Abstract
Longevity is influenced by various factors, including fatty acid composition and free radical stress, which relate to the membrane pacemaker and rate of living hypotheses. While these aspects are well-documented in some long-lived species, they remain largely unexplored in tree squirrels. This study aimed to compare oxidative stress, antioxidant activity, nitrosative stress, and lipid composition between the long-lived Persian squirrel (Sciurus anomalus) and the short-lived Wistar rat across age cohorts (younger and older). Tissue homogenates from skin, liver, skeletal muscle, spleen, lung, and kidney were analysed for lipid composition (monounsaturated fatty acids (MUFA), polyunsaturated fatty acids (PUFA), arachidonic to linoleic acid ratio, peroxidation index, and unsaturation index. Oxidative, nitrosative, and antioxidant markers were assessed, including NADPH oxidase, superoxide dismutase, catalase, glutathione peroxidase, glutathione S-transferase (GST), nitric oxide synthase, superoxide, hydrogen peroxide, nitric oxide, malondialdehyde, 4-hydroxynonenal, and total antioxidant capacity (TAC). Squirrels demonstrated higher GST activity, lower free radical stress, lower PUFA, and higher MUFA compared to rats. Antioxidant activities, except for TAC were negatively correlated with longevity. Older squirrels exhibited similar oxidative, nitrosative, and antioxidant profiles to younger squirrels, whereas younger rats displayed highly susceptible fatty acids, similar to older rats. The Persian squirrel’s longevity appears closely linked to fatty acid composition and free radical resistance, likely due to increased GST activity. We propose GST’s multifunctional role in reducing inflammation, enhancing immune response, providing disease resistance, and antioxidant activity contributes significantly to the longevity of the Persian squirrel.
Supplementary Information
The online version contains supplementary material available at 10.1007/s11357-025-01668-9.
Keywords: Ageing, Lipids, Free radical, Oxidative stress, Tree squirrel, Glutathione-S-transferase
Introduction
Aging is a gradual and inevitable biological process described as the deterioration of the physiological function, which concurrently increases susceptibility to disease and death [1–5]. As such, gerontology remains the subject of intense investigation, as the elucidation of the mechanisms involved in this process are essential to improve the quality of life [6].
Oxidative stress is thought to be involved in mediating the aging process, where the oxidative stress theory of aging posits that reactive oxygen species (ROS) (a by-product of cellular metabolism) causes cellular attrition over time, subsequently damaging proteins, lipids, and DNA that leads to eventual death [7, 8]. Oxidative stress may be defined as the process where the balance between oxidative damage forming compounds like ROS and antioxidants are out of balance [9–11]. Some examples of cellular attrition include mitochondrial dysfunction and increased risk of age-related diseases [12] and overall senescence [9, 13]. Antioxidants are the primary means of combating an imbalance of ROS through either enzymatic or non-enzymatic means as they inhibit radical formation, where this compromised redox environment results in the expression of antioxidant enzymes [10, 14, 15]. Additionally, where dietary antioxidants i.e. non-enzymatic and endogenously created antioxidants (e.g. glutathione (GSH)) can further contribute to ROS detoxification [9] promoting healthy living and increase longevity, antioxidants do not relate to the maximum lifespan potential (MLSP) of a species, as these factors are genetically determined [16]. Furthermore, several studies have found a negative or no correlation of antioxidants enzymes with longevity, where these correlations are highly dependent on the specific antioxidant being measured and which tissue it is being measured in (reviewed by Shields, Traa and Van Raamsdonk [5]). Crucially, recent evidence suggests that aging is evolutionary conserved and associated with consistent age-related changes to DNA over time, and not random accumulation to cellular methylation [17]. Ultimately, ROS remains crucial for normal physiological function and signalling, but not all ROS can be scavenged and some damage will always transpire [18–23]. Two hypotheses have been put forward to currently explain the longevity of animals regardless of species, body size or metabolic rate which include the “membrane pacemaker theory” [24–26] and the “rate of living theory” [27–29] that may still mediate the ageing process.
The “membrane pacemaker theory” or the “homeoviscous-longevity adaptation” theory is an extension of the oxidative stress theory of aging and proposes that the risk of membrane fatty acid composition to oxidative damage and lipid peroxidation is directly linked to healthy aging and longevity [24–26, 30–33]. Fatty acids differ in their susceptibility to oxidation, where lipids containing more than one double bond are highly susceptible to peroxidation [30, 32, 34, 35]. The relevance of lipid composition to aging has found that a higher proportion of polyunsaturated fatty acids (PUFA), especially omega- 6, in the membrane positively correlates with metabolism and negatively correlates with body size and longevity [36–40]. Additionally, higher monounsaturated fatty acid (MUFA) content promotes longevity [25, 30, 34, 38, 41–44]. Once oxidation occurs, the resultant ROS can interfere with membrane structure, compromising fluidity and other relevant properties of functional membranes [38, 45–47]. Furthermore, these free radical intermediates can be more dangerous than the ROS that produce them, as these products have a longer half-life, and their non-charged structure allows them to leave the cell to compromise cell integrity far from the site of origin [48]. The “rate of living” hypothesis in contrast proposes that longer-lived animals produce less free radicals over time and thus accrue less oxidative damage with time [27, 29, 49]. Furthermore, the “membrane pacemaker” and the “rate of living” hypotheses are not mutually exclusive, and both can act synergistically.
The study of aging has used laboratory rodent models such as mice and rats, where a great deal of progress has been made into their gerontological research [5, 50–53]. The use of rats and mice is beneficial as they are small, rapidly reproducing, and easy to maintain in captivity [50, 51, 54, 55]. Additionally, studies focusing on the comparative differences between species of similar size and disparate longevity provide significant insights into the aging mechanisms and the physiological basis of longevity [5, 50, 51]. The disparate maximum longevity differences observed in the order Rodentia may be attributed to variable selection for lifestyle strategies in reproductive fitness (as argued by r and k selection) [56]. Rats are typically r-selected species, selecting for a rapid lifestyle and maximising reproductive success, whereas other long-lived rodents such as squirrels demonstrate traits of K-selection, such as low reproductive rate and high parental investment [57]. K-selected traits that would benefit this selection pressure may have coevolved to promote extended maximum lifespan (e.g. rate of senescence, increased repair mechanisms) [58]. From general long-lived rodent models [50] the squirrels of the family Sciuridae provide a unique opportunity to study gerontological research.
The Sciuridae contains the tree squirrels, it has been suggested that their arboreal lifestyle has promoted their longevity, as they live longer than their terrestrial squirrel counterparts [59–63]. Some of these squirrels are now continents apart despite them being closely related within the same family this being a consequence of geographic isolation [64–66]. This may lead to novel mechanisms for extended maximum lifespan within this family. Furthermore, these squirrels vary in their body mass, where body mass has been shown to be an important correlate of longevity [67–69]. Despite this, there is a clear lack of research on long-lived squirrels when compared to mice models and/or comparisons between long-lived squirrels, and as a consequence the mechanisms behind squirrel longevity remain elusive. Investigating tree squirrels may provide further information associated with delayed aging that will enhance our understand of ageing with potential medical benefits.
Longevity and maximum lifespan potential research on tree squirrels are lacking primarily due to their arboreal nature and wariness, resulting in difficulties with their capture [70, 71]. Due to the lack of research in this growing field, it is difficult to dissociate physiological mechanisms and intrinsic mechanisms inherent to the species that may or may not contribute to their longevity. An example includes the evolutionary mechanisms due to an arboreal life style that can reduce extrinsic mortality and its selection for longevity [63, 72, 73], but how these mechanisms manifests are still unknown. In order to identify factors known to affect longevity in long-lived species, we set out to investigate oxidative stress, antioxidants, nitrosative and fatty acids composition and indices as these factors are commonly associated with aging and longevity [8, 26, 74]. The exact characteristics of fatty acids that affect longevity however are unknown, which in turn requires a detailed insight into the relationship of fatty acid profiles [43] and likely their relationship with oxidative stress variables across a wide array of tissues.
Few studies have compared lipid composition and oxidative stress simultaneously in a wide array of tissues. Furthermore, we wanted to not just investigate species related differences, but whether the variables differ between younger and older animals, as such we compared younger and older animals within and between species. As such, we selected a wide range of tissue types to investigate “the membrane pacemaker” and “the rate of living” hypotheses. In order to investigate these hypotheses, we 1) determined species differences across all tissues based on antioxidant, oxidative, nitrosative, lipid indices and lipid composition variables 2) if antioxidant, oxidative, nitrosative, lipid indices and lipid composition variables have an age-related pattern in rats compared to squirrels between age cohorts (younger and older) 3) determine whether differences observed between the species are a consequence of ageing or species differences.
Material and methods
Ethical clearance
Research Ethics Committees of the College of Sciences, University of Tehran (IR.UT.SCIENCE.REC.1401.010) approved all animal protocols.
Experimental animals and housing conditions
Ten Wistar rats Rattus norvegicus domestica were obtained from the Zoological Laboratory of Biochemistry—Biophysics Research Centre at the University of Tehran and maintained under the international standards of care for laboratory animals. The rats were provided with a standard laboratory rat diet and water as the composition of the standard rat chow diet is given in the supplementary information (SI) (Table S1). Information regarding diet and the influence it would have on fatty acids are provided in the SI. The rats were kept in these conditions for three months before being euthanised.
Ten adult Persian squirrels Sciurus anomalus were captured with baited traps from their natural environment in the Zagros Mountains. Squirrels were placed in wooden boxes and transported to Pardisan Park Environmental Protection Organization. Animals were individually transferred and housed in cages of 1 m2 in the isolation room at Pardisan Park for one week. Each individual was transferred to a cage of 2 m x 3 m or 3 m x 4 m. These cages were placed outside at Pardisan Park, and were designed to resemble the natural environment with tree trunks, nests and stone floors. The squirrels were fed daily with fresh vegetables and a fruit diet, supplemented with a protein-rich cereal (whole wheat, corn and soybean). The squirrels were kept in these conditions for three months.
Experimental design
Studies focusing on the comparative differences between species of similar size and disparate longevity provide significant insights into the aging mechanisms. Wistar rats are considered to have an maximum lifespan of 3.8 years with an average body mass of 300 g [75]. The maximum lifespan of adult Persian squirrels is deemed to be about 15 years based on the oldest recorded age of the squirrel [76]. Unfortunately, Persian squirrels are illegally hunted and bought and sold as pets [77]. Persian squirrels are also difficult to maintain in captivity due to their dietary constraints and space requirements (requiring space to jump and move arboreally), thus their maximum lifespan may well be more than 15 years, but limited in captivity as a result of artificial housing.
The exact age of the rat was known from laboratory life history. To predict the ages of wild-caught Persian squirrels, the eye lens weight technique was applied using known-age individuals from captivity through two approaches: (1) a regression equation of lens weight against age and (2) a regression equation of lens weight against body weight [78, 79]. Using the age data, we separated each species into two age cohorts representing equivalent chronological ages (Fig. 1). Chronological age is defined as the actual amount of time that has passed since an organism's birth. Estimated chronological age was determined by multiplying an individual's age by their MLSP. The younger cohort represents an estimated 12% of the chronological age, while the older cohort represents an estimated 40% (Fig. 1). A sufficient sample size for squirrels above this chronological age could not be attained.
Fig. 1.
The Wistar Rat (left) and Persian Squirrel (Sciurus anomalus) (right) ages in month and body mass in parentheses. Younger individuals have an estimated chronological age of 12% and older individuals 40% based on their maximum lifespan potential (not shown), where chronological age is defined as the actual amount of time that has passed since an organism's birth. Values represent mean ± SD
We proceeded to compared fatty acid profiles and oxidative stress between rats and squirrels from different age cohorts in several tissues (spleen, skin, liver, kidney, lung, muscle). We first assessed lipid composition by determining which fatty acids are present in tissues to determine the distribution and composition of saturated fatty acid (SFA), MUFA and PUFA. We also measured antioxidant activity (superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPX), glutathione S-transferase (GST) and total antioxidant capacity (TAC)), oxidative stress (lipid peroxidation or damage markers malondialdehyde (MDA) and 4-Hydroxynonenal (HNE), superoxide and hydrogen peroxide (H2O2)), nitrosative markers (NADPH oxidase (NOX), and nitric oxide synthase (NOS), and nitric oxide (NO: a measure of reactive nitrogen species (RNS)). We also analysed fatty acid indices (peroxidation index (PI), unsaturation index (UI), arachidonic/linoleic acid 20:4/18:2 ratio (A/L ratio)) as well as SFA, MUFA and PUFA ratios in all tissues. Formulas for various lipid indices as outlined by Pamplona, Barja and Portero‐Otín [30] are provided as SI (Table S2). Briefly, the PI explains the susceptibility of lipids to peroxidation, where a higher value indicates greater susceptibility. The UI explains the degree of unsaturation, where a higher value indicates a greater proportion of unsaturated fatty acids compared to saturated fatty acids. Animals with a lower A/L ratio are more resistant to lipid peroxidation and associated with animal longevity [33]. Lastly, since body mass between these animals were similar, we could reduce the variation between the species associated with body mass and size [67–69, 80].
Tissue collection
Animals were killed following anaesthesia (Ketamine: 80–90 mg/body mass (Bremer Pharma); Xylazine: 10–15 mg/body mass (Alfasan)). Tissues were immediately harvested and stored in liquid nitrogen until lipid extraction (< 3 weeks).
Preparation of fatty acids and profiling
A modified Folch method containing chloroform: methanol: water (2:1:0.8, v/v/v) was utilized for total lipid extraction and preparation [81]. Methyl ester of fatty acids was prepared from total lipid extract (0.1 g) using acidic methanol (1.0 mL; methanol: sulphuric acid in 80:20 v/v) at 80 °C for 2 h. Normal saline (NaCl 0.7%; 1.0 mL) and hexane (1.0 mL) were then added sequentially, and each step vortexed for 5.0 min, subsequently centrifuged at 3000 × g for 10 min. The profile of fatty acids was assessed using an Agilent 7890B GC 7955 A MSD gas chromatograph coupled with a single quadrupole mass spectrometer and equipped with a fused silica capillary HP- 5MS column (30 m × 0.25 mm; thickness 0.25 µm) as described previously [82]. Briefly, 1.0 µL of fatty acid extract diluted in hexane was injected into the column. Temperature was adjusted at 50 °C, rising to 300 °C. The electron impact ionization mode was used to detect mass spectrometry, and the mass spectrum was achieved from 40–650 Da at 70 eV electron ionization energy. The scanning time and split ratio were 58 min and 1:50, respectively. The quadrupole, source, and interface were adjusted to 150 °C, 230 °C, and 240 °C, respectively. The temperature program of the oven was set as follows: 60 °C (1.0 min), ramp 5 °C/min to 220 °C, then increased 3 °C/min to 280 °C.
NADH oxidase, antioxidant and detoxifying enzymes assay
Before experiments, rat and squirrel tissues (100 mg) were homogenized in 1 mL extraction buffer containing (5.0 g K2HPO4, 2.7 g KH2PO4, 0.150 g EDTA, 0.2 g mercaptoethanol, 0.09 g PMSF, 0.5 g Triton X- 100, 1 g PVP, 3 g Glycerol in 500 mL; pH 7.2). After centrifugation of homogenates for 10 min at 12,000 × g (4 °C), the Bradford assay was used to measure protein concentration in supernatants. NOX activity was assessed at 345 nm by adding 500 µL of tissue homogenate to 500 µL of K2HPO4- KH2PO4 (0.1 M; pH 7.5) reaction mixture buffer containing 0.1 mM NADPH, and 1.0 mM DTT [83]. SOD activity was monitored at 560 nm by adding 200 µL of tissue homogenate to 200 µL of K2HPO4- KH2PO4 reaction mixture buffer containing 50 M nitroblue tetrazolium (NBT), 100 M EDTA, 50 mM sodium carbonate, 12 mM methionine, and 10 M riboflavin [84]. CAT activity was assessed by monitoring H2O2 decomposition in the presence of 500 µL tissue homogenate at 240 nm by adding 500 µL of K2HPO4- KH2PO4 containing 10 mM H2O2. GPX activity was assessed at 340 nm by adding 500 µL of tissue homogenate to 500 µL of phosphate buffer (100 mM, pH 7.0) reaction mixture buffer containing 1.0 mM glutathione, 0.15 mM NADH, and 1.0 mM hydrogen peroxide [85]. GST activity was measured by observing the conjugation of 1-chloro- 2, 4-dinitrobenzene (CDNB) with reduced glutathione (GSH) in the presence of GST and monitoring absorbance at 340 nm [86]. NOS activity was assessed via NADPH oxidation to NADP + by measuring absorbance at 340 nm in a reaction mixture containing 10 mM Tris–HCl buffer (pH 8.0), 10 mM arginine, and 10 µM NADPH [85].
Determination of superoxide anion, hydrogen peroxide, nitric oxide and lipid peroxidation biomarkers
Superoxide anions were determined at 560 nm by NBT reduction in the presence of superoxide and formazan formation. The reaction was initiated by adding 500 µL of tissue extract to 500 µL of NBT (100 mM), then 50 µL of potassium hydroxide (1 M) + DMSO (0.1%) was added for dissolving the blue formazan particles. The standard superoxide-producing regent was EDTA (0.1 mM), sodium carbonate (50 mM), riboflavin (10 mM), and methionine (12 mM) [87]. The ferrous oxidation-xylenol orange (FOX) reagent contained 110 mM perchloric acid, 0.250 mM xylenol orange, and 0.250 mM ferrous ions was used to measure the H2O2 amount in tissue homogenate using H2O2 as standard. The 0.9 mL of tissue extract was mixed with 0.1 mL of methanol and 0.9 mL of FOX reagent sequentially and each step was incubated at room temperature for 30 min, subsequently, the absorbance was monitored at 560 nm [88]. The nitric oxide content was analysed by Griess reagent [0.1% naphthylethylenediamine dihydrochloride in H2O + 1% sulphanilamide in 2.5% H3PO4] using sodium nitrite as standard. Briefly, 500 µL of supernatant and 500 µL of Griess reagent were mixed and the absorbance was read at 540 nm [89]. The lipid peroxidation products including MDA and other thiobarbituric acid reactive substances achieved through thiobarbituric acid reaction mixture (50 µL of 8.1% SDS, 375 µL of 3.5 M (pH = 4) acetate buffer, 375 µL of 0.8% TBA solution, 100 µL H2O) and monitoring light absorbance at 532 nm [90]. A commercially available ELISA kit (#Ab238538, Abcam) was utilized to quantify the HNE concentration following the manufacturer's instructions.
Total antioxidant capacity of tissue homogenate
The TAC was assessed at 734 nm by mixing 20 µL of tissue with 1000 µL of 2, 2-azino-bis (3-ethylbenzothiazoline 6-sulfonic acid) (ABTS) radical solution (7 mM ABTS and 2.54 mM potassium persulfate; one day in dark). A calibration graph was drawn based on Trolox (10 mg/mL) as a standard reference [91].
Statistical analyses
Broad comparative analyses were performed first between squirrels and rats by comparing oxidative stress variables (NOX, SOD, CAT, GPx, GST, NOS, TAC, superoxide, H2O2, NO, MDA and HNE) and fatty acid indexes (UFA/SFA, MUFA/SFA, MUFA/SFA, PUFA/SFA, MUFA/PUFA ratios, in addition to UI and PI) between different tissues (lung, spleen, kidney, liver, skin and muscle) as well as by age group or cohort (older and younger) by principal component analysis (PCA) using R (version 4.3.3) [92]. Additionally, a PCA was used to determine the comparative differences of different fatty acids between squirrels and rats in addition to age between different tissues as well as age cohort. All PCAs were performed with the prcomp function using the ggplot2 package [93]. The number of PCs (principal components) was determined through Eigenvalues, skree plots, and the cumulative proportion of variance explained by the PCs [94]. The functions ggbiplot from the ggplot2 package [93] and cor were used to determine the relationship of the variables within each component and the loading of the variables within each PC was visualised as a biplot [95]. A correlation higher than 50 was considered to have a strong correlation, while. the ellipse probability was set to 68%. PCs were further analysed for their effect with respect to species, age and species*age interaction through MANOVA and linear models (lm). All models were corrected for multiple comparisons with either a sidak adjustment or Tukey’s honest significant difference. Only biologically relevant comparisons were pursued further, which include older rats and squirrels, younger rats and squirrels, and/or between younger and older rats and squirrels respectively.
Normality of the residuals was determined using the Shapiro wilk test. Heterogeneity of variance was determined using the Breusch-Pagan test. Models that were previously identified to have a significant effect of age, species or species*age were further analysed in lm. Linear models used age, species and sex, as well as the age*species interaction and body mass as a covariate using the base package in R. Variables that violated normality and heteroscedasticity, or had a unique distribution, or did not converge, were analysed using a generalised linear model (GLM) with a Gamma distribution and a log-link function or with a Tweedie distribution log-link function. Backward elimination was performed using the MASS package and the stepAIC function, where the best AIC model was retained. Post hoc tests were performed on models that retained species and age as fixed factors after backwards elimination, using the emmeans package. All variables were tested for multicollinearity using the car package with the vif function, where non-interactive fixed effects had a variance inflation factor of less than 5. Tissue comparisons were regrettably beyond the scope of the manuscript and were not interpreted. Furthermore, age effects without species were also not interpreted.
Results
Our main questions are whether there are larger differences between the squirrels and rats in all variables tested, and whether age differences exist between the age cohorts (younger rats and squirrels and older rats and squirrels) in these variables. Although several factors in our results show how variables differ with age regardless of species, it does not address our question thus these results were not interpreted. Furthermore, sex and body mass effects were also not elaborated on as these were for controlling observed variation.
PCA analyses
Specific information of the eigen value, cumulative variance, total variance and eigen vectors or loadings of all PCAs can be found in the SI (Table S3, Table S4).
Overall, there was no clear separation by species or age in the PCA plots for fatty acids composition (Fig. S1- 3). MANOVA of the lipid PCA (with 5 PCs) showed significant effects for species, age, and their interaction, with the species effect being larger than age (Table 1). Post hoc analyses revealed significant differences between the younger and older groups, as well as between younger and older squirrels (Table 1). For individual PCs, only PC1 (associated with C18:1n9 (oleic acid), C20:4n6 90 (arachidonic acid), and C6:0 caproic acid)) was significant, with post hoc analyses showing differences between older rats and squirrels, younger rats and squirrels, and between younger and older squirrels (Table 1). PC3 (associated with C12:0 (lauric acid) and C17:0 (margaric acid)) was significant, with post hoc analyses showing differences only between younger and older rats and between younger and older squirrels (Table 1). Post hoc analyses of PC4 and PC5 was not significant so these correlations were not pursued further (Table 1).
Table 1.
The MANOVA and linear model outputs, statistical significance and post hoc comparisons of the principal components determined from the principal component analyses for lipid composition and for oxidative, nitrosative, antioxidant and lipid indices between rats and squirrels from two different age group cohorts (younger and older) across all tissues
| Variables | Model (Predictors and Family) | Pilai, F, df (num, den), p-value | Model Fit (adjusted R2, F-statistic, df, p value) | Significant Effects (t value, significance) | Post hoc Comparisons (t value, significance) |
|---|---|---|---|---|---|
| Lipid composition cbind(PC1, PC2, PC3, PC4, PC5) | MANOVA (Species:AgeGroup, Gaussian) | Species (0.47094, 33.825, (3,114), **), AgeGroup (0.73521, 105.512, (3,114), **), AgeGroup:Species (0.12057, 5.210, (3,114, **) | Species **, AgeGroup **, AgeGroup:Species ** | Rat Older > Rat Younger: df = 116, t = 7.302, ***, Squirrel Older > Squirrel Younger: df = 116, t = 3.701, **, Rat Younger < Squirrel Younger: df = 116, t = − 5.402, *** | |
| Lipid composition PC1 | lm(Species:AgeGroup, Gaussian) | 0.5085, 42.04 on 3 and 116 DF, *** | Species (t = 4.310: ↑ squirrels ***), Species:AgeGroup (t = 3.539, ↑ in younger squirrels ***) |
Rat Older < Squirrel Older: t = − 4.310, ***, Squirrel Older < Squirrel Younger: t = − 5.727, ***, Rat Younger < Squirrel Younger: t = − 9.315, *** |
|
| Lipid composition PC2 | lm(Species:AgeGroup, Gaussian) | 0.5928, 58.76 on 3 and 116 DF, *** | Species (t = 8.813: ↑ squirrels ***) | Rat Older < Squirrel Older: t = − 8.813, ***, Rat Younger < Squirrel Younger: t = − 9.925, *** | |
| Lipid composition PC3 | lm(Species:AgeGroup, Gaussian) | 0.4042, 7.91 on 3 and 116 DF, *** |
Species (t = − 2.434: ↓ squirrels ***) AgeGroup(t = 5.767, ↑ in younger squirrels ***) |
Rat Older < Rat Younger: t = − 5.767, ***, Squirrel Older < Squirrel Younger: t = − 6.463, *** | |
| Lipid composition PC4 | lm(Species:AgeGroup, Gaussian) | 0.04307, 2.785 on 3 and 116 DF, * | AgeGroup (t = − 2.668: ↓ in younger **) | NS | |
| Lipid composition PC5 | lm(Species:AgeGroup, Gaussian) | – | – 0,1872, 0.2711 on 3 and 116 DF, NS | NS | NS |
| Oxidative, Nitrosative, Antioxidant and Lipid Indices Variables cbind(PC1, PC2, PC3) | MANOVA (Species:AgeGroup, Gaussian) | Species (0.77230, 128.888, (3, 114), ***, AgeGroup (0.08040, 3.322, (3, 114), *) | Species ***, AgeGroup * | Rat Younger < Squirrel Younger: df = 116, t = − 4.013, *** | |
| Oxidative, Nitrosative, Antioxidant and Lipid Indices PC1 | lm(Species:AgeGroup, Gaussian) | 0.128, 6.825 on 3 and 116 DF, *** |
Species (t = − 3.561: ↓ squirrels ***), AgeGroup (t = − 3.106: ↓ in younger **) |
Rat Older > Squirrel Older: t = 3.561, **, Rat Older > Rat Younger: t = 3.106, * | |
| Oxidative, Nitrosative, Antioxidant and Lipid Indices PC2 | lm(Species:AgeGroup, Gaussian) | 0.5928, 58.76 on 3 and 116 DF, *** | Species (t = 8.813: ↑ squirrels ***) | Rat Older < Squirrel Older: t = − 8.813, ***, Rat Younger < Squirrel Younger: t = − 9.925, *** | |
| Oxidative, Oxidative, Nitrosative, Antioxidant and Lipid Indices PC3 | lm(Species:AgeGroup, Gaussian) | 0.06434, 3.727 on 3 and 116 DF, * | Species (t = 8.813: ↑ squirrels *), | NS |
Significance levels: *** (p < 0.001), ** (p < 0.01), * (p < 0.05), NS (not significant)
For antioxidant, oxidative stress, nitrosative and lipid indices, there was no clear separation observed for age or species in PC1 and PC3, but PC2 clearly separated the variables (Fig. 2; Fig. S4; Fig. S5). MANOVA of the oxidative stress PCA (with 3 PCs) revealed significant species and age effects, with the species effect being larger, but the interaction was not significant (Table 1). Post hoc analyses indicated that only the interaction between younger rats and squirrels was significant (Table 1). Individual PC analyses found that PC1 (associated with NOX, SOD, CAT, GPX, NOS, superoxide, H2O2, NO, and TAC) showed significant species and age effects, but no interaction, with post hoc analyses revealing significant differences only between older rats and squirrels, and between older and younger rats (Table 1). PC2 (associated with HNE, MDA, GST, UI, PI, A:L, MUFA/SFA, MUFA/PUFA ratio) found a significant species effect, with post hoc analyses showing differences between older rats and squirrels and between older and younger rats (Table 1). PC3 showed a significant species difference, but post hoc analyses found no significant differences between groups (Table 1).
Fig. 2.
Principal component analysis illustrating the relationships between PC1 and PC2 for oxidative status, nitrosative and antioxidant markers, which include markers such as NADPH oxidase (NOX), superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPX), glutathione S-transferase (GST) and nitric oxide synthase (NOS), the level of reactive free radical markers superoxide, hydrogen peroxide (H2O2), Nitric Oxide (NO), lipid peroxidation markers malondialdehyde (MDA) and 4-Hydroxynonenal (HNE), total antioxidant capacity (TAC) and lipid indices such as MUFA/SFA, PUFA/SFA, MUFA/PUFA and UFA/SFA ratios, unsaturation index (UI), peroxidation index (PI) and arachidonic/linoleic acid 20:4/18:2 ratio (A:L) in the Persian squirrel (n = 10) and the Wistar rat (n = 10) between the different tissues (spleen, skin, liver, kidney, lung, muscle: n = 5 for each tissue) between 5 younger (squirrel = blue, rat = orange) and 5 older (squirrel = purple, rat = red) individuals. Variance explained by PC1 is 42.6% and PC2 is 24.9%, with a cumulative variance of 67.5%. The direction of the arrows represents the loadings or eigenvectors of the variables within a PC
Squirrel age comparison
Lipid PCA analyses (PC1 & PC3) revealed differences in oleic acid, arachidonic acid, caproic acid, lauric acid, and margaric acid (Table 2; Table S3; Fig. S1-3). Caproic and margaric acids levels were not significant between younger and older squirrels (Fig. 3a-b. Table 2). Lauric acid was higher in the liver, kidney, and lung of older squirrels (Fig. 3c; Table 2). Oleic acid was higher in the skin of older squirrels, and arachidonic acid was higher in the skin and liver of older squirrels (Fig. 3d-e; Table 2).
Table 2.
The linear and general linear model outputs, statistical significance and post hoc comparisons of the specific lipids (oleic, arachidonic, caproic, margaric, lauric) between rats and squirrels from two different age group cohorts (younger and older) between a variety of tissues (lung, spleen, liver, kidney, muscle, skin)
| Variable | Model (Predictors and Family) | Deviance (Null and Residual) | Model Fit (adjusted R2, F-statistic, df, p value) | Significant Effects (t value, significance) | Post-hoc Comparisons (t value, significance) |
|---|---|---|---|---|---|
| Lung_Oleic_Acid | lm(Species + BodyMass, Gaussian) | 0.6458, 18.32 on 2 and 17 DF, *** | Species (t = 5.818: ↑ squirrels ***), BodyMass (t = 2.318: ↑ *) | N/A | |
| Lung_Arachidonic_Acid | lm(Species + BodyMass, Gaussian) | 0.8894, 77.41 on 2 and 17 DF, *** | Species (t = − 12.406: ↓ squirrels ***), Body Mass (t = − 2.358: ↓*) | N/A | |
| Lung_Caproic_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 7.5224 on 19 degrees of freedom, Residual deviance: 1.6301 on 14 degrees of freedom | NS | N/A | |
| Lung_Margaric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 0.12573 on 19 degrees of freedom, Residual deviance: 0.03485 on 14 degrees of freedom | NS | N/A | |
| Lung_Lauric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 0.629728 on 19 degrees of freedom, Residual deviance: 0.099261 on 14 degrees of freedom | AgeGroup (t = − 2.604: ↓ in younger *), | t = 2.604: Older Rat > Younger Rat * | |
| Spleen_Oleic_Acid | glm(AgeGroup:Species + AgeGroup:Species, Gamma(link ="log")) | Null deviance: 5.54400 on 19 degrees of freedom, Residual deviance: 0.38921 on 16 degrees of freedom | AgeGroup (t = − 2.226: ↓ in younger *), Species (t = 9.162: ↑ squirrels ***), AgeGroup:Species (t = 2.165: ↑ in younger squirrels *) |
t = − 9.162: Older Rat < Older Squirrel ***, t = − 12.224: Younger Rat < Younger Squirrel *** |
|
| Spleen_Arachidonic_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 1651.6 on 19 degrees of freedom, Residual deviance: 145.5 on 14 degrees of freedom | Species (t = − 2.405: ↓ squirrels *) | NS | |
| Spleen_Caproic_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 28.13930 on 19 degrees of freedom, Residual deviance: 0.36651 on 14 degrees of freedom | NS | N/A | |
| Spleen_Margaric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 0.47303 on 19 degrees of freedom, Residual deviance: 0.16422 on 14 degrees of freedom | Species (t = − 2.338 ↓ in squirrels *), Sex (t = 2.262: ↑ in male *) | NS | |
| Spleen_Lauric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 1.02641 on 19 degrees of freedom, Residual deviance: 0.20969 on 14 degrees of freedom | AgeGroup (t = − 2.451: ↓ in younger *) | NS | |
| Liver_Oleic_Acid | lm(AgeGroup:Species + Sex + BodyMass, Gaussian) | 0.8164, 17.89 on 5 and 14 DF, *** | Species (t = 4.113: ↑ squirrels **), AgeGroup:Species (t = 2.525: ↑ in younger squirrels *) |
t = − 4.113: Older Rat < Older Squirrel **, t = − 8.062: Younger Rat < Younger Squirrel *** |
|
| Liver_Arachidonic_Acid | glm(AgeGroup:Species + AgeGroup:SpeciesGamma(link ="log")) | Null deviance: 24.495 on 19 degrees of freedom, Residual deviance: 5.928 on 16 degrees of freedom | AgeGroup:Species (t = − 4.743: ↓ younger squirrels) |
t = 8.323: Younger Rat > Younger Squirrel ***, t = 5.424: Older Squirrel > Younger Squirrel *** |
|
| Liver_Caproic_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 6.9907 on 19 degrees of freedom, Residual deviance: 1.5094 on 14 degrees of freedom | NS | N/A | |
| Liver_Margaric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 1.0547 on 19 degrees of freedom, Residual deviance: 0.3573 on 14 degrees of freedom | NS | N/A | |
| Liver_Lauric_Acid | lm(AgeGroup:Species + Sex + BodyMass, Gaussian) | 0.5439, 8.552 on 3 and 16 DF, ** | AgeGroup (t = − 2.178: ↓ in younger *) | t = 4.447: Older Squirrel > Younger Squirrel ** | |
| Kidney_Oleic_Acid | lm(AgeGroup:Species + AgeGroup:Species, Gaussian) | 0.8364, 33.37 on 3 and 16 DF, *** | Species (t = 8.307: ↑ squirrels ***) |
t = − 8.307: Older Rat < Older Squirrel *** t = − 5.526: Younger Rat < Younger Squirrel *** |
|
| Kidney_Arachidonic_Acid |
lm(AgeGroup:Species + BodyMass + AgeGroup:Species, Gaussian) |
0.8964, 42.11 on 4 and 15 DF, *** | AgeGroup (t = − 2.639: ↓ in younger *), Species (t = − 10.980: ↓ squirrels ***), BodyMass (t = − 2.742: ↓ *), AgeGroup:Species (t = − 3.156: ↓ in younger squirrels **) |
t = 10.980: Older Rat > Older Squirrel *** t = 6.841: Younger Rat > Younger Squirrel *** |
|
| Kidney_Caproic_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 0.0000e + 00 on 19 degrees of freedom, Residual deviance: 9.7578e- 22 on 14 degrees of freedom | NS | N/A | |
| Kidney_Margaric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 1.09809 on 19 degrees of freedom, Residual deviance: 0.28638 on 14 degrees of freedom | Species (t = 2.698 ↑ in squirrels *), Sex (t = − 2.8762: ↓ in male *) | N/A | |
| Kidney_Lauric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Gaussian) | 0.8604, 30.28 on 4 and 15 DF, *** |
AgeGroup (t = − 2.945: ↓ *), Species (t = 6.444: ↑ in squirrels ***), AgeGroup:Species (t = − 4.724: ↓ in younger squirrels ***) |
NS | |
| Muscle_Oleic_Acid | lm(AgeGroup:Species + Sex + BodyMass, Gaussian) | 0.3219, 2.804 on 5 and 14 DF, NS | NS | NS | |
| Muscle_Arachidonic_Acid | glm(AgeGroup:Species + AgeGroup:Species, Gamma(link ="log")) | Null deviance: 5.3436 on 19 degrees of freedom, Residual deviance: 3.3114 on 16 degrees of freedom |
AgeGroup (t = 2.168: ↑ in younger *), Species (t = 2.844: ↓ squirrels *), AgeGroup:Species (t = − 2.532: ↓ in younger squirrels *) |
NS | |
| Muscle_Caproic_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 0.0000e + 00 on 19 degrees of freedom, Residual deviance: 9.7578e- 22 on 14 degrees of freedom | NS | N/A | |
| Muscle_Margaric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 1.07209 on 19 degrees of freedom, Residual deviance: 0.59816 on 14 degrees of freedom | NS | N/A | |
| Muscle_Lauric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 0.52785 on 19 degrees of freedom, Residual deviance: 0.27001 on 14 degrees of freedom | NS | N/A | |
| Skin_Oleic_Acid | lm(AgeGroup:Species + Sex + AgeGroup:Species, Gaussian) | 0.6103, 8.44 on 4 and 15 DF, *** | Species (t = 4.722: ↑ squirrels ***), SexMale (t = − 2.284: ↓*), AgeGroup:Species (t = − 3.098: ↓ in younger squirrels **) |
t = − 4.722: Older Rat < Older Squirrel **, t = 4.032: Older Squirrel > Younger Squirrel ** |
|
| Skin_Arachidonic_Acid | glm(AgeGroup:Species + AgeGroup:Species, Gamma(link ="log")) | Null deviance: 99.276 on 19 degrees of freedom, Residual deviance: 15.202 on 16 degrees of freedom | AgeGroup (t = − 6.263: ↓ in younger ***), AgeGroup:Species (t = − 13.049: ↓ in younger squirrels ***) |
t = 6.263: Older Rat > Younger Rat ***, t = 18.501: Younger Rat > Younger Squirrel ***, t = 24.718: Older Squirrel > Younger Squirrel *** |
|
| Skin_Caproic_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 10.3268 on 19 degrees of freedom, Residual deviance: 2.2407 on 14 degrees of freedom | NS | N/A | |
| Skin_Margaric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 0.77086 on 19 degrees of freedom, Residual deviance: 0.15308 on 14 degrees of freedom | BodyMass (t = 2.637: ↑ *) | N/A | |
| Skin_Lauric_Acid | glm(AgeGroup:Species + Sex + BodyMass, Tweedie) | Null deviance: 15.5339 on 19 degrees of freedom, Residual deviance: 1.2407 on 14 degrees of freedom | Species (t = − 4.839: ↓ in squirrel ***) | t = 4.839: Older Rat > Older Squirrel *** |
Significance levels: *** (p < 0.001), ** (p < 0.01), * (p < 0.05), NS (not significant)
Fig. 3.
The boxplot variable distributions of a) arachidonic acid, b) caproic acid, c) lauric acid, d) margaric acid and e) oleic acid between squirrels (n = 10) and rats (n = 10) between different age cohorts (older (n = 5): circle, and younger (n = 5): triangle) across a variety of tissues (kidney: red, liver: blue, lung: green, muscle: purple, skin: orange, spleen: yellow)
Oxidative, antioxidant, nitrosative and lipid indices PCA linear models found no significant differences between younger and older squirrels across all tissues, suggesting age-related antioxidant, nitrosative, oxidative markers and lipid indices are not significantly different between age cohorts showing a delayed ageing phenotype in squirrels (Fig. 4a-e; Fig. 5a-g; Fig. 6a-f; Fig. S4-5; Table 1; Table S4).
Fig. 4.
The boxplot variable distributions of a) catalase (CAT), b) glutathione peroxidase (GPX), c) glutathione-S-transferase (GST), d) superoxide dismutase (SOD) and e) total antioxidant capacity (TAC) between squirrels (n = 10) and rats (n = 10) between different age cohorts (older (n = 5): circle, and younger (n = 5): triangle) across a variety of tissues (kidney: red, liver: blue, lung: green, muscle: purple, skin: orange, spleen: yellow)
Fig. 5.
The boxplot variable distributions of a) hydrogen peroxide (H2O2), b) 4-Hydroxynonenal (HNE), c) malondialdehyde (MDA), d) nitric oxide (NO), e) nitric oxide synthase (NOS), f) NADPH oxidase (NOX), g) superoxide between squirrels (n = 10) and rats (n = 10) between different age cohorts (older (n = 5): circle, and younger (n = 5): triangle) across a variety of tissues (kidney: red, liver: blue, lung: green, muscle: purple, skin: orange, spleen: yellow)
Fig. 6.
The boxplot variable distributions of a) arachidonic/linoleic acid 20:4/18:2 ratio (A/L ratio), b) monounsaturated fatty acid (MUFA)/polyunsaturated fatty acid (PUFA) ratio, c) MUFA/saturated fatty acid (SFA) ratio, d) peroxidation index (PI), e) PUFA/SFA ratio and f) unsaturation index (UI) between squirrels (n = 10) and rats (n = 10) between different age cohorts (older (n = 5): circle, and younger (n = 5): triangle) across a variety of tissues (kidney: red, liver: blue, lung: green, muscle: purple, skin: orange, spleen: yellow)
Rat age comparison
For the lipid PCA analyses, only PC3, which included lauric acid and margaric acid, was significantly different between younger and older rats (Table 2; Table S3; Fig. S1-3). Furthermore, no significant differences were observed between arachidonic and oleic acid between younger and older rats (Table 2; Fig. 3d-e). Margaric acid was not significantly different across all tissues in the GLMs comparing younger and older rats (Table 2; Fig. 3b). In contrast, lauric acid showed significant differences in the lung and kidney, with higher amounts observed in older rats (Table 2; Fig. 3c). No differences between A/L and oleic acid suggest that rats do not protect their fatty acids from free radical stress.
Following the oxidative stress PCA, PC1 included variables related to antioxidants, nitrosative and oxidative stress (NOX, SOD, CAT, GPX, NOS, superoxide, H2O2, NO, TAC), while PC2 included lipid peroxidation and fatty acid indices (HNE, MDA, GST, UI, PI, A:L, MUFA/SFA, MUFA/PUFA) (Table S3- 4). Older rats showed significantly higher antioxidant markers: CAT (skin, spleen) (Fig. 4a), GPX (kidney, muscle, spleen) (Fig. 4b), and SOD (liver) (Fig. 4d) (Table 3). Older rats also had higher oxidative stress markers: H2O2 (lung, skin) (Fig. 5a), MDA (muscle, skin, spleen), (Fig. 5c), HNE (skin), and superoxide (all tissues except skin) (Fig. 5g) (Table 3). Nitrosative markers were also elevated in older rats, including NO (lung, muscle, skin, spleen) (Fig. 5d), NOS (muscle, skin, spleen) (Fig. 5e) and NOX (all tissues except lung) (Fig. 5g; Table 3). For fatty acid indices, older rats had lower PI and UI (muscle, spleen) (Fig. 6d and Fig. 6f), lower UI (liver) (Fig. 6f), and higher PI (skin) (Fig. 6d; Table 3). Significant differences were found in the liver and spleen for MUFA/SFA, PUFA/SFA, and MUFA/PUFA ratios, where the liver had a higher MUFA/SFA ratio (Fig. 6c), but lower PUFA/SFA ratio (Fig. 6e), while the spleen showed a lower PUFA/SFA ratio, but higher MUFA/PUFA ratio (Fig. 6b; Table 3).
Table 3.
The linear and general linear model outputs, statistical significance and post hoc comparisons for oxidative (malondialdehyde (MDA) and 4-Hydroxynonenal (HNE), superoxide and hydrogen peroxide (H2O2)), nitrosative (NADPH oxidase (NOX), and nitric oxide synthase (NOS), and nitric oxide (NO: a measure of reactive nitrogen species (RNS)), antioxidant (superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GPX), glutathione S-transferase (GST) and total antioxidant capacity (TAC)) and lipid indices (MUFA/SFA, PUFA/SFA, MUFA/PUFA and UFA/SFA ratios, unsaturation index (UI), peroxidation index (PI) and arachidonic/linoleic acid ratio (A/L)) between rats and squirrels from two different age group cohorts (younger and older) between a variety of tissues (lung, spleen, liver, kidney, muscle, skin)
| Variable | Model (Predictors and Family) | Deviance (Null and Residual) | Model Fit (adjusted R2, F-statistic, df, significance | Significant Effects (t value, significance) | Post-hoc Comparisons (t value, significance) |
|---|---|---|---|---|---|
| Liver_NOX | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | N/A | 0.7171, 17.05 on 3 and 16 DF, *** | AgeGroup (t = − 5.572: ↓ in younger ***), Species (t = − 4.623: ↓ in squirrels ***), AgeGroup:Species (t = 2.525: ↑ in younger squirrels *) |
t = 5.572: Older Rat > Younger Rat **, t = 4.623: Older Rat > Older Squirrel ** |
| Liver_SOD | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | N/A | 0.8064,27.39 on 3 and 16 DF, *** | AgeGroup (t = − 4.816: ↓ in younger ***), Species (t = − 7.735: ↓ in squirrels ***), AgeGroup:Species (t = 3.263: ↑ in younger squirrels **) |
t = 4.816: Older Rat > Younger Rat **, t = 7.735: Older Rat > Older Squirrel ***, t = 3.12: Younger Rat > Younger Squirrel * |
| Liver_CAT | lm (Species, BodyMass, Gaussian) | N/A | 0.5506, 12.64 on 2 and 17 DF, *** | Species (t = − 3.158: ↓ in squirrels **), BodyMass (t = 3.529: ↑ **) | N/A |
| Liver_GPX | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | N/A | 0.7516, 15.37 on 4 and 15 DF, *** | Species (t = 4.835: ↑ in squirrels ***), Sex (t = − 3.399: ↓ in males **) |
t = − 4.835: Older Rat < Older Squirrel **, t = 4.568: Older Squirrel > Younger Squirrel ** |
| Liver_GST | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | N/A | 0.8326, 32.49 on 3 and 16 DF, *** | Species (t = 8.354: ↑ in squirrels ***), AgeGroup:Species (t = − 2.328: ↓ in younger squirrels *) |
t = − 8.354: Older Rat < Older Squirrel ***, t = − 5.061: Younger Rat < Younger Squirrel ** |
| Liver_NOS | lm (AgeGroup, Species, Sex, BodyMass, AgeGroup:Species, Gaussian) | N/A | 0.7403, 11.83 on 5 and 14 DF, *** | AgeGroup (t = − 6.387: ↓ in younger ***), Species (t = − 4.596: ↓ in squirrels ***), Sex (t = 3.940: ↑ in males **), BodyMass (t = − 3.789: ↓ **), AgeGroup:Species (t = 5.533: ↑ in younger squirrels ***) |
t = 6.387: Older Rat > Younger Rat **, t = 4.596: Older Rat > Older Squirrel **, t = − 3.295: Younger Rat < Younger Squirrel *, t = 3.015: Older Squirrel > Younger Squirrel * |
| Liver_Superoxide | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | N/A | 0.6257, 11.59 on 3 and 16 DF, *** | AgeGroup (t = − 4.848: ↓ in younger ***), Species (t = − 4.249: ↓ in squirrels ***), AgeGroup:Species (t = 2.780: ↑ in younger squirrels *) |
t = 4.848: Older Rat > Younger Rat **, t = 4.249: Older Rat > Older Squirrel ** |
| Liver_H2O2 | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | N/A | 0.2916, 2.956 on 4 and 15 DF, NS | AgeGroup (t = − 2.393: ↓ in younger *), Sex (t = 2.327: ↑ in males *), AgeGroup:Species (t = 2.204: ↑ in younger squirrels *) | NS |
| Liver_NO | lm (AgeGroup, Sex, BodyMass, Gaussian) | N/A | 0.3041, 3.767 on 3 and 16 DF, * | AgeGroup (t = − 2.791: ↓ in younger *) | N/A |
| Liver_MDA | lm (AgeGroup, Species, Gaussian) | N/A | 0.8301, 47.41 on 2 and 17 DF, *** | Species (t = − 9.612: ↓ in squirrels ***) |
t = 9.612: Older Rat > Older Squirrel ***, t = 9.612: Younger Rat > Younger Squirrel *** |
| Liver_HNE | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.9145, 68.76 on 3 and 16 DF, *** | AgeGroup (t = − 2.706: ↓ in younger *), Species (t = − 11.109: ↓ in squirrels ***) |
t = 11.109: Older Rat > Older Squirrel ***, t = 8.830: Younger Rat > Younger Squirrel *** |
|
| Liver_TAC | lm (AgeGroup, Species, Gaussian) | 0.607, 15.67 on 2 and 17 DF, *** | AgeGroup (t = − 3.482: ↓in younger **), Species (t = 4.384: ↑ in squirrels ***) |
t = 3.482: Older Rat > Younger Rat *, t = − 4.384: Older Rat < Older Squirrel **, t = − 4.384: Younger Rat < Younger Squirrel **, t = 3.482: Older Squirrel > Younger Squirrel * |
|
| Liver_UI | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 0.77008 on 19 degrees of freedom Residual deviance: 0.23840 on 16 degrees of freedom |
AgeGroup (t = 3.415: ↑ in younger **), AgeGroup:Species (t = − 3.799: ↓ in younger squirrels **) |
t = − 3.415: Older Rat < Younger Rat *, t = 5.667: Younger Rat > Younger Squirrel *** |
|
| Liver_PI | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 5.7501 on 19 degrees of freedom Residual deviance: 1.3610 on 16 degrees of freedom |
AgeGroup (t = 2.202: ↑ in younger *), AgeGroup:Species (t = − 3.766: ↓ in younger squirrels **) |
t = 6.832: Younger Rat > Younger Squirrel ***, t = 3.124: Younger Squirrel < Older Squirrel * |
|
| Liver_UFA/SFA | lm (AgeGroup, Species, Gaussian) | 0.7524, 29.87 on 2 and 17 DF, *** | AgeGroup (t = 2.233: ↑ in younger *), Species (t = 7.400: ↑ in squirrels ***) |
t = − 7.400: Older Rat < Older Squirrel ***, t = − 7.400: Younger Rat < Younger Squirrel *** |
|
| Liver_MUFA/SFA | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 5.21157 on 19 degrees of freedom Residual deviance: 0.80567 on 16 degrees of freedom |
AgeGroup (t = − 3.059: ↓ **), Species (t = 4.537: ↑ ***), AgeGroup:Species: (t = 3.458: ↑ in younger squirrels **) |
t = 3.059: Older Rat > Younger Rat *, t = − 4.537: Older Rat < Older Squirrel **, t = − 9.427: Younger Rat < Younger Squirrel *** |
|
| Liver_PUFA/SFA | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | 0.4829, 5.436 on 4 and 15 DF, ** | AgeGroup (t = 4.417: ↑ in younger ***), Species (t = 2.447: ↑ in squirrels *), AgeGroup:Species (t = − 3.444: ↓ in younger squirrels **) | t = − 4.417: Older Rat < Younger Rat ** | |
| Liver_MUFA/PUFA | lm (AgeGroup, Species, Sex, BodyMass, AgeGroup:Species, Gaussian) | 0.7292, 11.23 on 5 and 14 DF, *** | AgeGroup:Species (t = 3.036: ↑in younger squirrels **) | t = − 6.609: Younger Rat < Younger Squirrel ** | |
| Liver_ A/L | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 29.8342 on 19 degrees of freedom Residual deviance: 7.7222 on 16 degrees of freedom |
AgeGroup:Species (t = − 4.134: ↓ in younger squirrels ***) |
t = 7.812: Older Rat > Older Squirrel ***, t = 7.812: Younger Rat > Younger Squirrel ***, t = 5.281: Older Squirrel > Younger Squirrel *** |
|
| Kidney_NOX | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.8796, 35.69 on 4 and 15 DF, *** | AgeGroup (t = − 7.376: ↓ in younger ***), Species (t = − 8.571: ↓ in squirrels ***), BodyMass (t = − 2.549: ↓ *), AgeGroup:Species (t = 4.717: ↑ younger squirrels***) |
t = 7.376: Older Rat > Younger Rat ***, t = 8.571: Older Rat > Older Squirrel ***, t = 3.642: Older Squirrel > Younger Squirrel * |
|
| Kidney_SOD | lm (Species, BodyMass, Gaussian) | 0.7779, 34.28 on 2 and 17 DF, *** | Species (t = − 7.909: ↓ in squirrels ***) | N/A | |
| Kidney_CAT | glm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 4.23982 on 19 degrees of freedom Residual deviance: 0.73134 on 15 degrees of freedom |
Species (t = − 6.139: ↓ in squirrels ***) |
t = 6.139: Older Rat > Older Squirrel **, t = 3.568: Younger Rat > Younger Squirrel * |
|
| Kidney_GPX | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.4283, 5.744 on 3 and 16 DF, ** | AgeGroup (t = − 3.712: ↓ in younger **) | t = 3.712: Older Rat > Younger Rat **, | |
| Kidney_GST | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | 0.7950, 19.42 on 4 and 15 DF, *** | Species (t = 4.716: ↑ in squirrels ***), Sex (t = 2.576: ↑ in males *), AgeGroup:Species (t = − 6.919: ↓ younger squirrels ***) |
t = − 4.716: Older Rat < Older Squirrel **, t = 5.068: Younger Rat > Younger Squirrel **, t = 7.705: Older Squirrel > Younger Squirrel *** |
|
| Kidney_NOS | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.1739, 2.333 on 3 and 16 DF, NS | Species (t = − 2.527: ↓ in squirrels *) | NS | |
| Kidney_Superoxide | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.9303, 64.4 on 4 and 15 DF, *** | AgeGroup (t = − 9.438: ↓ in younger ***), Species (t = − 12.609: ↓ in squirrels ***), BodyMass (t = − 3.004: ↓**), AgeGroup:Species (t = 7.204: ↑ in younger squirrels ***) |
t = 9.438: Older Rat > Younger Rat ***, t = 12.609: Older Rat > Older Squirrel ***, t = 3.530: Older Squirrel > Younger Squirrel * |
|
| Kidney_H2O2 | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.7579, 20.83 on 3 and 16 DF, *** | AgeGroup (t = − 2.181: ↓ in younger *), Species (t = − 6.455: ↓ in squirrels ***) |
t = 6.455: Older Rat > Older Squirrel ***, t = 4.289: Younger Rat > Younger Squirrel ** |
|
| Kidney_NO | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.3359, 4.203 on 3 and 16 DF, * | AgeGroup (t = − 2.456: ↓ in younger *), Species (t = − 2.904: ↓ in squirrels *) | t = 2.904: Older Rat > Older Squirrel * | |
| Kidney_MDA | lm (AgeGroup, Species, BodyMass, Gaussian) | 0.7395, 18.98 on 3 and 16 DF, *** | Species (t = − 6.709: ↓ in squirrels ***) |
t = 6.709: Older Rat > Older Squirrel ***, t = 6.709: Younger Rat > Younger Squirrel *** |
|
| Kidney_HNE | lm (Species, Sex, Gaussian) | 0.7113, 24.41 on 2 and 17 DF, *** | Species (t = − 6.605: ↓ in squirrels ***) | N/A | |
| Kidney_TAC | lm (Species, BodyMass, Gaussian) | 0.4587, 9.051 on 2 and 17 DF, ** | Species (t = 3.840: ↑ in squirrels **), BodyMass (t = 2.255: ↑*) | N/A | |
| Kidney_UI | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.6953, 11.84 on 4 and 15 DF, *** | Species (t = − 5.650: ↓ in squirrels ***), AgeGroup:Species (t = 3.861: ↑ in younger squirrels **) | t = 5.650: Older Rat > Older Squirrel ** | |
| Kidney_PI | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.84, 25.93 on 4 and 15 DF, *** | Species (t = − 8.922: ↓ in squirrels ***), BodyMass (t = − 2.163: ↓*), AgeGroup:Species (t = 3.684: ↑ in younger squirrels **) |
t = 8.922: Older Rat > Older Squirrel ***, t = 3.960: Younger Rat > Younger Squirrel ** |
|
| Kidney_UFA/SFA | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 2.6241 on 19 degrees of freedom Residual deviance: 0.6515 on 16 degrees of freedom |
Species (t = 2.648: ↑ in squirrels *) |
t = − 5.381: Younger Rat < Younger Squirrel **, t = − 3.268: Older Squirrel < Younger Squirrel * |
|
| Kidney_MUFA/SFA | lm (Species, Gaussian) | 0.6595, 37.81 on 1 and 18 DF, *** | Species (t = 6.149: ↑ in squirrels ***) | N/A | |
| Kidney_PUFA/SFA | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.6217, 11.41 on 3 and 16 DF, *** | AgeGroup:Species (t = 3.411: ↑ in younger squirrels **) |
t = − 4.193: Younger Rat < Younger Squirrel **, t = − 5.262: Older Squirrel < Younger Squirrel ** |
|
| Kidney_MUFA/PUFA | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 5.0149 on 19 degrees of freedom Residual deviance: 1.2911 on 16 degrees of freedom |
Species (t = 5.300: ↑ in squirrels ***), AgeGroup:Species (t = − 2.746: ↓ in younger squirrels *) |
t = − 5.300: Older Rat < Older Squirrel **, t = 4.147: Older Squirrel > Younger Squirrel ** |
|
| Kidney_ A/L | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.8196, 22.58 on 4 and 15 DF, *** | AgeGroup (t = − 3.120: ↓ in younger**), Species (t = − 7.850: ↓ in squirrels ***), BodyMass (t = − 2.726: ↓*) | t = 3.120: Older Rat > Younger Rat *, t = 7.850: Older Rat > Older Squirrel ***, t = 5.395: Younger Rat > Younger Squirrel *** | |
| Lung_NOX | lm (Species, Gaussian) | 0.915, 204.9 on 1 and 18 DF, *** | Species (t = 14.31: ↑ in squirrels ***) | N/A | |
| Lung_SOD | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.9101, 49.09 on 4 and 15 DF, *** | Species (t = 7.229: ↑ in squirrels ***), AgeGroup:Species (t = 3.070: ↑ in younger squirrels **) |
t = − 7.229: Older Rat < Older Squirrel ***, t = − 11.858: Younger Rat < Younger Squirrel *** |
|
| Lung_CAT | lm (Species, Sex, BodyMass, Gaussian) | 0.6831, 14.65 on 3 and 16 DF, *** | Species (t = 5.723: ↑ in squirrels ***), Sex (t = − 2.189: ↓ in males *), BodyMass (t = 3.523: ↑**) | N/A | |
| Lung_GPX | lm (Species, BodyMass, Gaussian) | 0.5849, 14.39 on 2 and 17 DF, *** | Species (t = 3.891: ↑ in squirrels **), BodyMass (t = 4.110: ↑***) | N/A | |
| Lung_GST | lm (Species, Gaussian) | 0.889, 153.2 on 1 and 18 DF, *** | Species (t = 12.38: ↑ in squirrels ***) | N/A | |
| Lung_NOS | lm (AgeGroup, Species, Gaussian) | 0.7415, 28.26 on 2 and 17 DF, *** | Species (t = 7.236: ↑ in squirrels ***) |
t = − 7.236: Older Rat < Older Squirrel ***, t = − 7.236: Younger Rat < Younger Squirrel *** |
|
| Lung_Superoxide | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.8162, 29.12 on 3 and 16 DF, *** | AgeGroup (t = − 4.098: ↓ in younger ***), Species (t = 3.542: ↑ in squirrels **), AgeGroup:Species (t = 3.365: ↑ in younger squirrels**) |
t = 4.098: Older Rat > Younger Rat **, t = − 3.542: Older Rat < Older Squirrel *, t = − 8.301: Younger Rat < Younger Squirrel *** |
|
| Lung_H2O2 | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.7899, 24.81 on 3 and 16 DF, *** | AgeGroup (t = − 4.130: ↓ in younger ***), Species (t = 3.490: ↑ in squirrels **), AgeGroup:Species (t = 2.627: ↑ in younger squirrels *) |
t = 4.130: Older Rat > Younger Rat **, t = − 3.490: Older Rat < Older Squirrel *, t = − 7.205: Younger Rat < Younger Squirrel *** |
|
| Lung_NO | lm (AgeGroup, Species, Gaussian) | 0.7856, 35.82 on 2 and 17 DF, *** | Species (t = 7.672: ↑ in squirrels **), AgeGroup (t = − 3.573: ↓ in younger ***) |
t = 3.573: Older Rat > Younger Rat *, t = − 7.672: Older Rat < Older Squirrel ***, t = − 7.672: Younger Rat < Younger Squirrel ***, t = 3.573: Older Squirrel > Younger Squirrel * |
|
| Lung_MDA | lm (Species, Gaussian) | 0.8372, 98.74 on 1 and 18 DF, *** | Species (t = − 9.937: ↓ in squirrels ***) | N/A | |
| Lung_HNE | lm (Species, Gaussian) | 0.8905,155.5 on 1 and 18 DF, *** | Species (t = − 12.47: ↓ in squirrels ***) | N/A | |
| Lung_TAC | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.9339, 90.44 on 3 and 16 DF, *** | Species (t = 12.692: ↑ in squirrels ***) |
t = − 12.692: Older Rat < Older Squirrel ***, t = − 9.994: Younger Rat < Younger Squirrel ***, t = 3.626: Older Squirrel > Younger Squirrel * |
|
| Lung_UI | lm (Species, Gaussian) | 0.5542, 24.62 on 1 and 18 DF, *** | Species (t = − 4.961: ↓ in squirrels ***) | N/A | |
| Lung_PI | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.8561, 29.26 on 4 and 15 DF, *** | Species (t = − 6.378: ↓ in squirrels ***) |
t = 6.378: Older Rat > Older Squirrel **, t = 8.654: Younger Rat > Younger Squirrel *** |
|
| Lung_UFA/SFA | lm (Species, Gaussian) | 0.2898, 8.753 on 1 and 18 DF, ** | Species (t = 2.959: ↑ in squirrels **) | N/A | |
| Lung_MUFA/SFA | lm (AgeGroup, Species, Gaussian) | 0.6054, 15.58 on 2 and 17 DF, *** | Species (t = 5.369: ↑ in squirrels) |
t = − 5.369: Older Rat < Older Squirrel **, t = − 5.369: Younger Rat < Younger Squirrel ** |
|
| Lung_PUFA/SFA | glm (AgeGroup, Species, Gamma(link ="log")) |
Null deviance: 2.1244 on 19 degrees of freedom Residual deviance: 1.6744 on 17 degrees of freedom |
NS | N/A | |
| Lung_MUFA/PUFA | lm (Species, Gaussian) | 0.5874, 28.05 on 1 and 18 DF, *** | Species (t = 5.296: ↑ in squirrels ***) | N/A | |
| Lung_ A/L | lm (Species, Sex, Gaussian) | 0.7241, 25.93 on 2 and 17 DF, *** | Species (t = − 7.132: ↓ in squirrels ***) | N/A | |
| Spleen_NOX | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 22.72810 on 19 degrees of freedom Residual deviance: 0.42939 on 16 degrees of freedom |
AgeGroup (t = − 3.325: ↓ in younger **), Species (t = − 23.829: ↓ in squirrels***), AgeGroup:Species (t = 2.234: ↑ in younger squirrels *) |
t = 3.325: Older Rat > Younger Rat *, t = 23.829: Older Rat > Older Squirrel ***, t = 20.670: Younger Rat > Younger Squirrel *** |
|
| Spleen_SOD | glm (Species, Gamma(link ="log")) |
Null deviance: 23.46291 on 19 degrees of freedom Residual deviance: 0.51316 on 18 degrees of freedom |
Species (t = − 30.51: ↓ in squirrels ***) | N/A | |
| Spleen_CAT | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.9436, 106.9 on 3 and 16 DF, *** | AgeGroup (t = − 8.779: ↓ in younger ***), Species (t = − 15.066: ↓ in squirrels ***), AgeGroup:Species (t = 5.710: ↑ in younger squirrels ***) |
t = 8.779: Older Rat > Younger Rat ***, t = 15.066: Older Rat > Older Squirrel ***, t = 6.287: Younger Rat > Younger Squirrel *** |
|
| Spleen_GPX | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.879, 35.52 on 4 and 15 DF, *** | AgeGroup (t = − 5.361: ↓ in younger ***), Species (t = − 9.132: ↓ in squirrels ***), AgeGroup:Species (t = 3.136: ↑ in younger squirrels **) |
t = 5.361: Older Rat > Younger Rat **, t = 9.132: Older Rat > Older Squirrel ***, t = 4.959: Younger Rat > Younger Squirrel ***, t = 2.929: Older Squirrel > Younger Squirrel * |
|
| Spleen_GST | lm (AgeGroup, Species, AgeGroup:Species | 0.6495, 12.73 on 3 and 16 DF, *** | Species (t = 5.707: ↑ in squirrels ***), AgeGroup:Species (t = − 2.714: ↓ in younger squirrels *) |
t = − 5.707: Older Rat < Older Squirrel ***, t = 2.953: Older Squirrel > Younger Squirrel * |
|
| Spleen_NOS | lm (AgeGroup, Species, AgeGroup:Species | 0.8512, 37.22 on 3 and 16 DF, *** | AgeGroup (t = − 4.475: ↓ in younger ***), Species (t = − 9.076: ↓ in squirrels ***), AgeGroup:Species (t = 3.264: ↑ in younger squirrels **) |
t = 4.475: Older Rat > Younger Rat **, t = 9.076: Older Rat > Older Squirrel ***, t = 4.460: Younger Rat > Younger Squirrel ** |
|
| Spleen_Superoxide | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 18.26080 on 19 degrees of freedom Residual deviance: 0.56231 on 16 degrees of freedom |
AgeGroup (t = 7.072: ↓ in younger ***), Species (t = − 19.149: ↓ in squirrels ***), AgeGroup:Species (t = 3.697: ↑ in younger squirrels **) |
t = 7.072: Older Rat > Younger Rat ***, t = 19.149: Older Rat > Older Squirrel ***, t = 13.921: Younger Rat > Younger Squirrel *** |
|
| Spleen_H2O2 | lm (Species, Gaussian) | 0.9486, 351.4 on 1 and 18 DF, *** | Species (t = − 18.75: ↓ in squirrels ***) | N/A | |
| Spleen_NO | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 13.14865 on 19 degrees of freedom Residual deviance: 0.47642 on 16 degrees of freedom |
AgeGroup (t = − 5.930: ↓ in younger ***), Species (t = − 17.551: ↓ in squirrels ***), AgeGroup:Species (t = 5.023: ↑ in younger squirrels ***) |
t = 5.930: Older Rat > Younger Rat ***, t = 17.551: Older Rat > Older Squirrel ***, t = 10.448: Younger Rat > Younger Squirrel *** |
|
| Spleen_MDA | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.9245, 59.12 on 4 and 15 DF, *** | AgeGroup (t = − 3.181: ↓ in younger **), Species (t = − 11.688: ↓ in squirrels ***), BodyMass (t = − 2.557: ↓ *) |
t = 3.181: Older Rat > Younger Rat *, t = 11.688: Older Rat > Older Squirrel ***, t = 9.977: Younger Rat > Younger Squirrel *** |
|
| Spleen_HNE | glm (Species, Gamma(link ="log")) |
Null deviance: 12.8028 on 19 degrees of freedom Residual deviance: 1.0427 on 18 degrees of freedom |
Species (t = − 15.31: ↓ in squirrels ***) | N/A | |
| Spleen_TAC | lm (Species, BodyMass, Gaussian) | 0.9368, 141.9 on 2 and 17 DF, *** | Species (t = − 16.249: ↓ in squirrels ***), BodyMass (t = 2.563: ↑*) | N/A | |
| Spleen_UI | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | 0.7466, 14.99 on 4 and 15 DF, *** | AgeGroup (t = 4.723: ↑ in younger ***), Sex (t = 2.376: ↑ in males *), AgeGroup:Species (t = − 4.360: ↓ in younger squirrels ***) |
t = − 4.723: Older Rat < Younger Rat **, t = 6.648: Younger Rat > Younger Squirrel *** |
|
| Spleen_PI | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | 0.8533, 28.64 on 4 and 15 DF, *** | AgeGroup (t = 4.833: ↑ in younger ***), Species (t = − 3.212: ↓ in squirrels **), Sex (t = 2.357: ↑ in males *), AgeGroup:Species (t = − 4.304: ↓ in younger squirrels ***) |
t = − 4.833: Older Rat < Younger Rat **, t = 3.212: Older Rat > Older Squirrel *, t = 9.235: Younger Rat > Younger Squirrel *** |
|
| Spleen_UFA/SFA | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.7767, 17.52 on 4 and 15 DF, *** | AgeGroup (t = 2.744: ↑ in younger *), Species (t = 7.384: ↑ in squirrels ***), AgeGroup:Species (t = − 2.683: ↓ in younger squirrels *) |
t = − 7.384: Older Rat < Older Squirrel ***, t = − 3.800: Younger Rat < Younger Squirrel ** |
|
| Spleen_MUFA/SFA | lm (AgeGroup, Species, Sex, BodyMass, Gaussian) | 0.9082, 48.01 on 4 and 15 DF, *** | Species (t = 13.519: ↑ in squirrels ***) |
t = − 13.519: Older Rat < Older Squirrel ***, t = − 13.519: Younger Rat < Younger Squirrel *** |
|
| Spleen_PUFA/SFA | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | 0.4908, 5.579 on 4 and 15 DF, ** | AgeGroup (t = 3.161: ↑ in younger **), Sex (t = 2.278: ↑ in males *), AgeGroup:Species (t = − 3.233: ↓ in younger squirrels **) |
t = − 3.161: Older Rat < Younger Rat *, t = 3.516: Younger Rat > Younger Squirrel * |
|
| Spleen_MUFA/PUFA | glm (AgeGroup, Species, Sex, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 13.3603 on 19 degrees of freedom Residual deviance: 1.4796 on 15 degrees of freedom |
AgeGroup (t = − 3.624: ↓ in younger **), Species (t = 6.159: ↑ in squirrels ***), Sex (t = − 2.528: ↓ in males *), AgeGroup:Species (t = 3.436: ↑ in younger squirrels **) |
t = 3.624: Older Rat > Younger Rat *, t = − 6.159: Older Rat < Older Squirrel ***, t = − 10.931: Younger Rat < Younger Squirrel *** |
|
| Spleen_ A/L | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 55.200 on 19 degrees of freedom Residual deviance: 25.776 on 16 degrees of freedom |
AgeGroup:Species (t = − 2.469: ↓ in younger squirrels *) | t = 5.323: Younger Rat > Younger Squirrel *** | |
| Muscle_NOX | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.4894, 7.071 on 3 and 16 DF, ** | AgeGroup (t = − 4.524: ↓ in younger ***), AgeGroup:Species (t = 2.821: ↑ in younger squirrels *) | t = 4.524: Older Rat > Younger Rat ** | |
| Muscle_SOD | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.717, 13.03 on 4 and 15 DF, *** | Species (t = − 5.289: ↓ in squirrels ***), AgeGroup:Species (t = 2.759: ↑ in younger squirrels *) | t = 5.289: Older Rat > Older Squirrel ** | |
| Muscle_CAT | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.7585, 20.89 on 3 and 16 DF, *** | AgeGroup (t = − 2.541: ↓ *), Species (t = − 6.457: ↓ ***) |
t = 6.457: Older Rat > Older Squirrel ***, t = 4.141: Younger Rat > Younger Squirrel ** |
|
| Muscle_GPX | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.6391, 12.21 on 3 and 16 DF, *** | AgeGroup (t = − 5.416: ↓in younger ***), Species (t = − 2.908: ↓ squirrels *), AgeGroup:Species (t = 2.369: ↑ in younger squirrels *) |
t = 5.416: Older Rat > Younger Rat **, t = 2.908: Older Rat > Older Squirrel * |
|
| Muscle_GST | glm (Species, Gamma(link ="log")) |
Null deviance: 1.6848 on 19 degrees of freedom Residual deviance: 1.0104 on 18 degrees of freedom |
Species (t = 3.356: ↑ in squirrels **) | N/A | |
| Muscle_NOS | lm (AgeGroup, Species, Sex, BodyMass, AgeGroup:Species | 0.935, 55.69 on 5 and 14 DF, *** | AgeGroup (t = − 6.210: ↓ in younger ***), Species (t = − 13.863: ↓ in squirrels ***), SexMale (t = 2.333: ↑ in males *), BodyMass (t = − 3.479: ↓ **), AgeGroup:Species (t = 5.025: ↑ in younger squirrels ***) |
t = 6.210: Older Rat > Younger Rat **, t = 13.863: Older Rat > Older Squirrel ***, t = 7.240: Younger Rat > Younger Squirrel *** |
|
| Muscle_Superoxide | glm (AgeGroup, Species, Sex, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 6.62267 on 19 degrees of freedom Residual deviance: 0.15145 on 15 degrees of freedom |
AgeGroup (t = : ↓ in younger ***), Species (t = − 18.755: ↓ ***), AgeGroup:Species (t = 2.923: ↑ in younger squirrels *) |
t = 7.545 Older Rat > Younger Rat ***, t = 18.755: Older Rat > Older Squirrel *** t = 14.449: Younger Rat > Younger Squirrel ***, t = 3.354: Older Squirrel > Younger Squirrel * |
|
| Muscle_H2O2 | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.8724, 44.3 on 3 and 16 DF, *** | Species (t = − 9.520: ↓ in squirrels ***), AgeGroup:Species (t = 2.136: ↑ in younger squirrels *) |
t = 9.520: Older Rat > Older Squirrel ***, t = 6.499: Younger Rat > Younger Squirrel *** |
|
| Muscle_NO | glm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 10.00736 on 19 degrees of freedom Residual deviance: 0.65428 on 15 degrees of freedom |
AgeGroup (t = − 3.959: ↓ in younger **), Species (t = − 11.976: ↓ in squirrels ***), AgeGroup:Species (t = 4.486: ↑ in younger squirrels ***) |
t = 3.959: Older Rat > Younger Rat **, t = 11.976: Older Rat > Older Squirrel ***, t = 5.971: Younger Rat > Younger Squirrel ** |
|
| Muscle_MDA | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.8837, 49.1 on 3 and 16 DF, *** | AgeGroup (t = − 4.980: ↓ in younger ***), Species (t = − 10.214: ↓ in squirrels ***), AgeGroup:Species (t = 3.378: ↑ in younger squirrels **) |
t = 4.980: Older Rat > Younger Rat **, t = 10.214: Older Rat > Older Squirrel ***, t = 5.437: Younger Rat > Younger Squirrel ** |
|
| Muscle_HNE | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.6003, 10.51 on 3 and 16 DF, *** | AgeGroup (t = :− 2.508 ↓ in younger *), Species (t = − 4.895: ↓ in squirrels ***) | t = 4.895: Older Rat > Older Squirrel ** | |
| Muscle_TAC | lm (Species, Sex, BodyMass, Gaussian) | 0.9063, 62.26 on 3 and 16 DF, *** | Species (t = − 12.208: ↓ in squirrels ***), BodyMass (t = 5.002: ↑ ***) | N/A | |
| Muscle_UI | lm (AgeGroup, Species, Gaussian) | 0.8206, 44.46 on 2 and 17 DF, *** | AgeGroup (t = 3.66: ↑ in younger **), Species (t = 8.69: ↑ in squirrels ***) |
t = − 3.660: Older Rat < Younger Rat **, t = − 8.690: Older Rat < Older Squirrel ***, t = − 8.690: Younger Rat < Younger Squirrel *, t = − 3.660: Older Squirrel > Younger Squirrel ** |
|
| Muscle_PI | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 1.95644 on 19 degrees of freedom Residual deviance: 0.72281 on 16 degrees of freedom |
AgeGroup (t = 3.288: ↑ in younger **), Species (t = 4.422: ↑ in squirrels ***), AgeGroup:Species (t = − 2.295: ↓ in younger squirrels *) |
t = − 3.288: Older Rat < Younger Rat *, t = − 4.422: Older Rat < Older Squirrel ** |
|
| Muscle_UFA/SFA | lm (AgeGroup:Species + Sex + BodyMass, Gaussian) | 0.7228, 10.91 on 5 and 14 DF, *** | Species (t = 2.973: ↑ in squirrels *) | t = − 2.973: Older Rat < Older Squirrel *, t = − 5.974: Younger Rat < Younger Squirrel *, t = − 2.937: Older Squirrel < Younger Squirrel *, | |
| Muscle_MUFA/SFA | lm (AgeGroup, Species, Sex, BodyMass, Gaussian) | 0.3948, 4.098 on 4 and 15 DF, * | Species (t = 3.304: ↑ in squirrels **), Sex (t = − 2.447: ↓ in males *), BodyMass (t = 2.421: ↓ *) |
t = − 3.304: Older Rat < Older Squirrel *, t = − 3.304: Younger Rat < Younger Squirrel *, |
|
| Muscle_PUFA/SFA | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.8248, 30.82 on 3 and 16 DF, *** | Species (t = 4.007: ↑ in squirrels **), AgeGroup:Species (t = 2.483: ↑ in younger squirrels *) |
t = − 4.007: Older Rat < Older Squirrel **, t = − 7.518: Younger Rat < Younger Squirrel ***, t = 4.909: Older Squirrel < Younger Squirrel ** |
|
| Muscle_MUFA/PUFA | glm (AgeGroup, Species, Sex, BodyMass, Gamma(link ="log")) |
Null deviance: 4.5402 on 19 degrees of freedom Residual deviance: 1.0561 on 15 degrees of freedom |
Species (t = − 3.348: ↓ in squirrels **), BodyMass (t = 2.622: ↑ *) |
t = :3.348 Older Rat > Older Squirrel *, t = 3.348: Younger Rat > Younger Squirrel * |
|
| Muscle_ A/L | glm (AgeGroup, Species, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 8.9954 on 19 degrees of freedom Residual deviance: 5.8238 on 16 degrees of freedom |
AgeGroup:Species (t = − 2.506 ↓ in younger squirrels *) | NS | |
| Skin_NOX | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.8114, 21.44 on 4 and 15 DF, *** | AgeGroup (t = − 5.320: ↓ younger ***), Species (t = − 7.428: ↓ in squirrels ***), BodyMass (t = − 2.512: ↓ *), AgeGroup:Species (t = 3.341: ↑ in younger squirrel **) |
t = 5.320: Older Rat > Younger Rat **, t = 7.428: Older Rat > Older Squirrel ***, t = 2.905: Younger Rat > Younger Squirrel * |
|
| Skin_SOD | glm (Species |
Null deviance: 1.61704 on 19 degrees of freedom Residual deviance: 0.35661 on 18 degrees of freedom |
Species (t = − 7.648: ↓ in squirrels ***) | N/A | |
| Skin_CAT | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.4963, 5.681 on 4 and 15 DF, ** | AgeGroup (t = − 3.640: ↓in younger **), AgeGroup:Species (t = 2.731: ↑ in younger squirrel *) |
t = 3.640: Older Rat > Younger Rat *, t = − 3.350: Younger Rat < Younger Squirrel * |
|
| Skin_GPX | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.5632, 9.167 on 3 and 16 DF, *** | AgeGroup (t = − 2.420: ↓ in younger *) | t = 4.537: Older Squirrel > Younger Squirrel ** | |
| Skin_GST | lm (AgeGroup, Species, Sex, AgeGroup:Species, Gaussian) | 0.8787,35.42 on 4 and 15 DF, *** | Species (t = 9.353: ↑ in squirrels ***) |
t = − 9.353: Older Rat < Older Squirrel ***, t = − 6.966: Younger Rat < Younger Squirrel *** |
|
| Skin_NOS | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.8331, 24.71 on 4 and 15 DF, *** | AgeGroup (t = − 6.986: ↓ in younger ***), Species (t = − 7.987: ↓ squirrels ***), BodyMass (t = − 2.824: ↓*), AgeGroup:Species (t = 5.541: ↑ in younger squirrel ***) |
t = 6.986: Older Rat > Younger Rat ***, t = 7.987: Older Rat > Older Squirrel *** |
|
| Skin_Superoxide | lm (AgeGroup, Species, BodyMass, AgeGroup:Species, Gaussian) | 0.586, 7.725 on 4 and 15 DF, ** | Species (t = − 3.024: ↓ in squirrels **), AgeGroup:Species (t = 3.175: ↑ in younger squirrel **) | t = 3.024: Older Rat > Older Squirrel * | |
| Skin_H2O2 | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.5162, 7.759 on 3 and 16 DF, ** | AgeGroup (t = − 4.669: ↓ in younger ***), Species (t = :− 2.871 ↓ squirrels *), AgeGroup:Species (t = 4.157: ↑ in younger squirrel ***) |
t = 4.669: Older Rat > Younger Rat **, t = 2.871: Older Rat > Older Squirrel *, t = − 3.008: Younger Rat < Younger Squirrel * |
|
| Skin_NO | lm (AgeGroup, Species, Sex, BodyMass, AgeGroup:Species, Gaussian) | 0.9112, 40.01 on 5 and 14 DF, *** | AgeGroup (t = − 10.796: ↓ in younger ***), Species (t = − 10.047: ↓ squirrels ***), BodyMass (t = − 5.766: ↓ ***), AgeGroup:Species (t = 9.710: ↑ in younger squirrel ***) |
t = 10.796: Older Rat > Younger Rat ***, t = 10.047: Older Rat > Older Squirrel ***, t = − 3.689: Younger Rat < Younger Squirrel *, t = 4.787: Older Squirrel > Younger Squirrel ** |
|
| Skin_MDA | glm (AgeGroup + Species + AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 6.43955 on 19 degrees of freedom Residual deviance: 0.67711 on 16 degrees of freedom |
AgeGroup (t = − 3.233: ↓ in younger **), Species (t = − 9.953: ↓ squirrels ***), AgeGroup:Species (t = 2.125: ↑ in younger squirrel *) |
t = 3.233: Older Rat > Younger Rat *, t = 9.953: Older Rat > Older Squirrel ***, t = 6.948: Younger Rat < Younger Squirrel *** |
|
| Skin_HNE | lm (AgeGroup, Species, Sex, BodyMass, AgeGroup:Species, Gaussian) | 0.8558, 23.54 on 5 and 14 DF, *** | AgeGroup (t = − 3.406: ↓ in younger **), Species (t = − 8.421: ↓ squirrels ***), BodyMass (t = − 2.649: ↓*) |
t = 3.406: Older Rat > Younger Rat *, t = 8.421: Older Rat > Older Squirrel ***, t = 6.214: Younger Rat > Younger Squirrel *** |
|
| Skin_TAC | lm (AgeGroup, Species, Gaussian) | 0.9477, 154.1 on 2 and 17 DF, *** | AgeGroup (t = − 6.706: ↓ in younger ***), Species (t = 16.222: ↑ squirrels ***) |
t = 6.706: Older Rat > Younger Rat ***, t = − 16.222: Older Rat < Older Squirrel ***, t = − 16.222: Younger Rat < Younger Squirrel ***, t = 6.706: Older Squirrel > Younger Squirrel *** |
|
| Skin_UI | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.8073, 27.54 on 3 and 16 DF, *** | AgeGroup (t = − 2.646: ↓ in younger *), AgeGroup:Species (t = − 3.567: ↓ in younger squirrel **) |
t = 5.390: Younger Rat > Younger Squirrel **, t = 7.691: Older Squirrel > Younger Squirrel *** |
|
| Skin_PI | lm (AgeGroup, Species, Gaussian) | 0.6891, 22.06 on 2 and 17 DF, *** | AgeGroup (t = − 6.174: ↓ in younger ***), Species (t = − 2.450: ↓ squirrels *) |
t = 6.174: Older Rat > Younger Rat **, t = 6.174: Older Squirrel > Younger Squirrel ** |
|
| Skin_UFA/SFA | glm (AgeGroup, Species, Sex, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 3.09098 on 19 degrees of freedom Residual deviance: 0.58773 on 15 degrees of freedom |
Sex (t = − 2.288: ↓ in males), AgeGroup:Species (t = − 5.483: ↓ in younger squirrels) |
t = 6.054: Younger Rat > Younger Squirrel **, t = 7.420: Older Squirrel > Younger Squirrel *** |
|
| Skin_MUFA/SFA | glm (AgeGroup, Species, Sex, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 2.49684 on 19 degrees of freedom Residual deviance: 0.64054 on 15 degrees of freedom |
Species (t = 3.218: ↑ squirrels **), Sex (t = − 2.403: ↓ in males *), AgeGroup:Species (t = − 5.390: ↓ in younger squirrels ***) |
t = − 3.218: Older Rat < Older Squirrel *, t = 4.398: Younger Rat > Younger Squirrel **, t = 6.067: Older Squirrel > Younger Squirrel ** |
|
| Skin_PUFA/SFA | glm (AgeGroup, Species, Sex, AgeGroup:Species, Gamma(link ="log")) |
Null deviance: 6.9686 on 19 degrees of freedom Residual deviance: 0.7969 on 15 degrees of freedom |
AgeGroup:Species (t = − 5.740: ↓ in younger squirrels ***) |
t = 8.869: Younger Rat > Younger Squirrel ***, t = 9.451: Older Squirrel > Younger Squirrel *** |
|
| Skin_MUFA/PUFA | lm (AgeGroup, Species, AgeGroup:Species, Gaussian) | 0.8933, 54 on 3 and 16 DF, *** | AgeGroup (t = 2.521: ↑ in younger *), Species (t = 3.779: ↑ squirrels **), AgeGroup:Species (t = 4.035: ↑ in younger squirrels ***) |
t = − 3.779: Older Rat < Older Squirrel **, t = − 9.485: Younger Rat < Younger Squirrel ***, t = − 8.228: Older Squirrel < Younger Squirrel *** |
|
| Skin_ A/L | lm (AgeGroup, Sex, BodyMass, Gaussian) | 0.6238, 11.5 on 3 and 16 DF, *** | AgeGroup (t = − 4.239: ↓ in younger ***), BodyMass (t = − 2.188: ↓*) | N/A |
Significance levels: *** (p < 0.001), ** (p < 0.01), * (p < 0.05), NS (not significant)
In summary, older rats exhibit higher levels of antioxidants, as well as increased oxidative stress and nitrosative markers, compared to younger rats. This pattern indicates that older rats experience heightened oxidative stress as they age.
Species comparisons
Caproic acid showed no significant differences between species (Fig. 3a; Table 2). Margaric acid was lower in squirrels only in the spleen, but post hoc analyses found no significance (Fig. 3b; Table 2). Interestingly, body mass was negatively associated with margaric acid only in the kidney, and males had higher amounts of margaric acid in the spleen and kidney (Fig. 3b; Table 2). Oleic acid was higher in all squirrel tissues except for the muscle, while arachidonic acid was lower across all squirrel tissues except for the muscle, where it was higher (Fig. 3d-e; Table 2). Post hoc analyses confirmed these patterns across most tissues and age cohorts, with oleic acid being higher (Fig. 3d) and arachidonic acid being lower in the spleen, liver, and kidney for younger and older squirrels compared to rats (Fig. 3e) (Table 2). In the skin, oleic acid was higher only in older squirrels (Fig. 3d) while arachidonic acid was lower only in younger squirrels (Fig. 3e) (Table 2). Squirrels generally have higher oleic acid and lower arachidonic acid compared to rats, indicating a more resistant lipid composition profile as opposed to rats.
CAT is lower in the liver, kidney, muscle, and spleen, but higher in the lung and skin (Fig. 4a; Table 3). Post hoc analyses show higher activity in the kidney and spleen across both ages, lower in the muscle for older squirrels, and higher in the skin for younger squirrels (Fig. 4a) (Table 3). GPX is higher in the muscle and spleen, but lower in the liver and lung (Fig. 4b; Table 3). Post hoc analyses confirm higher levels in the liver and lower in the spleen across both age cohorts, with higher activity in older squirrels compared to older rats (Fig. 4b; Table 3). GST is higher in all squirrel tissues, although younger squirrels have lower GST in the kidney compared to younger rats (Fig. 4c; Table 3). SOD is lower in the liver, kidney, spleen, muscle, and skin, with post hoc analyses confirming lower activity in the liver across both ages and the muscle in older squirrels, but higher activity in the lung across both ages (Fig. 4d; Table 3). TAC is higher in most tissues, except for the spleen and muscle where it is lower (Fig. 4e; Table 3). Post hoc analyses confirm higher levels in the liver, lung, and skin across both ages of squirrel (Fig. 4e; Table 3). Overall, squirrels show reduced SOD and CAT activity across most tissues, except the lung (SOD) and skin (CAT) where levels are higher. Additionally, squirrels showed a higher GST and TAC, suggesting a compensatory mechanism for antioxidant defence. Most differences are consistent across both age cohorts.
H2O2 is higher in the squirrel lung, but lower in all other tissues except the liver (Fig. 5a; Table 3). Post hoc analyses show lower levels in the kidney and muscle across both age cohorts, while older squirrels have lower H2O2 than older rats, younger squirrels have higher H2O2 than younger rats (Fig. 5a; Table 3). Oxidative damage markers (MDA and HNE) are lower in all squirrel tissues, consistently across both age cohorts (Fig. 5b-c; Table 3). Post hoc analyses show HNE is lower in the liver and skin across both age cohorts, and in the muscle only in older squirrels (Fig. 5b-c; Table 3). Superoxide is lower in the liver, kidney, spleen, muscle, and skin (Fig. 5g; Table 3). Post hoc analyses show lower levels in the spleen and muscle across both age cohorts, and in the liver, kidney, and skin only in older squirrels. In the lung, superoxide is higher in squirrels across both age cohorts (Fig. 5g; Table 3). Overall, squirrels exhibit lower free radical production and oxidative damage compared to rats, even in younger squirrels. Despite higher superoxide and H2O2 levels in the lung, oxidative damage remains low, suggesting effective defence mechanisms against free radical insults.
NO follows a similar pattern, being lower in the kidney, spleen, muscle, and skin, but higher in the lung (Fig. 5d; Table 3). Post hoc analyses reveal lower NO in the spleen and muscle across both age cohorts, lower in the kidney only for older squirrels, and in the skin, lower in older squirrels but higher in younger squirrels compared to rats (Fig. 5d; Table 3). The lung shows higher NO levels across both age cohorts (Fig. 5d; Table 3). NOS is lower in the liver, kidney, spleen, muscle, and skin in squirrels (Fig. 5e; Table 3). Post hoc analyses show lower NOS in the spleen and muscle across both age cohorts, and lower levels in the skin only in older squirrels (Fig. 5e; Table 3). In the liver, NOS is lower in older squirrels but higher in younger squirrels compared to rats (Fig. 5e; Table 3). NOX is reduced in the liver, kidney, spleen, and skin but elevated in the lung of squirrels (Fig. 5f; Table 3). Post hoc analyses indicate lower NOX in the spleen and skin across both age cohorts, and lower in the liver and kidney only for older squirrels (Fig. 5f; Table 3). The lung shows higher NOX levels in squirrels of both age cohorts (Fig. 5f; Table 3). Squirrels generally show lower levels of NOS, NO, and NOX across most tissues, but higher levels in the lung compared to rats, with the exception of the lung, where levels are higher in squirrels. This implies squirrel tissues have increased resistance to nitrosative stress, whereas this was absent in rats.
A/L ratio is lower in all tissues except the skin, with consistent reductions in the kidney and liver across both age cohorts and in the spleen only for younger squirrels (Table 3). The lung and spleen show no significant differences after post hoc analysis (Fig. 6a; Table 3). The MUFA/PUFA ratio was consistently higher in squirrels compared to rats across most tissues and age groups, particularly in the liver, lung, muscle, and spleen. Older squirrels displayed higher ratios than older rats in the liver, lung, muscle, and spleen (Fig. 6b; Table 3). Younger squirrels also maintained higher ratios than younger rats across these tissues (Fig. 6b; Table 3). No significant differences were detected in the kidney across age groups or species (Fig. 6b; Table 3). These results indicate that squirrels generally maintain higher MUFA/PUFA ratios than rats across various tissues suggesting higher free radical resistant UFA in squirrels. The MUFA/SFA ratio is higher in all squirrel tissues and consistent across both age cohorts for the liver, spleen, lung, and muscle (Table 3). In the skin, the ratio is higher in older squirrels but lower in younger squirrels compared to rats (Table 3). No significant differences were observed for the kidney (Fig. 6c; Table 3). PI levels are lower in the liver, kidney, lung, spleen, and skin, but higher in the muscle for squirrels (Fig. 6d; Table 3). Post hoc analyses show that PI is consistently lower in the kidney, lung, and spleen across both age cohorts, and lower in the liver only for younger squirrels (Fig. 6d; Table 3). The skin shows lower PI, but this is not significant (Fig. 6d; Table 3). In the muscle, PI is higher in older squirrels compared to older rats (Fig. 6d; Table 3). The PUFA/SFA ratio is higher in the liver, kidney, and muscle, lower in the skin, and shows no significant differences in the lung and spleen (Fig. 6e; Table 3). In the muscle, the ratio is higher across both age cohorts for squirrels, but higher only in the younger group for the liver (Fig. 6e;Table 3). Post hoc analyses show lower PUFA/SFA in younger squirrels compared to younger rats for the spleen and skin (Fig. 6e; Table 3). Squirrels have lower UI levels in most tissues compared to rats, with younger squirrels showing lower UI in the liver, spleen, and skin, and older squirrels showing lower UI overall (Fig. 6f; Table 3). UI is higher in the muscle across both age cohorts for squirrels (Fig. 6f; Table 3). Collectively, these results suggest that squirrels have lower UI, PI, and A/L ratios but higher MUFA/SFA, PUFA/MUFA, and PUFA/SFA ratios, indicating greater lipid free radical resistance. The higher unsaturation ratios and lower PI and A/L ratios likely reflect higher levels of free radical-resistant linoleic acid and lower levels of free radical-prone arachidonic acid in squirrels compared to rats.
Discussion
Previously research has shown that fatty acid composition and oxidative stress play crucial roles in determining species longevity [27, 30, 96]. Factors such as the rate of free radical production, accumulation of oxidative damage, and lipid composition favouring resistance to oxidation contribute significantly to variations in lifespan [27, 30, 49, 97]. Therefore, our study aimed to comprehensively assess oxidative stress and lipid composition across multiple tissues between rats and squirrels of differing age groups, to elucidate the primary factors influencing differences in longevity. We identified central themes typically associated with longevity in Persian squirrels when compared to Wistar rats. These include highly resistant fatty acids, reduced free radical production, lower oxidative damage, and lower antioxidant activity overall, except for the enzyme GST. These facets are discussed below.
Lipids can exist in a variety of forms and chemical compositions that influence their susceptibility to oxidative damage which, in turn, contributes to oxidative stress [30, 32, 34, 35]. The degree of unsaturation and the position of double bonds on the carbon chain are critical factors contributing to oxidative stress resistance and are integral to the membrane pacemaker theory [38, 98]. Despite having high amounts of UFAs, squirrels consistently exhibited higher levels of MUFA, particularly oleic acid, and a lower PI. This lower PI is largely a consequence of reduced arachidonic acid content, which is highly susceptible to oxidative stress [25, 30, 38, 41–44, 97]. In contrast, older rats displayed a profile with higher MUFA content and lower PUFA content compared to younger rats. However, differences in oleic acid and arachidonic acid between younger and older rats were absent. Furthermore, the A/L ratio was not significantly different between younger and older rats, suggesting that rats do not rely on different fatty acids to mitigate oxidative damage within tissues. These findings suggest that the tissues of Persian squirrels are generally composed of highly resistant fatty acids, resembling other long-lived species [32–34, 97].
The oxidative stress theory of aging posits that accumulated oxidative damage over time promotes aging [29, 49, 99]. Although younger and older rats did not differ in their rate of free radical production in our study, they did differ in accrued oxidative damage, aligning with the typical aging process described by the oxidative stress theory [7, 100]. Conversely, squirrels exhibited reduced free radical production across most tissues, except for the lung, accompanied by consistently low oxidative damage in all tissues regardless of age. This pattern demonstrates that squirrels maintain a delayed aging phenotype from a younger age that persists as they age, forming a critical factor contributing to their extended lifespan when compared to rats. This observation also aligns with findings from Lambert et al. [101] who reported that low rates of H2O2 production in a diverse group of species are associated with maximum lifespan potential.
Antioxidant levels did not appear to contribute significantly to species differences in longevity, consistent with previous studies [102–104]. In retrospect, antioxidant activity was negatively associated with longevity [102, 103]. This observation can be explained by the fact that antioxidants generally respond to oxidative stress; therefore, if oxidative stress is low, antioxidant levels will be similarly low [105]. The squirrel reflects this fact with a lower overall antioxidant content and lower oxidative damage and free radical production across all tissues. The difference between younger and older rats also shows this, where younger rats have lower antioxidant activity and lower oxidative damage compared to older rats. For squirrels, the lung was the only tissue which had much higher antioxidant activity compared to rats, likely due to the high amounts of free radical production. Despite this increased free radical production, oxidative damage remained lower than in rats, which is attributable to the lower PI resulting from higher MUFA content in squirrel tissues.
One antioxidant enzyme stood out, namely GST, which showed a positive association in squirrels regardless of age across all tissues compared to those levels in rats. Furthermore, GST was the only antioxidant in the PCA as having a positive association at mitigating oxidative damage associated with lipid composition. Previous studies suggest that GST promotes longevity under normal physiological conditions, supporting its role as a potential longevity factor [106–109]. This enzyme was most prominently expressed in the skin, spleen, liver, and lung. The lung may protect against airborne pathogens and inflammation [110], the spleen filters blood and removes damaged red blood cells and pathogens while storing white blood cells and platelets essential for immune response [111], and the liver serves as the primary site of GSH synthesis, an important thiol for antioxidant activity [112, 113]. GSH is essential for GST function during phase 2 detoxification reactions [114]. As such, GST appears to act as a multifunctional tool for detoxification and antioxidant defence [115].
Dietary fatty acid profiles can significantly influence fatty acid composition and antioxidant availability in animals [16]. Both species were provided with diets that were similar in chemical composition and they were housed and maintained in identical environments. Therefore, variations in membrane composition are likely under genetic control. Although Persian squirrels in the wild consumes a higher lipid content diet, such as nuts [116], the diet provided in captivity aimed at a balanced composition. Despite this, TAC was higher in squirrel tissues, except for the spleen and muscle, suggesting that squirrels either obtained more antioxidants from their diet, or they used fewer antioxidants to detoxify free radicals. Our findings suggest that both of these possibilities may contribute to the observed differences. The activity of desaturases, which convert one lipid type to another (e.g., oleic acid to linoleic acid), was not investigated in this study. Future research on desaturase activity could provide valuable insights into the lower peroxidation index observed in Persian squirrel tissues and their potential resistance to oxidative stress.
While our study demonstrates that free radical stress resistance due to fatty acid composition is a key component of the longevity disparity observed between long-lived squirrels and short-lived rats, it is important to note that our findings are based on baseline values from unstressed animals. Crucial to our findings is to determine whether these free radical stress resistant patterns and the regulatory pathways associated with these patterns persist under stressed conditions, which should be done in future research. Furthermore, our study does not strictly investigate old animals, but older animals due to the limitations of obtaining old squirrels from the wild due to their high mortality rate, thus our data represents a perspective of progressive age differences within the early parts of each animals’ lifespan. Additionally, while our data strongly suggest that squirrels produce fewer free radicals and incur lower oxidative damage compared to rats, we did not directly measure free radical production. Furthermore, since we found more pronounced species differences than age-related differences, our study cannot fully elucidate all factors associated with Persian squirrel longevity. Future studies comparing different long-lived squirrel species could further enhance our understanding of traits inherent to squirrels’ longevity.
Conclusion
Our findings show that Persian squirrels differ from short-lived rats through reduced oxidative damage, lower free radical production, distinctive fatty acid composition, and the use of GST as a multifunctional enzyme. Together, these findings support both the “membrane pacemaker” and “rate of living” hypotheses as explanations for the differences in longevity between the two species.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
For assistance in the field, we would like to thank the formers head of Iran's Department of Environment, Dr. Isa Kalantari and his advisor khorasani, Dr. Ali Salajegheh and current Head of Iran's Department of Environment, Dr. Shina Ansari. We thank Elaheh Salehi for recommending the literature on wild tree squirrels. We are grateful to members of the evaluation and sustainable exploitation of wildlife groups, especially Mohamadreza Hoseini and Esmaeel Mousakhani, for their helpful care and sampling of the Persian squirrel specimens.
Author contribution
Study design: FS; conceptualization: FS, NCB, PJJ; methodology: FS, GHK; Investigation: FS, GHK; validation: FS, GHK, SHA; formal analysis: FS, PJJ; Resources: FS, GHK, SHA, BB, MGH; writing-original draft preparation: FS, PJJ; writing-review and editing: FS, PJJ, NCB; visualization: FS, PJJ; supervision: SHA.
Funding
Research Council of the University of Tehran (grant NO. 6401007/6/35) and Shiraz University (grant No. 88-GR-AGRST- 108) has funded this study.
Data availability
The corresponding author will deliver the information assisting the findings of this study upon reasonable request.
Declarations
Ethics approval and consent to participate
Research Ethics Committees of the College of Sciences, University of Tehran (IR.UT.SCIENCE.REC.1401.010) approved all animal protocols.
Consent for publication
Not applicable.
Competing interests
The authors declare no conflicts of interest. The sponsors had no role in the design of the study, in the collection, analyses, or the interpretation of the data, in the writing of the manuscript, or in the decision to publish the results.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.López-Otín C, Blasco MA, Partridge L, Serrano M, Kroemer G. The hallmarks of aging. Cell. 2013;153:1194–217. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Jacobs PJ, Hart DW, Merchant HN, Voigt C, Bennett NC. The Evolution and Ecology of Oxidative and Antioxidant Status: A Comparative Approach in African Mole-Rats. Antioxidants. 2023;12:1486. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Liguori, I.; Russo, G.; Curcio, F.; Bulli, G.; Aran, L.; Della-Morte, D.; Gargiulo, G.; Testa, G.; Cacciatore, F.; Bonaduce, D. 2018 Oxidative stress, aging, and diseases. Clinical interventions in aging , 757–772. [DOI] [PMC free article] [PubMed]
- 4.Hekimi S, Lapointe J, Wen Y. Taking a “good” look at free radicals in the aging process. Trends Cell Biol. 2011;21:569–76. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Shields HJ, Traa A, Van Raamsdonk JM. Beneficial and detrimental effects of reactive oxygen species on lifespan: a comprehensive review of comparative and experimental studies. Front Cell Dev Biol. 2021;9:181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.de Magalhaes JP. The scientific quest for lasting youth: prospects for curing aging. Rejuvenation Res. 2014;17:458–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Yang J, Luo J, Tian X, Zhao Y, Li Y, Wu X. Progress in Understanding Oxidative Stress, Aging, and Aging-Related Diseases. Antioxidants. 2024;13:394. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Maldonado E, Morales-Pison S, Urbina F, Solari A. Aging Hallmarks and the Role of Oxidative Stress. Antioxidants. 2023;12(3):651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Varesi A, Chirumbolo S, Campagnoli LIM, Pierella E, Piccini GB, Carrara A, Ricevuti G, Scassellati C, Bonvicini C, Pascale A. The role of antioxidants in the interplay between oxidative stress and senescence. Antioxidants. 2022;11:1224. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Mirończuk-Chodakowska I, Witkowska AM, Zujko ME. Endogenous non-enzymatic antioxidants in the human body. Adv Med Sci. 2018;63:68–78. [DOI] [PubMed] [Google Scholar]
- 11.Kurutas EB. The importance of antioxidants which play the role in cellular response against oxidative/nitrosative stress: current state. Nutr J. 2015;15:71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Amorim JA, Coppotelli G, Rolo AP, Palmeira CM, Ross JM, Sinclair DA. Mitochondrial and metabolic dysfunction in ageing and age-related diseases. Nat Rev Endocrinol. 2022;18:243–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Omidifar N, Moghadami M, Mousavi SM, Hashemi SA, Gholami A, Shokripour M, Sohrabi Z. Trends in natural nutrients for oxidative stress and cell senescence. Oxid Med Cell Longev. 2021;2021:7501424. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.George S, Abrahamse H. Redox potential of antioxidants in cancer progression and prevention. Antioxidants. 2020;9:1156. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Pisoschi AM, Pop A, Iordache F, Stanca L, Predoi G, Serban AI. Oxidative stress mitigation by antioxidants-an overview on their chemistry and influences on health status. Eur J Med Chem. 2021;209:112891. [DOI] [PubMed] [Google Scholar]
- 16.Varela-López A, Romero-Márquez JM, Navarro-Hortal MD, Ramirez-Tortosa CL, Battino M, Forbes-Hernández TY, Quiles JL. Dietary antioxidants and lifespan: Relevance of environmental conditions, diet, and genotype of experimental models. Exp Gerontol. 2023;178:112221. [DOI] [PubMed] [Google Scholar]
- 17.Haghani A, Li CZ, Robeck TR, Zhang J, Lu AT, Ablaeva J, Acosta-Rodríguez VA, Adams DM, Alagaili AN, Almunia J. DNA methylation networks underlying mammalian traits. Science. 2023;381:eabq5693. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Selman C, Blount JD, Nussey DH, Speakman JR. Oxidative damage, ageing, and life-history evolution: where now? Trends Ecol Evol. 2012;27:570–7. [DOI] [PubMed] [Google Scholar]
- 19.Monaghan P, Metcalfe NB, Torres R. Oxidative stress as a mediator of life history trade-offs: mechanisms, measurements and interpretation. Ecol Lett. 2009;12:75–92. [DOI] [PubMed] [Google Scholar]
- 20.Perez-Campo R, Lopez-Torres M, Cadenas S, Rojas C, Barja G. The rate of free radical production as a determinant of the rate of aging: evidence from the comparative approach. J Comp Physiol B. 1998;168:149–58. [DOI] [PubMed] [Google Scholar]
- 21.Pacifici RE, Davies KJ. Protein, lipid and DNA repair systems in oxidative stress: the free-radical theory of aging revisited. Gerontology. 1991;37:166–80. [DOI] [PubMed] [Google Scholar]
- 22.Rodriguez KA, Wywial E, Perez VI, Lambert AJ, Edrey YH, Lewis KN, Grimes K, Lindsey ML, Brand MD, Buffenstein R. Walking the oxidative stress tightrope: A perspective from the naked mole-rat, the longest-living rodent. Curr Pharm Des. 2011;17:2290–307. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Di Meo S, Reed TT, Venditti P, Victor VM. Role of ROS and RNS sources in physiological and pathological conditions. Oxid Med Cell Longev. 2016;2016:1245049. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Hulbert AJ. Membrane fatty acids as pacemakers of animal metabolism. Lipids. 2007;42:811–9. [DOI] [PubMed] [Google Scholar]
- 25.Mitchell TW, Buffenstein R, Hulbert A. Membrane phospholipid composition may contribute to exceptional longevity of the naked mole-rat (Heterocephalus glaber): a comparative study using shotgun lipidomics. Exp Gerontol. 2007;42:1053–62. [DOI] [PubMed] [Google Scholar]
- 26.Hulbert AJ. On the importance of fatty acid composition of membranes for aging. J Theor Biol. 2005;234:277–88. [DOI] [PubMed] [Google Scholar]
- 27.Barja G. Rate of generation of oxidative stress-related damage and animal longevity. Free Radical Biol Med. 2002;33:1167–72. [DOI] [PubMed] [Google Scholar]
- 28.Dammann, P. Slow aging in mammals—Lessons from African mole-rats and bats. In Proceedings of the Seminars in cell & developmental biology, 2017; pp. 154–163. [DOI] [PubMed]
- 29.Buffenstein R, Edrey YH, Yang T, Mele J. The oxidative stress theory of aging: embattled or invincible? Insights from non-traditional model organisms. Age. 2008;30:99–109. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Pamplona R, Barja G, Portero-Otín M. Membrane fatty acid unsaturation, protection against oxidative stress, and maximum life span: a homeoviscous-longevity adaptation? Ann N Y Acad Sci. 2002;959:475–90. [DOI] [PubMed] [Google Scholar]
- 31.Pamplona R, Portero-Otin M, Riba D, Ruiz C, Prat J, Bellmunt MJ, Barja G. Mitochondrial membrane peroxidizability index is inversely related to maximum life span in mammals. J Lipid Res. 1998;39:1989–94. [PubMed] [Google Scholar]
- 32.Pamplona R, Portero-Otın M, Requena J, Thorpe S, Herrero A, Barja G. A low degree of fatty acid unsaturation leads to lower lipid peroxidation and lipoxidation-derived protein modification in heart mitochondria of the longevous pigeon than in the short-lived rat. Mech Ageing Dev. 1999;106:283–96. [DOI] [PubMed] [Google Scholar]
- 33.Pamplona R, Prat J, Cadenas S, Rojas C, Perez-Campo R, Torres ML, Barja G. Low fatty acid unsaturation protects against lipid peroxidation in liver mitochondria from long-lived species: the pigeon and human case. Mech Ageing Dev. 1996;86:53–66. [DOI] [PubMed] [Google Scholar]
- 34.Pamplona R, Portero-Otín M, Riba D, Requena JR, Thorpe SR, López-Torres M, Barja G. Low fatty acid unsaturation: a mechanism for lowered lipoperoxidative modification of tissue proteins in mammalian species with long life spans. J Gerontol A Biol Sci Med Sci. 2000;55:B286–91. [DOI] [PubMed] [Google Scholar]
- 35.Frankel EN. Lipid oxidation: mechanisms, products and biological significance. J Am Oil Chem Soc. 1984;61:1908–17. [Google Scholar]
- 36.Hulbert A, Else PL. Mechanisms underlying the cost of living in animals. Annu Rev Physiol. 2000;62:207–35. [DOI] [PubMed] [Google Scholar]
- 37.Hulbert A, Faulks S, Buttemer W, Else P. Acyl composition of muscle membranes varies with body size in birds. J Exp Biol. 2002;205:3561–9. [DOI] [PubMed] [Google Scholar]
- 38.Hulbert A, Pamplona R, Buffenstein R, Buttemer W. Life and death: metabolic rate, membrane composition, and life span of animals. Physiol Rev. 2007;87:1175–213. [DOI] [PubMed] [Google Scholar]
- 39.Barja G. Free radicals and aging. Trends Neurosci. 2004;27:595–600. [DOI] [PubMed] [Google Scholar]
- 40.Couture P, Hulbert A. Membrane fatty acid composition of tissues is related to body mass of mammals. J Membr Biol. 1995;148:27–39. [DOI] [PubMed] [Google Scholar]
- 41.Gonzalez-Covarrubias V, Beekman M, Uh HW, Dane A, Troost J, Paliukhovich I, van der Kloet FM, Houwing-Duistermaat J, Vreeken RJ, Hankemeier T. Lipidomics of familial longevity. Aging Cell. 2013;12:426–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Haddad LS, Kelbert L, Hulbert A. Extended longevity of queen honey bees compared to workers is associated with peroxidation-resistant membranes. Exp Gerontol. 2007;42:601–9. [DOI] [PubMed] [Google Scholar]
- 43.Galván I, Naudí A, Erritzøe J, Møller AP, Barja G, Pamplona R. Long lifespans have evolved with long and monounsaturated fatty acids in birds. Evolution. 2015;69:2776–84. [DOI] [PubMed] [Google Scholar]
- 44.Buttemer WA, Battam H, Hulbert AJ. Fowl play and the price of petrel: long-living Procellariiformes have peroxidation-resistant membrane composition compared with short-living Galliformes. Biol Let. 2008;4:351–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Halliwell, B.; Gutteridge, J. Free radical in biology and medicine: 2nd ed; Oxford Science Publications: UK: Clarendon, 1989.
- 46.Spiteller G. Are changes of the cell membrane structure causally involved in the aging process? Ann N Y Acad Sci. 2002;959:30–44. [DOI] [PubMed] [Google Scholar]
- 47.Spiteller G. Are lipid peroxidation processes induced by changes in the cell wall structure and how are these processes connected with diseases? Med Hypotheses. 2003;60:69–83. [DOI] [PubMed] [Google Scholar]
- 48.Esterbauer H, Schaur RJ, Zollner H. Chemistry and biochemistry of 4-hydroxynonenal, malonaldehyde and related aldehydes. Free Radical Biol Med. 1991;11:81–128. [DOI] [PubMed] [Google Scholar]
- 49.Barja G. Updating the mitochondrial free radical theory of aging: an integrated view, key aspects, and confounding concepts. Antioxid Redox Signal. 2013;19:1420–45. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Gorbunova V, Bozzella MJ, Seluanov A. Rodents for comparative aging studies: from mice to beavers. Age. 2008;30:111–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Cohen, A. A. 2018 Aging across the tree of life: The importance of a comparative perspective for the use of animal models in aging. Biochimica et Biophysica Acta (BBA)-Molecular Basis of Disease 1864(9) 2680–2689 [DOI] [PubMed]
- 52.Holtze S, Gorshkova E, Braude S, Cellerino A, Dammann P, Hildebrandt TB, Hoeflich A, Hoffmann S, Koch P, Terzibasi Tozzini E. Alternative animal models of aging research. Front Mol Biosci. 2021;8:660959. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Kim SK. Common aging pathways in worms, flies, mice and humans. J Exp Biol. 2007;210:1607–12. [DOI] [PubMed] [Google Scholar]
- 54.Flurkey K, Currer JM, Harrison D. Mouse models in aging research. In The mouse in biomedical research; Elsevier: 2007; pp. 637–672.
- 55.Vanhooren V, Libert C. The mouse as a model organism in aging research: usefulness, pitfalls and possibilities. Ageing Res Rev. 2013;12:8–21. [DOI] [PubMed] [Google Scholar]
- 56.Kirkwood TB, Rose MR. Evolution of senescence: late survival sacrificed for reproduction. Philosophical Transactions of the Royal Society of London Series B: Biological Sciences. 1991;332:15–24. [DOI] [PubMed] [Google Scholar]
- 57.Lee JC, Osborn DA, Miller KV. Characteristics of a high density population of southern fox squirrels. Am Midl Nat. 2008;159:385–93. [Google Scholar]
- 58.Shilovsky GA, Putyatina TS, Markov AV. Evolution of longevity as a species-specific trait in mammals. Biochem Mosc. 2022;87:1579–99. [DOI] [PubMed] [Google Scholar]
- 59.Gorbunova V, Seluanov A, Zhang Z, Gladyshev VN, Vijg J. Comparative genetics of longevity and cancer: insights from long-lived rodents. Nat Rev Genet. 2014;15:531–40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 60.Tian X, Azpurua J, Hine C, Vaidya A, Myakishev-Rempel M, Ablaeva J, Mao Z, Nevo E, Gorbunova V, Seluanov A. High-molecular-mass hyaluronan mediates the cancer resistance of the naked mole rat. Nature. 2013;499:346–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Gorbunova V, Hine C, Tian X, Ablaeva J, Gudkov AV, Nevo E, Seluanov A. Cancer resistance in the blind mole rat is mediated by concerted necrotic cell death mechanism. Proc Natl Acad Sci. 2012;109:19392–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Seluanov A, Hine C, Bozzella M, Hall A, Sasahara TH, Ribeiro AA, Catania KC, Presgraves DC, Gorbunova V. Distinct tumor suppressor mechanisms evolve in rodent species that differ in size and lifespan. Aging Cell. 2008;7:813–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Shattuck MR, Williams SA. Arboreality has allowed for the evolution of increased longevity in mammals. Proc Natl Acad Sci. 2010;107:4635–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64.de Abreu-Jr EF, Pavan SE, Tsuchiya MT, Wilson DE, Percequillo AR, Maldonado JE. Museomics of tree squirrels: a dense taxon sampling of mitogenomes reveals hidden diversity, phenotypic convergence, and the need of a taxonomic overhaul. BMC Evol Biol. 2020;20:1–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Aghbolaghi MA, Ahmadzadeh F, Kiabi BH, Keyghobadi N. Evolutionary history of the Persian squirrel (Sciurus anomalus): It emerged on the Eurasian continent in the Miocene. Zool Anz. 2020;287:17–24. [Google Scholar]
- 66.Aghbolaghi MA, Ahmadzadeh F, Kiabi B, Keyghobadi N. The permanent inhabitant of the oak trees: phylogeography and genetic structure of the Persian squirrel (Sciurus anomalus). Biol J Lin Soc. 2019;127:197–212. [Google Scholar]
- 67.Austad SN, Fischer KE. Primate longevity: its place in the mammalian scheme. Am J Primatol. 1992;28:251–61. [DOI] [PubMed] [Google Scholar]
- 68.Lorenzini A, Tresini M, Austad SN, Cristofalo VJ. Cellular replicative capacity correlates primarily with species body mass not longevity. Mech Ageing Dev. 2005;126:1130–3. [DOI] [PubMed] [Google Scholar]
- 69.Seluanov A, Chen Z, Hine C, Sasahara TH, Ribeiro AA, Catania KC, Presgraves DC, Gorbunova V. Telomerase activity coevolves with body mass not lifespan. Aging Cell. 2007;6:45–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Gurnell, J.; Gurnell, J. The natural history of squirrels; Christopher Helm London: 1987.
- 71.Mantor M, Krause S, Hart LA. Trapping and handling squirrels: Trap modification and handling restraint to minimize injuries and stress. Wildl Soc Bull. 2014;38:152–9. [Google Scholar]
- 72.Promislow DE. Senescence in natural populations of mammals: a comparative study. Evolution. 1991;45:1869–87. [DOI] [PubMed] [Google Scholar]
- 73.Munshi-South J, Wilkinson GS. Bats and birds: exceptional longevity despite high metabolic rates. Ageing Res Rev. 2010;9:12–9. [DOI] [PubMed] [Google Scholar]
- 74.Liguori I, Russo G, Curcio F, Bulli G, Aran L, Della-Morte D, Gargiulo G, Testa G, Cacciatore F, Bonaduce D. Oxidative stress, aging, and diseases. Clin Interv Aging. 2018;13:757. 10.2147/CIA.S158513. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Turturro A, Witt WW, Lewis S, Hass BS, Lipman RD, Hart RW. Growth curves and survival characteristics of the animals used in the Biomarkers of Aging Program. J Gerontol A Biol Sci Med Sci. 1999;54:B492–501. [DOI] [PubMed] [Google Scholar]
- 76.Ghasempour R, Harsini J, Beigi A, Kaboli M. Public attitudes towards the management of invasive squirrel species: lessons learned from Iran. Int J Environ Sci Technol. 2024;21:1435–46. [Google Scholar]
- 77.Raza, H.A.; Fadhel, O.; Ararat, K.; Haba, M.K.; Salim, M.; Bachmann, A.; Chappell, B. Animal and bird trade and hunting in Iraq. Nature Iraq, Sulaimani, Iraq/Ministry of Environment, Baghdad 2011.
- 78.Tanikawa T. An eye-lens weight curve for determining age in black rats, Rattus rattus. J Mamm Soc Japan. 1993;18:49–51. [Google Scholar]
- 79.Yabe T. Eye lens weight as an age indicator in the Norway rat. J Mamm Soc Japan. 1979;8:54–5. [Google Scholar]
- 80.Speakman JR. Body size, energy metabolism and lifespan. J Exp Biol. 2005;208:1717–30. [DOI] [PubMed] [Google Scholar]
- 81.Saini RK, Prasad P, Shang X, Keum Y-S. Advances in lipid extraction methods—a review. Int J Mol Sci. 2021;22:13643. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 82.Heydari Koochi Z, Jahromi KG, Kavoosi G, Babaei S. Citrus peel waste essential oil: Chemical composition along with anti-amylase and anti-glucosidase potential. Int J Food Sci Technol. 2022;57:6795–804. [Google Scholar]
- 83.Kim SY, Jeong J-M, Kim SJ, Seo W, Kim M-H, Choi W-M, Yoo W, Lee J-H, Shim Y-R, Yi H-S. Pro-inflammatory hepatic macrophages generate ROS through NADPH oxidase 2 via endocytosis of monomeric TLR4–MD2 complex. Nat Commun. 2017;8:2247. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Alici, E.H.; Arabaci, G. 2016 Determination of SOD, POD, PPO and cat enzyme activities in Rumex obtusifolius L. Annu Res Rev Biol 1–7.
- 85.Aminizadeh M, Rahimi A, Sohrabi F, Kavoosi G. Development of antioxidant materials based on Persian gum and Zataria essential oil: Modulation of superoxide-producing and nitric oxide-producing enzymes in wheat seedlings. Biocatal Agric Biotechnol. 2021;34:102035. [Google Scholar]
- 86.Lin H, Wu H, Li H, Song A, Yin W. The essential role of GSTP1 I105V polymorphism in the prediction of CDNB metabolism and toxicity: In silico and in vitro insights. Toxicol In Vitro. 2023;90:105601. [DOI] [PubMed] [Google Scholar]
- 87.Sim Choi H, Woo Kim J, Cha YN, Kim C. A quantitative nitroblue tetrazolium assay for determining intracellular superoxide anion production in phagocytic cells. J Immunoassay Immunochem. 2006;27:31–44. [DOI] [PubMed] [Google Scholar]
- 88.Li D, Wang M, Cheng N, Xue X, Wu L, Cao W. A modified FOX-1 method for Micro-determination of hydrogen peroxide in honey samples. Food Chem. 2017;237:225–31. [DOI] [PubMed] [Google Scholar]
- 89.Vargas-Maya NI, Padilla-Vaca F, Romero-González OE, Rosales-Castillo EAS, Rangel-Serrano Á, Arias-Negrete S, Franco B. Refinement of the Griess method for measuring nitrite in biological samples. J Microbiol Methods. 2021;187:106260. [DOI] [PubMed] [Google Scholar]
- 90.De Leon, J.A.D.; Borges, C.R. 2020 Evaluation of oxidative stress in biological samples using the thiobarbituric acid reactive substances assay. JoVE e61122 [DOI] [PMC free article] [PubMed]
- 91.Silvestrini A, Meucci E, Ricerca BM, Mancini A. Total Antioxidant Capacity: Biochemical aspects and clinical significance. Int J Mol Sci. 2023;24:10978. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Team, R.D.C. R: A Language and Environment for Statistical Computing. Available online: https://www.R-project.org/. (accessed on 24 April).
- 93.Wickham, H.; Chang, W.; Wickham, M.H. Package ‘ggplot2’. Create elegant data visualisations using the grammar of graphics. Version 2016 2 1–189.
- 94.Abdi H, Williams LJ. Principal component analysis. Wiley Interdiscip Rev Comput Stat. 2010;2:433–59. [Google Scholar]
- 95.Gabriel KR. The biplot graphic display of matrices with application to principal component analysis. Biometrika. 1971;58:453–67. [Google Scholar]
- 96.Aiello A, Medoro A, Accardi G, Calabrò A, Carru C, Cannavo A, Caruso C, Candore G, Scapagnini G, Corbi G. Polyunsaturated fatty acid status and markers of oxidative stress and inflammation across the lifespan: A cross-sectional study in a cohort with long-lived individuals. Exp Gerontol. 2024;195:112531. [DOI] [PubMed] [Google Scholar]
- 97.Hulbert AJ, Faulks SC, Buffenstein R. Oxidation-resistant membrane phospholipids can explain longevity differences among the longest-living rodents and similarly-sized mice. J Gerontol A Biol Sci Med Sci. 2006;61:1009–18. [DOI] [PubMed] [Google Scholar]
- 98.Pamplona R, Costantini D. Molecular and structural antioxidant defenses against oxidative stress in animals. Am J Physiol Regul Integr Comp Physiol. 2011;301:R843–63. [DOI] [PubMed] [Google Scholar]
- 99.Gladyshev VN. The free radical theory of aging is dead Long live the damage theory! Antioxid Redox Signal. 2014;20:727–31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Maldonado E, Morales-Pison S, Urbina F, Solari A. Aging hallmarks and the role of oxidative stress. Antioxidants. 2023;12:651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Lambert AJ, Boysen HM, Buckingham JA, Yang T, Podlutsky A, Austad SN, Kunz TH, Buffenstein R, Brand MD. Low rates of hydrogen peroxide production by isolated heart mitochondria associate with long maximum lifespan in vertebrate homeotherms. Aging Cell. 2007;6:607–18. [DOI] [PubMed] [Google Scholar]
- 102.Andziak B, O’Connor TP, Buffenstein R. Antioxidants do not explain the disparate longevity between mice and the longest-living rodent, the naked mole-rat. Mech Ageing Dev. 2005;126:1206–12. [DOI] [PubMed] [Google Scholar]
- 103.Sohal R, Sohal BH, Brunk UT. Relationship between antioxidant defenses and longevity in different mammalian species. Mech Ageing Dev. 1990;53:217–27. [DOI] [PubMed] [Google Scholar]
- 104.Page MM, Richardson J, Wiens BE, Tiedtke E, Peters CW, Faure PA, Burness G, Stuart JA. Antioxidant enzyme activities are not broadly correlated with longevity in 14 vertebrate endotherm species. Age. 2010;32:255–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 105.Rahal A, Kumar A, Singh V, Yadav B, Tiwari R, Chakraborty S, Dhama K. Oxidative stress, prooxidants, and antioxidants: the interplay. Biomed Res Int. 2014;2014:761264. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 106.Townsend DM, Tew KD, Tapiero H. The importance of glutathione in human disease. Biomed Pharmacother. 2003;57:145–55. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 107.Toba G, Aigaki T. Disruption of the microsomal glutathione S-transferase-like gene reduces life span of Drosophila melanogaster. Gene. 2000;253:179–87. [DOI] [PubMed] [Google Scholar]
- 108.Lewis KN, Wason E, Edrey YH, Kristan DM, Nevo E, Buffenstein R. Regulation of Nrf2 signaling and longevity in naturally long-lived rodents. Proc Natl Acad Sci. 2015;112:3722–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 109.Ayyadevara S, Engle MR, Singh SP, Dandapat A, Lichti CF, Beneš H, Shmookler Reis RJ, Liebau E, Zimniak P. Lifespan and stress resistance of Caenorhabditis elegans are increased by expression of glutathione transferases capable of metabolizing the lipid peroxidation product 4-hydroxynonenal. Aging Cell. 2005;4:257–71. [DOI] [PubMed] [Google Scholar]
- 110.Thimmulappa RK, Chattopadhyay I, Rajasekaran S. Oxidative stress mechanisms in the pathogenesis of environmental lung diseases. Oxidative Stress Lung Dis. 2020;2:103–37. [Google Scholar]
- 111.Aliyu M, Zohora F, Saboor-Yaraghi AA. Spleen in innate and adaptive immunity regulation. AIMS Allergy Immunol. 2021;5:1–17. [Google Scholar]
- 112.Asantewaa G, Tuttle ET, Ward NP, Kang YP, Kim Y, Kavanagh ME, Girnius N, Chen Y, Rodriguez K, Hecht F. Glutathione synthesis in the mouse liver supports lipid abundance through NRF2 repression. Nat Commun. 2024;15:6152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Matuz-Mares D, Riveros-Rosas H, Vilchis-Landeros MM, Vázquez-Meza H. Glutathione participation in the prevention of cardiovascular diseases. Antioxidants. 2021;10:1220. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 114.Allocati N, Masulli M, Di Ilio C, Federici L. Glutathione transferases: substrates, inihibitors and pro-drugs in cancer and neurodegenerative diseases. Oncogenesis. 2018;7:8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 115.Eaton, D.L.; Bammler, T.K. 1999 Concise review of the glutathione S-transferase and their significance to toxicology. Toxicol Sci 49 [DOI] [PubMed]
- 116.Sadeghinezhad J, Tootian Z, Javadi F. Anatomical and histological structure of the tongue and histochemical characteristics of the lingual salivary glands in the Persian squirrel (Sciurus anomalus). Anat Sci Int. 2018;93:58–68. [DOI] [PubMed] [Google Scholar]
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Data Availability Statement
The corresponding author will deliver the information assisting the findings of this study upon reasonable request.