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- Experimental procedures
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Although generally considered as beneficial components of dietary fats, polyunsaturated fatty acids (PUFA) have been suspected to compromise maximum lifespan (MLSP) in mammals. Specifically, high amounts of phospholipid PUFAs are thought to impair lifespan due to an increase in the susceptibility of membranes to lipid peroxidation and its damaging effect on cellular molecules. Also, there is evidence from in vitro studies suggesting that highly unsaturated PUFAs elevate basal metabolic rate (BMR). Previous comparative studies in this context were based on small sample sizes, however, and, except for one study, failed to address possible confounding influences of body weight and taxonomic relations between species. Therefore, we determined phospholipid membrane composition in skeletal muscle from 42 mammalian species to test for a relation with published data on MLSP, and with literature data on BMR (30 species). Using statistical models that adjust for the effects of body weight and phylogeny, we found that among mammals, MLSP indeed decreases as the ratio of n−3 to n−6 PUFAs increases. In contrast to previous studies, we found, however, no relation between MLSP and either membrane unsaturation (i.e. PUFA content or number of double bonds) or to the very long-chain, highly unsaturated docosahexaenoic acid (DHA). Similarly, our data set gave no evidence for any notable relation between muscle phospholipid fatty acid composition and BMR, or MLSP and BMR in mammals. These results contradict the ‘membrane pacemaker theory of aging’, that is, the concept of a direct link between high amounts of membrane PUFAs, elevated BMR, and thus, impaired longevity.
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- Experimental procedures
- Supporting Information
Polyunsaturated fatty acids (PUFA) are essential components of dietary fats and have a number of important cellular functions including regulation of enzymes, ion pumps, and immune responses (Stubbs & Smith, 1984; Pond & Mattacks, 1998). In the context of aging, however, there are several arguments suggesting that PUFAs may adversely affect maximum lifespan (MLSP; Barja, 2004; Pamplona et al., 2004; Hulbert, 2005). Firstly, because PUFAs are located at the mitochondrial membrane, they are prone to lipid peroxidation, which results in extensive production of radical oxygen species (ROS) (reviewed in Hulbert, 2005). Radical oxygen species readily interact with macromolecules, cause accumulating tissue damage, and eventually lead to death from age according to the ‘free radical theory’ (Brand, 2000; Hulbert, 2003; Barja, 2004; Speakman, 2005a). Secondly, PUFAs are thought to raise metabolic rate, i.e. one of the factors that seems to be associated with short lifespans (Rubner, 1908; Pearl, 1928; Daan et al., 1996; but see Speakman et al., 2003, 2004). Species with a high basal metabolic rate (BMR), such as small mammals, also have high membrane PUFA contents (Hulbert, 2005). Furthermore, there is experimental evidence that PUFAs increase the activity of membrane associated metabolically active proteins, such as the sodium pump (Wu et al., 2001; Turner et al., 2003). Based on these observations, the ‘membrane pacemaker theory’ of metabolism (Hulbert & Else, 1999, 2000, 2004, 2005; Hulbert, 2003, 2005) suggests that high amounts of membrane PUFAs lead to elevated BMR and increased peroxidation of fatty acids, and thus impair MLSP in mammals.
Among PUFAs, one particular fatty acid, docosahexaenoic acid (DHA), which predominantly occurs in membranes of retina and brain, was shown to significantly increase Na+-K+-ATPase molecular activity (Turner et al., 2003). Therefore, DHA is thought to act as a particularly important pacemaker of BMR (Hulbert & Else, 1999, 2000, 2004, 2005). Accordingly, numerous studies have demonstrated a negative correlation between DHA content in tissue membranes and MLSP in mammals and birds (Pamplona et al., 1998, 1999a; Portero-Otin et al., 2001; Hulbert, 2003, 2005).
As pointed out by Speakman (2005b), there are, however, two major problems with these simple correlations between membrane fatty acid composition, metabolism, and longevity. First, correlations between these variables may be merely due to the fact that all of them are correlated to body weight, a most ‘pervasive trait that influences all aspects of organismal biology’ (Speakman, 2005b), but may have no actual functional relation to each other. Second, species in comparative data sets may not represent independent replicates, due to phylogenetically caused correlations. Fortunately, both of these problems can be overcome by employing statistical procedures that adjust for body weight and phylogenetic effects. In his reanalysis of relations between DHA, MLSP, and BMR, Speakman (2005b) found that indeed, after statistically adjusting for both body weight effects and phylogeny, there was no significant relation between MLSP and BMR, and only a weak relationship between MLSP and DHA. With regard to membrane fatty acids, this analysis was, however, limited to DHA, and to eight mammalian species only, which may have been one of the reasons for the observed lack of correlations with MLSP.
Therefore, we collected data on DHA and other PUFA muscle phospholipid contents in 42 mammalian species (Fig. 1, Table 1) and re-examined their possible effect on MLSP. In short, we found that after adjusting for the influence of body weight and phylogenetic correlations, MLSP was neither related to DHA content, nor to membrane unsaturation (i.e. PUFA content or number of double bonds). Interestingly, however, MLSP significantly decreased as the class of phospholipid n−3 PUFAs (including DHA) increased, and, consequently n−6 PUFAs decreased. This effect of the n−3/n−6 PUFA ratio appears to be independent from metabolic rate because we found no relation between any characteristic of membrane fatty acid composition and BMR.
Figure 1. Phylogeny of the 42 mammalian species for which phospholipid muscle fatty acid composition was determined. Branch lengths are arbitrary. This dendrogram was constructed using the statistical package R for Windows (version 2.3.1, R-Project, 2005).
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Table 1. Species investigated with body weights, maximum lifespan (MLSP) and fatty acid composition
| ||Body weight (kg)||MLSP (years)||SFA (%)||MUFA (%)||PUFA (%)||n−3 PUFA (%)||n−6 PUFA (%)|
| Antilope cervicapra||37||18||24.47|| 7.65||67.89||11.21||56.69|
| Axis axis||100||21||25.95|| 5.23||68.82||11.80||57.10|
| Bos taurus||369||20||29.66*||34.95*||34.54*|| 8.90*||25.20*|
| Capra ibex||40||14||23.01|| 6.60||70.39||19.74||50.64|
| Capreolus capreolus||20||15||27.05|| 7.60||65.40||15.01||50.30|
| Cervus dama||80||25||28.07|| 9.50||62.42||11.78||50.60|
| Cervus elaphus||150||27||24.64|| 8.51||63.81||14.66||49.15|
| Cervus nippon||52.5||15||24.60|| 6.20||69.20||20.30||48.90|
| Connochaetes taurinus||165||22||22.71|| 9.45||67.84|| 5.89||61.95|
| Giraffa camelopardalis||700||36||25.81||12.90||61.33|| 6.90||55.40|
| Ovis ammon||16.1||13||20.9||11.31||67.80||19.60||48.20|
| Rangifer tarandus||170||15||25.40*||17.30*||46.10*||14.20*||31.90*|
| Rupicapra rupicapra||35||22||24.41||12.90||62.70||21.60||41.12|
| Sus domestica||150||27||29.30*||18.15*||52.80*|| 3.90*||49.70*|
| Sus scrofa||50||21||27.30|| 8.06||64.65|| 6.98||57.66|
| Sus pekari||30||25||28.44|| 4.02||67.54|| 6.43||61.11|
| Tragelaphus oryx||400||25||27.40||11.37||61.23||16.21||45.02|
| Tragelaphus angasi||85||16||28.30||17.80||53.90||12.25||41.65|
| Acinonyx jubatus||55||15||27.32||22.90||49.79|| 7.31||42.48|
| Canis familiaris||20||30||31.65||10.06||58.30|| 3.56||54.74|
| Canis lupus||30||16||28.05||13.07||58.88|| 5.14||53.75|
| Martes foina||1.58||18||27.61||10.02||62.37|| 8.33||54.05|
| Meles meles||8.5||16||27.24|| 5.40||67.36|| 7.54||59.82|
| Mustela putorius||9.1||10||28.99|| 5.80||65.21||20.83||44.37|
| Panthera tigris||107||26||29.14||25.61||45.25|| 7.73||37.53|
| Vulpes vulpes||5.1||12||28.88||13.34||57.77|| 6.59||51.18|
| Erinacaeus europaeus||1||14||26.22||13.70||60.08||22.95||37.13|
| Neomys fodiens||0.01|| 3||32.30*||13.90*||53.80*||37.00*||16.80*|
| Sorex araneus||0.01|| 2||34.00*||11.90*||54.10*||41.90*||12.20*|
| Talpa europaea||0.07|| 4||27.17||11.26||61.57||46.80||14.77|
| Lepus europaeus||3.62||12||27.26*|| 5.66*||66.74*||22.54*||44.28*|
| Oryctolagus cuniculus||4.10||18||33.34*||15.09*||51.58*||12.65*||38.57*|
| Equus caballus||500||62||28.31|| 7.21||64.47|| 7.19||57.29|
| Loxodonta africana||4000||70||22.65||39.16||38.19|| 9.55||28.64|
| Capromys pilorides||4.5||11||30.90|| 5.57||63.53||17.86||45.68|
| Cavia aperea||0.19|| 8||32.93|| 6.07||61.00||10.70||50.30|
| Cricetus cricetus||0.09|| 4||26.05||15.85||58.10||29.09||29.01|
| Glis glis||0.09|| 9||27.18||19.89||52.93||20.29||32.64|
| Marmota marmota||4||15||30.30|| 8.38||59.43||20.36||39.07|
| Mus musculus||0.04|| 6||33.10*||15.00*||51.90*||20.90*||30.50*|
| Rattus norvegicus||0.58|| 3||35.00*||15.10*||49.90*||21.18*||36.50*|
| Sciurus vulgaris||0.32||12||27.08|| 6.43||66.50||22.11||44.39|
- Top of page
- Experimental procedures
- Supporting Information
We examined muscle phospholipid fatty acid composition of mammals from six taxonomic orders (see Supplementary Table S1). Muscle samples consisted of tissues from zoo animals in Vienna and Salzburg, Austria as well as from the zoo Munich, Germany (nSpecies = 13). Zoo animals were killed for population management reasons, or died subsequent to trauma. Additionally, we sampled muscle material from fresh road kills (nSpecies = 6), from animals shot during hunting (nSpecies = 10), and finally, from breeding-colony animals (nSpecies = 4). In each case, we took 0.5 g of hind leg muscle, Musculus vastus (M. vastus), from the fresh carcass. In small mammals, when we were unable to gain this amount of muscle material from the M. vastus alone, we used muscle tissues from the entire hind legs. We felt justified to pool tissues in these cases because muscle (and fibre type) specific differences in total phospholipid fatty acid profiles are very small compared to the range of between-species difference investigated here (Kriketos et al., 1995; Valencak et al., 2003), or not detectable at all (Blackard et al., 1997; Nikolaidis et al., 2006).
All animals investigated were healthy individuals of both sexes. To avoid any effect of maturational changes in phospholipid composition, we excluded juvenile specimens from our analysis, and only used adult individuals. We tested for, but found no effect of gender on any individual fatty acid or any of the fatty acid classes determined (ancova with adjustment for body weight, 0.10 < P < 0.84). Thus, we pooled data obtained from males and females. We only used fresh tissues without any discoloration or gunshot wounds (hunting samples) and collected warm road kills as described in Valencak et al. (2003). Immediately after sampling, muscles tissues were placed in plastic bags and stored at −18 °C.
Lipids were extracted from muscle samples (0.5 g each) and lipid classes were separated on silica gel thin layer chromatography plates. All solvents contained butylhydroxytoluol in order to avoid oxidative modification of PUFAs. Phospholipid extracts were transesterified under nitrogen, extracted into hexane, and analysed by GLC (PerkinElmer Autosystem XL with autosampler and FID; Norwalk, CA, USA) using a capillary column (HP INNOWax, 30 m × 0.25 mm; Hewlett Packard, Palo Alto, CA, USA). Fatty acid methyl esters were identified by comparing retention times with those of standards (Sigma-Aldrich, St. Louis, MO, USA), and peaks were integrated using the Turbochrom 4.1 Software (PerkinElmer). Details about chemical analyses and substances used are given elsewhere (Valencak et al., 2003). For 33 species, we determined the proportions of the following phospholipid acids: C14:0, C15:0, C16:0, C17:0, C18:0 (saturated fatty acids, SFA), C16:1n−7, C18:1n−9 (MUFA), C18:2n−6, C18:3n−3, C20:4n−6, C20:5n−3, C22:5n−3, and C22:6n−3 (PUFA). Exact proportions of single fatty acids are not given here but can be obtained from the authors. Sample sizes within species ranged from one to 244 individuals (given in Supplementary Table S1). We felt justified to include single specimens because fatty acid concentrations showed only little to moderate within-species variation [coefficients of variation for all fatty acid (classes) investigated ranged from 2.3% to 36.67%, overall mean: 15.68%]. Nonetheless, we computed additional bootstrap statistics (see below) to test for the possible effects of including species for which no replicates were available.
Our fatty acid data set was enlarged with values obtained from the literature (nSpecies = 9, see Supplementary Table S1). We included only studies in which the same set of fatty acids was given as determined in our laboratory (except for three cases in which C14:0, C15:0 or C17:0 had not been determined due to their negligible amount [typically < 1%]). We computed mean proportions for each species when more than one reference was available (Bos taurus, Sus domestica, Oryctolagus cuniculus).
Data on body weight were obtained, if possible, from the actual specimen used for tissue collection (n = 25), or taken from the literature (n = 9). Basal metabolic rates for 30 from our set of 42 mammal species could be compiled from different sources (Supplementary Table S1), and units were transformed into Watt if necessary.
Fatty acid percentages were combined into classes of SFA, MUFA, PUFA, UFA (unsaturated fatty acids) according to their degree of unsaturation, and, additionally, PUFAs were attributed to the n−3 and n−6 family. For all animals an unsaturation index (UI) was computed (Couture & Hulbert, 1995) as well as the ratio between n−3 and n−6 PUFAs. The UI represents the average number of double bonds per 100 fatty acid molecules (Couture & Hulbert, 1995: UI = (%MUFA × 1) + (%Dienoic × 2) + (%Trienoic × 3) + (%Tetraenoic × 4) + (%Pentaenoic × 5) + (%Hexaenoic × 6)). Further, a peroxidisability index (P1) was computed as follows and as given in Pamplona et al. (1998): PI = (%MUFA × 0.025) + (%Dienoic × 1) + (%Trienoic × 2) + (%Tetraenoic × 4) + (%Pentaenoic × 6) + (%Hexaenoic × 8).
Prior to computing regression models, longevity, body weight, and BMR data as well as fatty acid classes (n−3, n−6 PUFAs, UFAs, MUFAs, SFAs, UI) and single fatty acids (C18:3, C20:5, C22:5, C22:6) were log transformed to linearise these traits. To assess the impact of body weight, BMR and lifespan on fatty acid proportions, we used multiple linear regression models and entered either longevity or BMR as the response variable. Body weight was always entered as a covariate. We used this multiple regression approach, rather than comparing residuals from individual regressions against body weight (e.g. Speakman, 2005b), because it avoids bias and allows to estimate P values based on the correct degrees of freedom (with all variables entered into a single model; Freckleton, 2002). Note that this statistical procedure statistically eliminates possibly confounding effects of body mass on both MLSP and on phospholipid fatty acid composition. We computed separate analyses for each fatty acid class because percentages of fatty acids inevitably showed strong multicolinearity. While this leads to the problem of multiple P values, we followed the arguments by Nakagawa (2004) and did not employ further Bonferroni-like corrections. All analyses were carried out with the statistical package R (R-Project, Version 2.3.1; R Development Core Team, 2005). To illustrate the partial influence of variable correlations in multiple regression models, we used partial regression plots, also called added variable plots [function av. plot in library ‘car’, Fox (1997)], as described in Sall (1990).
To test for the influence of including data with very different levels of uncertainty for each species (i.e., replicates of fatty acid composition ranging from n = 1 to n = 244) we employed a bootstrap test with 10 000 runs per predictor variable investigated. For each run, we randomally picked data from only one individual per species (sampled with replacement, cf. Efron & Tibshirani, 1993) and recomputed regression models. Note that this procedure uses both random omission of species during each run and incorporation of all information available on within-species variation for each trait to assess possible bias, particularly that caused by including species represented by single specimens. We provide 95% confidence intervals of the coefficient estimates from this bootstrap procedure for comparison with conventional statistics based on means from each species.
To adjust for phylogenetic relationships, we used phylogenetic generalized least square (GLS) models (Garland & Ives, 2000). This approach is functionally identical to independent contrasts regressions (Garland & Ives, 2000). The distance matrix on which these models were based, was constructed from the phylogenetic relationships between the 42 mammalian species investigated shown in Fig. 1. The topology in this dendrogram was constructed combining information on evolutionary relationships given in two recent sources (Arnason et al., 2002; Murphy et al., 2001). Due to a large number of polytomies, we used arbitrary branch lengths (Pagel, 1992) to generate the distance matrix. It has been demonstrated that this use of arbitrary branch lengths has little effect on hypothesis testing in phylogenetic regressions (Garland & Diaz-Uriarte, 1999). Multiple linear GLS models with longevity and BMR as the dependent variable, and log transformed body weight as well as the content of muscle fatty acids as predictor variables were computed with the R-library PHYLOGR (Diaz-Uriarte & Garland, 2000).