Front. Mol. Neurosci. Frontiers in Molecular Neuroscience Front. Mol. Neurosci. 1662-5099 Frontiers Media S.A. 10.3389/fnmol.2023.1091305 Molecular Neuroscience Brief Research Report Transcriptome-wide selection and validation of a solid set of reference genes for gene expression studies in the cephalopod mollusk Octopus vulgaris ImperadorePamela1*† CagninStefano23† AllegrettiVittoria1 MillinoCaterina2 RaffiniFrancesca1 FioritoGraziano1 PonteGiovanna1 1Department of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, Napoli, Italy 2Department of Biology, University of Padova, Padova, Italy 3CIR-Myo Myology Center, University of Padova, Padova, Italy

Edited by: Margaret S. Saha, College of William & Mary, United States

Reviewed by: Camino Gestal, Spanish National Research Council (CSIC), Spain; Masa-aki Yoshida, Shimane University, Japan

 *Correspondence: Pamela Imperadore, imperadore.p@gmail.com; pamela.imperadore@szn.it;

†These authors have contributed equally to this work

This article was submitted to Methods and Model Organisms, a section of the journal Frontiers in Molecular Neuroscience

 17 05 2023 2023 16 1091305 06 11 2022 20 02 2023 Copyright © 2023 Imperadore, Cagnin, Allegretti, Millino, Raffini, Fiorito and Ponte. 2023 Imperadore, Cagnin, Allegretti, Millino, Raffini, Fiorito and Ponte

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Octopus vulgaris is a cephalopod mollusk and an active marine predator that has been at the center of a number of studies focused on the understanding of neural and biological plasticity. Studies on the machinery involved in e.g., learning and memory, regeneration, and neuromodulation are required to shed light on the conserved and/or unique mechanisms that these animals have evolved. Analysis of gene expression is one of the most essential means to expand our understanding of biological machinery, and the selection of an appropriate set of reference genes is the prerequisite for the quantitative real-time polymerase chain reaction (qRT-PCR). Here we selected 77 candidate reference genes (RGs) from a pool of stable and relatively high-expressed transcripts identified from the full-length transcriptome of O. vulgaris, and we evaluated their expression stabilities in different tissues through geNorm, NormFinder, Bestkeeper, Delta-CT method, and RefFinder. Although various algorithms provided different assemblages of the most stable reference genes for the different kinds of tissues tested here, a comprehensive ranking revealed RGs specific to the nervous system (Ov-RNF7 and Ov-RIOK2) and Ov-EIF2A and Ov-CUL1 across all considered tissues. Furthermore, we validated RGs by assessing the expression profiles of nine target genes (Ov-Naa15, Ov-Ltv1, Ov-CG9286, Ov-EIF3M, Ov-NOB1, Ov-CSDE1, Ov-Abi2, Ov-Homer2, and Ov-Snx20) in different areas of the octopus nervous system (gastric ganglion, as control). Our study allowed us to identify the most extensive set of stable reference genes currently available for the nervous system and appendages of adult O. vulgaris.

 Octopus vulgaris reference genes qRT-PCR cephalopods nervous system molecular fingerprint Introduction

Cephalopods and particularly the common octopus, Octopus vulgaris, are among the key invertebrate organisms recognized for their complex neural organization. The common octopus is an iconic species among cephalopods, at the center of a long tradition of research in diverse aspects of its biology and physiology (e.g., O’Brien et al., 2019).

The taxon belongs to Lophotrochozoa (i.e., a protostome animal), and thus it is very distant from vertebrates. Nevertheless, octopuses are known for possessing the largest nervous system among invertebrates in terms of the number of cells and body-to-brain size (Young, 1963; Packard and Albergoni, 1970; Giuditta et al., 1971; Packard, 1972), as well as their intricate neural network and manifold cellular complexity (Young, 1932; Shigeno and Ragsdale, 2015; Chung et al., 2022; Styfhals et al., 2022; Chung et al., 2023), with remarkable functional analogies to vertebrates (Shigeno et al., 2015, 2018). Octopus vulgaris has also served as an organism of study for the identification of the neural correlates of learning and memory and the search for a model of the brain (Young, 1964; Kandel, 1979; review in: Hochner et al., 2006; Borrelli and Fiorito, 2008; Marini et al., 2017). Nowadays, these mollusks continue to inspire the search for the biological and neural machinery underlying plasticity and cognition (Edelman and Seth, 2009; Albertin and Simakov, 2020; Ponte et al., 2022).

Over the last decade, a significant increase in the efforts of the scientific community has facilitated the release of a large set of genomic data (see Supplementary Info) for various cephalopod species, including the transcriptomes of O. vulgaris (Zhang et al., 2012; Petrosino, 2015; Petrosino et al., 2015; Liscovitch-Brauer et al., 2017; Petrosino et al., 2022; Prado-Álvarez et al., 2022; Styfhals et al., 2022) and reference genomes for more than 10 species (Supplementary Table 1). Although these resources still do not comprehensively represent the rich biological diversity of the approximately 800 living cephalopod species, their availability has greatly contributed to illuminating the biological and physiological complexity of these organisms and the ‘innovations’ they provided during their evolution (Albertin and Simakov, 2020; Albertin et al., 2022; Macchi et al., 2022; Schmidbaur et al., 2022).

These datasets, however, are not sufficient for the understanding of the molecular machinery implicated in neural plasticity (sensu: Cavallaro et al., 2002; Martinez et al., 2007; Asok et al., 2019). In addition, the current knowledge of the gene expression changes occurring in these animals during learning, memory, and behavioral plasticity is still poor. Only a few available studies are focused on some candidate molecules that are potentially involved in given functions. Thus, to the best of our knowledge, an investigation of the differential gene expression occurring in the brain of any cephalopod is still lacking. Here we contribute with a first step to fulfil this gap.

The accurate analysis of gene expression relies on the quantitative real-time polymerase chain reaction (qRT-PCR), one of the most utilized tools for assessing gene levels in different samples in experimental or biological conditions (Bustin, 2002; Huggett et al., 2005; Nolan et al., 2006). The technique offers numerous advantages (Bustin, 2002; Bustin et al., 2005). Nevertheless, its reliability and accuracy are based on the choice of reference genes (RGs) required for normalizing the expression levels of a given target gene. An ideal RG should have a moderate and stable expression level in different tissues, across biological phenomena, and under different experimental treatments (Huggett et al., 2005; Udvardi et al., 2008).

Most of the commonly used RGs for data normalization are the so-called housekeeping genes (e.g., elongation factor 1α, α-tubulin and β-tubulin, β-actin, and ubiquitin). Although widely employed in several species, in some instances they might lack the required stability when tested in different organisms and/or experimental contexts. In some circumstances, they may not match the requirements of an ideal candidate RG (Rubie et al., 2005; Hong et al., 2008; Eisenberg and Levanon, 2013).

In cephalopods, previous studies identified several candidate RGs (Supplementary Table 2) in a number of tissues (mainly brain masses), but to the best of our knowledge, they never encompassed testing of the peripheral ganglia or arms.

Our approach to build a list of potential stable reference genes in octopus was based on: (i) increasing the number of tissues to consider and (ii) exploring the available transcriptomes for O. vulgaris (Zhang et al., 2012; Petrosino, 2015; Petrosino et al., 2015, 2022). We selected genes that appeared stable and uniform in different tissues through in silico characterization of transcriptomes. Finally, we explored relative gene expression through qRT-PCR experiments by using a subset of target genes of the known expression in silico, thus validating our data and the use of the selected RGs in the brain and other ganglia. This approach allowed us to identify the most extensive set of stable reference genes currently available for adult O. vulgaris in the central and peripheral nervous system and in complex structures such as arms.

 Materials and methods <italic>In silico</italic> selection of candidate reference genes

Potential RGs were identified through in silico analysis of the RNA-seq available for O. vulgaris (for details, including assembly methods, see: Petrosino, 2015; Petrosino et al., 2015, 2022). The data included whole transcriptomes from nine tissues: the lobes of the adult octopus’ central nervous system (optic lobes, OL; supra-, SEM, and sub-oesophageal masses, SUB); the first anterior right arm (R1) with its distal extremity (Tip_R1), a proximal portion (ARM_R1), and muscle tissue (MUSC_R1; i.e., only muscle bundles, not the skin and arm nerve cord); the fourth posterior right arm (R4) with its proximal portion (ARM_R4); and two peripheral ganglia i.e. the left stellate and gastric ganglia (StG and GG, respectively; Figure 1).

Schematic outline of Octopus vulgaris anatomy. Tissues sampled for transcriptomic analysis and biological validation are highlighted here. Black dotted rectangles and lines identify tissues included both in RNA-seq analysis and RT-qPCR experiments: supraoesophageal mass (SEM), suboesophageal mass (SUB) and left optic lobe (OL), gastric ganglion (GG), stellate ganglion (StG), R1 arm tip (Tip_R1), a piece of R1 and R4 arms (ARM_R1 and ARM_R4), and a piece of muscle from arm R1 (MUSC_R1). Green circles identify tissues only included in RT-qPCR experiments, i.e., a posterior portion of the left gill (GILL), a piece of muscles from the ventral side of the mantle without the skin (MANT), and R4 arm tip (Tip_R4).

As aforementioned, our rationale was to extend the biological diversity of the considered tissues. In addition, the anterior versus posterior arms were included on the basis of the scientific evidence of the potential variety of behavioral functions these may achieve (e.g., Mather, 1998; Huffard et al., 2005; Amodio et al., 2021).

Details on RNA isolation, quality and quantity assessment, and libraries construction are available in Petrosino (2015) and Petrosino et al. (2022) and not provided herein. Raw reads were analyzed by Trimmomatic (Bolger et al., 2014), which served for the filtering and trimming of low-quality bases. Normalization was performed, and the remaining reads were assembled in putative clustered transcripts to select unique sequences using Trinity (Grabherr et al., 2011). The raw reads were then mapped to the assembled transcriptome to measure the expression levels. Only annotated transcripts with a relative abundance greater than 1.5 counts per million (TPM) in all the biological replicates were considered. The annotation of these transcripts was finalized using the Annocript pipeline (Musacchia et al., 2015; Petrosino, 2015), thus counting 21,030 protein-coding sequences.

In order to identify the candidate RGs, we selected sequences based on their coefficient of variation (CV) of the relative abundance (TPM) of each transcript, i.e., the ratio of the standard deviation to the group mean of each transcript identified for four groups of tissues, as follows: (i) all the available tissues from adult individuals of O. vulgaris (Adult); (ii) the brain masses (Brain: SEM, SUB, and OL); (iii) The nervous tissues (Nervous: including tissues already listed in Brain group, plus StG and GG); (iv) tissues belonging to the arm (Arm: Tip_R1, ARM_R1, ARM R4, and MUSCLE_R1).

Genes were considered stable when their transcript’s CV was lower than 15%. For the Adult group, a cut-off of 20% CV was used to account for the higher tissue variability. Some genes were included in more than one group according to their CV values (Table 1 and Supplementary Figure 1).

Genes identified in whole transcriptomes and validated in RT-qPCR experiments.

Transcript ID Group Gene name Description Accession number CV%
c35016_g13_i1 Nervous system Ov-Gsk3b Glycogen synthase kinase-3 beta MW800694 3.89
c34071_g2_i1 Nervous system Ov-mts Serine/threonine protein phosphatase PP2A MW800693 4.27
c30725_g11_i1 Nervous System Ov-timm Mitochondrial import inner membrane translocase subunit Tim22 MW800652 4.40
c36083_g5_i1 Nervous System Ov-SUCLG2 Succinate––CoA ligase [GDP-forming] subunit beta, mitochondrial MW800659 4.52
c33604_g6_i1 Nervous System Ov-CHCHD7 Coiled-coil-helix-coiled-coil-helix domain-containing protein 7 MW800655 4.63
c32222_g5_i1 Nervous system Ov-UBE2F NEDD8-conjugating enzyme UBE2F MW800681 5.16
c34932_g8_i1 Nervous system Ov-MTX1 Metaxin-1 MW800712 5.19
c31554_g1_i3 Nervous System Ov-gk5 Putative glycerol kinase 5 MW800648 5.74
c35771_g14_i2 Nervous system Ov-Gnaq Guanine nucleotide-binding protein G(q) subunit alpha MW800695 5.92
c35786_g9_i1 Nervous System Ov-Naa15 N-alpha-acetyltransferase 15 NatA auxiliary subunit MW800658 6.35
c30400_g11_i1 Nervous System Ov-wdr44 WD repeat-containing protein 44 MW800651 6.97
c17784_g1_i1 Nervous System Ov-Klhdc Kelch domain-containing protein 4 MW800649 6.98
c35707_g2_i1 Nervous System Ov-PRMT5 Protein arginine N-methyltransferase 5 MW800660 7.15
c32096_g14_i2 Nervous System Ov-Canx Calnexin MW800654 7.33
c35499_g5_i1 Nervous System Ov-ube2c Ubiquitin-conjugating enzyme E2 C MW800657 7.79
c33913_g6_i1 Nervous System Ov-PTPN12 Tyrosine-protein phosphatase non-receptor type 12 MW800656 9.22
c31227_g1_i2 Nervous System Ov-tollip Toll-interacting protein MW800653 9.24
c31322_g1_i1 Nervous System Ov-prrc1 Protein PRRC1-A MW800647 9.63
c28856_g1_i2 Nervous System Ov-CUL1 Cullin-1 MW800650 9.91
c32222_g5_i1 ADULT Ov-UBE2F NEDD8-conjugating enzyme UBE2F MW800681 8.81
c35707_g2_i1 ADULT Ov-PRMT5 Protein arginine N-methyltransferase 5 MW800660 10.25
c25466_g1_i1 ADULT Ov-Ltv1 Protein LTV1 homolog MW800662 11.27
c35311_g1_i1 ADULT Ov-CPIJ005834 Elongation factor G mitochondrial MW800676 12.23
c35010_g2_i4 ADULT Ov-EIF2A Eukaryotic translation initiation factor 2A MW800674 12.66
c29044_g1_i1 ADULT Ov-rpf1 Ribosome production factor 1 MW800663 12.73
c31610_g1_i1 ADULT Ov-slc25a40 Solute carrier family 25 member 40 MW800667 12.79
c33222_g7_i1 ADULT Ov-RIOK2 Serine/threonine protein kinase RIO2 MW800670 12.85
c32170_g13_i2 ADULT Ov-Dap3 28S ribosomal protein S29, mitochondrial MW800668 12.87
c34313_g4_i1 ADULT Ov-Ppm1b Protein phosphatase 1B MW800677 14.23
c34059_g14_i1 ADULT Ov-ATPAF2 ATP synthase mitochondrial F1 complex assembly factor 2 MW800671 14.36
c30066_g9_i1 ADULT Ov-NOB1 RNA-binding protein NOB1 MW800666 14.38
c32751_g1_i1 ADULT Ov-flr Actin-interacting protein 1 MW800669 15.29
c34776_g5_i1 ADULT Ov-usp10 Ubiquitin carboxyl-terminal hydrolase 10 MW800673 15.39
c35032_g7_i2 ADULT Ov-Dnaja3 DnaJ homolog subfamily A member 3, mitochondrial MW800675 15.61
c34087_g16_i1 ADULT Ov-CSDE1 Cold shock domain-containing protein E1 MW800672 15.63
c29524_g1_i1 ADULT Ov-EIF3M Eukaryotic translation initiation factor 3 subunit M MW800665 15.71
c36175_g1_i1 ADULT Ov-BTBD17 BTB/POZ domain-containing protein 17 MW800661 15.76
c29430_g1_i1 ADULT Ov-CG9286 Protein BCCIP homolog MW800664 16.21
c34939_g11_i1 ARM Ov-ESR16 Ecdysteroid-regulated 16 kDa protein MW800722 3.38
c35194_g4_i2 ARM Ov-C2CD2 C2 domain containing protein 2 ×2 MW800723 3.69
c32350_g3_i1 ARM Ov-nAChRalpha1 Acetylcholine receptor subunit alpha-like 1 MW800709 3.71
c29941_g6_i1 ARM Ov-14-3-3zeta 14–3-3 protein zeta MW800678 4.27
c36050_g13_i1 ARM Ov-Sdhd Succinate dehydrogenase ubiquinone cytochrome b small subunit, mitochondrial MW800689 5.11
c34295_g8_i1 ARM Ov-Vbp1 Prefoldin subunit 3 MW800685 5.33
c34563_g2_i1 ARM Ov-PCK1 Phosphoenolpyruvate carboxykinase cytosolic GTP MW800686 5.51
c35194_g4_i1 ARM Ov-C2CD2 C2 domain containing protein 2 ×1 MW800687 5.53
c35789_g7_i1 ARM Ov-MRM2 rRNA methyltransferase 2, mitochondrial MW800688 6.77
c32876_g12_i1 ARM Ov-RNF7 RING-box protein 2 MW800710 6.90
c30691_g3_i1 ARM Ov-RSU1 Ras suppressor protein 1 MW800679 7.13
c31105_g4_i1 ARM Ov-BTBD2 BTB/POZ domain-containing protein 2 MW800680 7.22
c26803_g1_i1 ARM Ov-RAD23B UV excision repair protein RAD23 homolog B MW800690 7.25
c28934_g1_i1 ARM Ov-UGP2 UTP––glucose-1-phosphate uridylyltransferase MW800692 7.39
c28702_g2_i1 ARM Ov-Abhd18 Protein ABHD18 MW800691 8.08
c33117_g3_i1 ARM Ov-Rnd3 Rho-related GTP-binding protein RhoE MW800683 8.09
c32876_g7_i5 ARM Ov-KCMF1 E3 ubiquitin-protein ligase KCMF1 MW800682 9.26
c33305_g9_i1 ARM Ov-Abi2 Abl interactor 2 MW800684 10.22
c32222_g5_i1 ARM Ov-UBE2F NEDD8-conjugating enzyme UBE2F MW800681 11.77
c34071_g2_i1 BRAIN Ov-mts Serine/threonine protein phosphatase PP2A MW800693 0.43
c28771_g3_i1 BRAIN Ov-AP5Z1 AP-5 complex subunit zeta-1 MW800696 0.99
c34716_g8_i1 BRAIN Ov-USP15 Ubiquitin carboxyl-terminal hydrolase 15 MW800700 1.29
c35771_g14_i2 BRAIN Ov-Gnaq Guanine nucleotide-binding protein G(q) subunit alpha MW800695 1.60
c35361_g5_i1 BRAIN Ov-Fam160a2 FTS and hook-interacting protein-like MW800703 1.93
c34932_g8_i1 BRAIN Ov-MTX1 Metaxin-1 MW800712 2.14
c34844_g11_i1 BRAIN Ov-WBP2 WW domain-binding protein 2 MW800701 2.39
c30165_g11_i1 BRAIN Ov-wls Protein wntless MW800721 3.05
c31295_g14_i1 BRAIN Ov-AP1M1 AP-1 complex subunit mu-1 MW800711 3.05
c35016_g13_i1 BRAIN Ov-Gsk3b Glycogen synthase kinase-3 beta MW800694 3.19
c35896_g5_i1 BRAIN Ov-Snx25 Sorting nexin-25 MW800705 3.36
c35327_g8_i2 BRAIN Ov-FBXO38 F-box only protein 38 MW800708 3.53
c35373_g3_i2 BRAIN Ov-Snx20 Sorting nexin-20 MW800704 3.65
c30947_g6_i1 BRAIN Ov-syvn1 E3 ubiquitin-protein ligase synoviolin MW800698 3.85
c32955_g4_i1 BRAIN Ov-Homer2 Homer protein homolog 2 MW800699 4.14
c35037_g6_i2 BRAIN Ov-PIP4K2B Phosphatidylinositol 5-phosphate 4-kinase type-2 beta MW800702 4.94
c34087_g16_i1 BRAIN Ov-CSDE1 Cold shock domain-containing protein E1 MW800672 5.09
c36137_g10_i4 BRAIN Ov-AGL Glycogen debranching enzyme MW800706 5.44
c29565_g1_i1 BRAIN Ov-CERK Ceramide kinase MW800697 5.52
c34695_g13_i5 BRAIN Ov-CNBP Cellular nucleic acid-binding protein MW800707 5.96
from previously published studies
c26807_g1_i1 Previously published Ov-eef1a Elongation factor 1-alpha (Xu and Zheng, 2018) MW800714 16.81
c2281_g1_i1 Previously published Ov-Rpl6 60S ribosomal protein L6 (Xu and Zheng, 2018) MW800718 24.75
c5816_g1_i1 Previously published Ov-Rps27a Ubiquitin-40S ribosomal protein S27a (Sirakov et al., 2009) MW800713 30.40
c29373_g3_i1 Previously published Ov-RPS18 40S ribosomal protein S18 (Imperadore, 2017) MW800720 33.43
c12855_g1_i1 Previously published Ov-TUBG1 Tubulin gamma-1 chain (Xu and Zheng, 2018) MW800715 36.73
c34110_g1_i1 Previously published Ov-MRPS5 28S ribosomal protein S5, mitochondrial (Xu and Zheng, 2018) MW800716 38.56
c30772_g3_i11 Previously published Ov-RpL23 60S ribosomal protein L23 (Imperadore, 2017) MW800719 42.20
c36025_g3_i2 Previously published Ov-Tuba1a Tubulin alpha-1A chain (Sirakov et al., 2009) MW800717 80.40

List and details of the candidate reference genes for Octopus vulgaris identified and validated in this study (see the main text for details). Highlighted transcripts are those that have been identified as stable in different tissue groups. CV, Coefficient of Variation.

Eight RGs from previous studies on cephalopods (Sirakov et al., 2009; García-Fernández et al., 2016; Baldascino et al., 2017; Imperadore, 2017; Xu and Zheng, 2018; Whang et al., 2020; see also Table 1 and Supplementary Table 2) were also included for subsequent validation analyses.

 Sample collection and processing for RT-qPCR

To test the selected candidate RGs, tissues were harvested from five adult specimens of O. vulgaris (Supplementary Table 3) that did not show any signs of lesions, aberrant formations, or regenerating parts. From each octopus, 12 tissues were collected: SEM; SUB; OL; GG; StG; a portion of muscle from the ventral side of the mantle (MANT); arm tips from the anterior (Tip_R1) and posterior arms (Tip_R4); ARM_R1 and ARM_R4; MUSC_R1; and the left gill (GILL), considered here as a reference tissue for an internal organ differing from the muscles and nervous structures (Figure 1). The tissues were processed for RNA extraction; RNA integrity was tested using Agilent Bioanalyzer 2100 (see Supplementary Figure 2) and cDNA synthesis; cDNA samples were stored at −20°C until use (see Supplementary Info for specimens handling, sample harvesting, tissue processing, and RNA and cDNA processing and synthesis).

 Primer design and amplification efficiency analysis for qRT-PCR

Primer3 Plus software (Untergasser et al., 2012) was used to design specific primers (Supplementary Table 4) to amplify the candidate genes. The following parameters were utilized: optimal melting temperature at 60°C, amplicon size 100–200 bp, and primer size between 18 and 27 bp (optimum set at 20 bp). Template RNA sequences were retrieved from previously mentioned RNA-seq studies. To obtain the most efficient primer couples, hairpin, homodimer, and heterodimer structures were evaluated for each primer couple using the Multiple Primer Analyzer1 (modified after Breslauer et al., 1986). In addition, 12 primer couples from eight genes were selected from the literature and slightly modified, when needed, to match with O. vulgaris sequences, or they were designed ex novo based on published ones (Supplementary Table 4).

RT-qPCR was performed on four-fold cDNA dilutions (from 10 ng/μL to 0.15625 ng/μL; see Supplementary Info) to calculate the primers’ efficiency, using the formula E=[(10−1/m)−1]×100 where m is the slope of the linear interpolation of dots representing Ct in the function of log10 [cDNA concentration].

To estimate the gene expression in each tissue, the primers were tested for RT-qPCR on individual samples in technical triplicates by using 2 μl cDNA [1.25 ng/μl] (see Supplementary Info for details).

 Expression stability

The 12 tissues included in this study are highly diverse in structure, function, and gene expression profile. Thus, to account for this variability (with highest variability showed by the arm tips), we considered three groups for the expression stability analyses: Nervous (SEM, SUB, OL, GG, and StG); all tissues excluding the arm tips (Allex: Nervous, plus GILL, MANT, ARM_R1, ARM_R4, and MUSC_R1); and Adult (all the tissues including the arm tips; see also above and Figure 1).

The expression stability of each candidate gene across all samples within each tissue group was investigated using the mean Ct values and four different algorithms: geNorm (Vandesompele et al., 2002), NormFinder (Mestdagh et al., 2009), BestKeeper (Pfaffl et al., 2004), and the Delta-CT method (2−ΔΔCT) (Livak and Schmittgen, 2001). geNorm estimates the average pairwise variation in a specific gene with all the other potential reference genes. NormFinder computes the stability value for each gene according to their minimum variance. Both the geNorm and NormFinder values are lower for more stable genes (Amable et al., 2013). BestKeeper relies on the concept that the more stable the gene expression, the lower the Ct variation if the cDNA quantity is constant (Amable et al., 2013). Finally, the Delta Ct algorithm (Livak and Schmittgen, 2001) takes into account the expression of each gene in all samples and its standard deviation (SD); the gene with the lowest SD is considered the most appropriate reference gene (Silver et al., 2006).

The results from these approaches were integrated in RefFinder (Xie et al., 2012) to obtain an overall rank of expression stability for each of the three tissue groups. The method ranks each gene in each group and calculates the geometric mean of ranks for each gene. More stable genes show smaller geometric means, as they are ranked higher by all the methods.

 Validation of reference genes

To validate the reliability of the data normalization, the combination of the two most stable candidate RGs, and of the most stable and unstable reference genes for the Nervous group were used to analyze the expression levels of the target genes. When two RGs were utilized for normalization, we relied on their geometric mean. The relative quantification of nine target genes was calculated for the Nervous group following Pfaffl's (2001) method, which takes into account the primer efficiencies of both targets and RGs.

 Data analysis

Following Zar (1999), statistical significance was assessed after an ANOVA test, followed by Bonferroni multiple comparison tests. For all analyses, we used SPSS (rel. 18.0, SPSS Inc. - Chicago, 2009), with the exceptions mentioned above. All tests were two-tailed, and the alpha was set at 0.05.

 Results <italic>In silico</italic> identification of candidate reference genes

Candidate RGs were identified from the transcriptome of O. vulgaris (Petrosino, 2015; Petrosino et al., 2022). Genes with a relatively stable expression in silico in four tissue groups (Adult, Brain, Nervous, and Arm) were selected according to the relative abundance of each transcript (TPM counts) and their coefficient of variation (CV). Using a CV cut-off of 20%, 32 transcripts (out a total of 64,477 unique transcripts) were selected for Adult. The CV cut-off was decreased to 15% to identify the most stable transcripts in the Brain (1,540 transcripts), Nervous system (357), and Arm (125). A total of 2,145 transcripts was identified. Because the annotation results were not further curated in the original studies (Musacchia et al., 2015; Petrosino, 2015; Petrosino et al., 2015, 2022), we excluded non-annotated transcripts, thus identifying 88 potential RGs for the four tissue groups (Table 1). Seven of them resulted shared among more than one group (highlighted in Table 1).

We observed the highest variability in CVs among samples belonging to the Adult group (19 genes; mean CV = 13.6%; CV range: 8.8–16.2%; Table 1). In this set, 12 genes showed CV values lower than 15% (i.e., Ov-UBE2F, Ov-PRMT5, Ov-LTV1, Ov-CPIJ005834, Ov-EIF2A, Ov-rpf1, Ov-slc25a40, Ov-RIOK2, Ov-Dap3, Ov-Ppm1b, Ov-ATPAF2, and Ov-NOB1; Table 1). Lower CV values were observed when the Brain group was considered (20 genes; mean CV = 3.28%; CV range: 0.4–6.0%; Table 1), with seven genes exhibiting CV values below 3% (i.e., Ov-mts, Ov-AP5Z1, Ov-USP15, Ov-Gnaq, Ov-Fam160a2, Ov-MTX1, and Ov-WBP2; Table 1). Nineteen candidate RGs were identified for the Nervous group (mean CV = 6.5%; CV range: 3.9–9.9%; Table 1), with average CVs higher than those observed for the Brain group. In this case, 12 genes had CV values lower than 7% (i.e., Ov-Gsk3b, Ov-mts, Ov-timm, Ov-SUCLG2, Ov-CHCHD7, Ov-UBE2F, Ov-MTX1, Ov-gk5, Ov-Gnaq, Ov-Naa15, Ov-wdr44, and Ov-Klhdc). For the Arm group (19 genes; mean CV = 6.7%; CV range: 3.4–11.8%), we observed similar CVs to the Nervous group, with 9 genes (10 transcripts) having CVs lower than 7% (i.e., Ov-ESR16, Ov-C2CD2, Ov-nAChRalpha1, Ov-14-3-3zeta, Ov-Sdhd, Ov-Vbp1, Ov-PCK1, Ov-MRM2, and Ov-RNF7; Table 1).

A total of 69 candidate RGs were selected for biological validation. In addition, we considered eight RGs used in previous studies (Sirakov et al., 2009; Imperadore, 2017; Xu and Zheng, 2018), raising the final number of genes to be tested through qRT-PCR experiments to 77 (Table 1).

 Candidate reference genes and their expression profiles

Eighty-one primer couples for the selected putative RGs were designed and tested for specificity and efficiency through standard PCR and qRT-PCR reactions (Supplementary Info), using the total mRNA extracted from 12 tissues (Figure 1) belonging to five O. vulgaris specimens. Three primer couples (i.e., Ov-wls, Ov-ESR16, and Ov-C2CD2 isoform X2) exhibited no or multiple amplification products when tested for standard PCR and were excluded from subsequent analyses. All other primer couples resulted in a single amplification product at the expected amplicon size (Supplementary Figure 3) and were therefore tested for RT-qPCR. The primer sequences, amplicon size, product Tm, and amplification efficiencies are shown in Supplementary Table 4.

A total of 59 primer pairs showed amplification efficiencies between 98 and 102%, while 19 did not fall within this range and were excluded from further analyses (Supplementary Table 4).

The expression levels of the final list of candidate RGs (n = 59) were estimated in each tissue sample (technical triplicates) through qRT-PCR. The reference genes displayed a wide range of transcription levels, with average Ct values ranging from 18.17 to 37.27 (Supplementary Table 5). Ov-Tuba1a showed the lowest mean Ct (21.63), i.e., the highest abundance in tissues. High expression levels were also noted for Ov-Rpl6, Ov-tollip, and Ov-RPS18 (mean Ct = 24.15, 24.40, and 24.79, respectively). In an opposite trend, Ov-NOB1 and Ov-TUBG1-FR presented a relatively low expression level (mean Ct = 31.31 in both cases; Supplementary Table 5).

 Analysis of expression stability of the candidate reference genes

For the expression stability analyses, three tissue groups of increasing biological variability were considered (Nervous, ALLex, and Adult).

Our results suggested that the most suitable reference genes differed among the approaches used for the identification of RGs (see Methods), as well as among the groups considered, likely owing to their substantial tissue diversity (Table 2).

Outcomes of the analysis for reference genes stability after application of the four algorithms (i.e., GeNorm, NormFinder, BestKeeper, and DeltaCt).

GeNorm Algorithm NormFinder algorithm
Nervous Allex Adult Nervous Allex Adult
Gene name Stab. value Gene name Stab. value Gene name Stab. value Gene name Stab. value Gene name Stab. value Gene name Stab. value
1 Ov-CHCHD7/ Ov-RNF7 0.271 Ov-CUL1/ Ov-Naa15 0.386 Ov-Vbp1 0.544 Ov-RIOK2 0.238 Ov-EIF2A 0.371 Ov-EIF2A 0.382
2 Ov-RIOK2 0.297 Ov-RIOK2 0.480 Ov-EIF2A 0.556 Ov-slc25a40 0.278 Ov-Naa15 0.373 Ov-CUL1 0.390
3 Ov-UBE2F 0.317 Ov-slc25a40 0.520 Ov-RAD23B 0.573 Ov-RNF7 0.296 Ov-Ppm1b 0.376 Ov-Ppm1b 0.469
4 Ov-slc25a40 0.333 Ov-usp10 0.534 Ov-syvn1 0.603 Ov-CHCHD7 0.299 Ov-CUL1 0.388 Ov-Vbp1 0.505
5 Ov-BTBD17 0.355 Ov-KCMF1 0.544 Ov-CUL1 0.625 Ov-Ppm1b 0.330 Ov-RIOK2 0.411 Ov-syvn1 0.527
6 Ov-Naa15 0.372 Ov-Ppm1b 0.555 Ov-Ppm1b 0.674 Ov-syvn1 0.336 Ov-KCMF1 0.444 Ov-slc25a40 0.584
7 Ov-Ppm1b 0.396 Ov-EIF2A 0.562 Ov-CHCHD7 0.691 Ov-UBE2F 0.340 Ov-usp10 0.477 Ov-UBE2F 0.597
8 Ov-CUL1 0.415 Ov-EIF3M 0.576 Ov-RIOK2 0.707 Ov-Naa15 0.349 Ov-EIF3M 0.495 Ov-RAD23B 0.599
9 Ov-Snx25 0.432 Ov-CHCHD7 0.611 Ov-UBE2F 0.722 Ov-BTBD17 0.354 Ov-syvn1 0.499 Ov-Rnd3 0.627
10 Ov-Dnaja3 0.445 Ov-syvn1 0.619 Ov-RNF7 0.738 Ov-Abi2 0.373 Ov-slc25a40 0.500 Ov-RIOK2 0.637
11 Ov-usp10 0.454 Ov-Vbp1 0.627 Ov-Rnd3 0.769 Ov-EIF2A 0.374 Ov-Vbp1 0.505 Ov-CHCHD7 0.680
12 Ov-Fam160a2 0.462 Ov-RAD23B 0.644 Ov-syvn1 0.782 Ov-Ltv1 0.399 Ov-CHCHD7 0.538 Ov-Dap3 0.686
13 Ov-PTPN12 0.470 Ov-UBE2F 0.653 Ov-MRM2 0.805 Ov-usp10 0.405 Ov-RAD23B 0.539 Ov-MRM2 0.687
14 Ov-ATPAF2 0.478 Ov-Dap3 0.661 Ov-Fam160a2 0.814 Ov-AP5Z1 0.406 Ov-Dap3 0.558 Ov-usp10 0.689
15 Ov-AP5Z1 0.485 Ov-ATPAF2 0.668 Ov-usp10 0.824 Ov-CUL1 0.419 Ov-UBE2F 0.562 Ov-RNF7 0.696
16 Ov-syvn1 0.491 Ov-Rnd3 0.675 Ov-Dap3 0.834 Ov-EIF3M 0.420 Ov-ATPAF2 0.574 Ov-gk5 0.697
17 Ov-EIF2A 0.498 Ov-RNF7 0.682 Ov-gk5 0.843 Ov-CSDE1 0.422 Ov-MRPS5_FR 0.585 Ov-Fam160a2 0.735
18 Ov-Abi2 0.503 Ov-Ltv1 0.689 Ov-ATPAF2 0.854 Ov-PTPN12 0.423 Ov-MRM2 0.592 Ov-Naa15 0.740
19 Ov-KCMF1 0.508 Ov-MRM2 0.695 Ov-CG9286 0.863 Ov-Snx25 0.424 Ov-Rnd3 0.595 Ov-CPIJ005834 0.762
20 Ov-Ltv1 0.515 Ov-Fam160a2 0.703 Ov-Ltv1 0.879 Ov-BTBD2 0.438 Ov-Ltv1 0.606 Ov-Ltv1 0.762
21 Ov-CSDE1 0.520 Ov-MRPS5_FR 0.710 Ov-CPIJ005834 0.886 Ov-KCMF1 0.439 Ov-RNF7 0.612 Ov-Sdhd 0.766
22 Ov-prrc1 0.525 Ov-MRPS5_F1R1 0.732 Ov-Naa15 0.893 Ov-MRPS5_FR 0.457 Ov-Fam160a2 0.646 Ov-CG9286 0.799
23 Ov-BTBD2 0.529 Ov-CG9286 0.742 Ov-Sdhd 0.900 Ov-Rnd3 0.457 Ov-MRPS5_F1R1 0.660 Ov-ATPAF2 0.806
24 Ov-tollip 0.534 Ov-gk5 0.751 Ov-EIF3M 0.915 Ov-Dnaja3 0.461 Ov-gk5 0.703 Ov-EIF3M 0.849
25 Ov-EIF3M 0.540 Ov-mts 0.768 Ov-prrc1 0.925 Ov-Vbp1 0.464 Ov-CG9286 0.729 Ov-MRPS5_FR 0.856
26 Ov-Rnd3 0.545 Ov-CPIJ005834 0.778 Ov-Abhd18 0.954 Ov-ATPAF2 0.478 Ov-mts 0.744 Ov-prrc1 0.863
27 Ov-MRPS5_FR 0.551 Ov-prrc1 0.787 Ov-MRPS5_FR 0.965 Ov-MRM2 0.486 Ov-CPIJ005834 0.771 Ov-Abhd18 0.890
28 Ov-Vbp1 0.558 Ov-PTPN12 0.797 Ov-KCMF1 0.976 Ov-prrc1 0.489 Ov-PTPN12 0.774 Ov-KCMF1 0.951
29 Ov-Dap3 0.565 Ov-RPS18 0.807 Ov-C2CD2 0.998 Ov-Fam160a2 0.502 Ov-prrc1 0.802 Ov-rpf1 0.969
30 Ov-CG9286 0.571 Ov-Sdhd 0.817 Ov-UGP2 1.009 Ov-tollip 0.504 Ov-Sdhd 0.809 Ov-MRPS5_F1R1 0.974
31 Ov-MRM2 0.578 Ov-Abhd18 0.837 Ov-CSDE1 1.018 Ov-Dap3 0.513 Ov-RPS18 0.835 Ov-SUCLG2 0.997
32 Ov-UGP2 0.585 Ov-SUCLG2 0.870 Ov-rpf1 1.027 Ov-CG9286 0.546 Ov-Abhd18 0.862 Ov-CSDE1 1.034
33 Ov-RAD23B 0.592 Ov-tollip 0.880 Ov-MRPS5_F1R1 1.046 Ov-UGP2 0.568 Ov-tollip 0.916 Ov-timm 1.035
34 Ov-Sdhd 0.599 Ov-Abi2 0.891 Ov-Abi2 1.055 Ov-RAD23B 0.578 Ov-Abi2 0.926 Ov-C2CD2 1.037
35 Ov-WBP2 0.606 Ov-rpf1 0.902 Ov-SUCLG2 1.064 Ov-gk5 0.579 Ov-SUCLG2 0.928 Ov-UGP2 1.048
36 Ov-gk5 0.614 Ov-RpL23 0.935 Ov-timm 1.073 Ov-WBP2 0.611 Ov-rpf1 0.972 Ov-WBP2 1.056
37 Ov-RPS18 0.621 Ov-C2CD2 0.957 Ov-WBP2 1.093 Ov-Sdhd 0.616 Ov-RpL23 1.023 Ov-Abi2 1.063
38 Ov-mts 0.630 Ov-CSDE1 0.969 Ov-PTPN12 1.103 Ov-RPS18 0.623 Ov-C2CD2 1.034 Ov-PTPN12 1.134
39 Ov-MRPS5_F1R1 0.641 Ov-UGP2 0.990 Ov-BTBD17 1.115 Ov-MRPS5_F1R1 0.702 Ov-NOB1 1.069 Ov-BTBD17 1.181
40 Ov-CPIJ005834 0.651 Ov-NOB1 1.000 Ov-AP5Z1 1.127 Ov-mts 0.709 Ov-timm 1.071 Ov-AP5Z1 1.233
41 Ov-TUBG1_FR 0.661 Ov-timm 1.010 Ov-mts 1.140 Ov-CPIJ005834 0.727 Ov-CSDE1 1.077 Ov-mts 1.277
42 Ov-C2CD2 0.674 Ov-WBP2 1.020 Ov-Snx25 1.179 Ov-TUBG1_FR 0.745 Ov-WBP2 1.082 Ov-RPS18 1.304
43 Ov-SUCLG2 0.690 Ov-Snx25 1.029 Ov-tollip 1.193 Ov-C2CD2 0.825 Ov-UGP2 1.113 Ov-Snx25 1.369
44 Ov-rpf1 0.705 Ov-BTBD17 1.052 Ov-BTBD2 1.206 Ov-PCK1 0.888 Ov-Snx25 1.122 Ov-tollip 1.378
45 Ov-PCK1 0.720 Ov-Dnaja3 1.063 Ov-AGL 1.231 Ov-rpf1 0.894 Ov-Dnaja3 1.211 Ov-PCK1 1.382
46 Ov-Tuba1a 0.738 Ov-Tuba1a 1.075 Ov-RPS18 1.245 Ov-SUCLG2 0.919 Ov-Tuba1a 1.216 Ov-BTBD2 1.398
47 Ov-AGL 0.758 Ov-AP5Z1 1.087 Ov-PIP4K2B 1.271 Ov-Tuba1a 1.035 Ov-BTBD17 1.216 Ov-AGL 1.425
48 Ov-Abhd18 0.777 Ov-TUBG1_FR 1.100 Ov-PCK1 1.284 Ov-AGL 1.061 Ov-TUBG1_FR 1.267 Ov-PIP4K2B 1.450
49 Ov-RpL23 0.798 Ov-Homer2 1.112 Ov-Dnaja3 1.298 Ov-Abhd18 1.095 Ov-AP5Z1 1.284 Ov-Dnaja3 1.544
50 Ov-NOB1 0.819 Ov-BTBD2 1.127 Ov-Snx20 1.314 Ov-RpL23 1.175 Ov-Homer2 1.312 Ov-Snx20 1.551
51 Ov-Rpl6 0.842 Ov-AGL 1.141 Ov-NOB1 1.330 Ov-NOB1 1.252 Ov-PIP4K2B 1.386 Ov-NOB1 1.576
52 Ov-Homer2 0.867 Ov-PIP4K2B 1.155 Ov-Homer2 1.346 Ov-Rpl6 1.283 Ov-PCK1 1.403 Ov-TUBG1_FR 1.655
53 Ov-timm 0.893 Ov-PCK1 1.169 Ov-wdr44 1.361 Ov-Homer2 1.365 Ov-BTBD2 1.437 Ov-Homer2 1.672
54 Ov-TUBG1_F1R1 0.920 Ov-wdr44 1.183 Ov-TUBG1_FR 1.396 Ov-timm 1.460 Ov-AGL 1.442 Ov-wdr44 1.684
55 Ov-Snx20 0.953 Ov-TUBG1_F1R1 1.197 Ov-TUBG1_F1R1 1.413 Ov-TUBG1_F1R1 1.512 Ov-TUBG1_F1R1 1.442 Ov-TUBG1_F1R1 1.719
56 Ov-PIP4K2B 0.990 Ov-Snx20 1.212 Ov-RpL23 1.434 Ov-Snx20 1.699 Ov-wdr44 1.443 Ov-RpL23 1.916
57 Ov-wdr44 1.031 Ov-Rps27a_FR 1.261 Ov-Rpl6 1.465 Ov-PIP4K2B 1.888 Ov-Snx20 1.535 Ov-Rpl6 2.370
58 Ov-Rps27a_FR 1.090 Ov-Rpl6 1.297 Ov-Rps27a_FR 1.500 Ov-wdr44 2.035 Ov-Rps27a_FR 2.047 Ov-Rps27a_FR 2.539
59 Ov-Tuba1a 1.537 Ov-Rps27a_FR 2.687 Ov-Rpl6 2.443 Ov-Tuba1a 2.677
BestKeeper algorithm Delta Ct method
Nervous Allex Adult Nervous Allex Adult
Gene name Std. dev Gene name Std. dev Gene name Std. dev Gene name Std. dev Gene name Std. dev Gene name Std. dev
1 Ov-Abi2 0.610 Ov-Ltv1 0.850 Ov-Ltv1 0.750 Ov-RIOK2 0.780 Ov-Naa15 1.020 Ov-EIF2A 1.190
2 Ov-RPS18 0.610 Ov-RPS18 0.910 Ov-RpL23 0.750 Ov-RNF7 0.790 Ov-EIF2A 1.020 Ov-CUL1 1.190
3 Ov-RpL23 0.630 Ov-Abi2 0.920 Ov-RPS18 0.800 Ov-slc25a40 0.790 Ov-Ppm1b 1.020 Ov-Ppm1b 1.210
4 Ov-EIF3M 0.640 Ov-RpL23 0.930 Ov-prrc1 0.820 Ov-CHCHD7 0.790 Ov-CUL1 1.030 Ov-Vbp1 1.240
5 Ov-RNF7 0.690 Ov-EIF3M 0.940 Ov-gk5 0.860 Ov-UBE2F 0.810 Ov-RIOK2 1.040 Ov-syvn1 1.240
6 Ov-BTBD17 0.700 Ov-BTBD17 0.980 Ov-BTBD17 0.860 Ov-Ppm1b 0.820 Ov-KCMF1 1.050 Ov-slc25a40 1.260
7 Ov-Sdhd 0.710 Ov-RIOK2 0.990 Ov-slc25a40 0.890 Ov-Naa15 0.820 Ov-usp10 1.060 Ov-UBE2F 1.270
8 Ov-MRM2 0.730 Ov-gk5 1.010 Ov-Rnd3 0.900 Ov-BTBD17 0.820 Ov-EIF3M 1.080 Ov-RAD23B 1.280
9 Ov-RAD23B 0.740 Ov-NOB1 1.030 Ov-CPIJ005834 0.930 Ov-syvn1 0.820 Ov-syvn1 1.080 Ov-Rnd3 1.290
10 Ov-CHCHD7 0.760 Ov-MRPS5_F1R1 1.050 Ov-EIF3M 0.940 Ov-EIF2A 0.830 Ov-Vbp1 1.080 Ov-RIOK2 1.300
11 Ov-syvn1 0.770 Ov-Rnd3 1.060 Ov-Abi2 0.940 Ov-Abi2 0.830 Ov-slc25a40 1.080 Ov-usp10 1.300
12 Ov-EIF2A 0.770 Ov-UBE2F 1.100 Ov-MRPS5_F1R1 0.950 Ov-usp10 0.850 Ov-CHCHD7 1.090 Ov-MRM2 1.310
13 Ov-MRPS5_F1R1 0.770 Ov-RAD23B 1.130 Ov-RIOK2 0.960 Ov-CUL1 0.850 Ov-UBE2F 1.100 Ov-Dap3 1.310
14 Ov-RIOK2 0.800 Ov-CPIJ005834 1.130 Ov-usp10 0.970 Ov-EIF3M 0.850 Ov-RAD23B 1.100 Ov-CHCHD7 1.320
15 Ov-Ltv1 0.810 Ov-EIF2A 1.140 Ov-NOB1 1.000 Ov-Ltv1 0.860 Ov-Dap3 1.110 Ov-RNF7 1.320
16 Ov-CPIJ005834 0.810 Ov-syvn1 1.150 Ov-BTBD2 1.000 Ov-AP5Z1 0.860 Ov-ATPAF2 1.120 Ov-gk5 1.330
17 Ov-UBE2F 0.810 Ov-slc25a40 1.170 Ov-UBE2F 1.000 Ov-PTPN12 0.860 Ov-MRM2 1.130 Ov-Fam160a2 1.330
18 Ov-Vbp1 0.810 Ov-Sdhd 1.170 Ov-KCMF1 1.020 Ov-Snx25 0.860 Ov-Rnd3 1.130 Ov-Naa15 1.350
19 Ov-BTBD2 0.850 Ov-CUL1 1.180 Ov-MRM2 1.020 Ov-CSDE1 0.870 Ov-Ltv1 1.130 Ov-Sdhd 1.360
20 Ov-Rnd3 0.850 Ov-usp10 1.200 Ov-EIF2A 1.040 Ov-KCMF1 0.870 Ov-MRPS5_FR 1.130 Ov-Ltv1 1.360
21 Ov-NOB1 0.860 Ov-prrc1 1.230 Ov-Sdhd 1.040 Ov-Dnaja3 0.870 Ov-RNF7 1.130 Ov-CPIJ005834 1.370
22 Ov-MRPS5_FR 0.870 NAA15 1.230 NAA15 1.050 Ov-MRPS5_FR 0.880 Ov-Fam160a2 1.150 Ov-ATPAF2 1.380
23 Ov-UGP2 0.890 Ov-MRM2 1.230 Ov-Dap3 1.060 Ov-BTBD2 0.880 Ov-MRPS5_F1R1 1.170 Ov-CG9286 1.380
24 Ov-WBP2 0.890 Ov-MRPS5_FR 1.250 Ov-CSDE1 1.060 Ov-Rnd3 0.880 Ov-gk5 1.190 Ov-prrc1 1.410
25 Ov-slc25a40 0.890 Ov-timm 1.260 Ov-UGP2 1.070 Ov-ATPAF2 0.890 Ov-mts 1.210 Ov-EIF3M 1.410
26 Ov-Ppm1b 0.890 Ov-UGP2 1.280 Ov-syvn1 1.080 Ov-Vbp1 0.890 Ov-CG9286 1.210 Ov-MRPS5_FR 1.440
27 Ov-prrc1 0.900 Ov-rpf1 1.290 Ov-CUL1 1.080 Ov-prrc1 0.900 Ov-PTPN12 1.230 Ov-Abhd18 1.450
28 Ov-Tuba1a 0.900 Ov-BTBD2 1.300 Ov-CG9286 1.090 Ov-tollip 0.900 Ov-CPIJ005834 1.240 Ov-KCMF1 1.450
29 Ov-timm 0.910 Ov-Vbp1 1.300 Ov-C2CD2 1.110 Ov-Fam160a2 0.900 Ov-prrc1 1.250 Ov-rpf1 1.500
30 Ov-AP5Z1 0.920 Ov-Dap3 1.310 Ov-RAD23B 1.130 Ov-MRM2 0.900 Ov-Sdhd 1.250 Ov-MRPS5_F1R1 1.510
31 Ov-gk5 0.930 CHCHD 1.330 Ov-ATPAF2 1.140 Ov-Dap3 0.920 Ov-RPS18 1.280 Ov-CSDE1 1.510
32 Ov-CG9286 0.940 Ov-CG9286 1.330 Ov-rpf1 1.160 Ov-CG9286 0.940 Ov-Abhd18 1.310 Ov-C2CD2 1.510
33 Ov-Naa15 0.940 Ov-CSDE1 1.330 Ov-Dnaja3 1.160 Ov-UGP2 0.940 Ov-Abi2 1.320 Ov-SUCLG2 1.520
34 Ov-PCK1 0.950 Ov-Ppm1b 1.330 Ov-Ppm1b 1.160 Ov-RAD23B 0.950 Ov-tollip 1.330 Ov-UGP2 1.530
35 Ov-tollip 0.960 Ov-RNF7 1.340 Ov-SUCLG2 1.190 Ov-gk5 0.960 Ov-SUCLG2 1.350 Ov-WBP2 1.540
36 Ov-CUL1 0.960 Ov-PCK1 1.360 Ov-MRPS5_FR 1.190 Ov-Sdhd 0.970 Ov-rpf1 1.380 Ov-timm 1.550
37 Ov-ATPAF2 0.960 Ov-ATPAF2 1.370 Ov-Abhd18 1.200 Ov-WBP2 0.980 Ov-C2CD2 1.400 Ov-Abi2 1.550
38 Ov-Dap3 0.970 Ov-Abhd18 1.380 Ov-timm 1.220 Ov-RPS18 0.980 Ov-RpL23 1.420 Ov-PTPN12 1.580
39 Ov-TUBG1_FR 0.980 Ov-C2CD2 1.380 Ov-tollip 1.230 Ov-mts 1.030 Ov-CSDE1 1.430 Ov-BTBD17 1.650
40 Ov-usp10 0.980 Ov-KCMF1 1.420 Ov-RNF7 1.250 Ov-MRPS5_F1R1 1.040 Ov-WBP2 1.440 Ov-AP5Z1 1.670
41 Ov-CSDE1 1.010 Ov-TUBG1_FR 1.480 CHCHD 1.270 Ov-CPIJ005834 1.060 Ov-NOB1 1.450 Ov-mts 1.670
42 Ov-PTPN12 1.020 Ov-SUCLG2 1.500 Ov-Vbp1 1.280 Ov-TUBG1_FR 1.070 Ov-UGP2 1.450 Ov-RPS18 1.730
43 Ov-KCMF1 1.030 Ov-Rps27a_FR 1.520 Ov-Rps27a_FR 1.300 Ov-C2CD2 1.130 Ov-timm 1.460 Ov-Snx25 1.770
44 Ov-Snx25 1.030 Ov-Fam160a2 1.540 Ov-PTPN12 1.340 Ov-PCK1 1.190 Ov-Snx25 1.470 Ov-tollip 1.770
45 Ov-Dnaja3 1.050 Ov-Dnaja3 1.650 Ov-PCK1 1.390 Ov-rpf1 1.190 Ov-Tuba1a 1.550 Ov-BTBD2 1.780
46 Ov-Abhd18 1.070 Ov-PTPN12 1.660 Ov-Fam160a2 1.420 Ov-SUCLG2 1.200 Ov-BTBD17 1.560 Ov-AGL 1.800
47 Ov-Fam160a2 1.080 Ov-tollip 1.670 Ov-AGL 1.440 Ov-Tuba1a 1.290 Ov-Dnaja3 1.560 Ov-PCK1 1.810
48 Ov-rpf1 1.120 Ov-PIP4K2B 1.720 Ov-TUBG1_FR 1.440 Ov-AGL 1.330 Ov-AP5Z1 1.590 Ov-PIP4K2B 1.840
49 Ov-TUBG1_F1R1 1.130 Ov-AGL 1.750 Ov-mts 1.460 Ov-Abhd18 1.360 Ov-TUBG1_FR 1.610 Ov-Dnaja3 1.910
50 Ov-Rpl6 1.140 Ov-Snx20 1.770 Ov-wdr44 1.490 Ov-RpL23 1.410 Ov-Homer2 1.630 Ov-Snx20 1.930
51 Ov-SUCLG2 1.180 Ov-Rpl6 1.770 Ov-PIP4K2B 1.500 Ov-NOB1 1.470 Ov-PIP4K2B 1.710 Ov-NOB1 1.940
52 Ov-AGL 1.230 Ov-WBP2 1.800 Ov-TUBG1_F1R1 1.500 Ov-Rpl6 1.510 Ov-BTBD2 1.710 Ov-Homer2 2.010
53 Ov-C2CD2 1.250 Ov-TUBG1_F1R1 1.830 Ov-WBP2 1.650 Ov-Homer2 1.570 Ov-PCK1 1.710 Ov-TUBG1_FR 2.020
54 Ov-mts 1.250 Ov-mts 1.840 Ov-Rpl6 1.680 Ov-timm 1.660 Ov-AGL 1.720 Ov-wdr44 2.030
55 Ov-Homer2 1.370 Ov-Tuba1a 1.850 Ov-Snx20 1.700 Ov-TUBG1_F1R1 1.710 Ov-wdr44 1.750 Ov-TUBG1_F1R1 2.080
56 Ov-Snx20 1.400 Ov-wdr44 1.890 Ov-Tuba1a 1.700 Ov-Snx20 1.880 Ov-TUBG1_F1R1 1.760 Ov-RpL23 2.220
57 Ov-Rps27a_FR 1.420 Ov-AP5Z1 2.020 Ov-Homer2 1.820 Ov-PIP4K2B 2.050 Ov-Snx20 1.820 Ov-Rpl6 2.640
58 Ov-PIP4K2B 1.660 Ov-Snx25 2.060 Ov-Snx25 1.860 Ov-wdr44 2.180 Ov-Rps27a_FR 2.280 Ov-Rps27a_FR 2.800
59 Ov-wdr44 1.710 Ov-Homer2 2.100 Ov-AP5Z1 2.050 Ov-Rps27a_FR 2.800 Ov-Rpl6 2.640 Ov-Tuba1a 2.920
RefFinder
Nervous Allex Adult
Gene name Geomean of ranking values Gene name Geomean of ranking values Gene name Geomean of ranking values
1 Ov-RNF7 2.340 Ov-Naa15 2.170 Ov-EIF2A 1.970
2 Ov-RIOK2 2.550 Ov-CUL1 4.560 Ov-CUL1 4.420
3 Ov-CHCHD7 3.560 Ov-EIF2A 4.920 Ov-RAD23B 5.630
4 Ov-slc25a40 5.130 Ov-RIOK2 5.850 Ov-Vbp1 6.110
5 Ov-UBE2F 6.770 Ov-slc25a40 6.510 Ov-syvn1 6.320
6 Ov-BTBD17 7.140 Ov-Ppm1b 6.980 Ov-Ppm1b 6.500
7 Ov-Abi2 8.040 Ov-KCMF1 7.650 Ov-UBE2F 8.010
8 Ov-Ppm1b 8.710 Ov-usp10 7.650 Ov-slc25a40 9.260
9 Ov-syvn1 10.020 Ov-EIF3M 9.240 Ov-Ltv1 9.810
10 Ov-Naa15 10.580 Ov-Ltv1 9.390 Ov-Rnd3 10.260
11 Ov-EIF3M 12.350 Ov-Rnd3 12.330 Ov-RIOK2 11.470
12 Ov-EIF2A 12.420 Ov-Dap3 13.960 Ov-usp10 13.000
13 Ov-RPS18 15.200 Ov-syvn1 14.050 Ov-gk5 13.210
14 Ov-CUL1 15.740 Ov-UBE2F 14.460 Ov-MRM2 13.780
15 Ov-Ltv1 15.920 Ov-RpL23 15.600 Ov-Dap3 15.610
16 Ov-usp10 16.440 Ov-ATPAF2 16.460 Ov-RNF7 15.840
17 Ov-AP5Z1 18.110 Ov-gk5 16.790 Ov-CHCHD7 16.430
18 Ov-Snx25 19.580 Ov-prrc1 16.900 Ov-EIF3M 17.500
19 Ov-PTPN12 20.600 Ov-RPS18 17.150 Ov-Abi2 18.500
20 Ov-MRM2 21.160 Ov-Vbp1 17.390 Ov-CPIJ005834 18.830
21 Ov-BTBD2 21.160 Ov-RAD23B 17.820 Ov-Fam160a2 18.870
22 Ov-Dnaja3 22.350 Ov-MRM2 19.230 Ov-Sdhd 18.990
23 Ov-Rnd3 23.120 Ov-MRPS5_F1R1 20.170 Ov-Naa15 20.060
24 Ov-CSDE1 23.230 Ov-CHCHD7 20.220 Ov-RPS18 20.070
25 Ov-Vbp1 23.430 Ov-CPIJ005834 20.510 Ov-ATPAF2 22.590
26 Ov-MRPS5_FR 23.880 Ov-MRPS5_FR 23.270 Ov-prrc1 23.480
27 Ov-Sdhd 23.900 Ov-RNF7 23.770 Ov-CG9286 23.490
28 Ov-RAD23B 24.390 Ov-Abi2 24.500 Ov-BTBD17 24.110
29 Ov-ATPAF2 24.510 Ov-CG9286 25.190 Ov-MRPS5_F1R1 24.470
30 Ov-KCMF1 24.660 Ov-BTBD17 26.120 Ov-MRPS5_FR 27.340
31 Ov-RpL23 24.750 Ov-Fam160a2 26.450 Ov-RpL23 28.950
32 Ov-Fam160a2 26.300 Ov-Sdhd 26.650 Ov-Abhd18 29.040
33 Ov-prrc1 26.420 Ov-mts 31.030 KCMF1 29.500
34 Ov-tollip 30.000 Ov-NOB1 32.210 Ov-UGP2 29.830
35 Ov-MRPS5_F1R1 30.010 Ov-Abhd18 33.440 Ov-rpf1 29.920
36 Ov-UGP2 31.090 Ov-PTPN12 33.530 Ov-CSDE1 30.710
37 Ov-CG9286 31.990 Ov-CSDE1 33.740 Ov-C2CD2 31.240
38 Ov-Dap3 32.350 Ov-C2CD2 34.100 Ov-timm 32.860
39 Ov-WBP2 33.310 Ov-SUCLG2 34.490 Ov-NOB1 33.370
40 Ov-CPIJ005834 33.370 Ov-rpf1 35.440 Ov-SUCLG2 34.730
41 Ov-gk5 34.430 Ov-UGP2 35.770 Ov-BTBD2 38.610
42 Ov-NOB1 40.850 Ov-tollip 36.210 Ov-PTPN12 40.120
43 Ov-Tuba1a 41.290 Ov-BTBD2 38.930 Ov-WBP2 40.280
44 Ov-TUBG1_FR 41.490 Ov-timm 40.960 Ov-mts 43.380
45 Ov-PCK1 41.710 Ov-Dnaja3 42.420 Ov-PCK1 44.430
46 Ov-mts 42.380 Ov-WBP2 45.300 Ov-AP5Z1 44.510
47 Ov-CSDE1 45.520 Ov-Snx25 46.880 Ov-tollip 44.980
48 Ov-rpf1 45.730 Ov-TUBG1_FR 48.240 Ov-AGL 45.950
49 Ov-timm 46.230 Ov-Tuba1a 48.880 Ov-Snx25 47.140
50 Ov-SUCLG2 46.680 Ov-AGL 50.720 Ov-Dnaja3 47.720
51 Ov-Abhd18 48.230 Ov-AP5Z1 50.800 Ov-PIP4K2B 47.750
52 Ov-AGL 48.970 Ov-PCK1 51.830 Ov-Snx20 50.000
53 Ov-Rpl6 51.490 Ov-Homer2 52.170 Ov-TUBG1_FR 50.400
54 Ov-TUBG1_F1R1 53.430 Ov-PIP4K2B 52.240 Ov-wdr44 52.720
55 Ov-Homer2 53.490 Ov-wdr44 53.460 Ov-Rps27a_FR 53.820
56 Ov-Snx20 56.000 Ov-Rps27a_FR 53.820 Ov-TUBG1_F1R1 54.240
57 Ov-PIP4K2B 57.250 Ov-TUBG1_F1R1 54.710 Ov-Homer2 54.440
58 Ov-wdr44 58.250 Ov-Snx20 56.750 Ov-Rpl6 55.440
59 Ov-Rps27a_FR 58.490 Ov-Rpl6 57.710 Ov-Tuba1a 57.970

The results from the four approaches were integrated using RefFinder. The integration of the results was done calculating the geometric mean of the rank of each gene in all algorithms. In boldface common genes for the first 10 positions are highlighted for each algorithm. Integration of the different algorithms using the geometric mean of the rank of each gene is also provided. Stab. value, Stability value; Std. dev, Standard deviation; Geomean, geometric mean.

geNorm analysis. Ov-CHCHD7 and Ov-RNF7 were identified as the two most correlated genes and therefore scored as the most stable RGs for the Nervous tissues (Table 2), followed by Ov-RIOK2, Ov-UBE2F, Ov-slc25a40, Ov-BTBD17, Ov-Naa15, Ov-Ppm1b, Ov-CUL1, Ov-Snx25, and Ov-Dnaja3 (Table 2). Interestingly, the genes Ov-RpL23, Ov-Rpl6, Ov-TuBg1-F1R1, and Ov-Rps27a-FR, recently utilized as RGs in RT-qPCR experiments in cephalopods (Supplementary Table 2), were demonstrated to be among the most unstable genes (Table 2).

When additional tissues were considered (Allex), Ov-CUL1 and Ov-Naa15 were identified as the two most correlated genes, followed by Ov-RIOK2, Ov-slc25a40, Ov-usp10, Ov-KCMF1, Ov-Ppm1b, Ov-EIF2A, Ov-EIF3M, Ov-CHCHD7, and Ov-syvn1. The least stable genes included Ov-Rpl6, Ov-Rps27a-FR, Ov-TUBG1-F1R1, and Ov-TUBG1-FR (Table 2). In the analysis of all the tissues (Adult), Ov-Vbp1, Ov-EIF2A, Ov-RAD23B, Ov-syvn1, Ov-CUL1, Ov-Ppm1b, Ov-CHCHD7, Ov-RIOK2, Ov-UBE2F, and Ov-RNF7 emerged as the most stable genes. Similarly to Nervous and Allex, Ov-TUBG1-F1R1, Ov-TUBG1-FR, Ov-RpL23, Ov-Rpl6, Ov-Rps27a-FR, and Ov-Tuba1a were the least stable genes. Among the 10 most stable genes, only Ov-Ppm1b, Ov-CHCHD7, and Ov-CUL1 were shared by the three groups, while Ov-RIOK2, Ov-slc25a40, Ov-Naa15, Ov-RNF7, Ov-UBE2F, and Ov-EIF2A were shared between two groups (Table 2).

NormFinder analysis. We identified Ov-RIOK2, Ov-slc25a40, Ov-RNF7, Ov-CHCHD7, Ov-Ppm1b, Ov-syvn1, Ov-UBE2F, Ov-Naa15, Ov-BTBD17, and Ov-Abi2 as the most stable genes (Nervous, Table 2). For the Allex and Adult groups, some of the top genes in Nervous ranked at lower values (e.g., Ov-RIOK2 and Ov-slc25a40), while others were considered more stable (e.g., Ov-EIF2A and Ov-CUL1; Table 2). Ov-Naa15, Ov-EIF2A, Ov-CUL1, and Ov-UBE2F were identified as stable reference genes in both groups, while Ov-RIOK2, Ov-slc25a40, Ov-Ppm1b, and Ov-syvn1 were shared in all the considered tissue groups (Table 2).

BestKeeper analysis. Ov-Abi2, Ov-Ltv1, and Ov-gk5 were shown to be the most stable genes, which were shared between two groups, while Ov-RPS18, Ov-RpL23, Ov-EIF3M, and Ov-BTBD17 were shared between the three groups (see Table 2). Ov-RPS18 and Ov-RpL23 were identified as suitable reference genes by this algorithm.

Delta Ct method. Ov-RIOK2, Ov-RNF7, Ov-slc25a40, Ov-CHCHD7, Ov-UBE2F, Ov-Ppm1b, Ov-Naa15, Ov-BTBD17, Ov-syvn1, and Ov-EIF2A emerged as the most stable genes for the Nervous tissues (Table 2). However, several other genes showed a comparable standard deviation (Table 2). Ov-Naa15, Ov-EIF2A, Ov-Ppm1b, Ov-CUL1, Ov-RIOK2, Ov-KCMF1, Ov-usp10, Ov-EIF3M, Ov-syvn1, and Ov-Vbp1 were selected as references for the Allex group. When all tissues were considered (Adult), Ov-EIF2A, Ov-CUL1, Ov-Ppm1b, Ov-Vbp1, Ov-syvn1, Ov-slc25a40, Ov-UBE2F, Ov-RAD23B, Ov-Rnd3, and Ov-RIOK2 were identified as the most stable reference genes. Ov-slc25a40, Ov-Naa15, Ov-Vbp1, Ov-CUL1, and Ov-UBE2F were shared between two groups, while Ov-Ppm1b, Ov-syvn1, Ov-RIOK,2 and Ov-EIF2A were shared between the three groups (Table 2).

 Comprehensive ranking of the reference genes

By comparing the 10 most stable genes identified by the four approaches in the same tissue group (Nervous), Ov-RIOK2, Ov-UBE2F, Ov-slc25a40, Ov-Naa15, and Ov-Ppm1b were identified as common in at least three algorithms, while Ov-CHCHD7, Ov-RNF7, and Ov-BTBD17 resulted from all the four methods (Table 2). When arms but not tips were included as tissues (Allex group), Ov-CUL1, Ov-Naa15, Ov-RIOK2, Ov-usp10, Ov-KCMF1, Ov-EIF2A, and Ov-Ppm1b (Table 2) emerged as the best reference genes using the three approaches, while Ov-EIF3M was shared among the four methods (Table 2). The analysis performed considering the Adult tissues led to the identification of Ov-RAD23B, Ov-EIF2A, Ov-Vbp1, Ov-syvn1, Ov-CUL1, Ov-Ppm1b, and Ov-UBE2F as reference genes by the three approaches, but none of them were shared in all the considered methods.

The results from the four approaches were integrated using RefFinder (Xie et al., 2012). Overall, the top 10 most stable genes in the Nervous tissues were Ov-RNF7, Ov-RIOK2, Ov-CHCHD7, Ov-slc25a40, Ov-UBE2F, Ov-BTBD17, Ov-Abi2, Ov-Ppm1b, Ov-syvn1, and Ov-Naa15 (Table 2). For Allex, the most stable genes were Ov-Naa15, Ov-CUL1, Ov-EIF2A, Ov-RIOK2, Ov-slc25a40, Ov-Ppm1b, Ov-KCMF1, Ov-usp10, Ov-EIF3M, and Ov-Ltv1 (Table 2). When all tissues were considered (Adults), Ov-EIF2A, Ov-CUL1, Ov-RAD23B, Ov-Vbp1, Ov-syvn1, Ov-Ppm1b, Ov-UBE2F, Ov-slc25a40, Ov-Ltv1, and Ov-Rnd3 were identified as reference genes, with Ov-EIF2A proving to be the best reference gene (see the geometric mean of the rank, Table 2). We also plotted the raw Ct for the best RGs identified (Supplementary Figure 4). The most stable genes shared by the combination of tissues were Ov-Naa15 and Ov-RIOK2 (Nervous and Allex); Ov-CUL1, Ov-EIF2A, and Ov-Ltv1 (Allex and Adult); and Ov-syvn1 and Ov-UBE2F (Nervous and Adult). Meanwhile, the Ov-slc25a40 and Ov-Ppm1b results were shared among the three groups (Table 2).

 Reference genes validation

To investigate the reliability of the selected candidate RGs, the expression profiles of nine target genes (i.e., Ov-Naa15, Ov-Ltv1, Ov-CG9286, Ov-EIF3M, Ov-NOB1, Ov-CSDE1, Ov-Abi2, Ov-Homer2, and Ov-Snx20) were assessed in tissues belonging to the nervous system. The role of these genes (see Supplementary Info: Selected target genes for validation) is still unknown in cephalopods. The selection was based on their known functions in different organisms, particularly those related to their involvement in neuronal signaling, cytoskeleton functions, axon guidance, synaptogenesis, and behavioral plasticity.

This also allowed comparison of gene expression profiles among brain masses and peripheral ganglia (Nervous). The gastric ganglion (GG) was considered as the reference ‘tissue’. A combination of the two most stable (Ov-RNF7 and Ov-RIOK2), the most stable (Ov-RNF7), and the least stable (most unstable; Ov-Rps27a-FR) RGs was used to normalize the expression of the target genes.

When the best RGs combination and the most stable gene were used for normalization of the expression of target genes in the nervous system of O. vulgaris, similar expression profiles were obtained for Ov-CSDE1 and Ov-Homer2 in the SEM, SUB, and OL, but the StG showed a significantly lower expression (Figure 2). A similar trend was also highlighted for Ov-Snx20 (Figure 2) that showed a lower expression in the StG compared to the SEM and SUB. No significant differences resulted for the other target genes considered (Figure 2).

Relative expression levels of target genes: Ov-Naa15, Ov-Ltv1, Ov-CG9286, Ov-EIF3M, Ov-NOB1, Ov-CSDE1, Ov-Abi2, Ov-Homer2, and Ov-Snx20 across all considered tissues (optic Lobe: OL; supraesophageal mass: SEM; suboesophageal mass: SUB; stellate ganglion: StG) normalized by the most stable reference gene combination (white bars), the most stable gene (grey), and the most unstable gene (black). Significant differences were assessed after ANOVA (p < 0.05, Bonferroni post hoc). See main text for details.

When the least stable reference gene Ov-Rps27a-FR was used for normalization, none of the nine genes investigated showed any significant change in expression except for Ov-Homer2, which appeared to be less expressed in the StG compared to the OL (Figure 2).

 Discussion

Exploring gene expression in the nervous system and other tissues helps to find molecular correlates of biological and neural plasticity, learning, and memory. In cephalopods, the study of the molecular machinery occurring in these processes is still limited. Few studies of cuttlefish (Agin et al., 2000, 2001, 2003; Focareta et al., 2014; Focareta and Cole, 2016; Bian et al., 2018), squid (Giuditta et al., 2002; Kimbell and McFall-Ngai, 2003; Burbach et al., 2019), and octopus (Zarrella, 2011; Zarrella et al., 2015; van Giesen et al., 2020; see also Prado-Álvarez et al., 2022) have been based on an exiguous number of specific candidate molecules involved in given functions or biological aspects of cephalopod plasticity.

Despite the availability of a few candidate RGs (review in Supplementary Table 2), the application of qRT-PCR in O. vulgaris also appears limited. Our approach was to expand the list of potential stable RGs in octopus through the use of the available transcriptomes (Petrosino, 2015; Petrosino et al., 2015, 2022), with the aim of facilitating a large-scale analysis of gene expression profiles under various conditions in different tissues.

We focused on genes that demonstrated stability and a uniform predicted expression within different tissues (peripheral and central nervous system and appendages). We explored their relative gene expression through qRT-PCR experiments using a subset of target genes. This approach allowed us to identify the most extensive set of stable reference genes currently available for the adult O. vulgaris.

Through in silico analysis of the octopus transcriptome, we found more than 2000 candidate RGs. However, we tested less than hundreds because of limitations in gene annotation. We identified a list of stable and uniformly expressed RGs across different body parts in adult individuals and in tissues including the nervous tissues (e.g., brain, gastric and stellate ganglia, and arm; Figure 1). The gene expression profiles of these potential RGs (n = 59) were assessed via qRT-PCR, and their stability was calculated and analyzed using different algorithms. The analysis of potential RGs in O. vulgaris revealed that there was no single reference gene that exhibited a constant expression level in all the samples, similarly to what has been reported in other organisms (e.g., Guo et al., 2014; Gao et al., 2017; Jin et al., 2019).

Via RefFinder, we identified RGs specific to the nervous system (Nervous, Ov-RNF7, and Ov-RIOK2), all tissues but the arm tips (Allex, Ov-Naa15, and Ov-CUL1), or those that are transcriptionally stable across all considered tissues (Adult, Ov-EIF2A, and Ov-CUL1, Table 2). In addition, Ov-slc25a40 and Ov-Ppm1b were identified as shared best reference genes in the Nervous, Allex, and Adult groups of tissues (Table 2). Notably, the arm tips showed the highest variation in gene expression among the analyzed anatomical structures, likely due to the biological peculiarities of the octopus’ arm that maintains the ability of regeneration and indeterminate growth throughout adult ontogeny (Fossati et al., 2013, 2015; Nödl et al., 2015; Zullo et al., 2017; Tarazona et al., 2019; e.g., Zullo et al., 2019; van Giesen et al., 2020; see also De Sio and Imperadore, 2023).

The identified RGs are related to ubiquitination, rRNA processing, translation, and post-translational protein modifications, which are housekeeping functions in line with the typical references. Interestingly, none of them has ever been used as references in cephalopods before. Our approach—i.e., a large number of candidate transcripts and several tissues belonging to putatively different cell types (Styfhals et al., 2022)—provided more than 70 candidate RGs for O. vulgaris.

We also validated RGs by assessing the expression profiles of nine target genes (Ov-Naa15, Ov-Ltv1, Ov-CG9286, Ov-EIF3M, Ov-NOB1, Ov-CSDE1, Ov-Abi2, Ov-Homer2, and Ov-Snx20) in different tissues of the octopus nervous system. The expression after normalization by Ov-RNF7 and Ov-RIOK2 (the most stable RGs) differed from that of Ov-Rps27a-FR (the least stable gene), which is commonly used as an RG for data normalization (Figure 2).

In conclusion, we utilized different algorithms to evaluate the expression profiles of tens of candidate RGs of O. vulgaris. We identified those that can be used in the normalization of the qRT-PCR data and suggested RGs that can be used cautiously with different tissue groups.

Our findings will aid future investigations of the transcriptional landscape of cephalopods and facilitate the study of the molecular basis of neural plasticity and other phenomena.

 Data availability statement

The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding author.

 Ethics statement

Ethical review and approval was not required for the animal study because killing animals solely for tissue removal does not require authorization from the National Competent Authority under Directive 2010/63/EU (European Parliament & Council of the European Union, 2010) and its transposition into National legislation. Sampling of octopuses from artisanal fishermen included in this study was authorized by the local Animal Welfare Body (Ethical Clearance: case 5/2021/ec AWB-SZN).

 Author contributions

PI and SC performed the experiments, data curation, and analysis and contributed to the study conceptualization. VA and CM contributed to the experiments and data curation. PI, SC, and GF contributed to writing the original draft. GF and GP contributed to the study conceptualization, investigation, writing, and funding acquisition. All authors contributed to the article and approved the submitted version.

 Funding

This work was supported by the Stazione Zoologica Anton Dohrn intramural research fund granted to GF and GP and by a HSA-Ceph 1/2019 grant to GP. VA was supported by a short-term fellowship from the Stazione Zoologica Anton Dohrn.

 Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be identified as a potential conflict of interest.

 Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

We thank Elena Baldascino, Giuseppe Petrosino, and Remo Sanges for their advice and support.

 Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnmol.2023.1091305/full#supplementary-material

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