Matchmaking: SK channels, C‐boutons and motor units
Bibliographic record
Abstract
For several decades, there has been much fascination with the large cholinergic boutons that form synapses on spinal motoneurons. These boutons are associated with motoneuron subsurface cisterns and hence are called C-boutons. In recent years, several groups have been studying the anatomy and physiology of these relatively large synapses that densely innervate the somata and proximal dendrites of α-motoneurons. For example, we have learned about some molecules clustered in the membrane at the postsynaptic site (mAChR2, Kv2.1), and that these synapses can increase motoneuronal excitability by reducing the post-spike afterhyperpolarisation (AHP; Miles et al. 2007). This leads to an increase in motoneuron firing rate and hence an increase in muscle contraction force. Now, in this issue of The Journal of Physiology, the work by Deardorff et al. (2013) adds to this understanding by showing that two subtypes of channels responsible for the AHP are also clustered at this site, and that different types of motoneurons have different complements of these subtypes. This opens the door to the future possibility of understanding differences between fast and slow motoneurons from a molecular point of view during development and injury. In motoneurons, the AHP functions to limit the maximum rate of firing and to reduce variability in inter-spike intervals. The duration of the AHP following a single action potential differs between motoneurons, and is correlated to the type of muscle fibre innervated by that particular motoneuron (Bakels and Kernell, 1993). Motoneurons that innervate the fastest twitch fibres have the shortest AHPs, thus allowing them to fire at higher rates than those innervating slow twitch fibres, which have longer duration AHPs. In other words, the AHP of a given motoneuron is matched to the contractile speed of the muscle fibre type it innervates. However, the molecular basis underlying the differences in AHP durations and the matching of motoneuron and muscle properties remain unresolved. Now, Deardorff and colleagues beautifully demonstrate that in addition to mAChR2 receptors and KV2.1 channels, SK channels – small conductance calcium-activated potassium channels responsible for the AHP – cluster at C-boutons. They demonstrate in breath-taking images that SK channels, in fact, interdigitate with KV2.1 channels at these synaptic sites. Furthermore, although both SK2 and SK3 channels are seen in this location in all cat and smaller rodent α-motoneurons, larger mouse and rat motoneurons do not express SK3. Through in vivo intracellular recordings in adult rats, they demonstrate that motoneurons lacking SK3 channel clusters have shorter duration AHPs. It thus seems probable that the relative proportion of SK2 and SK3 channels controls the overall AHP duration, with SK3 channels lengthening the AHP duration. This SK isoform distribution would then phenomenologically account for the match between motoneuron and muscle properties. Of course, many questions remain unanswered. There is a third isoform of apamin-sensitive SK channels, SK1, but their expression in motoneurons is not known. It has been shown that in HEK cells, SK1 proteins can interact with both SK2 and SK3 proteins and modulate channel properties and channel trafficking to the cell membrane (Monaghan et al. 2004). Furthermore, it is not known how mAChR2 activation at C-bouton synapses leads to a reduction in AHP or how different motoneuron types respond to this activation. Interestingly, the behavioural deficit noted in mice lacking functional C-boutons was more apparent in swimming than in walking mice (Zagoraiou et al. 2009). Swimming may require a steeper recruitment gain in motor pools (Hultborn et al. 2004), leading to earlier and greater recruitment of fast motoneurons and fast-twitch muscle fibres. In that case, might differential modulation of SK2 and SK3 channels via mAChR2 activation alter this gain to result in greater force production? As to why these channels are clustered at synaptic sites, we might suppose that their function depends on tight control of calcium nanodomains, which may be regulated by subsurface cisterns. But how do these molecules all coalesce at this site? Presumably, there are as yet unidentified scaffolding complexes. Interestingly, as demonstrated in this paper, KV2.1 channels at sites distant to C-bouton synapses are not associated with SK channels, indicating that there may be different mechanisms that anchor these proteins to particular sites. We would expect that in the near future, additional molecules will be discovered that will shed light on the clustering process and the regulation of local calcium activity. Furthermore, how are motoneuron properties matched to the properties of the muscle fibres they innervate – which is the chicken and which is the egg? Could the complement of SK channel subtypes be the sole (or main) factor that leads to motoneuron subtypes? When during development do the SK channels appear – before or after muscle innervation? Answers to these questions will significantly further our understanding of neuromuscular development. We conclude that while there is clearly more to be discovered about C-boutons and some important questions remain, this synapse may provide a window through which we can further understand motoneuronal subtype development. This is an important question not only for physiologists, but also for those studying disease mechanisms, given that the most susceptible motoneurons in motoneuron diseases such as amyotrophic lateral sclerosis are fast motoneurons. In addition, there are changes to motoneuron fast–slow properties following injury or in response to exercise that are not yet understood. The work by Deardorff et al. could certainly help to further our understanding of “the speed match between motoneurons and muscle units” (Bakels and Kernell, 1993).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".