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Identification and selection of optimal reference genes for qPCR-based gene expression analysis in Fucus distichus under various abiotic stresses

Quantitative gene expression analysis is an important tool in the scientist’s belt. The identification of evenly expressed reference genes is necessary for accurate quantitative gene expression analysis, whether by traditional RT-PCR (reverse-transcription polymerase chain reaction) or by qRT-PCR (quantitative real-time PCR; qPCR). In the Stramenopiles (the major line of eukaryotes that includes brown algae) there is a noted lack of known reference genes for such studies, largely due to the absence of available molecular tools. Here we present a set of nine reference genes (Elongation Factor 1 alpha (EF1A), Elongation Factor 2 alpha (EF2A), Elongation Factor 1 beta (EF1B), 14-3-3 Protein, Ubiquitin Conjugating Enzyme (UBCE2), Glyceraldehyde-3-phosphate Dehydrogenase (GAPDH), Actin Related Protein Complex (ARP2/3), Ribosomal Protein (40s; S23), and Actin) for the brown alga Fucus distichus. These reference genes were tested on adult sporophytes across six abiotic stress conditions (desiccation, light and temperature modification, hormone addition, pollutant exposure, nutrient addition, and wounding). Suitability of these genes as reference genes was quantitatively evaluated across conditions using standard methods and the majority of the tested genes were evaluated favorably. However, we show that normalization genes should be chosen on a condition-by-condition basis. We provide a recommendation that at least two reference genes be used per experiment, a list of recommended pairs for the conditions tested here, and a procedure for identifying a suitable set for an experimenter’s unique design. With the recent expansion of interest in brown algal biology and accompanied molecular tools development, the variety of experimental conditions tested here makes this study a valuable resource for future work in basic biology and understanding stress responses in the brown algal lineage.

reaction) or by qRT-PCR (quantitative real-time PCR; qPCR). In the Stramenopiles (the major line 23 of eukaryotes that includes brown algae) there is a noted lack of known reference genes for such 24 studies, largely due to the absence of available molecular tools. Here we present a set of nine 25 reference genes (Elongation Factor 1 alpha (EF1A), Elongation Factor 2 alpha (EF2A), Elongation 26

3-phosphate Dehydrogenase (GAPDH), Actin Related Protein Complex (ARP2/3), Ribosomal 28
Protein (40s; S23), and Actin) for the brown alga Fucus distichus. These reference genes were 29 tested on adult sporophytes across six abiotic stress conditions (desiccation, light and 30 temperature modification, hormone addition, pollutant exposure, nutrient addition, and wounding). 31 Suitability of these genes as reference genes was quantitatively evaluated across conditions 32 using standard methods and the majority of the tested genes were evaluated favorably. However, 33 expressed gene was EF1A (Cq mean = 20.6), followed by 40S. The lowest expression level was 151 observed for GAPDH (Cq mean = 26.9) (Fig 1). For each gene, the variation across samples did not 152 exceed 3 cycles, which suggests a relatively constant expression in all conditions. All nine 153 candidate genes were determined as suitably 'consistent' (qualitatively) across conditions to move 154 forward in our analyses. The stability of expression for a given reference gene is a quantitative measure by which 165 the suitability of the reference gene may be assessed. Stability refers to how invariant the 166 expression pattern of a given gene is in an experimental setup. The stability of our nine candidate 167 reference genes was evaluated across all 15 conditions and 2 time-points (Fig. 2). Stability was 168 analyzed using three algorithms: geNorm (8), NormFinder (11), and BestKeeper (12). These 169 algorithms rank the reference genes according to the calculated gene expression stability values 170 and acceptability threshold (geNorm (M<1.5); NormFinder (SV<1.0)) or standard deviation and 9 threshold (SD<1.0, BestKeeper) (detailed in Materials and Methods). Figure 2 shows the 172 calculated stability metric for each gene in all samples combined, by method, ranked from best to 173 worst (left to right). 174 The rankings generated by geNorm and NormFinder were similar; EF1B and GAPDH 175 were identified as the most stable genes, followed by ACT, 14-3-3, EF1A, and 40S (Fig. 2AB). 176 BestKeeper, on the other hand, ranked 40S and 14-3-3 as the most stable (Fig. 2C). The three 177 least stable genes identified by all three algorithms were UBCE2, EF2A, and ARP2/3 (Fig. 2ABC). 178 To create a consensus of the best-to-worst gene ranking across all three programs, a rank 179 aggregation method was performed using a Cross Entropy Monte Carlo algorithm (R-package 180 RankAggreg; See Materials and Methods). This analysis indicated that GAPDH and EF1B were 181 the two most stable genes and EF2A and ARP2/3 the most unstable (Fig. 2D). Based on these 182 analyses, GAPDH and EF1B are recommended as general normalization genes for qPCR gene 183 expression studied in Fucus. Fucus is an intertidal alga that is challenged by a very harsh natural environment where it 197 undergoes daily desiccation and osmotic shock (group 1 -physiological stress); furthermore, 198 these environments are under the constant pressure of pollutants, such as copper and 199 herbicides/pesticides (group 2 -pollution); also, brown algae serve as food for marine organisms 200 such as mollusks, so the effect of grazing (proxied by mechanical wounding) was examined as 201 well (group 3 -wounding); in Stramenopiles, phytohormones have been found to influence 202 developmental processes (49-51) and as such we have tested the effect of auxin and gibberellic 203 acid (group 4 -hormones); Fucus was also grown with and without nutrients to identify the best 204 housekeeping genes for nutritional studies (group 5 -nutrients); lastly, we cultured  geNorm, BestKeeper, and NormFinder showed differences in the ranking of candidate 218 reference genes within treatment groups, to some extent (Fig 3). To achieve a consensus ranking 219 across all three methods that could be used for recommendation of normalization genes by 220 11 condition, we again performed a rank aggregation analysis (Fig 3,

red line and left to right order). 221
For pollution, hormone, nutrient and temperature-light treatments there was less variation in 222 ranking by the three algorithms and the consensus appears to be a reasonable recommendation. 223 However, for physiological stress and wounding, the results of the three algorithms differed more, 224 and the consensus seemed qualitatively less reliable. As such, for these two conditions, we 225 recommend using the ranking produced by geNorm or NormFinder; our recommendation here is Identifying the optimal number of reference genes for normalization 234 Our previous analyses allowed us to provide recommendations for normalization genes to 235 be used in general and specific experimental designs. However, using a single reference gene 236 during qPCR experiments can cause bias and the use of multiple reference genes is suggested 237 as standard practice (8). To determine the optimal number of reference genes to be used in Fucus 238 qPCR experiments, pairwise variation V n/n+1 was calculated for the proposed reference genes 239 using the geNorm algorithm (8). In this method, the reference gene variances were combined 240 additively and successively according to geNorm rank. After each addition the pairwise variance 241 was calculated between that sum (V n+1 ) and the variance of the prior sum (V n ); for example, V 2/3 242 was the pairwise variance calculated when using the top two genes was compared with using the 243 top three. In general, the more reference genes that are added, the lower the successive pairwise 244 variance becomes. When the pairwise variance was below 0.15, n number of normalization genes 245 was considered sufficient. 246 When all conditions were pooled, to simulate a general experiment, V 2/3 was below the 247 0.15 threshold, suggesting that using the top two stable reference genes was sufficient (Fig 4;  248 V 2/3 =0.145; optimal reference gene set GAPDH+EF1B). When the analysis was performed by 249 condition group, V 2/3 was below 0.15 for all conditions except pollution (physiological 250 stress=0.130, hormones=0.096, nutrients=0.103, temperature-light=0.120, wounding=0.105, 251 pollution = 0.173; Fig. 4). For pollution stress, V 3/4 was less than the threshold (V 3/4 = 0.145) 252 suggesting the need for three reference genes for these experiments (Fig 4). Taken together, two 253 stable reference genes are sufficient for most conditions tested here. By combining the 254 recommended number of reference genes from this analysis with the consensus rank achieved 255 for stability, we generated a table of most recommended and least recommended reference genes 256 (Table 2)  with 1) normalization to the two most stable reference genes (GAPDH and EF1A) or 2) with 280 normalization to the least stable genes (EF2A and ARP2/3) for physiological stress (Table 2). 281 Gene expression analysis with the most stable pair (EF1A + GAPDH) showed a significant 282 increase of Hsp70 cDNA levels with 0.5x ASW treatment and a significant decrease in levels with 283 the 2x ASW stress and desiccation treatments (Fig 5A). Conversely, normalizing to the 284 ARP2/3/EF2A (least stable) reference gene set resulted in a loss of significant differential 285 expression in the 0.5X ASW treatment (Fig 5B). Hsp90 cDNA levels did not show significant 286 change with treatment using either normalization set (Fig 5). These data indicate that using the 287 most stable pair of normalization genes allowed for the capture of more information on differential 288 gene expression for Hsp70; however, even the lowest-ranked normalization pair did result in 289 some differential expression detection. Using a suboptimal normalization pair would likely be most 290 detrimental when high Cq variance within technical replicates or between biological replicates is 291 present. As such, using the most recommended set of normalization genes, in general or by 292 condition, is likely to increase experimental sensitivity and reduce false negatives. The use of stable and suitable reference genes when analyzing qPCR data is of utmost 302 importance to correctly assess, present, and interpret gene expression in any experimental 303 15 design. In this study, we analyzed the suitability of nine candidate genes for normalization of gene 304 expression data obtained from qPCR in a brown alga Fucus distichus. This paper reports 305 recommendations for reference gene pairs to be used in Fucus experimental qPCR studies (Table  306 2). The recommendation is a result of two analyses: stability assessment and calculation of an 307 optimal number of reference genes to be used. Below we discuss the outcomes and comparative 308 merits of these analyses. 309 310

Assessing stability 311
The expression stability of the candidate genes was tested using the geNorm, 312 NormFinder, and BestKeeper algorithms. Our analysis with the three algorithms showed that the 313 stability of most candidate genes strongly depended on the experimental condition used, with 314 GAPDH being the most stable gene in most conditions and ARP2/3 being the least stable in most 315 conditions. We did observe some discrepancies in ranking candidate gene stability within 316 conditions when using these three different algorithms. 317 The differences in stability values observed between different algorithms were not entirely 318 unexpected, taking into account that there are differences inherent in the three approaches 319 (8,11,12). geNorm is based on the principle that the most stable housekeeping genes should have 320 a nearly identical variation in expression ratio, or co-expression pattern. This approach, however, 321 may identify co-differentially expressed genes as highly stable in the chosen experimental system 322 (8,11). Conversely, NormFinder is not affected by the co-differential expression problem and as 323 such should be more robust; it estimates both inter-and intra-group variation and then combines 324 them into a stability value (11). BestKeeper, on the other hand, uses raw Cp values as input to 325 identify the best among the investigated candidate genes. It uses a Pearson correlation analysis, 326 a parametric method, which is valid for normally distributed data with a homogeneous variance; 327 if the data do not match these dependencies it may lead to a false interpretation of the obtained 328 results. 329 When the stability of genes in all the treatments was tested together, some discrepancies 330 were detected in the ranking of the most stable candidate reference genes using the three 331 algorithms, but there was an agreement between ranking the least stable genes. GAPDH and 332 EF1B were ranked as the most stable housekeeping genes tested by geNorm and NormFinder, 333 however, BestKeeper only placed them 3 rd and 5 th , respectively. The least stable genes were 334 identified as ARP2/3, EF2alpha, and UBCE. This is contradictory to results in Ectocarpus 335 siliculosus, where both ARP2/3 and UBCE were the most stable genes and GAPDH was one of 336 the most unstable genes (13). It may be that patterns of gene expression vary between different 337 brown algal species, which further emphasizes the need to precisely define best reference genes 338 for specific studies. 339 According to our results, in four out of seven tested conditions GAPDH and EF1B were 340 the most stable reference gene candidates. It is valuable to note that GAPDH and EF1B had 341 What is perhaps most pertinent to note is that even though ranking differences were 358 observed between the different algorithms, the majority of the calculated stability values were still 359 suitable as the fell under the rejection threshold (geNorm >1.5, BestKeeper>1, NormFinder>1; 360 provide evidence that normalization using a single gene can lead to an inaccurate normalization 370 of up to 25% of cases (with up to 6.4-fold difference). In our experiments and analyses, we could 371 conclude that in all treatment groups, except pollution, using the two most stable genes should 372 be sufficient for normalization. For the pollution treatment, our experiment would require three 373 normalization genes according to analysis. We strongly recommend that experimental designs 374 for Fucus RT-PCR and qPCR include at least two reference normalization genes. 375 376

Evaluating normalization pairs in a desiccation and salinity stress study 377
Overall, our recommendation of a 'best' normalization pair for physiological stress 378 condition (GAPDH and EF1A) proved to be more sound when compared to the 'least' suitable 379 pair when evaluating the differential gene expression of Hsp70 during salinity stress and 380 desiccation. The statistically significant difference in the expression of Hsp70 in low salinity failed 381 to be detected when normalizing to the two least stable genes. This has previously been shown 382 in other reports as well, where the choice of unstable genes led to misinterpretation of expression 383 data (47,59,60). It should be noted that we have assumed that the differential expression of Hsp70 384 in low salinity is true and not a false positive; we believe this to be the most likely case given the 385 higher stability of the 'best' pair. 386 387

Conclusion 388
The regulation of genes involved in algal biology including stress responses and 389 development is complex, and exploring the molecular mechanisms behind them requires high 390 precision and understanding. The selection of suitable reference genes to test qPCR based gene 391 expression is a necessary first step in understanding key molecular networks in the brown algal 392 lineage. Here we recommend the usage of specific reference gene sets in specific conditions 393 when performing comparable experiments in the brown alga Fucus distichus. In general, at least 394 two reference genes are recommended, and the best 'general' pair to use are GAPDH and EF1B. 395 When designing new experiments, we recommend checking the top five general recommended 396 genes from this study (Table 2) to test their stability in one's experimental system, as conducted 397 here, before deciding on the best normalization gene set for your study. apical segments were transferred into Petri dishes with 15 different treatments, one segment per 408 condition (Fig. ST1). Three biological replicates were obtained for each treatment at two time-409 points: 3 hours and 3 days after treatment start, resulting in 90 samples in total. A summary of 410 the treatments may be found in Table S1. 411 The chemical treatments were: control ASW with nutrients (PES), nutrient-deficient ASW, 412 0.2µg/L imidacloprid (Marathon 1%, OHP, Inc., USA), 50µM indole-3-acetic acid (IAA; 413 Cat#102037, MP Biochemicals, Irvine, CA.), 50µM gibberellic acid (GA; Cat#G7645, Sigma). An 414 equal volume of absolute ethanol was used as a control for the GA and IAA treatment. In addition, 415 a saline shock was performed using a hypersaline solution (2x ASW) and a hyposaline solution 416 (0.5x ASW). The desiccation treatment was affected by placing the algal segments onto dry Petri 417 dishes, after blotting gently, under the same environmental conditions as in other treatments. A 418 mechanical wounding treatment, to simulate the effect of grazing, was performed by damaging 419 the algal segments with a razor blade in several places along the thallus. Alteration of light and 420 temperature was achieved as follows: a portion of samples was cultured under modified light 421