# Henry Markram and colleagues | Breakdown and repair of metabolism in the aging brain
Aging-associated changes in metabolism alter electrophysiological characteristics
We show for the first time how aging in the metabolic system leads to changes in the generation of action potentials by both synaptic input (Figure 3) and current injection (Presentation 1: Supplementary Figure S5). Age-related differences in neuronal firing characteristics evoked by current injection are particularly important for decomposing NGV energy use because this type of stimulation protocol excludes the metabolic demand caused by glutamate release. We found similar changes in metabolic profiles following synaptic input and current injection (Presentation 1: Supplementary Figure S14), suggesting that metabolic changes mostly impact the action potential generation ability of neurons. However, the model would require a more detailed molecular coupling between the metabolic system and the entire glutamate cycle to strengthen this prediction.
We found that changes in action potential shape and size are caused by a reduction in Na+/K±ATPase expression in the aged brain, supporting a recent theory of non-canonical control of neuronal energy status (35). To better understand whether other aspects of the metabolic system, such as reduced supply of ATP, also contribute to these changes, we increased the Na+/K±ATPase expression levels in the aged brain model to match the young brain while leaving all other aspects of the aging metabolic system in their aged state. There were no significant differences in action potentials at low frequencies (4–8 Hz) and only slight changes at much higher frequencies (78–79 Hz), suggesting that the decreased expression of the Na+/K±ATPase pump is the main factor impairing the ability of neurons to generate action potentials. However, it is still possible that other aspects of the NGV metabolic network become more important after sustained neuronal activity, such as those used during intense cognitive demand.
Lower supply and demand for energy in the aged brain
Although energy deficiency is a prominent hypothesis in brain aging (12), it is not clear if the supply is limited and/or demand is reduced; it is also unclear whether astrocytes and neurons are impacted in the same way. Adenylate energy charge (AEC), a widely used proxy for cellular energy availability (36), is higher in the young state than in the aged (Figure 4B). However, this value does not separate supply from demand. To separate the two factors, we first computed the total ATP cost of firing action potentials. We found that the young brain model consumes approximately 2 billion ATP molecules per second per NGV unit (where one unit is one neuron, one astrocyte, and their associated extracellular matrix and capillaries) with 8 Hz firing, while the aged brain model consumes around 1.8 billion molecules per second per unit, which aligns well with literature estimates (37–39). We found that ATP production is lower in the aged cytosol of both neurons and astrocytes and in aged neuronal mitochondria (Presentation 1: Supplementary Figure S2). However, ATP consumption is also lower (Figure 4C) due to the lower levels of Na+/K±ATPase (40, 41), and therefore ATP supply is not necessarily a limiting factor. Nevertheless, while reduced ATP supply does not seem to limit action potential generation in the acute state, a persistently lower ATP supply may still cause Na+/K±ATPase expression to decrease, thereby impairing action potential generation over a longer period.
We also found that neurons and astrocytes are differentially affected by aging. Normally, astrocytic Na+/K±ATPases consume slightly less than two-thirds as much ATP as neuronal Na+/K±ATPases (Figure 4D). In astrocytes, the ATP supply is only reduced in the cytosol and not in the mitochondria, and the catalytic subunit of the Na+/K±ATPases expression is unchanged with aging. While ATP consumption of the Na+/K±ATPase pump in neurons decreases with aging (Figure 4C), it slightly increases in astrocytes—resulting in an increase in the ratio of astrocyte to neuron Na+/K±ATPase ATP consumption from around 0.69 in the young brain to around 0.72 in the aged. Since astrocytes do not need to fire action potentials, this finding suggests that there is an increased demand on astrocytes to support the neurons to clear extracellular K+ in order to help neurons generate their action potentials.
The model shows that Na+/K+ pump ATP use in the astrocyte is comparable with that of the neuron (Figure 4D), consistent with recent evidence (42). In line with previous studies (43), mitochondrial ATP production as a share of total ATP production is higher in neurons than in astrocytes, at 84% versus 70% (Presentation 1: Supplementary Figure S2).
Applying the RNAseq data (26, 27) to the respective metabolic pathways revealed that succinate dehydrogenase (SDH) is differentially affected by aging in neurons and astrocytes. SDH is a mitochondrial energy nexus and serves as complex II of the mitochondrial electron transport chain (ETC). SDH connects the tricarboxylic acid cycle (TCA) to the ETC. This result indicates that pre- and post-SDH enzymes of TCA (fumarase and succinate CoA ligase) display opposite changes in aged neurons and astrocytes. SDH itself decreases more in aged neurons than in aged astrocytes. In neurons, aging reduces both succinate CoA ligase and SDH, while increasing fumarase. Unlike in neurons, succinate CoA ligase levels rise in astrocytes during aging. SDH decreases slightly while fumarase levels decline further.
^polina’s phd thesis, uses julia too (tho idk about neuroblox that SUNY stony brook prof uses)
optimization using a combination of diet (lower blood glucose and higher blood beta-hydroxybutyrate), exercise (higher blood lactate), and NAD-related supplementation and modulation of the cytosol-mitochondria NAD-associated reducing equivalents shuttle (hereafter referred to as DEN therapy), resulted in increase ATP levels in both neurons and astrocytes towards values of the young metabolic system comparable to that of the top-scoring drug targets (Fig. 5d, Supplementary Table 3