Scaling mitochondrial volume in heart to body mass

Hoppeler, Hans; Lindstedt, Stan L.; Claassen, Horst; Taylor, Charles R.; Mathieu, Odile; Weibel, Ewald R.

doi:10.1016/0034-5687(84)90018-5

Cited by 74 publications

(61 citation statements)

References 14 publications

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“…Allometry in V max values for citrate synthase, a Krebs cycle enzyme that serves as an index of oxidative capacity, is observed in the skeletal muscles of mammals (Emmett and Hochachka, 1981) and fishes Somero and Childress, 1990). Consistent with these patterns is the decline in mitochondrial content observed in skeletal muscle, heart, liver, kidney and brain with increasing mass (Else and Hulbert, 1985a,b;Hoppeler et al, 1984;Mathieu et al, 1981). Because enzymes and mitochondria usually do not operate at their maximum capacities, allometry in flux capacities provides only a partial explanation for allometry in cellular O 2 consumption rates.…”

Section: Causation In Metabolic Scalingmentioning

confidence: 95%

Multi-level regulation and metabolic scaling

Suarez

Darveau

2005

Journal of Experimental Biology

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Metabolic rates scale allometrically, such that a unit mass of elephant has a metabolic rate much less than that of a unit mass of mouse. To empiricists, the question 'what causes the allometric scaling of metabolic rates?' is one that is inextricably linked to the question 'what determines metabolic rates?' This is because whatever phenomena determine metabolic rates should presumably play major roles in driving their allometric scaling. The fundamental importance of both questions has been recognized by biologists for a century, and studies addressing them have had long and illustrious histories. Introductions to the phenomenon of metabolic scaling and accounts of the historical development of ideas and studies devoted to the subject can be found in excellent books (Calder, 1984;Schmidt-Nielsen, 1984) and reviews (Calder, 1981;Hoppeler et al., 1980;Porter, 2001;Taylor, 1987;Weibel, 1987) and are beyond the scope of this article. Here, we discuss some empirical data from the literature of comparative physiology to address the issue of what determines metabolic rates in animals. We relate this information to the allometric scaling of metabolic rates and comment on recently proposed models for metabolic scaling. Animals as the sum of their partsWhen data for basal metabolic rate, BMR, are plotted against body mass, M b , on logarithmic coordinates, the slope of the linear relationship, referred to as the allometric exponent, b, in the equation:is significantly less than 1.0, where a is the vertical intercept (Schmidt-Nielsen, 1984 Metabolic control analysis has revealed that flux through pathways is the consequence of system properties, i.e. shared control by multiple steps, as well as the kinetic effects of various pathways and processes over each other. This implies that the allometric scaling of flux rates must be understood in terms of properties that pertain to the regulation of flux rates. In contrast, proponents of models considering the scaling of branching or fractal-like systems suggest that supply rates determine metabolic rates. Therefore, the allometric scaling of supply alone provides a sufficient explanation for the allometric scaling of metabolism. Examination of empirical data from the literature of comparative physiology reveals that basal metabolic rates (BMR) are driven by rates of energy expenditure within internal organs and that the allometric scaling of BMR can be understood in terms of the scaling of the masses and metabolic rates of internal organs. Organ metabolic rates represent the sum of tissue metabolic rates while, within tissues, cellular metabolic rates are the outcome of shared regulation by multiple processes. Maximal metabolic rates (MMR, measured as maximum rates of O 2 consumption, V O ∑ max ) during exercise also scale allometrically, are also subject to control by multiple processes, but are due mainly to O 2 consumption by locomotory muscles. Thus, analyses of the scaling of MMR must consider the scaling of both muscle mass and muscle energy expenditure. Consistent with t...

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Section: Causation In Metabolic Scalingmentioning

confidence: 95%

Multi-level regulation and metabolic scaling

Suarez

Darveau

2005

Journal of Experimental Biology

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“…[25,37]. Note 2: the allometric constants for mitochondrial volume fraction were selected from [34][35][36] for heart, liver and kidney respectively, for BS mass ranging from 0.02 kg to 250 kg. For kidney, the kidney cells are assumed to follow same allometric relation as reported for modular TAL.…”

Section: Derived Allometric Constantsmentioning

confidence: 99%

Biological Aging and Life Span Based on Entropy Stress via Organ and Mitochondrial Metabolic Loading

Annamalai

Nanda

2017

Entropy

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Abstract:The energy for sustaining life is released through the oxidation of glucose, fats, and proteins. A part of the energy released within each cell is stored as chemical energy of Adenosine Tri-Phosphate molecules, which is essential for performing life-sustaining functions, while the remainder is released as heat in order to maintain isothermal state of the body. Earlier literature introduced the availability concepts from thermodynamics, related the specific irreversibility and entropy generation rates to metabolic efficiency and energy release rate of organ k, computed whole body specific entropy generation rate of whole body at any given age as a sum of entropy generation within four vital organs Brain, Heart, Kidney, Liver (BHKL) with 5th organ being the rest of organs (R5) and estimated the life span using an upper limit on lifetime entropy generated per unit mass of body, σ M,life . The organ entropy stress expressed in terms of lifetime specific entropy generated per unit mass of body organs (kJ/(K kg of organ k)) was used to rank organs and heart ranked highest while liver ranked lowest. The present work includes the effects of (1) two additional organs: adipose tissue (AT) and skeletal muscles (SM) which are of importance to athletes; (2) proportions of nutrients oxidized which affects blood temperature and metabolic efficiencies; (3) conversion of the entropy stress from organ/cellular level to mitochondrial level; and (4) use these parameters as metabolism-based biomarkers for quantifying the biological aging process in reaching the limit of σ M,life . Based on the 7-organ model and Elia constants for organ metabolic rates for a male of 84 kg steady mass and using basic and derived allometric constants of organs, the lifetime energy expenditure is estimated to be 2725 MJ/kg body mass while lifetime entropy generated is 6050 kJ/(K kg body mass) with contributions of 190; 1835.0; 610; 290; 700; 1470 and 95 kJ/K contributed by AT-BHKL-SM-R7 to 1 kg body mass over life time. The corresponding life time entropy stresses of organs are: 1.2; 60.5; 110.5; 110.5; 50.5; 3.5; 3.0 MJ/K per kg organ mass. Thus, among vital organs highest stress is for heart and kidney and lowest stress is for liver. The 5-organ model (BHKL and R5) also shows similar ranking. Based on mitochondrial volume and 5-organ model, the entropy stresses of organs expressed in kJ/K per cm 3 of Mito volume are: 12,670; 5465; 2855; 4730 kJ/cm 3 of Mito for BHKL indicating brain to be highly stressed and liver to be least stressed. Thus, the organ entropy stress ranking based on unit volume of mitochondria within an organ (kJ/(K cm 3 of Mito of organ k)) differs from entropy stress based on unit mass of organ. Based on metabolic loading, the brains of athletes already under extreme mitochondrial stress and facing reduced metabolic efficiency under concussion are subjected to more increased stress. In the absence of non-intrusive measurements for estimating organ-based metabolic rates which can serve as metabolism-based biomarkers for biol...

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“…If b=1 then heart mass increases in direct proportion to body mass (isometric), if b>1 then heart mass increases faster than body mass (positive allometry or hyperallometry), and if b<1 then heart mass increases slower than body mass (negative allometry or hypoallometry). Phylogenetic analysis across a range of differentsized mammalian species shows that heart mass increases with body mass more-or-less isometrically, with an exponent (b) between 0.96 and 1.06 (Bishop, 1997;Brody, 1945;Holt et al, 1968;Hoppeler et al, 1984;Lindstedt and Schaeffer, 2002;Prothero, 1979;Seymour and Blaylock, 2000;Stahl, 1965). Analyses restricted to marsupial species also trend toward isometry, with an exponent between 0.94 and 1.05 (Dawson and Needham, 1981;Dawson et al, 2003).…”

Section: Introductionmentioning

confidence: 97%

Scaling of left ventricle cardiomyocyte ultrastructure across development in the kangaroo Macropus fuliginosus

Snelling

Taggart

Maloney

et al. 2015

Journal of Experimental Biology

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The heart and left ventricle of the marsupial western grey kangaroo Macropus fuliginosus exhibit biphasic allometric growth, whereby a negative shift in the trajectory of cardiac growth occurs at pouch exit. In this study, we used transmission electron microscopy to examine the scaling of left ventricle cardiomyocyte ultrastructure across development in the western grey kangaroo over a 190-fold body mass range (0.355−67.5 kg). The volume-density (%) of myofibrils, mitochondria, sarcoplasmic reticuli and T-tubules increase significantly during in-pouch growth, such that the absolute volume (ml) of these organelles scales with body mass (M b ; kg) with steep hyperallometry: , respectively. The steep hyperallometry of organelle volumes and volume-densities across in-pouch growth is consistent with the improved contractile performance of isolated cardiac muscle during fetal development in placental mammals, and is probably critical in augmenting cardiac output to levels necessary for endothermy and independent locomotion in the young kangaroo as it prepares for pouch exit. The shallow hypoallometry of organelle volumes during post-pouch growth suggests a decrease in relative cardiac requirements as body mass increases in free-roaming kangaroos, which is possibly because the energy required for hopping is independent of speed, and the capacity for energy storage during hopping could increase as the kangaroo grows.

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Scaling mitochondrial volume in heart to body mass

Cited by 74 publications

References 14 publications

Multi-level regulation and metabolic scaling

Multi-level regulation and metabolic scaling

Biological Aging and Life Span Based on Entropy Stress via Organ and Mitochondrial Metabolic Loading

Scaling of left ventricle cardiomyocyte ultrastructure across development in the kangaroo Macropus fuliginosus

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