Decision letter: Gene and protein expression and metabolic flux analysis reveals metabolic scaling in liver ex vivo and in vivo

Abstract

Full text Figures and data Side by side Abstract Editor's evaluation Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract Metabolic scaling, the inverse correlation of metabolic rates to body mass, has been appreciated for more than 80 years. Studies of metabolic scaling have largely been restricted to mathematical modeling of caloric intake and oxygen consumption, and mostly rely on computational modeling. The possibility that other metabolic processes scale with body size has not been comprehensively studied. To address this gap in knowledge, we employed a systems approach including transcriptomics, proteomics, and measurement of in vitro and in vivo metabolic fluxes. Gene expression in livers of five species spanning a 30,000-fold range in mass revealed differential expression according to body mass of genes related to cytosolic and mitochondrial metabolic processes, and to detoxication of oxidative damage. To determine whether flux through key metabolic pathways is ordered inversely to body size, we applied stable isotope tracer methodology to study multiple cellular compartments, tissues, and species. Comparing C57BL/6 J mice with Sprague-Dawley rats, we demonstrate that while ordering of metabolic fluxes is not observed in in vitro cell-autonomous settings, it is present in liver slices and in vivo. Together, these data reveal that metabolic scaling extends beyond oxygen consumption to other aspects of metabolism, and is regulated at the level of gene and protein expression, enzyme activity, and substrate supply. Editor's evaluation Key metabolic processes have been shown to scale inversely with the body mass of different animals. This study provides direct evidence for metabolic scaling of key metabolic fluxes in the livers of mice and rats, as well as species-specific differences in the transcription and expression of enzymes involved in energy metabolism that could contribute to metabolic scaling. The finding suggests that metabolic scaling likely reflects multiple levels of regulation and have broad implications for studying animal metabolism and physiology. https://doi.org/10.7554/eLife.78335.sa0 Decision letter Reviews on Sciety eLife's review process Introduction In 1932, Max Kleiber published a seminal study (Kleiber, 1932), integrating prior reports demonstrating a phenomenon that came to be termed ‘Kleiber’s law,’ or the principle of metabolic scaling. Metabolic scaling refers to the phenomenon that the metabolic processes in many animals, if not all, scale inversely to three-quarters of their body mass (West et al., 1997). In simpler terms, there is a reduction in metabolic rate as body size increases. For example, an elephant is 25 million times larger than a fruit fly, yet its energy expenditure is only 20 thousand times higher; thus, from the fruit fly to elephant, the metabolic rate per gram of body weight scales down 1250 times. While there is experimental evidence for metabolic scaling from bacteria to large mammals, data have been generated almost exclusively from observations of caloric intake and oxygen consumption, with gene and protein expression, and substrate fluxes almost entirely unexplored. The concept of hierarchical regulation, whereby gene expression initiates the cascade that allows for the flux of metabolic pathways (Rossell et al., 2005; Suarez and Moyes, 2012), provides a systems framework to begin to understand scaling. Beginning at the transcriptional level, we studied liver gene expression across five species: mice (Mus musculus), rats (Rattus norvegicus), monkeys (Macaca mulatta), humans (Homo sapiens), and cattle (bos taurus), species with a 30,000-fold range of average body weight in adults (from 30 g in mice, to 900 kg in cattle). Numerous metabolic genes related to glycolysis, gluconeogenesis, fatty acid metabolism, oxygen consumption, electron transport, and redox function, and detoxification of oxidative damage, were expressed at levels inverse to body size. Further analysis of liver proteomics revealed that approximately half of the genes in the liver that were expressed inversely proportionally to body size at the transcriptional level, were also expressed at levels inversely proportional to body size at the level of protein expression. To determine if gene and protein expression would correlate with enzyme activity and metabolic flux, we performed a comprehensive assessment of liver metabolism in vivo and in vitro using modified Positional Isotopomer NMR Tracer Analysis (PINTA) (Perry et al., 2017b) and stable isotope-derived turnover (Perry et al., 2015) methods. Our analysis shows that rats exhibit lower metabolic rates when compared to mice in and ex vivo; however, no significant differences were observed when we isolated hepatocytes and cultured them in vitro under identical conditions. Taken together, this study demonstrates the variation of metabolic fluxes according to body size, extending prior studies of metabolic scaling, and provides unique insight into the regulation of metabolic flux across species. Results Genes within the liver that are expressed inversely proportional to body weight are predominantly metabolic genes We examined gene expression in livers from mice (Mus musculus), rats (Rattus norvegicus), monkeys (Macaca mulatta), humans (Homo sapiens), and cattle (Bos taurus). Using recent advances in high throughput mRNA sequencing and bioinformatics tools that allow for intra-species data preprocessing (Bray et al., 2016; Conesa et al., 2016; Ritchie et al., 2015), we searched for a set of genes in the liver, the metabolic hub of mammals, whose expression correlates inversely with body mass. After normalizing for differences in transcript length and abundance across species, we filtered out genes that followed the pattern of mouse >rat > monkey >human > cow. The genes that met these criteria were predominantly related to metabolic pathways, including pyruvate metabolism, amino acid metabolism, and glucose metabolism (Figure 1A). Genes from this list were further restricted to genes involved in amino acid, carbohydrate, energy, lipid, vitamin, and TCA cycle metabolism, and demonstrated a range of degrees of inverse correlation with body mass, with only TCA cycle genes clustering together (Figure 1B). Figure 1 Download asset Open asset Genes that follow the pattern of allometric scaling are most strongly related to metabolism. (A) KEGG Pathway enrichment of all genes that are expressed with an inverse correlation to body mass, and (B) clustering heatmap of scaled genes that belong to one of six Reactome metabolic superpathways. All samples were obtained from males. For clarity, the human gene (and style of writing human gene names) are shown. RAPGEF, rap guanine nucleotide exchange factor; ELOVL2, Elongation of Very Long Chain Fatty Acids-Like 2; MDH1, malate dehydrogenase 1; LIPE, hormone-sensitive lipase E; PANK1, pantothenate kinase 1; PGK1, phosphoglycerate kinase 1; SDC4, syndecan 4; ALDH7A1, aldehyde dehydrogenase 7 family member A1; GPX1, glutathione peroxidase 1; GLUL, glutamate-ammonia ligase; SORD, sorbitol dehydrogenase; TDO2, tryptophan 2,3-dioxygenase; DLST, dihydrolipoamide S-succinyltransferase; ACACA, acetyl-CoA carboxylase-alpha; ADIPOR1, adiponectin receptor-1; GPT, glutamic-pyruvate transaminase; HS3ST3B1, heparan sulfate-glucosamine 3-sulfotransferase 3B1; PSMD5, proteasome 26 S subunit, non-ATPase-5; COX8A, cytochrome c oxidase subunit 8 A; NDUFA9, NADH:ubiquinone oxidoreductase subunit A9. Genes encoding enzymes involved in hepatic metabolism are expressed inversely proportionally to body mass and involve metabolite detoxification, intertissue metabolism, substrate metabolism, electron transport, and NAD metabolism In order to further understand the functional aspects of the metabolic genes that are expressed inversely proportionally to body size, the gene list from Figure 1B was categorized into several functional categories, converging on optimizing energy provision, oxidative metabolism, and damage control from oxidative stress and ammonia (Figure 2). Furthermore, to understand whether or not certain genes that are expressed inversely proportionally to body size involved anabolic or catabolic processes, they were further classified by their properties to be energy suppliers or consumers. Eleven of sixteen critical metabolic enzymes that scaled required molecular oxygen, NAD+/NADH, or ATP/ADP for function, possibly indicating exquisite regulation of energy-consuming processes at the individual gene level. Genes involved in the detoxication of lipid peroxidation-derived aldehydes (ALDH7A1), hydrogen peroxide (GPX1), and ammonia (GLUL) suggest scaling of damage control mechanisms that are associated with increased oxidative metabolism across species (Figure 2A). The inverse correlation between body size and expression of genes that are associated with interorgan crosstalk is consistent with scaling in vivo which would not be expected in plated cells. For example, the differentially expressed genes include GPT1, which is involved in recycling skeletal muscle-derived alanine back to liver-derived glucose (Felig and Wahren, 1971; Petersen et al., 2019), and the adiponectin receptor (ADIPOR1), which binds an adipose tissue-derived hormone that regulates gluconeogenesis and fatty acid oxidation (Lin and Accili, 2011; Li et al., 2020 Figure 2B). Genes involved in fatty acid metabolism included the rate-limiting steps of the synthesis of CoA (PANK1), of de novo fatty acid synthesis (ACACA), and of fatty acid elongation (ELOVL2), in addition to the oxidation of diacylglycerols (LIPE) (Figure 2C). NAD and ATP-dependent genes involved in glycolysis (PDK1), fructose/glucose metabolism (SORD1), and DLST of the TCA cycle also correlated inversely with body size (Figure 2D–E). Differentially regulated genes also couple oxygen consumption to NAD provision (MDH1, TDO2), and are involved with the function of the electron transport chain (subunits of complex I, NDUFA9, and complex IV, COX8A, which catalyzes oxygen accepting the final electrons of the electron transport chain) (Figure 2F–G). Figure 2 with 1 supplement see all Download asset Open asset Metabolic genes that are expressed inversely proportionally to body size implicate key pathways in substrate and nucleotide supply, glucose and fatty acid flux, oxygen consumption, and detoxification pathways. mRNA expression of key regulatory genes related to metabolite detoxication (A), intertissue metabolism (B), fatty acid metabolism (C), glucose metabolism (D), tricarboxylic acid (TCA) cycle, NAD metabolism (F), and the electron transport chain (G) in mice, rats, monkeys, humans, and cattle. Bars denote expression levels by an organism, following the same order shown in the cartoon of organisms. Expression was normalized to counts per million and was then further normalized for sequencing depth and transcript length. All genes met an adjusted p-value threshold of 0.01 using a one-way ANOVA with the Bonferroni correction for multiple comparisons. All samples were obtained from males (n=2 replicates per species). ALDH7A1, aldehyde dehydrogenase 7 family member A1; GPX1, glutathione peroxidase 1; GLUL, glutamate-ammonia ligase; GPT, glutamic-pyruvate transaminase; ADIPOR1, adiponectin receptor-1; LIPE, hormone-sensitive lipase E; PANK1, pantothenate kinase 1; ACACA, acetyl-CoA carboxylase-alpha; ELOVL2, Elongation of Very Long Chain Fatty Acids-Like 2; SORD, sorbitol dehydrogenase; PGK1, phosphoglycerate kinase 1; DLST, dihydrolipoamide S-succinyltransferase; TDO2, tryptophan 2,3-dioxygenase; MDH1, malate dehydrogenase 1; NDUFA9, NADH:ubiquinone oxidoreductase subunit A9; COX8A, cytochrome c oxidase subunit 8 A. Figure 2—source data 1 Source data for Figure 2 and Figure 2—figure supplement 1. https://cdn.elifesciences.org/articles/78335/elife-78335-fig2-data1-v1.xlsx Download elife-78335-fig2-data1-v1.xlsx To examine the possibility that the inverse correlation between body mass and gene expression observed in the transcriptomics analysis could be a consequence of global alterations in mRNA (for example, as a consequence of alterations in RNA turnover rates), we performed targeted quantitative polymerase chain reaction (qPCR), measuring in liver tissue abundance of mRNA encoding several enzymes that were found to scale in the five-species transcriptomics analysis, relative to the common housekeeping gene β-actin (Actb). We found that all three enzymes (Glul, Lipe, and Dlst) scaled relative to Actb (Figure 2—figure supplement 1A–C), whereas structural genes (collagenase 3 [Mmp3] and Larp1) did not (Figure 2—figure supplement 1D–E), indicating that the differences in metabolic gene expression observed across species is likely not a result of global changes in RNA levels. In addition to transcriptomics, we assessed proteomics data to evaluate the protein levels corresponding to the genes that were found to be expressed inversely proportionally to body size at the level of mRNA expression. Our proteomics data were limited to mouse, rat, and human, as all the open-source proteomic databases that we identified lacked data from monkey or cow. An important limitation for finding such data is that even with careful post-processing, we cannot combine data from different studies, because differences in methods of tissue preparation may influence results. Therefore, we were limited to a single experiment that had generated proteomics data for mouse, rat, and human using the same experimental procedures. The dataset contained protein expression corresponding to eight of the twenty genes identified to scale in our transcriptomics data analysis. Of these, three (GLUL, GPX1, and MDH1) were found to follow a reverse correlation with body size (Figure 3A–C). Interestingly, one of these proteins (GLUL) was also found to be expressed inversely proportionally to body size in the left ventricle of the heart (Figure 3D). Additionally, we measured liver transaminase concentrations and observed that both alanine aminotransferase (ALT) and aspartate aminotransferase (AST) exhibited lower concentrations in humans as compared to rats and rats as compared to mice (Figure 3E–F), consistent with scaling at the level of protein expression as well as mRNA expression. Finally, we utilized established enzymatic assays to measure the activity of peroxidase and pyruvate carboxylase in the livers of mice and rats. 30–40% lower activity of each enzyme per mg tissue was observed in rats as compared to mice (Figure 3—figure supplement 1A–B), suggesting scaling at the level of metabolic enzyme activity. Figure 3 with 1 supplement see all Download asset Open asset Proteomics reveals a negative correlation between body size and the expression of some liver proteins. Liver (A) glutamate-ammonia ligase (GLUL), (B) glutathione peroxidase 1 (GPX1), and (C) malate dehydrogenase 1 (MDH1) protein expression. (D) GLUL protein expression in the left ventricle of the heart. The proteomics analysis was performed on n=1 per species, so statistical comparisons were not possible. (E) Plasma alanine aminotransferase (ALT) and (F) aspartate aminotransferase (AST) concentrations (for both transaminases, n=5 per species). *p<0.05, ***p<0.001, ****p<0.0001. Figure 3—source data 1 Source data for Figure 3 and Figure 3—figure supplement 1. https://cdn.elifesciences.org/articles/78335/elife-78335-fig3-data1-v1.xlsx Download elife-78335-fig3-data1-v1.xlsx Metabolic rates of mouse vs. rat hepatocytes in vitro are not significantly different Considering prior data reporting higher oxygen consumption per unit body mass in smaller as compared to larger animals (Gilman et al., 2013; Brody, 1945; Urbina and Glover, 2013), we first asked whether these differences were cell-intrinsic, or whether in vivo or hepatocyte-extrinsic signals are required. We incubated plated hepatocytes in [3-13C] lactate and first validated that the data met the assumptions of PINTA, including reaching steady-state in [13C] lactate and glucose enrichment, and producing glucose at a linear rate throughout the 6 hr incubation (Figure 4—figure supplement 1A–C). Consistent with the possibility that hepatocyte-extrinsic signals are primarily responsible for metabolic scaling, when we used PINTA to assess cytosolic and mitochondrial fluxes, we observed no significant differences between species in any of the fluxes measured in plated hepatocytes: glucose production, VPC, VCS, the contribution of glucose or fatty acids to the tricarboxylic acid (TCA) cycle, or lipolysis (Figure 4A–H, Figure 4—figure supplement 1D–F). Similarly, a mitochondrial stress test in plated hepatocytes revealed no difference in any parameter: neither basal mitochondrial and non-mitochondrial respiration, ATP production, maximal (uncoupled) respiration, spare respiratory capacity, nor proton leak differed between plated hepatocytes from mice and rats (Figure 4I). Previous studies have demonstrated scaling in vitro in cell suspensions only when analyzed immediately after hepatocyte isolation (Porter and Brand, 1995), and have suggested that the phenomenon of scaling gradually disappears around 24 hr post removal (Brown et al., 2007), similar to the conditions in which we performed these studies. Most prior in vitro studies have also demonstrated an absence of scaling, in contrast to in vivo (Glazier, 2015), and we extend these results to gluconeogenic and lipolytic fluxes in hepatocytes, glucose production in liver slices, and multimodal flux analysis in vivo. Figure 4 with 1 supplement see all Download asset Open asset Metabolic fluxes are not different between mouse and rat hepatocytes in vitro. (A) Study design. This figure was made using Biorender.com. (B) Tracer labeling strategy. (C) Glucose production. (D) Gluconeogenesis from pyruvate (pyruvate carboxylase flux, VPC). (E) Citrate synthase flux (VCS), i.e., mitochondrial oxidation. (F) Pyruvate dehydrogenase flux (VPDH), i.e., the contribution of glucose via glycolysis to total mitochondrial oxidation. (G) Non-esterified fatty acid (NEFA) production. (H) The contribution of fatty acid oxidation to citrate synthase flux. (I) Oxygen consumption rate (OCR) during a mitochondrial stress test. In all panels, hepatocytes from wild-type males were studied, and groups were compared using the two-tailed unpaired Student’s t-test. No significant differences were observed. In all panels, the mean ± SEM. of six biological replicates (averaged from three technical replicates per biological replicate) is shown. Figure 4—source data 1 Source data for Figure 4 and Figure 4—figure supplement 1. https://cdn.elifesciences.org/articles/78335/elife-78335-fig4-data1-v1.xlsx Download elife-78335-fig4-data1-v1.xlsx Glucose production per gram tissue is higher ex vivo in liver slices from mice than in rats Next, considering that hepatocytes comprise approximately 70–80% of liver mass and that their culture in vitro does not replicate in vivo conditions (Krebs, 1950), we asked whether glucose production would be different between mice and rats in slices of liver. Indeed, we found that liver glucose production per gram liver mass was threefold greater in mouse liver slices as compared to rats (Figure 5A–B), suggesting that hepatocyte-extrinsic signals (for example, from other liver cell types) are involved in liver metabolic scaling. Figure 5 Download asset Open asset Glucose production scales ex vivo in liver slices. (A) Study design. This figure was made using Biorender.com. (B) Glucose production. Groups were compared by the two-tailed unpaired Student’s t-test. Liver slices from male, wild-type animals (n=4 mice and 2 rats, three technical replicates per biological replicate) were studied. Figure 5—source data 1 Source data for Figure 5. https://cdn.elifesciences.org/articles/78335/elife-78335-fig5-data1-v1.xlsx Download elife-78335-fig5-data1-v1.xlsx Metabolic rates in multiple tissue types are higher in vivo in mice relative to rats We utilized multimodal stable isotope metabolic flux analysis to compare rats and mice with respect to a panel of metabolic fluxes (Figure 6A). First, we validated tracer assumptions in vivo, including the metabolic and isotopic steady state in plasma and negligible liver glycogen concentrations, although in the recently fed state, hepatic glycogenolysis was higher in mice than that in rats (Figure 6—figure supplement 1A–G). Using PINTA, we found that both endogenous glucose production and gluconeogenesis from pyruvate (VPC) per gram liver were more than twofold higher in mice than rats (Figure 6B–C), although the fractional contribution of pyruvate to gluconeogenesis did not differ between mice and rats (Figure 6—figure supplement 1H). Mitochondrial oxidation scaled similarly, increasing threefold in mice as compared to rats studied under the same conditions, due to increases in both glucose oxidation (pyruvate dehydrogenase flux, VPDH) and fatty acid oxidation (Figure 6D–F), associated with an increase in the ratio of pyruvate carboxylase anaplerosis to citrate synthase flux (VPC/VCS) without any difference in the fraction of VCS flux fueled by glucose through PDH (Figure 6—figure supplement 1I–J). While we did not have the capacity to measure liver fluxes in larger mammals in the current study, endogenous glucose production, VPC, and VCS previously measured using PINTA were 50–60% lower in overnight fasted humans than in rats (Petersen et al., 2019), assuming a liver size of 1500 g in humans. These differences in metabolic fluxes according to body size applied not only to rodent liver metabolism but also to adipose tissue metabolism: whole-body fatty acid turnover, reflecting lipolysis, was 2.5-fold higher in mice than in rats (Figure 6G). No sex differences were observed in any of the measured fluxes (Figure 6—figure supplement 1K–P). Taken together, these data emphasize the inadequacy of common in vitro methods as a readout of in vivo metabolism: whereas in vivo mitochondrial oxidation (TCA cycle flux) was threefold higher in mice than in rats, in vitro measurements of oxygen consumption throughout a mitochondrial stress test, TCA cycle flux, and glucose production were not different between the species (Figure 4). Figure 6 with 1 supplement see all Download asset Open asset Analysis of systemic metabolic fluxes suggests in vivo metabolic scaling in mice vs. rats. (A) Study design. (B) Endogenous glucose production. (C) Gluconeogenesis from pyruvate (VPC). (D) VCS, i.e., mitochondrial oxidation. (E) VPDH, i.e., the contribution of glucose via glycolysis to total mitochondrial oxidation. (F) Palmitate (fatty acid) turnover. (G) The contribution of fatty acid oxidation to citrate synthase flux. In all panels, groups were compared using the two-tailed unpaired Student’s t-test. Male rodents (n=4 mice and 6 rats) were studied. Figure 6—source data 1 Source data for Figure 6 and Figure 6—figure supplement 1. https://cdn.elifesciences.org/articles/78335/elife-78335-fig6-data1-v1.xlsx Download elife-78335-fig6-data1-v1.xlsx Classical clustering defined species-specific clusters based on in vivo metabolic fluxes but not in vitro fluxes A clustering dendrogram was applied to our in vitro flux data and showed no distinct clustering between species (Figure 7A–B). However, the in vivo metabolic flux data led to distinct clustering of rats and mice (Figure 7C), providing a classical clustering-based objective analysis of in vitro versus in vivo metabolic flux. Figure 7 Download asset Open asset Comparison of in vitro and in vivo results. (A) Study workflow. (B) Clustering heatmap demonstrating the absence of metabolic differences in vitro. (C) Clustering heatmap demonstrating metabolic differences between mice and rats in vivo. In panels (B) and (C), mouse and rat color legends correspond to the species label attached to the dendrogram on the leftmost of each graph. Vpc = pyruvate carboxylase flux, Vcs = citrate synthase flux, Vpdh = pyruvate dehydrogenase flux, Vfao = fatty acid oxidation, NEFA = non-esterified fatty acid concentrations. All data presented in Figures 3 and 5 were utilized in the classical clustering analysis and are included in this figure. Discussion Oxygen consumption has been shown to scale inversely with body mass in species ranging in mass across 20 orders of magnitude, from 10–14 to 106 grams (Ernest et al., 2003; Gillooly et al., 2001; Kleiber, 1932; Makarieva et al., 2005a; Makarieva et al., 2008; Savage et al., 2004; West et al., 2002). This phenomenon has been most studied in mammals, but is highly conserved, having also been shown to occur in prokaryotes (Fenchel and Finlay, 1983; Makarieva et al., 2005a; Makarieva et al., 2005a; Makarieva et al., 2008; Moses et al., 2008), plants (Makarieva et al., 2005b; Mori et al., 2010; Reich et al., 2006), insects (Chown et al., 2007; Maino and Kearney, 2014; Makarieva et al., 2005a), fish (Clarke and Johnston, 1999; Gjoni et al., 2020; Rubalcaba et al., 2020), and birds (Glazier, 2008; Hudson et al., 2013; Makarieva et al., 2005b). However, a major limitation of prior studies in this field has been that observations have been largely limited to oxygen consumption and caloric intake, leaving other metabolic processes unexplored. This study sought to address this issue by examining the generalizability of the inverse relationship between body mass and metabolic rates, using both experimental measurements and previously assembled databases that have not previously been employed in this context. It is important to note that the metabolic processes which we observed to be higher in mice as compared to rats did not necessarily adhere quantitatively to the classic metabolic scaling relationship, with metabolic rates proportional to three-quarters of body mass. This speaks to the idea that the scaling relationship is multidimensional: it is entirely conceivable that whole-body oxygen consumption could be proportional to three-quarters of body mass, while other metabolic processes may exhibit a different scaling relationship. Further studies across species beyond rodents will be required to address this question. The possibility that gene expression, as reflected by mRNA abundance, may also scale with body mass has not been previously addressed. We observed that the expression of key genes in glycolysis, gluconeogenesis, fatty acid metabolism, NAD synthesis and transport, mitochondrial oxygen consumption, and protection from oxidative damage scale with body mass. More compelling, however, is the observation that those genes for which an inverse relationship of expression with body mass is observed, are not randomly distributed across the genome. Rather, the collection of genes whose expression is inversely correlated with body mass is enriched for genes related to metabolic processes, and whose corresponding proteins’ enzymatic action are constrained by the supply of substrate, NAD, ATP, or oxygen. The notion that body mass is a variable related to the level of expression of certain genes has not previously been considered as an aspect of metabolic scaling. However, it should be noted that metabolic scaling cannot fully be explained at the transcriptional level, because many rate-limiting enzymes in the metabolic processes measured in vivo did not scale at the transcriptional level, and only approximately half of genes that scaled at the level of mRNA scaled at the level of protein. Thus, it is likely that both transcriptional and other mechanisms – such as enzyme activity – are responsible for variations in metabolic flux per unit mass, inversely proportionally to body size. Additionally, the currently available data do not allow us to assess whether the expression of certain isoforms of key metabolic enzymes scales differentially across species. It is also informative to contrast the lack of an inverse relationship between body size and metabolic fluxes per tissue weight (oxygen consumption, mitochondrial oxidation, lipolysis, and glucose production in hepatocytes) measured in the in vitro setting, to our findings in vivo (where all fluxes proceeded faster in mice than in rats). This emphasizes the need to employ tracer methods in vivo to generate a comprehensive picture of differences in metabolic fluxes between species. Our findings emphasize that measurement of oxygen consumption in vitro may fail to detect any influence of scaling processes present in vivo. Glucose production was threefold higher in mouse liver slices relative to rat liver slices, but did not significantly differ between plated hepatocytes from mice and rats. Future studies using metabolic flux analysis may have the further capacity to generate new insights as to the scope and mechanism of metabolic scaling (Wiechert, 2001). For example, our data do not allow us to ascertain whether differences in oxygen consumption orchestrate metabolic alterations, as has been suggested in the setting of cancer cells (Nakazawa et al., 2016), or metabolic alterations require changes in oxygen consumption. Additionally, there are limitations to the fact that metabolic flux studies were performed only in the two arguably most related species included in the transcriptomics analysis (with monkey and human perhaps being similarly related). Our laboratory does not have the capacity to perform flux analysis in larger or smaller species, but we fully recognize that widening our scope beyond the 10-fold range in body size between mice and r