Alexandre Crepin

Gabriel Bidaux

Fabien Vanden-Abeele

Etienne Dewailly

Vincent Goffin #

Natalia Prevarskaya

Christian Slomianny
email: christian.slomianny@univ-lille1.fr

Prolactin stimulates prostate cell proliferation by increasing endoplasmic reticulum content due to SERCA 2b overexpression

Keywords: Prolactin, prostate, calcium, SERCA, proliferation PRL, prolactin, ER, endoplasmic reticulum, [Ca 2+ ] cyt , cytosolic calcium concentration, SERCA 2b, sarcoendoplasmic calcium ATPase 2b, TNT, Tris-NaCl-Tween buffer, HBSS, Hank siRNA, small interference RNA, TG, thapsigargin, PCNA, Proliferating Cell Nuclear Antigen, TRPM8, transient receptor potential

Prolactin (PRL) has been implicated in the differentiation and proliferation of numerous tissues, including the prostate gland. More, variations in calcium concentration within the endoplasmic reticulum ([Ca 2+ ] ER ) may play a role in cell growth. However, few studies have focused on the regulation of calcium homeostasis by prolactin. This present study evaluates the regulation of calcium pools as well as the possible role of [Ca 2+ ] ER variations as a signal for growth modulation by PRL. We show that PRL stimulates the proliferation of normal SV40 immortalized epithelial prostatic (PNT1A) cells with a maximal effect at a dose of 100 ng/ml. We also show that PRL (100 ng/ml) increases the [Ca 2+ ] ER both mesured by indirect quantification with Fura-2AM after application of 1 µM thapsigargin and by direct quantification with MagFura-2AM within the endoplasmic reticulum. Western blot analysis show that the sarcoendoplasmic calcium ATPase 2b (SERCA 2b) are overexpressed in PNT1A cells treated with 100 ng/ml PRL for 24 hours. A siRNA SERCA2a/b, used to downregulate endogenous SERCA 2b expression, reduces PNT1A cells proliferation and the [Ca 2+ ] ER . We thus identify the [Ca 2+ ] ER and SERCA 2b as a central actor for proliferation induced by PRL.

INTRODUCTION

Due to the constant increase in life expectancy, benign prostate hyperplasia and prostate cancer have became very common diseases. Prostate cancer is now the second cause of death induced by cancer among men. It is clearly established that the growth, differentiation and programmed cell death of prostatic cells are regulated by androgens . For this reason, the main treatment for prostate tumours consists of the inhibition of cell growth by suppressing the action or production of endogenous androgens by chemical or surgical castration. This results only in a temporary regression of the disease, since almost all tumours continue to progress.

Therefore, other non-steroid factors or hormones, like prolactin (PRL), are considered to be important for the normal and pathological development of the prostate gland. Indeed, PRL serum levels increase with age, whereas androgen levels decrease, suggesting that the role of PRL in the development of prostate hyperplasia and cancer becomes more important with age. PRL is a polypeptidic hormone, belonging to the cytokine super-family, produced mainly in the pituitary gland, but is also synthesized by peripheral tissues like mammary or prostate glands. Studies of several teams including ours have shown that PRL is one of the non-steroidal factors involved both in prostate cell proliferation and in the development of benign prostate hyperplasia and prostate cancer .

Previous research in our laboratory has demonstrated the crucial role played by Ca 2+ pools in prostate . Indeed Ca 2+ homeostasis modulation is a mechanism common to number of signal transduction pathways regulated a wide range of cell phenomena. Cytosolic Ca 2+ is known to control numerous cell functions, including contraction, gene expression and proliferation . The maintenance of Ca 2+ levels within the lumen of the endoplasmic reticulum is another critical factor for cell growth . The endoplasmic reticulum is a dynamic environment perfectly designed for protein synthesis, post-traductional modifications and medium was replaced every 48 h. Proliferation was determined with the CellTiter 96® AQ ueous Non-Radioactive Cell Proliferation Assay kit (Promega, Madison, USA). siRNA cell transfection -PNT1A cells were transfected overnight with 40 nM siRNASERCA 2a/b, using JetSI TM ENDO transfection reagent (Polyplus transfection SA, Illkrich, France). Ready-to-use siRNASERCA 2a/b (processing option: A4) with the sense sequence of: 5'-CAAAGUUCCUGCUGAUAUA(dTdT)-3' was synthesized by Dharmacon Inc (Lafayette, USA). Control siRNA experiments were performed either by applying the transfection reagent alone (vehicle) or by transfecting siRNATRPM8 .

Ca 2+ Measurements using Fura-2 AM -Cells were grown on glass coverslips and before each imaging experiment, the culture medium was replaced by a Hank's Balanced Salt Solution (HBSS) containing (in mM): NaCl, 142; KCl, 5.6; MgCl2, 1; CaCl 2, 2; Na2PO 4 , 0.34; KH2PO 4 , 0.44; HEPES, 10; glucose, 5.6; and buffered to pH 7.4 with NaOH. Cells were loaded with Fura-2 by exposure to 2 µM fura-2 acetoxymethyl ester (Fura-2 AM) (Calbiochem, Meudon, France) in HBSS for 1 h at 37°C. After incubation, cells were washed three times in the same dye-free solution. When a Ca 2+ -free medium was required, CaCl 2 was omitted and replaced by equimolar MgCl 2 . Intracellular Ca 2+ was measured by an imaging system (Quanticell 900, Visitech, UK). The glass coverslip was mounted in a chamber on a Nikon microscope equipped for fluorescence. Fura-2 fluorescence was excited at 340 and 380

Direct quantification of [Ca 2+

] ER -For imaging Ca 2+ within the ER, PNT1A cells were loaded with 2 µM of the AM-ester derivative of Mag-fura 2 for 45 min at 37°C. After incubation with the dye, the cells were briefly rinsed in a high K+ solution (in mM): 125 KCl, 25 NaCl, 10 HEPES, 0.1 MgCl2 [pH 7.2], and then exposed for 2 min to an "intracellular buffer" at 37°C and 5 mg/ml digitonin. Digitonin-permeabilized cells were continuously perfused with digitonin-free "intracellular buffer" supplement with 0.2 mM Mg-ATP and free [Ca 2+ ] clamped to 170 nM using Ca 2+ /EGTA buffer. The Mag-fura 2 fluorescence ratio was calibrated using exposure to 10 µM ionomycin and 15 mM Ca 2+ or 10 mM EGTA, assuming a dissociation constant for Ca 2+ -Mag-fura 2 at room temperature of 53 µM . Ratio imaging measurements of Mag-fura 2 fluorescence were made using a commercial imaging sytem (Quanticell 900, Applied Imaging, UK).

Western Blot -Following the treatments (PRL or siRNA treatment), PNT1A cells were lyzed with RIPA buffer (Triton X100 1%, Na Deoxicholate 1%, NaCl 0.15M, PO4Na2/K pH 7.2 20mM) + 5mM EDTA + 5mM sodium orthovanadate + anti-proteases cocktail (Sigma P 8340, St Quentin Fallavier, France) for 30 minutes on ice. Insoluble material was discarded by centrifugation at 10,000g for 10 minutes at 4°C and the amount of proteins was assessed by the Bicinchoninic acid (BCA) method (Pierce, Chemical Co., Rockford, IL). Equal amounts of proteins were subjected to 16 or 10% SDS-polyacrylamide gel electrophoresis. Finally, the proteins were transferred onto nitrocellulose membrane using a semi-dry electroblotter (Biorad). After the transfer, the membrane was stained with Ponceau Red and the tracks were cut into thin strips (2mm wide) that were further processed for immuno-detection. The strips were blocked for 30 minutes at room temperature in TNT buffer ( 15 Frank Wuytack), rabbit antibody anti-Calreticulin (Stressgen Biotechnologies, San Diego, CA), rabbit antibody anti-Jak2 or rabbit antibody anti-phosphoJak2 diluted in TNT + milk (1/500 for anti-Actine, 1/1000 for anti-PCNA, 1/5000 for anti-SERCA 2b, 1/10000 for anti-Calreticulin, 1/1000 for anti-Jak2 or 1/1000 for anti-phosphoJak2) for 1 hour at room temperature. After thorough washes in TNT, the strips were treated with the corresponding horseradish peroxydase-linked secondary antibodies (anti-mouse AP192P or anti-rabbit AP182P, Chemicon, Temecula, CA) diluted in TNT + milk (1/20,000) for 1 hour at room temperature. After several washes in TNT, the strips were processed for chemiluminescent detection using the Supersignal West Pico chemiluminescent substrate (Pierce, Rockford, IL), according to the manufacturer instructions. The blots were finally exposed to X-Omat AR films (Eastman Kodak Company, Rochester, NY). The intensity of the signals was evaluated by densitometry and semi-quantified using the ratio between the rate of protein of interest divided by the actin or calnexin rate for each experiment. Each experiment presented was repeated at least twice.

Indirect Immunofluorescence -PNT1A cells were grown on glass coverslips. After 10 min cold (-20°C) acetone permeabilization, the cells were blocked with PBS-gelatine (Phosphate buffer saline, gelatine 1.2%) for 30 minutes at 37°C and then incubated with a mouse Reagents and Chemicals -Human recombiant prolactin (PRL) was kindly provided by Vincent Goffin . Fura-2 AM and Mag Fura-2 AM were obtained from Calbiochem (Meudon, France) and thapsigargin (TG) from Sigma (France).

Data analysis -The data were analyzed using Origin 5.0 (Microcal Software Inc., Northampton, MA, USA). Results were expressed as mean + standard error of the mean (sem). Each experiment was repeated several times, n indicates the number of cells used to express mean and sem and N the number of experiments. Statistical analysis was performed using the student's t-test (P<0.05 considered as significant) and ANOVA tests followed by Tukey-Kramer post-tests.

RESULTS

PNT1A cell growth is stimulated by PRL -PRL stimulates PNT1A cell growth in a dosedependant manner (Fig. 1 A). 50 -1000 ng/ml PRL increased cell growth after 4 days treatment, as estimated by CellTiter 96® AQ ueous Non-Radioactive Cell Proliferation Assay kit. The maximal effect was observed at a 100 ng/ml dose. At this concentration, PRL induced an increase of proliferation to 122.2% + 6.5% compared to control conditions. We have also checked the effect of PRL on the expression of PCNA (Proliferating Cell Nuclear Antigen), a proliferation marker. Western blot analysis (Fig. 1 B) showed that PCNA was weakly expressed in PNT1A cells under control conditions. PCNA proteins were 2.3 fold overexpressed in PNT1A cells treated for 24 hours with 100 ng/ml PRL (Fig. 1 B) corresponding to the maximal effect observed in the proliferative assay. The PRL induced increase in the PCNA expression was confirmed by immunocytochemical assays (Fig. 1 Copyright 2006 Biochemical Society Indeed, in treated cells with 100 ng/ml PRL for 24 hours, the PCNA nuclear positive cells was higher (45%) than in control cells (22%). These data were confirmed using an another proliferation marker, the nuclear antigen Ki67 (data not shown).

The ER Ca 2+ content is increased in PNT1A cells pretreated with PRL -As the maintenance of luminal Ca 2+ within the endoplasmic reticulum has been demonstrated to be essential for cell growth in different cell models , we investigated whether the Ca 2+ pool content was modulated in PRL stimulated PNT1A cells. The basal cytosolic Ca 2+ level was not affected when PNT1A cell growth was stimulated by PRL (Fig. 2 A). Under control conditions, the [Ca 2+ ] cyt content of PNT1A cells was 49.7 nM ± 1.2 nM. No significant increase in [Ca 2+ ] cyt was observed in cells treated with 100 ng/ml PRL (50.2 nM ± 1.2 nM). However, the Ca 2+ release from the intracellular stores induced by 1 µM Thapsigargin in cells treated for 24 hours with 100 ng/ml PRL was higher than that in control cells (Fig. 2 B). It is important to point out the remarkable similarity between the extent of cell proliferation (Fig. 1 A) and the amplitude of the TG-induced Ca 2+ release from intracellular stores (Fig. 2 B). The maximal amplitude of TG-induced Ca 2+ release (669.2 nM ± 44.2 nM), corresponding to 156.1 % ± 10.3 % of the TG response observed under control conditions (428.8 nM ± 18.7 nM), was measured in cells with a maximal growth rate (treated with 100 ng/ml PRL). This result suggests that there is a close connection between the Ca 2+ pool content and the proliferation of PNT1A cells. Moreover, 50 µM AG490, a potent and specific inhibitor of Jak2 kinase activity (the major PRL receptor associated kinase) made PRL incapable of stimulating PNT1A cell growth and the amplitude of the TG-induced Ca 2+ release from intracellular stores in treated cells with AG490 and PRL was the same as under control conditions (data not shown). The higher [Ca 2+ ] ER in PRL treated cells, estimated by TG-induced Ca 2+ release method, has been confirmed using the direct quantification of luminal [Ca 2+ ] ER (Fig. 2 C). The luminal [Ca 2+ ] ER was significantly higher in PRL-treated cells (ratio 0.95 ± 0.02) than in control cells (ratio

5

  nm and emitted fluorescence was measured at 510 nm. The [Ca 2+ ] cyt was derived from the ratio of the fluorescence intensities for each of the excitation wavelengths (F 340 /F 380 ), and from the Grynkiewicz equation. Thapsigargin (TG), the most potent selective inhibitor of Sarco(endo)plasmic reticulum Ca 2+ -ATPase (SERCA) is often used to estimate Ca 2+ pool content. In a Ca 2+ -free medium, TG induces an increase in [Ca 2+ ] cyt , due to Ca 2+ release from intracellular stores. Endoplasmic reticulum (ER) Ca 2+ content could be estimated in Fura-2 Biochemical Journal Immediate Publication. Published on 12 Sep 2006 as manuscript BJ20060870 Copyright 2006 Biochemical Society loaded cells by measuring the difference between basal [Ca 2+ ] cyt and maximum [Ca 2+ ] cyt at peak induced by TG.

  mM Tris buffer pH 8, 140mM NaCl, 0.05% Tween 20) + 5% skimmed dry milk and then incubated with : mouse 6 Biochemical Journal Immediate Publication. Published on 12 Sep 2006 as manuscript BJ20060870 Copyright 2006 Biochemical Society antibody anti-Actine (pan Ab-5, Neomarkers, Fremont, CA), rabbit antibody anti-PCNA (sc-56 S, Santa Cruz), rabbit antibody anti-SERCA 2b (poly clonal rabbit IgG, gift from Prof.

  antibody anti PNCA diluted in PBS-gelatine (1/100) for 1 hour at 37°C. After thorough washes in PBS-gelatine, the slides were treated with Alexa fluor 488-labeled secondary antibody (Molecular Probes, USA) diluted in PBS-gelatine (1/4000) for 1 hour at room temperature with blue-evans (1/50 000). After two washes in PBS-gelatine and in PBS, the slides were mounted with Mowiol® and watched through a confocal microscope (Zeiss LSM 510). The acquisition parameters were kept identical throughout the whole experiment to 7 Biochemical Journal Immediate Publication. Published on 12 Sep 2006 as manuscript BJ20060870 Copyright 2006 Biochemical Society allow comparison of the intensity of the fluorescence level. The intensity of the signals was directly quantified on the confocal microscope with LSM examiner software (AIM 3.2, Zeiss, Le Pecq, France). Each experiment presented was repeated at least three times.

Biochemical Journal Immediate Publication. Published on 12 Sep 2006 as manuscript BJ20060870

  C).

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Copyright 2006 Biochemical Society 0.72 ± 0.02). The functional activity of the sarcoendoplasmic calcium ATPase has been confirmed using the direct quantification of luminal [Ca 2+ ] ER (Fig. 2 D) using the classical protocol to measure the Ca 2+ re-uptake into ER after inositol triphosphate (IP3)-induced release . As demonstrated by the d[Ca 2+ ]/dt max , the luminal [Ca 2+ ] ER is more rapidly refilled in PRL-treated cells (8.5 µM.s -1 ) than in control cells (2 µM.s -1 ) confirming that the sarcoendoplasmic calcium ATPase are more active in PRL-treated cells than in control cells. Thus, our results clearly demonstrate PRL induces the increase in the ER Ca 2+ content, suggesting that the cell growth stimulation by PRL could be linked to the high ER Ca 2+ luminal concentration.

SERCA2b protein expression in PNT1A cells is regulated by PRL -We have further

investigated the effect of PRL on the expression of SERCAs (Sarco(endo)plasmic reticulum Ca 2+ -ATPase) proteins known to play an important role in the maintenance of luminal [Ca 2+ ] ER . Moreover, it has been suggested that SERCA proteins regulate cell growth by controlling the expression of growth and transformation related genes . We therefore investigated the effect of PRL on the expression of three SERCA isoforms: SERCA2a (the cardiac/slow skeletal muscle isoform), SERCA2b (the ubiquitously expressed isoform) and SERCA 3. In ours experiments, SERCA2a and SERCA 3 protein expression was not detected under control conditions or in PRL-treated cells. However, western blot analysis showed that SERCA2b was constitutively expressed in control cells and overexpressed (3.6 fold) in cells treated with 100 ng/ml PRL for 24 hours (Fig. 3 A). This PRL induced up-regulation of SERCA2b expression in PNT1A cells seemed to be dependent on tyrosine kinase activity.

After a brief (3 hours) PRL treatment, western blot analysis has shown that Jak2 kinase (the major PRL receptor associated kinase) was more phosporylated in PRL-treated cells than in control cells (Fig. 3 C). When cells were treated with 100 ng/ml PRL in the presence of 50 µM Jak2 kinase inhibitor AG490, we did not observe up-regulation of SERCA2b Copyright 2006 Biochemical Society (Fig. 3 A). Treating PNT1A cells with AG490 alone had no effect on SERCA 2b expression under control conditions (datas not shown). We also studied calreticulin expression, the luminal Ca 2+ binding/storage chaperon, to confirm that SERCA2b overexpression was specific to PRL and was not due to the proliferation increase in proteins synthesis. We therefore compared the expression of calreticulin in control and in PRL-treated cells, using semi-quantitative western blot analysis. PRL treatment inducing SERCA 2b overexpression did not modified calreticulin expression (Fig. 3 B).

SERCA2b protein expression in PNT1A cells is closely linked to cell proliferation -siRNA

technology was used to downregulate the endogenous SERCA 2b expression in PNT1A cells with a siRNA SERCA2a/b. Western blotting with anti-SERCA 2b antibody on the first and 3nd day after transfection demonstrates that the level of SERCA 2b protein was decreased by 70% compared to control (Fig. 4 A), indicating the effectiveness of siRNA to downregulate endogenous SERCA 2b expression. Then, we tested the siRNA SERCA2a/b effect on PNT1A proliferation. We observed a 13.6% reduction in the proliferation of the SiRNA SERCA2a/b treated cells compared to control (Fig. 4 B). The result was also confirmed by PCNA expression method. Western blot analysis shows a down-regulation of PCNA expression in siRNA treated cells (Fig. 4 C). Then, we investigated whether the Ca 2+ pool content was modulated in siRNA SERCA 2a/b treated PNT1A cells. The basal cytosolic Ca 2+ level was affected, an increase in [Ca 2+ ] cyt was observed in cells treated with siRNA SERCA2a/b (111.7 nM ± 10.1 nM) compared to controls (64.9 nM ± 2.8 nM) (Fig. 5 A). The Ca 2+ release induced by 1 µM Tapsygargin in siRNA SERCA 2a/b treated cells was about 52 % smaller than that in control cells (Fig. 5 B). Because of the higher basal cytosolic Ca 2+ level, the resulting driving force may reduce the Ca 2+ release in siRNA SERCA 2a/b treated cells. Therefore, these results have been confirmed using the direct quantification of luminal [Ca 2+ ] ER (Fig. 5 Copyright 2006 Biochemical Society control cells (ratio 0.87 ± 0.04). These results show that, in PNT1A human prostate normal cells, SERCA2b is a common target of physiological stimuli controlling cell growth by PRL.

DISCUSSION

It has been established that prolactin and its receptors are expressed in the prostate.

These datas have provided significant support for the existence of an autocrine/paracrine loop of PRL in prostate cells. have shown that prolactin induced proliferation and survival of prostate epithelial cells in the long term organ culture of rat prostate tissue explant. In addition, reported dramatic prostate enlargement in prolactin transgenic mice. In ours previous studies, we have established that a chronic hyperprolactinemia, caused by sulpiride, induced an enlargement and an inflammation of the lateral rat prostate . PRL stimulates the proliferation of LNCaP prostate cancer cells . In this study, we show that PRL stimulates the proliferation of the normal SV40 immortalized prostatic PNT1A cells. Ours experiments are consistent with those reported by , shown that PRL induced the proliferation in LNCaP, DU145 and PC3 cancer cells. These authors have also found that PRL stimulates the proliferation of PC3 cells in a dose-dependant manner. The mechanisms by wich PRL stimulates prostate cell proliferation remain poorly studied.

Ca 2+ homeostasis modulation is involved in number of signal transduction pathways regulating into a wide range of cell phenomena. Cytosolic Ca 2+ is known to control numerous cell functions, including contraction, gene expression and proliferation . However, it has been documented that Ca 2+ pools control numerous cell functions, including protein synthesis and cell proliferation and maintenance of Ca 2+ levels within the lumen of the endoplasmic reticulum is critical for cell growth . Indeed, a range of Ca 2+ -binding proteins within the ER lumen are involved in the folding and correct assembly of proteins. The increased effectiveness of these proteins may be the primary reason for growth progression. An Copyright 2006 Biochemical Society alternative or additional possibility is that high Ca 2+ concentration within the ER may promote important growth factor-initiated, InsP 3 -mediated Ca 2+ signals that are generated through release Ca 2+ from ER and that are essential for cells to progress through cell cycle and maintain growth. We therefore investigated the potential variation in PNT1A Ca 2+ pool content linked to PRL increased proliferation. Our results show that the PRL induced proliferation of PNT1A cells is at least in part due to [Ca 2+ ] ER increase and the SERCA 2b overexpression by the Jak2 pathway. We could not observe any Ca 2+ modifications in the cytoplasm or any modifications in the SERCA 2b activity following quick PRL traitment. So, we suggest that PRL promotes, via the Jak2-Stat5 transduction signal, the SERCA 2b gene transcription which increase the [Ca 2+ ] ER and then cell growth. The intracellular calcium ATPase and the Ca 2+ pool content have already closely associated with cell growth and could be modulated by hormones or growth factors like platelet-derived growth factor . We have shown that the Epidermial Growth factor modulated prostate cancer LNCaP cell growth and it was correlated with intracellular calcium pool content : The rate of cell growth was correlated with an increase in the calcium pool filling state, whereas growth-inhibited cells shown a reduced calcium pool load. More, we also demonstrated that the Insulin Growth Factor, which increased prostate cancer LNCaP cell growth, induced an increase in [Ca 2+ ] ER and was associated with an overexpression of SERCA 2b . To our knowledge, in this study we show for the first time that PRL stimulates cell proliferation via the overexpression of SERCA 2b and the subsequent upregulation of the ER Ca 2+ content.

To study the consequence of a reduction of [Ca 2+ ] ER of the cell growth and to confirm the implication of SERCA 2b in PRL-induced proliferation, we used the downregulation of SERCA 2b protein by siRNA strategy. Our results shows that a reduction in Ca androgen-independent human prostate cancer cells . In ours experiments, we also found that cytosolic Ca 2+ in siRNA SERCA 2b treated cells was higher than in control cells. This could be due to the reduced calcium pool load or to a mechanism of compensation for Ca 2+ signaling via up-regulation of membrane Ca 2+ channels and exchangers. have shown that the reduction of SERCA expression by gene silencing was associated with the up-regulation of transient receptor potential (TRP) channel proteins (TRPC4 and TRPC5) and Na + / Ca 2+ exchanger in cardiac myocytes. Store deficiency was in this study compensated for by Ca 2+ fluxes through the plasma membrane.

In summary, our study identified and characterized for the first time the intracellular Ca 2+ homeostasis mechanisms involved in PRL regulation of the PNT1A prostate cell growth.

We have shown that PRL increases the intracellular stores Ca 2+ amount in this proliferative cells through the overexpression of SERCA 2b calcium pumps. Using siRNA technology, we have specifically down-regulated SERCA 2b expression and shown that the intracellular stores Ca 2+ amount was proportionally reduced. As a consequence, this reduces the PNT1A cells proliferation. We suggest that [Ca 2+ ] ER exerts a profound control over cell growth and it is a crucial element in the PRL signal transduction.

ACKNOWLEDGMENTS


Florentin Remot
email: florentin.remot@gmail.com

Victor Ronget

Hannah Froy

Benjamin Rey

Jean-Michel Gaillard

Daniel H Nussey

Jean-François Lemaître Lemaître

No sex differences in adult telomere length across vertebrates: a meta-analysis

Keywords: Organismal and evolutionary biology telomere dynamics, sexual size dimorphism, heterogametic sex, ageing, longevity, life-history strategies

à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Telomeres are DNA structures composed of non-coding sequences that are repeated in tandem at the extremity of linear chromosomes. Telomeres are essential to the maintenance of genomic integrity and © 2020 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

protect coding DNA against two major deleterious processes: 'the end replication problem' and oxidative damage [START_REF] Aubert | Telomeres and aging[END_REF]. In the absence of telomerase, an enzyme that acts to elongate telomeres, telomeres unavoidably shorten with each DNA replication cycle. Critically short telomeres are associated with genomic instability (e.g. chromosome fusions, chromosome breaks), cell cycle arrest and can trigger apoptosis. The shortening of telomere sequences with increasing age is now considered as a key physiological mechanism underlying ageing [START_REF] López-Otín | The hallmarks of aging[END_REF]. While a causal association among telomere length (TL), age-specific diseases and survival prospects remains uncertain [START_REF] Simons | Questioning causal involvement of telomeres in aging[END_REF], telomere dynamics have been associated with several age-related disorders such as cardiovascular diseases [START_REF] Aviv | Reflections on telomere dynamics and ageing-related diseases in humans[END_REF] in humans. In addition, shorter telomeres are associated with a higher mortality risk in humans [START_REF] Wang | Telomere length and all-cause mortality: a meta-analysis[END_REF] and in several populations of vertebrates [START_REF] Wilbourn | The relationship between telomere length and mortality risk in non-model vertebrate systems: a meta-analysis[END_REF].

Women and men show striking differences in adult health and lifespan, the shorter lifespan of men being a particularly robust feature across populations worldwide [START_REF] Austad | Sex differences in lifespan[END_REF]. Among the many complex genetic and physiological factors that might govern those sex differences, a possible role played by sex-specific telomere dynamics has been proposed [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF]. Recently, a meta-analysis on sex differences in TL in humans (compiling 40 studies) revealed that adult women have on average slightly longer telomeres than adult men (i.e. 176 bp longer in women than in men of similar age). However, this study also revealed high heterogeneity in the magnitude and direction of the sex differences observed across studies (I 2 = 91.4%) [START_REF] Gardner | Gender and telomere length: systematic review and meta-analysis[END_REF]. So far, most human studies have been cross-sectional and have focused on middleaged or elderly individuals. Thus, it has not been possible to determine whether average sex differences in adult TL result from differences determined genetically or in early life and maintained through adulthood, or from differences in the rate of telomere attrition (i.e. telomeres shortening faster in men over time), or both. A recent study based on umbilical cord blood found 144 bp longer telomeres in girls than in boys [START_REF] Factor-Litvak | Aviv royalsocietypublishing[END_REF], suggesting that sex differences in human TL are already present at birth. However, these results are inconsistent with previous observations on humans that show no gender difference in TL at the start of life (at birth and in utero) [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF].

Until now, two main evolutionary hypotheses have been proposed to explain why TL could differ between sexes [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF]. First, the heterogametic disadvantage hypothesis predicts that the heterogametic sex (e.g. XY males in mammals) should have shorter telomeres than the homogametic sex (e.g. females XX in mammals) if the Y chromosome contains fewer telomere maintenance alleles [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF]. Moreover, if males carry a deleterious mutation in telomere maintenance alleles on the X chromosome, this mutation will always be expressed in males while in females it can be compensated by the second X chromosome [START_REF] Marais | Sex gap in aging and longevity: can sex chromosomes play a role?[END_REF]. Therefore, the heterogametic disadvantage hypothesis is not based on a difference in telomere length between the X and Y chromosomes, but on a difference in gene content, especially those involved in telomere maintenance. For instance, the DKC1 gene that codes for dyskerin, a protein involved in the stability of telomerase, is located on the X chromosome [START_REF] Aubert | Telomeres and aging[END_REF]. Mutations of this gene induce dyskeratosis congenita whose symptoms resemble premature ageing and critically short telomeres. In birds, due to their ZW sex-determination system (i.e. males are ZZ and females are ZW), this hypothesis predicts shorter telomeres in females, assuming that telomere maintenance alleles are located on the homogametic sex chromosome (Z chromosome). In reptiles and fish, predictions are more complicated and will depend on the sex-determination system of each species. This hypothesis is indirectly supported by a recent meta-analysis, which showed that in vertebrates the heterogametic sex lives shorter than the homogametic sex [START_REF] Xirocostas | The sex with the reduced sex chromosome dies earlier: a comparison across the tree of life[END_REF]. Second, the sexual selection hypothesis posits that the sex growing faster and maintaining a larger body mass throughout the lifetime should display shorter telomeres due to increased cell replication during the growth phase and the regeneration of tissues along the life course [START_REF] Young | The role of telomeres in the mechanisms and evolution of life-history tradeoffs and ageing[END_REF][START_REF] Stindl | Tying it all together: telomeres, sexual size dimorphism and the gender gap in life expectancy[END_REF]. Mammals, for instance, tend to show often a strong male-bias in body size (associated with polygynous mating systems) [START_REF] Lindenfors | Sexual size dimorphism in mammals. In Sex, size and gender roles: evolutionary studies of sexual size dimorphism[END_REF], meaning that, following the sexual selection hypothesis, males should have shorter telomeres than females. However, unlike the heterogametic disadvantage hypothesis, the sexual selection hypothesis makes predictions within groups as well as across groups because variation in sexual size dimorphism occurs both among and within vertebrate groups. So far, the vast majority of research on the genetic and physiological determinants of sex differences in ageing has focused on laboratory organisms [START_REF] Austad | Sex differences in lifespan[END_REF] (but see [START_REF] Marais | Sex gap in aging and longevity: can sex chromosomes play a role?[END_REF]). To fully understand the universal mechanisms that modulate ageing, we need to know whether mechanisms that have been suggested to contribute to ageing, such as telomere dynamics, are shared between different lineages or if these mechanisms are species-specific [START_REF] Partridge | Mechanisms of ageing: public or private?[END_REF]. To evaluate the role of evolutionary forces in shaping sex differences in vertebrate TL, it is necessary to test these hypotheses across a wide range of species displaying contrasted life-history strategies.

A previous qualitative review of sex differences in TL across animals suggested that telomeres may be longer in females than in males in mammals, but not in birds [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF]. However, these observations were made on a limited number of species (i.e. 20 species of birds and 5 species of mammals), with a bias toward laboratory rodents in mammals. Laboratory rodents are known to display unusual telomere dynamics, which are royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548 neither representative of mammals in general nor their wild counterpart (i.e. telomeres in laboratory mice are known to be particularly long compared with those in wild-derived mice, see [START_REF] Hemann | Wild-derived inbred mouse strains have short telomeres[END_REF][START_REF] Gomes | Comparative biology of mammalian telomeres: hypotheses on ancestral states and the roles of telomeres in longevity determination[END_REF]). They may therefore provide only limited insight into the evolutionary forces that have shaped sex differences in TL across vertebrates. The recent surge of studies investigating telomere dynamics in wild populations [START_REF] Wilbourn | The relationship between telomere length and mortality risk in non-model vertebrate systems: a meta-analysis[END_REF][START_REF] Monaghan | Understanding diversity in telomere dynamics[END_REF][START_REF] Chatelain | The association between stressors and telomeres in non-human vertebrates: a meta-analysis[END_REF] now provides the necessary material to test whether consistent sex differences in mean TL actually occur across vertebrates and to quantify the magnitude of these differences. In this article, we used a meta-analytic approach to examine sex differences in mean TL across non-human vertebrates and to test the main evolutionary hypotheses proposed to explain such sex differences. According to the heterogametic sex hypothesis, we predicted that the heterogametic sex should have shorter telomeres than the homogametic sex. According to the sexual selection hypothesis, we expected that the magnitude of sex differences in mean TL would be positively associated with the intensity of sexual size dimorphism. Finally, we examined whether sex differences in mean TL could explain differences in life expectancy between the sexes, which are widely observed among vertebrates [START_REF] Marais | Sex gap in aging and longevity: can sex chromosomes play a role?[END_REF][START_REF] Lemaître | Sex differences in adult lifespan and aging rates of mortality across wild mammals[END_REF]. We predicted that, across species, the longer lived sex should have longer telomeres.

Material and methods

Literature survey

Relevant articles were collected by entering the following keywords 'telom à NOT (clinic à OR hospital)' in the 'topic' window of the ISI Web of Science database. The keywords telom à allowed us to find articles not only about telomeres but also about telomerase, and the use of 'NOT (clinic à OR hospital)' enabled us to exclude many studies exclusively focused on humans. The literature search was restricted to the following Web of Science Categories: 'Evolutionary biology', 'Marine freshwater biology', 'Multidisciplinary sciences', 'Geriatrics gerontology', 'Physiology', 'Zoology', 'Environmental sciences', 'Fisheries', 'Ecology', 'Agriculture dairy animal sciences' and 'Veterinary sciences'. The search was conducted in January 2019. Using this protocol, 5029 articles were gathered. We performed a first selection based on the title and the abstract to exclude single sex or human studies. All the remaining articles (N = 324) were then read in full to exclude studies according to a predefined set of criteria (figure 1). In experimental studies, we excluded the 'treatment group' because the treatment can influence telomere dynamics [START_REF] Monaghan | Understanding diversity in telomere dynamics[END_REF], potentially in a sex-specific way, but we retained the 'control group'. Similarly, we excluded studies that measured telomeres in cell cultures, in laboratory strains or in domesticated species because these measurements were unlikely to reflect telomere length in natural conditions [START_REF] Hemann | Wild-derived inbred mouse strains have short telomeres[END_REF]. We also excluded data on neonates (i.e. before fledging in birds, before weaning in mammals) because the number of newborn individuals (which is higher than older individuals) and the different dynamics of telomere between them [START_REF] Rollings | Age-related sex differences in body condition and telomere dynamics of red-sided garter snakes[END_REF] may mask sex differences in TL if these differences occur later in life. Finally, we checked the full reference lists of the 324 selected articles and of the two meta-analyses performed on TL across several populations of vertebrates [START_REF] Wang | Telomere length and all-cause mortality: a meta-analysis[END_REF][START_REF] Hemann | Wild-derived inbred mouse strains have short telomeres[END_REF] to check that we had not missed any relevant article with our literature search protocol. This procedure allowed us to include data from two PhD theses [START_REF] Foote | Avian telomere dynamics[END_REF][START_REF] Izzo | Patterns of telomere length change with age in aquatic vertebrates and the phylogenetic distribution of the pattern among jawed vertebrates[END_REF] in our analyses. It appeared that some datasets were published, partially or entirely, in more than one article (i.e. 12 datasets or subset of datasets were published more than once). In those cases, we selected the article with the largest dataset or if the datasets were identical, the most recent. By applying this research protocol and the exclusion criteria, we obtained a total of 58 articles covering 52 vertebrate species (figure 1).

Extraction of effect sizes

TL was frequently reported as mean absolute or relative TL (depending on the measurement method, see below) with the standard deviation (or the standard error) and the sample size. When the full dataset was available (e.g. data published in research data repositories such as Dryad), the mean and the standard deviation of TL for both sexes were calculated from the raw data. When the TLs were only graphically displayed in the papers, we extracted the data using the WebPlotDigitizer software [START_REF] Rohatgi | WebPlotDigitizer[END_REF], and then calculated the mean and the standard deviation of TL for each sex separately. When no data were reported in the article and no dataset was publicly available, we requested the following information from the corresponding authors: mean TL, standard deviation and sample size for both sexes.

To be comparable across studies, effect sizes of the mean differences in TL need to be standardized. The most commonly used standardized mean difference is the 'Hedge's g' (or 'Hedge's d') (see [START_REF] Koricheva | Handbook of meta-analysis in ecology and evolution[END_REF] for further details), which is not affected by unequal sampling variance. Females were set as the reference royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548 group. Therefore, a positive value of g means that males have longer telomeres than females, whereas a negative value of g means that females have longer telomeres than males. For the meta-analysis, the interpretation of the effect size followed the Cohen's rule of thumb [START_REF] Cohen | Statistical power analysis for the behavioral sciences[END_REF] stating that g absolute values of 0.2, 0.5 and 0.8 represent low, medium, and large differences, respectively (effects size of each study are summarized in table 1).

Additional information extracted from the articles or the literature

All information required for the identification of the paper (title, first author, year of publication, journal, species and population studied) was recorded. We also extracted information regarding the method of TL measurement (e.g. TRFS for telomere restriction fragment followed by a Southern blot, TRFI for telomere restriction fragment followed by an in-gel hybridization and quantitative PCR), the biological tissue sampled to measure telomeres (e.g. red blood cell, white blood cell or other tissues), and whether telomeres were measured in free-living or captive animals. We also created a data extraction quality index (whether the effect size was computed from a graph [i.e. low-quality index] or directly from data in the article [i.e. high-quality index]). To assess the factors of variation in the magnitude of sex differences in TL, we then searched for the following information in the literature for the set of the 52 vertebrate species included in the meta-analysis: female age at first reproduction (log-transformed), mean adult body mass (log-transformed), mating system and mean annual adult survival for both sexes. All variables are fully described in electronic supplementary material, Methods and Results and were included as moderators in the meta-regression separately (i.e. subset analysis) to avoid overfitting the model. royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548

Table 1. Summary of the statistics and of the potential driving factors compiled in the meta-analysis. For each study, the sex difference in TL (g), its variance and its sample size are reported. The tissue sampled is reported as RBC for red blood cells, WBC for white blood cells and other for all other types of tissue. The method of TL measurement is reported as TRFS for telomere restriction fragment followed by a Southern blot, TRFI for telomere restriction fragment followed by an in-gel hybridization, and qPCR. See electronic supplementary material, Methods and Results for further information.

General model

The meta-analytic model was run using R (v. 3.3.1, [START_REF] Core | R: a language and environment for statistical computing[END_REF]). To account for the non-independence of observations due to pseudo-replication (multiple effect sizes calculated within the same study, species, population or by the same authors), linear mixed-effects models were run with the package MCMCglmm [START_REF] Hadfield | MCMC Methods for multiresponse generalized linear mixed models: the MCMCglmm R package[END_REF]. As related species are more prone to share common trait values, our meta-analytic models controlled for phylogeny [START_REF] Nakagawa | Methodological issues and advances in biological meta-analysis[END_REF]. The website http://www.timetree.org/ [START_REF] Kumar | TimeTree: a resource for timelines, timetrees, and divergence times[END_REF] provided us with a phylogenetic tree (see electronic supplementary material, figure S1) which we further used to control for phylogenetic relatedness among species. This website gathered data from more than 2000 articles published between 1987 and 2013 to estimate divergence times between species and to construct a phylogenetic tree at the vertebrate level. A covariance matrix among the species was then extracted from the tree.

In the meta-analytic model, effect sizes were entered as the dependent variable and the sampling variance associated with each effect size was also included in the model using the mev argument. We included the following variables as random factors: the phylogeny (using the variance-covariance matrix), the population (to control for the fact that several populations were sometimes sampled within a given species), the sample (to control for the fact that effect sizes were measured from the same individuals but with different methods or in different tissues) and the species (i.e. to take into account that several effect sizes were measured for a given species and can share biological characteristics that are independent of the phylogenetic relatedness such as the habitat). Since we had no a priori information on the parameters of the meta-analytic model, we used a non-informative prior (inverse Wishart prior with ν = 0.02 and V = 1). To test if the prior had any impact on the results, we re-ran the meta-analysis using a new parameter expanded prior for the random effect (ν = 1, V = 1, alpha.mu = 0 and alpha.V = 1000). Since our results remained unchanged they did not appear to be sensitive to prior specification. The model was run with 1 000 000 iterations (burn-in = 1000 and thinning interval = 50) twice, and convergence of the model was assessed with the Gelman-Rubin diagnostic [START_REF] Gelman | Inference from iterative simulation using multiple sequences[END_REF] using the gelman.diag function in R (cut-off value = 1.1, [START_REF] Brooks | General methods for monitoring convergence of iterative simulations[END_REF]).

From the meta-analytic model, we computed the posterior distribution of the meta-analysis mean and its highest posterior density credible interval (HPDI) at 95%. The effect size was considered significant if the credible interval did not overlap 0. To quantify the heterogeneity in the data accounting for random factors, the I 2 and their HPDI at 95% [START_REF] Nakagawa | Methodological issues and advances in biological meta-analysis[END_REF] were calculated for each random factor with I 2 = 25, 50 and 75% considered as low, moderate and high heterogeneity, respectively. In addition, we also computed the H 2 , which is the percentage of the between-study variance explained by the phylogenetic effect. The proportion of the total variance explained by the between-study variance, I 2 tot , was also calculated [START_REF] Nakagawa | Methodological issues and advances in biological meta-analysis[END_REF]. Finally, to assess whether the effect sizes estimated from populations with small sample size could bias our results, we also ran the meta-analytic model including only estimates that were based on 40 or more data points. Overall, results were qualitatively unchanged (see electronic supplementary material, table S1).

Age-correction of telomere length

Two other meta-analyses (based on the same procedure as described above) were then performed to establish whether age effects biased the results in any way. First, we restricted our meta-analysis to measurements made on adults only to remove the effect of a possible sex difference in telomere shortening of juveniles (i.e. before the age at first reproduction) [START_REF] Rollings | Age-related sex differences in body condition and telomere dynamics of red-sided garter snakes[END_REF]. This meta-analysis included 46 articles and is referred to as 'meta-analysis 2' below. Second, we performed a separate analysis using agecorrected adult TL (referred to as 'meta-analysis 3' below) to correct for possible sex differences in telomere dynamics over the entire life course [START_REF] Rollings | Age-related sex differences in body condition and telomere dynamics of red-sided garter snakes[END_REF] and for the fact that TL could have been measured at different ages in males and females. For each dataset in which individual ages were available, we first established the best fitting model describing TL as a function of age by comparing three models: no effect of age, a linear relationship with age and a quadratic relationship with age (see electronic supplementary material, table S2 for the full list of models). We compared models using the Akaike information criterion (AIC [START_REF] Burnham | Model selection and multimodel inference: a practical information-theoretic approach[END_REF]), selecting the model with the lowest value. When models were within two AIC units, the simpler model was retained. Then, for each sex, we computed the age-corrected TL as the residuals from the selected model. Like in the general meta-analysis, we computed the effect size as the mean difference of the age-corrected TL of both sexes divided by their pooled standard deviation. This meta-analysis included 30 effect sizes (encompassing 23 articles) and is referred to as 'meta-analysis 3' below.

Publication bias

As studies reporting statistically significant results may be more likely to be published than those reporting non-statistically significant ones (i.e. file drawer problem [START_REF] Rosenthal | The file drawer problem and tolerance for null results[END_REF]), the mean effect size might be overestimated. To test for possible publication bias, we used funnel plots to represent the precision of each study (i.e. the inverse of the standard error) against the effect size value of the study (see electronic supplementary material, figure S2). When the data points are symmetrically distributed around the mean, this indicates an absence of publication bias. In addition to funnel plots, we performed an Egger's regression [START_REF] Egger | Bias in meta-analysis detected by a simple, graphical test[END_REF], which is a linear regression of the effect size value of each study against their precision. However, because the different effect sizes are not independent, we fitted the linear regression on the residuals of the meta-analysis (which are independent) against its precision [START_REF] Nakagawa | Methodological issues and advances in biological meta-analysis[END_REF]. In the absence of publication bias, the intercept of the regression should not differ from zero. Finally, we assessed whether our data were subjected to a time-lag bias (i.e. the decrease of the effect size over time) by including the publication year as a moderator in the meta-regression [START_REF] Nakagawa | Meta-evaluation of meta-analysis: ten appraisal questions for biologists[END_REF].

Outlier analysis

To avoid bias due to extreme and possibly spurious sex differences in TL, we checked for the presence of outliers. To identify outliers, we first calculated studentized deleted residuals, considering any study with studentized deleted residuals greater than 1.96 in absolute value as a potential outlier [START_REF] Viechtbauer | Outlier and influence diagnostics for meta-analysis[END_REF]. Then, the influence of such outliers was examined by calculating the DFFITS (a measure of how the predicted value change if an observation is excluded) and the Cook's distance (see [START_REF] Viechtbauer | Outlier and influence diagnostics for meta-analysis[END_REF] for more information). This diagnostic was done on the residuals of the meta-analysis using the package metafor [START_REF] Viechtbauer | Conducting meta-analyses in R with the metafor package[END_REF]. Only one study, which focused on mandrills (Mandrillux sphinx) [START_REF] Beaulieu | Mind the cell: seasonal variation in telomere length mirrors changes in leucocyte profile[END_REF] was detected as a possible outlier (g ± 95% CI = 1.28 ± 0.4, studentized deleted residuals = 4.05) (see electronic supplementary material, figure S3). All our analyses were therefore performed with and without the data from this species to assess its effects on the overall results.

2.8. The effect of sex differences in telomere length on sex differences in adult life expectancy

To assess whether sex differences in adult life expectancy could be explained by sex differences in TL, we performed a phylogenetically controlled comparative analysis. The adult life expectancy for both sexes of royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548 each species was estimated using the formula: s=ð1 À sÞ where s is the mean annual adult survival [START_REF] Seber | Estimation of animal abundance[END_REF]. Sex differences in adult life expectancy were then estimated as the log-transformed differences of the adult life expectancy between sexes. A Bayesian linear mixed model using MCMCglmm was built, with the sex difference in adult life expectancy as the dependent variable, the sex difference in TL (non-corrected for age with juveniles and adults) as the independent variable and with both female body mass and age at first reproduction as covariates. The random factors phylogeny and species as described for the meta-analysis model were also included in the model. Like for previous metaanalyses, models were run with non-informative priors (inverse Wishart prior with ν = 0.02 and V = 1), with 1 000 000 iterations and the convergence was assessed by performing a Gelman-Rubin diagnostic [START_REF] Gelman | Inference from iterative simulation using multiple sequences[END_REF]. Models were then ranked using the deviance information criterion (DIC) [START_REF] Spiegelhalter | Bayesian measures of model complexity and fit[END_REF].

Results

The full dataset included 52 species that mostly corresponded to birds and mammals (55% and 23% of the species included, respectively, table 2).

To examine variation in sex differences in TL across vertebrates, we performed three separate metaanalyses (i.e. the first using data on juveniles and adults non-adjusted for age, the second using data on adults non-adjusted for age and the third using data on adults adjusted for age). Because the results were qualitatively similar for the three meta-analyses, we only present results from the first one, based on the largest dataset. Results from meta-analyses 2 and 3 are reported in electronic supplementary material, tables S3-S5.

The meta-analysis revealed no detectable sex differences in TL (mean = -0.038, HPDI = [-0.299: 0.233]) (figure 2). The random factors 'species', 'population' and 'sample' accounted for a low (all specific I 2 , the measure of the total variance explained by each random factor, values were smaller than or close to 0.25) but a similar proportion of heterogeneity observed among studies (table 3). We also found a low effect of the phylogeny with a H 2 (the percentage of the between-study variance explained by the phylogenetic effect) around 25% (table 3). The proportion of the total variance explained by the between-study variance (I 2 tot = 0.71, table 3) corresponded to a moderate heterogeneity value.

There was no detectable publication bias (see electronic supplementary material, figure S2) because data points displayed on the funnel plot, which represent studies, were symmetrically distributed around the mean. Moreover, the intercept of the Egger's regression did not differ from zero (mean ± s.e. = -0.001 ± 0.060). Effect sizes slightly increased over time (slope of the meta-regression = 0.018, royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548 HPDI = [-0.003 : 0.038]), but this relationship was not statistically significant, providing no evidence for a time-lag bias (see electronic supplementary material, figure S4).

There was no evidence of sex differences in TL in any of the four vertebrate classes (figure 2). Sex differences in TL were not influenced by the method used to extract data from the original paper (mean difference in data extraction quality index: low quality versus high quality = -0.224, HPDI = [-0.479 : 0.039]), or whether the study populations were wild or captive (mean difference: 0.160, HPDI = [-0.138 : 0.455]). Likewise, neither the method used to measure TL nor the nature of the biological tissues had any detectable influence on sex differences in TL (figure 2). However, we found a small trend of longer TL in females when using the TRFI method, but this trend was not statistically significant (mean = -0.267, HPDI = [-0.730 : 0.141]). We did not detect any association between sex differences in TL and age at first reproduction (linear regression slope = 0.041, HPDI = [-0.068 : 0.148]).

An unexpected positive association between sex differences in TL and sexual size dimorphism was detected (slope = 0.522, HPDI = [0.043 : 0.967]), meaning that the larger the males are compared with the females, the longer their telomeres will be compared with females. However, when the study on mandrills, which was identified as an outlier, was removed from the analyses the intervals of the estimated effect of the sexual size dimorphism on the sex difference in TL included zero (slope = 0.212, ). Sex differences in TL did not differ between mating systems (i.e. another proxy of the sex differences in life-history strategies, figure 2). Sex differences in TL were not associated with sex differences in adult life expectancy. The model with the lowest DIC did not include sex differences in TL (slope of -0.009, credible interval 95% = [-0.092 : 0.072], N = 36 species, for the model that included only sex differences in TL, figure 3). Results were qualitatively unchanged when mammals and birds were analysed separately (see electronic supplementary material, table S6 for model selection tables).

Discussion

Once technical and biological confounding factors were taken into account, our meta-analysis revealed a lack of consistent sex differences in mean TL, both across vertebrates and within vertebrate classes. In addition, we did not find any sex differences in mean TL using age-corrected effect sizes (see electronic supplementary material, table S5), which suggest an absence of sex differences in telomere attrition patterns. Therefore, sex differences in mean TL that have been reported among several human populations [START_REF] Gardner | Gender and telomere length: systematic review and meta-analysis[END_REF] cannot be generalized to other mammals or other classes of vertebrates. Our results contrast with previous findings from a study that compiled 49 effect sizes and encompassed five species of mammals (mostly laboratory rodents and humans) [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF], which suggested that, in mammals, telomeres are generally longer in females than in males. Our meta-analysis was performed on a wider range of mammalian populations in the wild (i.e. 12 species encompassing five orders) and does not support any consistent sex difference in TL in mammals. Thus, we found no support for the heterogametic sex hypothesis as an explanation for the sex differences in mean TL observed in humans [START_REF] Gardner | Gender and telomere length: systematic review and meta-analysis[END_REF]. In mammals, homogametic females had statistically longer telomeres than males in only 2 of 12 species, while in birds, homogametic males showed statistical evidence for longer telomere than females in only 4 of 28 species. It is therefore unlikely that differences in sex chromosome content explain the sex differences in TL across vertebrates. However, we cannot exclude that this lack of relationship is due to a taxonomic bias in the species included in our dataset, which favoured birds and mammals (40 species out of 52). These two vertebrate classes display a single genetic sexdetermination system (i.e. ZZ/ZW and XX/XY, respectively). Adding data on reptile, fish and amphibian species (i.e. taxa that contains both XX/XY and ZZ/ZW sex-determination system), would allow disentangling the effect of the sex chromosome (heterogametic disadvantage hypothesis) from the effect of phylogeny.

Sex differences in life-history strategies are expected to be positively associated with sex differences in adult TL. More specifically, in species subjected to intense sexual selection, males allocate a substantial Absence of association between sex differences in adult life expectancy (log-transformed) and the effect size of the mean sex difference in TL for each species included in the analysis. The zero value corresponds to an absence of difference in adult life expectancy ( y-axis) and in TL (x-axis). The black line represents the slope of the relationship between sex differences in adult life expectancy and sex differences in TL for the model that only includes the effect size of the mean sex difference in TL.

royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548 amount of resources to sexual competition (e.g. growth and maintenance of costly secondary sexual traits) that might cause an increase in both the rate of cell division and the amount of oxidative damage [START_REF] Garratt | Oxidative stress and condition-dependent sexual signals: more than just seeing red[END_REF] and ultimately lead to a faster rate of telomere attrition [START_REF] Reichert | Does oxidative stress shorten telomeres in vivo? A review[END_REF]. Contrary to our expectation, we found a positive association between the intensity of sexual size dimorphism and the amount of sex differences in telomere length. This result was largely driven by one study [START_REF] Beaulieu | Mind the cell: seasonal variation in telomere length mirrors changes in leucocyte profile[END_REF] on mandrill, a polygynous species where males are about three times bigger than females [100] and have longer telomeres (36% longer). However, this relationship was non-statistically significant when this species was removed from the analysis, suggesting that the sexual size dimorphism is unlikely to be universally linked with the sex difference in TL. Taken together, these results suggest that sexual selection has, at best, a limited influence on the evolution of sex differences in telomere length. Consistent with this conclusion, intraspecific studies to date testing the same hypothesis have revealed contrasting results. In the Australian painted dragon (Ctenophorus pictus) in which male coloration during the breeding season is tightly linked to the reproductive allocation and success, males that are better able to maintain their coloration also underwent more telomere erosion [101]. Conversely, in the common yellowthroat (Geothlypis trichas) males with higher UV brightness, a trait that is positively linked to reproductive success experienced a slower rate of telomere erosion [102]. Since sexual differentiation and many aspects of male and female physiology are driven by steroid sex hormones, it has been proposed that sex hormones could mediate sex differences in TL, notably by modulating the level of oxidative stress [START_REF] Barrett | Sex differences in telomeres and lifespan[END_REF]. Accordingly, oestrogens would protect females against oxidative stress while testosterone would decrease resistance to oxidative stress in males [103]. As the level of testosterone increases with the intensity of sexual size dimorphism and with the expression of costly male secondary sexual traits [103], our results indirectly challenge the belief that sex hormones play a key role in mediating sex differences in TL in humans.

The discrepancy in terms of sex-specific TL between humans and other vertebrate species may in part be relative to the exceptionally high longevity of humans. Since human TL is generally measured in the elderly (mean age in [START_REF] Gardner | Gender and telomere length: systematic review and meta-analysis[END_REF] ranging from 37 to 89.9 years old with an overall mean of 55.8 years old), the reported sex differences in TL might be particularly pronounced at old ages (although we note that sex differences were also found in umbilical cord blood, [START_REF] Factor-Litvak | Aviv royalsocietypublishing[END_REF]). By contrast, most individuals measured in vertebrate populations in the wild are juveniles and young adults. Thus, sex differences in TL may not be observed in these studies if they occur in late adulthood. This hypothesis remains to be tested by measuring sex-specific TL in old individuals of other very long-lived vertebrate species such as naked mole rats, elephants, bats, seabirds or turtles. In addition, due to cultural habits, human TL is more prone to suffer from lifestyle and environmental factors than that of animals. Factors such as stress, diet, physical activity and harmful consumption are known to influence telomere length (see [104] for a review), possibly in a sex-specific way. However, results about lifestyle and TL are contrasted (see [105] where no effect of smoking was found on telomere attrition) and no consensus has been reached yet.

The comparative analysis we performed indicates that sex differences in adult life expectancy are not related to sex differences in telomere length across our set of vertebrate species. This finding matches the results of a recent meta-analysis performed across 20 vertebrate species, which reported that there is an association between telomere length and mortality risk but that this association is not sex-specific [START_REF] Wilbourn | The relationship between telomere length and mortality risk in non-model vertebrate systems: a meta-analysis[END_REF]. Our starting hypothesis was based on the existence of a causal relationship between TL and ageing, which remains debated [START_REF] Simons | Questioning causal involvement of telomeres in aging[END_REF]. A lack of causal association between TL and ageing would obviously explain the absence of sex differences in telomere length, as well as the lack of association between sex differences in telomere length and sex differences in lifespan we report. Taken together, these results suggest that telomere dynamics do not provide an explanation for the sex differences in longevity observed across vertebrates. On the other hand, if TL and ageing are causally linked, our findings might come from a lack of power of our dataset. Note, however, that under that scenario it would be quite difficult to explain the trend for a positive association between longer relative TL in males and intensity of sexual size dimorphism.

The heterogeneity analysis revealed that the proportion of the total variance explained by the between-study variance was quite low compared with that generally reported in meta-analyses in the field of evolutionary biology. Indeed, the average heterogeneity value computed in evolutionary meta-analyses is around 92% because these analyses are not confined to a single species like metaanalyses commonly performed in medical sciences, which probably increases heterogeneity [106]. Hence, our measure of the total variance explained by the between-study variance (I 2 = 0.71) shows that heterogeneity within studies (i.e. the sampling variances associated with each effect size) accounts for a substantial proportion of the total variance in TL. This result could be due to a large number of studies with a small sample size that were included in our meta-analysis. Indeed, 30% of the studies royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548 in our meta-analysis had a sample size less than or equal to 20 individuals, which could impair our capacity to detect effects from variables included as moderators (i.e. the meta-analyses could be underpowered if the overall effect is expected to be small [START_REF] Nakagawa | Meta-evaluation of meta-analysis: ten appraisal questions for biologists[END_REF]). Interestingly, sex differences reported in humans are also weak in magnitude but generally based on much larger datasets (e.g. over 100 000 participants for the biggest study to date, [107]), which results in statistically significant sex differences. Therefore, if there are consistent but very weak differences between sexes in mammals, we might have not enough power to detect them. However, when our analysis was performed on effect sizes calculated from studies with more than 40 individuals, our measure of heterogeneity increased (I 2 = 0.84) (see electronic supplementary material, table S1), but results were qualitatively unchanged, suggesting that the overall absence of sex differences we found was not due to a power issue. However, it is worth noticing that for some moderators (e.g. only 9 effect sizes measured in white blood cells, only 11 effect sizes measured using TRFI), the number of computed effect sizes was very low, which might have prevented us to detect an effect of the tissue or the method in the meta-regression.

Our results were robust to publication bias, which contrasts with a previous meta-analysis looking at the relationship between TL and mortality in non-model vertebrates [START_REF] Wilbourn | The relationship between telomere length and mortality risk in non-model vertebrate systems: a meta-analysis[END_REF] where a substantial publication bias was reported. The absence of a publication bias in our analyses could be explained by the fact that sex-specific TL measurements we compiled for the meta-analysis were generally not the main focus of the papers we retrieved.

We failed to find any consistent sex differences in TL across vertebrates or within any single class of vertebrates. This absence of a difference does not support the main evolutionary hypotheses (i.e. the heterogametic sex disadvantage and the life-history hypotheses) that have been proposed to explain sex differences in TL in humans. Our findings also call into question the possible role played by sex hormones in sex differences in TL. Testing hypotheses across broad sets of species beyond humans is essential to understand the evolutionary roots of sex differences in TL. The increasing number of publications on many different species should allow clarifying the causes and consequences of TL in the next future and should be beneficial for both evolutionary biology and medical sciences.

Figure 1 .

 1 Figure 1. PRISMA flow diagram (from the PRISMA statement,[START_REF] Liberati | The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration[END_REF]) for the meta-analysis.

Figure 2 .

 2 Figure 2. Mean of each moderator included in the meta-regression. All means are presented with their 95% highest posterior density intervals and sample size. A negative value means that females have longer telomeres than males while a positive value means that males have longer telomeres than females. Hedge's g corresponds to the value of the effect size and HPDI corresponds to the lower and upper high posterior density limits of the credible interval.

Figure 3 .

 3 Figure 3. Absence of association between sex differences in adult life expectancy (log-transformed) and the effect size of the mean sex difference in TL for each species included in the analysis. The zero value corresponds to an absence of difference in adult life expectancy ( y-axis) and in TL (x-axis). The black line represents the slope of the relationship between sex differences in adult life expectancy and sex differences in TL for the model that only includes the effect size of the mean sex difference in TL.

Table 1 .

 1 (Continued.)

	species BIRDS Acrocephalus arundinaceus Acrocephalus sechellensis Aptenodytes patagonicus Aptenodytes patagonicus Aptenodytes patagonicus Branta leucopsis Calidris alpina Corvus monedula Diomedea exulans Fregata minor Hirundo rustica	study [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39]	g -0.086 0.174 0.115 -0.158 -0.094 0.678 -0.177 0.242 0.423 -0.076 0.17	variance 0.059 0.005 0.08 0.038 0.027 0.141 0.178 0.084 0.068 0.229 0.031	sample size 68 837 50 106 146 34 24 48 60 22 130	tissue RBC RBC RBC RBC RBC RBC RBC RBC RBC RBC RBC	method qPCR qPCR qPCR qPCR qPCR TRFS TRFI TRFI TRFS TRFI qPCR	royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548
	Larus crassirostris	[40]	-0.278	0.051	82	RBC	TRFS
	Larus crassirostris	[41]	-0.214	0.072	72	RBC	TRFS
	Leucophaeus pipixcan	[42]	-0.437	0.205	20	RBC	qPCR
	Luscinia svecica	[43]	-0.15	0.042	97	RBC	qPCR
	Macronectes giganteus	[44]	-0.653	0.092	47	RBC	TRFS
	Macronectes halli	[24]	-1.016	0.124	37	RBC	TRFS
	Parus caeruleus	[45]	0.384	0.079	56	RBC	qPCR
	Parus major	[46]	-0.483	0.099	42	RBC	TRFI
	Parus major	[47]	0.483	0.053	79	RBC	qPCR
	Parus major	[47]	0.018	0.054	76	RBC	qPCR
	Riparia riparia	[35]	-0.158	0.267	16	RBC	TRFI
	Rissa tridactyla	[48]	0.457	0.111	38	RBC	TRFS
	Sterna hirundo	[49]	0.139	0.017	233	RBC	TRFI
	Strigops habroptila	[50]	0.6	0.062	67	RBC	TRFS
	Strix aluco	[51]	0.06	0.026	158	RBC	qPCR
	Tachycineta bicolor	[52]	0.187	0.057	82	RBC	qPCR
	Tachymarptis melba	[53]	0.044	0.041	96	RBC	qPCR
	Taeniopygia guttata	[54]	-0.307	0.229	19	RBC	TRFI
	Taeniopygia guttata	[55]	0.375	0.042	99	RBC	qPCR
	Taeniopygia guttata	[55]	0.128	0.052	79	RBC	qPCR
	Thalassarche melanophrys	[56]	0.091	0.079	51	other	TRFS
	Turdus merula	[57]	-0.206	0.102	40	RBC	TRFI
	Turdus merula	[57]	-0.607	0.192	22	RBC	TRFI
	Uria lomvia	[58]	0.33	0.235	35	RBC	qPCR
	Uria lomvia	[58]	0.956	0.147	47	RBC	TRFS
	Uria lomvia	[59]	0.578	0.101	60	RBC	TRFS
	Vultur gryphus	[60]	0.566	0.21	20	RBC	qPCR
							(Continued.)

Table 1 .

 1 (Continued.)

	species REPTILES Alligator mississippiensis Chlamydosaurus kingii Lacerta agilis Liasis fuscus Thamnophis sirtalis Zootoca vivipara Zootoca vivipara Zootoca vivipara Zootoca vivipara Zootoca vivipara Zootoca vivipara Zootoca vivipara Zootoca vivipara Zootoca vivipara	study [79] [80] [81] [82] [22] [83] [83] [83] [83] [83] [83] [83] [83] [83]	g -0.239 0.131 -0.323 -0.909 -0.638 -0.621 0.254 0.687 0.257 0.591 0.601 0.157 -0.216 -0.39	variance 0.167 0.102 0.025 0.123 0.06 0.436 0.403 0.424 0.403 0.417 0.418 0.401 0.402 0.408	sample size 24 112 161 45 72 10 10 10 10 10 10 10 10 10	tissue RBC RBC RBC RBC RBC RBC RBC RBC RBC RBC RBC RBC RBC RBC	method TRFI qPCR TRFS TRFS qPCR TRFS TRFS TRFS TRFS TRFS TRFS TRFS TRFS TRFS	royalsocietypublishing.org/journal/rsos R. Soc. Open Sci. 7: 200548
	Zootoca vivipara	[83]	0.033	0.4	10	RBC	TRFS

Table 2 .

 2 Number of studies, species and effect sizes included in the meta-analysis performed on juveniles and adults nonadjusted for age.

	number of studies	total	58
		mammals	13
		birds	33
		reptiles	6
		fish	6
	number of species	total	52
		mammals	13
		birds	28
		reptiles	6
		fish	5
	number of effect sizes	total	93
		mammals	31
		birds	38
		reptiles	15
		fish	9

Table 3

 3

	mean	HPDI

. I 2 values associated with each random effect (population, species and sample) and the phylogenetic heritability H 2 value, for the meta-analysis performed on juveniles and adults non-corrected for age. HPDI corresponds to the lower and upper high posterior density limits of the credible interval. HPDI = [-0.233 : 0.674]

Acknowledgements. All authors would like to thank the following for providing data, in alphabetical order: Pierre Bize, Donald Blomqvist, Andréaz Dupoué, Britt Heidinger, Patrik Karell, Yuichi Mizutani, Pat Monaghan, Jenny Ouyang, Pablo Salmon, Simon Verhulst and Rebecca Young. We also want to thank Jack Cerchiara, Alessandra Costanzo, Mark Haussmann, Sin-Yeon Kim, Nuria Mach, Marco Parolini, Sophie Reichert and Oscar Vedder for providing data or information we were not able to include in the meta-analysis. We are grateful to two anonymous reviewers for their comments that markedly improved our manuscript. We acknowledge BBSRC funding to D.H.N.

Data accessibility. Data used in the meta-analyses are published as the electronic supplementary material. Authors' contributions. F.R., D.H.N. and J.-F.L. conceived the study with inputs from H.F., B.R. and J.-M.G. F.R compiled the dataset. F.R. and V.R. analysed the data with inputs from J.-F.L. and J.-M.G. F.R. wrote the first draft of the paper and then received input from all authors.

Competing interests. The authors declare no competing interests. Funding. F.R. is funded by the French Ministry of Education and Research. J.-F.L. B.R. and J.-M.G. are supported by a grant from the Agence Nationale de la Recherche (grant no. ANR-15-CE32-0002-01 to J.-F.L.). This work is supported by the Charity Open Access Fund and the Royal Society international exchange grant: Telomere dynamics in wild populations of vertebrates (grant no. IEC\R2\181087). This study was performed within the framework of the LABEX ECOFECT (grant no. ANR-11-LABX-0048) of Université de Lyon, within the programme 'Investissements d'Avenir' (grant no. ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).