Study areas, competitive level of red deer populations and trait measurements
The study was conducted on Iberian red deer (Cervus e. hispanicus) in southwestern Spain. In this study area, red deer occur in hunting estates under two types of different management systems that lead to neat differences in population structure and in the level of male intrasexual competition [26, 27]: (1) nearly natural population structure, in fenced estates, with high intrasexual competition for mates (HC), and (2) female-biased populations with young males, in unfenced estates, with low competition situation (LC). To investigate differences between males subjected to different populational conditions, 19 samples were collected in areas with high mating competition (HC) and 26 were collected in areas with low mating competition (LC) [26, 27]. Population identity was introduced as random factor in models. The variables used in this study were age (in years), the month and the season when samples were collected, and the sizes (in centimeters) of the dark ventral area (Supplementary Table S1). The age of male red deer was estimated, as in previous studies [28] by counting cementum growth marks at the interradicular pad under the first molar [29] and by checking eruption patterns in younger animals. For the dark ventral patch, longitudinal size from the penis to the end of the dark spot at the breast or the base of the neck was measured with a ruler [23]. The size of the dark ventral patch has been shown to follow a binomial distribution that reveals its role as a discrete sexual signal [22]. Hence, we used here as in previous works the size of the dark ventral patch (DVP) in two categories [21], small (corresponding to low trait expression, LTE, in previous papers) and large (corresponding to high trait expression, HTE).
Sample collection
Since previous studies have shown that the mechanism of pigmentation is based on the excretion of high amounts of norepinephrine metabolite deposited on the fur by the urinating behavior during the rut [23], adrenal glands, being responsible for the synthesis and the secretion of norepinephrine, were collected from 45 Iberian male red deer culled during hunting seasons from 2018/2019 to 2021/2022 in south-western Spain. For this study, all samples were collected from carcasses of individuals that did not show clinical signs of disease and that were hunted during ordinary hunting activities based on official hunting plans. This means that our study never provoked hunters to shoot additional animals specifically for the purpose of this research.
Our access to the hunting estates was approved by their management authorities while ethical issues regarding this study were revised and agreed by the Wildlife Research Unit at the University of Cordoba (UIRCP-UCO). This study was carried out in line with ARRIVE guidelines as our methods and experiment were performed in accordance with relevant regulations.
Once collected, the samples were cut into slices less than 0.5 cm thick and immediately immersed in a sufficient volume of RNAlater™ RNA Stabilization Reagent. Samples were kept at 4°C in the field and then stored at − 20°C till use.
RNA isolation and cDNA synthesis
RNA extraction from adrenal glands preserved in RNAlater™ was carried out using the commercial RNeasy Mini Kit (Qiagen) according to the manufacturer’s protocol. Total RNA was extracted from 30 mg of RNAlater™ stabilized tissue; we tried tissue disruption both with the Tissue-Lyser method and by using liquid nitrogen to find the best alternative. 600 µL of lysis buffer were added and after centrifugation (3 minutes; 16,000 xg), the whole supernatant was passed to a new tube. Next, 900 µL of 70% EtOH were added and after pipetting, a volume of 700 µL were transferred to a spin column and centrifugated for 15 s at 8,000 xg. Once discarded the first flow-through, centrifugation was repeated with the remaining volume using the same column. Removal of gDNA was performed by treating the RNA retained in the column with DNase I (QIAGEN) following the manufacturer protocol. Lastly, the Filter Cartridge was transferred to a new tube and the RNA eluted with 50 µL RNeasy-free water (preheated to ~ 75°C), denatured by heating at 55–65°C for 10 min, immediately cooled on ice for at least 5 min, and kept at -80°C till use. Purity and concentration of isolated RNA preparation was determined spectrophotometrically using a Nanodrop® one spectrophotometer (Thermo Scientific) while its integrity was determined using an Agilent 2100 Bioanalyzer (Agilent Scientific Instruments) system, which assigned an RNA Integrity Number (RIN) from 1 (totally degraded) to 10 (completely intact) to each sample. The iScript cDNA Synthesis Kit (BIORAD) was used to generate cDNA according to manufacturer’s instructions.
Primer design and qRT-PCR conditions
Primer pairs for absolute quantification of the transcriptional levels of the dopamine beta hydroxylase (DBH) and the tyrosine hydroxylase (TH) genes were designed with the Oligo 7 software (Molecular Biology Insights) (Table 1).
Table 1
Primers used in this work.
Use | | GenBank Acc. Number | | | Sequence 5´-3´b | | Lengthc (bp) | Amplification eficiencyd (R2) | | Reference | |
| | Targeta | | | | | | |
Determination of C. elaphus gene sequencee | | | | | | |
| | DBH | | NW_001493002.2 | | F: | ACCCGGCAGCCTCCCGCTCACCAGG | | 421 | naf | | This study | |
| | | | R: | AGGGGGAGTGAGCAGGGCGATGGGGGTC | | |
qRT-PCR quantificationg | | | | | | |
| | DBH | | XM_043916405 | | F: | GAGGCCATCAACACCTCCGGCTTGCAC | | 127 | 0,9979 (99,81%) | | This study | |
| | | | R: | TGAGGACATCGGGGGCGCGGATCTCCATG | | | |
| | TH10 − 11 | | OWK17426 | | F: | CTGGGGCACGTGCCCATGCTCGCT | | 166 | 0,9897 (98,96%) | | This study | |
| | | | R: | AGGCCTTCACCTCCCCGTTCTGCTTGCAC | | | |
| | TH2 − 3 | | XM_043923566 | | F: | GGCCAAGGGCTTCCGCCGGGCCGTC | | 173 | 0,9977 (99,56%) | | This study | |
| | | | R: | CCAGGCTGCCCGCCTCCCTGGGTTCCGAG | | | |
Calibrator geneh | | | | | | |
| | A170 | | U57413 | | F: | GGAAGAGAAGCCGCCTGACACCCACT | | 113 | 1.0001 (99.9%) | | [30] |
| | | | R: | CCCGTCAGGTTTGCTGACTTCCGAAG | | | |
aGene symbols are according to the NCBI Gene database. bSequence of forward (F) and reverse (R) primers. cPCR product size in base pair (bp). dThe real-time PCR efficiencies (E) were calculated from each standard curve according to the equation E = 10(−1/slope)-1. E is in the range from 0 (minimum value) to 1 (maximum and optimum), i.e., E = 1 is equal to 100% efficiency. ePrimers based on Bos taurus sequence designed to determine the orthologous C. elaphus sequence. fNot applicable. gPrimers based on C. elaphus sequences were designed for absolute quantification by real-time RT-PCR. hInter-Run Calibrator gene: A170 transcripts are quantified in the same calibrator RNA sample included in each run to combine or compare data from different runs/experiments. |
We used primers previously designed (Pelayo et al., 2012) against the Bos taurus DBH gene sequence (NW_001493002.2) to amplify the orthologous fragment in C. elaphus cDNA. With the obtained C. elaphus DBH sequences we designed specific primers to be used in qRT-PCR. Using the red deer gene sequence deposited in the GenBank database (NCBI, https://www.ncbi.nlm.nih.gov/genbank/) we designed TH primers.
Since TH mRNA in other species like humans [31] has been shown to present alternative splicing, we designed two primer pairs to quantify TH mRNA isoforms. The primer pair TH10 − 11 allows the quantification of any TH isoform variant; in contrast, the pair TH2 − 3 only quantifies the splicing variants carrying the exon 2. All primers for qRT-PCR were targeted towards the protein-coding region of the mRNAs, having high melting temperature (Tm ≥ 73°C) and optimal 3‘ΔG ( ≥ − 6 kcal/mol) values, to obtain the highest specificity and performance. Primers specificity was evaluated by 1% agarose gel electrophoresis of their PCR amplicons while their efficiency was assessed by amplifying in quadruplicate a 10-fold dilution series (resulting in a concentration range from 20 to 2×105 pg) of a mixture of all cDNA samples [32]. The slope of the standard curve achieved by plotting the obtained CT values against the RNA amount per well was used to calculate the efficiency according to the equation E = 10(− 1/slope) −1 [32]. Real-time PCRs were performed in quadruplicate (four technical replicates for each biological replicate and transcript) as detailed previously [30]. No primer dimers were detected, and the investigated transcripts showed optimal PCR efficiencies (from 0.98 to 1.00).
The high Tm of the designed primers allowed a two-step protocol including a denaturation step (94°C, 15 s) and a hybridization/extension step (70ºC, 30 s) for each of the 40 cycles performed after the polymerase activation step (98°C, 4 minutes). Reactions were performed and fluorescence read in a CFX-96 Touch Real-Time PCR Detection System (Bio-Rad), using 50 ng of cDNA (2 µl) per reaction and the SSo Advanced Universal SYBR® Green Supermix (Bio-Rad) kit, according to the manufacturer’s instructions. After each PCR reaction, a melting curve analysis was run to ensure the specificity of amplification.
Stablished the fluorescence threshold by using a calibrator gene [30, 33], the absence of signal in the negative control (non-template sample) was checked before reading the CT values. Then, the CT values were transferred to Microsoft Excel converted into absolute transcript numbers by using a calibration curve constructed with an external standard as previously described [30, 33] and normalized by using stable genes under our experimental conditions. Then, the mean of the transcript values obtained was calculated as the average of the four replicates if the SD was less than 20%; otherwise, the transcript value of the outlying sample was removed from the study and the remaining data (at least n = 3) were reanalyzed [34].
Selection of reference genes
Primer pairs for performing stability analysis to identify genes to be used as normalizers (reference genes) in our qRT-PCR study have already been described [34]. Following the MIQE guidelines (https://www. gene-quantification.de/miqe-index.html) as previously described [35–37], we determined the CT values for each candidate gene (Pgk1, Rplp0 and Gapdh) in all adrenal gland mRNA samples, regardless of their provenance (fenced or unfenced farms). The obtained CT values were used in the free web tool RefFinder (www.heartcure.com.au/reffinder/?type=reference), which pointed out Pgk1 and Rplp0 as stable genes under our experimental conditions (Fig. 2).
Quality control for qRT-PCR
We analyzed the expression at the transcriptional level of two genes, TH and DBH, involved in the catecholamines metabolic pathway, by using the qRT-PCR methodology according to the MIQE Guidelines [35–37]. To ensure RNA quality, adrenal glands samples were collected and conserved in RNAlater™ solution and kept frozen till use [34]. RNA was extracted with the RNeasy Mini Kit (Qiagen) following the manufacturer recommendations. RNA preparations were considered pure when their ratios of absorbance A260/A280 and A260/A230 were in the range of 2.0–2.2. RNA integrity was evaluated by using an Agilent 2100 Bioanalyzer: all RNA samples in this study were of sufficient quality to be used in transcript quantification by qRT-PCR.
The success of PCR is highly dependent on primer design, as primers are the main determinants of its specificity and sensitivity [34]. Primers to amplify the red deer TH and DBH genes were carefully designed to assure total specificity and efficiency in the amplification reactions, using the OLIGO 7 Primer Analysis Software (Molecular Biology Insights, Inc., http://www.oligo.net) as previously described [34]. After optimizing PCR conditions (annealing temperature and concentration) for each primer pair, primer’s specificity was experimentally evaluated by visualizing the PCR products in agarose gels, which were excised and sequenced and by carrying out melting curves at the end of the PCR reaction. In all cases, primer pairs targeting TH and DBH produced one specific amplicon of the expected size and sequence, exhibiting, in the melting curve, a single peak.
Dopamine measurement
Dopamine is an important compound in the DOPEG biosynthetic route (see Fig. 1 and below, Fig. 4) and it may be depleted by the action of DBH to produce norepinephrine. To measure plasma dopamine levels, blood samples were collected from the infraorbital sinus in 10 ml anticoagulant EDTA-containing tubes. Samples were kept on ice in the field and then centrifuged for 20 minutes at approximately 1,000xg; they were subsequently stored at -20°C for later use. We decided to work with 6 samples (3 HC and 3 LC) as a test to confirm our previous gene expression results.
For quantitative measure of dopamine in serum, we used the Enzyme-Linked Immunosorbent Assay Kit (ELISA Cloud-Clone Corp.) following the manufacturer’s protocol. This pan-species assay has excellent specificity and elevated sensitivity for the detection of dopamine. It is a competitive inhibition enzyme immunoassay technique that allowed us to measure the concentration of dopamine in the samples.
Every sample was duplicated, as recommended, at optimal sample dilution and at 1/6 dilution, obtaining repetitive results.
Statistical analyses
We used a linear mixed-effects regression model (LMM) to predict the effect of the intrasexual mating competition level in the population (LC and HC) and sexual trait development (dark ventral patch (small vs. large) on the expression of the DBH gene controlling for population identity nested within season, and season, as random sources of variation. For TH, we performed a linear model for the most common isoform, but without random effects due to the small sample size that produced model singularity.
For confirmatory checking of the role of TH transcripts and DA plasma concentrations in the metabolic route, unpaired Student’s t-tests were employed for comparisons, with significance level at two-tailed p < 0.05.
We checked for normality (using Shapiro - Wilks test), the lack of heteroscedasticity of the model' residuals, the presence of outliers and singularity of the model. To avoid multicollinearity between variables, we calculated the variance inflation factors (VIFs) [38] of each model using the package “usdm” [39]. We did not find any evidence of collinearity for any of the models (VIF < 3). We calculated the coefficients of the final model using the REML-restricted maximum likelihood method [40]. The variance explained by the models was represented as marginal R² (variance explained by fixed effects) and conditional R² (variance explained by random and fixed effects) following a method developed for linear mixed-effects models [41].
This statistical analysis and graphics were conducted in R v.3.6.1 [42] using the package lme4 [43] and ggeffects [44] packages.