Organ function declines with age, and large-scale transcriptomic analyses have highlighted differential aging trajectories across tissues. The mechanism underlying shared and organ-selective functional changes across the lifespan, however, still remains poorly understood. Given the central role of mitochondria in powering cellular processes needed to maintain tissue health, we therefore undertook a systematic assessment of respiratory activity across 33 different tissues in young (2.5 months) and old (20 months) mice of both sexes. Our high-resolution mitochondrial respiration atlas reveals: 1) within any group of mice, mitochondrial activity varies widely across tissues, with the highest values consistently seen in heart, brown fat, and kidney; 2) biological sex is a significant but minor contributor to mitochondrial respiration, and its contributions are tissue-specific, with major differences seen in the pancreas, stomach, and white adipose tissue; 3) age is a dominant factor affecting mitochondrial activity, especially across most brain regions, different fat depots, skeletal muscle groups, eyes, and different regions of the gastrointestinal tract; 4) age-effects can be sex- and tissue-specific, with some of the largest effects seen in pancreas, heart, adipose tissue, and skeletal muscle; and 5) while aging alters the functional trajectories of mitochondria in a majority of tissues, some are remarkably resilient to age-induced changes. Altogether, our data provide the most comprehensive compendium of mitochondrial respiration and illuminate functional signatures of aging across diverse tissues and organ systems.
All the main figures (Fig. 2-8) have been updated. The discussion section has been significantly expanded.
Introduction
Aging is a complex biological phenomenon that results in functional decline across tissues and organs systems (1). Age is one of the most significant contributors to disease risk (2), and the aging process is influenced by a variety of genetic and environmental factors at the tissue and organismal level (3–5). The application of omics technologies in recent years has led to unprecedented insights into the complex biology of aging (6). Bulk and single-cell transcriptomic analyses of mouse tissues across the lifespan have shown that aging tempo and trajectory, as indicated by tissue transcriptomic signatures, varies widely across tissues (7–9). Remarkably, many of the transcriptomic signatures of aging across tissues can be significantly reversed by caloric restriction or rejuvenated by the transfusion of young blood (10, 11). Large-scale proteomic analyses of human plasma also reveal distinct waves of changes across the lifespan, which are associated with distinct biological pathways and affect age-related phenotypic traits and diseases (12, 13).
Despite enormous progress, there is no consensus regarding the mechanisms underlying aging at the cellular or tissue level. Although many non-mutually exclusive hypotheses have been put forth to explain the root cause of aging—oxidative damage, genomic instability, epigenetic changes, loss of proteostasis, mitochondrial dysfunction, DNA damage, telomere shortening, cellular senescence, stem cell exhaustion—it remains a major challenge to distinguish between the driver and passenger mechanisms of aging (14). Nevertheless, efforts to understand the proximal and ultimate cause of aging will facilitate development of therapeutics aimed to improve healthy aging (15, 16).
In the present study, we focused on aging from a mitochondrial perspective, as this organelle is known to play an important role in the aging process (17–20). Mitochondria supply the bulk of the energy needed to maintain tissue health and repair tissue damage, and their function tends to decline with age. Over time, damage accumulates in mitochondrial DNA, proteins, and lipids, which compromises their functional integrity and leads to dysregulated metabolism and increased oxidative stress. Recent transcriptomic analyses have highlighted major reductions in electron transport chain genes across the lifespan (7), and these changes can be significantly reversed by the transfusion of young blood into older mice (10).
Aging-associated reduction in mitochondrial OXPHOS genes appear to be conserved between human, mouse, fly, and worm (21). Accordingly, mitochondrial dysfunction has been implicated in various age- related diseases, including neurodegenerative disorders, cardiovascular diseases, and metabolic syndromes (22). Boosting mitochondrial health has been shown to delay age-related decline in organ function (23–26).
Given the central role of mitochondria in tissue health, we aimed to address the extent and magnitude of aging-induced changes in mitochondrial function across tissues and organ systems. Although many studies have examined mitochondrial respiratory capacity in various tissues, the scale was limited in that only a very small number of tissues could be interrogated at the same time. This is largely due to the inherent low-throughput method of assessing respiration which requires freshly isolated mitochondria or cells from tissues (27). Consequently, it was not previously feasible to have a comprehensive and systems-level analysis of mitochondrial function across many tissues and the lifespan.
This barrier, however, has been recently overcome. An innovative method by Acin-Perez and coworkers has made it possible to now assess mitochondrial function in previously frozen tissues (28). This new method circumvents the need to isolate mitochondria at the time of tissue harvest, allowing many tissues to be collected, frozen, and assayed at a later time. We adopted the new method in a standardized workflow to profile mitochondrial activity in 33 tissues from young and old mice of both sexes. The dataset consists of a total of 1320 tissue samples from 40 mice and 3960 high-resolution respirometry assays encompassing three technical replicates. Our study represents the largest and the most comprehensive tissue respirometry analysis to date. Our data provide an unprecedented view on the variations and changes in mitochondrial functional capacity across tissues, sex, and age, thus informing ongoing studies on the causes and consequences of aging.
ResultsPan-tissue mitochondrial respiration atlas overview, work-flow, and analysis pipeline
To assay mitochondrial respiration and its maintenance across age and sex, we collected 33 tissues from young (2.5 months; ∼18-year-equivalent in human) or old (20 months; ∼65-year-equivalent in human) male or female mice (n = 10 mice per age and sex). The tissues collected include different brain regions (hippocampus, cortex, cerebellum, and hypothalamus), different sections of the GI tract (stomach, duodenum, ileum, jejunum, cecum, proximal colon, and distal colon), various fat depots (gonadal, inguinal, mesenteric), different skeletal muscle groups (tongue, diaphragm, quadriceps complex, hamstrings, gastrocnemius, plantaris, and soleus), reproductive organs (testis and fallopian tubes), as well as liver, pancreas, heart atria and ventricles, spleen, kidney cortex and medulla, eyes, and skin (Figure 1A). Our aim was to provide a comprehensive systems-level view of mitochondrial respiration across tissues, sex, and age.
Pan-tissue mitochondrial respiration atlas overview, work-flow, and analysis pipeline. A.
Study overview highlighting the 33 different tissues collected from four different groups of mice (n = 10 / group) for respirometry analysis and mitochondrial content quantification. The four groups of mice are young male, young female, old male, and old female. Young = 10-week-old; old = 80-week-old. B. General schematic showing the preparation of samples for respirometry analysis. C. General representation of the electron transport chain to illustrate the key components of the respirometry assay used to assess mitochondrial function, the associated data, and examples of subsequent data analysis. AA, Antimycin A; Rot, Rotenone; TMPD, N, N, N’, N’-tetramethyl-p-phenylenediamine; Asc, Ascorbate; NADH, nicotinamide adenine dinucleotide.
Our standardized workflow involved thawing, mincing, and homogenization of frozen tissue samples in buffer. Samples were centrifuged to pellet cell debris and the supernatant was collected for immediate protein and mitochondrial content quantification (via MitoTracker Deep Red, MTDR). Sample respiration rates were then assayed using a Seahorse XFe96 Analyzer (Figure 1B). The basic respiration assay consisted of four sequential steps: first, baseline unstimulated measurements were obtained. Then, NADH was used to assess respiration via mitochondrial complex I (CI), or succinate was used as to assess respiration via mitochondrial complex II (CII) in the presence of rotenone (Rot, a CI inhibitor). Following this, samples were exposed to rotenone and antimycin A (AA, a complex III inhibitor) to silence respiration. Then, TMPD in the presence of ascorbate was used to assess respiration through mitochondrial complex IV (CIV), via donation of electrons to cytochrome c (Figure 1C). Detailed information of the methodology can be found in the methods section, which closely follows the method first described by Acin-Perez et al (28). High resolution respirometry data for each of the 33 tissues were used in all subsequent comparisons (Figure 1 -figure supplement 1-33; Figure 1 - Source data 1; Figure 1 – source data 2).
Mitochondrial function across different organ systems in male and female mice
The first analysis made was within a group (male, female, young, or old) across all tissues. This allowed us to focus on shared and unique mitochondrial properties across different tissues within a single mouse system. Ranking young male or young female tissues by their respiration via CI (NADH-stimulated), CII (succinate-stimulated), or CIV (TMPD and ascorbate-stimulated) showed that both sexes have the greatest oxygen consumption in the heart atria and ventricles, brown adipose tissue (BAT), kidney cortex and medulla, and the lowest respiration in the colon (distal or proximal), plantaris muscle, jejunum, ileum, and mesenteric white adipose tissue (mesWAT) (Figure 2A-B).
Tissue-by-tissue analysis of mitochondrial function in male and female mice. A.
Young male mitochondrial respiration through complex I (CI, NADH-stimulated), complex II (CII, Succinate stimulated in the presence of Rotenone, a CI inhibitor), and at complex IV (CIV, via TMPD + Ascorbate). B. Young female mitochondrial respiration through CI, CII, and at CIV. C. Old male mitochondrial respiration through CI, CII, and at CIV. D. Old female respiration through CI, CII, and at CIV, respectively. n = 10 mice per tissue. All oxygen consumption rates (OCR) are normalized to mitochondrial content (based on MTDR). All data is represented as the mean with standard error, and organized highest (left) to lowest (right). Young = 10 weeks; Old = 80 weeks. BAT, brown adipose tissue; gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; Quad, quadriceps muscles; Vent, ventricles.
Ranking old male tissues by their respiration via CI, CII, or CIV showed that the heart atria and ventricles, BAT, diaphragm muscle, kidney cortex and medulla have the highest oxygen consumption, while the ileum, pancreas, skin, duodenum, stomach, distal colon, mesWAT, and inguinal white adipose tissue (iWAT) have the lowest (Figure 2C). Ranking all old female tissues by their respiration via CI, CII, or CIV showed that the heart atria and ventricles, BAT, kidney cortex, and diaphragm have the highest respiration, while the pancreas, duodenum, mesWAT, ileum, distal colon, eyes, and skin have the lowest (Figure 2D).
Our analyses revealed a wide range in mitochondrial respiratory capacity across nearly all tissues.
We observed tissue-types of high similarity, such as the different regions of the brain (Figure 2-figure supplement 1) and kidney cortex and medulla (Figure 2-figure supplement 2), as well as those displaying degrees of functional heterogeneity, such as the heart atria and ventricles (Figure 2-figure supplement 3), different skeletal muscle groups (Figure 2-figure supplement 4), various white adipose tissue depots (Figure 2-figure supplement 5), and sections of the gastrointestinal tract (Figure 2-figure supplement 6). Together, these data help illustrate both the heterogeneity and lack thereof in mitochondrial function across different tissues and organ systems. Remarkably, even when age and sex are controlled for, mitochondria within a single tissue-classification or organ system can have distinct respiration signatures.
Sex differences in mitochondrial function in young mice
The first pan-tissue comparison of mitochondrial function across groups was made between young male and female mice. In this comparison, we focused on the effects of sex on mitochondrial respiration in young mice. A systems-level view of mitochondrial respiration via CI, CII, or CIV across 32 tissues (omitting reproductive tissues) showed that young male and female mice have similar distributions in oxygen consumption rate (OCR; Figure 3A-C, left panel). Individual tissue-level analysis of respiration through CI showed only two significant differences between young male and female mice, in the quadriceps muscle complex and gonadal white adipose tissue (gWAT) (Figure 3A, right panel).
Tissue-by-tissue analysis of young male and female mitochondrial function. A.
(left) Systems-level view of mitochondrial respiration (NADH-stimulated) through complex I (CI). (right) Mitochondrial respiration through CI across all young tissues. B. (left) Systems-level view of mitochondrial respiration through complex II (CII, Succinate-stimulated in the presence of rotenone to inhibit CI). (right) Mitochondrial respiration through CII across all tissues. C. (left) Systems-level view of mitochondrial respiration at complex IV (CIV) in the presence of rotenone and antimycin A to inhibit CI and CIII, respectively. (right) Mitochondrial respiration at CIV across all tissues. All data is presented as the mean with standard error, and organized highest to lowest for young male values. D. Heat map view of mitochondrial function across all tissues assayed (omitting reproductive organs). Data is represented as young male/female. Tissues with elevated respiration in males appear red while the same for females appear blue. Data is organized highest to lowest by summation of young male CI, CII, and CIV respiration values. n = 10 young male (YM, 10 weeks) and 10 young female (YF, 10 weeks) per tissue assayed. All oxygen consumption rates (OCR) are normalized to mitochondrial content (based on MTDR). Statistical significance is represented as: a = p < 0.05, b = p < 0.01, c = p < 0.001, and d = p < 0.0001. BAT, brown adipose tissue; gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; Quad, quadriceps muscles; Vent, ventricles.
Individual tissue comparisons of respiration through CII showed that young males have higher mitochondrial respiration in the kidney medulla, stomach, quadriceps muscle complex, hippocampus, and gastrocnemius muscle, and reduced mitochondrial activity in gWAT and ileum relative to young females (Figure 3B, right panel). Respiration at CIV showed that young males have higher mitochondrial activity in the BAT, heart ventricles, kidney medulla, hippocampus, stomach, quadriceps muscle complex, and gastrocnemius muscle and reduced mitochondrial activity in the gWAT and duodenum when compared to young females (Figure 3C, right panel). A relative and global view of the sex- specific differences across young male and female mice showed that males have higher mitochondrial respiration in the stomach, kidney, and skeletal muscle, whereas females have higher mitochondrial respiration in the gastrointestinal tract and different fat depots (gWAT and mesWAT) (Figure 3D).
Effects of age on male mitochondrial function
The second tissue-by-tissue comparison of mitochondrial function was made across age, where we focused on the effects of age on mitochondrial respiration in male mice. A systems-level view of respiration via CI, CII, or CIV across all 33 tissues showed that old and young male mice have similar distributions of OCR (Figure 4A-C, left panel). However, an individual tissue view showed many differences in response to age. Old males showed a striking increase in mitochondrial activity via CI in the heart atria, skeletal muscles (diaphragm, soleus, tongue, plantaris, gastrocnemius), testis, mesWAT, and ileum, and reduced mitochondrial respiration in the brain cortex, cerebellum, hippocampus, liver, eyes, skin, iWAT, BAT, and stomach relative to young males (Figure 4A, right panel). Viewing respiration through CII also showed that old males have markedly higher mitochondrial activity in the heart ventricles, skeletal muscles (diaphragm and plantaris), jejunum, ileum, and mesWAT when compared to young males (Figure 4B, right panel). In contrast, old males had reduced mitochondrial activity via CII in the brain (cortex and hippocampus), BAT, stomach, quadriceps muscle complex, liver, and eyes when compared to young males. Respiration at CIV showed that old males have markedly elevated mitochondrial activity in the skeletal muscle (diaphragm, soleus, plantaris), ileum, and cecum, and reduced mitochondrial activity in the brain (cortex, cerebellum, hippocampus), BAT, liver, spleen, eyes, quadriceps muscle complex, pancreas, duodenum, stomach, and iWAT, relative to young males (Figure 4C, right panel). A relative and global view of the age-specific differences across male mice showed that old males have significantly elevated mitochondrial activity in the skeletal muscles, mesWAT, GI tract, and heart, with a concomitant reduction in mitochondrial activity in the stomach, iWAT, gWAT, BAT, eyes, and nearly all brain regions (Figure 4D). Together, these data indicate age has a strong effect in modulating mitochondrial respiration in male mice.
Tissue-by-tissue analysis of young and old male mitochondrial function. A.
(left) Systems- level view of mitochondrial respiration (NADH-stimulated) through complex I (CI). (right) Mitochondrial respiration through CI across all male tissues. B. (left) Systems-level view of mitochondrial respiration (Succinate-stimulated in the presence of rotenone to inhibit CI) through complex II (CII). (right) Mitochondrial respiration through CII across all male tissues. C. (left) Systems-level view of mitochondrial respiration at complex IV (CIV) in the presence of rotenone and antimycin A to inhibit CI and CIII, respectively. (right) Mitochondrial respiration at CIV across all male tissues. All data is presented as the mean with standard error and organized highest to lowest for old male values. D. Heat map view of mitochondrial function across all male tissues assayed. Data is presented as old/young male. Tissues with elevated respiration in old males appear red while the same for young males appear blue. Data is organized highest to lowest by summation of old male CI, CII, and CIV respiration values. n = 10 old male (OM, 80 weeks) and 10 young male (YM, 10 weeks) per tissue assayed. All oxygen consumption rates (OCR) are normalized to mitochondrial content (based on MTDR). Statistical significance is represented as, a = p < 0.05, b = p < 0.01, c = p < 0.001, and d = p < 0.0001. BAT, brown adipose tissue; gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; Quad, quadriceps muscles; Vent, ventricles.
Effects of age on female mitochondrial function
The third tissue-by-tissue comparison of mitochondrial function was made between old and young female mice. Systems-level analysis of respiration via CI, CII, or CIV across all 33 tissues showed a similar OCR distribution between old and young female mice (Figure 5A-C, left panel). Individual tissue comparisons, however, showed many significant differences in response to age. Respiration through CI showed that old females have elevated mitochondrial activity in the heart ventricles, skeletal muscles (diaphragm, soleus, tongue, and plantaris), and mesWAT, and a concomitant reduction in mitochondrial activity in the BAT, kidney cortex, brain (cortex and cerebellum), gWAT, iWAT, eyes, and distal colon when compared to young females (Figure 5A, right panel). Respiration through CII also showed that old females have increased mitochondrial respiration in skeletal muscles (diaphragm and plantaris), and mesWAT relative to young females (Figure 5B, right panel). In contrast, old females had reduced mitochondrial respiration via CII in the liver, brain (cortex and cerebellum), gWAT, fallopian tubes, eyes, skin, BAT, heart atria, and pancreas when compared to young females. Respiration at CIV likewise showed that old females have elevated mitochondrial activity in the skeletal muscle (diaphragm, plantaris, and soleus) and mesWAT relative to young females; concomitantly, old females had reduced mitochondrial activity in the heart atria, brain (cortex and cerebellum), liver, stomach, lung, eyes, jejunum, duodenum, pancreas, and skin when compared to young females (Figure 5C, right panel). A relative and global view of the age-specific differences across female mice showed that old females have elevated mitochondrial respiration in the skeletal muscles, mesenteric fat, and heart ventricles, and a concomitant reduction in mitochondrial activity in the eyes, skin, duodenum, gWAT, iWAT, BAT, and nearly all brain regions when compared to young females. Similar to males, these data also indicate age has a strong effect in modulating mitochondrial activity in female mice.
Tissue-by-tissue analysis of young and old female mitochondrial function. A.
(left) Systems-level view of mitochondrial respiration (NADH-stimulated) through complex I (CI). (right) Mitochondrial respiration through CI across all female tissues. B. (left) Systems-level view of mitochondrial respiration (Succinate-stimulated in the presence of rotenone to inhibit CI) through complex II (CII). (right) Mitochondrial respiration through CII across all female tissues. C. (left) Systems-level view of mitochondrial respiration at complex IV (CIV) in the presence of rotenone and antimycin A to inhibit CI and CIII, respectively. (right) Mitochondrial respiration at CIV across all female tissues. All data is organized highest to lowest for old female values. D. Heat map view of mitochondrial function across all female tissues assayed. Data is presented as old/young female, so tissues with elevated mitochondrial respiration in old females appear red while the same for young females appear blue. Data is organized highest to lowest by summation of old female CI, CII, and CIV respiration values. n = 10 old female (OF, 80 weeks) and 10 young female (YF, 10 weeks) per tissue assayed. All oxygen consumption ratea (OCR) are normalized to mitochondrial content (based on MTDR). Statistical significance is represented as, a = p < 0.05, b = p < 0.01, c = p < 0.001, and d = p < 0.0001. BAT, brown adipose tissue; gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; Quad, quadriceps muscles; Vent, ventricles.
Age differentially affects mitochondrial function in male and female mice
The fourth tissue-by-tissue comparison of mitochondrial function was made between old male and female mice, where we focused on the effects of sex on mitochondrial respiration in old mice. A systems-level view of respiration through CI, CII, or CIV across all 33 tissues showed no differences in OCR distribution across sex in old mice (Figure 6A-C, left panel). At the individual tissue level, however, old male mice had greater mitochondrial activity via CI in the heart atria, kidney (medulla and cortex), lung and ileum, and lower respiration in the BAT and stomach as compared to old females. Respiration through CII showed that old males have higher mitochondrial activity in the kidney (cortex and medulla), lung, and pancreas, and reduced mitochondrial activity in the stomach, relative to old females (Figure 6B, right panel). Respiration at CIV showed that old males have higher mitochondrial respiration in the heart atria, kidney medulla, soleus muscle, lungs, and eyes, and reduced mitochondrial activity in the cerebellum, iWAT, and mesWAT, when compared to old females (Figure 6C, right panel). A global and relative view of the sex-specific differences across age showed that old males have elevated mitochondrial activity in the distal colon, lung, eyes, pancreas, soleus, kidney, and heart when compared to old females, whereas old females have higher mitochondrial activity in all adipose tissue depots, stomach, and nearly all brain regions when compared to old males (Figure 6D).
Tissue-by-tissue analysis of old male and female mitochondrial function. A.
(left) Systems- level view of mitochondrial respiration (NADH-stimulated) through complex I (CI). (right) Mitochondrial respiration through CI across all old tissues. B. (left) Systems-level view of mitochondrial respiration (Succinate-stimulated in the presence of rotenone to inhibit CI) through complex II (CII). (right) Mitochondrial respiration through CII across all tissues. C. (left) Systems level view of mitochondrial respiration at complex IV (CIV) in the presence of rotenone and antimycin A to inhibit CI and CIII, respectively. (right) Mitochondrial respiration at CIV across all tissues. All data is presented as the mean with standard error and organized highest to lowest for male values. D. Heat map view of mitochondrial function across all tissues assayed. Data is represented as old male/female, so tissues with elevated mitochondrial respiration in males appear red while the same for females appear blue. Data is organized highest to lowest by summation of male CI, CII, and CIV respiration values. n = 10 old male (OM, 80 weeks) and 10 old female (OF, 80 weeks) per tissue assayed. All oxygen consumption rates (OCR) are normalized to mitochondrial content (based on MTDR). Statistical significance is represented as, a = p < 0.05, b = p < 0.01, c = p < 0.001, and d = p < 0.0001. BAT, brown adipose tissue; gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; Quad, quadriceps muscles; Vent, ventricles.
Age has a much larger effect than sex on mitochondrial function
To assess the relative contributions of sex and age to mitochondrial respiration, we first counted the total number of significant differences from all across-group comparisons made in Figures 3-6, which was 128 in total. Clustering significant differences by the type of respiration affected showed CIV to have the most with 49 differences, followed by CI with 41 differences, and CII with 38 differences (Figure 7A).
Mitochondrial respiration is affected by sex but dominated by age. A.
Total number of statistically significant differences across all tissue-by-tissue comparisons, grouped by the component measured (CI = respiration through complex I, CII = respiration through complex II, or CIV = respiration through complex IV). B. Total number of significant differences across tissue-by-tissue comparisons grouped by the specific comparison, colored to represent the mitochondrial component in which respiration began (CI, CII, or CIV), and summed to the right of each histogram to highlight the number of significant findings per comparison. To underscore the number of statistically significant sex- or age- associated differences, the total number of significant findings from YM-by-YF and OM-by-OF (sex effect), and OM-by-YM and OF-by-YF (age effect) were summed. C. Quantification of the total number of tissues affected in each tissue-by-tissue comparison. Data are colored based on the specific comparison and grouped as sex- or age-associated to illustrate the effect-type. D. Cumulative absolute difference of means for CI, CII, and CIV (from left to right) grouped by effect-type, sex (originating from YM-by-YF or OM-by-OF) or age (originating from OM-by-YM or OF-by-YF). These graphs do not indicate directionality of the change, only the absolute cumulative magnitude. Each box within the histogram represents a unique tissue. All histograms are organized lowest (left) to highest (right) in degree of tissue contribution to total change given the comparison type. The top contributors to sex- or age-associated changes are highlighted with non-greyscale colors and their relative percentage of contribution to the total cumulative difference is provided. E. Principal Component Analysis (PCA) of all tissues and groups for CI, CII, and CIV (from left to right). F. Pearson correlation heat maps of all tissues combined from young and old, male and female mice. From top to bottom and left to right the samples are organized by group as follows: YF, YM, OF, and OM (n = 10 mice per group). YM = young male, YF = young female, OM = old male, OF = old female. Young = 10 weeks, Old = 80 weeks. BAT, brown adipose tissue; gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; Quad, quadriceps muscles; At, atria; Vent, ventricles.
Grouping significant differences by the across-group comparison in which they occurred showed the young male (YM)-by-old male (OM) comparison to have the most significant differences with 48 in total, followed by young female (YF)-by-old female (OF) with 42, OM-by-OF with 20, and YM-by-YF with 18 significant differences (Figure 7B). Clustering the significant difference counts by type, sex (originating from YM-by-YF or OM-by-OF) or age (originating from YM-by-OM or YF-by-OF), we saw that although both sex and age exerted an effect on mitochondrial respiration, it was age that resulted in the highest number of significant changes (Figure 7B). We next sought to quantify the number of tissues significantly affected in at least one mitochondrial parameter per across-group comparison. There were 10 tissues with a significant difference when comparing young male and female, 13 when comparing old male and female, 26 tissues when comparing young and old male, and 21 for young and old female (Figure 7C). Together, these data indicate that the majority of significant differences observed are the result of age.
Next, we calculated the absolute difference of means and summed the resultant values based on their effect-type, sex (originating from YM-by-YF or OM-by-OF) or age (originating from YM-by-YF or OM- by-OF) (Figure 7D). This analysis offered an added magnitude perspective to our view of changes in mitochondrial respiration across sex and age. It allowed us to see that respiration via CI, CII, and CIV all have a greater magnitude of difference as a result of age as compared to sex. Each graph in Figure 7D shows ranked values (lowest/left to highest/right) within the age and sex groupings with the top tissues colored and labeled with their percent contribution to the total. The highest contributors to the differences in respiration via CI as a result of age were heart (ventricles and atria), BAT, and diaphragm muscle, while those for sex were heart (ventricles and atria) and BAT. The highest contributors to the differences in respiration through CII as a result of age were BAT, diaphragm muscle, heart ventricles, and kidney cortex, while those for sex were kidney (medulla and cortex) and heart (ventricles and atria). The highest contributors to the differences in respiration at CIV as a result of age were BAT, diaphragm, kidney cortex, and eyes, while those for sex were BAT and heart ventricles, and kidney (medulla and cortex). It is important to note that these rankings do not indicate directionality of a difference, only total magnitude. Additionally, these changes are represented as the absolute magnitude of difference. Therefore, tissues with lower absolute oxygen consumption values would skew towards lower ranking, even though the relative change occurring within that tissue may be large.
Principle component (PC) analysis of each mitochondrial respiration dataset showed a primary and consistent separation across PC1 as a result of age (Figure 7E). A small percentage of the variance can be explained through PC2 as a result of sex, although this effect was not as consistent as that seen with age. Pearson correlation heat maps further highlighted age as a key factor relating samples across groups (Figure 7F). Male and female samples of the same age positively correlated with one another, while young and old samples regardless of sex showed a negative correlation. These combined data indicate that while sex does affect mitochondrial respiration, age is the dominant factor.
The sex-specific impact of age on mitochondrial function
To investigate the sex-dependent effects on mitochondrial function across age, we first calculated all of the relative change values for males and females from young to old. Summation of relative change values of all tissues within a specific mitochondrial parameter—respiration through CI, CII, or CIV—allowed us to view sex-specific systems-level effects across age (Figure 8A-B). Both the male and female systems showed a relative net increase in CI and decrease in CIV activity with age. Interestingly, with respect to CII activity, the male and female systems had divergent responses. The net relative CII activity increased in males and decreased in females with age. These data suggest that: 1) independent of sex, CI and CIV are uniquely regulated, showing net opposite responses with age, and 2) CII activity is the most sex- affected respiratory component across age and tissues.
The effects of age on mitochondrial respiration occur in sex-specific ways. A-B.
Systems- level view of the total relative change (sum of all relative changes) present at each mitochondrial parameter (respiration via CI, CII, or CIV) for males (M) and females (F), respectively. All change is old (O) compared to young (Y). C. Heat maps of O / Y data for each mitochondrial parameter per tissue across lifespan. Male and female values are grouped and organized by greatest (top) to least (bottom) sum of relative change per tissue. Tissues with the largest positive relative change as a result of aging are located on the top-most region of the heat maps, while those with the largest negative relative change are at the bottom. The middle of each heat map represents tissues with little relative response to aging or with opposite effect directions across sex. D. Graphs showing magnitude of sex-effect with age across all tissues and mitochondrial parameters (CI, CII, or CIV). The blue line and circles represent log2(old male/young male) data and the red line and boxes represent log2(old female/young female) data; both linked to the left y-axis. The black line and triangles represent the absolute difference of male and female relative change data (linked to the right y-axis). All data is organized from left to right by highest to lowest absolute difference of relative change across sex. Tissues with the greatest relative difference across sex are located to the left-most region of the graph, while those with the most similar response to aging are located to the right. A hollow blue circle, red circle, and black triangle represent a tissue with a divergent sex-specific age response. E. Summary diagrams classifying the relative trends of each mitochondrial parameter assayed per tissue as age- or age-and-sex-specific. Age-specific effects have a shared relative change direction across sex. Tissues in the red “M + F Increased” box have a positive relative change for both males and females. Tissues in the blue “M + F Decreased” box have a negative relative change for both males and females. Tissues in the Purple box labeled “Divergent” have opposing log2(O/Y) directions (signs, + or -) for male and female values; these tissues display relative mitochondrial changes that are age-and-sex-specific. F. Summary diagram showing tissues with a shared increase (red box) or decrease (blue box) consistent across all complexes (CI, CII, and CIV). Tissues within the shared increase or decrease classifications are grouped by their tissue-type, for example. PL, DI, SL, and HV are all muscle, CO, HI, HY, and CE are all brain and so on. CI = respiration measured through complex I stimulated by NADH; CII = respiration through complex II stimulated by succinate in the presence of rotenone; CIV = respiration at complex IV stimulated by TMPD and ascorbate in the presence of rotenone and antimycin A. Tissues: BT = brown adipose tissue, CC = cecum, CE = cerebellum, CO =brain cortex, DI = diaphragm, DC = distal colon, DU = duodenum, EY = eyes, GS = gastrocnemius, GW = gonadal white adipose tissue, HS = hamstring, HA = heart atria, HV = heart ventricles, HI = hippocampus, HY = hypothalamus, IL = ileum, IW = inguinal white adipose tissue, JE = jejunum, KC = kidney cortex, KM = kidney medulla, LV = liver, LN = lung, MW = mesenteric white adipose tissue, PN = pancreas, PL = plantaris, PC = proximal colon, QD = quadriceps, SK = skin, SL = soleus, SP = spleen, ST = stomach, TN = tongue. Reproductive organs are omitted from cross-sex analysis.
Heat maps of the individual tissue-level data presented in Figure 8A-B further highlight the tissue-level similarities and differences across age, sex, and mitochondrial respiration type (i.e., through CI, CII, or CIV). For example, respiration through CI, CII, or CIV showed that the plantaris muscles (PL), diaphragm muscle (DI), jejunum (JE), mesenteric white adipose tissue (MW), and ileum (IL) have the greatest positive relative change in response to age, while the eye (EY), skin (SK), stomach (ST), and BAT (BT) were among the tissues that showed the greatest negative relative change with age (Figure 8C).
To determine the tissues with the strongest sex-specific age effects, we organized male and female data by the largest (left) to smallest (right) difference of relative change. This multi-dimensional view highlights differences within a tissue across sex in response to age (Figure 8D). The largest sex- specific effects of age on respiration via CI were found in the stomach (ST), jejunum (JE), distal colon (DC), quadriceps muscles (QD), and pancreas (PN); in contrast, the gWAT (GW), kidney cortex (KC), diaphragm (DI), cecum (CC), soleus muscle (SL), and cerebellum (CE) displayed the highest similarity of change (Figure 8D, top panel). The largest sex-specific effects of age on respiration through CII were found in the stomach (ST), duodenum (DU), distal colon (DC), pancreas (PN), and jejunum (JE); in contrast, the liver (LV), hamstring muscle (HS), kidney medulla (KM), spleen (SP), and skin (SK) displayed the smallest sex-differences (Figure 8D, middle panel). Finally, the most sex-specific effects of age on respiration at CIV were found in mesenteric white adipose tissue (MW), duodenum (DU), jejunum (JE), skin (SK), and distal colon (DC); in contrast, the tongue (TN), gastrocnemius muscle (GS), heart ventricles (HV), hypothalamus (HY), and diaphragm (DI) displayed the greatest similarity of change with age (Figure 8D, bottom panel).
All sexually divergent tissues are marked specifically within each graph per mitochondrial respiration-type. Respiration via CI showed 7 tissues with sexual divergence in response to aging, and these were stomach (ST), distal colon (DC), pancreas (PN), lung (LN), heart atria (HA), quadriceps muscle complex (QD), and kidney medulla (KM) (Figure 8D, top panel). Respiration through CII showed 5 tissues with sexual divergence in response to aging, and these were duodenum (DU), pancreas (PN), lung (LN), quadriceps muscle complex (QD), and distal colon (DC) (Figure 8D, second panel). Respiration at CIV also showed 5 tissues with sexual divergence in response to aging, and these were jejunum (JE), distal colon (DC), hamstring (HS), quadriceps muscle complex (QD), and kidney medulla (KM) (Figure 8D, bottom). Together, these data show the relative magnitude of change across sex and age, and highlight the tissue- and sex-specific regulation of mitochondrial respiration with aging.
We further grouped the above data by directionality to visualize male and female tissue responses to aging (Figure 8E). From this, we can quickly visualize tissues displaying a shared direction of change in respiration via CI, CII, or CIV across sex and age. Additionally, some tissues responded in an age-and- sex-specific manner (i.e., display divergence in Figure 8D). This data clustering summarizes the shared (age-specific) or opposite (sex-specific) directionality of mitochondrial function across tissues in response to age.
It should be noted, however, that the mitochondrial activity of a particular tissue could increase or decrease in a sex-specific way even though the directionality is the same. For example, respiration through CII for the stomach (ST) showed male and female values decreasing with age, yet the male had a larger relative decrease when compared to female (Figure 8D, middle panel). Another consideration should be given to the divergent group, which displayed varying degrees of divergence. For example, CI- associated respiration of the stomach (Figure 8D, top panel) showed a strong divergence given that male had a strong decline in function while the female increased over time. On the other hand, the CI- associated respiration of the kidney medulla showed a weak divergence; although the directionality of change was opposite, the magnitude was small.
Lastly, we grouped all the tissues with a consistent shared (across sex) relative increase or decrease in CI-, CII-, and CIV-associated respiration in response to age (Figure 8F). The tissues with a shared (across sex) increased response to aging were plantaris (PL), diaphragm (DI), soleus (SL), heart ventricle (HV), ileum (IL), cecum (CC), proximal colon (PC), and mesenteric fat (MW). Interestingly, PL, DI, and SL are all skeletal muscles and IL, CC, and PC are all part of the digestive tract. Also of note, mitochondrial respiration in the gastrocnemius (GT), hamstring (HS), and tongue (TN) muscles were not uniformly increasing with age. In fact, these muscles showed reduced mitochondrial respiration with age, in striking contrast to other skeletal muscle types assayed. The tissues with a shared (across sex) decrease in response to aging were brown fat (BT), gonadal fat (GW), inguinal fat (IW), brain cortex (CO), hippocampus (HI), hypothalamus (HY), cerebellum (CE), liver (LV), kidney cortex (KC), eye (EY), skin (SK), and spleen (SP). Different fat depots and most of the brain regions showed a consistent decline in respiratory function with age, regardless of sex. The kidney cortex (KC) oddly showed a respiratory effect distinct from the medulla, which was almost completely resistant to age-related respiratory changes. The eyes (EY) not only showed a consistent decline in respiration with age, but also had one of the largest combined relative effects. Together, the shared and divergent analyses presented (Figure 8E and F) illuminate the modulation of mitochondrial function across age.
Discussion
Here, we provide the most comprehensive catalog of sex- and age-associated mitochondrial respiration signatures across diverse tissues and organs system. One important aspect of our study to emphasize is the experimental uniformity. All the tissue samples were collected from the same set of mice, and processed and analyzed in a uniform and standardized manner on the same respirometry platform in a single laboratory. Combined with the ten biological replicates, our experimental setup and workflow markedly enhanced the rigor and reduced the variations in data quality. From the mitochondrial respiration compendium, we can clearly see that mitochondrial function varies widely across tissues. This is perhaps not surprising given the highly variable energetic demands of different tissues and the known differences in the composition of mitochondrial proteomes across tissues (29, 30). Importantly, our data provide evidence that aging has a disproportionately larger effect than sex on mitochondrial activity across tissues.
The transcriptomic dataset generated by the Tabula Muris Consortium clearly indicates organ- specific temporal signatures across the lifespan (7–9), and this is assumed to track closely with tissue function even though no equivalent systematic interrogation of tissue function across the lifespan has been performed. In this regard, our mitochondrial activity atlas aligned and corroborated with the conclusion from large-scale transcriptomic analyses that the aging process and trajectory can vary substantially across tissues (7, 8). This reinforces the notion that while an organism ages, the aging- associated decline in organ function is not uniform across tissues and organ systems. Given that genetics and environmental factors all contribute to tissue health and their functional breakdown with age, this further exemplifies the heterogenous process of aging across a population.
Although sex is a significant biological variable influencing many aspects of physiology and pathophysiology (31, 32), and sex hormones are known to affect mitochondrial function (33–35), we were surprised that sex has a relatively modest impact on respiratory capacity in mitochondria across tissues and age. The modest impact of sex on mitochondrial respiration across tissues is also concordant with the relatively minor impact of sex on tissue transcriptome across the lifespan (7, 11). The caveat, however, is that our study is based on a single strain of mice with a uniform genetic background. The contribution of biological sex may be more pronounced in an outbred population where sex hormones interact with other genetic and environmental determinants to influence mitochondrial activity across tissues and age.
Aging is a dynamic process that unfolds across the lifespan, which involves changes at the molecular, cellular, biochemical, and tissue level. Although the magnitude of change may vary between individuals, with increasing age, humans generally experience a gradual and progressive decline in cognitive function, visual acuity, skin elasticity, motor control, digestive function, fertility, and metabolism (e.g., increased adiposity) (1, 3). Interestingly, in our dataset, we also observed an aging- associated decline in mitochondrial respiration in most brain regions, eyes, skin, fallopian tubes, different fat depots and BAT, pancreas, and the digestive tracts in male and/or female mice. This age-dependent reduction in mitochondrial function likely contributes, at least in part, to the functional decline across different organ systems. Depending on the tissue, the magnitude of change in mitochondrial respiration in old mice, when compared to young mice, can be substantial or relatively modest. This variability is consistent with the heterogenous rate of aging across tissues (3).
The underlying cause of reduced mitochondrial respiration in aging is not well understood and it may involve multiple mechanisms. Transcriptomic analyses have consistently shown that aging induces a reduction in mitochondrial OXPHOS genes (7, 8), and this likely is one of the mechanisms that contributes to the reduced mitochondrial activity we observed across many tissues in old mice. What causes an age-dependent decrease in mitochondrial OXPHOS genes across tissues, however, is largely unknown. Other potential mechanisms may involve age-dependent accumulation of oxidative damage in mitochondrial DNA, proteins, and lipids that adversely affect mitochondrial function (20, 36). The damage to mitochondrial integrity in aging is frequently attributed to reactive oxygen species (ROS) that are naturally generated as byproducts of electron transfer through the mitochondrial OXPHOS complexes (37), or to inadequate stress response pathways (e.g., proteostasis and mitophagy) (18) . While cells have mechanisms to guard against oxidative stress due to mitochondrial activity, these antioxidant defense mechanisms are known to decline with age despite an age-dependent increase in ROS production (38, 39). Similarly, the integrated stress response pathways also tend to diminished with age (18). Thus, it is often thought the gradual accumulation of ROS with age, coupled with a decline in mitochondria’s ability to handle proteotoxic stress, progressively compromises mitochondrial function.
It should be pointed out that changes in cell, tissue, and organ function are often counteracted by homeostatic and compensatory responses. While many tissues showed a significant reduction in mitochondrial function, some tissues also showed a concomitant increase in mitochondrial activity with age. For examples, we observed an increase in mitochondrial respiration in the heart, different skeletal muscle groups, and mesenteric fat in old male and/or female mice. Increased mitochondrial respiration is seen in both the oxidative (diaphragm, soleus, tongue) and glycolytic (plantaris) muscle fibers. This increase in mitochondrial activity is not attributed to differences in mitochondrial content since the oxygen consumption rate (OCR) is normalized to mitochondrial content.
Skeletal muscle mass is known to decrease with age (40), and it is frequently but not always accompanied by a reduction in skeletal muscle mitochondrial function in vivo (41–45) and in vitro (42, 46–50). Some studies, however, have also suggested that the energetic efficiency of mitochondria in skeletal muscle— located beneath the sarcolemmal membrane or between the myofibrils—appears to increase with age (51, 52). While we observed an increase in the maximal uncoupled respiration in skeletal muscle lysates of aged mice, our results cannot be directly compared to prior studies that examine coupled respiration in intact tissue or isolated mitochondria. It has been shown that skeletal muscle mitochondrial number increases, whereas mitochondrial size decreases, with age (53). Additionally, the ultrastructure of mitochondria may also change with age (54). Whether these parameters correlate with and contribute to age-dependent changes in mitochondrial respiration remains to be established. With the general observation that organ function declines with age, our data suggests that some tissues may increase their mitochondrial respiration as a compensatory response to meet the energetic demands resulting from reduced mass (e.g., sarcopenia) and/or declining efficiency of organ function. This hypothesis awaits future experimental verification.
It is worth noting that our respirometry analysis assesses NADH-dependent respiration through CI, succinate-dependent respiration through CII, and TMPD/ascorbate-dependent respiration at CIV. Regardless of whether electrons enter the respiratory chain through CI or CII, or at CIV, oxygen (the final electron acceptor) is only consumed at CIV. Since the oxygen consumption rate (OCR) is normalized to mitochondrial content, the variations we observed in OCR within any given tissue through CI or CII, or at CIV, likely reflect how tightly coupled respiratory complex chains consisting of CI+CIII2+CIV and CII+CIII2+CIV are relative to CIV alone. Although our data suggest that sex and age affect the flow of electrons through respiratory complex CI+CIII2+CIV and CII+CIII2+CIV across tissues, this remains speculative given that our assays were not performed on intact mitochondria.
Every respirometry method has its advantages and limitations (27). Unlike the mitochondrial respiration analysis in intact cells or isolated mitochondria, our respirometry analyses were performed in mitochondria-enriched lysates derived from frozen tissues (28). In an intact mitochondrion, the rate of respiration is regulated by both substrate supply across the mitochondrial membrane and the rate of NADH and FADH production via the TCA cycle and fatty acid β-oxidation. In the absence of an intact mitochondrial membrane, our assays measure maximal mitochondrial respiration through CI, CII, or CIV. Thus, our mitochondrial activity reflects maximal respiration across tissues. In an in vivo milieu, mitochondrial activity is likely regulated and may not reach its maximal capacity as reflected in our assay. However, the major advantage of the frozen-tissue method is that it is now feasible to carry out large- scale respirometry analyses, which outweigh its limitations.
It should be emphasized that our mitochondrial respiration represents the average values for each tissue, even though all tissues consist of multiple distinct cell types that may vary in their cellular composition and mitochondrial activity across sex and age. While individual cell types can be isolated prior to respirometry analysis, this labor-intensive method is not suited for assessing mitochondrial respiration at scale (i.e., across a large number of tissues from the same animal). In the future, this challenge may be overcome with the development of new technologies that allow for high-resolution mitochondrial respirometry at cell-type or single-cell resolution in situ.
In summary, our mitochondria functional signatures demonstrate similar and divergent responses to aging across tissues. Whether changes in tissue mitochondrial respiration reflect the cause, consequence, or both, of aging remains to be determined. We anticipate that the integration of this knowledge and approach with other large-scale omics data will help uncover potential genetic, epigenetic, and biochemical mechanisms linking mitochondrial health and organismal aging.
Materials and methodsMouse model
All wild-type C57BL/6J male and female mice were purchased from the Jackson Laboratory and fed a standard chow (Envigo; 2018SX). In total, the young group was comprised of 10 male and 10 female 10- week-old mice, and the old group was comprised of 10 male and 10 female 80-week-old mice. Mice were housed in polycarbonate cages on a 12h:12h light-dark photocycle with ad libitum access to water and food. All mice were fasted for 2 h prior to euthanasia and dissection. Tissues were collected, snap-frozen in liquid nitrogen, and kept at -80°C until analysis. All mouse protocols were approved by the Institutional Animal Care and Use Committee of the Johns Hopkins University School of Medicine (animal protocol # MO22M367). All animal experiments were conducted in accordance with the National Institute of Health guidelines and followed the standards established by the Animal Welfare Acts.
Comprehensive multi-organ dissection
Each mouse was dissected cleanly, swiftly, and in a concerted manner by three people. Each dissection took approximately 8-10 min, with 33 tissues collected per dissection. After euthanasia, blood was collected via decapitation. The head was then immediately given to dissector one to collect brain regions (hypothalamus, cerebellum, hippocampus, and cortex), eyes, and tongue. Simultaneously, the visceral cavity was opened by dissector two. Inguinal white adipose tissue (iWAT) was collected immediately after opening the abdominal skin. After that the abdominal muscle was cut and gonadal white adipose tissue (gWAT) and testes or fallopian tubes were collected. Following this, the visceral organs were partitioned in two groups. Dissector two promptly dissected liver, stomach, kidneys (further separated the cortex and medulla), spleen, diaphragm, heart (further divided into atria and ventricles), lungs, brown adipose tissue (BAT), and skin (cleared of hair using Topical Nair lotion, cleaned, then collected). At the same time, dissector three was collecting the pancreas, mesenteric white adipose tissue (mesWAT), small intestine (further split into duodenum, jejunum, and ileum), cecum, and large intestine (further split into proximal and distal colon). As soon as tissue collection in the head was finished, the mouse carcass was cut transversely at the lumbar spine and handed to dissector one for muscle dissection. Dissector one then rapidly and precisely anatomized (bilaterally) the quadriceps (entire complex – rectus femoris, vastus lateralis, vastus intermedius, and vastus medialis), hamstrings (biceps femoris), gastrocnemius, plantaris, and soleus muscles. All tissues were washed with sterile 1X PBS to remove residual blood prior to snap freezing. Additionally, the stomach, small intestine, and large intestine were cleared and cleaned of debris with PBS prior to freezing. The Cecum, however, was kept whole containing all fecal/food matter and microorganisms present. All dissected tissues were snap frozen in liquid nitrogen, and stored at -80°C for later analysis.
Respirometry of frozen tissue samples
Respirometry was conducted on frozen tissue samples to assay for mitochondrial activity as described previously (28), using a Seahorse XFe96 Analyzer. Samples were thawed in 1X MAS buffer (70 mM sucrose, 220 mM mannitol, 5 mM KH2PO4, 5 mM MgCl2, 1 mM EGTA, 2 mM HEPES pH 7.4), finely minced with scissors, then homogenized with a glass Dounce homogenizer on ice. The entire sample, bilateral for skeletal muscles, was homogenized and used for respirometry with the exception of liver in which only a piece was homogenized. This was done to provide a true tissue average and avoid regional differences in mitochondria. The resulting homogenate was spun at 1000 g for 10 min at 4°C. The supernatant was collected and immediately used for protein quantification by BCA assay (Thermo Scientific, 23225). Each well of the Seahorse microplate was loaded with the designated amount (in µg) of homogenate protein (Table 1). Each biological replicate was comprised of three technical replicates. Samples from all tissues were treated separately with NADH (1 mM) as a complex I substrate or Succinate (a complex II substrate, 5 mM) in the presence of rotenone (a complex I inhibitor, 2 µM), then with the inhibitors rotenone (2 µM) and Antimycin A (4 µM), followed by TMPD (N,N,N’,N’- Tetramethyl-p-Phenylenediamine; also known as Wurster’s Reagent, 0.45 mM) and Ascorbate (Vitamin C, 1 mM) to activate complex IV, and finally treated with Azide (40 mM) to assess non-mitochondrial respiration.
Assay parameters for Seahorse-based respirometry analysis across all tissues.
Table shows tissues used, approximate size per tissue homogenized for analysis, amount of 1X MAS buffer used for homogenization, and the amount of protein used for respiration analysis across all tissues.
Quantification of mitochondrial content
Mitochondrial content of homogenates used for respirometry was quantified with the membrane potential- independent mitochondrial dye MitoTracker Deep Red FM (MTDR, Invitrogen, M22426) as described previously (28). Briefly, lysates were incubated with MTDR (1 µM) for 10 min at 37°C, then centrifuged at 2000 g for 5 min at 4°C. The supernatant was carefully removed and replaced with 1X MAS solution and fluorescence was read with excitation and emission wavelengths of 625 nm and 670 nm, respectively. All measurements were read with a BioTek Synergy HTX Multimode Plate Reader (Agilent). All samples from a single tissue-type were measured on the same plate, in duplicate, with an equal gain setting of 90 to ensure comparability across samples. To minimize non-specific background signal contribution, control wells were loaded with MTDR + 1X MAS and subtracted from all sample values.
Acknowledgements
This work was supported by the National Institutes of Health (DK084171 to GWW). D.C.S. was supported by an NIH T32 training grant (HL007534).
Author contributions
DCS and GWW contributed to the experimental design; DCS, MS, FC, and GWW performed the experiments; DCS and GWW analyzed and interpreted the data; DCS and GWW drafted the paper with inputs and edits from all authors.
Competing interests
We declare that none of the authors has a conflict of interest.
Data availability
All the data presented are included in the main text or as supplementary tables and figures.
AbbreviationsBAT
Brown adipose tissue
CI
Mitochondrial complex I
CII
Mitochondrial complex II
CIII
Mitochondrial complex IIII
CIV
Mitochondrial complex IV
FADH
flavin adenine dinucleotide
gWAT
Gonadal white adipose tissue
iWAT
Inguinal white adipose tissue
NADH
nicotinamide adenine dinucleotide
OCR
Oxygen consumption rate
Rot
Rotenone
TMPD
N,N,N’,N’-Tetramethyl-p-Phenylenediamine
ReferencesLopez-Otin, C., Blasco, M. A., Partridge, L., Serrano, M., and Kroemer, G. (2013) The hallmarks of aging. Cell153, 1194–1217Kennedy, B. K., Berger, S. L., Brunet, A., Campisi, J., Cuervo, A. M., Epel, E. S., Franceschi, C., Lithgow, G. J., Morimoto, R. I., Pessin, J. E., Rando, T. A., Richardson, A., Schadt, E. E., Wyss- Coray, T., and Sierra, F. (2014) Geroscience: linking aging to chronic disease. Cell159, 709–713Tian, Y. E., Cropley, V., Maier, A. B., Lautenschlager, N. T., Breakspear, M., and Zalesky, A. (2023) Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality. Nat Med29, 1221–1231Nie, C., Li, Y., Li, R., Yan, Y., Zhang, D., Li, T., Li, Z., Sun, Y., Zhen, H., Ding, J., Wan, Z., Gong, J., Shi, Y., Huang, Z., Wu, Y., Cai, K., Zong, Y., Wang, Z., Wang, R., Jian, M., Jin, X., Wang, J., Yang, H., Han, J. J., Zhang, X., Franceschi, C., Kennedy, B. K., and Xu, X. (2022) Distinct biological ages of organs and systems identified from a multi-omics study. Cell reports38, 110459Kuo, P. L., Schrack, J. A., Levine, M. E., Shardell, M. D., Simonsick, E. M., Chia, C. W., Moore, A. Z., Tanaka, T., An, Y., Karikkineth, A., AlGhatrif, M., Elango, P., Zukley, L. M., Egan, J. M., de Cabo, R., Resnick, S. M. and Ferrucci, L. (2022) Longitudinal phenotypic aging metrics in the Baltimore Longitudinal Study of Aging. Nat Aging2, 635–643Rutledge, J., Oh, H., and Wyss-Coray, T. (2022) Measuring biological age using omics data. Nat Rev Genet23, 715–727Schaum, N., Lehallier, B., Hahn, O., Palovics, R., Hosseinzadeh, S., Lee, S. E., Sit, R., Lee, D. P., Losada, P. M., Zardeneta, M. E., Fehlmann, T., Webber, J. T., McGeever, A., Calcuttawala, K., Zhang, H., Berdnik, D., Mathur, V., Tan, W., Zee, A., Tan, M., Tabula Muris, C., Pisco, A. O., Karkanias, J., Neff, N. F., Keller, A., Darmanis, S., Quake, S. R., and Wyss-Coray, T. (2020) Ageing hallmarks exhibit organ-specific temporal signatures. Nature583, 596–602Zhang, M. J., Pisco, A. O., Darmanis, S., and Zou, J. (2021) Mouse aging cell atlas analysis reveals global and cell type-specific aging signatures. Elife10The Tabula Muris Consortium (2020) A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature583, 590–595Palovics, R., Keller, A., Schaum, N., Tan, W., Fehlmann, T., Borja, M., Kern, F., Bonanno, L., Calcuttawala, K., Webber, J., McGeever, A., Tabula Muris, C., Luo, J., Pisco, A. O., Karkanias, J., Neff, N. F., Darmanis, S., Quake, S. R., and Wyss-Coray, T. (2022) Molecular hallmarks of heterochronic parabiosis at single-cell resolution. Nature603, 309–314Ma, S., Sun, S., Geng, L., Song, M., Wang, W., Ye, Y., Ji, Q., Zou, Z., Wang, S., He, X., Li, W., Esteban, C. R., Long, X., Guo, G., Chan, P., Zhou, Q., Belmonte, J. C. I., Zhang, W., Qu, J., and Liu, G. H. (2020) Caloric Restriction Reprograms the Single-Cell Transcriptional Landscape of Rattus Norvegicus Aging. Cell180, 984–1001.Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., Moran Losada, P., Berdnik, D., Keller, A., Verghese, J., Sathyan, S., Franceschi, C., Milman, S., Barzilai, N., and Wyss-Coray, T. (2019) Undulating changes in human plasma proteome profiles across the lifespan. Nat Med25, 1843–1850Oh, H. S., Rutledge, J., Nachun, D., Palovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., Wang, L., Timsina, J., Western, D., Liu, M., Kohlfeld, P., Budde, J., Wilson, E. N., Guen, Y., Maurer, T. M., Haney, M., Yang, A. C., He, Z., Greicius, M. D., Andreasson, K. I., Sathyan, S., Weiss, E. F., Milman, S., Barzilai, N., Cruchaga, C., Wagner A. D., Mormino, E., Lehallier, B., Henderson, V. W., Longo, F. M., Montgomery, S. B. and Wyss-Coray, T. (2023) Organ aging signatures in the plasma proteome track health and disease. Nature624, 164–172de Magalhaes, J. P. (2024) Distinguishing between driver and passenger mechanisms of aging. Nat GenetKaeberlein, M., Rabinovitch, P. S., and Martin, G. M. (2015) Healthy aging: The ultimate preventative medicine. Science350, 1191–1193Campisi, J., Kapahi, P., Lithgow, G. J., Melov, S., Newman, J. C., and Verdin, E. (2019) From discoveries in ageing research to therapeutics for healthy ageing. Nature571, 183–192Jang, J. Y., Blum, A., Liu, J., and Finkel, T. (2018) The role of mitochondria in aging. J Clin Invest128, 3662–3670Lima, T., Li, T. Y., Mottis, A., and Auwerx, J. (2022) Pleiotropic effects of mitochondria in aging. Nat Aging2, 199–213Lanza, I. R., and Nair, K. S. (2010) Mitochondrial function as a determinant of life span. Pflugers Arch459, 277–289Sun, N., Youle, R. J., and Finkel, T. (2016) The Mitochondrial Basis of Aging. Mol Cell61, 654–666Zahn, J. M., Poosala, S., Owen, A. B., Ingram, D. K., Lustig, A., Carter, A., Weeraratna, A. T., Taub, D. D., Gorospe, M., Mazan-Mamczarz, K., Lakatta, E. G., Boheler, K. R., Xu, X., Mattson, M. P., Falco, G., Ko, M. S., Schlessinger, D., Firman, J., Kummerfeld, S. K., Wood, W. H., 3rd, Zonderman, A. B., Kim, S. K., and Becker, K. G. (2007) AGEMAP: a gene expression database for aging in mice. PLoS Genet3, e201Haas, R. H. (2019) Mitochondrial Dysfunction in Aging and Diseases of Aging. Biology (Basel)8Foote, K., Reinhold, J., Yu, E. P. K., Figg, N. L., Finigan, A., Murphy, M. P., and Bennett, M. R. (2018) Restoring mitochondrial DNA copy number preserves mitochondrial function and delays vascular aging in mice. Aging Cell17, e12773A.Lima, T. I., Laurila, P. P., Wohlwend, M., Morel, J. D., Goeminne, L. J. E., Li, H., Romani, M., Li, X., Oh, C. M., Park, D., Rodriguez-Lopez, S., Ivanisevic, J., Gallart-Ayala, H., Crisol, B., Delort, F., Batonnet-Pichon, S., Silveira, L. R., Sankabattula Pavani Veera Venkata, L., Padala, A. K., Jain, S., and Auwerx, J, (2023) Inhibiting de novo ceramide synthesis restores mitochondrial and protein homeostasis in muscle aging. Sci Transl Med15, eade6509Chiao, Y. A., Zhang, H., Sweetwyne, M., Whitson, J., Ting, Y. S., Basisty, N., Pino, L. K., Quarles, E., Nguyen, N. H., Campbell, M. D., Zhang, T., Gaffrey, M. J., Merrihew, G., Wang, L., Yue, Y., Duan, D., Granzier, H. L., Szeto, H. H., Qian, W. J., Marcinek, D., MacCoss, M. J., and Rabinovitch, P. (2020) Late-life restoration of mitochondrial function reverses cardiac dysfunction in old mice. Elife9Nilsson, M. I., and Tarnopolsky, M. A. (2019) Mitochondria and Aging-The Role of Exercise as a Countermeasure. Biology (Basel)8Salabei, J. K., Gibb, A. A., and Hill, B. G. (2014) Comprehensive measurement of respiratory activity in permeabilized cells using extracellular flux analysis. Nat Protoc9, 421–438Acin-Perez, R., Benador, I. Y., Petcherski, A., Veliova, M., Benavides, G. A., Lagarrigue, S., Caudal, A., Vergnes, L., Murphy, A. N., Karamanlidis, G., Tian, R., Reue, K., Wanagat, J., Sacks, H., Amati, F., Darley-Usmar, V. M., Liesa, M., Divakaruni, A. S., Stiles, L., and Shirihai, O. S. (2020) A novel approach to measure mitochondrial respiration in frozen biological samples. EMBO J39, e104073Mootha, V. K., Bunkenborg, J., Olsen, J. V., Hjerrild, M., Wisniewski, J. R., Stahl, E., Bolouri, M. S., Ray, H. N., Sihag, S., Kamal, M., Patterson, N., Lander, E. S., and Mann, M. (2003) Integrated analysis of protein composition, tissue diversity, and gene regulation in mouse mitochondria. Cell115, 629–640Pagliarini, D. J., Calvo, S. E., Chang, B., Sheth, S. A., Vafai, S. B., Ong, S. E., Walford, G. A., Sugiana, C., Boneh, A., Chen, W. K., Hill, D. E., Vidal, M., Evans, J. G., Thorburn, D. R., Carr, S. A., and Mootha, V. K. (2008) A mitochondrial protein compendium elucidates complex I disease biology. Cell134, 112–123Mauvais-Jarvis, F., Bairey Merz, N., Barnes, P. J., Brinton, R. D., Carrero, J. J., DeMeo, D. L., De Vries, G. J., Epperson, C. N., Govindan, R., Klein, S. L., Lonardo, A., Maki, P. M., McCullough, L. D., Regitz-Zagrosek, V., Regensteiner, J. G., Rubin, J. B., Sandberg, K., and Suzuki, A. (2020) Sex and gender: modifiers of health, disease, and medicine. Lancet396, 565–582Austad, S. N., and Fischer, K. E. (2016) Sex Differences in Lifespan. Cell Metab23, 1022–1033Klinge, C. M. (2020) Estrogenic control of mitochondrial function. Redox Biol31, 101435Gaignard, P., Liere, P., Therond, P., Schumacher, M., Slama, A., and Guennoun, R. (2017) Role of Sex Hormones on Brain Mitochondrial Function, with Special Reference to Aging and Neurodegenerative Diseases. Front Aging Neurosci9, 406Sultanova, R. F., Schibalski, R., Yankelevich, I. A., Stadler, K., and Ilatovskaya, D. V. (2020) Sex differences in renal mitochondrial function: a hormone-gous opportunity for research. Am J Physiol Renal Physiol319, F1117–F1124Sastre, J., Pallardo, F. V., Garcia de la Asuncion, J., and Vina, J. (2000) Mitochondria, oxidative stress and aging. Free Radic Res32, 189–198Orrenius, S., Gogvadze, V., and Zhivotovsky, B. (2007) Mitochondrial oxidative stress: implications for cell death. Annu Rev Pharmacol Toxicol47, 143–183Jones, D. P., Mody, V. C., Jr., Carlson, J. L., Lynn, M. J., and Sternberg, P., Jr. (2002) Redox analysis of human plasma allows separation of pro-oxidant events of aging from decline in antioxidant defenses. Free Radic Biol Med33, 1290–1300Kozakiewicz, M., Kornatowski, M., Krzywinska, O., and Kedziora-Kornatowska, K. (2019) Changes in the blood antioxidant defense of advanced age people. Clin Interv Aging14, 763–771Wilkinson, D. J., Piasecki, M., and Atherton, P. J. (2018) The age-related loss of skeletal muscle mass and function: Measurement and physiology of muscle fibre atrophy and muscle fibre loss in humans. Ageing Res Rev47, 123–132Gouspillou, G., Bourdel-Marchasson, I., Rouland, R., Calmettes, G., Biran, M., Deschodt-Arsac, V., Miraux, S., Thiaudiere, E., Pasdois, P., Detaille, D., Franconi, J. M., Babot, M., Trezeguet, V., Arsac, L., and Diolez, P. (2014) Mitochondrial energetics is impaired in vivo in aged skeletal muscle. Aging Cell13, 39–48Short, K. R., Bigelow, M. L., Kahl, J., Singh, R., Coenen-Schimke, J., Raghavakaimal, S., and Nair, K. S. (2005) Decline in skeletal muscle mitochondrial function with aging in humans. Proc Natl Acad Sci U S A102, 5618–5623Conley, K. E., Jubrias, S. A., and Esselman, P. C. (2000) Oxidative capacity and ageing in human muscle. J Physiol526 Pt 1, 203–210Kent-Braun, J. A., and Ng, A. V. (2000) Skeletal muscle oxidative capacity in young and older women and men. J Appl Physiol(1985)89, 1072-1078Chilibeck, P. D., McCreary, C. R., Marsh, G. D., Paterson, D. H., Noble, E. G., Taylor, A. W., and Thompson, R. T. (1998) Evaluation of muscle oxidative potential by 31P-MRS during incremental exercise in old and young humans. Eur J Appl Physiol Occup Physiol78, 460–465Mansouri, A., Muller, F. L., Liu, Y., Ng, R., Faulkner, J., Hamilton, M., Richardson, A., Huang, T. T., Epstein, C. J., and Van Remmen, H. (2006) Alterations in mitochondrial function, hydrogen peroxide release and oxidative damage in mouse hind-limb skeletal muscle during aging. Mech Ageing Dev127, 298–306Kumaran, S., Panneerselvam, K. S., Shila, S., Sivarajan, K., and Panneerselvam, C. (2005) Age- associated deficit of mitochondrial oxidative phosphorylation in skeletal muscle: role of carnitine and lipoic acid. Mol Cell Biochem280, 83–89Tonkonogi, M., Fernstrom, M., Walsh, B., Ji, L. L., Rooyackers, O., Hammarqvist, F., Wernerman, J., and Sahlin, K. (2003) Reduced oxidative power but unchanged antioxidative capacity in skeletal muscle from aged humans. Pflugers Arch446, 261–269Drew, B., Phaneuf, S., Dirks, A., Selman, C., Gredilla, R., Lezza, A., Barja, G., and Leeuwenburgh, C. (2003) Effects of aging and caloric restriction on mitochondrial energy production in gastrocnemius muscle and heart. Am J Physiol Regul Integr Comp Physiol284, R474–480Picard, M., Ritchie, D., Wright, K. J., Romestaing, C., Thomas, M. M., Rowan, S. L., Taivassalo, T., and Hepple, R. T. (2010) Mitochondrial functional impairment with aging is exaggerated in isolated mitochondria compared to permeabilized myofibers. Aging Cell9, 1032–1046Crescenzo, R., Bianco, F., Mazzoli, A., Giacco, A., Liverini, G., and Iossa, S. (2015) Skeletal muscle mitochondrial energetic efficiency and aging. Int J Mol Sci16, 10674–10685Crescenzo, R., Bianco, F., Mazzoli, A., Giacco, A., Liverini, G., and Iossa, S. (2014) Alterations in proton leak, oxidative status and uncoupling protein 3 content in skeletal muscle subsarcolemmal and intermyofibrillar mitochondria in old rats. BMC Geriatr14, 79Del Campo, A., Contreras-Hernandez, I., Castro-Sepulveda, M., Campos, C. A., Figueroa, R., Tevy, M. F., Eisner, V., Casas, M., and Jaimovich, E. (2018) Muscle function decline and mitochondria changes in middle age precede sarcopenia in mice. Aging (Albany NY)10, 34–55Brandt, T., Mourier, A., Tain, L. S., Partridge, L., Larsson, N. G., and Kuhlbrandt, W. (2017) Changes of mitochondrial ultrastructure and function during ageing in mice and Drosophila. Elife610.7554/eLife.96926.2.sa4eLife AssessmentSeldinMarcus MReviewing EditorUniversity of California, IrvineIrvineUnited States of AmericaCompellingImportant
This important study provides a comprehensive assessment of mitochondrial function across age and sex in mice. The strength of evidence supporting this resource is compelling, given the exhaustive number of tissues profiled and in-depth analyses performed.
In this study, Sarver and colleagues carried out an exhaustive analysis of the functioning of various components (Complex I/II/IV) of the mitochondrial electron transport chain (ETC) using a real-time cell metabolic analysis technique (commonly referred as Seahorse oxygen consumption rate (OCR) assay). The authors aimed to generate an atlas of ETC function in about 3 dozen tissue types isolated from all major mammalian organ systems. They used a recently published improvised method by which ETC function can be quantified in freshly frozen tissues. This method enabled them to collect data from almost all organ systems from the same mouse and use many biological replicates (10 mice/experiment) required for an unbiased and statistically robust analysis. Moreover, they studied the influence of sex (male and female) and aging (young adult and old age) on ETC function in these organ systems. The main findings of this study are (1) cells in the heart and kidneys have very active ETC complexes compared to other organ systems, (2) the sex of the mice has little influence on the ETC function, and (3) aging undermined the mitochondrial function in most tissue, but surprisingly in some tissue aging promoted the activity of ETC complexes (e.g., Quadriceps, plantaris muscle, and Diaphragm).
Comments on revised version:
The revised manuscript has improved significantly, addressing some of my previous concerns in the discussion. There is no doubt the method used to estimate the maximal uncoupled respiration rate in mitochondria across different organ systems and ages is excellent for getting an overview of the mitochondrial state. However, the correlation between the measured maximal respiration rate and the actual mitochondrial ATP production is still not adequately addressed. The authors could performed few straight forward experiments on freshly isolated mitochondria from 1-2 tissue samples of their choice to provide data linking maximal respiration rates with mitochondrial ATP production. Providing evidence that directly links maximal respiration rates with mitochondrial ATP production would help readers understand how mitochondrial function is affected in various tissues.
The authors utilize a new technique to measure mitochondrial respiration from frozen tissue extracts, which goes around the historical problem of purifying mitochondria prior to analysis, a process that requires a fair amount of time and cannot be easily scaled up.
Strengths:
A comprehensive analysis of mitochondrial respiration across tissues, sexes, and two different ages provides foundational knowledge needed in the field.
Weaknesses:
While many of the findings are mostly descriptive, this paper provides a large amount of data for the community and can be used as a reference for further studies. As the authors suggest, this is a new atlas of mitochondrial function in mouse. The inclusion of a middle aged time point and a slightly older young point (3-6 months) would be beneficial to the study.
The aim of the study was to map, a) whether different tissues exhibit different metabolic profiles (this is known already), what differences are found between female and male mice and how the profiles changes with age. In particular, the study recorded the activity of respirasomes, i.e. the concerted activity of mitochondrial respiratory complex chains consisting of CI+CIII2+CIV, CII+CIII2+CIV or CIV alone.
The strength is certainly the atlas of oxidative metabolism in the whole mouse body, the inclusion of the two different sexes and the comparison between young and old mice. The measurement was performed on frozen tissue, which is possible as already shown (Acin-Perez et al, EMBO J, 2020).
Weakness:
The assay reveals the maximum capacity of enzyme activity, which is an artificial situation and may differ from in vivo respiration, as the authors themselves discuss. The material used was a very crude preparation of cells containing mitochondria and other cytosolic compounds and organelles. Thus, the conditions are not well defined and the respiratory chain activity was certainly uncoupled from ATP synthesis. Preparation of more pure mitochondria and testing for coupling would allow evaluation of additional parameters: P/O ratios, feedback mechanism, basal respiration, and ATP-coupled respiration, which reflect in vivo conditions much better. The discussion is rather descriptive and cautious and could lead to some speculations about what could cause the differences in respiration and also what consequences these could have, or what certain changes imply.
Nevertheless, this study is an important step towards this kind of analysis.
The following is the authors’ response to the original reviews.
Public Reviews:
Reviewer #1 (Public Review):
Although this study provides a comprehensive outlook on the ETC function in various tissues, the main caveat is that it's too technical and descriptive. The authors didn't invest much effort in putting their findings in the context of the biological function of the tissue analyzed, i.e., some tissues might be more glycolytic than others and have low ETC activity.
To better contextualize our results, we have added substantial amount of new information to the Discussion Section.
Also, it is unclear what slight changes in the activity of one or the other ETC complex mean in terms of mitochondrial ATP production.
Unfortunately, the method we used can only determine oxygen consumption rate through complex I (CI), CII, or CIV. It cannot tell us about ATP production. This method only measures maximal uncoupled respiration.
Likely, these small changes reported do not affect the mitochondrial respiration.
We are indeed looking at mitochondrial respiration. Some changes are more dramatic while others are much more modest. We are looking at the normal aging process across tissues (focusing on mitochondrial respiration) and not pathological states. As such, we expect many of the changes in mitochondrial respiration across tissues to be mild or relatively modest. After all, aging is slow and progressive. In fact, the variations we observed in mitochondrial respiration across tissues are consistent with the known heterogenous rate of aging across tissues.
With such a detailed dataset, the study falls short of deriving more functionally relevant conclusions about the heterogeneity of mitochondrial function in various tissues. In the current format, the readers get lost in the large amount of data presented in a technical manner.
We agree that the paper contains a large amount of information. In the revised manuscript, we did our best to contextualize our results by substantially expanding the Discussion Section.
Also, it is highly recommended that all the raw data and the values be made available as an Excel sheet (or other user-friendly formats) as a resource to the community.
We included all the data in two excel sheets (Figure 1 – data source 1; Figure 1 – data source 2). We presented them in such as way that it will be easy for other investigators to follow and re-use our dataset in their own studies for comparison.
Major concerns
(1) In this study, the authors used the method developed by Acin-Perez and colleagues (EMBO J, 2020) to analyze ETC complex activities in mitochondria derived from the snap-frozen tissue samples. However, the preservation of cellular/mitochondrial integrity in different types of tissues after being snap-frozen was not validated.
All the samples are actually maximally preserved due to being snap frozen. Freezing the samples disrupts the mitochondria to produce membrane fragments. Subsequent thawing, mincing, and homogenization in a non-detergent based buffer (mannose-sucrose) ensures that all tissue samples are maximally disrupted into fragments which contain ETC units in various combinations. This allows the assay to give an accurate representation of maximal respiratory capacity given the ETC units present in a tissue sample.
Since aging has been identified as the most important effector in this study, it is essential to validate how aging affects respiration in various fresh frozen tissues. Such analysis will ensure that the results presented are not due to the differential preservation of the mitochondrial respiration in the frozen tissue. In addition, such validations will further strengthen the conclusions and promote the broad usability of this "new" method.
The reason we adopted this method is because it has been rigorously validated in the original publication (PMID: 32432379) and a subsequent methods paper (PMID: 33320426). The authors in the original paper benchmarked their frozen tissue method with freshly isolated mitochondria from the same set of tissues. Their work showed highly comparable mitochondrial respiration from frozen tissues and isolated mitochondria. For this reason, we did not repeat those validation studies.
(2) In this study, the authors sampled the maximal activity of ETC complex I, II, and IV, but throughout the manuscript, they discussed the data in the context of mitochondrial function.
We apologize that we did not make it clearer in our manuscript. We corrected this in our revised manuscript (the Discussion Section). Our method we measure respiration starting at Complex I (CI; via NADH), starting at CII (via succinate), or starting at CIV (using TMPD and ascorbate). Regardless of whether electrons (donated by the substrate) enter the respiratory chain through CI, CII or CIV, oxygen (as the final electron acceptor) is only consumed at CIV. Therefor, the method measures mitochondrial respiration and function through CI, CII, or CIV. This high-resolution respirometry analysis method is different from the classic enzymatic method of assessing CI, CII, or CIV activity individually; the enzymatic method does not actually measure oxygen consumption due to electrons flowing through the respiratory complexes.
However, it is unclear how the changes in CI, CII, and CIV activity affect overall mitochondrial function (if at all) and how small changes seen in the maximal activity of one or more complexes affect the efficiency and efficacy of ATP production (OxPhos).
Please see the preceding response to the previous question. The method is measuring mitochondrial respiration through CI, CII or CIV. The limitation of this method is that it is maximal uncoupled respiration; namely, mitochondrial respiration is not coupled to ATP synthesis since the measurements are not performed on intact mitochondria. As such, we cannot say anything about the efficiency and efficacy of ATP production. This will be an interesting future studies to further investigating tissue level variations of mitochondrial OXPHOS.
The authors report huge variability between the activity of different complexes - in some tissues all three complexes (CI, CII, and CIV) and often in others, just one complex was affected. For example, as presented in Figure 4, there is no difference in CI activity in the hippocampus and cerebellum, but there is a slight change in CII and CIV activity. In contrast, in heart atria, there is a change in the activity of CI but not in CII and CIV. However, the authors still suggest that there is a significant difference in mitochondrial activity (e.g., "Old males showed a striking increase in mitochondrial activity via CI in the heart atria....reduced mitochondrial respiration in the brain cortex..." - Lines 5-7, Page 9). Until and unless a clear justification is provided, the authors should not make these broad claims on mitochondrial respiration based on small changes in the activity of one or more complexes (CI/CII/CIV). With such a data-heavy and descriptive study, it is confusing to track what is relevant and what is not for the functioning of mitochondria.
We have attempted to address these issues in the revised Discussion section.
(3) What do differences in the ETC complex CI, CII, and CIV activity in the same tissue mean? What role does the differential activity of these complexes (CI, CII, and CIV) play in mitochondrial function? What do changes in Oxphos mean for different tissues? Does that mean the tissue (cells involved) shift more towards glycolysis to derive their energy? In the best world, a few experiments related to the glycolytic state of the cells would have been ideal to solidify their finding further. The authors could have easily used ECAR measurements for some tissues to support their key conclusions.
We have attempted to address these issues in the revised Discussion section. The frozen tissue method does not involve intact mitochondria. As such, the method cannot measure ECAR, which requires the presence of intact mitochondria.
(4) The authors further analyzed parameters that significantly changed across their study (Figure 7, 98 data points analyzed). The main caveat of such analysis is that some tissue types would be represented three or even more times (due to changes in the activity of all three complexes - CI, CII, and CIV, and across different ages and sexes), and some just once. Such a method of analysis will skew the interpretation towards a few over-represented organ/tissue systems. Perhaps the authors should separately analyze tissue where all three complexes are affected from those with just one affected complex.
Figure 7 summarizes the differences between male vs female, and between young vs old. All the tissue-by-tissue comparisons (data separated by CI-linked respiration, CII-linked respiration, and CIV-linked respiration) can be found in earlier figures (Figure 1-6).
The focus of Figure 7 is to helps us better appreciate all the changes seen in the preceding Figure 1-6:
Panel A and B indicate all changes that are considered significant
Panel C indicates total tissues with at least one significantly affected respiration
Panel D indicates total magnitude of change (i.e., which tissue has the highest OCR) offering a non-relative view
Panel E indicates whole body separations
Panel F indicates whole body separations and age vs sex clustering
(5) The current protocol does not provide cell-type-specific resolution and will be unable to identify the cellular source of mitochondrial respiration. This becomes important, especially for those organ systems with tremendous cellular heterogeneity, such as the brain. The authors should discuss whether the observed changes result from an altered mitochondria respiratory capacity or if changes in proportions of cell types in the different conditions studied (young vs. aged) might also contribute to differential mitochondrial respiration.
We agree with the reviewer that this is a limitation of the method. We have addressed this issue in the revised Discussion section.
(6) Another critical concern of this study is that the same datasets were repeatedly analyzed and reanalyzed throughout the study with almost the same conclusion - namely, aging affects mitochondrial function, and sex-specific differences are limited to very few organs. Although this study has considerable potential, the authors missed the chance to add new insights into the distinct characteristics of mitochondrial activity in various tissue and organ systems. The author should invest significant efforts in putting their data in the context of mitochondrial function.
We have attempted to address these issues in the revised Discussion section.
Reviewer #2 (Public Review):
Summary:
The authors utilize a new technique to measure mitochondrial respiration from frozen tissue extracts, which goes around the historical problem of purifying mitochondria prior to analysis, a process that requires a fair amount of time and cannot be easily scaled up.
Strengths:
A comprehensive analysis of mitochondrial respiration across tissues, sexes, and two different ages provides foundational knowledge needed in the field.
Weaknesses:
While many of the findings are mostly descriptive, this paper provides a large amount of data for the community and can be used as a reference for further studies. As the authors suggest, this is a new atlas of mitochondrial function in mouse. The inclusion of a middle aged time point and a slightly older young point (3-6 months) would be beneficial to the study.
We agreed with the reviewer that inclusion of additional time points (e.g., 3-6 months) would further strengthen the study. However, the cost, labor, and time associated with another set of samples (660 tissue samples from male and female mice and 1980 respirometry assays) are too high for our lab with limited budget and manpower. Regrettably, we will not be able to carry out the extra work as requested by the reviewer.
Reviewer #3 (Public Review):
The aim of the study was to map, a) whether different tissues exhibit different metabolic profiles (this is known already), what differences are found between female and male mice and how the profiles changes with age. In particular, the study recorded the activity of respirasomes, i.e. the concerted activity of mitochondrial respiratory complex chains consisting of CI+CIII2+CIV, CII+CIII2+CIV or CIV alone.
The strength is certainly the atlas of oxidative metabolism in the whole mouse body, the inclusion of the two different sexes and the comparison between young and old mice. The measurement was performed on frozen tissue, which is possible as already shown (Acin-Perez et al, EMBO J, 2020).
Weakness:
The assay reveals the maximum capacity of enzyme activity, which is an artificial situation and may differ from in vivo respiration, as the authors themselves discuss. The material used was a very crude preparation of cells containing mitochondria and other cytosolic compounds and organelles. Thus, the conditions are not well defined and the respiratory chain activity was certainly uncoupled from ATP synthesis. Preparation of more pure mitochondria and testing for coupling would allow evaluation of additional parameters: P/O ratios, feedback mechanism, basal respiration, and ATP-coupled respiration, which reflect in vivo conditions much better. The discussion is rather descriptive and cautious and could lead to some speculations about what could cause the differences in respiration and also what consequences these could have, or what certain changes imply.
Nevertheless, this study is an important step towards this kind of analysis.
We have attempted to address some of these issues in the revised Discussion Section. The frozen tissue method can only measure maximal uncoupled respiration. Because we are not measuring mitochondrial respiration using intact mitochondria, several of the functional parameters the reviewer alluded to (e.g., P/O ratios, feedback mechanism, basal respiration, and ATP-coupled respiration) simply cannot be obtained with the current set of samples. Nevertheless, we agree that all the additional data (if obtained) would be very informative.
Reviewer #1 (Recommendations For The Authors):
(1) For most of the comparative analysis, the authors normalized OCR/min to MitoTracker Deep RedFM (MTDR) fluorescence intensity. Why was the data normalized to the total protein content not used for comparative analysis? Is there a correlation between MTDR fluorescence and the protein content across different tissues?
Given that we used the crude extract method, total protein content does not equal total mitochondrial protein content. This is why the MTDR method was used, as this represents a high throughput method of assessing mitochondrial mass in this volume of samples. In general, the total protein concentration is used to ensure the respiration intensity was approximately the same across all samples loaded into the Seahorse machine.
(2) To test the mitochondrial isolation yield, the authors should run immunoblot against canonical mitochondrial proteins in both homogenates and mitochondrial-containing supernatants and show that the protocol followed effectively enriched mitochondria in the supernatant fraction. This would also strengthen the notion that the "µg protein" value used to normalize the total mitochondrial content comes from isolated mitochondria and not other extra-mitochondrial proteins.
Because we are using crude tissue lysate (from frozen tissue), the total ug protein content does not come from isolated mitochondria; for this reason, it was not used and this is why MTDR was. Total mitochondrial protein content is subject to change depending on tissue for non-mitochondrial reasons. This method does not use isolated mitochondria; we only use tissue lysates enriched for mitochondrial proteins. This method has been rigorously validated in the original study (PMID: 32432379) and a subsequent methods paper (PMID: 33320426). In those studies, the authors had performed requisite quality checks the reviewer has asked for (e.g., immunoblot against canonical mitochondrial proteins in both homogenates and mitochondrial-containing supernatants to show effective enrichment of mitochondrial proteins). For this reason, we did not repeat this.
(3) MitoTracker loads into mitochondria in a membrane potential-dependent manner. The authors should rule out the possibility that samples from different ages and sexes might have different mitochondrial membrane potentials and exhibit a differential MitoTracker loading capacity. This becomes relevant for data normalization based on MTDR (MTDR/µg protein) since it was assumed that loading capacity is the same for mitochondria across different tissue and age groups.
MitoTracker Deep Red is not membrane potential dependent and can be effectively used to quantify mitochondrial mass even when mitochondrial membrane potential is lost. This is highlighted in the original study (PMID: 32432379).
(4) Page 11, line 3 typo - across, not cross.
Response: We have fixed the typo.
Reviewer #2 (Recommendations For The Authors):
If possible, I would include a middle aged time point between 12 and 14 months of age.
We agreed with reviewer that inclusion of additional time points (e.g., 3-6 months) would further strengthen the study. However, the cost, labor, and time associated with another set of samples (660 tissue samples from male and female mice and 1980 respirometry assays) are too high for our lab with limited budget and manpower. Regrettably, we will not be able to carry out the extra work as requested by the reviewer.
Reviewer #3 (Recommendations For The Authors):
Overall, the work is well done and the data are well processed making them easy to understand. Some minor adjustments would improve the manuscript further:
- Significance OCR in Figure 2, maybe add error bars?
We have added the error bars and statistical significance to revised Figure 2.
- Tissue comparison A-C, right panel: graphs are cropped
We are not sure what the reviewer meant here. We have double checked all our revised figures to make sure nothing is accidentally cropped.
- Heart ventricle: Old males and females have higher CI- and CII-dependent respiration than young males and females? Only CIV respiration is lower?
Comparing old to young male or female heart ventricle respiration via CI or CII shows an increase in maximal capacity with age. CIV-linked respiration is in the upward direction as well, although not significant, when comparing old to young. When comparing the respiration values among themselves within a mouse, i.e. old male CI- or CII-linked respiration compared to old male CIV- linked respiration, we can see that the old male CIV-linked respiration is very similar. When comparing the same in the old female mouse, there appears to be something special about electrons entering through CI as compared to CII or CIV, as CI-linked respiration appears to be elevated compared to both CII and CIV. Although we do not know if this is significantly different, the trend in the data is clear. We do not know the exact reason as to why this occurred in the heart ventricles. To differing degrees, the connected nature of CI-, CII-, and CIV-linked respirations seems to be in a generally similar style in most skeletal muscles as well, and the old male heart atria. Again, the root of this discrepancy is unknown and potentially indicates an interesting physiologic trait of certain types of muscle and merits further exploration.
- What is plotted in Fig.3: The mean of all OCR of all tissues? A,B,C: Plot with break in x-axis to expand the violin, add mean/median values as numbers to the graph (same for Fig4)
The left most side of Figure 3 A, B, and C shows the average OCR/MTDR value across all tissues in a group. Each tissue assayed is represented in the violin plot as an open circle.
- Fig. 3D: add YM/YF to graph for better understanding, same in following figures
This is in the scale bar next to all heat maps presented in the figures. We also added to the revised figure as well to improve clarity.
- Additional figures: x-axis title (time) is missing in OCR graphs
Time has been added to the x axis of all additional figures for clarity.
- Also a more general question is: where the concentrations of substrates and inhibitors optimized before starting the series of experiments?
All the details of assay optimization was carried out in the original study (PMID: 32432379) and the subsequent methods paper (PMID: 33320426). Because we had to survey 33 different tissues, we tested and optimized the “optimal” protein concentrations we need to use; the primary goal of this was to balance enough respiration signal without too much respiration signal across all tissue types as to keep all the diverse tissues analyzed under the Seahorse machine’s capabilities of detection. Through our optimization of mostly the very high respiring tissues like heart and kidney, we were also able to prove that all substrates and inhibitors were in saturating concentrations since we could get respiration to go higher if more sample was added and that all signal could be lost in these samples with the same amount of inhibitors.