Expression of genes involved in brain GABAergic neurotransmission in three-spined stickleback exposed to near-future CO2.

Change in the activity of the main inhibitory receptor, GABAA, has been suggested to be a general mechanism behind the behavioural alterations reported in ocean acidification studies on fish. It has been proposed that regulatory acid-base mechanisms in response to high CO2 alter the neuronal Cl- and HCO3- gradients that are important for GABAA receptor function. Here, we report a comprehensive analysis of gene expression of GABAA receptor subunits and of genes involved in GABAergic transmission in the brain of fish exposed to near-future CO2. Altogether, 56 mRNA transcripts were quantified in brains of three-spined stickleback (Gasterosteus aculeatus) kept in control pCO2 (333 ± 30 μatm CO2) or at high pCO2 levels (991 ± 57 μatm) for 43 days. The gene expression analysis included GABAA receptor subunits (α1-6, β1-3, γ1-3, δ, π and ρ1-3), enzymes and transporters involved in GABA metabolism (GAD1-2, GABAT and GAT1-3), GABAA receptor-associated proteins (GABARAP and GABARAPL), ion cotransporters (KCC1-4, NKCC1, ClC21-3, AE3 and NDAE) and carbonic anhydrase (CAII). Exposure to high CO2 had only minor effects on the expression of genes involved in GABAergic neurotransmission. There were significant increases in the mRNA levels of α family subunits of the GABAA receptor, with a more pronounced expression of α12, α3, α4 and α6b. No changes were detected in the expression of other GABAA subunits or in genes related to receptor turnover, GABA metabolism or ion transport. Although the minor changes seen for mRNA levels might reflect compensatory mechanisms in the high-CO2 conditions, these were apparently insufficient to restore normal neural function, because the behavioural changes persisted within the time frame studied.

Introduction

The ongoing increase of CO2 levels in the atmosphere and the resultant changes in the ocean chemistry are leading to what is commonly referred to as ocean acidification. In their most recent assessment report, the Intergovernmental Panel on Climate Change (IPCC) predicted an increase in the atmospheric CO2 concentration from the present level of 400 μatm to 800–1150 μatm within this century (Collins ). These changes in the atmosphere can then lead to a decrease in average ocean pH of up to 0.32, with severe consequences for marine ecosystems (Doney ; Ciais ).

Numerous studies on ocean acidification have reported alterations in behaviour and sensory responses in both tropical and temperate fish after sustained exposure to predicted near-future CO2 levels. The sensory systems affected include olfaction, hearing and vision (Munday ; Dixson ; Ferrari ; Simpson ; Forsgren ; Chung ; Rossi ). Other neural challenges detected involve brain lateralization (Domenici ; Nilsson ; Jutfelt ; Lai ), learning (Ferrari ), anxiety (Hamilton ), boldness and activity (Munday ; Jutfelt ). Nonetheless, a few studies on temperate species (Atlantic cod, Gadus morhua, and Atlantic silverside, Menidia menidia) find resilience against elevated ambient CO2, which can be related to adaptations in species experiencing a strong variation in the partial pressure of CO2 (pCO2) in their current habitat (Murray ; Jutfelt and Hedgärde, 2015).

Studies using an antagonist (gabazine) or an agonist (muscimol) of the γ-aminobutyric acid receptor A (GABAA receptor) have indicated that an altered function of this inhibitory neurotransmitter receptor underlies these behavioural abnormalities. In particular, gabazine has been found to restore much of the altered behaviours (Nilsson ; Chivers ; Chung ; Hamilton ; Lai ). The GABAA receptor is an ion channel with conductance for Cl− and HCO3−, and these are the same two ions that are involved in pH regulation in fish exposed to elevated CO2. Thus, when fish are exposed to high CO2 levels, the reduction in blood pH is countered by accumulation of HCO3− in blood and tissues (Ishimatsu ; Brauner and Baker, 2009), accompanied by a release of H+ and Cl− over the gills into the ambient water. This led Nilsson to suggest that pH-regulatory changes in fish exposed to high CO2 alter the gradients of Cl− and HCO3− over neuronal membranes in a way that renders some GABAA receptors depolarizing (i.e. excitatory) rather than hyperpolarizing (i.e. inhibitory).

The GABAA receptor is the major inhibitory neurotransmitter receptor in the vertebrate brain, and ~30% of all synapses respond to GABA (Bloom and Iversen, 1971; Kaila, 1994; Somogyi ; Sieghart and Sperk, 2002). It is expressed throughout the central nervous system, and its role has been linked to important processes such as brain development, neural migration and excitability, network interaction in the cerebral cortex, memory, learning, cognition, vigilance and behaviour (Sieghart and Sperk, 2002; Makkar ; Luscher ).

The GABAA receptor is a ligand-gated ion channel composed by pentameric assemblies of subunits, arranged to form a central selective anion channel (Bormann ). To date, a total of 19 genes have been found to encode for GABAA receptor subunits in mammals, namely α1–6, β1–3, γ1–3, δ, π, ε, θ and ρ1–3 (reviewed by Farrant and Kaila, 2007). However, information on GABAA receptor composition in fish is very scarce. An immunochemistry analysis has confirmed a widespread distribution of the receptor in Atlantic salmon (Salmo salar) brain (Anzelius ). Ellefsen surveyed mRNA transcripts of GABAA subunits in the anoxia-tolerant crucian carp (Carassius carassius), quantifying the effect of anoxia on the mRNA expression of subunits α1–6, β2, γ2 and δ1–2. Cocco recently profiled the expression of GABAA subunits in zebrafish (Danio rerio) brain, showing α1, β2, γ2 and δ to be the most prominently expressed subunits.

The combination of different subunits in the pentameric GABAA receptor can give rise to diverse receptor subtypes, with distinct physiological and pharmacological properties (Herd ). Generally, a combination of the two most highly expressed subunits, α and β, is sufficient to form a functional GABAA receptor, while the presence of a third subunit is also often observed (reviewed by Farrant and Kaila, 2007). Indeed, the most predominant GABAA receptor stoichiometry among mammals is a heteromeric receptor composed of two α, two β and one γ subunit, with the most common combination being α1, β2 and γ2 subunits (Fritschy ; McKernan and Whiting, 1996; Pirker ; Sieghart and Sperk, 2002; Whiting, 2003; Benke ). In other GABAA receptors, the γ subunit is replaced by δ, π or ε (forming αβδ, αβπ or αβε), whereas the θ subunit might replace the β subunit (Sieghart and Sperk, 2002; reviewed by Farrant and Kaila, 2007).

Activation of the receptor takes place when two GABA molecules bind to the extracellular domains between the α and β subunit, triggering a rapid conformational change in the transmembrane region that allows movement of Cl− and HCO3− through the channel (Bormann ). Intracellular and extracellular [Cl−] and [HCO3−] are important for setting the EGABAA reversal potential. In most mature mammalian neurons, GABAA receptor activation reduces the excitatory neurotransmission through membrane hyperpolarization caused by a net influx of negatively charged Cl− ions into the neuron, with a smaller component of HCO3− flowing out (reviewed by Farrant and Nusser, 2005). However, in the fetal mammalian brain, and in some conditions of neuronal overactivity, such as in epilepsy, anion gradients are reversed as a result of increased intracellular [Cl−] and/or intracellular [HCO3−] linked to a different or altered expression of ion transporters. The Cl− gradient across neuronal membranes has been shown to depend largely on two ion-exchange mechanisms (Delpire, 2000). The K+–Cl− cotransporters (KCC) are responsible for K+-coupled Cl− outward transport in central neurons. In contrast, the Na+–K+–2Cl− cotransporter (NKCC) family is responsible for transporting Cl− into cells through a Na+–K+ -coupled Cl− inward transport. The high intracellular [Cl−] that makes GABAA receptors excitatory in developing fetal brains has been linked to an upregulation of NKCC1 mRNA expression, whereas the low intracellular [Cl−] in mature neurons is attributable to an upregulation of KCC2 mRNA expression (Delpire, 2000).

Additional mechanisms may influence anion gradients across neural membranes. One is the voltage-gated Cl− channel 2 (ClC2), which has an important role in determining the intracellular [Cl−] through chloride extrusion in neurons expressing inhibitory GABAA receptors and directly reducing excitability (Ratté and Prescott, 2011). The anion exchanger 3 (AE3) affects both [Cl−] and [HCO3−] by exchanging Cl− and HCO3− over cell membranes, while the Na+-driven anion exchanger (NDAE) can influence intracellular [Cl−] through a Na+-coupled HCO3− outward transport (Romero ; Casey ). Finally, intracellular [HCO3−] is influenced by the rate of hydration of intracellular CO2 through the action of carbonic anhydrases (CAs; Lindskog, 1997).

The function of the GABAergic transmission is also affected by the timing of GABA release and clearance in the extracellular space. Extracellular GABA in not subject to enzymatic degradation, but its turnover relies on diffusion and uptake by specific GABA transporters, GAT1–3 (reviewed by Scimemi, 2014). GABA is susequently processed in the neurons by GABA aminotransferase (GABAT) and glutamate decarboxylases (GAD1–2, also known as GAD67 and GAD65; Delpire, 2000). The clustering, targeting and degradation of the GABAA receptor in the post-synaptic area is regulated by GABAA receptor-associated proteins (GABARAP and GABARAPL; Nemos ). Both proteins belong to a microtubule-associated protein family.

Interestingly, in some fish the behavioural dysfunctions observed in hypercapnia set only in after several days of exposure to high CO2 and then persist for several days after normal CO2 levels have been restored (Munday ). This led Lai to propose that gene transcription may be involved. This could include the expression of GABAA receptor genes and the genes encoding for proteins responsible for establishing Cl− and HCO3− ion gradients over neuronal membranes.

The three-spined stickleback (Gasterosteus aculeatus) should provide a good model for investigating the effects of CO2 on gene expression because its genome has been sequenced and annotated (Kingsley, 2003). Importantly, sustained high-CO2 exposure has been shown to alter three-spined stickleback behaviour (Jutfelt ; Näslund ), and this impairment can be reversed by treatment with the GABAA antagonist gabazine (Lai ). Thus, as in other fishes, the neural effects of high-CO2 exposure on three-spined stickleback appear to depend on altered GABAA receptor function.

We hypothesized that the proposed ion disturbances leading to altered GABAA receptor function in brains of hypercapnic fish lead to alterations in the expression of genes related to the function of these systems. Consequently, we have quantified the mRNA transcription levels of 56 genes involved in GABAergic transmission and anion regulation in brains of three-spined stickleback exposed to present and predicted future CO2 levels. Our analysis included the expression of 28 genes encoding for the GABAA receptor subunits in three-spined stickleback, six for GABA transporters (GAT1–3), GABA aminotransferase (GABAT), three for glutamate decarboxylases (GAD1–2), three for GABAA receptor-associated protein and protein-like (GABARAP and GABARAPL), 14 for ion cotransporters (KCCs, NKCCs, ClC2s, AE3 and NDAE) and two for carbonic anhydrases (CAII and CAVII).

Materials and methods

Experimental animals

One hundred marine female three-spine sticklebacks weighing 1.24 ± 0.07 g were caught in Fiskebäckskil, Sweden, during July–August 2012 and were randomly distributed into ten 25 litre glass aquaria of 10 individuals each in Sven Lovén Centre for Marine Sciences, Kristineberg, Sweden. The aquaria were constantly supplied with water at 17.6 ± 1.2°C (SD) and salinity 24.2 ± 3.4 PSU (SD). Chemical parameters such as salinity, oxygen saturation, temperature and pCO2 were measured daily, and alkalinity was measured weekly. Further details are given by Jutfelt , who published behavioural data from the same groups of fish.

The fish were divided into two experimental groups (distributed in duplicate aquaria for each group), where one group was exposed to increased pCO2 (991.3 ± 56.6 μatm), while the other served as a control and was exposed to present-day CO2 levels (333.0 ± 30.0 μatm pCO2; Jutfelt ). Fish were kept in a 14 h–10 h light–dark cycle and fed ad libitum twice daily with frozen Artemia nauplii. The exposures lasted for 43 days. Upon termination of exposure and behavioural studies, 12 individuals weighing 2.06 ± 0.14 g from the control group and 12 individuals weighing 1.58 ± 0.18 g from the CO2 group were killed using an overdose of 2-phenoxyethanol in seawater. For the gene expression analysis, the whole brains were rapidly dissected, snap-frozen in liquid nitrogen and stored at −80°C until further use. Prior to downstream experiments, samples were transferred on dry ice to the Department of Biosciences, University of Oslo, Norway.

Animal experiments were carried out in accordance with national regulations and were approved by the ethical committee on animal experiments of Gothenburg, Sweden (ethical permit: Fredrik Jutfelt 100-2010 and 151-2011).

Quantification of mRNA expression using qPCR

RNA extraction and cDNA synthesis

Total RNA was extracted from brains using 15 µl/mg TRIzol® reagent (Invitrogen, Carlsbad, CA, USA). A NanoDrop 2000 UV-Vis Spectrophotometer (Thermo Fisher Scientific, Rockland, DE, USA) and a 2100 BioAnalyzer with RNA 6000 Nano Lab Chip Kit (Agilent Technologies, Palo, Alto, CA, USA) were used to assess the quantity and quality of the extracted total RNA. Prior to cDNA synthesis, 1 µg of total RNA was treated with TURBO DNase using TURBO DNAse-free kit (Ambion Applied Biosystems, Foster City, CA, USA) to avoid any remnants of genomic DNA. Subsequently, cDNA was synthesized in duplicate from each total RNA sample using SuperScript III reverse transcriptase (Invitrogen) and oligo(dT)18 in a total reaction volume of 20 µl. All procedures were carried out in accordance with the manufacturer's protocols.

Real-time RT-PCR primer design

To our knowledge, expression analyses of the GABAA subunits or genes linked with the GABAA activity studied here have previously not been described in stickleback. Therefore, a total of 56 gene-specific real-time rt-PCR (qPCR) primer pairs were designed from stickleback gene sequences retrieved from the Ensembl database (http://www.ensembl.org/index.html; see Table 1 for accession numbers). For each transcript, a minimum of three primer pairs were initially designed for each nucleotide sequence using Primer3 (http://primer3.ut.ee) and synthesized by ThermoScientific (Ulm, Germany). Emphasis was put on designing primers spanning exon–exon junctions to avoid amplification of any remnant genomic DNA. All primers were analysed for crossing point (Cp) values, primer efficiencies (E) and melting peaks, and their products were sequenced by GATC (Cologne, Germany), ensuring amplification of a single amplicon. The primer pairs showing the highest efficiency, lowest crossing point value and a single melting peak curve were selected for qPCR and are listed in Table 1.

Table 1:

Primer sequences for qPCR in three-spined stickleback

Gene	GenBank ID		Primers for real-time PCR
(A)
					E	Cp
Ubiquitin	ENSGACG00000008021	ubc	F	AGACGGGCATAGCACTTGC	1.894 ± 0.001	22.17 ± 0.06
			R	CAGGACAAGGAAGGCATCC
Ribosomal protein L13A	ENSGACT00000012382	rpl13A	F	CACCTTGGTCAACTTGAACAGTG	1.897 ± 0.006	21.86 ± 0.16
			R	TCCCTCCGCCCTACGAC
GABAAα1 (1of2)	ENSGACT00000027474	α11	F	GGCAGAGTGTGGATTCTGGT	1.896 ± 0.003	25.62 ± 0.11
			R	GGACGGACTCTCTGTTGAGC
GABAAα1 (2of2)	ENSGACT00000027475	α12	F	GCTATGACAATCGCCTCAGG	1.878 ± 0.000	26.75 ± 0.17
			R	TTGTGGAAGAAGGTGTCGGG
GABAAα2	ENSGACT00000024778	α2	F	GAGGATTTCCCCATGGACTT	1.910 ± 0.002	28.14 ± 0.21
			R	CTCCTTCCACCTCCACAGAG
GABAAα3	ENSGACT00000026865	α3	F	CACCCTGAGCATCAGTGCTA	1.846 ± 0.002	32.11 ± 0.27
			R	CGTCGACGATTCTCTTCTCC
GABAAα4	ENSGACT00000024781	α4	F	TTTTGGACCGACTTCTGGAC	1.882 ± 0.001	31.19 ± 0.19
			R	ATTTCCACATCCGAGACAGG
GABAAα5 (1of2)	ENSGACT00000018222	α51	F	TCCCGCCTCAATCAATACCA	1.865 ± 0.003	31.73 ± 0.22
			R	CGGCATGTAGGTCTGGATGA
GABAAα5 (2of2)	ENSGACT00000019800	α52	F	ATGCCTATCCGGTGTCAGAG	1.877 ± 0.001	28.92 ± 0.13
			R	TCAGGTAGAAGTGGGCCATC
GABAAα6a	ENSGACT00000024057	α6a	F	GGTCCATTTCCACCTGCAGA	1.838 ± 0.006	33.83 ± 0.03
			R	GCTCAAGGTGGTCATGGTCA
GABAAα6b	ENSGACT00000027476	α6b	F	CGCCTGATGAACTTCCCCAT	1.898 ± 0.001	26.48 ± 0.22
			R	GACACCGTCTGACCGATGAG
GABAAβ1	ENSGACT00000017426	β1	F	GGCGTGGAAAACATTGAACT	1.885 ± 0.005	27.08 ± 0.17
			R	AGACCCAGGACAAGATGGTG
GABAAβ2 (1of2)	(i) ENSGACT00000024053	β21i	F	AAGATGAGACCCGACCCCAA	1.906 ± 0.000	27.42 ± 0.14
			R	TGCTCGCCTAGTCCTAATGC
	(ii) ENSGACT00000024054	β21ii	F	ATCCCGAAACCGCCTCAAAA	1.894 ± 0.003	27.68 ± 0.14
			R	CCTGTCCACGGTTTCCTTCA
GABAAβ2 (2of2)	ENSGACT00000027477	β22	F	CGCTGCTTGTATGATGGACC	1.907 ± 0.009	29.63 ± 0.52
			R	AGGCAGTTCGATCTTGTCCA
GABAAβ3 (1of2)	(i) ENSGACT00000018209	β31at	F	AGGGATACGACATCCGTCTG	1.811 ± 0.002	34.34 ± 0.20
	(ii) ENSGACT00000018213		R	CGTAGGCCAGTCTCTTGTCC
GABAAβ3 (2of2)	(i) ENSGACT00000019821	β32at	F	ACGTACATGCCATCGATCCT	1.853 ± 0.000	28.37 ± 0.04
	(ii) ENSGACT00000019826		R	GGTGTTGATCGTGGTCATCG
	(iii) ENSGACT00000019833
GABAAγ1	ENSGACT00000026662	γ1	F	ATCAATTACCGGTGGCAGAG	1.820 ± 0.000	29.71 ± 0.09
			R	GGAGACCCAAGACAAGACCA
GABAAγ2	(i) ENSGACT00000027471	γ2at	F	GACAAACCAAGAAGGGCAAA	1.874 ± 0.001	29.59 ± 0.10
	(ii) ENSGACT00000027472		R	GGCACAATGTTGGTCATCTG
	(iii) ENSGACT00000027473
GABAAγ3 (1of2)	ENSGACT00000018227	γ31	F	GCTGTCTGTCCTTCTCACCT	1.820 ± 0.003	33.08 ± 0.11
			R	CGCAGCTTCTTGTCGTACTC
GABAAγ3 (2of2)	ENSGACT00000019780	γ32	F	GGCTCCGAAACACAACAGAT	1.858 ± 0.014	31.51 ± 0.28
			R	ATGGTGAAGTAGCCCATTCG
GABAAδ	ENSGACT00000007260	δ	F	CTGGAGCTCTCCCAGTTCAC	1.917 ± 0.003	25.02 ± 0.16
			R	GCAGGATGGAAGGCATGTAT
GABAAπ (1of2)	ENSGACT00000003740	π1	F	TTCTGCCTCCCACCATTCAT	1.835 ± 0.002	32.40 ± 0.31
			R	TTGTTGCCCTCGAAACCAAG
GABAAπ (2of2)	ENSGACT00000024472	π2	F	AGGCCATCGATGTTTACCTG	1.883 ± 0.003	32.33 ± 0.13
			R	CGAAGCTCCCTGTGTAGGTC
GABAAρ1 (1of2)	ENSGACT00000012171	ρ11	F	GTCACTGTTACCGCCATGTG	1.830 ± 0.002	29.3 ± 0.15
			R	TGGTGGTGTGGAATTTCTGA
GABAAρ1 (2of2)	(i) ENSGACT00000016158	ρ12at	F	CACTAAAGTCTGGGGTCCGA	1.896 ± 0.008	33.61 ± 0.03
	(ii) ENSGACT00000016168		R	TTGGTGTTGCTCTTGAAGGC
GABAAρ2a (1of2)	ENSGACT00000016151	ρ2a1	F	GGCAGCCTGTAACATGGACT	1.877 ± 0.001	30.68 ± 0.08
			R	CGTGGTGTGGAACTTCTGGA
GABAAρ2a (2of2)	ENSGACT00000017273	ρ2a2	F	GCATGCAACATGGATTTCAG	1.888 ± 0.001	32.5 ± 0.09
			R	GGATGAGGAACTGGGACAGA
GABAAρ3a	(i) ENSGACT00000027385	ρ3aat	F	AGCAGTACGGAGAGAACACC	1.894 ± 0.001	32.9 ± 0.28
	(ii) ENSGACT00000027384		R	GCATTGCAAAGTCGTGGTCT
GABAAρ3b	ENSGACT00000002161	ρ3b	F	CTTCATCCACGACACCACCA	1.885 ± 0.001	30.46 ± 0.07
			R	GGGAAGCTGCTGAAGTCCAT
(B)
Ubiquitin	ENSGACG00000008021	ubc	F	AGACGGGCATAGCACTTGC	1.904 ± 0.003	22.08 ± 0.01
			R	CAGGACAAGGAAGGCATCC
Ribosomal protein L13A	ENSGACT00000012382	rpl13A	F	CACCTTGGTCAACTTGAACAGTG	1.902 ± 0.010	22.07 ± 0.26
			R	TCCCTCCGCCCTACGAC
GAT11	ENSGACG00000009684	GAT11	F	CAGTGCAGATGGTTCCCCTC	1.8794 ± 0.000	25.19 ± 0.04
			R	GCGGGGTTCTGATTCTGGTT
GAT12	(i) ENSGACT00000020044	GAT12at	F	AGAGTACGTGTTCCCAGCATG	1.879 ± 0.003	26.21 ± 0.00
	(ii) ENSGACT00000020046		R	ATAGGTTCGCTGCGTTGGTC
GAT21	(i) ENSGACT00000004780	GAT21at	F	TGCGTTGATCAAGTACTCTCCT	1.894 ± 0.000	26.97 ± 0.14
	(ii) ENSGACT00000004778		R	CTTGGTTTTCGGCAAGTCGG
GAT22	ENSGACT00000025159	GAT22	F	TGTCTGCATTGCTTGGGTCT	1.891 ± 0.000	29.62 ± 0.09
			R	AATCGCATAACCCCACCAGG
GAT23	(i) ENSGACT00000001890	GAT23at	F	GGTCTGGAAGCCCTCGTAAC	1.920 ± 0.002	28.40 ± 0.08
	(ii) ENSGACT00000001897
	(iii) ENSGACT00000001899		R	GAGAGTCATCCCACTGCAGG
GAT3	(i) ENSGACT00000009625	GAT3at	F	GCGGGATGTGTTTGCTGTTT	1.901 ± 0.003	26.51 ± 0.03
	(ii) ENSGACT00000009632		R	CCAGTCAGGGTAGGTGTACA
GABAT	(i) ENSGACT00000006245	GABATat	F	TGTCCGATCCAAGCAGTCTG	1.855 ± 0.005	27.73 ± 0.04
	(ii) ENSGACT00000006238		R	CATTGTCTGAACCCGGGACA
GAD1a	ENSGACT00000006685	GAD1a	F	GGGACACCTTGAAGTACGGA	1.858 ± 0.001	30.74 ± 0.02
			R	CATGAGCACAAAGACAGGGG
GAD1b	ENSGACT00000017175	GAD1b	F	CCATTGGGTTTGAGCAGCAC	1.891 ± 0.002	25.93 ± 0.09
			R	CATGTCTCTCAGGCTGGGTG
GAD2	ENSGACT00000006820	GAD2	F	ACCTCTCTTCGCCATAACCG	1.876 ± 0.005	30.15 ± 0.15
			R	ATCATCTTGTGCGGGTTCCA
GABARAP	(i) ENSGACG00000025686	GABARAPat	F	ATATCTCGTCCCCTCCGACC	1.913 ± 0.003	23.05 ± 0.09
	(ii) ENSGACG00000025685		R	CGCTCTCATCACTGTAGGCA
GABARAPL1	ENSGACG00000013851	GABARAPL1	F	AGGTGAGGAGAGCAGAAGGA	1.933 ± 0.000	24.74 ± 0.12
			R	GGGGAAGGGAGTTGTTGACA
GABARAPL2	(i) ENSGACG00000002829	GABARAPL2at	F	AAGTACCTGGTGCCCTCTGA	1.913 ± 0.007	26.72 ± 0.05
	(ii) ENSGACG00000002816
	(iii) ENSGACG00000002836		R	TTTTCGTACAGCTGCCCCAT
(C)
Ubiquitin	ENSGACG00000008021	ubc	F	AGACGGGCATAGCACTTGC	1.894 ± 0.001	22.17 ± 0.06
			R	CAGGACAAGGAAGGCATCC
Ribosomal protein L13A	ENSGACT00000012382	rpl13A	F	CACCTTGGTCAACTTGAACAGTG	1.897 ± 0.006	21.86 ± 0.16
			R	TCCCTCCGCCCTACGAC
KCC1	ENSGACT00000022002	KCC1	F	ACAACGGAGAGCCTACATGG	1.844 ± 0.001	30.21 ± 0.24
			R	TCATGCCTAGGAAGGACAGC
KCC2a	ENSGACT00000007272	KCC2a	F	AGAGCAGAACGTGGAACAGC	1.803 ± 0.001	29.42 ± 0.44
			R	GCACGCTGAGACTGTTCGTA
KCC2b	ENSGACT00000003694	KCC2b	F	AGAACATCTCCAGCTACCCG	1.909 ± 0.030	32.59 ± 0.07
			R	CGCAGGTGATAGAGGAAGGT
KCC3	ENSGACT00000025029	KCC3	F	GGCGCTCATGTTCATATCCT	1.901 ± 0.001	30.93 ± 0.17
			R	GCGTCCTCGTCCAGTTTTAG
KCC4a	ENSGACT00000019164	KCC4a	F	GCCAAGAACATCGACCATTT	1.903 ± 0.003	32.52 ± 0.16
			R	CACCACAGCATCCAGACGTA
KCC4b	(i) ENSGACT00000001353	KCC4b	F	AAAGACACAGAGGCCAGGAA	1.864 ± 0.003	31.23 ± 0.20
	(ii) ENSGACT00000001355		R	CCATGAGGATTGTGTTGTGC
NKCC1 (1of2)	ENSGACT00000019494	NKCC1i	F	TCCGAATCCTGTCCCTCCAA	1.831 ± 0.000	27.86 ± 0.00
			R	ATGGTTCCTTTGCCCTGCTT
	ENSGACT00000019488	NKCC1ii	F	TTAAACTCCCCGCGATGCTT	1.858 ± 0.000	32.56 ± 0.34
			R	GTTGTCGGTGATCCTCCAGG
NKCC1 (2of2)	(i) ENSGACT00000024304	NKCC2at	F	GAGTCTTGGCCCAGAGTTTG	1.915 ± 0.004	29.28 ± 0.36
	(ii) ENSGACT00000024305		R	GCGGATATCGTTGAGTTCGT
ClC2 (1of3)	(i) ENSGACT00000000266	ClC21at	F	CCAGAGAAAGAAGGCCTGGA	1.919 ± 0.004	26.77 ± 0.01
	(ii) ENSGACT00000000267		R	CATCCTCCACATCTGCGTCG
ClC2 (2of3)	ENSGACT00000013852	ClC22	F	TTAAAACACGGTTCCGGCTC	1.886 ± 0.001	29.34 ± 0.16
			R	ATCAGCCGGTTCAGGTAGAC
ClC2 (3of3)	ENSGACT00000017906	ClC23	F	GGCCAAAGTCATCGGTCTGA	1.884 ± 0.002	30.28 ± 0.19
			R	CCACCGAAAAGAGGAGCCAT
NDAE	ENSGACT00000000854	NDAE	F	TCCTCATGTGTGCGTTCCTC	1.867 ± 0.001	30.47 ± 0.28
			R	AACCTCGGTCGTCTCTGGTA
AE3	ENSGACT00000003278	AE3	F	GGAGCAATTATGACCTGCGG	1.865 ± 0.001	30.65 ± 0.12
			R	GACACCGCGATGACTTCTTC
CAII	ENSGACT00000006681	CAII	F	CTGACTTCGACCCTTCCACC	1.910 ± 0.000	24.94 ± 0.14
			R	GCAGCTCGCGGAATTTCTTC

Primer sequences used for qPCR: (A) GABAA receptor subunits qPCR primers; (B) GAT, GABAT, GAD, GABARAP and GABARAPL qPCR primers; and (C) Ion cotransporters qPCR primers. Abbreviations: F, forward primer; and R, reverse primer. The lower case number and/or letter represents a paralogue sequence and/or splice variance, respectively: 1, paralogue 1; 2, paralogue 2; i, splice variant 1; ii, splice variant 2; and at, primer pair that does not discriminate between splice variants. Priming efficiencies (E) and crossing point (Cp) values are given in the two rightmost columns. Values are means ± SEM.

For genes with known paralogues or splice variants, efforts were made to design transcript-specific primers when possible, in order to discriminate between closely related transcripts. A comparison aiming at determining identities between genes was carried out using a global alignment (NCBI-Needleman-Wunsch Global Align Nucleotide Sequences; blast.ncbi.nlm.nih.gov), which is a sequence alignment method based on the Needleman–Wunsch algorithm (Needleman and Wunsch, 1969) used to find the best optimal alignment along two sequences (Table S1).

Thirty-five gene sequences for GABAA receptor subunits were retrieved from the Ensemble stickleback database. Diverse paralogue sequences exist in the GABAA subunit families, except for the δ subunit, which has only one known gene variant (see Table 1A). The majority of these sequences showed a distant relationship (identitites ranging from 37 to 78%; Table S1A–E), and qPCR primers were directed at conserved regions. In contrast, paralogues belonging to the subunits β3, γ2 and ρ1 showed a close identity (51–100%), and qPCR primers were directed at poorly conserved regions and analysed as single transcripts (Table S1B, C and E). Among all paralogues, Ensembl presents alternative splice variants for β21, β31 and β32 (Table 1A). In the γ family, γ2 is the only subunit that splices for three alternative transcripts (Table 1A), and for the ρ subunits, two alternative variants are known to be present for ρ12 and ρ3a: ρ12i, ρ12ii and ρ3ai, ρ3aii (Table 1A). Altogether, a total of 28 qPCR primers were designed for the gene expression analysis of subunits (some sequences showed too much similarity to allow for the design of specific primers).

For the genes involved in GABA turnover, 11 different gene paralogues were found to encode for GAT, three for GAD, one for GABAT, two for GABARAP and four for GABARAPL. We designed a total of 13 qPCR primers, of which some will work for more than one transcript. Moreover, four different genes encoding for ClC2, seven for KCCs and four for NKCC1 are found in the stickleback genome, whereas AE3, NDAE and CAII have only one variant (Table 1). A total of 15 qPCR primers were designed for the ion cotransporter analysis. Effort was made to design primers able to detect CAVII, but we were unsuccessful in detecting this transcript.

As for the GABAA subunits, splice variants are present for some of the members of GAT and GAD families (Table 1B). The gene GAT1 splices for two splice variants (Table 1B). The other transporters, GAT21, GAT23 and GAT3, splice into two (GAT21i and GAT21ii), three (GAT23i, GAT23ii and GAT23iii) and two variants (GAT3i and GAT3ii), respectively (Table 1B). The GABARAPL2 gene encodes for three splice variants, GABARAPL2i and GABARAPL2ii (Table 1B). In the KCC family, two KCC4b splice variants are known, KCC4bi and KCC4bii (Table 1C). Likewise for ClC21, there are two alternative splice variants (ClC21i and ClC21ii; Table 1C). Two NKCC1 paralogues exist (NKCC11 and NKCC12), both having alternative splice variants (Table 1C).

Quantitative PCR

Quantitative PCR was carried out in duplicates using 1:30 diluted cDNA (3 μl), LightCycler 480 SYBR Green I Master Mix (5 μl; Roche Diagnostics, Basel, Switzerland), primers (1 μl; 5 μM) and nuclease-free water (1 μl; Ambion Applied Biosystems). The reaction mix and samples were loaded onto 384 multiwell plates (Roche Diagnostics) using an Agilent Bravo robot (Agilent Technologies, USA) The following qPCR program was used: (i) 95°C for 10 min; (ii) 95°C for 10 s; (iii) 60°C for 10 s; (iv) 72°C for 13 s; and (v) repeat steps (ii) to (iv) 42 times. A melting curve analysis was performed for each amplicon after the qPCR program. Ubiquitin (ubc) and ribosomal protein L13A (rpl13A) were used as reference genes for normalization, as they have previously been demonstrated to be the most stably expressed genes in the three-spined stickleback (Hibbeler ) (Table 1). The geometric average of their expression was used to normalize the data sets, because this method has been shown to be a prerequisite for an accurate qPCR expression analysis leading to the possibility of studying small expression differences (Vandesompele ; Hellemans ).

The Cp values and priming efficiencies for each reaction were calculated using the second derivative maximum method (Roche Lightcycler 480; Rasmussen, 2001) and the LinRegPCR software (Ruijter ), respectively. Subsequently, relative mRNA expression levels were calculated using the following formula: Where ga is the geometric average of the two reference genes; tar is the gene of interest, E is priming efficiency and Cp is the crossing point.

Given that duplicate cDNA syntheses were performed, and each of these were analysed in duplicates in the qPCR analyses, four data points were present for each original sample for each primer pair used, and their means were used in the mRNA expression calculations.

Statistical analysis

All statistical analyses were performed using GraphPad Prism (GraphPad Software; version 6.0d; Mac OS X). Normality and homogeneity of variance were assessed using the D'Agostino & Pearson omnibus normality test and F-test. According to function, data were grouped into seven families as follows: (i) GABAA α subunits; (ii) GABAA β subunits; (iii) GABAA γ subunits; (iv) GABAA δ, π and ρ subunits; (v) GAT, GAD and GABAT; (vi) GABARAP and GABARAPL; and (vii) KCC, NKCC, ClC2, AE3, NDAE and CAVII. Two-way analysis of variance (ANOVA) followed by the Sidak post hoc test was used to examine differences in expression between the genes within the families and between the two treatment groups. A value of P < 0.05 was considered significant. All data are presented as means ± SEM, unless otherwise stated.

Results

As listed in Table 1A, GABAA subunits can be regrouped into six families: α(1–6), β(1–3), γ(1–3), δ, π and ρ(1–3). In contrast to mammals, no genes encoding for ε and θ subunits are present in the stickleback genome. All GABAA paralogues retrived on the Ensembl database were found to be expressed in the three-spined stickleback brain (Fig. 1A–D). Expression within each gene family was analysed using two-way ANOVA, with subunit and CO2 treatment as the two variables. Not surprisingly, the mRNA transcripts levels differed significantly for the different subunits (Fig. 1; two-way ANOVA, P < 0.001). The most highly expressed GABAA subunits in the three-spined stickleback belonged to the α, β, γ and δ families (Fig. 1A–D), and within these families the expression was dominated by α11 and α12, α6b, β1, γ1 (there was only a single isoform for the δ subunit). Among the ρ subunits, ρ11 was the most abundant (Fig. 1D). The π subunits showed the lowest expression levels (Fig. 1D). Exposure of three-spined stickleback to elevated pCO2 (~990 μatm) resulted in significantly altered expression levels for relatively few of the GABAA subunits investigated. The α family subunits, which are composed of nine different isoforms, showed a significantly higher expression in high-CO2 fish compared with control fish (Fig. 1A; two-way ANOVA, P = 0.0165). All α subunits showed a numerically higher mean expression in the high-CO2 group, with increased mRNA transcription levels of 24, 48, 50 and 25% for the α12 α3, α4 and α6b, respectively, although the post hoc test failed to identify significant treatment effects for any individual isoform (Fig. 1A; Sidak post hoc test, P > 0.05). The high-CO2 exposure did not significantly affect the expression of the β (two-way ANOVA, P = 0.6639), γ (two-way ANOVA, P = 0.1861), δ, π or ρ subunits (two-way ANOVA, P = 0.7189; Fig. 1B–D).

Figure 1:

Messenger RNA expression levels of GABAA receptor subunits. Data were normalized to the geometric average of the reference genes ribosomal protein L13A (rpl13A) and ubiquitin (ubc) and grouped into four families as follows: α subunits (A); β subunits (B); γ subunits (C) and δ, π and ρ subunits (D). Each family was analysed by two-way ANOVA followed by Sidak post hoc test. Open and filled columns represent three-spined sticklebacks exposed to control water (n = 12) and high-CO2 water (n = 12) for 43 days. Values are shown as means + SEM.

Of the genes involved in GABA turnover included in Ensembl Genome Browser (GAT, GAD, GABAT, GABARAP and GABARAPL), all were found to be expressed in three-spined stickleback brain (Figs 2 and 3). Within families, there were significant differences in the mRNA abundance of the paralogue members (two-way ANOVA, P < 0.001). In the control group, the GAT1 paralogues (GAT11 and GAT12at) were more abundant than GAT21–3 (Fig. 2). In the GAD family, GAD1b displayed higher mRNA expression levels than the GAD1a and GAD2 transcripts (Fig. 2). GABARAP was almost four times more highly expressed than GABARAPL1 and eight times more highly expressed than GABARAPL2 (Fig. 3). In our experiment, none of the transporters or enzymes involved in GABA metabolism displayed significant alterations in expression in response to high-CO2 treatment (Figs 2 and 3; two-way ANOVA, P > 0.05).

Figure 2:

Messenger RNA expression levels of GAT, GABAT and GAD genes. Data were normalized to the geometric average of the reference genes ribosomal protein L13A (rpl13A) and ubiquitin (ubc). Data were analysed by two-way ANOVA followed by Sidak post hoc test. Open and filled columns represent three-spined sticklebacks exposed to control water (n = 12) and high-CO2 water (n = 12) for 43 days. Values are shown as means + SEM.

Figure 3:

Messenger RNA expression levels of GABARAP and GABARAPL genes. Data were normalized to the geometric average of the reference genes ribosomal protein L13A (rpl13A) and ubiquitin (ubc). Data were analysed by two-way ANOVA followed by Sidak post hoc test. Open and filled columns represent three-spined sticklebacks exposed to control water (n = 12) and high-CO2 water (n = 12) for 43 days. Values are shown as means + SEM.

Of the genes involved in ion transport, the expression level of the transcripts differed between the members of the gene families (two-way ANOVA, P < 0.0001; Fig. 4). KCC2a, ClC21at and NKCC11i were the most highly expressed transcripts among the ion transporters (Fig. 4). Exposure to high CO2 did not cause significant changes in mRNA expression levels for the ion transporter transcripts in high-CO2-treated fish compared with control fish (Fig. 4; two-way ANOVA, P > 0.05).

Figure 4:

Messenger RNA expression levels of KCCs, NKCC1, ClC2, NDAE, AE3 ion cotransporters and CAII enzyme. Data were normalized to the geometric average of the reference genes ribosomal protein L13A (rpl13A) and ubiquitin (ubc). Data were analysed by two-way ANOVA followed by Sidak post hoc test. Open and filled columns represent three-spined sticklebacks exposed to control water (n = 12) and high-CO2 water (n = 12) for 43 days. Values are shown as means + SEM.

Discussion

Changes in the function of GABAA receptors caused by altered ion gradients have been suggested as a general mechanism behind the behavioural disturbances seen in CO2-exposed fish (Nilsson ; Chivers ; Chung ; Hamilton ; Lai ). Exposure to high CO2 levels triggers acid–base adjustments in fish involving altered levels of Cl− and HCO3− (Brauner and Baker, 2009), which have been suggested to change neuronal membrane gradients of these ions, switching some GABAA receptors from being inhibitory to excitatory (Nilsson ). Based on the scarce data available, calculations of GABAA equilibrium potentials of neurons in fish exposed to near-future pCO2 are consistent in showing a possibility for a shift in the GABAA receptor equilibrium potential from causing hyperpolarization to depolarization (Heuer and Grosell, 2014; Nilsson and Lefevre, 2016; Heuer ; Regan ). Here, we hypothesized that an increase in CO2 levels in the marine environment, triggering acid–base regulatory mechanisms in fish, could lead to changes in the expression of genes involved in regulating GABAA receptor function and neuronal ion distribution. Such molecular changes could be adaptive. However, the persistence of the behavioural disturbances reported in some experiments, and a lack of transgenerational acclimation (Welch ), suggest that possible molecular responses are insufficient, or even maladaptive.

The present study is the first comprehensive expression analysis focused on genes involved in GABAergic transmission in fish exposed to elevated CO2. The fish in this study were the same individuals as those previously examined behaviourally (Jutfelt ), where the behavioural alterations, including reduced exploratory behaviour and lateralization, were characterized and found to persist for the whole experimental period. Of the 28 GABAA receptor subunits examined herein, there was a significant effect of the high-CO2 treatment on the mRNA expression level for the α family subunits, all showing a tendency to be more highly expressed in the CO2 group. This could suggest some subunit rearrangement of GABAA receptors in this group, assuming that gene expression is reflected in protein expression. Changes in the GABAA receptor composition are often suggested to induce a change in the receptor function (reviewed by Farrant and Kaila, 2007). The α subunits play important roles on desensitization and deactivation of the receptor, because the GABA binding is presumed to take place at the α–β interfaces (Böhme ). In the present experiment, changes at the mRNA expression level of the α subunit family after exposure to high CO2 might indicate possible compensatory mechanisms used by three-spined stickleback to restore proper GABAA receptor function, but further investigations are required. However, if adaptive, the changes are apparently not sufficient, because the behavioural alterations detected in the same individuals by Jutfelt remained, and if anything increased, during the 43 day exposure period. Also, we cannot exclude the possibility that the changes detected in this study are maladaptive rather than adaptive and contribute to the behavioural alterations.

As mentioned, the three dominant subunits that make up the mammalian receptor are α, β and γ (reviewed by Farrant and Kaila, 2007), and our data show that these subunit families are also highly expressed in three-spined stickleback, but it is striking that the δ subunit is also expressed at a level similar to the most highly expressed α subunits (Fig. 1). Although the mammalian receptors are dominated by subunits α1 β2 and γ2 (Pirker ), the most predominantly expressed subunits in three-spined stickleback were found to be α1, α6b, β1, γ1 and δ. Receptors that comprise γ2 in association with α1, α2 or α3 subunits are normally localized to post-synaptic membranes, where they mediate a phasic inhibition involving a rapid and brief inhibitory post-synaptic potential in response to GABA in the synaptic cleft (reviewed by Farrant and Nusser, 2005). In contrast, GABA leaking out of the synapse can activate extrasynaptic GABAA receptors made up of δ, α4 or α6 subunits, and these are mainly responsible for slower, but longer-lasting inhibitory post-synaptic potentials, causing tonic inhibition (reviewed by Farrant and Nusser, 2005). Consequently, the high expression of the extrasynaptic δ and the α6b subunits might indicate a more important role of extrasynaptic GABAA receptors in three-spined stickleback compared with mammals. Interestingly, previous studies on crucian carp (Carassius carassius) and zebrafish (Danio rerio) brain found a dominating expression of the δ subunits (Ellefsen ; Cocco ). In light of this high expression of δ subunits, it is tempting to suggest that extrasynaptic GABAA receptors causing tonic inhibition play more important roles in fish than in mammals. In contrast to both crucian carp and zebrafish, three-spined stickleback express alternative splice variants for β2, β31, β32, ρ12 and ρ3a. The only common subunit that exhibits alternative splicing in all three species is γ2 (Ellefsen ; Cocco ). The presence of numerous splice transcripts in three-spined stickleback brain could mean that GABAA receptor isoforms are particularly diverse in this species, and fishes differ in the degree to which alternative splicing is used to modulate GABAA receptor function (see also Cresko ).

Perhaps surprisingly, exposure to high CO2 did not result in significant changes in the mRNA expression levels for ion cotransporters. Heuer showed that the increase in plasma partial pressure of CO2 (in millimetres of mercury) in spiny damselfish (Acanthochormis polyacanthus) kept in high-CO2 conditions was accompanied by increases in intracellular and extracellular HCO3− concentrations, with an assumed decrease in intracellular Cl− (Heuer ). Based on their measurements, they calculated a positive deviation in the EGABAA resting potential in fish exposed to 1900 µatm CO2, consistent with a shift in the GABA action towards depolarization (excitation) rather than hyperpolarization (inhibition). Such alterations could be compensated for by changes in the expression of the Cl− transporters NKCC1 and KCC2 in fish exposed to high CO2. As mentioned, these transporters are known to play important roles in setting the reversal potential for Cl− (ECl) in the mammalian central nervous system, leading to a shift of the GABAA receptor function from excitatory in immature neurons to inhibitory in mature neurons (Delpire, 2000). In any case, this does not appear to happen in CO2-exposed stickleback in the present conditions, as we found no significant changes in the expression of NKCC1 and KCC2, or in other Cl− and HCO3− transporters.

Our gene expression results are in agreement with the recent findings of Schunter on juvenile spiny damselfish (Acanthochromis polyacanthus). In a transcriptome and proteome analysis, they investigated the molecular responses of offspring of CO2-tolerant and CO2-sensitive parents reared in control or high-CO2 conditions. The main molecular differences between the two groups were found among genes involved in circadian rhythm control, such as bmal1, clock, per1 and nr1d1 (Schunter ). In contrast, the GABAA receptor genes were expressed at similar levels across treatments. The only possible change seen in the GABAergic system was at the protein level of an enzyme that may participate in GABA synthesis, aldehyde dehydrogenase 9 member 1 (Al9A1), which were more highly expressed in the offspring of the CO2-tolerant parents (Schunter ).

Conclusions and perspectives

In general, the present findings show that exposure of three-spined stickleback to elevated CO2 resulted in only few and minor changes in the expression of genes involved in GABAergic neurotransmission in the brain. If these few adjustments reflect compensatory mechanisms they are apparently not sufficient, because the behavioural dysfunctions remained during the course of the 43 day high-CO2 exposure (Jutfelt ). Thus, the present results, together with results reporting that aberrant behaviours displayed by fish exposed to elevated pCO2 are persistent and not reduced even by transgenerational acclimation (Welch ), lead to the worrying conclusion that fish might be incapable of adaptive responses to these new conditions. Given that globally sustained pCO2 levels >500 µatm have probably not occurred on earth during the last 30 million years (Beerling and Royer, 2011), we may have to face the conclusion that many present-day fishes do not possess the genes and mechanisms necessary to cope with the projected near-future elevation of CO2 levels.

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