Cyanobacteria are a multifaceted group of photosynthetic prokaryotes, with a large morphological diversity including unicellular, colonial and filamentous forms. Some filamentous strains exhibit cellular differentiation, with cells specialized in photosynthesis (vegetative cells) and in nitrogen fixation (hetero- cysts) [1–2]. Besides the morphological diversity, cyanobacteria are versatile microorganisms with simple growth requirements that are able to produce several secondary metabolites of commercial interest [3–5]. To exploit this biotechnological potential and use cyanobacteria as cell factories it is necessary to understand their physiology and metabolism at a systems level. The comprehensive evaluation of gene expression patterns is an important part of this characterization and, at present, the quantitative real-time polymerase chain reaction (RT-qPCR) is considered the gold standard for measurement of transcript abundance . This technique allows the simultaneous amplification and quantification of a target amplicon by measuring the fluorescence increment in each PCR cycle in a fast, extremely sensitive and accurate way . However, it has been shown that the poor design ofRT-qPCR experiments can lead to erroneous or biologically irrelevant data . A number of parameters such as the sample origin and amount, RNA quality and integrity, PCR efficiency, qPCR protocol and validation have been shown to compromise the quality of the final RT-qPCR experiment [8–10]. Additionally, the
use, followed by ACT and S15, whereas α-TUB and CYCL showed the least stable mRNA contents. Using the coffee expression profiles of the gene encoding the large-subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase (RLS), results from the in silico aggregation and experimental validation of the best number ofreferencegenes showed that two referencegenes are adequate to normalize RT-qPCR data. Altogether, this work highlights the importance of an adequate selectionofreferencegenes for each single or combined experimental condition and constitutes the basis to accurately study molecular responses of Coffea spp. in a context of climate changes and global warming.
In this study, eEF-1a, which has been proven to be a suitablereference gene for expression normalization in O. sativa and C. sativus [5,12], also ranked at the top when evaluated by geNorm, NormFinder and deltaCt across our four experimental sets. Being an important specific protein factor involved in the process of protein translation, eIF-4a had the same performance as eEF-1a in most experimental treatment conditions in our study. Our study showed that eIF-4a had a relatively high expression under most experimental conditions, which is in agreement with the study of Zhu et al. in C. papaya . eIF-4a also performed well in Musa paradisiaca, Lycoris longituba, Hevea brasiliensis and Coffea spp. [47–50]. The low copy number of APRT in the sugarcane genome, which was reported by Casu et al. , provides an advantage in analyzing gene copy number. However, in the present study the expression of APRT was found to be easily affected by abiotic stress conditions (3 rd set) and thus its application is limited. With more copies in the sugarcane genome than APRT , PRR displayed variable performance under different stresses and in different sugarcane tissues.
Rotator cuff tear is one of the most common causes of shoulder dysfunction. Gene expres- sion analysis may be a useful tool for understanding tendon tears and the failure of cuff healing, and reverse-transcription quantitative polymerase chain reaction (RT-qPCR) has become an effective method for such studies. However, this technique requires the use ofsuitablereferencegenes for data normalization. Here, we evaluate the suitability of six ref- erence genes (18S, ACTB, B2M, GAPDH, HPRT1 and TBP) using samples from the rotator cuff tendons of 28 individuals with tendon tears (3 tendons regions) and 8 controls (2 tendon regions); for the tear patients, we evaluated ruptured and non-ruptured tendon samples. The stability of the candidate referencegenes was determined using the NormFinder, geN- orm, BestKeeper and DataAssist software packages. Overall, HPRT1 was the best single reference gene, and HPRT1+TBP composed the best pair and HPRT1+TBP+ACTB com- posed the best trio ofreferencegenes from the analysis of different groups, including the si- multaneous analysis of all tissue samples. To identify the optimal combination ofreferencegenes, we evaluated the expression of COL1A1 and COL3A1, and no obvious differences were observed when using 2, 3 or 4 referencegenes for most of the analyses. However, COL3A1 expression differed between ruptured and non-ruptured (posterior superior region) tendons of patients only when normalized by HPRT1+TBP+B2M and HPRT1+TBP. On the other hand, the comparison between these two groups using the best trio ofreferencegenes (HPRT1+TBP+ACTB) and 4 referencegenes did not revealed a significant difference in COL3A1 expression. Consequently, the use ofsuitablereferencegenes for a reliable gene expression evaluation by RT-qPCR should consider the type of tendon samples in- vestigated. HPRT1+TBP+ACTB seems to be the best combination ofreferencegenes for the analysis of involving different tendon samples of individuals with rotator cuff tears.
sequences used in the qRT-PCR analyses are presented in Table 1. The primers were further validated for unique amplicon using Primer-BLAST (http://www.ncbi.nlm.nih.gov/tools/ primer-blast/). Standard RT-PCR was performed for all the primer pairs and a single amplifica- tion product of the expected size for each gene was obtained by electrophoresis on a 2% agarose gel. Primer amplification efficiency was determined from standard curve generated by serial di- lution of cDNA (10 fold each) for each gene in triplicate. Correlation coefficients (R 2 values) and amplification efficiencies (E) for each primer pairs were calculated from slope of regression line by plotting mean Cq values against the log cDNA dilution factor in Microsoft Excel using equation E = (10 (1/-slope −1) X 100. Real-time amplification reactions were performed in 96 well plates using SYBR Green detection chemistry and run in triplicate on 96-wells plates with the StepOne Plus Real-time PCR machine (Applied Biosystems). Reactions were prepared in a total volume of 20 μl containing: 1 μl of 10 fold diluted template, 0.5 μl of each amplification primer (1μM), 10μl of 2X Fast SYBR Green (Applied Biosystems) and final volume of 20 μL with sterile nuclease free water. Non-template controls (NTC) were also included for each primer pair. The cycling conditions were set as default: initial denaturation step of 95°C for 20 sec to activate the Taq DNA polymerase, followed by 40 cycles of denaturation at 95°C for 3s, annealing at 60°C for 30s. The melting curve was generated by heating the amplicon from 60 to 90°C. Baseline and threshold cycles (Ct) were automatically determined using the Ste- pOne Plus Software version 2.3 (Applied Biosystems). All the experiments were done ac- cording to MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines .
In previous studies of Chinese jujube, ZjH3 was reported as the most suitable housekeeping gene for evaluating jujube fruit-bearing shoot development by semi-quantitative RT-PCR , whereas, its expression was not the most stable at different fruit developmental stages, tissues and genotypes by RT-qPCR. In the present study, the expression of ZjH3 was relatively stable only at fruit developmental stages. Above difference should be caused by the different genes, tissues and conditions tested. In addition, we found that the M values of most of tested genesin our study and in Zhang et al.  were lower than 1.5, meaning that these tested genes had stable expression levels and could be applied as referencegenes under some specified conditions. Thus, the screening result was significantly influenced by the genes evaluated at the same time and the genes selected were relatively stable compared to others. The referencegenes selected in present study and previous studies [3, 4] provide more choices for further molecular mechanism studies in Chinese jujube. When normalizing with the most stable referencegenes, we should comprehensively consider additional factors and employ multiple programs.
The validation ofreferencegenes has been carried out in several cucurbitaceous crops, including melons, cucumbers, and zucchi- nis. In cucumbers, EF1a, Fbox, CAC and TIP41 have been demonstrated to be stable genes under different abiotic stresses, growth regulator treatments, and nitrogen nutrition situations [46,47]. In another study on cucumbers, EF1a, UBIep, and TUA were also shown to be the most stable . However, studies in zucchini showed that the combination of UFP, EF1a, RPL36aA, PP2A, and CAC genes was the best strategy for reliable normalization . RPL2, ACT, and cyclophilin were found to be the best three referencegenesin melon stems infected with Fusarium wilt . In the present study, the combination of CmADP and CmUBIep, CmRPL, CmRAN, CmACT, and CmEF1a was found to be a suitable multiple reference set for normalization of gene expression in all samples evaluated using geNorm. The optimal referencegenes identified in melons were different from those identified in cucumbers and zucchinis, suggesting that identification of the most stable referencegenesin each species is essential. The best referencegenes identified in melon roots and leaves in the present study were also different from those identified in melon stems . This further highlighted the organ-specific characteristic of these referencegenes. Accordingly, it is necessary to systematically validate the stability of the expression of candidate referencegenes prior to their use inRT-qPCR normalization.
Referencegenes have to be validated and consistently expressed under various circumstances. Widely used referencegenes for expression analyses include GAPDH, ACTB, and HPRT (Blaha et al., 2015; Boosani, Dhar & Agrawal, 2015; Wang et al., 2016; Zhang et al., 2016b; Zhao et al., 2015). These genes are reported to have consistent expression levels under various conditions such as different organs and different developmental stages (Tang et al., 2007). However, expression of these selected referencegenes has not proven to be as stable as originally presumed, and their expression can be highly variable under different conditions (Jain et al., 2006; Wan et al., 2010; Wang et al., 2015). Therefore, qPCR has been used to identify appropriate referencegenesin humans (Andersen, Jensen & Orntoft, 2004; Warrington et al., 2000), animals (McCulloch et al., 2012; Martinez-Giner et al., 2013; Robledo et al., 2014; Tatsumi et al., 2008), and plants (Hu et al., 2009; Huis, Hawkins & Neutelings, 2010; Jain et al., 2006; Zemp, Minder & Widmer, 2014). In addition, the use of more than one reference gene might be necessary to accurately normalize gene expression levels and avoid relative errors (Jian et al., 2008; Ohl et al., 2005).
Overall, nearly all referencegenes had a suitable performance and showed very high stability measurements in all experimental conditions tested. To decide the best genes to be used in our forthcoming qPCR gene expression analysis, we proceeded a comprehensive ranking by order- ing the ten candidate genes according to their stability classification given in each program and then classified them by a simple ranking average. This strategy to evaluate individual gene sta- bility taking into account the outputs from the three algorithms proved to be effective, as previ- ously suggested . Therefore, the combined analysis of the ranking order was able to deliver a common single list of stable genes, which will be effectively used in our experimental condi- tions. TIP41 gene was identified as the most stable gene considering the both entire datasets here analyzed. Likewise, TIP41 gene, coding for a TIP41-like family protein, was also recom- mended as internal reference gene in a consensus ranking ofreferencegenes during tomato development  and also in Arabidopsis  and Brassica napus vegetative tissues .
Shoulder instability is a common shoulder injury, and patients present with plastic deformation of the glenohumeral capsule. Gene expression analysis may be a useful tool for increasing the general understanding of capsule deformation, and reverse-transcription quantitative polymerase chain reaction (RT-qPCR) has become an effective method for such studies. Although RT-qPCR is highly sensitive and specific, it requires the use ofsuitablereferencegenes for data normalization to guarantee meaningful and reproducible results. In the present study, we evaluated the suitability of a set ofreferencegenes using samples from the glenohumeral capsules of individuals with and without shoulder instability. We analyzed the expression of six commonly used referencegenes (ACTB, B2M, GAPDH, HPRT1, TBP and TFRC) in the antero- inferior, antero-superior and posterior portions of the glenohumeral capsules of cases and controls. The stability of the candidate reference gene expression was determined using four software packages: NormFinder, geNorm, BestKeeper and DataAssist. Overall, HPRT1 was the best single reference gene, and HPRT1 and B2M composed the best pair ofreferencegenes from different analysis groups, including simultaneous analysis of all tissue samples. GenEx software was used to identify the optimal number ofreferencegenes to be used for normalization and demonstrated that the accumulated standard deviation resulting from the use of 2 referencegenes was similar to that resulting from the use of 3 or more referencegenes. To identify the optimal combination ofreferencegenes, we evaluated the expression of COL1A1. Although the use of different reference gene combinations yielded variable normalized quantities, the relative quantities within sample groups were similar and confirmed that no obvious differences were observed when using 2, 3 or 4 referencegenes. Consequently, the use of 2 stable referencegenes for normalization, especially HPRT1 and B2M, is a reliable method for evaluating gene expression by RT-qPCR.
Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is a powerful technique for confirming gene expression differences or measuring transcript abundance due to its sensitivity, reproducibility, simplicity and high-throughput [5,6]. With this assay, the common method is normalization of gene expression using an endogenous reference gene. Ideal referencegenes should be sufficiently abundant and have stable expression across differ- ent tissues and cell lines under different experimental conditions, but the ideal and universal reference gene does not exist in practice . Inaccurate normalization can cause inadequate quantification and incorrect conclusions [7,8]. Currently, several mathematical approaches in- cluding geNorm , NormFinder , Bestkeeper  and comparative delta Ct  have been developed to assist appropriate reference gene selection and geNorm provides a measure of the minimum optimal number ofreferencegenes to normalize .
Studying its expression is a critical component in determining the function of a given gene in molecular biology and such studies help us to understand the growth and development of different species. RT-qPCR is the most commonly used method in such studies and the use of appropriate referencegenes is essential in accurately determining the expression quantity of a given gene . Ideal referencegenes are those which give constant expression levels. However, such genes may not exist as plant growth is affected by environments. Different results can be obtained with the use of different referencegenes and inaccurate assessment of gene expression could be obtained if suitablereferencegenes are not used. For example, Mafra et al. analysed the relative expression levels of WRKY70 (transcription factor) in citrus challenged with fungal pathogens and found that, when Figure 1. Candidate reference gene expression levels in different samples. Expression data displayed as Ct values for each reference gene in all samples. A line across the box is depicted as the median. The box indicates the 25th and 75th percentiles. Whiskers represent the maximum and minimum values.
Methods for the quantification of accurate gene expres- sion have an increasingly important role in studies aiming for the reliable examination of expression profiles gener- ated by high-throughput approaches. Real-time reverse transcription quantitative PCR (qRT-PCR) has emerged as one of the most powerful tools for this purpose. Given the extreme sensitivity of qRT-PCR, a careful and stringent selectionof a proper constitutively expressed control gene is required to account for differences in the amount and quality of starting RNA and in cDNA synthesis efficiency. Adequate normalizations presume the use of an internal control, often referred to as a housekeeping or reference gene, whose expression levels should not significantly vary among tissues and experimental situations analyzed [1,2]. Genes most commonly applied as references in qRT-PCR studies include: beta actin (ACTB), glyceral- deyde-3-phosphate dehydrogenase (GAPDH), beta glu- curonidase (GUSB), hypoxanthine guanine phosphoribosyl transferase (HPRT1) and ribosome small subunit (18S) ribosomal RNA [1-3]. However, several reports have mentioned these classical housekeeping genes as showing variable expression levels in different experimental conditions [3-9]. Furthermore, the same gene revealed as almost invariant for certain tissues or cell types or could present highly variable expression levels in other tissues or experimental conditions [2,9,10]. Thus, it is clear that suitable control genes are extremely specific for particular sample sets and experimental models, being a crucial component in assessing confident gene expres- sion patterns. It has been strongly suggested that more than one stable expressed reference gene should be used to avoid misinterpretation of gene expression data [6,7,11-13].
Referencegenes are commonly used for normalization of target gene expression during RT-qPCR analysis. How- ever, no housekeeping genes or referencegenes have been identified to be stable across different tissue types or under different experimental conditions. To identify the most suitablereferencegenes for RT-qPCR analysis of target gene expression in the hepatopancreas of crucian carp (Carassius auratus) under various conditions (sex, age, wa- ter temperature, and drug treatments), seven referencegenes, including beta actin (ACTB), beta-2 microglobulin (B2M), embryonic elongation factor-1 alpha (EEF1A), glyceraldehyde phosphate dehydrogenase (GAPDH), alpha tubulin (TUBA), ribosomal protein l8 (RPL8) and glucose-6-phosphate dehydrogenase (G6PDH), were evaluated in this study. The stability and ranking of gene expression were analyzed using three different statistical programs: GeNorm, Normfinder and Bestkeeper. The expression errors associated with selectionof the genes were assessed by the relative quantity of CYP4T. The results indicated that all the seven genes exhibited variability under the experi- mental conditions of this research, and the combination of ACTB/TUBA/EEF1A or of ACTB/EEF1A was the best can- didate that raised the accuracy of quantitative analysis of gene expression. The findings highlighted the importance of validation of housekeeping genes for research on gene expression under different conditions of experiment and species.
In order to test the applicability of this candidate reference gene set on different experimental conditions, we performed a similar evaluation ofsuitablereferencegenesin acute phase of epileptogenesis induced by the injection of pilocarpine in the rat hippocampus. The comparison between the Ct raw data of intrahippocampal PILO injected and control groups showed significant differences for B2m, Actb, Polr1a and Gusb, but not for Gapdh, Tubb2a, Ppia and Rplp1 (Figure S1). However, these differences are likely to be due to the preparation of the samples in the multistep process from tissue homogenization to RT-qPCR assay since analysis by geNorm, NormFinder and Bestkeeper indicated that all the analysed mRNAs were stable in the analysed conditions. Similarly to the systemic-PILO-model, all programs ranked Act and Rplp1 as the most stable genes. In contrast, B2m was pointed out as the worst candidate gene by the three programs (data not shown). The relative expression of Gfap, normalized by selected referencegenes, was compared between intrahippocam- pal PILO injected rats and controls (Figure 5). Interestingly, only when B2m was used as normalizer, the increase of Gfap mRNA was not revealed in the contralateral hippocampus of rats sacrificed 24 hours after SE.
Selectionof candidate referencegenes and mRNA transcript levels To ensure accurate analysis of gene expression, ten candidate referencegenes (Table 1) were investigated to determine how stable each gene is for sex and time-point of development. The candidate referencegenes selected included those genes commonly used inRT-qPCR experiments (such as Gapdh and Actb) and genes were stably expressed in multiple mouse adult tissues, but had not been tested for suitability as referencegenes with mouse embryo tissues (Kouadjo et al., 2007). RT-qPCR was carried out for brain tissue samples across three time points: E11.5, E12.5 and E15.5 using both male and female samples. These time points were chosen for analysis as the sex determination in mouse occurs between E11.0 and E12.0. During this 24 h period, considerable changes also occur with respect to neuronal development, in particular, the formation of the primary brain vesicles (Stiles & Jernigan, 2010). By E12.5, cellular proliferation is increased resulting in the expansion of neuronal precursors and formation of cortical layers (Finlay & Darlington, 1995). Neuronal differentiation, axonal branching and synaptogenesis is taking place around day 15.5 (summarized in Fig. 1) (Sur & Rubenstein, 2005) The raw Ct values for each reference gene across time and each sex are plotted in Fig. 2.
(2006) reported that, in dogs, the most stable control genes were ribosomal protein S5 in the liver, kidney, and mammary glands, beta 2-microglobulin (B2M ) in the left ventricle, and ribosomal protein L8 (RPL8) in the prostate, indicating each tissue type has its specific stably-expressed HKG even within the same species. Vorachek, Bobe & Hall (2013) and Vorachek et al. (2013) reported that for neutrophils, the most stable gene was glucose- 6-phosphate dehydrogenase (G6PD) in sheep, while in bovine calves, the most stable genes were phosphoglycerate kinase I (PGK1) and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ ); however, G6PD was ranked fifth in 10 genes tested. It has been suggested that using an inappropriate reference gene could lead to incorrect normalized data, leading to misinterpretation of the results (Dheda et al., 2005). Therefore, selecting a suitablereference gene is needed when studying a new species or tissue type.
To exhibit the influence ofreferencegenes on the expression profiles of target genes, cells cultured in GM, IM and CM were harvested at day 7 followed by qPCR assay. Then the data of fold change for chondrogenesis, hypertrophy and endochondral ossification related genes – Collagen type I, Collagen type II, Transcription factor SOX-9 and Collagen type X (Col1, Col2, Sox9 and ColX) was obtained using DDCt methods. In the calculation, various referencegenes were employed including widely used genes (designated as GAPDH, Actb and 18 s), the stable referencegenes demonstrated in this study (designated as Eef1a1, Hprt, Tbp and Ppia), the combination of Eef1a1 & Hprt and Ppia & Hprt (designated as E&H and P&H, the geometric mean of two referencegenes Ct value) as well as the combination of sixteen referencegenes (designated as ‘‘All’’, the geometric mean of all referencegenes Ct value). As shown in Fig. 3, the expression profiles of Col1, Col2, Sox9 and ColX in Ppia, Hprt, E&H and P&H groups indicated similar to those in ‘‘All’’ group while the trends of these target genes expression in GAPDH, Actb, 18 s, Eef1a1 and Tbp groups exhibited inconsistent with those in ‘‘All’’ group. Compared with ‘‘All’’ group in which there was no significance between CM and IM, the expression level of Col2 in GAPDH group suggested that CM was more beneficial to Col2 synthesis than IM while contrary results showed up in 18 s, Eef1a1
Real-time reverse transcription PCR (RT-qPCR) is a preferred method for rapid and accurate quantification of gene expression studies. Appropriate application ofRT-qPCR requires accurate normalization though the use ofreferencegenes. As no single reference gene is universally suitable for all experiments, thus reference gene(s) validation under different experimental conditions is crucial for RT-qPCR analysis. To date, only a few studies on referencegenes have been done in other plants but none in papaya. In the present work, we selected 21 candidate referencegenes, and evaluated their expression stability in 246 papaya fruit samples using three algorithms, geNorm, NormFinder and RefFinder. The samples consisted of 13 sets collected under different experimental conditions, including various tissues, different storage temperatures, different cultivars, developmental stages, postharvest ripening, modified atmosphere packaging, 1- methylcyclopropene (1-MCP) treatment, hot water treatment, biotic stress and hormone treatment. Our results demonstrated that expression stability varied greatly between referencegenes and that different suitablereference gene(s) or combination ofreferencegenes for normalization should be validated according to the experimental conditions. In general, the internal referencegenes EIF (Eukaryotic initiation factor 4A), TBP1 (TATA binding protein 1) and TBP2 (TATA binding protein 2) genes had a good performance under most experimental conditions, whereas the most widely present used referencegenes, ACTIN (Actin 2), 18S rRNA (18S ribosomal RNA) and GAPDH (Glyceraldehyde-3-phosphate dehydrogenase) were not suitablein many experimental conditions. In addition, two commonly used programs, geNorm and Normfinder, were proved sufficient for the validation. This work provides the first systematic analysis for the selectionof superior referencegenes for accurate transcript normalization in papaya under different experimental conditions.
A proteína NodC atua na biossíntese da estrutura básica dos fatores Nod (Spaink et al., 1994), mais especificamente como um determinante importante do tamanho da cadeia do fator Nod (Kamst et al., 1997). Ausmees et al. (2004), em estudo com Rhizobium sp. NGR234, relataram que os genes nodABC são transcritos em poucos minutos após a indução por flavonoides, por participarem da fase inicial do processo de nodulação. De modo semelhante, Viprey et al. (1998), também com Rhizobium sp. NGR 234 descreveram que genes do operon nodABC possuem maior expressão no período anterior a uma hora de indução pelo flavonoide. De fato, neste trabalho com a CPAC 15 foi evidenciado que o incremento na expressão do gene nodC pode ocorrer rapidamente após o contato com o indutor (15 min), embora níveis mais elevados de expressão gênica tenham sido observados quando a estirpe foi cultivada na presença de indutores por 48 horas, desde a fase inicial de crescimento. Contudo, o efeito dos indutores pode ser diferente para outras classes de genes. Como exemplo, a expressão de genes relacionados ao SSTT Figura 2. Expressão dos genes nopP, nodW e nodC de