Metabolic regulation by the PGC-1α and PGC-1β coactivators in larval zebrafish (Danio rerio)

Comparative Biochemistry and Physiology, Part A 234 (2019) 60–67

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Metabolic regulation by the PGC-1α and PGC-1β coactivators in larval zebrafish (Danio rerio)☆,☆☆ Caleb Northam, Christophe M.R. LeMoine

T

⁎

Department of Biology, Brandon University, Brandon, Manitoba R7A 6A9, Canada

A R T I C LE I N FO

A B S T R A C T

Keywords: Mitochondria ERRα NRF-1 PPAR Citrate synthase Lipid oxidation

The peroxisome proliferator activated receptor γ coactivator-1 (PGC-1) family is composed of three coactivators whose role in regulating mammalian bioenergetics regulation is clear, but is much less certain in other vertebrates. Current evidence suggests that in fish, PGC-1α and PGC-1β may exhibit much less redundancy in the control of fatty acid oxidation and mitochondrial biogenesis compared to mammals. To assess these roles directly, we knocked down PGC-1α and PGC-1β expression with morpholinos in zebrafish embryos, and we investigated the resulting molecular and physiological phenotypes. First, we found no effects of either morpholinos on larval hatching, heart rates and oxygen consumption over the first few days of development. Next, at 3 days post fertilization (dpf), we confirmed by real time PCR a specific knock down of both coactivators, that resulted in a significant reduction in the transcript levels of citrate synthase (CS), 3-hydroxyacyl-CoA dehydrogenase (HOAD), and medium-chain acyl-coenzyme A dehydrogenase (MCAD) in both morphant groups. However, there was no effect on transcription factors' gene expression except for a marked reduction in estrogen related receptor α (ERRα) transcripts in PGC-1α morphants. Finally, we assessed whole embryonic enzyme activity for CS, cytochrome oxidase (COX), HOAD and carnitine palmitoyltransferase I (CPT-1) at 4 dpf. The only significant effect of the knockdown was a reduced CS activity in PGC-1α morphants and a counterintuitive increase of cytochrome oxidase activity in PGC-1β morphants. Overall, our results indicate that in larval zebrafish, PGC-1α and PGC-1β both play a role in regulating expression of important mitochondrial genes potentially through ERRα.

1. Introduction Under aerobic conditions, mitochondrial metabolism is a central provider of cellular energy in the form of ATP. While the overall mitochondrial architecture and organization is generally conserved among organisms, there can be enormous differences in mitochondrial content and capacity among different tissues and in response to various physiological constraints (Hood et al., 2006; Kraft et al., 2006; Lyons et al., 2006; Moyes et al., 1997; Nicholls et al., 1986). This inherent plasticity of the organelle provides metabolic flexibility that certainly plays an integral part in the resilience of species to environmental and physiological change (Seebacher et al., 2010). Although critical, this capacity to modulate mitochondrial processes is complicated by the genetic compromise formed by the mitochondrial-nuclear partnership. Indeed, of endosymbiotic origin, over evolutionary time the organelle surrendered a large portion of its genome to the nucleus (Woodson and Chory,

2008; Zimorski et al., 2014). Consequently, to promote mitochondrial biogenesis, cells must coordinate the expression of thousands of genes on both nuclear and mitochondrial genomes to produce a fully functional organelle (Garesse and Vallejo, 2001; Jornayvaz and Shulman, 2010). In mammals, this coordination is believed to be primarily assumed by a small family of transcriptional coactivators, the peroxisome proliferators-activated receptor gamma coactivator 1 (PGC-1). These proteins have a modular structure that allows them to bind to and modulate the activity of a plethora of transcription factors (Handschin and Spiegelman, 2006; Scarpulla, 2011). For example, the mammalian PGC1α can activate the nuclear respiratory factor 1 (NRF-1) (Virbasius and Scarpulla, 1994). In turn, this activation upregulates the expression of several electron transport chain subunits and the mitochondrial transcription factor A (TFAM), thereby playing an integral role in mitochondrial biogenesis and the regulation of oxidative metabolism

☆ This article is part of a special issue entitled: 13th International Congress on the Biology of Fish: Select papers from the Growth and Metabolism of Fishes and the Muscle Growth and Development symposia, edited by: Dr. Brian Small, Dr. JOAQUIM Gutiérrez, Dr. Peggy Biga and Dr. Brian Peterson. ☆☆ This article is a part of the Special Issue on Papers from XIIIth ICBF ⁎ Corresponding author. E-mail address: [email protected] (C.M.R. LeMoine).

https://doi.org/10.1016/j.cbpa.2019.04.011 Received 1 December 2018; Received in revised form 8 March 2019; Accepted 11 April 2019 Available online 18 April 2019 1095-6433/ © 2019 Elsevier Inc. All rights reserved.

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Diego, CA) heated to 26 °C and filled with recirculating UV treated dechlorinated city of Brandon water. The adult zebrafish were fed once (or twice prior to breeding) a day with Adult zebrafish Complete Diet (Zeigler, Gardners, PA) and once daily with freshly hatched Artemia nauplii. The adults were kept in a 13:11 h light-dark cycle. Embryos were obtained by randomly breeding ~30 adults each in 6 holding tanks using standard procedures. The embryos were reared in Petri dishes containing E3 embryo medium (5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, 0.33 mM MgSO4, 0.0006 mM methylene blue) in an incubator at 28 °C. All manipulations were approved by the Brandon University Animal Care Committee in accordance with guidelines from the Canadian Council on Animal Care.

(Choi et al., 2002; Virbasius and Scarpulla, 1994). Other binding partners of the PGC-1 family includes several members of the nuclear hormone receptor superfamily, such as the estrogen related receptor α (ERRα) and PPARα (Handschin and Spiegelman, 2006, Scarpulla, 2011,). In several species, PPARα plays a central role in the regulation of multiple enzymes involved in β-oxidation (Barger and Kelly, 2000; Kondo et al., 2010). Similarly, the orphan receptor ERRα regulates enzymes of this pathway but also seems to be involved in the regulation of mitochondrial genes such as citrate synthase (CS)(Berger and Moller, 2002; Van Bilsen et al., 2002). In mammals, the three PGC-1 appear to have overlapping roles in controlling gene expression though they are individually responsive to different bioenergetics constraints (Fernandez-Marcos and Auwerx, 2011; Handschin and Spiegelman, 2006; Villena, 2015). Indeed, PGC-1α has been associated with brown fat differentiation, thermogenesis, and muscle adaptation to exercise in rodents (Baar et al., 2002; Goto et al., 2000; Puigserver et al., 1998), while PGC-1β plays an important role in hepatic lipid homeostasis and cardiac function (Lelliott et al., 2006). The third and least studied member of the family, PGC-1 related coactivator (PRC), is ubiquitously expressed across tissue in mammals and seems mostly responsive to cell cycle signals (Andersson and Scarpulla, 2001). While these pathways have started to be elucidated in rodents and humans, there is still a paucity of information on whether these “master controlling” roles are generally conserved in other species. Recent work in Drosophila suggests that the PGC-1 homologue (Spargel) carries some metabolic regulation reminiscent of some of the functions of the PGC-1 in mammals, but the exact pathways involved have yet to be elucidated (Diop et al., 2015, Mukherjee et al., 2014, Tiefenböck et al., 2010, Tinkerhess et al., 2012). In non-mammalian vertebrates, the picture is less clear as the majority of evidence collected thus far points to similar roles in tetrapods (Hirabayashi et al., 2005; Mujahid, 2010; Seebacher et al., 2009), but divergent functions in most fish species (Magnoni et al., 2013; Orczewska et al., 2010; Windisch et al., 2011). Indeed, work in teleost fish has so far found very tenuous evidence of any involvement of PGC-1α with the regulation of respiratory genes, but some indication that it can play a regulatory role in the lipid oxidation axis (Bremer and Moyes, 2011, Bremer et al., 2012, LeMoine et al., 2008, LeMoine et al., 2010a, McClelland et al., 2006). Further, molecular evidences suggest that this disconnect is potentially due to structural divergence of the domains allowing interactions with the various binding partners, potentially shifting the role of PGC-1α and β in the teleost lineage (LeMoine et al., 2010b). Consequently, to determine the actual function of these coactivators in teleosts, we here undertake the first step in the functional characterization of PGC-1α and β in the zebrafish, by using a morpholino knock down approach. Using zebrafish as a model system allows us to capitalize on its many experimental advantages from a genetics and physiological perspective, and to start deciphering crucial regulatory pathways in an organism that is becoming an important biomedical model of metabolic disturbances (Seth et al., 2013). Our work could therefore provide much needed clarity on the respective roles of these coactivators in regulating metabolism and their effects at various levels of organization in a favoured experimental fish model. With our experimental approach, we evaluate the phenotypic impact of a severe reduction in gene expression of both PGC-1α and β, and assess the impact of this knockdown at the genetic, enzymatic and whole organismal level. Our results indicate that both coactivators appear to have overlapping roles in regulating metabolic gene expression, but that the regulatory pathways involved may differ between the two paralogues.

2.2. Morpholino gene knockdown A splice-blocking morpholino targeting the junction of exon 6 and intron 6 of the zebrafish PGC-1α gene (ENSDART00000097710.6) and a splice-blocking morpholino targeting the junction of exon 5 and intron 5 of the zebrafish PGC-1β gene (ENSDART00000170871.2) were designed (Gene Tools, Philomath, OR). A micromanipulator (MM3301R, World Precision Instruments, Sarasota, FL), a Picopump (PV830, World Precision Instruments, Sarasota, FL), and pulled 1.0 mm borosilicate glass micropipettes (World Precision Instruments, Sarasota, FL) were used to inject 1-cell to 8-cell embryos with ~1 nL of the morpholino for PGC-1α [GCAAAACCCATGTTTGTTTACCTGG], the morpholino for PGC-1β [CGATGTGTTCTTGAATTCTTACCGT] or a control morpholino [CCTCTTACCTCAGTTACAATTTATA] at 0.375–0.5 mM MO and 0.05% Phenol Red in sterile nuclease-free water. The morpholinos were labelled with the fluorescent molecule fluorescein which allowed uptake of the morpholino to be observed by fluorescence microscopy (Olympus, Richmond Hill, ON). Only embryos that successfully took up the morpholino, and exhibiting whole body fluorescence by 24 hpf were used for further testing. For each separate injection/breeding event (19–25 events per morpholino), half the embryos was injected with PGC-1 morpholinos while the other half was injected with another one (usually the control morpholino). This effectively allowed us to monitor inter-clutch variability. Further, we monitored hatching rates at 48–52 hpf to assess the effect of each morpholino on this developmental hallmark. 2.3. Heart rate To reduce intrinsic activity, with minor effect on basal heart rate (Tzaneva and Perry, 2016), each embryo (75–80 hpf) was placed in 100 mg/L tricaine, in a temperature controlled petri dish (28 °C) for 3 min. Video was then taken of the embryo's heart for approximately 1 min, embryo's position being adjusted as necessary for a clear view of the heart, while still in the 28 °C tricaine solution. We recorded the heart rate using LG K4 (LG Electronics, Seoul, South Korea), and a Smartphone digiscoping adapter (Gosky, Yuyao, China) with a dissecting microscope (SMZ1000, Nikon, Tokyo, Japan). Videos were analyzed at reduced speed to quantify the number of heart beats per minute for each embryo. 2.4. Respirometry We measured 3 and 4 dpf morphants oxygen consumption rates using a high definition respirometry system (LoligoSystems, Viborg, Denmark). Briefly, oxygen sensor spots were mounted inside glass chambers, and read using fiber optics and OXY-4 mini system (PreSens, Regensburg, Germany). The respirometry tubes were maintained at 28 °C by a water bath, and within each chamber the water was circulated by a stir bar at 140–150 rpm. For 4 dpf larvae, a mesh was used to separate larvae from the stir bar. Sensors were calibrated using 0.02 g/ ml sodium sulfite solution and oxygenated E3. Respirometry was carried out by placing 5 or 10 larvae in to a respirometry chamber

2. Materials and methods 2.1. Animals Adult zebrafish (Danio rerio) were obtained from Pet Lovers, Brandon, MB, and kept in an aquatic housing system (Aquaneering, San 61

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containing E3, they were then allowed to acclimate for 20 min followed by oxygen measurement for the next 20–30 min. Our preliminary analyses suggested that while our protocol may not be removing entirely the effect of handling stress on O2 consumption rates, this acclimation period was sufficient to see a marked reduction in larval respiration rates. During each trial, data were collected continuously in real-time using AutoResp ver. 2.1.2 software (LoligoSystems). The slope of a linear portion of the resulting O2 concentration trace was then calculated, we then accounted for the volume of the respirometry chamber and the number of fish assayed to obtain the oxygen consumption rates per fish. The rates of O2 depletion from empty respirometers (i.e., no fish) were also taken to subtract background O2 consumption for each trial.

annealing/extension (60 °C). No template controls were run to ensure lack of contamination, and melt curve analysis was run for each sample to ensure specific amplification. For each primer set, we ran negative cDNA reactions (omitting reverse transcriptase) to ensure the lack of contribution of contaminating DNA to the signal. Relative gene expression was obtained using ΔCt with the geometric mean of elongation factor 1α (EF1α) and ribosomal protein L13a (RPL 13a) used as reference genes (Ibarra et al., 2017). The efficiency of each primer set (ranging from 0.85–1.00) was assessed using cDNA standard curves and accounted for in the calculations.

2.5. Real-time PCR analysis

Embryos were homogenized in 10 μL/fish (~1:10) of enzyme extraction media (20 mM HEPES pH 7.0, 1 mM EDTA, and 0.1% Triton X100). Enzyme assays were run in triplicate with a SpectraMax Plus 384 (Molecular Devices, San Jose, CA) as described previously (LeMoine et al., 2006; LeMoine et al., 2008). Briefly, we assayed cytochrome oxidase (COX) immediately after homogenization at 550 nm (20 mM Tris pH 8.0, 0.5% TWEEN 20, and 50 μM reduced cytochrome c) while the other enzymes were assessed after one freeze-thaw cycle. HOAD activity was assayed at 340 nm in 20 mM imidazole pH 7.0, 0.1% Triton X-100, 0.15 mM NADH and 0.1 mM acetoacetyl CoA to start the reaction. Carnitine palmitoyltransferase (CPT) activity was assessed at 412 nm in 20 mM Tris pH 8.0, 0.1 mM DTNB, and 0.1 mM palmitoyl CoA and 5 mM L-carnitine (omitted to assess deacylase activity). Citrate synthase (CS) activity was assayed at 412 nm in 20 mM Tris pH 8.0, 0.05% Triton X-100, 0.1 mM DTNB, 0.3 mM acetyl CoA, and 0.5 mM oxaloacetate. LDH activity was assayed at 340 nm in 50 mM HEPES pH 7.0, 0.15 mM NADH, and 0.2 mM pyruvate-Na. Enzyme activity was normalized to protein content as estimated by a Bradford assay following manufacturer's instructions (Bio Basic Inc., Markham, ON).

2.6. Enzyme assays

Embryos were harvested at 3 dpf, frozen and stored at −80 °C. For each sample 20–30 embryos were homogenized in QIAzol Lysis Reagent and RNA was extracted using RNeasy® Plus Universal Mini Kit according to manufacturer's instructions (QIAGEN, Mississauga, ON). RNA concentration and quality (230/260 nm and 260/280 nm ratios) were assessed using NanoPhotometer® NP80 (IMPLEN, Munich, Germany). RNA was then reverse transcribed using GoScript™ Reverse Transcription System (Promega, Madison, WI), and the resulting cDNA was diluted four-fold in nuclease-free water. We carried real-time PCR analysis in a Rotor-Gene Q (QIAGEN). Each sample was assayed in duplicate in a 15 μL mix containing 7.5 μL of 2× SYBR Green PCR Master Mix (QIAGEN), 0.7 μM of each primer (Table 1), and 1.0 μL of cDNA as per manufacturer's instructions. Cycling conditions were 2 min of 95 °C initial heat activation, and 40 cycles of 5 s denaturation (95 °C), and 10 s combined annealing/extension (60 °C). The only exception was 3-hydroxyacyl-CoA dehydrogenase (HOAD) primers which was run with 2× SYR Green PCR Master Mix with Green-2-Go Mastermix (BIO BASIC, Makham, ON) and used cycling conditions of 10 min of 95 °C enzyme activation, and 40 cycles of 3 s denaturation (95 °C) and 60 s

Table 1 Real-time qPCR primers used in this study. Primers non-referenced were designed using the Primer 3 software. All primers exhibited efficiency > 85%, generating amplicons ranging from 104 to 241 bp. Gene Target

Accession Number

Forward Primer (5′-3′)

Reverse Primer (5′-3′)

EF1αa RPL 13ab PGC-1α PGC-1β PRCc NRF-1d ERRα PPARα-ae TFAM CSf COX-IV-1g COX-1d HOADd MCADh CPT1h

AY422992.1 NM_212784.1 ENSDART00000097710.6 ENSDART00000170871.2 XM_001338200.7 NM_131680.2 XM_009295473.3 XM_005164753.4 NM_001077389.1 BC166040.1 BC164879.1 MG736333.1 NM_001003515.1 NM_213010.2 BC083470.1

GTGCTGTGCTGATTGTTGCT TCTGGAGGACTGTAAGAGGTATGC ACCAACCATCTTGCCACTTC CAGCGAAGAGGAGATTACGG CTCCAACAAAGGAGCCTGAG AGGCCCTGAGGACTATCGTT AGATGTGGCATCTGGCTACC GAACGCAACTGCAAGATCCAG GCGAAAGATTGCCCAGCAGT ATCCGTTTCCGTGGTTACAG CAAGTTTGTGCAGCAGCTG ACTTAGCCAACCAGGAGCAC CCACAGGACATTCAGTGGTG CAGAAAGAGTTCCAGGAGGTG TATGACCGTTCAGACGCAGA

TGTATGCGCTGACTTCCTTG AGACGCACAATCTTGAGAGCAG ATTACTCAGCCTGGGCCTTT GAGTCTGCTCAAAGGGCTTG TTGCGCTGACAATCACTAGG GCTCCAGTGCCAACCTGTAT CCAAGCGAACTCCTTCTTTG CCAAAACGAATAGCGTTGTGG TTGTCGTTTTTCCTCCGCAAA AGACAGCCAACTGACCTGCT CAAAGAAGAAGATTCCTGCAAC GGGTGGAAGAAGTCAGAAGC GTCAGTGCCATGAACGACAG TGTCCGTTCATTAGACCCAG TACAGGCAGATGTGGCAGAG

EF1α, elongation factor 1α; RPL 13a, ribosomal protein L13a; PGC-1α, peroxisome proliferator activated receptor (PPAR)γ coactivator-1α; PGC-1β, peroxisome proliferator activated receptor (PPAR)γ coactivator-1β; PRC, peroxisome proliferator activated receptor γ coactivator (PGC) related coactivator; NRF-1, nuclear respiratory factor-1; ERRα, estrogen related receptor α; TFAM, mitochondrial transcription factor A; CS, citrate synthase; COXIV-1, cytochrome oxidase IV-1; COXI, cytochrome c oxidase I; PPARα-a, peroxisome proliferator activated receptor α-a; HOAD, 3-hydroxyacyl-CoA dehydrogenase; MCAD, medium-chain acyl-coenzyme A dehydrogenase; CPT1, carnitine palmitoyltransferase 1. a (LeMoine et al., 2010a); b (Ibarra et al., 2017); c (Artuso et al., 2012); d (McClelland et al., 2006); e (Zheng et al., 2010); f (Thomas et al., 2013); g (Duggan et al., 2011a, 2011b); h (Liu et al., 2008). 62

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Relative Gene Expression

A) 1.4 1.2

a

a

1.4

0.8

0.8

0.6

0.6

b

0.4

1.2

b

b

PGC-1α

PGC-1β PGC-1α MO

0.2 0

PRC

NRF-1

ERRα

Control MO

PGC-1β MO

D)

a

1.4 1.2

PPARα

PGC-1α MO

TFAM

PGC-1β MO

a

a

1

1 0.8

0.8

b

0.6

0.6

0.4

0.4

0.2

0.2

0

ab

0.4

0.2

1.4

a

1.2 1

Control MO

Relative Gene Expression

B)

1

0

C)

a

CS

COXIV

0

COXI

b

b

HOAD

MCAD

CPT-1

Fig. 1. Effects of PGC-1α (grey) and PGC-1β (white) knockdowns on gene expression in 3 dpf zebrafish larvae. We used real time RT-PCR to assess the relative gene expression of the PGC-1 coactivators (A), putative PGC-1 associated transcription factors (B). In addition we measured potential target metabolic transcript levels including oxidative phosphorylation and Krebs cycle genes (C) as well as genes associated with lipid homeostasis (D). Different letters indicate a statistical significance between treatment groups (n = 8–9).

2.7. Data analyses

3.1. Hatching rates, heart rates and oxygen consumption

Statistical analyses were performed using the SigmaPlot 11 software (Systat, San Jose, CA). Data were analyzed using a 2-way ANOVA (respirometry), or a one-way ANOVA followed by Holm-Sidak test. When the data was not normally distributed as assessed by a Shapiro-Wilk test, they were analyzed by ANOVA on ranks (hatching data) or a Kruskal-Wallis followed by Tukey test when appropriate (qPCR: COX4, CPT1, TFAM, PGC-1α, PGC-1β. enzyme: CPT).

As many morpholinos can induce severe dose-dependent developmental and physiological effects, we started by assessing several physiological indicators to characterize the respective morphants phenotypes. First, we recorded the hatching rate at ~48 hpf and our results indicate no evident difference between fish injected with control morpholinos and either of the PGC-1 morpholinos (Table S1). As seen in previous work (e.g., LeMoine et al., 2018) there was some inherent variability in hatching rates (9.6–19.9% at 50 hpf), which were mostly clutch dependent, but our data overall suggest that none of the morpholinos have extensive effects on this developmental hallmark. Similarly, heart rates recorded at 75–80 hpf showed no obvious effects in any of the morphants (Table S1), and the values obtained were within the range of previous reports at that developmental stage (215.38–223.00 bpm; Jacob et al., 2002, Pylatiuk et al., 2014). Of note, in PGC-1α or β null mice, resting heart rates show no impact of the knockout in vivo (Lai et al., 2008; Lelliott et al., 2006; Leone et al., 2005). Thus it appears that at least at this stage of development, as in mammals, the zebrafish PGC-1α or β do not play an important part in regulating resting cardiac function. Furthermore, as the PGC-1 coactivators have a presumably important role in regulating metabolic pathways, we assessed if the various morphants show differences in their oxygen consumption at 72 and 96 hpf (Table S1). Our data reveal no difference among the various morphant groups, though there was an expected significant increase in oxygen consumption between 72 and 96 hpf larvae comparable to previous reports (Table S1, Bang et al., 2004, Pelster et al., 2005). In mammalian models, there does not seem to be any effects of PGC-1β loss of function on routine metabolic rates, while the effect of PGC-1α knockout is study dependent and seem to be primarily related to thermogenesis, a physiological constraint that would not affect

3. Results and discussion Organisms have an outstanding ability to modulate their mitochondrial metabolism in response to a variety of physiological and environmental constraints. Our current understanding of the pathways responsible for this plasticity primarily relies on knowledge accumulated from mammalian models, and pinpoints the PGC-1 family as a central regulatory hub of metabolic regulation (Scarpulla, 2011). Interestingly, multiple lines of evidence now suggest that this central molecular regulatory hub may not be completely conserved across vertebrates, and seems particularly divergent in fish (e.g., LeMoine et al., 2010b; Orczewska et al., 2010; Windisch et al., 2011). However, most of these studies are correlative in nature, and so far there has been no direct evidence that these coactivators play a direct role in metabolic regulation in teleosts. Therefore, our main aim was to disrupt gene expression of two coactivators in zebrafish embryos to assess some of the putative regulatory roles of the coactivators in this model. Specifically, we used morpholinos directed at PGC-1α and β to transiently alter proper splicing of the transcript and thereby knock down their gene expression levels.

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synthase, a mitochondrial matrix enzyme typically used as an indicator of mitochondrial content (i.e., Larsen et al., 2012), were reduced by close to 50% in both PGC-1 morphants (Fig. 1C), demonstrating a marked reduction in transcriptional control of mitochondrial content in these fish. In contrast, COX-4 expression showed a marginal, but not significant (p = 0.06), reduction in both PGC-1 morphants (Fig. 1C). Further, the mitochondrially encoded COX-1 was unaffected by either of the knockdowns (Fig. 1C). These results are interesting as in most mammalian models, genes associated with the electron transport chain typically show a good correlation with PGC-1α/β, which is not the case in our fish model (Lelliott et al., 2006; Leone et al., 2005; Schreiber et al., 2004; Shao et al., 2010). In contrast, we see a strong transcriptional effect on CS (Fig. 1C), confirming an important role for the coactivators in regulating aspects of mitochondrial content. Thus taken together, given the overall lack of response along the NRF-1 axis (NRF1, TFAM, COXI, COXIV) in either morphants, our results support a decoupling of the PGC-1α/β and NRF-1 pathways in fish as previously suggested (LeMoine et al., 2008; LeMoine et al., 2010a; LeMoine et al., 2010b). In contrast, the transcript levels of two fatty acid oxidation genes, MCAD and HOAD, showed over 50% reduction in both the PGC-1α and β morphants (Fig. 1D). CPT-1 gene expression also showed a trend toward reduced levels, though this was not statistically significant (Fig. 1D). Thus it appears that both coactivators do play a role in the regulation of lipid oxidation. These respective downregulation of MCAD and HOAD in both PGC-1 morphants could result from changes in two pathways which unfortunately cannot be teased apart with certainty here. In mammals, PGC-1α/β can physically interact with both ERRα and PPARα and both these transcription factors have an effect on fatty acid oxidation gene expression, particularly MCAD and HOAD (Barger and Kelly, 2000; Huss et al., 2004; Giguere, 2008; Sladek et al., 1997). Thus, reduced MCAD and HOAD levels could just result from the reduced PGC-1α and β levels, and consequently a reduced coactivation of PPARα or ERRα. However, ERRα is also downregulated in PGC-1α, and marginally reduced in PGC-1β (Fig. 1A), and this transcription factor is known to regulate both FAO genes and CS in mammals (Giguere, 2008; Huss et al., 2004; Rangwala et al., 2007; Sladek et al., 1997). Therefore, the concomitant downregulation of CS, MCAD and HOAD strongly suggests that these effects are mediated by the ERRα pathway. It is also interesting to note that overall we observe very similar effects of the two coactivators on metabolic gene expression, indicating that under these conditions PGC-1α and β exhibit functional redundancy in zebrafish and can regulate the expression of components of the Krebs cycle and lipid metabolism.

ectothermic zebrafish larvae (Lelliott et al., 2006; Leone et al., 2005; Lin et al., 2004; Liu et al., 2007; Sonoda et al., 2007). Therefore, overall we do not see major impact, at least at the macroscopic level, on development, heart rate or metabolic rate in PGC-1α or β knock downs. 3.2. PGC-1 gene expression Next, we used real-time RT-PCR to assess the extent of the morpholino knockdown, and their effects on various transcript levels. First, we focused on assessing transcript abundance of the PGC-1 coactivators in 3 dpf larvae (Fig. 1). Our results demonstrate that the PGC-1α morphants exhibited a 74% reduction in PGC-1α, while PGC-1β expression was down 89% in the PGC-1β morphants (Fig. 1A). Interestingly, we found no increase in gene expression of the other PGC-1 family members, including PRC, in any of our morphants (Fig. 1A), suggesting a lack of compensatory response sometimes experienced in knockout/knockdown experiments (i.e. Lelliott et al., 2006; Sonoda et al., 2007). Actually, PGC-1α knockdown resulted in a ~40% reduction in mean PGC-1β expression, though this result failed to reach significance. The extent of the knockdown that we report here is in line with morpholino studies in zebrafish (i.e., Nasevicius and Ekker, 2000; Summerton, 2007). Thus, given the relative lack of major developmental effects (see above, Table S1), and the resulting knockdowns (Fig. 1A), we are confident that the dosage administered is sufficient for efficient knockdown of our target genes with minimal direct toxic effect of the injection (as discussed in Bill et al., 2009). 3.3. Transcription factor gene expression Next, we explored the effects of the knockdowns on the transcript abundance of several transcription factors that are known PGC-1 binding partners in mammals (NRF-1, ERRα and PPARα), as well as the mRNA levels of TFAM, a PGC-1α/NRF-1 dependent mitochondrial transcription factor (Scarpulla, 2011). Of the four transcription factors investigated, only ERRα showed a significant effect from knockdown, with an approximate 45% downregulation in PGC-1α morphants relative to controls (Fig. 1B). This pattern is consistent with previous work in murine models where ERRα expression and its effects on mitochondrial gene expression are modulated in response to PGC-1α expression (Mootha et al., 2004; Schreiber et al., 2004; Wrann et al., 2013). In contrast, PGC-1β morphants only showed a nonsignificant trend toward reduced ERRα expression (Fig. 1B), which is surprising as PGC-1β is strongly associated with ERRα in mammalian models, where both partners regulate mitochondrial physiology (Ju et al., 2012; Rodríguez-Calvo et al., 2006; Shao et al., 2010). However, in our model the direct regulation of ERRα by PGC-1β is not as evident as it is for PGC-1α (Fig. 1B), potentially suggesting that the two coactivators differ in their ability to interact or regulate ERRα expression. The transcript levels of the other three transcription factors (NRF-1, PPARα, and TFAM) showed overall very little effect of the knockdowns, potentially reflecting a reduced codependence between the coactivators and these transcription factors in developing zebrafish larvae (Fig. 1B). Indeed, in most mammalian PGC-1 knockout or overexpression models there is a concomitant change in the gene expression of these various transcription factors (Lelliott et al., 2006; Leone et al., 2005; Schreiber et al., 2004; Shao et al., 2010). In particular, the expression of NRF1 and TFAM is usually closely associated with PGC-1α levels (e.g., Leone et al., 2005; Liu et al., 2007), an association that we evidently do not see here indicating a potential separation of the PGC-1 and NRF-1 axes in zebrafish (Fig. 1B).

3.5. Metabolic enzyme activities Finally, we assessed whether PGC-1 knockdown was further reflected at the enzymatic level. We measured enzyme activity at 4 dpf for two oxidative phosphorylation enzymes (CS and COX) and two fatty acid oxidation enzymes (HOAD and CPT-1), as well as LDH as a nonmitochondrial enzyme as an endogenous control (Fig. 2). We selected this time point taking into consideration the latency between changes in gene expression and protein/enzyme levels, as well as the fact that it is at the tail end of the efficacy period for morpholino knockdowns (Bill et al., 2009). The enzymatic activity of CS in both control and PGC-1β morphants exhibited rates similar to previously reported values at that stage of development (Pasha and Moon, 2017), but this activity was significantly reduced by approximately one fifth in PGC-1α morphants (Fig. 2). This apparent difference between the effects of the two morphants on CS activity is potentially due to slightly, though not significantly, higher levels of CS mRNA in PGC-1β morphants, which could be reflective of a difference in the pathways affected by both coactivators. Another possibility is that different, but redundant, pathways are involved in the respective regulation of CS by PGC-1α and β, which would be reflected by different temporal responses and different

3.4. Metabolic gene expression At the next regulatory level we examined the changes in mRNA levels of metabolic genes involved in oxidative phosphorylation, Krebs cycle, and fatty acid oxidation (Fig. 1C,D). Transcript levels of citrate 64

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Relative Enzyme Activity

2

b

1.5

a 1

Although we could argue that we are only seeing the early effects of gene expression downregulation, since we are only 4 days into development, the differential effects of the two PGC-1 knockdowns may have to do with the pathways affected which we can't fully tease apart under our current experimental conditions. However, we now have directly demonstrated that, at least in the developing zebrafish larvae, PGC-1α and β play an important role in metabolic regulation. Indeed, the coactivators strongly affect gene expression of multiple metabolic genes, and these alterations could in turn affect mitochondrial metabolic pathways. While our results clearly show an involvement for these coactivators under normal developmental constraints, the caveat of our gene targeting technique is that it is only transient and the phenotypes can only be assessed over a period of a few days. Furthermore, given the size of the larvae at that developmental stage, all our assays are performed on pools of whole larvae and thus tissue-specific effects of the knockdowns cannot be evaluated and would require a different experimental approach. While we are therefore able to comment on the basal role in the transcriptional regulation of mitochondrial maintenance at the level of the entire larvae, it is clear that the extensive function of the coactivators in responding to metabolic stress (i.e., exercise, diet, temperature) cannot be assessed under these conditions. Consequently, current efforts to generate stable mutant lines should alleviate this knowledge gap. Nevertheless, we find that PGC-1α and β carry largely redundant roles in regulating metabolic gene expression during early development in a model vertebrate reminiscent of traditional mammalian models. Our results favor a role in controlling the ERRα pathway rather than the NRF-1 pathway which is overall consistent with previous work (LeMoine et al., 2008; LeMoine et al., 2010a; LeMoine et al., 2010b). However, as is the case in mammals, it appears that these coactivators, while providing an important controlling hub for metabolic gene expression are only modulators rather than master controllers of gene expression as they are dispensable with limited macroscopic impact on the developing larva.

a

a

b

0.5

0

CS

COX Control MO

HOAD

CPT-1

PGC-1α MO

PGC-1β MO

LDH

Fig. 2. Enzymatic activities for PGC-1α MO (grey), PGC-1β MO (white) and control MO (black) larvae. CS, COX, HOAD, CPT-1, and LDH activity per mg of protein were measured for control, PGC-1α and PGC-1β MO larvae at 4 dpf (n = 9) and expressed as a function of control fish (for control in mU.mg−1 protein: CS = 0.40 ± 0.02; COX = 0.45 ± 0.02; HOAD = 0.03 ± 0.002; CPT = 0.01 ± 0.004). Letters indicate statistically significant differences between treatments.

enzymatic phenotypes at this particular time point. In contrast, COX activity remained unchanged in PGC-1α morphants but significantly increased by ~20% in PGC-1β larvae (Fig. 2). This suggests a lack of correspondence between transcript levels and the enzyme activities as transcripts levels of COXI and IV, both cytochrome oxidase subunits, were relatively unchanged in morphants at day 3 (Fig. 1C). Although a similar disconnect has previously been documented for COX in several fish species (Duggan et al., 2011a, 2011b), it could also indicate that other pathways may have been affected in PGC1β morphants, leading to a temporally different compensatory response in cytochrome oxidase activity in these animals. Finally, the enzymes associated with fatty acid oxidation, HOAD and CPT-1, showed no differences between any of the treatments (Fig. 2), following the trend of the glycolytic enzyme LDH. Although it should be noted that CPT-1 enzymes activities were relatively low, with high levels of endogenous deacylase activity (also detected by this assay), thus resulting in inflated variability for that assay. In contrast, the lack of effect on HOAD activity is surprising considering the robust downregulation of the transcripts levels at 3 dpf (Fig. 1D). However, considering these mismatches between gene expression and enzymatic activities, it is quite likely that the temporal constraints associated with morpholino knockdown prevented us to detect the full extent of the effects of the knockdown at the enzyme level.

Acknowledgments This work was supported by an NSERC Discovery grant and a Canadian Foundation for Innovation Grant to C.L.M. Further, C.N. was supported by NSERC-USRA fellowships. The authors would also like to thank Dr. Greenwood for the use of his CFI-funded microscopy suite, and two anonymous reviewers for their constructive comments. Conflicts of interest The authors declare no conflicts of interests. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.cbpa.2019.04.011.

3.6. Role of PGC-1α and β in zebrafish larvae

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