Kit (+\u0026thinsp;gDNA wiper) (Code No R312-02, Vazyme, Nanjing, China) was used with 1 ug of total RNA. Both oligo (dT) primers and random primers were used in each PCR reaction mixture. Before obtaining first-strand cDNA, genomic DNA contamination was removed from isolated RNA using DNase provided in the kit. The cDNA was stored at \u0026minus;\u0026thinsp;20\u0026deg;C until use.\u003c/p\u003e \u003cp\u003e \u003cb\u003eReference gene selection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on previous studies [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the four nuclear reference genes and 14 mitochondrial genes selected were: \u003cem\u003esec3\u003c/em\u003e, \u003cem\u003eEF1-α\u003c/em\u003e, \u003cem\u003eActin\u003c/em\u003e, \u003cem\u003etubulin\u003c/em\u003e, and \u003cem\u003eATP1\u003c/em\u003e, \u003cem\u003eATP4\u003c/em\u003e, \u003cem\u003eATP6\u003c/em\u003e, \u003cem\u003eATP9\u003c/em\u003e, \u003cem\u003ecob\u003c/em\u003e, \u003cem\u003ecox2\u003c/em\u003e, \u003cem\u003enad1\u003c/em\u003e, \u003cem\u003enad2\u003c/em\u003e, \u003cem\u003enad3\u003c/em\u003e, \u003cem\u003enad5\u003c/em\u003e, \u003cem\u003enad6\u003c/em\u003e, \u003cem\u003erps19\u003c/em\u003e, \u003cem\u003erps3\u003c/em\u003e and \u003cem\u003erps4\u003c/em\u003e. From previous research on reference genes for potato under abiotic stress [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], four nuclear genes were also studied. Target-specific primers of 14 mitochondrial genes were designed using Primer-BLAST [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] with a melting temperature (Tm) between 57\u0026deg;C and 63\u0026deg;C, primer length of 19\u0026ndash;21 nucleotides, and amplicon size between 78 and 126 bp. The names and sequences of primers of candidate reference genes are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Primer specificity was confirmed through melting curve analysis of qRT-PCR reactions.\u003c/p\u003e\n\u003ch3\u003eReal-time polymerase chain reaction\u003c/h3\u003e\n\u003cp\u003eReal-time PCR was performed using SYBR Green Premix Pro Taq HS qPCR Kit III (High Rox Plus) (Code No AG11738, Accurate Biotechnology, Hunan, China). To assess the specificity of the primers, melt curve analysis was undertaken, initially using mixed samples containing all the tissues at varying concentrations of cDNA (5\u003csup\u003e0\u003c/sup\u003e, 5\u003csup\u003e1\u003c/sup\u003e, 5\u003csup\u003e2\u003c/sup\u003e, 5\u003csup\u003e3\u003c/sup\u003e, 5\u003csup\u003e4\u003c/sup\u003e, 5\u003csup\u003e5\u003c/sup\u003e). A negative control using distilled water instead of cDNA was included at every stage of the experiment. The respective qRT-PCR efficiencies (\u003cem\u003eE\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) for each gene were calculated based on the slope. Each reaction consisted of a 10 \u0026micro;L volume, and two-step PCR was used. Each sample included three biological replicates, and each biological replicate was done in triplicate for technical replication.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eTo identify the most stable gene expression values, GeNorm [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], BestKeeper [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], NormFinder [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] and Delta Ct [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] tools were used. BestKeeper and Delta Ct directly employ the Ct value for stability analysis, avoiding an additional conversion step. These two tools estimate the standard deviation values (SD) and variation coefficient (CV) of each reference gene based on their Ct values. The reference genes with the lowest SD and CV are considered the most stably expressed. In contrast, GeNorm and Normfinder tools transform raw Ct values into relative quantities through the formula 2\u003csup\u003e\u0026minus;ΔCt\u003c/sup\u003e (ΔCt\u0026thinsp;=\u0026thinsp;each corresponding Ct value\u0026thinsp;\u0026minus;\u0026thinsp;lowest Ct value). GeNorm introduces expression stability values (\u003cem\u003eM\u003c/em\u003e) to investigate the most stable reference gene. As for GeNorm, the stability value (SV) is applied to NormFinder to estimate expression variation among the tested candidate reference genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTissues from eight stages of potato growth and development were analysed, together with six stages of anther development, to identify the most stable reference genes for study of expression patterns of mitochondrial genes, especially CMS genes. PCR efficiency and correlation coefficient data for the candidate reference genes are summarised in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results indicate that PCR efficiency for the candidate genes tested ranged from 92.3\u0026ndash;111.4%, with an \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e between 0.992 and 0.999. According to the information for publication of quantitative real-time PCR experiments (MIQE) guidelines, PCR efficiency should be in the order of 80% \u0026le; \u003cem\u003eE\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;120% to ensure the reliability of experiments [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The additional test of melt curve analysis is usually done to confirm the specificity of primer annealing. A single peak for a melt curve analysis, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-a, indicates a single PCR product for each of the candidate genes - that is, that PCR amplification was specific for each of the genes studied.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate reference genes and primer sequences\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimer sequence (5'-3')\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLength\u003c/p\u003e \u003cp\u003e(bp)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eE\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResources\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TGGTCTCAGTTGGGGATGG\u003c/p\u003e \u003cp\u003eR: ACACCGCTGGCAAATTCAAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e111.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CGGTGTAGCTCGAAAGCAGA\u003c/p\u003e \u003cp\u003eR: AAGAGAATCCCCCACCCGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TTCGTGCTGAACCCGGTAAA\u003c/p\u003e \u003cp\u003eR: AAAGTGACCGAGATGCGAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CTTCAGCGGGAGCTGCTATC\u003c/p\u003e \u003cp\u003eR: CCAATGATGGATTTCGCGCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TGGGTTCTCCGTGGACAATG\u003c/p\u003e \u003cp\u003eR: GCGGCCAGATGAAGAAGACT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecox2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CTCGTCCCATACCTTCTGCC\u003c/p\u003e \u003cp\u003eR: TCTCACCCAGCCCTACCTAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TATGGGTCCGTGCAGCATTT\u003c/p\u003e \u003cp\u003eR: CACCAGAAACGGGGACTACC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GGCTAACGGGGGTATTCCTG\u003c/p\u003e \u003cp\u003eR: TAGCATTACGGCAAACCCGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: AGTGATCAGCCCGCTAGTTTC\u003c/p\u003e \u003cp\u003eR: GCATCACCGGAAGGATCGAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e105.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: AAAGGGAACGAGGAGGCAAG\u003c/p\u003e \u003cp\u003eR: ATTCCTGAGTGCAGGTTCGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: ACGGTTTATGCCGGAAAGGT\u003c/p\u003e \u003cp\u003eR: AGCCCCAATCATGGCTACTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e98.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: CGGAATTCGTTGATTGCTCCG\u003c/p\u003e \u003cp\u003eR: TCGAAGGTCTTCGTTTCCGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GTGCTTCTCCGATTGCTCAAG\u003c/p\u003e \u003cp\u003eR: CCCCTCCACCCCCTTTTTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e101.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: TCAAGCAAGGCAGCCGATAA\u003c/p\u003e \u003cp\u003eR: GCGGGTTCTCGCATCATTTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e103.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: AGGAGCATCCTGTCCTCCTAA\u003c/p\u003e \u003cp\u003eR: CACCATCACCAGAGTCCAACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEF1-α\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GATGGTCAGACCCGTGAACA\u003c/p\u003e \u003cp\u003eR: CCTTGGAGTACTTCGGGGTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e106.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esec3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GCTTGCACACGCCATATCAAT\u003c/p\u003e \u003cp\u003eR: TGGATTTTACCACCTTCCGCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e107.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etubulin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eF: GGGAATAACTGGGCGAAAGGT\u003c/p\u003e \u003cp\u003eR: CCTCCACCAAGTGAGTGACAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eF\u003c/em\u003e forward primer, \u003cem\u003eR\u003c/em\u003e reverse primer, \u003cem\u003eE\u003c/em\u003e PCR efficiency, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e regression coefficient.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of expression stability of selected reference genes\u003c/h2\u003e \u003cp\u003eAbsolute Ct values in qRT-PCR reflect transcript abundance, and the results obtained show the stability of expression of the candidate reference genes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-b and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, overall, the mean Ct values of the reference genes ranged from 17.96 to 24.90 in all groups. This analysis shows that the most highly expressed candidate reference gene was \u003cem\u003enad1\u003c/em\u003e, which is involved cellular energy metabolism [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], with a mean Ct of 17.96 in 8 different tissues, and 18.27 in anther developmental stages. The three nuclear reference genes, \u003cem\u003eactin\u003c/em\u003e, \u003cem\u003esec3\u003c/em\u003e and \u003cem\u003etubulin\u003c/em\u003e, had a relatively lower expression level: \u003cem\u003esec3\u003c/em\u003e had the highest mean Ct value in all eight stages of potato development, and in the stages of anther development studied. The Coefficient of Variance (CV) as anther standard was used to assess the variability of experimental replicates and further visualise the fluctuation in the expression of reference genes. As shown in Table. 1, in group1, \u003cem\u003enad3\u003c/em\u003e had the lowest expression, CV of 2.96%, closely followed by \u003cem\u003eATP4\u003c/em\u003e at 3.53%, nad5 at 3.54% and \u003cem\u003eATP1\u003c/em\u003e at 3.57%. Interestingly, the two conventional nuclear reference genes had fairly high CVs (\u003cem\u003etubulin\u003c/em\u003e at 6.40% and \u003cem\u003eEF1-α\u003c/em\u003e at 5.68%). However, in group 2, the nuclear candidate reference genes displayed better stability (\u003cem\u003eactin\u003c/em\u003e at 1.02%, \u003cem\u003esec3\u003c/em\u003e at 1.38% and \u003cem\u003eEF1-α\u003c/em\u003e at 5.68% CV), with \u003cem\u003etubulin\u003c/em\u003e the least stable at 5.00% CV.\u003c/p\u003e \u003cp\u003eFour reliable statistical analytic tools, NormFinder, Detal Ct, GeNorm and BestKeeper were applied to evaluate the stability of gene expression. These tools were employed to validate the results and gain a deeper understanding of the dataset. By applying these statistical tools, we evaluated the stability of mitochondrial genes as reference genes during anther development, providing crucial insights for the relatively quantitative analysis of CMS genes, thereby enhancing the identification of CMS-associated genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of candidate reference gene Ct value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eGroup1: Different tissue types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eGroup 2: Different anther developmental stages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecox2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.804\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.752\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEF1-α\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSec3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etubulin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSD measures the dispersion of data points from the mean Ct and was calculated using the STDEV.S function in Excel, CV was calculated using the formula: CV (%)= [Standard Deviation / Mean (Average Ct)]\u0026times;100%\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeNorm analysis\u003c/h3\u003e\n\u003cp\u003eGeNorm analysis determines the normalisation value based on the geometric mean of various candidate reference genes and mean pairwise variation of each gene from all the reference genes in a given set of samples. To identify the most stable reference genes, the internal control gene-stability measure stability value (M) was defined as the average pairwise-to-decreasing expression stability. A lower M value indicates higher stability, with a recommended cut-off of less than 1.5 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. We analysed our data and found that all 18 candidate reference genes exhibited high expression stability with low (\u0026lt;\u0026thinsp;0.9) M values among the two experimental sets, and these were much lower than the default threshold of 1.5 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). More specifically, in the first group of sample tissues, the results indicate that the combination of two mitochondrial genes nad5 and nad6 were the most stable reference genes with M values of 0.276, followed by nad2, at nearly 0.4, and the third, cob, with M values 0.462. Interestingly, the M values of four nuclear candidate genes were either the highest M (tubulin at 0.836) or a significantly higher M (Actin at 0.64, sec3 at 0.675, EF1-α at 0.709). In the group of samples from anthers, cob and nad1 exhibited the lowest M value, and tubulin had the highest M value, indicating that cob and nad1 were most stably expressed and tubulin the least.\u003c/p\u003e\n\u003ch3\u003eNormFinder analysis\u003c/h3\u003e\n\u003cp\u003eNormFinder calculates the SV using an ANOVA-based model to estimate expression variation among the tested candidate genes. A higher SV indicates lower stability. By considering both intra-group and inter-group variations, NormFinder ranks the candidate genes based on their stability [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The outcome of the NormFinder analysis for the 18 candidate reference genes is provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was a significant gap between the top and bottom of SV in the first group of different tissues. The results highlight nad1 as the most stable gene, with a stability value of 0.216, surpassing the others. ATP1 and rps19 had close stability values, 0.241 and 0.249, respectively. In contrast, the four nuclear candidate reference genes exhibited poorer expression stability in all of these samples. However, in the second group, the overall SV for each candidate gene was relatively low, with the lowest SV of 0.282 for tubulin. The distinct difference from the first group is that the nuclear gene Actin displayed the minimum SV with 0.074, indicating that Actin was most stably expressed.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDelta Ct (ΔCt)\u003c/h2\u003e \u003cp\u003eThe ΔCt method is used to identify useful reference genes, by comparing the relative expression of \u0026lsquo;pairs-of-genes\u0026rsquo; in each sample. Genes remaining constant in distinct samples are regarded as expressed stably. However, fluctuations in ΔCt values suggest that one or both genes may have variable expression. Introducing a third, fourth, or even fifth gene into the comparisons allows a deeper analysis, revealing which pairs demonstrate less variability. In turn, this helps identify the gene(s) with stable expression across the tested samples, enabling a process of ranking or discarding based on the results obtained. Expression stabilities are determined from the mean SDs [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Data analysis using the ΔCt method suggested that the five most stable genes in the eight tissue samples under normal conditions were nad1\u0026thinsp;\u0026gt;\u0026thinsp;nad2 and rps19\u0026thinsp;\u0026gt;\u0026thinsp;ATP1\u0026thinsp;\u0026gt;\u0026thinsp;cob, which were all mitochondrial genes (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The rank of all the nuclear candidate reference genes was lower. However, for the different anther development stages, Actin, a potential nuclear candidate reference gene, exhibited the smallest mean SD, together with nad2 and ATP9. Because of their similar expression stability, the ΔCt method these three genes shared the same ranking. To ensure reliability, we further validated the selection using additional statistical tools. However, tubulin was ranked last. These results are similar to those from GeNorm and NormFinder - tubulin exhibited the poorest stability among the two experiment sets.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStability values of candidate reference genes calculated by four algorithms in two groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eGroup 1: Different tissue types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c13\" namest=\"c8\"\u003e \u003cp\u003eGroup 2: Different anther developmental stages\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeNorm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormFinder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔCt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eBestKeeper\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGeNorm\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNormFinder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eΔCt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eBestKeeper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eATP9\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecob\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.462\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecob | nad1 0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ecox2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ecob | nad1 0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enad5 | nad6 0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003enad6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enad5 | nad6 0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps19\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003erps4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEF1-α\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003esec3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003etubulin\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eBestKeeper analysis\u003c/h2\u003e \u003cp\u003eBestKeeper evaluates the stability of candidate genes by analysing several parameters, including SD, CV, and Pearson correlation coefficient (\u003cem\u003er\u003c/em\u003e). Genes exhibiting the highest \u003cem\u003er\u003c/em\u003e value and an SD\u0026thinsp;\u0026gt;\u0026thinsp;1 are considered to be the most stably expressed. The number of candidate genes that can be analysed at one time is limited to ten candidate genes by BestKeeper. Hence, in the first group, eight candidate genes, including all four nuclear candidate genes and \u003cem\u003erps4\u003c/em\u003e, \u003cem\u003erps3\u003c/em\u003e, \u003cem\u003eATP4\u003c/em\u003e and \u003cem\u003eATP9\u003c/em\u003e, were eliminated by the results from GeNorm, NormFinder and Delta Ct, as they consistently ranked the lowest in all three software tools (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003enad1\u003c/em\u003e was considered the most stable reference gene in the eight different tissues, with the highest \u003cem\u003er\u003c/em\u003e value at 0.952 and SD at 0.64. \u003cem\u003enad2\u003c/em\u003e was placed second, demonstrating strong stability in all evaluations with \u003cem\u003erps19\u003c/em\u003e third, followed by \u003cem\u003ecox2\u003c/em\u003e. \u003cem\u003eATP6\u003c/em\u003e occupied the fifth position in terms of stability across the tools. Using the same analytical approach for the second group of samples, the eight candidate genes - \u003cem\u003ecox2\u003c/em\u003e, \u003cem\u003eATP4\u003c/em\u003e, \u003cem\u003eATP6\u003c/em\u003e, \u003cem\u003enad3\u003c/em\u003e, \u003cem\u003enad5\u003c/em\u003e, \u003cem\u003erps3\u003c/em\u003e, \u003cem\u003erps4\u003c/em\u003e, and tubulin were removed from the BestKeeper analysis. \u003cem\u003enad1\u003c/em\u003e was also the most stably expressed in the different anther tissues/ developmental stages, with the highest \u003cem\u003er\u003c/em\u003e value at 0.924 and SD at 0.39, followed by \u003cem\u003ecob\u003c/em\u003e and \u003cem\u003enad2\u003c/em\u003e. Notably, the ranking of \u003cem\u003eActin\u003c/em\u003e decreased, and it was placed sixth using the BestKeeper tool.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOrdering of reference genes from the results of the four analytical tools\u003c/h2\u003e \u003cp\u003eA comprehensive comparative ranking of the effectiveness and stability of candidate reference genes is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which combines the ranking results from the four tools, and the average rank for each tool. Despite some differences in the results obtained from these tools, three genes \u003cem\u003enad1\u003c/em\u003e, \u003cem\u003enad2\u003c/em\u003e and \u003cem\u003erps19\u003c/em\u003e emerged as stably genes expressed in different tissues, with a consistent ranking from 1 to 3 based on NormFinder, BestKeeper and Detal Ct analysis. In particular, the mitochondrial gene, \u003cem\u003enad1\u003c/em\u003e, was ranked first using three tools. In contrast, four previously reported nuclear reference genes from the first group were less stable as shown by NormFinder, GeNorm and Detal Ct analysis. Similarly, there was a fluctuation in their rank in tissues of different stages of anther development. In contrast, the rank of \u003cem\u003enad2\u003c/em\u003e was consistent and was always in the top three for all analytical tools. It also was ranked at the top in the second group, although for this group, \u003cem\u003eActin\u003c/em\u003e could also be considered to be useful, since in different anther developmental stages, it ranked second in NormFinder and Detal Ct analyses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eqRT-PCR is a widely used method to examine gene expression patterns [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Ensuring the precision of experiments relies on the qRT-PCR assay itself, including primer design and length, and the PCR conditions, but most importantly, on selecting reliable internal controls. It requires an appropriate reference gene to normalise the target transcript levels. While reference genes generally exhibit stable expression under standard conditions, they are involved in essential cellular processes, and their expression can fluctuate under specific circumstances, such as environmental stress or during different developmental stages [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, the availability of suitable reference genes for accurate normalisation in mitochondrial genes remains limited. In eukaryotes, poly(A) tails are present on almost every mRNA [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], whereas in plant mitochondria, polyadenylated mitochondrial transcripts are rare and unstable because polyadenylation of mRNAs in chloroplasts serves as an RNA degradation signal [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Reverse transcriptase PCR (RT-PCR), a step before qRT-PCR is used to synthesise cDNA, but RT-PCR is usually undertaken with Oligo (dT) primers which target mRNAs with poly(A) tails. The synthesis of mitochondrial mRNAs and their patterns of expression have rarely been studied. Thus, selecting reference genes tailored for measuring mitochondrial gene expression, based on their stability and consistency of expression under different experimental conditions, is required to ensure accurate and dependable analysis of gene expression.\u003c/p\u003e \u003cp\u003eMitochondrial gene expression analysis is clearly important for studying mitochondrial gene function. Many CMS genes originate from mitochondrial gene rearrangements during evolution. When substoichiometric shifting (SSS) occurs, the copy number of specific mitochondrial molecules can increase, leading to the preferential expression of CMS-associated genes previously present at low levels [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe application of a three-line CMS breeding system to development the hybrid population has proved to be a successful approach for exploiting heterosis to improve yield in rice (\u003cem\u003eOryza sativa\u003c/em\u003e) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. However, there is limited knowledge of CMS genes in potato, which has slowed the development of hybrid potato breeding. qRT-PCR analysis is widely used to study gene-expression patterns, and it an be used to identify CMS genes in potato. However, accurate qRT-PCR analysis of mitochondrial genes is challenging because of limited flexibility of primer design and the lack of appropriate reference genes to standardise gene expression levels. If a common internal reference gene is selected without screening, it is likely to reduce the accuracy of quantitative analyses, and could result in incorrect conclusions. To improve the accuracy of gene-expression analyses, we studied and selected reliable internal control genes systematically for normalisation of mitochondrial gene expression in a range of potato tissue and anther developmental stages.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eE\u003c/em\u003e values of the eighteen candidate reference gene primer pairs ranged from 92.3\u0026ndash;111.4%, with \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e values were 0.960\u0026ndash;0.999 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These results demonstrate the high accuracy, efficiency, and sensitivity of the primer pairs used for reference gene selection. In addition, the mean Ct values of the candidate reference genes ranged from 17.964 (\u003cem\u003enad1\u003c/em\u003e) to 24.717 (\u003cem\u003esec3\u003c/em\u003e) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the mean Ct values for four nuclear candidate reference genes were not consistent in potato under abiotic stress [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], suggesting that no reference gene is equally expressed under different conditions. Therefore, it is evident that selecting appropriate reference genes for normalisation of mitochondrial genes under specific experimental conditions is required.\u003c/p\u003e \u003cp\u003eFour well-established algorithms (Delta Ct, GeNorm, NormFinder and BestKeeper) were used to identify stably expressed reference genes [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The operating principle of NormFinder is similar to that of the GeNorm program; however, GeNorm has the added capability to identify optimal reference gene combinations and determine the ideal number of reference genes. In contrast to GeNorm and NormFinder, BestKeeper and Delta Ct software directly make calculations using Ct values [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The results of NormFinder and Delta Ct were generally similar in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) as the top two candidate reference genes (\u003cem\u003enad1\u003c/em\u003e and \u003cem\u003enad2)\u003c/em\u003e selected by these two algorithms remained consistent in all tissues studied, whereas four nuclear candidate reference genes did not meet the required criteria in eight different tissue types of potato. Overall, \u003cem\u003enad2\u003c/em\u003e was identified as the best candidate reference gene for different anther developmental stages, although \u003cem\u003eActin\u003c/em\u003e also performed well, albeit with more variation. These result show that selection of reliable reference genes requires analysis of expression under different conditions or treatments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis is the first detailed study to select the best candidate mitochondrial reference genes which can be used in qRT-PCR expression studies in different tissues and developmental stages of potato to normalise gene expression. The results show that the most suitable reference gene is \u003cem\u003enad1\u003c/em\u003e for expression studies in eight potato tissues, whereas \u003cem\u003enad2\u003c/em\u003e is the most appropriate gene for normalising expression in developing anthers. Overall expression of \u003cem\u003enad2\u003c/em\u003e was most stable in all of the tissues studied.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCytoplasmic male sterility (CMS)\u003c/p\u003e\n\u003cp\u003equantitative real-time polymerase chain reaction (qRT-PCR)\u003c/p\u003e\n\u003cp\u003eminimum information for publication of quantitative real-time PCR experiments (MIQE)\u003c/p\u003e\n\u003cp\u003eadenosine triphosphate (ATP)\u003c/p\u003e\n\u003cp\u003eapocytochrome b gene (\u003cem\u003ecob\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003esubunit 2 of cytochrome c oxidase (\u003cem\u003ecox2\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eNADH dehydrogenase subunit (\u003cem\u003enad\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003ecytoplasmic ribosomal protein (\u003cem\u003erps\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eElongation factor 1-alpha (\u003cem\u003eEF1-\u0026alpha;\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eexocyst complex component (\u003cem\u003esec3\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003emelting temperature (Tm)\u003c/p\u003e\n\u003cp\u003ePCR efficiency (\u003cem\u003eE\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eCoefficient of variance (CV)\u003c/p\u003e\n\u003cp\u003eDelta Ct (\u0026Delta;Ct)\u003c/p\u003e\n\u003cp\u003estandard deviation (SD)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Professor Chunzhi Zhang and her team at the Agricultural Genomics Institute at Shenzhen for providing plant materials and glasshouse facilities used in this study. We also thank Professor Yonglin Ren, Murdoch University, for liaising between these two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by a grant to Li Yuan from the China Postdoctoral Science Foundation ID: 2024M753583, and a Murdoch University International PhD Scholarship to QL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors: *Qing Li, School of Agricultural Sciences, Western Australian State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Western Australia 6150, Australia and Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China\u003c/p\u003e\n\u003cp\u003e*Jing Xu, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120, China and College of Agronomy, Qingdao Agricultural University, Qingdao, China.\u003c/p\u003e\n\u003cp\u003eLi Yuan, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangdong 518120.\u003c/p\u003e\n\u003cp\u003eMichael G. K. Jones, School of Agricultural Sciences, Western Australian State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Murdoch University, Perth, Western Australia 6150, Australia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQL and LY contributed equally to this article. Study conception and design: LY, QL, LY; data collection: JX, LY; analysis and interpretation of results: QL, JX, MJ; draft manuscript preparation: MJ, QL. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMichael G. K. Jones ([email protected])\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHochholdinger F, Baldauf JA. Heterosis in plants. Curr Biol [Internet]. 2018;28:R1089\u0026ndash;92. 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Available from: https://bmcplantbiol.biomedcentral.com/articles/10.1186/s12870-024-04924-w\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Expression reference genes, qRT-PCR, mitochondria, tissue expression, anther, potato, nad1, nad2","lastPublishedDoi":"10.21203/rs.3.rs-6373075/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6373075/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePotato is the most widely grown tuber crop worldwide and a staple food in many countries: it has become the focus of many molecular breeding studies. One topical area is breeding potato seeds, especially advancing male sterile plants, focusing on developing cytoplasmic male sterility (CMS) as a breeding tool. A major obstacle has been the identification of mitochondrial genes for CMS. Quantifying the expression of candidate CMS genes is a critical aspect needed for the validation of gene expression levels for all organisms, and quantitative real-time polymerase chain reaction (qRT-PCR) is a powerful tool for this purpose. However, selecting appropriate internal control genes for normalisation of mitochondrial gene expression presents specific challenges. The aim of this study was to identify suitable reference genes best suited for analysis of mitochondrial gene expression in different tissues and developmental stages of potato, particularly in developing anthers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe assessed the expression of eighteen candidate internal control genes, including four previously studied nuclear reference genes and fourteen mitochondrial candidate reference genes. By studying gene expression in a range of tissues, the genes \u003cem\u003enad1\u003c/em\u003e and \u003cem\u003enad2\u003c/em\u003e were the most stable reference genes, since they were expressed most consistently using four different analytical tools, GeNorm, Delta Ct, Bestkeeper and NormFinder. In contrast, expression levels of the conventional nuclear reference genes were more variable. The comprehensively ranked first candidate gene, \u003cem\u003enad2\u003c/em\u003e is proposed as the preferred choice as a reference gene, especially when studying different stages of anther development. Notably, \u003cem\u003eactin\u003c/em\u003e, the most widely used marker expression gene, worked well in some cases, but there was significant variation in its rankings, for example, using the Bestkeeper tool it was ranked sixth.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results indicate that \u003cem\u003enad1\u003c/em\u003e and \u003cem\u003enad2\u003c/em\u003e respectively were the most stably expressed marker genes in 8 different tissues and stages of anther development. This study provides valuable support for future research on mitochondrial gene expression in potato, specifically for identifying patterns of expression of CMS genes, and can be a valuable tool to quantify gene expression for other \u003cem\u003eSolanaceae\u003c/em\u003e species.\u003c/p\u003e","manuscriptTitle":"Selection and optimisation reference genes for normalisation of mitochondrial gene-expression by qRT-PCR in different potato tissues and during anther development","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 08:44:10","doi":"10.21203/rs.3.rs-6373075/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-09T23:39:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-09T12:04:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T12:01:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-04-04T03:56:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cb8241a9-3351-4e56-9a03-2caa240c8781","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:04:04+00:00","versionOfRecord":{"articleIdentity":"rs-6373075","link":"https://doi.org/10.1186/s12870-025-07301-3","journal":{"identity":"bmc-plant-biology","isVorOnly":false,"title":"BMC Plant Biology"},"publishedOn":"2025-10-08 15:57:48","publishedOnDateReadable":"October 8th, 2025"},"versionCreatedAt":"2025-04-10 08:44:10","video":"","vorDoi":"10.1186/s12870-025-07301-3","vorDoiUrl":"https://doi.org/10.1186/s12870-025-07301-3","workflowStages":[]},"version":"v1","identity":"rs-6373075","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6373075","identity":"rs-6373075","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}