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Record W1595832798 · doi:10.1113/jp271075

Coupling cellular metabolism to neuronal signalling

2015· editorial· en· W1595832798 on OpenAlexafffundabout
Derek Bowie, David Attwell

Bibliographic record

VenueThe Journal of Physiology · 2015
Typeeditorial
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsCoupling (piping)SignallingMetabolismNeuroscienceChemistryCellular metabolismCell biologyBiophysicsBiologyBiochemistryMaterials science

Abstract

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Although it has long been appreciated that much of the brain's energy is devoted to neuronal signalling, it is unclear if and how these two processes are coupled. To explore this emerging area of neuroscience, The Journal of Physiology sponsored a symposium at The Physiological Society's annual meeting, Physiology 2014, in London to bring together five researchers (see Fig. 1) whose work is at the forefront of this rapidly developing field of study. This issue of The Journal of Physiology brings together six timely review articles from some of these speakers and other researchers whose work captures some of the ideas, discussions and debates that arose during the 2014 Symposium on ‘Coupling cellular metabolism to neuronal signalling’. Derek Bowie from McGill University presented recent data from his lab demonstrating that mitochondrial derived reactive oxygen species (ROS) strengthen inhibitory transmission by GABAA receptors in cerebellar stellate cells (Accardi et al. 2014). Although often associated with oxidative stress in neurodegenerative disease, these observations suggest that ROS act as a putative homeostatic signalling molecule at central synapses. An additional surprise was that ROS strengthen GABAergic synapses by recruiting α3-containing GABA receptors rather than accumulating more α1-containing receptors resident to stellate cell synapses. Since α3-receptors are targeted to postsynaptic sites that are functionally distinct from those for α1-receptors, it was concluded that ROS strengthen only a subset of all inhibitory synapses. In keeping with this, recent work from their lab has shown that insulin strengthens GABAergic synapses of cerebellar granule cells by a similar mechanism (Accardi et al. 2015). Taken together, a general mechanism is proposed whereby α1-containing GABA receptors resident to inhibitory synapses form the hardwiring of neuronal circuits with receptors of a different subunit composition fulfilling a fundamental but unappreciated role in synapse strengthening. Engl and Attwell from University College London review the non-signalling or house-keeping energy expenditure of the brain (Engl & Attwell, 2015). The Attwell lab has already developed a metabolic budget for the cost of signalling in the brain, estimating that as much as 80% is consumed on reversing sodium ion gradients generated by action potential firing and glutamatergic transmission (Attwell & Laughlin, 2001; Harris et al. 2012). Although the brain is thought to spend less than half of its total energy on non-signalling tasks, it remains to be established how energy usage is distributed amongst the different processes. To better understand this distribution, the authors reviewed published experimental data on energy use by the major subcellular processes involved in housekeeping such as actin treadmilling, protein and lipid synthesis, and proton leak in mitochondria. An unexpected challenge was that results from different studies were often incompatible due to differences in animal models used and differences in their developmental stage and experimental protocols therein. As a result, these confounding factors introduced a degree of uncertainty in drawing firm conclusions about each housekeeping activity. The authors conclude by highlighting the need for more research on this topic to better define how the brain expends energy on non-signalling tasks. Lee and colleagues from New York University School of Medicine examine the emerging idea that ROS lie at the hub of a number of signalling pathways in the brain (Lee et al. 2015). The authors focus on the role of hydrogen peroxide (H2O2) which the Rice lab has shown to be an endogenous modulator of dopamine release in the striatum (Chen et al. 2001). This observation is clinically relevant since the loss of dopaminergic innervation from the substantia nigra to the striatum is a key pathology that underlies early symptoms of Parkinsonism (Lotharius & Brundin, 2002). The authors describe how an elevation in the concentration of H2O2 derived from the mitochondria inhibits dopamine release by acting on ATP-sensitive K+ channels (Avshalumov et al. 2005). In contrast, H2O2 enhances the activity of the non-selective cation channel TRPM2 expressed by GABAergic projection neurons in the dorsal striatum and projecting neurons from the substantia nigra pars reticulata (Lee et al. 2013). Accordingly, mitochondrial H2O2 plays a key role in establishing the balance between inhibitory and excitatory input into and within the striatum. Given that the striatum is a key target in the treatment of Parkinson's disease, further insight into the role of H2O2 in this CNS region may provide clues into how to treat this most debilitating disease. The role of cytosolic and mitochondrial calcium (Ca2+) in shaping the cell's bioenergetics is the focus of the review by Llorente-Folch and colleagues (Llorente-Folch et al. 2015) from the Universidad Autónoma de Madrid and University College London. Although the mitochondria have long been associated with buffering cytosolic Ca2+ (Miller, 1991), the authors point out that a key role of sequestered Ca2+ is to stimulate ATP production. Newly synthesized ATP is then used to meet the cell's energy demands during periods of heightened activity. Intracellular Ca2+ achieves this in a number of ways. Elevations in cytosolic Ca2+ stimulate the transfer of metabolites, such as pyruvate, across the inner mitochondrial membrane whereas the citric acid cycle and ATP synthase activity are upregulated by [Ca2+] rises within the mitochondrial matrix. A further complication is that different Ca2+ loads alter mitochondrial bioenergetics by distinct mechanisms. For example, the aspartate–glutamate exchanger Aralar/AGC1 (Slc25a12), a component of the malate–aspartate shuttle (MAS), responds to small and modest workloads through changes in cytosolic Ca2+. In contrast, large workloads stimulate ATP production in the absence of the MAS and rely upon stimulating cellular respiration by elevating matrix [Ca2+]. The authors conclude by providing examples of how deficits in Ca2+ handling impact mitochondrial bioenergetics that in turn lead to pathology and disease. Roger Thompson from the University of Calgary reviews the role played by pannexin channels in the neurotoxicity and ensuing cell death accompanying cerebral ischaemia (Thompson, 2015). Pannexin channels are a family of membrane-bound proteins that are homologous to innexins found in invertebrates (Dahl & Muller, 2014). In vertebrates, pannexins form transmembrane channels that permit the transfer of small and large molecules between intracellular and extracellular space (Dahl & Muller, 2014). Although the overactivation of NMDA-type ionotropic glutamate receptors (NMDARs) is one of the primary triggers of ischaemic cell death (Choi, 1992), the author presents a compelling argument linking NMDAR signalling to the activation of pannexin channels via Src family kinases (Thompson et al. 2006, 2008). In the hippocampus, the C-terminal region of pannexin1 (Panx1) is the main target of Src kinases, which may directly regulate channel gating by phosphorylation or through allosteric coupling between the channel and the kinase. Since an interfering peptide can disrupt the regulation of Panx1 by NMDARs, it is proposed that understanding this mechanism may lead to novel therapies for cerebral ischaemia. The article by Brisco and Hass from the University of British Columbia examines emerging data that link mitochondria and the transcription factor myocyte enhancer factor 2 (MEF2) to the structural and functional plasticity of glutamatergic synapses (Brusco & Haas, 2015). The Haas lab first identified a MEF2 signalling pathway in sculpting neuronal circuits of the optic tectum of the developing Xenopus brain (Chen et al. 2012). As its name implies, MEF2 was originally discovered because of its role in differentiating myocytes (Lilly et al. 1994) before its widespread expression was noted in the vertebrate brain and subsequently linked to synapse development (Flavell et al. 2008). The authors argue that MEF2 and mitochondrial function are closely aligned since MEF2 is a target of mitochondrial apoptotic caspases and also controls mitochondrial genome transcription responsible for the production of ROS. Most unexpectedly, the authors conclude that numerous mitochondrial pathways normally associated with apoptotic cell death are, in fact, also important players in the normal physiology of healthy neurons where they mediate plasticity and metaplasticity mechanisms. Finally, Bagriantsev and Gracheva from Yale University review the complex molecular mechanisms that give rise to temperature adaptation in many vertebrate species (Bagriantsev & Gracheva, 2015). The authors highlight the central role of the transient receptor potential (TRP) family of ion channels in the sensory perception of temperature. Some TRP channels, such as TRPM8, contribute to the detection of cold in somatosensory neurons, whereas others, such as TRPV1, detect hot temperatures in excess of 42°C. A major challenge in understanding how somatosensory systems have adapted during evolution is limited by the available experimental models. For example, most studies have attempted to understand temperature adaptation by focusing on TRP channel biology in mouse and rat animal models. Though valuable, the authors argue that the way forward is to seek out evolutionarily distant vertebrate species. Only then will it be possible to pinpoint the general principles by which all species sense and regulate their temperature. None declared.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.256
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2015
Admission routes3
Has abstractyes

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