1. Computational and Systems Biology
Download icon

A cross-sectional study of functional and metabolic changes during aging through the lifespan in male mice

  1. Michael A Petr
  2. Irene Alfaras
  3. Melissa Krawcyzk
  4. Woei-Nan Bair
  5. Sarah J Mitchell
  6. Christopher H Morrell
  7. Stephanie A Studenski
  8. Nathan L Price
  9. Kenneth W Fishbein
  10. Richard G Spencer
  11. Morten Scheibye-Knudsen
  12. Edward G Lakatta
  13. Luigi Ferrucci
  14. Miguel A Aon
  15. Michel Bernier
  16. Rafael de Cabo  Is a corresponding author
  1. Center for Healthy Aging, ICMM, University of Copenhagen, Denmark
  2. Translational Gerontology Branch, National Institute on Aging, NIH, United States
  3. Laboratory of Cardiovascular Science, National Institute on Aging, NIH, United States
  4. Department of Physical Therapy, University of Sciences, United States
  5. Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, United States
  6. Integrative Cell Signaling and Neurobiology of Metabolism Program, Yale University School of Medicine, United States
  7. Vascular Biology and Therapeutics Program, Yale University School of Medicine, United States
  8. Department of Comparative Medicine, Yale University School of Medicine, United States
  9. Department of Pathology, Yale University School of Medicine, United States
  10. Laboratory of Clinical Investigation, National Institute on Aging, NIH, United States
Research Article
  • Cited 1
  • Views 2,048
  • Annotations
Cite this article as: eLife 2021;10:e62952 doi: 10.7554/eLife.62952

Abstract

Aging is associated with distinct phenotypical, physiological, and functional changes, leading to disease and death. The progression of aging-related traits varies widely among individuals, influenced by their environment, lifestyle, and genetics. In this study, we conducted physiologic and functional tests cross-sectionally throughout the entire lifespan of male C57BL/6N mice. In parallel, metabolomics analyses in serum, brain, liver, heart, and skeletal muscle were also performed to identify signatures associated with frailty and age-dependent functional decline. Our findings indicate that declines in gait speed as a function of age and frailty are associated with a dramatic increase in the energetic cost of physical activity and decreases in working capacity. Aging and functional decline prompt organs to rewire their metabolism and substrate selection and toward redox-related pathways, mainly in liver and heart. Collectively, the data provide a framework to further understand and characterize processes of aging at the individual organism and organ levels.

Introduction

Genetics, lifestyle, and environmental factors contribute to different rates of aging. Across the lifespan, most species undergo complex phenotypical and functional changes that affect all organs of the body and the systems that allow communication and coordination between tissues. While this concept of aging has existed for a very long time, several milestones have recently been achieved in aging research, both in terms of the identification and modulation of pathways critical to aging in model organisms, and the characterization of factors relevant to human aging. In particular, important progress has been made in comparing the phenotypes and rate of decline due to chronological aging versus disease-related factors, the relationship between health risk factors and aging, and the impact of behavioral trends on health and risk of disease promotion in humans (Healthy Aging: Lessons from the Baltimore Longitudinal Study of Aging, NIA, NIH, US DHHS, July 2010). There is significant variability in survival in the older population that cannot be explained solely by age and sex alone.

Under well-controlled environmental conditions, laboratory mice can live up to 4 years (Miller et al., 2002). A wide range of physiological, functional, behavioral, and pathological changes occur with age that determine how long mice live. Mice develop many of the histopathological characteristics of human aging during their lifespan, such as lymphocyte infiltration, tissue inflammation and necrosis, cancer, and amyloidosis (Mitchell et al., 2016; Mitchell et al., 2019). Understanding the process of normal aging in mice and identifying murine aging predictors and biomarkers will aid in the development of interventions capable of improving health and quality of life that are translatable to humans.

Research on aging is shifting its focus toward understanding the underlying differences in the aging phenotype of chronological and biological aging. Resilience and associated compensatory homeostatic mechanisms slow functional decline, resulting in great variability in survival (Ferrucci et al., 2020). For instance, physical performance metrics have provided important information about individualized estimates of survival as evidenced by the association of age-related decline in gait speed with reduced overall survival in humans (Studenski et al., 2011; White et al., 2013). However, clinically detectable changes currently utilized to characterize the aging phenotype are only apparent when compensatory mechanisms begin to fail (Ferrucci et al., 2020). In this context, the nature of the processes (e.g. metabolic, immunologic) and underlying mechanisms causing these compensatory maladaptations are not well understood (Ferrucci et al., 2020; Adelnia et al., 2020). All hallmarks of aging exhibit prominent anabolic and catabolic effects (López-Otín et al., 2013). In light of previous cross-sectional health studies in mice (Justice et al., 2014; Fischer et al., 2016), we performed an extensive characterization of the group differences in several relevant phenotypes, including glucoregulatory and physical performance metrics, in a cohort of male C57BL/6N mice ranging from 3 to 36 months of age. In addition, untargeted metabolite profiling of multiple organs and serum was conducted in this cross-sectional study, allowing a thorough characterization of the functional and molecular changes that occur with age. We report group differences in many functional and phenotypic biomarkers of aging in mice and provide an initial characterization of the specific biomolecular changes that occur in different organs that may contribute to these age-related conditions.

Results

Physiological, biochemical, and physical behavioral tests in a cross-sectional study of aging in mice

To characterize aging phenotypes in a cohort of male C57BL/6N mice, we utilized physiological, biochemical, and physical behavior tests to cross-sectionally assess group differences in these traits with age. Age group classification was as follows: Young (Y), (3–8 months; n = 23); Adult (A), (13–23 months; n = 26), and Old (O) (27–36 months; n = 22) (Figure 1A).

Figure 1 with 1 supplement see all
Impact of age and frailty on motor function, gait, and glucose homeostasis in male C57BL/6N mice.

(A) Flowchart depicting analyses performed in the study. (B–D) Effects of age on frailty index, motor function, and gait speed in mice. (E–F) Mean and maximum tail height as a function of age. (G–J) Negative correlation between gait speed, motor function, and tail height with frailty. (K) Dependence of fasting blood glucose with age. (L) Scatter plot depicting insulin sensitivity as measured by the HOMA-IR index. (M) Relationship between fasting blood glucose and frailty index. Glucose measurement was performed 3 months after frailty assessment. (B–M): Mice ranged in age from 3 to 33 months. Y: young (3–8 mo, n = 23); A, adult (13–23 mo, n = 26); O: old (27–33 mo, n = 22). The actual number of data points shown on the graphs varies as not all mice were tested for any given intervention.

Frailty was the first characteristic assessed due to its high human relevance as an established aging biomarker and good predictor of clinical outcomes. It is characterized by loss of gait speed, fatigue, loss of body weight and muscle strength, and cognitive decline (Palliyaguru et al., 2019). Here, we used a multimodal approach to assess the impact of aging on the development of frailty and the associated impairment of several related phenotypic measures. The frailty index (FI) is a compilation of 31 clinical parameters, with 0 being not frail and one being frail (Whitehead et al., 2014). The values obtained from FI assessment increased progressively among different groups of mice, passing from 0.13 ± 0.01 in group Y to 0.24 ± 0.01 in group A, and 0.31 ± 0.02 in group O (Figure 1B), in agreement with earlier results (Whitehead et al., 2014). The FI increased linearly as a function of age (Figure 1B, p=3.80e-16, r2 = 0.615), with age accounting for 61.5% of FI variation. These group differences in FI were associated with reduced forelimb muscle strength, expressed as latency to fall from a wire (p=0.0033, r2 = 0.113), and gait speed (p=2.73e-16, r2 = 0.608) (Figure 1C,D). Age accounted for 52.5% and 46.5% of the mean (p=2.61e-13) and maximal (p=1.87e-11) tail height variation, respectively (Figure 1E,F). Given the strong relationship between FI vs. age and FI’s significance as a metric of age-dependent decline, we related other functional parameters, for example, wire hang, tail height and gait speed, to FI. Gait speed exhibited the strongest relationship with FI from that of age itself, since FI can explain ~33% of the gait speed decrease (Figure 1G) compared to only ~17–25% of the other variables (Figure 1H–J). As expected, clear deficits in muscle strength (wire hang test and mean/maximal tail height) and walking speed were significantly related with FI. Interestingly, since total gait speed variation could, in principle, be explained by age and FI (Figure 1B and G, respectively), it can be considered a major biomarker of aging-dependent decline. After adjusting for age, frailty lost the association with gait speed (Figure 1G, p=0.477) but it remained strongly associated with forelimb muscle strength (Figure 1H, p=0.00088). After including age, FI was only marginally associated with mean tail height (Figure 1I, p=0.0792) while still being associated with maximal tail height (Figure 1J, p=0.0495). The effects of frailty on muscle strength were maintained after correction for age, indicating the presence of independent risk factors beyond chronological age that could reduce physical performance in the oldest mice.

Given the adaptive role of insulin resistance in aging (Barzilai and Ferrucci, 2012), venous blood was collected from 6 hr fasted mice. Blood glucose, plasma insulin concentrations, and HOMA-IR index, a surrogate for estimating insulin resistance (Matthews et al., 1985), were largely similar from 6 to 16 months and were observed to be lower after 19 months of age (Figure 1K,L, Figure 1—figure supplement 1A). In contrast to physical performance measurements, only a weak association existed between fasting blood glucose and the FI score (Figure 1M). These results may reflect a survivorship bias of the long-living mice (Scheen, 2017), although other effects such as reduced body weight and age-associated regional distribution among fat depots could have profound metabolic implications vis-à-vis changes observed in fasting blood glucose and insulin resistance (Girard and Lafontan, 2008; Hardy et al., 2012; Tran et al., 2008; Shuster et al., 2012).

In order to understand the impact of body composition on overall fitness, body composition analysis was performed next using low-field NMR spectroscopy (Figure 2A). Group differences in body weight and whole-body fat percentage were noted with a steady increase in values up to ~19 months and a decline thereafter (p=7.87e-07, r2 = 0.319 and p=2.71e–07, r2 = 0.340, respectively), whereas a weak positive association was observed between fasting glucose levels and percent body fat (Figure 1—figure supplement 1B). To determine body fat distribution, a 7T MRI body scanner was used to quantify the subcutaneous/visceral fat ratio, which revealed a linear reduction in visceral adiposity as a function of age (p=3.38e-05, r2 = 0.2996) (Figure 2B). The whole-body lean tissue percentage was largely preserved during aging (p=2.15e-05, r2 = 0.250) (Figure 2A). Representative MRI scans from groups Y, A, and O are depicted (Figure 1—figure supplement 1C).

Figure 2 with 1 supplement see all
Impact of age on body composition measurements, bone density, and in vivo metabolic function in male C57BL/6N mice.

(A) Body weight (left panel), percentages of whole-body fat mass (middle panel) and lean tissue mass (right panel) as assessed by low-field nuclear magnetic resonance imaging. (B) Ratio of subcutaneous to visceral fat calculated from abdominal 7-Tesla MRI scanning. (C) In vivo microCT analysis of the tibia was carried out to measure cortical thickness, trabecular bone mineral density (BMD), as well as fat and lean mass around the tibia expressed as percentage of the total body weight. (D) Metabolic parameters derived from the metabolic treadmill as a function of age. Energetic cost (left panel); heat generation (second panel); RER, respiratory exchange ratio (third panel); and Work (right panel). Energetic cost was determined from VO2 per unit of body weight and gait speed while work was expressed as mJoules. (E–G) Multiple regression analyses with frailty index (E, F) and gait velocity (G) as response variables. The modeling approach consisted of fitting a series of four regressions for each of the response variables. The explanatory variables included in the multiple regression modeling are depicted in Figure 2—source data 1. (E) left panel, Effect of gait velocity on the relationship between frailty and age; right panel, Impact of age on the interaction between frailty and gait velocity. (F) left panel, Effect of bone cortical thickness on the relationship between frailty and age; right panel, Impact of age on the interaction between frailty and bone cortical thickness. (G) left panel, Effect of energetic costs on the relationship between gait velocity and age; right panel, Impact of age on the interaction between gait velocity and energetic costs. Although this is a cross-sectional study, the points were joined to help the reader see the various associations without suggesting that there is causal association; for example, that animals with higher age tend to have higher frailty within the same gait velocity (E, left panel). (H–K) Mice at different ages were placed into metabolic cages for 72 hr to measure VO2, VCO2, RER, heat production, and ambulatory activity counts. The values associated with the first 12 hr acclimatization phase (L1) were discarded. (H) Body weight as mice entered the metabolic cage. (I) Averaged hourly RER trajectories were captured during two dark/light cycles in young (n = 8), adult (n = 22) and old (n = 18) mice. ZT, zeitgeber time is defined as the 12:12 hr light/dark cycle that synchronizes organismal biological rhythms. Each point represents mean ± SEM. (J) Scatter plot depicting RER values averaged over 48 hr for individual mice in the three age groups. (K) Relationship between RER and age. Data in (H) and (J) were analyzed using one-way ANOVA with Dunnett’s post-hoc analysis. *, p<0.05; **, p<0.01; ****, p<0.0001. (L) Diagram depicting the impact of age on body composition and energetic parameters in AL-fed male mice.

Figure 2—source data 1

Multiple regression analyses results for frailty index and gait velocity.

https://cdn.elifesciences.org/articles/62952/elife-62952-fig2-data1-v2.docx
Figure 2—source data 2

Area under the curves (AUCs) from the averaged, hourly trajectories of various energetic parameters that were captured during two dark/light cycles in young, adult, and old mice.

https://cdn.elifesciences.org/articles/62952/elife-62952-fig2-data2-v2.docx
Figure 2—source data 3

Distribution of various energetic parameters in male mice in various age groups.

https://cdn.elifesciences.org/articles/62952/elife-62952-fig2-data3-v2.docx
Figure 2—source data 4

Linear regression of the metabolic cage data obtained from the whole cohort of male mice and after segregation by age groups.

https://cdn.elifesciences.org/articles/62952/elife-62952-fig2-data4-v2.docx
Figure 2—source data 5

Generalized linear model (GLM) to assess potential significant interaction effect in energetic parameters between mice of various age groups.

https://cdn.elifesciences.org/articles/62952/elife-62952-fig2-data5-v2.docx

Age-associated bone loss is reflective of a general functional decline. Old mice exhibited marked and significantly lower femoral cortical bone thickness (p=7.20e-07, r2 = 0.458) and trabecular bone mineral density (BMD) (p=0.0184, r2 = 0.112) compared to the other two groups of mice (Figure 2C). Representative micro-CT scans of the femoral region are depicted (Figure 1—figure supplement 1D). No association between BMD and body weight was found (Figure 1—figure supplement 1E). Remarkably, there was a lower percentage of fat around the tibia per unit of body mass in animals of increased age, with the oldest group of mice being the most affected (p=0.002, r2 = 0.248), whereas the percentage of lean tissue surrounding the tibia trended higher in group O compared to group A (Figure 2C). The presence of weaker bones is suggestive of age-related osteoporosis.

To assess whether the lower gait speed in old animals was caused by an altered cardio-metabolic phenotype, mice walked on a metabolic treadmill at their age-group's average gait speed (determined from the MotoRater data, see Figure 1D), while VO2 and VCO2 were simultaneously monitored. Although variability exists within age groups, there is a steady decline in gait speed measured from progressively older cohorts of mice, and therefore average gait speed emulated the natural walking pattern, which is defined as walking without hesitation (e.g. stopping frequently/exploring/sniffing) not sprinting across the platform. Remarkably, the energetic cost of walking increased exponentially from ~20 months of age and was paralleled by a sustained decrease in work capacity (Figure 2D, 1st and 4th panels). The running VO2 values did not differ over the entire activity episode (800 s.) across age, thus ruling out a role for fatigue in the higher energetic cost in the oldest mice (data not shown). Respiratory exchange ratio (RER, defined as VCO2/VO2) varies between ~0.7 (i.e. predominant use of fats as fuel source) and 1.0 (i.e. exclusive carbohydrate utilization), with intermediate RER values implying mixed substrate use. Interestingly, RER and heat dissipation values, obtained during the treadmill monitoring, decreased from ~0.85 to ~0.80, and from 5 to 3 kJ h−1, respectively, indicating higher utilization of lipid than glucose as an energy source, and less heat dissipation in the older groups of animals (Figure 2D, 3rd and 2nd panels). The differences in work economy and heat production between the three age groups were maintained after adjusting for lean mass (data not shown). The diminished heat dissipation vs. age might not be due to an increase in the efficiency of energy use by the group O, but rather a predominant overall decrease in work capacity, that is, by working less, the mice dissipate less heat (Figure 2D, 2nd and 4th panels). These results are also consistent with the observation that, compared with young animals, older mice consume significantly more O2 (Figure 2D, 1st panel) and released more CO2 per unit gait speed (not shown), because enhanced lipid catabolism demands more O2.

Multiple linear regression analysis showed that from the initial set of explanatory variables, the interaction of gait velocity and age was found to be significant (Figure 2E, left panel). Frailty status was more apparent with age as gait velocity increases: Specifically, at younger ages, the frailty index was inversely correlated with gait velocity while at older ages frailty status showed strong association with lower gait velocity (Figure 2E, right panel). Studies have shown that higher level of HOMA-IR is significantly associated with frailty in the elderly, but such association is not observed in the middle-aged population (Peng et al., 2019); moreover, fasting plasma glucose does not differ between frail and non-frail individuals (Kalyani et al., 2012). Here, positive associations between HOMA-IR tertiles (Q1, median, Q3) and the risk of frailty were observed in old animals, while fasting blood glucose tertiles and frailty status were negatively correlated (Figure 2—source data 1). By incorporating bone density variables into the starting model, we found that frailty increases more steeply with age as cortical thickness increases (Figure 2F, left panel) and that the effect of cortical thickness on frailty index is much larger in older ages (Figure 2F, right panel). The introduction of variables such as percent lean mass, percent fat and visceral-to-subcutaneous fat ratio as well as metabolic treadmill parameters such as energetic cost, heat generation or RER did not improve the initial model (Figure 2—source data 1).

Of all the experimental variables analyzed, age and energetic costs were the only explanatory variables for gait speed (Figure 2—source data 1). Gait speed tended to be lower in older animals than in group Y, consistent with the observations made in the human population (Studenski et al., 2011). Moreover, at higher energetic costs there was a stronger decreasing relationship between gait velocity and age (Figure 2G, left panel). There was a marked positive association between gait velocity and energetic costs in groups Y and A, whereas no significant relationship was observed in group O (Figure 2G, right panel).

To better understand the impact of age on energy balance and availability in mice, we conducted indirect calorimetry experiments in metabolic chambers for 72 hr to collect specific measurements such as the VCO2, VO2, RER, energy expenditure (EE), and ambulatory activity counts. Mice from Group A (12–20 mo) were significantly heavier than group Y (7 mo) and group O (26–36 mo) animals as they entered the metabolic cages (Figure 2H). Hourly trajectories of energetic parameters were captured over two 24 hr cycles (D1-L2-D2-L3) and then were averaged. Distinctive diurnal patterns of O2 consumption and CO2 production were observed in the three groups of mice (Figure 2—figure supplement 1A) that led to differences in the calculated values for RER (Figure 2I), with significantly higher area under the curve (AUC) in group O vs. groups Y and A, indicative of greater metabolic flexibility (AUC for Y, 2.563 ± 0.1163 [CI 2.335–2.791]; A, 2.311 ± 0.2188 [CI 1.883–2.74]; O, 3.277 ± 0.1421 [CI 2.999–3.556]). Moreover, EE was greater in group A compared with the other two groups consistent with their higher O2 consumption (Figure 2—figure supplement 1A). While these differences are largely reflective of the higher body mass of group A mice, it was also clear that the relationships between these metabolic parameters and body weight were altered with age (Figure 2—source data 2). Lastly, the higher ambulatory activity during the dark cycle was significantly lower in group O mice (Figure 2—figure supplement 1A, Figure 2—source data 2) and showed a strong negative correlation with age across the entire pool of animals. The diurnal changes in RER in the three age groups showed statistically significant differences in the dark cycle (D) between group Y vs. group O and group A vs. group O (Figure 2J), with higher RER in the oldest group of mice (Figure 2K). No difference in RER could be observed in the light phase between the different groups of mice. Scatter plots of the averaged 48 hr values of various energetic parameters from groups Y (n = 8), A (n = 22), and O (n = 18) mice showed statistically significant differences in group Y vs. group A mice and A vs. O mice in VO2 and EE (Figure 2—figure supplement 1B, Figure 2—source data 3). In contrast, there were no differences between groups Y and O mice in any of the measures except for the ambulatory activity (Figure 2—figure supplement 1G, Figure 2—source data 3).

Consistent with the lower gait speed observed in older animals, metabolic cage analysis reveals a strong negative correlation between age and ambulatory activity (Figure 2—figure supplement 1C), whereas no other respiratory parameters correlated significantly with age (Figure 2—source data 4). As expected, body weight was positively correlated with EE, oxygen consumption, and CO2 production (Figure 2—figure supplement 1D, Figure 2—source data 4). The observed differences in the AUC of respiratory parameters were primarily a result of the higher body weight of group A mice, which is why a generalized linear model (ANCOVA) analysis was used to determine differences in the relationship of body weight with VO2, VCO2, and EE (Figure 2—figure supplement 1E, Figure 2—source data 5). This analysis demonstrated statistically significant differences in the relationship between body weight and respiratory parameters when comparing group Y vs. A mice and group Y vs. O mice. These metabolic alterations may complement many of the differences that were observed in body composition and other related parameters.

Overall, the data presented in this section show that gait speed is a major biomarker of healthspan, and that frailty, in addition to age, is able to explain to a great extent its functional deterioration. In contrast, morphometrics (e.g. body weight, % fat and % lean mass) and glucose homeostasis (e.g. blood glucose, insulin, and HOMA2-IR) metrics show a more nuanced relationship with age. In fact, many of these factors (e.g. blood glucose, insulin, body weight, % fat) do not start to show a reduction in values until after ~19 mo, whereas others (e.g. bone density parameters, % fat around tibia) steadily decline in the values from various groups of mice with age. Important exceptions to this include % lean mass, % lean mass around the tibia per whole body weight, and the ratio of subcutaneous/visceral fat, that tend to be higher in group O animals. These findings provide a coherent picture of the bioenergetic changes that occur as a function of age, marked by an exponential increase in the energetic cost of physical activity, which is paralleled by a sustained decrease in work (energy). Together with gait speed, these parameters constitute landmarks of aging-dependent decline (Figure 2L), which led us to explore in further detail how the metabolomic landscape was altered with age in different tissues.

Untargeted metabolomics analysis

To determine whether the differences in healthspan assessments, such as physical function, FI, energetics, and work economy can be linked to metabolic remodeling, we performed untargeted metabolomics analysis. We used serum and four different organs (brain, heart, liver, and skeletal muscle) of the same mice and compared the three different life stages: Y, A, and O. Depending upon the organ, a total of 133 to 177 metabolites was detected. Combining univariate and multivariate statistics we identified 49 metabolites that were significantly different in serum and across organs as a function of age, of which 46 were shared. Figure 3 depicts a heat map of the averaged levels of the 46 shared metabolites after normalization as a function of age. The metabolite abundance in each mouse is provided (Figure 3—figure supplement 1).

Figure 3 with 1 supplement see all
Heatmap depicting levels of modulated metabolites and associated pathways in brain, heart, liver, serum, and skeletal muscle from young, middle-age, and old mice.

There were 46 metabolites involved in more than 10 defined pathways that were shared between serum and various organs across the three age groups. Each row represents a single metabolite, and each column depicts averaged values for each tissue and age group (In each tissue, n = 11–12 animals for young; n = 7 for adult; and n = 11–12 for old). For each metabolite, standardized abundance was calculated. The pseudocolor scaling of the standardized expression is from low (blue) to high (red).

Several features are worth highlighting: (i) The liver was the organ exhibiting the highest abundance of metabolite intermediates from all major pathways involved in macronutrient metabolism (glucose, lipids, amino acids) and redox-related pathways (nicotinamide, glutathione, pentose phosphate); (ii) heart and serum had the next highest metabolite abundance; (iii) across age and organ, there was an apparent qualitative similarity of the metabolites’ pattern of abundance or depletion; however, significant quantitative differences in metabolite levels also exist, as revealed by multivariate statistics (see Figure 4); (iv) several metabolites from redox-related pathways, such as pentose-phosphate, NAD+ salvage, methionine cycle, and transsulfuration, exhibited significant differences with age; and (v) expectedly, relative accumulation of glutamate/glutamine in the brain, and of lactate in serum was observed – the latter concomitant with depletion in the liver where it is utilized for gluconeogenesis.

Figure 4 with 1 supplement see all
Untargeted metabolomics performed in multiple organs and serum from young, adult, and old male C57BL/6N mice.

(A) Three-dimensional principal component analysis (PCA) depicting the effect of age across various organs and serum. (B) Brain, heart, liver, serum, and skeletal muscle metabolite profiles from young (Y, black symbols), adult (A, blue symbols) or old (O, red symbols) mice were analyzed by Partial Least Square Determinant Analysis (PLS-DA). A statistically significant degree of separation was observed between age groups. The ellipses correspond to 95% confidence intervals for a normal distribution. Each principal component is labeled with the corresponding percent values. (C) Variable in projection (VIP) scores of positively (red boxes) and negatively (green boxes) correlating metabolites in each tissue or serum as a function of age. (D) Correlation coefficients of the top 25 metabolites that correlated positively (orange bars) and negatively (green bars) as a function of age for the indicated tissues and serum. Blue arrowhead, fatty acids; green arrowhead, amino acids and related; red arrowhead, redox and related.

Applying principal component analysis (PCA), a multivariate clustering method, to the metabolomic dataset, we found a clear separation between the metabolite profiles among organs and serum for each age group (Figure 4A).

Adaptative redox-related metabolic remodeling is a common trait in old mice

We next determined if groups of metabolites were different in each organ in Y, A, and O mice. As revealed by partial least squares discriminant analysis (PLSDA), a supervised clustering method, brain and heart exhibited a net separation between the three groups of mice. In contrast, in liver, skeletal muscle and serum, Y and O animals were clearly separated while group A mice partially overlapped with both groups (Figure 4B). To identify the main metabolites responsible for age-dependent metabolic differences, we analyzed the top 20–25 metabolites in each organ, ranked based on the variable importance in projection (VIP) score as a function of age (Figure 4C); the mini heat map on the right shows each metabolite’s level variation within the different age groups.

The age dependence of each metabolite’s concentration was further assessed by quantifying the Pearson correlation coefficient as a function of age; positive and negative values denote greater or lower levels in older mice. Depicted in Figure 4D are the brain, heart, liver, serum and skeletal muscle results. For example, the top sixteen VIP score-ranked metabolites (VIP >1.0) in the liver are also among the most important ones increasing (F6P, ribulose 5P, G6P, glutathione) or decreasing (pyruvate, xylitol, sorbitol, nicotinamide) with age. Extending this analysis to the other organs enabled us to visualize the emergence of a consistent ‘meta-pattern’ of metabolic change characterized by metabolites from redox-related pathways, such as pentose phosphate (G6P, ribulose 5P, ribose 5P), NAD+ salvage (nicotinamide), methionine cycle (methionine) and transsulfuration (cysteine, taurine, glutamate, glycine, glutathione) (Figure 4D). Unlike in liver and the heart, where higher relative accumulation levels of those metabolites were detected in older animals, skeletal muscle exhibited opposite comportment whereas the brain displayed an intermediate behavior. Specifically, G6P, ribulose 5P, ribose 5P and glutathione were elevated with age in liver and/or heart (Figure 4D, Figure 4—figure supplement 1). There was also an apparent difference in the response of these organs to oxidative stress, as revealed by lower nicotinamide and cysteine in the liver, and higher methionine sulfoxide (a biomarker of oxidative stress that originates from oxidation of methionine) in the heart from aged mice. In skeletal muscle, nicotinamide, methionine and cysteine were lower in group O mice, whereas in the brain, nicotinamide was relatively low, in contrast to methionine, which was modestly higher, while methionine sulfoxide was diminished (Figure 4D, Figure 4—figure supplement 1). Interestingly, the higher levels of methionine sulfoxide in the serum from old animals along with lower levels of nicotinamide are consistent with increased oxidative stress as a function of age (Figure 4D).

Together, these data suggest that as mice age, organs such as liver and heart, which are exposed to higher oxidative stress as a result of their function (detoxification in the liver and energy generation in the heart), remodel their metabolism toward higher expression/activity of redox-related metabolic pathways, for example, pentose phosphate, NAD+ salvage and transsulfuration. Oppositely, skeletal muscle and brain do not appear to be able to remodel those pathways thus becoming more vulnerable to increased oxidative stress with aging. This metabolic pattern agrees with the idea of different rates of aging among organs.

Age and organ-specific metabolic remodeling

Next, we addressed the metabolic pathways involved in this remodeling, according to metabolite intermediates present in the profile. Serum metabolite profiles from old mice showed accumulation of intermediates from urea cycle function (urea, glutamine, ornithine), a pattern consistent with predominant amino acid degradation in the liver, and significant accumulation of the branched-chain amino acids (BCAAs) valine, isoleucine and leucine (Figure 4D, Figure 4—figure supplement 1). Remarkably, the three top-ranked metabolites, glucose, 3-hydroxybutyrate (3-HB, a ketone body), and glycerol appear to be lower in the serum of old mice, suggesting a reduced supply of these fuel substrates from liver and adipose tissue to heart, brain, and skeletal muscle (Figure 4D, Figure 4—figure supplement 1). Together, the lower levels of glycerol and serum fatty acids (myristic, linoleic, oleic, and palmitoleic, but not arachidonic) suggests potential liver and adipose tissue dysfunction in aged mice, in agreement with the body composition data and the metabolic cage data.

In liver, depletion of glucose, pyruvate, sorbitol, fructose 1P (F1P), xylitol, maltose, and maltotriose with age suggests active glycolysis and glycogen breakdown, less utilization of the polyol pathway, and hepatic release to the circulation (Figure 4D, Figure 4—figure supplement 1). The accumulation of tyrosine, tryptophan, glutamine, and glutamate, concomitant with the depletion of alanine and valine (but not glycine), is mirrored by the serum metabolite profile, consistent with active amino acid degradation and urea cycle function.

The heart shows a remarkable accrual of BCAAs with age. Glucose degradation pathways appear to be all very active including polyols (fructose, F1P) and glycogenolysis (maltose) also consistent with glucose 1P (G1P) depletion (Figure 4D, Figure 4—figure supplement 1). Higher levels of the tricarboxylic acid intermediates fumarate, malate, and succinate suggest active mitochondrial function. Reduced production of 3-HB further contributes to the loss of 3HB circulating levels and reveals less fatty acid oxidation. In the brain, the accumulation of the top-ranked fatty acid ω6 linoleic acid together with stearic and palmitoleic acids is notably associated with the increased abundance of 3-HB, a preferred substrate of this organ (Figure 4D, Figure 4—figure supplement 1). The higher levels of urea and glutamine in older mice, accompanied by depletion of ornithine, suggest active urea cycle function, a trait consistent with depletion of several amino acids (isoleucine, serine, tyrosine, glutamate) (Figure 4D, Figure 4—figure supplement 1).

Expectedly, the skeletal muscle from old mice exhibits high glucose catabolism as suggested by the accumulation of maltose, lactate and maltotriose. Higher levels of urea concomitant with lower levels of several amino acids, such as leucine, tyrosine, valine, serine, and cysteine, indicate of active amino acid degradation accompanied by urea cycle function (Figure 4D, Figure 4—figure supplement 1).

Metabolite set enrichment analysis (MSEA) shows the statistical significance of the metabolic pathways involved for each organ. The results obtained support the idea that metabolic remodeling in old mice occurs via redox-related pathways in liver and heart as compared to skeletal muscle and brain (Figure 5). MSEA also confirmed the relevance of the urea cycle and ammonia cycling likely due to amino acid degradation in skeletal muscle, brain, and liver, a metabolic trend that is also reflected by the serum metabolite profile. With aging, and in an amphibolic organ like the liver, amino acid degradation could be triggered in parallel with protein biosynthesis, as indicated by tissue enrichment (Figure 5). In the aging phenotype of predominantly postmitotic catabolic organs like skeletal muscle, heart, and brain, protein synthesis might be less significant compared to that in liver.

Pathway enrichment in brain, heart, liver, serum, and skeletal muscle Metabolite set enrichment analysis (MSEA) showing the statistical significance of the metabolic pathways involved for each organ.

Note the metabolic remodeling occurring with age, as it pertains to redox-related pathways (green symbols), urea cycle and ammonia cycling through amino acid degradation (dark blue symbols), and one-carbon metabolism (light green symbols).

Together, the data obtained show that each organ exhibits an age-specific metabolic remodeling which is, in part, associated with its function, for example, prominence of redox-related pathways in liver and heart, active glucose catabolism in skeletal muscle, activation of glucose metabolism in the heart and gluconeogenesis and urea cycle function in liver. Statistically significant pathways found by MSEA in old mice are consistent with major hallmarks of metabolic remodeling identified by other types of analysis. Many of the differences observed in this metabolomic profiling can be linked to the phenotypic alterations that were observed in group O mice, either as factors that may be involved in promoting or may arise as a result of these differences.

Patterns of metabolic signatures of biological aging from covariation of metabolites with physiological metrics

In order to cope with the increased energetic cost of maintaining health and energy balance, each organ must fine-tune their metabolism and substrate selection. To assess the strength of the links between metabolites and phenotypic outcomes, we generated correlation matrices between energetic cost, RER, or frailty index (FI) and metabolites representative of major pathways involved in the metabolic remodeling of the four organs (heart, skeletal muscle, liver, and brain) and serum. Firstly, a correlation matrix with the 46 significantly changed metabolites was constructed to investigate their covariation, independently of age and organ (Figure 6A). A big cluster of positively correlated metabolites accompanied by four minor ones were found, from which 24 metabolites from all clusters and representative pathways were selected. The energetic cost of walking and RER are two metrics closely related to gait speed, whereas FI, a major biomarker of aging, is only remotely related to metabolism. Next, the Pearson correlation coefficient for each of the 24 metabolites was calculated with respect to the energetic cost, RER or FI from scatter plot matrices (‘r’ with p<0.05) for each of the four organs and serum (see Figure 6—figure supplement 1 as an example and Figure 6—source data 1). Hierarchically clustered heatmaps allowed for visualization of all correlation coefficients (positive and negative) corresponding to the 24 metabolites as a function of the phenotypic outcome in each organ and serum (Figure 6B–D). Broadly speaking, the increase in energetic cost, RER, or FI was correlated with organ-specific patterns of increased abundance or depletion of select metabolites.

Figure 6 with 1 supplement see all
Heatmaps of hierarchically clustered correlation coefficients of representative metabolites from organs and serum vs. different physiological metrics.

(A) Correlation matrix of the 46 metabolites significantly changed across age, organs and serum. Using the statistical module of MetaboAnalyst, we determined the clusters of normalized metabolites covarying positively or negatively, independently of organ or serum and age. The type (positive or negative) and strength (color intensity) of correlation are coded brown and blue, respectively, normalized between 1 and −1 according to the bar on the right. (B–D) Twenty-four significantly changed metabolites representative from all pathways that covary independently from organs or serum and age (see panel A) were correlated with energetic cost (B), RER (C) or frailty index (FI) (D). Using the software Origin v. 2021, we performed scatter plot correlation matrices (see Figure 6—figure supplement 1) to determine the Pearson correlation coefficient, r (p<0.05), for each of the 24 metabolites in each organ and serum vs. each of the physiological metrics analyzed. Displayed are in the form of hierarchically clustered heatmaps (dendogram on the left) corresponding to the ensemble of correlation coefficients, positive (red) and negative (blue), exhibited by each metabolite vs. the specified physiological metric.

Figure 6—source data 1

The Pearson correlation coefficient, r, value for each metabolite vs. energetic cost, RER, and Frailty index.

https://cdn.elifesciences.org/articles/62952/elife-62952-fig6-data1-v2.xlsx

With respect to the energetic cost of walking (Figure 6B), a group of metabolites show a strong negative correlation in liver, of which only pyruvate, palmitoleic acid, and glucose were shared by the heart. A second cluster of negatively correlated metabolites represented by nicotinamide, alanine, and myristic acid was present in heart, and to a lesser extent in liver and brain. BCAAs (valine, isoleucine, leucine) and several other metabolites, including 3-hydroxybutyrate (3-HB), correlated positively with energetic cost in the heart, while being negatively linked in liver. On the other hand, glutathione correlated positively with energetic cost in both organs (heart > liver). For many metabolites, including glutathione, glycerol, and methionine sulfoxide, the brain presented a distinct pattern with respect to the other organs. The skeletal muscle resembled the heart profile with some exceptions.

Taken as a whole, the increase in energetic cost correlates with a pattern of metabolites related to substrate utilization. This pattern is consistent with a mixture of glucose, lipid, and amino acid metabolism in liver, and more predominant glucose and lipid metabolism in the heart, whereas lipids appeared to prevail over glucose in skeletal muscle. Remarkably, brain displayed a pronounced amino acid catabolism in addition to the expected dependence on glucose and ketone bodies. Regarding redox metabolism, heart exhibited the highest correlation between energetic cost and glutathione, followed by liver then SKM, while ribose from the pentose phosphate pathway was positively correlated in heart only, and the oxidative stress biomarker methionine sulfoxide showed a positive correlation in both cardiac and SKM, suggesting a more oxidative environment in these two organs along with brain.

As a function of RER (Figure 6C), liver and heart shared a cluster of both negatively and positively correlated metabolites. Again, the brain exhibited a clearly distinct pattern with respect to the other organs consisting of mostly positively correlated metabolites, whereas skeletal muscle presented a more mixed pattern with two noticeable clusters of negatively and positively correlated metabolites. An elevated RER is indicative of an increase in the proportion of carbohydrates utilized for energy production. Consistent with this, we observed an increasing abundance of glucose intermediates in both liver and heart concomitant with depletion of lipids and amino acids. In this regard, SKM and brain presented a distinct pattern which was more based on glucose rather than lipid metabolism. The increasingly mixed glucose and lipid utilization led to a trend toward higher abundance of methionine sulfoxide and depletion in glutathione in response to increased RER in heart, liver, and SKM, while brain had markedly less methionine sulfoxide, but greater accumulation of methionine and nicotinamide compared to glutathione. This pattern is consistent with systemic oxidative stress and lower antioxidant capacity.

Increasing energetic cost and higher RER elicited an enrichment of several lipids and amino acids, but depletion of glycerol in serum (Figure 6B and C). These patterns are consistent with the metabolic remodeling exhibited by liver, a major sink of lipids and amino acids, and unveiled a reduced supply of 3-HB and glycerol from liver and adipose tissue to heart, brain, and skeletal muscle, at least in old mice.

The prevalent pattern of metabolites correlating positively with increasing FI (Figure 6D) appears to be inconsistent with the pattern profiles revealed by energetic cost and RER. This is expected given the less direct mechanistic relationship between metabolism and FI since the latter derives from a compilation of many different phenotypic features (Palliyaguru et al., 2019). Energetic cost results from the ratio of respiration (VO2) over gait speed (Figure 2D), while RER includes only respiration-related variables (CO2 release over O2 uptake). Hence, because the latter is more directly related to metabolism, the mechanistic links between RER and metabolism can be more directly envisaged. Nevertheless, the energetic cost of walking/running is a relevant metric because of the presence of gait speed, a major biomarker of functional performance during aging. Remarkably, energetic cost and RER rendered a similar pattern of metabolic profiles (Figure 6B and C).

Discussion

In this cross-sectional study, male mice of various ages (3–36 months) were assessed for functional differences associated with aging, while pursuing the detection of major biomarkers and underlying energetic and metabolic mechanisms. Metabolic remodeling in serum, liver, brain, cardiac, and skeletal muscle, their interactions and consequences for diminishing bodily functions, morphometry and biochemistry, were extensively tested and analyzed. We performed an in-depth characterization of the phenotypic differences that exist between groups Y, A, and O animals. This analysis demonstrated that (i) gait speed decline, which appears to be fully explained by age and frailty, is a major functional biomarker of aging in mice; (ii) the deterioration of locomotor activity is associated with a dramatic increase in the energetic cost of physical activity from age ~19 months, and was paralleled by a sustained decrease of working capacity; (iii) other morphometric (e.g. body weight, % fat and lean) and biochemical (e.g. blood glucose, insulin, HOMA-IR) metrics show a more subtle relationship with age. To further explore the molecular changes that may contribute to or result from these alterations, we performed metabolomic analysis in a number of key metabolic tissues and showed that (iv) different organs remodel their metabolism in response to specific functional demands, for example, energy supply and detoxification; specifically, unlike brain and skeletal muscle, liver and heart in old mice rewire metabolism toward higher expression/activity of redox-related metabolic pathways such as pentose phosphate, NAD+ salvage, and transsulfuration; (v) depletion of glucose, 3-HB, and glycerol in serum from old mice is consistent with reduced supply of these fuel substrates from liver and adipose tissue to other organs; (vi) aging in mice favors up-modulation of glucose metabolism in cardiac and skeletal muscle as well as in liver, where gluconeogenesis and urea cycle are also enhanced, as well as similar but less pronounced pattern in brain. We then evaluated the associations between metabolites and phenotypic covariates (energetic cost of walking/running, RER, and frailty) by selecting the 24 most representative metabolites independently from age and organ. The main results showed that (vii) variance in energetic cost and RER can be explained by a distinct signature pattern of metabolic remodeling in the liver (e.g. mixed glucose, lipid, and amino acid metabolism), cardiac and skeletal muscle (e.g. glucose and lipids), and brain (mixture of amino acids in addition to glucose and ketone body catabolism). The enhancement in energy demand elicited an increasingly mixed substrate utilization (glucose, lipids, amino acids) leading to a rise in oxidative stress in organs that have lower antioxidant capacity relative to the heart and liver. (viii) Frailty, as a complex phenotype less directly related to metabolism, did not show a consistent metabolic pattern compared to the other two physiological metrics.

Overall, the ensemble of data obtained from the cross-sectional correlation linking key metabolites to relevant phenotypes identified in this study is consistent with signatures of biological aging. These signatures correspond to patterns of metabolic remodeling that characterize age-associated changes in functional trajectory of mice coping with the increasing energetic cost to maintain health, energy, and redox balance. While many of these molecular alterations are likely to promote aging and aging-associated functional decline, other alterations are expected to maintain functional capacity with age.

The search for the biological mechanisms underlying healthy aging has shifted the focus of this research field toward the extension of healthspan and lifespan, and to delaying the onset of disease (Longo and Panda, 2016; de Cabo and Mattson, 2019; Mitchell et al., 2019; Di Francesco et al., 2018; Aon et al., 2020). This shift is helping to understand the differences between biological and chronological aging, underscoring that aging is not just the passage of time while emphasizing the notion that individuals age at a different pace (Ferrucci et al., 2020). For instance, the rate of change and onset of increase or decline in functional, behavioral, morphometric, and biochemical organismic indicators exhibit distinct time course as a function of age. This behavior agrees with the notion of biological rather than chronological aging, thereby showing that aging follows the trajectory of the organism and not merely the passage of time.

Body composition plays an important role in determining metabolic health, as can be judged by fat and lean content. Consistent with the body weight trajectory, younger male mice have a higher percent fat content than old animals due to larger visceral fat depots. Interestingly, a positive association was observed between fasting glucose levels and percent body fat, which, in turn, correlates with extended lifespan in mice (Mitchell et al., 2016). Age-associated regional distribution among fat depots has strong metabolic implications. Visceral fat accumulation is associated with insulin resistance (Girard and Lafontan, 2008; Hardy et al., 2012), while subcutaneous fat plays a role in reducing insulin levels and improving insulin sensitivity (Tran et al., 2008; Shuster et al., 2012). Here, the increase in body fat percentage from young to adult mice was coincident with a greater subcutaneous/visceral fat ratio, which, in turn, was associated with a significant drop in circulating insulin levels and HOMA-IR. Noteworthily, the oldest mice had the highest ratio of subcutaneous-to-visceral fat and a likelihood of being insulin sensitive (lowest HOMA-IR). We surmise that in order to preserve body fat for insulation, the oldest mice fed ad libitum must eat a significantly greater amount of food despite their low body weight, resulting in negative energy balance. Since the food intake in proportion to body weight is increased and the energy expenditure is decreased in these animals, they are able to rely less heavily on fat oxidation and more on carbohydrate utilization for energy needs, yielding RER values close to 1.0 in the dark phase.

In vivo imaging using MRI and microCT enabled a precise monitoring of body composition and bone density. These assessments illustrated the marked and significant decline in bone thickness and mineral density, and the reduction in fat and relative increase in lean body composition as a function of age (Figure 2A–C). These measurements are also of great interest in human aging; for example, the relationship between brain structure and composition with gait in humans has been described (Rosso et al., 2017; for the Health ABC Study et al., 2016). In vivo imaging techniques were recently reviewed in the context of their importance to studying aging (Dall'Ara et al., 2016).

Previously, it was demonstrated that the frailty index, as measured with the Mouse Clinical FI, increases with age and is significantly reduced in response to CR in male C57BL/6J mice (Whitehead et al., 2014; Kane et al., 2016). Frail animals lack resilience, causing them to die earlier due to the rapid deterioration of several tissue/organ systems. In this study, frailty was associated with an age-dependent decline in physical strength, tail height, gait speed, and circulating glucose levels. The rising energetic cost of walking is associated with steeper decline in gait speed in the elderly (Schrack et al., 2016). The results reported herein support these findings. Specifically, the VO2 / gait speed ratio (in mL O2/Kg/m) is energetically and physiologically consistent with the energetic cost of, for example, walking or running (Figure 2D), to the extent that the cost of running depends upon the animal’s size rather than the speed at which an animal runs (Taylor et al., 1970; reviewed in Schmidt‐Nielsen, 1984). Although the impact of age on skeletal muscle mass or fiber type was not assessed or considered in the present study, tendon length and other aspects of muscle/tendon elasticity have been reported to be a major contributor of the biomechanics of work economy (Higham et al., 2011; Wilson and Lichtwark, 2011), and plans are underway to examine whether such changes in muscle/tendon properties are evident in old animals.

Biological aging represents a complex manifold and, in terms of the organism, widespread phenomenon. This is revealed by the intersection between its macroscopic manifestations such as physical performance and functional decline, with biological mechanisms deeply rooted in cellular energetics, repair, turnover, and detoxification. Our work highlights some of these potential functional/molecular interactions and demonstrates that these mechanisms operate differently at the level of an organ and its function, leading to a distinct and selective age-mediated impact, in turn, consistent with the differential metabolic remodeling observed.

Limitations of the study

This was a cross-sectional study in male C57Bl/6N mice rather than a longitudinal study of male and female mice of different genetic background which would have accounted for genetic variability and sex. Resilience as well as immune and blood pressure measurements (Bellantuono et al., 2020) were not carried out in this study. Future work will require, at least in part, untargeted proteomics and assessment of epigenetic marks in various organs. The age- and frailty-dependent reduction in gait speed and associated energetic cost indicates the need to assess mitochondrial function using high-resolution respirometry in permeabilized tissues and isolated mitochondria in parallel with skeletal muscle ultrastructure by electron microscopy. Other approaches include incorporating MRI of the brain to identify changes in brain structure and volume over time that correlate with collected variables such as motor function, as well as assessments of cognition.

Further integration of omics in an all-inclusive phenotypic investigation would be beneficial in order to unveil new mechanistic insights potentially translatable to humans. Examples of multi-tissue omics analysis include a recent transcriptome analysis in rats that revealed a top list of genes potentially important in aging (Shavlakadze et al., 2019).

Materials and methods

Key resources table
Reagent type
(species) or resource
DesignationSource or referenceIdentifiersAdditional information
Strain, strain background (M. musculus, males)C57BL/6NCrlCharles River LaboratoriesStrain Code 701a.k.a. NIA Aging Mouse Colony
Biological sample (M. musculus)Brain from
C57BL/6NCrl
B6MALMetabolomics analysis, see lines 249–352
Biological sample (M. musculus)Heart from
C57BL/6NCrl
B6MALMetabolomics analysis, see lines 249–352
Biological sample (M. musculus)Liver from
C57BL/6NCrl
B6MALMetabolomics analysis, see lines 249–352
Biological sample (M. musculus)Serum from
C57BL/6NCrl
B6MALMetabolomics analysis, see lines 249–352
Biological sample (M. musculus)Skeletal muscle from
C57BL/6NCrl
B6MALMetabolomics analysis, see lines 249–352
Commercial assay or kitMouse insulin ELISA kitCrystal Chem, IncCat# 90080
Software, algorithmMetaboAnalyst (versions 3.0, 4.0)Web-based resource
(McGill University, Canada)
https://www.metaboanalyst.ca/MetaboAnalyst/faces/home.xhtml
Software, algorithmOriginLab 2018OriginLab corporationhttps://www.originlab.com
Software, algorithmPrism 7.0GraphPadRRID:SCR_015807
Software, algorithmCanvas Draw six for macOSCanvas GFXRRID:SCR_014288
Software, algorithmMicrosoft Excel (version 16.19)Microsoft Corp.https://www.microsoft.com/en-gb/; RRID:SCR_016137
OtherTSE MotoRater systemTSE Systems
Other7T preclinical MRI scannerBruker Biospec 70/30
OtherOxymax Open Circuit Indirect CalorimetersColumbus Instrumentshttp://www.colinst.com/docs/OxymaxBrochure.pdf
OtherModular Metabolic TreadmillColumbus Instruments
OtherMinispec Whole Body Composition Analyzer LF90Bruker Opticshttps://www.bruker.com/products/mr/td-nmr/minispec-lf-series.html
OtherSkyscan 1076 micro-CT scannerBruker

Husbandry, diets, and dietary intervention in mice

Request a detailed protocol

Male C57BL/6N mice were obtained from the NIA Aging Mouse Colony (Charles River Laboratories, Kingston, NY). Animal rooms were maintained at 22.2 ± 1°C and 30–70% humidity. The lights were turned off at 6:00 PM and back on at 6:00 AM each day. Mice were group-housed up to four per cage with ad libitum access to food (Teklad Global 18% Protein Extruded Rodent Diet, #2018S, Envigo, Frederick, MD) and drinking water. In the case where an animal’s cage mate died during the course of the study, animals were not rehoused and, instead, were provided a scientific exception to justify the single housing of a social species. All animals were provided shepherd shacks and nestlets for enrichment. All animal protocols were approved by the Animal Care and Use Committee of the National Institute on Aging, NIH. All tests in this cross-sectional cohort of animals were performed by non-blinded investigators (MP, SJM, MK, KWF). There were three age groups of mice, in months: Young, 3–8 (n = 23); Adult, 13–23 (n = 27); and Old, 27–36 (n = 23). Two mice, one adult (A) and one old (O), died during experimentation, bringing the final number of groups A and O mice to 26 and 22, respectively.

Body composition

Request a detailed protocol

Unanesthetized mice were placed in the body comp clear plastic tube of a Bruker's Minispec Whole Body Composition Analyzer LF90 (Bruker Optics, Billerica, MA, USA). This low-field nuclear magnetic resonance device acquires and analyzes Time Domain-NMR signals from all protons in the entire sample volume and measures body fat, free body fluid, and lean tissue content. Young (n = 8), adult (n = 22), and old (n = 18) mice were studied.

Wire hang test

Request a detailed protocol

Each mouse was tested for forelimb strength using the wire hanging test. Prior to testing, mice were trained and acclimatized by placing them on the wire of the hanging bar apparatus for several min. The test can be used to measure decline in strength and neuromuscular function with age and consisted of measuring the time before the mouse falls from the hanging wire. The test was repeated two more times with approximately 30 min break between trials for each mouse.

Mouse gait speed and tail height analysis

Request a detailed protocol

Mice were acclimated to the TSE MotoRater system (TSE Systems, Inc Chesterfield, MO) for a week before gait and tail height recording as described (Bair et al., 2019). In brief, during gait assessment, the home cage was placed at the opposite end of the corridor to motivate the mouse to walk. A valid trial consisted of having the mouse walking the corridor length without stopping or turning in the opposite direction in the 70 cm mid-runway filming area. A custom in-house Python script was used to calculate mid- and maximum tail height, which was tracked by a semi-automated computer vision software package (TSE Systems). The Python script will be provided upon request for MotoRater system user. Gait speed was analyzed by PROC GLM model with SAS 9.4.

Frailty index

Request a detailed protocol

Mice are assessed for frailty using the Howlett and Rockwood clinical frailty index, which includes 31 different parameters (Feridooni et al., 2015; Bellantuono et al., 2020). In brief, the frailty index was calculated by summing the number of deficits in factors related to integument, physical/musculoskeletal, vestibulocochlear/auditory, ocular/nasal, digestive/urogenital, respiratory, and aspects of physical discomfort for each mouse and dividing by the total number of possible deficits. Along with these indices, mouse weight and body temperature were also scored. A higher frailty index, with a potential score ranging from 0 to 1, is associated with lower survivability in mice (Kane et al., 2016).

Magnetic resonance imaging

Request a detailed protocol

A 7T preclinical magnetic resonance imaging scanner (Bruker Biospec 70/30, Ettlingen, Germany) was used to measure volumes of visceral and subcutaneous fat from respiratory-gated axial fast spin echo images spanning the entire cranial-caudal extent of both kidneys. The ratio of total subcutaneous to visceral fat volume was calculated and compared as a function of age.

Bone imaging

Request a detailed protocol

Mice were anesthetized using avertin (tribromoethanol; Sigma-Aldrich, St-Louis, MO) and tibia were imaged by three-dimensional micro–computed tomography with a Bruker Skyscan 1076 micro-CT instrument for measurement and analysis of cortical and trabecular bones as previously described (Mercken et al., 2014). In brief, tibias were scanned at high resolution (1208 × 1080 pixels) with an isotropic voxel size of 18 μm and at 48 kV, 200 mA (0.5 mm Al filter). Microstructural properties of tibial cortical and trabecular bone were assessed at the distal metaphysis for trabecular parameters and the mid-diaphysis for cortical parameters, following the nomenclature guidelines outlined by Dempster et al., 2013. Trabecular bone was delineated via manual tracing and interpolation of trabeculae in a region of interest 0.25 mm to 1.75 mm proximal to the distal tibial growth plate. For cortical bone parameters, analysis was performed at a volume of interest beginning at the mid-diaphysis of the tibia and extending 0.6 mm distally. Percent fat and percent lean tissue around the tibia was also quantified by micro-CT and normalized per unit of body weight.

Metabolic treadmill

Request a detailed protocol

Mice were acclimated on the metabolic treadmills (Columbus Instruments International, Columbus, OH) at 5 m/min for 30 min the day prior to testing to ensure familiarity. On the day of testing, mice ran at their age group’s natural walking gait speed, assessed by the TSE MotoRater in earlier experiments, for 45 min. External motivators such as electric shock from an electrified metal grid located near the moving belt and/or pocking are used to entice mice to run. Mice unwilling or incapable to continue running despite receiving electric shocks more than five consecutive times within few seconds meet the criterion for exhaustion and the testing ends.

In vivo metabolic assessment

Request a detailed protocol

The metabolic rate of each mouse was assessed by indirect calorimetry in open-circuit Oxymax chambers using the Comprehensive Lab Animal Monitoring System (CLAMS; Columbus Instruments, Columbus, OH, USA). Mice were single housed with ad libitum access to water and food, and maintained at 20–22°C under a 12:12 hr light:dark cycle (light period 0600–1800). Food and water level in each chamber was checked once on a daily basis at approximately 8:30 AM and the amount of food left in the cage from the previous 24 hr of chamber time was collected and measured. All mice were acclimatized to monitoring cages for 12 hr prior to recording. Sample air was dried and passed through an oxygen sensor for determination of oxygen content. Oxygen consumption was determined by measuring oxygen concentration in air entering the chamber compared with air leaving the chamber. The sensor was calibrated against a standard gas mix containing defined quantities of oxygen, carbon dioxide and nitrogen. Constant airflow (0.5 L/min) was drawn through the chamber and monitored by a mass-sensitive flow meter. The concentrations of oxygen and carbon dioxide were monitored at the inlet and outlet of the sealed chambers to calculate oxygen consumption. Measurement in each chamber was recorded for 30 s at 30 min intervals for a total of 72 hr. Ambulatory activity (both horizontal and vertical) was also monitored by dual axis detection using infrared photocell technology. Metabolic parameters were analyzed using CalR software as previously described (Mina et al., 2018). Young (n = 8), adult (n = 22), and old (n = 18) mice were studied.

Blood and serum markers and HOMA-IR calculation

Request a detailed protocol

Glucose concentrations in blood were measured from the submandibular vein in 6 hr fasted mice with the Blood Glucose Monitoring System Breeze 2 (Bayer, Mishawaka, IN). Coagulated blood was centrifuged at 12,000 x g at 4°C for 10 min. Serum was aliquoted and kept frozen at −80°C. Insulin levels were determined according to the manufacturer’s protocol (Crystal Chem, Inc, Downers Grove, IL). Homeostasis model assessment-insulin resistance (HOMA-IR) was calculated to assess group differences in insulin resistance using the HOMA2 Calculator software available from the Oxford Centre for Diabetes, Endocrinology and Metabolism Diabetes Trials Unit website (http://www.dtu.ox.ac.uk/homacalculator/index.php).

Metabolomics analysis

Request a detailed protocol

Metabolomic analysis was performed by the West Coast Metabolomics Center at UC Davis (Davis, CA) in brain, heart, liver, skeletal muscle, and serum from nonfasted animals as previously described (Mitchell et al., 2018; Mitchell et al., 2016). In brief, tissue and serum were extracted in an acetonitrile:isopropanol:water (3:3:2) solution, vortexed, centrifuged, and the supernatants aliquoted for downstream analysis. After a series of evaporation and reconstitution steps in 50% acetonitrile, internal standards (C08-C30, fatty acid methyl esters) were added to the dried sample, which was then derivatized for trimethylsilylation of acidic protons. Data were acquired using the method as described by Fiehn, 2008 and summarized by Mitchell et al., 2016. In brief, metabolites were measured using a rtx5Sil-MS column (made of 95% dimethyl, 5% diphenyl-polysiloxane coated on fused silica; Restek Corporation; Bellefonte PA) protected by an empty guard column. This chromatography method yields excellent retention and separation of primary metabolite classes (amino acids, hydroxyl acids, carbohydrates, sugar acids, sterols, aromatics, nucleosides, amines, and miscellaneous compounds) with arrow peak widths of 2–3 s and very good within-series retention time reproducibility of better than 0.2 s absolute deviation of retention times. The mobile phase consisted of helium, with a flow rate of 1 mL/min, and injection volume of 0.5 μL. The following mass spectrometry parameters were used: a Leco Pegasus IV mass spectrometer with unit mass resolution at 17 spectra s-1 from 80 to 500 Da at −70 eV for elution of metabolites. As a quality control, for each sequence of sample extractions, one blank negative control was performed by applying the total procedure (e.g. all materials and plastic ware) without biological sample. Result files were transformed by calculating the sum intensities of all structurally identified compounds for each sample, and subsequently dividing all data associated with a sample by the corresponding metabolite sum. The resulting data were multiplied by a constant factor in order to obtain values without decimal places. Intensities of identified metabolites with more than one peak (e.g. for the syn- and anti-forms of methoximated reducing sugars) were summed to only one value in the transformed data set. The original non-transformed data set was retained. Relative metabolite levels represent the MS peak amplitude normalized with respect to the total metabolites returned, but disregarding unknowns that might potentially comprise artifact peaks or chemical contaminants.

Quantification and statistical analysis

Request a detailed protocol

No statistical method was used to predetermine sample size. GraphPad Prism version 7.0 was used to determine whether data sets passed the D’Agostino and Pearson normality test (α = 0.05). Those that failed included body composition data (NMR-generated morphometric analysis), physical performance/motor coordination results and MRI quantitative data, whereas metabolic outputs, locomotor activity between light/dark cycles, and bone imaging followed a normal data distribution. One-way ANOVA or Kruskal-Wallis H test was performed to determine if there were statistically significant differences between the three age groups for any given outcome. All regression models were either quadratic or linear regressions based on data’s fit. The R2, F-statistic and p-value are calculated through these models. Outliers were not omitted. As the mice aged, the different assessments took place. At times, the experiments were 3 or 6 months apart from previous assessments, which is why 36-month-old mice were enrolled in some measured outcomes, and not in others.

The polynomial variables for all curvilinear lines fitted to parameters are as follow:

  • Figure 1K (Glucose vs Age): −0.11821x2 + 3.22945x + 149.09292

  • Figure 2A (Body Weight vs Age): y = -0.046141x2 + 1.632253x + 25.087950

  • Figure 2A (%Fat vs Age): y = -0.040847x2 + 1.266635x + 11.621481

  • Figure 2A (%Lean vs Age): y = 0.021048x2 + −0.593099x + 62.762510

  • Figure 2C (%Lean Tibia vs Age): y = 0.003239x2 + −0.127683x + 3.903823

  • Figure 2D (Energetic cost vs Age): 0.8025x2 + −18.182x + 572.9071

  • Figure 2D (Heat vs Age): −0.001791x2 + 0.011949x + 5.174154

  • Figure 2K (RER vs Age, dark phase): y = 3.390e-04x2+−1.088e-02x+9.216e-01

Multiple linear regression analysis was used to examine the determinants of frailty index and gait speed, including, as covariates:

  1. Frailty index vs. age + gait velocity + BW + mean tail height + latency to fall from wire hang + glucose + HOMA-IR;

  2. #1 + %Lean mass + subcutaneous fat/visceral fat ratio;

  3. #1 + trabecular BMD + cortical thickness + %fat around tibia/BW + %lean around tibia/BW;

  4. #1 + Metabolic treadmill outputs (energetic cost + mean heat + RER).

The models were fitted with all explanatory variables as well as models that included interactions of Age with each of the other explanatory variables. All the interactions were then tested to assess whether they added anything to the original model (with only main effects). In almost all cases, the interactions did not improve the models. A backward elimination was then performed to obtain the final regression model. There was likely not enough power to detect significant interactions; however, these findings do not mean that significant interactions do not exist, but simply that we were not able to detect any significant interactions due largely to the limited size of this dataset. All analyses were performed with R Version 4.0.2 (R Development Core Team, 2020) with RStudio Version 1.3.1073.

Although dozens of different health parameters were evaluated on the same animals, these were largely the results of independent experiments to test distinct hypotheses. Therefore, no correction for multiple comparisons to the p-values was made as each experiment was considered to be a separate question. p Value ≤ 0.05 was considered statistically significant.

The software MetaboAnalyst, an integrated web-based platform for comprehensive analysis of metabolomics data, versions 3.0 (Xia and Wishart, 2016) and 4.0 (Chong et al., 2019), was utilized. Univariate (ANOVA), clustering (heat map, correlation matrix) and multivariate (partial least square discriminant; PLSD) statistical analyses were applied to the metabolite profiles (See Figures 36, Figure 3—figure supplement 1, Figure 4—figure supplement 1, Figure 6—figure supplement 1). PLSD analysis revealed that all tissues exhibited good separation as a function of age, and a subset of 49 metabolites responsible for the separation was identified. The autoscaling function of MetaboAnalyst used to normalize the metabolomics data closely resembles the Z ratio expression and requires the detection and removal of outliers.

One-way ANOVA (Prism 7.0, GraphPad Software, San Diego, CA, USA) was performed with Tukey’s multi-comparison test for the three age groups (young, adult, old) within each tissue. The statistical significance of the effect of age was evaluated for those metabolites that exhibited a significant difference in at least one of the tissues analyzed. Venn diagrams were plotted with CanvasDraw v. 5.0 for Mac (Canvas GFX, Inc, Plantation, FL).

Data availability

This study did not generate datasets or code.

References

    1. Barzilai N
    2. Ferrucci L
    (2012) Insulin resistance and aging: a cause or a protective response?
    The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 67:1329–1331.
    https://doi.org/10.1093/gerona/gls145
    1. Kalyani RR
    2. Varadhan R
    3. Weiss CO
    4. Fried LP
    5. Cappola AR
    (2012) Frailty status and altered Glucose-Insulin dynamics
    The Journals of Gerontology Series A: Biological Sciences and Medical Sciences 67:1300–1306.
    https://doi.org/10.1093/gerona/glr141
  1. Software
    1. R Development Core Team
    (2020) R: A Language and Environment for Statistical Computing
    R Foundation for Statistical Computing, Vienna, Austria.

Decision letter

  1. Jamie Justice
    Reviewing Editor; Wake Forest School of Medicine, United States
  2. Jessica K Tyler
    Senior Editor; Weill Cornell Medicine, United States

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This is a thorough characterization of phenotypes and metabolomic profiles across male C57Bl/6N mice ranging from 3-36 months coupled with a meta-analysis transcriptomic and metabolomics data from mice fed ad libitum or maintained chronically on calorie restriction (CR). The new analyses linking organ-specific patterns of increased abundance or depletion of metabolites with primary functional measures (energetic cost of walking, RER, and FI) are phenomenal. The interpretations of differences in small metabolite signatures in energetic cost and RER vs FI are insightful. Overall, the authors have turned a compelling submission to an exceptional manuscript that is likely to advance the field.

Decision letter after peer review:

Thank you for submitting your article "A cross-sectional study of functional and metabolic changes during aging through the lifespan in male mice" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Jessica Tyler as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

The authors performed a cross-sectional analysis of (1) physiologic and functional tests, and (2) untargeted metabolomics in multiple organs and tissues in male C57Bl/6N mice ranging from 3-36 months of age; and (3) transcriptomic and untargeted metabolomics data from liver of 24-mo-old mice fed ad libitum or maintained chronically on calorie restriction (CR). The impact of the present study is derived from the expansive breadth and depth of assessments performed.

Essential revisions:

A major limitation is use of a single sex and inbred strain, and lack of longitudinal assessment would strengthen study. While new experiments to address these deficiencies could enhance the study, all reviewers agree that these are addressed in discussion, are not essential for publication at this stage, and would require a timeline likely beyond the scope here.

Another over-arching concerned raised by reviewers is that the study is appears to be two or three separate studies combined together in one manuscript (healthspan assessment; metabolomics; caloric restriction). On one hand, this strengthens the study by providing an substantial amount of data. On the other hand, this also highlights a weakness: the study lacks cohesion, is highly descriptive, and does not make a convincing argument for causation (or even association with health). The major revision that is needed is a more cohesive narrative that unifies these areas.

1. A major element missing is omission of analyses and discussion linking healthspan assessment (physical function, frailty index, energetics, work economy) to the omics. If metabolomics and function performed in the same mice, it would be valuable to identify small metabolite signatures associated with gait speed or frailty index, etc.

2. Rationale for including CR data must be improved as cohorts, design, and measures are not consistent (different functional assessments, RNAseq included, etc).

3. Previous cross-sectional health studies in mice should be referenced for context (e.g. DOI: 10.1007/s11357-013-9589-9, 10.18632/aging.101059).

4. Figure 1. frailty and age are collinear; if age is used as a covariate is the effect of frailty removed?

5. Figure 2.

a. Legend and figure not aligned.

b. Panels 2E,F,G require clarification as interpretation is not intuitive. It looks as if higher gait velocity is associated with higher frailty index in old animals. Please consider alternative presentation format and check alignment with legend and text.

c. If curvilinear line fitted to parameters, please reference polynomial variables in methods.

d. Panel D – please report if differences in heat production and work economy are maintained after adjusting for lean mass. Also note if values generated differ over the entire activity episode or if energetic cost was initially similar across ages and then waned in old animals over time (if the latter, could there be a role for fatigue etc)

e. Consider segregating the Figure 2J and K data by light and dark cycle in addition to 24hr avg to better visualize the diurnal changes in these variables.

6. Interpretation of data in oldest group.

a. Additional comment on the extent that changes in the oldest group are an age effect per se versus negative energy balance effect are warranted (i.e. animals are in a gradual, chronic decline characterized by loss of weight and adiposity) – examples, reduction in HOMA-IR and circulating fatty acids. This deserve some discussion and delineation as such findings appear at odds with what is often thought to define metabolic changes with aging in mammalians, including humans, but appears periodically in studies using older rodents.

b. Authors propose "survivorship bias" in glucose homeostasis, but this is not consistent with other physiologic systems.

7. Describe feeding, collection of 24hr food intake. Comment of role of feeding in relation to energetics and raised RER in dark phase for oldest mice.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "A cross-sectional study of functional and metabolic changes during aging through the lifespan in male mice" for further consideration by eLife. Your revised article has been evaluated by Jessica Tyler (Senior Editor) and a Reviewing Editor.

This is a thorough characterization of phenotypes and metabolomic profiles across male C57Bl/6N mice ranging from 3-36 months coupled with a meta-analysis transcriptomic and metabolomics data from mice fed ad libitum or maintained chronically on calorie restriction (CR).

In general, this is a meritorious and comprehensive examination of phenotypic, functional, and metabolomic profiles across the lifespan in male C57BL/6J mice of anticipated interest to those in biology of aging, geroscience, and preclinical interventions testing. Though primary study design is cross-sectional and in a single mouse strain and sex, the impact of the present study is derived from (1) the expansive breadth and depth of assessments performed, (2) the pairing of functional and metabolomic profiles in a single study, and (3) the exploration of CR compared with ad libitum fed animals.

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

1. Reviewers requested authors evaluate the associations between key metabolites and frailty or functional measures. Please address.

2. The new Discussion section on metabolome and phenotypes is lengthy and somewhat reaching; please shorten.

3. Though rationale for including the CR data in introduction is improved, there is remaining reviewer concern that transcriptomic data in the CR study vs main study is imbalanced. Do the metabolomes of the CR animals look more like those of the adult animals in the main study?

Reviewer #1:

The authors were responsive to reviewer suggestion; most revisions and select omissions are justified. The revised Figure 2, additional analyses (e.g. age added to frailty model; diurnal patterns in RER ), text to add rational for CR meta-analysis, and clarifying text have improved readability and interpretation of findings. Some concerns either were not or could not be fully addressed. Overall the revisions have improved the quality and merit of the manuscript. This is a nice paper that is anticipated to have high impact.

1. Reviewers requested authors evaluate the associations between 'omics' and phenotypes (e.g. frailty); this was not addressed directly. Do key metabolites (e.g. pentose phosphate, NAD+ salvage and transsulfuration; depletion of glucose, 3-HB, and glycerol in serum) explain the variability in frailty with and without adjustment for chronologic age?

2. In lieu of modeling, the authors provided a lengthy and somewhat reaching review of multi-omics and phenotypes of aging in the discussion. A detailed description may be of interest in a dedicated review, but the Discussion section would benefit from a concise presentation.

Reviewer #2:

I found overall that the manuscript was much improved, and the majority of the comments were well addressed.

However, I still feel the rationale for including the CR data is not strong, and I would recommend it be removed. As there was no transcriptomic data for the main study, the CR transcriptome feels especially out of place. It would have also been helpful for the authors to show how the metabolomes changes in their cross sectional and CR studies. Do the metabolomes of the CR animals look more like those of the adult animals in the main study?

In addition, while the authors expanded on their metabolomics interpretations some, they did not correlated their metabolite values to the functional values measured.

https://doi.org/10.7554/eLife.62952.sa1

Author response

Essential revisions:

A major limitation is use of a single sex and inbred strain, and lack of longitudinal assessment would strengthen study. While new experiments to address these deficiencies could enhance the study, all reviewers agree that these are addressed in discussion, are not essential for publication at this stage, and would require a timeline likely beyond the scope here.

We are thankful to the reviewers for their appreciation of the study despite its inherent limitations as highlighted in the Discussion. We and others have demonstrated that genetic heterogeneity and sex can have dramatic impact on functional and phenotypic aging and the effectiveness of anti-aging interventions in mice (Mitchell et al., 2016; Gonzalez-Freire et al., 2020). The knowledge gained from the current cross-sectional study led us to the design and early implementation of the Study of Longitudinal Aging in Mice (SLAM) in which inbred C57BL/6J and outbred UM-HET3 mice of both sexes are longitudinally evaluated for functional, phenotypic and biological health, and collection of biospecimens is conducted throughout their lifespan (Palliyaguru et al., 2020). The SLAM study is at an early stage but eventually will provide the tools needed to identify and characterize phenotypic and biological predictors of mouse aging and age-associated conditions, assess whether these changes are consistent with alterations observed in human aging, and evaluate the utility of mouse models for anti-aging interventions in the clinic.

Another over-arching concerned raised by reviewers is that the study is appears to be two or three separate studies combined together in one manuscript (healthspan assessment; metabolomics; caloric restriction). On one hand, this strengthens the study by providing an substantial amount of data. On the other hand, this also highlights a weakness: the study lacks cohesion, is highly descriptive, and does not make a convincing argument for causation (or even association with health). The major revision that is needed is a more cohesive narrative that unifies these areas.

We thank the editors for this important guidance. We have made a significant effort to offer a more cohesive narrative of the various aspects of the study and provide arguments as to why multi-organ untargeted metabolomics and transcriptomics analyses along with a nutritional intervention were carried out in order to fill a gap of knowledge on the effect of age on key phenotypic aging domains associated with health and mortality. See our answers to Q1 and Q2.

1. A major element missing is omission of analyses and discussion linking healthspan assessment (physical function, frailty index, energetics, work economy) to the omics. If metabolomics and function performed in the same mice, it would be valuable to identify small metabolite signatures associated with gait speed or frailty index, etc.

The last few paragraphs of the Discussion section are now focused on presenting a narrative of what the ‘omics’ results mean with regard to the various healthspan outcomes while also describing the contribution our study makes to the field of aging.

2. Rationale for including CR data must be improved as cohorts, design, and measures are not consistent (different functional assessments, RNAseq included, etc).

While improved knowledge of the specific phenotypic and molecular changes that occur with aging is of considerable use to the research community, this type of characterization is further enhanced by the identification of specific alterations that are linked to lifespan extension through anti-aging interventions. One of the best non-pharmacological interventions that improves health and reduces mortality is calorie restriction (CR). CR contributes to negative energy balance by promoting loss of weight gain and reduction in adiposity, HOMA-IR, and circulating fatty acids compared to mice fed ad libitum. It is with that in mind that we performed a meta-analysis of an earlier study in which transcriptomic and untargeted metabolomics data were collected from liver of 24-mo-old C57BL/6 mice fed ad libitum or maintained chronically on CR (Mitchell et al., 2016). Although different functional assessments were carried out between the two studies, one common thread was our attempt to identify pathways enriched by both genes and metabolites that can be viewed as potential biomarkers of healthy aging in middle-aged mice. We found shared molecular and biochemical signatures between these two independent studies, which speaks to the robustness of these observations and highlights the importance of having the findings from the Mitchell et al. study to complement the current work. In the revised version of the manuscript, we have provided a more direct rationale for the inclusion of CR data and worked to better integrate the findings from this section into the discussion to more clearly demonstrate how these findings contribute to the overall conclusions of this work.

The rationale reads as follows: “In order to elucidate which of the specific phenotypic and molecular/metabolomic changes that occur with aging are altered in response to pro-longevity interventions, we conducted a multi-omics meta-analysis of the liver of 24-mo-old C57BL/6 male mice fed ad libitum (AL) or maintained chronically on 20% calorie restriction (CR) from one of our previous studies (Mitchell et al., 2016). These CR-fed mice had a ∼24% lifespan extension, which led us to hypothesize that pathways enriched by both genes and metabolites might be responsible for overcoming the impact of normal aging after lifelong CR and might be viewed as potential biomarkers of healthy aging in old mice. Indeed, many of the differences that are observed in animals on CR are the inverse of those observed in older cohorts of animals, while other age-related differences that are likely induced to preserve organ function with age are further enhanced in response to CR.”.

Moreover, we added the following statement: “Therefore, a key and consistent observation emerging from our two independent studies is the fact that the activation of redox-related pathways with age is observed in organs subjected to oxidative stress, such as liver and heart, which appears to be reinforced in CR-fed mice and may be a key driver of lifespan extension.”.

3. Previous cross-sectional health studies in mice should be referenced for context (e.g. DOI: 10.1007/s11357-013-9589-9, 10.18632/aging.101059).

The papers of Justice et al., 2014 and Fisher et al., 2016 have now been referenced.

4. Figure 1. frailty and age are collinear; if age is used as a covariate is the effect of frailty removed?

After adjusting for age, frailty is no longer associated with gait velocity (Figure 1G, p = 0.477) but it is still strongly associated with forelimb muscle strength (Figure 1H, p = 0.00088). After including age, frailty is only marginally associated with mean tail height (Figure 1I, p = 0.0792) while being still associated with maximal tail height (Figure 1J, p = 0.0495). The effects of frailty on muscle strength were maintained after correction for age, indicating the presence of independent risk factors beyond chronological age that could contribute to the reduction in physical performance in the oldest mice. A statement has been incorporated in the Results.

5. Figure 2.

a. Legend and figure not aligned.

We are sorry for the confusion. The legends for Figure 2 E-G were mistakenly omitted from the original submission, which has been since corrected. See comments in R5b.

b. Panels 2E,F,G require clarification as interpretation is not intuitive. It looks as if higher gait velocity is associated with higher frailty index in old animals. Please consider alternative presentation format and check alignment with legend and text.

In the previous multiple regression analysis, we utilized a set of values of the covariates to obtain the plots, and variables that were not included in the plot were set to their means. However, this led to unrealistic interpretations such as having a frail old mouse with higher gait velocity than a young, nonfrail animal. The problem is that in certain regions the covariates can only take on a particular set of values (not the whole range of covariates). Therefore, to create more appropriate visualizations of the models, we set up a grid of intervals for the two variables being plotted.

Using the first model as an example:

FrailtyIndex ~ Age_months + GaitVelocity_ms + Glucose + HOMAIR + Age_months:GaitVelocity_ms

For the first plot shown in Figure 2E, Age had intervals: [0,5] [5,10] [10,15] [15,20] [20,25] [25,30] [30,35] and Gait Velocity had intervals: [0.05,0.15] [0.15,0.25] [0.25,0.35]. The intervals for the legend variable (Gait Velocity) were usually chosen so that there were about the same number of (or, at least, “enough”) observations in each group.

Author response table 1
Age group
Gait group[0,5][5,10][10,15][15,20][20,25][25,30][30,35]
[0.05, 0.15]0011175
[0.15, 0.25]34413646
[0.25, 0.35]13301000

As can be seen in Author response table 1, certain combinations of these two group variables contain no observations. For example, we were unable to record old mice having high gait velocity (red font) nor were there any young mice walking at low gait velocity (blue font). Consequently, predictions should not be made in these regions. Other covariates in the model, Glucose + HOMA-IR, were set to their mean value in each grid cell to obtain model predictions. The x-values in the plot are the mean (Age) for each interval and the legend value is listed as the center of the intervals for that variable (e.g., 0.1, 0.2, and 0.3 for gait group). Although this is a cross-sectional study, the points were joined to help the reader see the association of Frailty with Age within the same gait velocity We are not suggesting that there is a causal association, just that animals with higher Age tend to have higher Frailty.

Although it does seem that there is a positive association between frailty index and gait speed in old mice (Figure 2E), the fact remains that many factors contribute to the frailty index score, as summarized in our recent review (Palliyaguru et al., 2019). Whether specific parameters related to ocular/nasal, digestive/urogenital and/or integumentary domains impact gait speed need to be firmly established.

The revised graphs depicted in Figure 2F,G are also only plotted over a reasonable region of the variables, with values of the other covariates in the model as mentioned above.

The legend for Figure 2 E-G has been incorporated into the document and it reads as follows:

“(E-G) Multiple regression analyses with frailty index (E, F) and gait velocity (G) as response variables. The modeling approach consisted of fitting a series of four regressions for each of the response variables. The explanatory variables included in the multiple regression modeling are depicted in Figure 2-Source data 1. (E) left panel, Effect of gait velocity on the relationship between frailty and age; right panel, Impact of age on the interaction between frailty and gait velocity. (F) left panel, Effect of bone cortical thickness on the relationship between frailty and age; right panel, Impact of age on the interaction between frailty and bone cortical thickness. (G) left panel, Effect of energetic costs on the relationship between gait velocity and age; right panel, Impact of age on the interaction between gait velocity and energetic costs. Although this is a cross-sectional study, the points were joined to help the reader see the various associations without suggesting that there is causal association; for example, that animals with higher age tend to have higher frailty within the same gait velocity (E, left panel).”

c. If curvilinear line fitted to parameters, please reference polynomial variables in methods.

As requested, we have provided the polynomial variables for all curvilinear lines fitted to parameters in the Methods section. These variables are as follow:

Figure 1K (Glucose vs Age): -0.11821x2 + 3.22945x + 149.09292

Figure 2A (Body Weight vs Age): y = -0.046141x2 + 1.632253x + 25.087950

Figure 2A (%Fat vs Age): y = -0.040847x2 + 1.266635x + 11.621481

Figure 2A (%Lean vs Age): y = 0.021048x2 + -0.593099x + 62.762510

Figure 2C (%Lean Tibia vs Age): y = 0.003239x2 + -0.127683x + 3.903823

Figure 2D (Energetic cost vs Age): 0.8025x2 + -18.182x + 572.9071

Figure 2D (Heat vs Age): -0.001791x2 + 0.011949x + 5.174154

Figure 2K (RER vs Age, dark phase): y = 3.390e-04x2+-1.088e-02x+9.216e-01

d. Panel D – please report if differences in heat production and work economy are maintained after adjusting for lean mass. Also note if values generated differ over the entire activity episode or if energetic cost was initially similar across ages and then waned in old animals over time (if the latter, could there be a role for fatigue etc)

The differences in work economy and heat production between the three age groups are maintained after adjusting for lean mass, although a much sharper drop in heat production between age groups can be observed after lean mass adjustment (compare bottom vs. upper right panels in Author response image 1). A statement has been incorporated in the Results section.

Author response image 1

With regard to the second comment, the metabolic treadmill speed was set according to the MotoRater average gait speed for each age group (units in m/min. 8-mo, 14.4; 21-mo, 10.8; 31-37-mo, 7.1). In Author response image 2, we show that the running VO2 values did not differ over the entire activity episode (800 sec) across age, hence ruling out a role for fatigue in the higher energetic cost in the oldest mice. A statement has been incorporated in the Results section.

Author response image 2

e. Consider segregating the Figure 2J and K data by light and dark cycle in addition to 24hr avg to better visualize the diurnal changes in these variables.

This is an excellent suggestion, indeed! The revised Figure 2J displays the diurnal changes in RER in the three age groups. The results indicate statistically significant differences in the dark cycle (D) between young (red) vs. old (black) and adult (blue) vs. old mice, with higher RER in the oldest group of mice (revised Figure 2K). These two new Figures are replacing the old ones as they provide stronger evidence of changes in RER between age groups. Note that no difference in RER could be observed in the light phase between the different groups of mice (r2 = 0.0763). To this end, a statement has been incorporated in the Results section.

6. Interpretation of data in oldest group.

a. Additional comment on the extent that changes in the oldest group are an age effect per se versus negative energy balance effect are warranted (i.e. animals are in a gradual, chronic decline characterized by loss of weight and adiposity) – examples, reduction in HOMA-IR and circulating fatty acids. This deserve some discussion and delineation as such findings appear at odds with what is often thought to define metabolic changes with aging in mammalians, including humans, but appears periodically in studies using older rodents.

The reviewer has two main assumptions: (1) Aging is a ‘gradual’ decline that can be dissociated from negative energy balance; and (2) the energetic balance is negative with age as revealed by weight loss and adiposity.

Regarding point #1, our data do not support this assumption because of the fact that the increase in energetic cost is highly nonlinear (exponential) from ∼15 months of age, thus strongly associated with aging.

For point #2, one of the reasons for the negative energy balance in the oldest mice is the higher energetic cost of movement in general, as reflected by gait speed and other indicators. If the animals spend more energy for the same amount of movement/activity, this can explain, at least in part, the reduction in adiposity and weight. It follows that at some point during aging, the energy demand of movement/activity in general should offset the intake of food further contributing to the negative energy balance. Although the life extending properties of long-term CR is also associated with an increase in negative energy balance e.g., lower body weight gain combined with a reduction in adiposity, it also triggers a panoply of healthy physiological responses leading to enhanced health and survival. In this context, we surmise that the survival of the oldest mice (31-36 months of age) was accompanied by the slowing down of phenotypic aging domains (Gonzalez-Freire et al., 2020).

Within aging research, mice have been used to identify and characterize some of the key contributors of health and survival (e.g., fasting blood glucose, adiposity, body weight), and have served as a preclinical model to test anti-aging interventions (Gonzalez-Freire et al., 2020). In a soon-to-be published study, we compared age-associated metabolic indices across three increasingly genetically complex mouse strains (C57BL/6, HET3, and Diversity Outbred) with nonhuman primates and humans. The association of age with blood glucose and the relationship between blood glucose and mortality differs considerably between mice (in either strain) and what is observed among nonhuman primates and humans (Palliyaguru, Shiroma et al., submitted). These findings clearly illustrate the potential pitfall of generalizing age-associated changes in specific metabolic trajectories and biomarkers in mice vs. humans. More work is required to better evaluate the effectiveness of anti-aging interventions in the clinic.

b. Authors propose "survivorship bias" in glucose homeostasis, but this is not consistent with other physiologic systems.

The initial sentence has been amended to acknowledge the reviewer’s reservation. It now reads: “These results may reflect a survivorship bias of the long-living mice (Scheen, 2017), although other effects such as reduced body weight and age-associated regional distribution among fat depots could have profound metabolic implications vis-à-vis changes observed in fasting blood glucose and insulin resistance (Girard and Lafontan, 2008; Hardy et al. 2012; Tran et al., 2008; Shuster et al., 2012).”

7. Describe feeding, collection of 24hr food intake. Comment of role of feeding in relation to energetics and raised RER in dark phase for oldest mice.

We surmise that the reviewer refers to data collection while the mice were temporarily single housed in the metabolic testing chamber (Figure 2I-K). Food and water level in each chamber was checked once on a daily basis at approximately 8:30 AM and the amount of food left in the cage from the previous 24 h of chamber time was collected and measured. The Methods section has been amended to provide more details on this.

With regard to the role of feeding in relation to energetics and higher RER in the dark phase for the oldest mice fed ad libitum, we assume that in order to preserve body fat for insulation, old mice must eat a significant amount of food despite their low body weight, resulting in negative energy balance. Since the food intake in proportion to body weight is increased and the energy expenditure is decreased in these animals, they are able to rely less heavily on fat oxidation and more on carbohydrate utilization for energy needs, yielding RER values close to 1.0 in the dark phase. A statement has been added to the Discussion that highlights this important concept.

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

1. Reviewers requested authors evaluate the associations between key metabolites and frailty or functional measures. Please address.

Following the Reviewers’ suggestion, we devoted renewed efforts in assessing the strength of the links between metabolites and functional measures (e.g., energetic cost of walking, RER and frailty index) by investigating the organs and serum fine-tuning their metabolism and substrate selection to cope with the increased energetic demand of maintaining health and energy balance. A whole new section devoted to this topic is now included in Results and in the first paragraph of the Discussion. A new set of figures (Figure 6 and associated Figure 6-supplemental figure 1) has been generated as well to support these new findings.

2. The new Discussion section on metabolome and phenotypes is lengthy and somewhat reaching; please shorten.

In the latest iteration of the paper, we have removed sections in the discussion that did not contribute to the main narrative of the study and reworded the ‘metabolome-phenotype’ section by discussing the new findings regarding the associations between key metabolites and functional measures. The reference list has been updated accordingly.

3. Though rationale for including the CR data in introduction is improved, there is remaining reviewer concern that transcriptomic data in the CR study vs main study is imbalanced. Do the metabolomes of the CR animals look more like those of the adult animals in the main study?

We have decided to remove the CR data from the current manuscript and focus instead on the main thrust of the study that is aimed to carry out a cross-sectional multi-organs metabolomic analysis in male mice fed ad libitum and its pairing with functional outcomes. In doing so, no further analysis was carried out with the CR dataset.

Reviewer #1:

The authors were responsive to reviewer suggestion; most revisions and select omissions are justified. The revised Figure 2, additional analyses (e.g. age added to frailty model; diurnal patterns in RER ), text to add rational for CR meta-analysis, and clarifying text have improved readability and interpretation of findings. Some concerns either were not or could not be fully addressed. Overall the revisions have improved the quality and merit of the manuscript. This is a nice paper that is anticipated to have high impact.

1. Reviewers requested authors evaluate the associations between 'omics' and phenotypes (e.g. frailty); this was not addressed directly. Do key metabolites (e.g. pentose phosphate, NAD+ salvage and transsulfuration; depletion of glucose, 3-HB, and glycerol in serum) explain the variability in frailty with and without adjustment for chronologic age?

In this revised version of the manuscript, we devoted renewed efforts in assessing the strength of the links between metabolites and functional measures (e.g., energetic cost of walking, RER and frailty index) by investigating the organs and serum fine-tuning their metabolism and substrate selection to cope with the increased demand of maintaining health and energy balance. A whole new section devoted to this topic is now included in Results and in the first paragraph of the Discussion. A new set of figures (Figure 6 and associated Figure 6-supplemental figure 1) has been generated as well to support these new findings.

2. In lieu of modeling, the authors provided a lengthy and somewhat reaching review of multi-omics and phenotypes of aging in the discussion. A detailed description may be of interest in a dedicated review, but the Discussion section would benefit from a concise presentation.

In the latest iteration of the paper, we have removed sections in the discussion that did not contribute to the main narrative of the study and reworded the ‘metabolome-phenotype’ section by discussing the new findings regarding the associations between key metabolites and functional measures. The reference list has been updated accordingly.

Reviewer #2:

I found overall that the manuscript was much improved, and the majority of the comments were well addressed.

However, I still feel the rationale for including the CR data is not strong, and I would recommend it be removed. As there was no transcriptomic data for the main study, the CR transcriptome feels especially out of place.

As per the reviewer’s recommendation, we have removed the CR data generated from one of our previous study (Mitchell et al., 2016) to better focus on the narrative of the cross-sectional study.

It would have also been helpful for the authors to show how the metabolomes changes in their cross sectional and CR studies. Do the metabolomes of the CR animals look more like those of the adult animals in the main study?

No further analysis was carried out with this dataset.

In addition, while the authors expanded on their metabolomics interpretations some, they did not correlated their metabolite values to the functional values measured.

In this revised version of the manuscript, we devoted renewed efforts in assessing the strength of the links between metabolites and functional measures (e.g., energetic cost of walking, RER and frailty index) by investigating the organs and serum fine-tuning their metabolism and substrate selection to cope with the increased demand of maintaining health and energy balance. A whole new section devoted to this topic is now included in Results and in the first paragraph of the Discussion. A new set of figures (Figure 6 and associated Figure 6-supplemental figure 1) has been generated as well to support these new findings.

https://doi.org/10.7554/eLife.62952.sa2

Article and author information

Author details

  1. Michael A Petr

    1. Center for Healthy Aging, ICMM, University of Copenhagen, Copenhagen, Denmark
    2. Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  2. Irene Alfaras

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Present address
    Aging Institute, University of Pittsburgh School of Medicine, Pittsburgh, United States
    Contribution
    Formal analysis, Investigation, Project administration, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Melissa Krawcyzk

    Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  4. Woei-Nan Bair

    1. Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    2. Department of Physical Therapy, University of Sciences, Philadelphia, United States
    Contribution
    Data curation, Formal analysis, Investigation
    Competing interests
    No competing interests declared
  5. Sarah J Mitchell

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Present address
    Department of Health Sciences and Technology, ETH Zürich, Zürich, Switzerland
    Contribution
    Data curation, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Christopher H Morrell

    Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Data curation, Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Stephanie A Studenski

    1. Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    2. Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, United States
    Contribution
    Conceptualization, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Nathan L Price

    1. Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    2. Integrative Cell Signaling and Neurobiology of Metabolism Program, Yale University School of Medicine, New Haven, United States
    3. Vascular Biology and Therapeutics Program, Yale University School of Medicine, New Haven, United States
    4. Department of Comparative Medicine, Yale University School of Medicine, New Haven, United States
    5. Department of Pathology, Yale University School of Medicine, New Haven, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  9. Kenneth W Fishbein

    Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Data curation, Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Richard G Spencer

    Laboratory of Clinical Investigation, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared
  11. Morten Scheibye-Knudsen

    Center for Healthy Aging, ICMM, University of Copenhagen, Copenhagen, Denmark
    Contribution
    Conceptualization, Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared
  12. Edward G Lakatta

    Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Conceptualization, Writing - review and editing
    Competing interests
    No competing interests declared
  13. Luigi Ferrucci

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Conceptualization, Resources, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6273-1613
  14. Miguel A Aon

    1. Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    2. Laboratory of Cardiovascular Science, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  15. Michel Bernier

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5948-368X
  16. Rafael de Cabo

    Translational Gerontology Branch, National Institute on Aging, NIH, Baltimore, United States
    Contribution
    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing - review and editing
    For correspondence
    decabora@grc.nia.nih.gov
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2830-5693

Funding

National Institute on Aging (AG000335-04)

  • Rafael de Cabo

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

This study was supported by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, Baltimore, MD, USA. We are grateful to the Comparative Medicine Section of the NIA, NIH for their exceptional animal care. We thank the NIA Aging Rodent Colony for providing aging mice for this study as part of their aging mouse resource.

Ethics

Animal experimentation: This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. All of the animals were handled according to approved institutional animal care and use committee (ACUC) protocol of the National Institute on Aging (Protocol Number: TGB-277-2022).

Senior Editor

  1. Jessica K Tyler, Weill Cornell Medicine, United States

Reviewing Editor

  1. Jamie Justice, Wake Forest School of Medicine, United States

Publication history

  1. Received: September 10, 2020
  2. Accepted: April 13, 2021
  3. Accepted Manuscript published: April 20, 2021 (version 1)
  4. Version of Record published: May 5, 2021 (version 2)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

Metrics

  • 2,048
    Page views
  • 307
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Computational and Systems Biology
    Pierpaolo Sorrentino et al.
    Short Report Updated

    Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p<0.0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.

    1. Computational and Systems Biology
    2. Neuroscience
    Matthew Rosenberg et al.
    Research Article Updated

    Animals learn certain complex tasks remarkably fast, sometimes after a single experience. What behavioral algorithms support this efficiency? Many contemporary studies based on two-alternative-forced-choice (2AFC) tasks observe only slow or incomplete learning. As an alternative, we study the unconstrained behavior of mice in a complex labyrinth and measure the dynamics of learning and the behaviors that enable it. A mouse in the labyrinth makes ~2000 navigation decisions per hour. The animal explores the maze, quickly discovers the location of a reward, and executes correct 10-bit choices after only 10 reward experiences — a learning rate 1000-fold higher than in 2AFC experiments. Many mice improve discontinuously from one minute to the next, suggesting moments of sudden insight about the structure of the labyrinth. The underlying search algorithm does not require a global memory of places visited and is largely explained by purely local turning rules.