• 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • Likewise and considering the aforementioned biological actio


    Likewise, and considering the aforementioned biological actions of ketogenic diet-derived MCFA on neuronal excitability, it could be postulated that specific receptors for fatty acids could also be involved. For example, the GPCR for medium to long chain fatty acids, FFA1/GPR40, might also contribute to regulate neuronal excitability. GPR40 is expressed in several 7545 sale regions, and it activates a signaling pathway that involves phosphatidylinositol metabolites and calcium mobilization [62]. The patho-physiological role of GPR40 has been investigated in the context of development, neurodegenerative diseases and neuroinflammation, but its direct effects in neuronal excitability have not been examined [62]. Further studies will be necessary to delineate whether GPR40 contributes to mediate MCFA effects on neuronal firing rates.
    Concluding remarks How can metabolism modulate neuronal excitability? In this review we have provided a brief overview on how different energy substrates can contribute to modulate neurotransmission independently of their value as a source of ATP (Fig. 1). These mechanisms are not always clearly defined, but the work done so far shows that they are highly diverse: changes in gene expression, chemical balance of neurotransmitters, roles as intracellular messengers or allosteric modulators, or roles as extracellular ligands of plasma membrane receptors, to cite some examples. Another important outstanding question is: 7545 sale How do neurons make a "decision" as to what carbon substrate they should use as a fuel? In extreme cases in which neuronal cells are exposed to an overwhelming amount of a given substrate with respect to other substrates, the decision is easy. This is what happens in individuals treated with the aforementioned ketogenic diet, in which the high amount of ketone bodies and the low fraction of carbohydrates available to neurons tilts the balance in favor of the former [18]. But what happens in situations when there are several nutrients to choose from? Are there any additional mechanisms that contribute to make the choice of fuel? Several efforts are underway to understand the mechanisms that dictate fuel choice. Certain signaling pathways integrate extracellular signals with other physiological cues to regulate metabolic flow to meet cellular needs [4]. The AMP-dependent kinase (AMPK) and the mTOR pathways are master regulators of nutrient and energy sensing and utilization [38], [63]. In fact, we have discussed above the potential implications of mTOR in neuronal excitability and epileptic seizures [39], [40]. In addition, other signaling pathways may also contribute to shape metabolic flow and fuel preference in the brain [4]. In this regard, it was shown that the BCL-2 protein BAD acts as a switch that sets the preference for glucose or ketone bodies depending on its phosphorylation status, thus affecting sensitivity to seizures [7]. Multiple signaling cascades can control phosphorylation of BAD [64], thus underscoring the idea that signaling cascades may contribute to set the fuel preference in neurons by modulating the activity of metabolic nodes such as BAD. Other members of the BCL-2 family of proteins have also been shown to influence energy metabolism in different ways [65]. The discovery of proteins that act as metabolic nodes may contribute to understand how signaling and metabolism are integrated and work together to modulate neuronal excitability.
    Acknowledgements We apologize to the authors whose original work could not be cited due to space limitations. This work was supported by grants from Spanish Ministerio de Economía y Competitividad (MINECO, grant BFU2016-77634-R), co-financed by Agencia Estatal de Investigación and Fondo Europeo de Desarrollo Regional (AEI/FEDER, EU); Hjärnfonden (FO2015-0067); Diabetesfonden (DIA2016-126); Petrus och Augusta Hedlunds Stiftelse (M-2016-0408) and Åke-Wibergs Stiftelse (M16-0072) to A.G.-C.; and grants from Karolinska Institutet Foundations to A.G.-C. and R.M.P.A. A.G.-C. is a recipient of a “Ramón y Cajal” fellowship (RYC-2014–15792) from Spanish Ministerio de Economía y Competitividad (MINECO). The authors declare no conflict of interest.