Peptidomics of a Single Identified Neuron Reveals Diversity of Multiple Neuropeptides with Convergent Actions on Cellular Excitability
Bibliographic record
Abstract
In contrast to classical transmitters, the detailed structures and cellular and synaptic actions of neuropeptides are less well described. Peptide mass profiling of single identified neurons of the mollusc Lymnaea stagnalis indicated the presence of 17 abundant neuropeptides in the cardiorespiratory neuron, visceral dorsal 1 (VD1), and a subset of 14 peptides in its electrically coupled counterpart, right parietal dorsal 2. Altogether, based on this and previous work, we showed that the high number of peptides arises from the expression and processing of four distinct peptide precursor proteins, including a novel one. Second, we established a variety of posttranslational modifications of the generated peptides, including phosphorylation, disulphide linkage, glycosylation, hydroxylation, N-terminal pyroglutamylation, and C-terminal amidation. Specific synapses between VD1 and its muscle targets were formed, and their synaptic physiology was investigated. Whole-cell voltage-clamp analysis of dissociated heart muscle cells revealed, as tested for a selection of representative family members and their modifications, that the peptides of VD1 exhibit convergent activation of a high-voltage-activated Ca current. Moreover, the differentially glycosylated and hydroxylated alpha2 peptides were more potent than the unmodified alpha2 peptide in enhancing these currents. Together, this study is the first to demonstrate that single neurons exhibit such a complex pattern of peptide gene expression, precursor processing, and differential peptide modifications along with a remarkable degree of convergence of neuromodulatory actions. This study thus underscores the importance of a detailed mass spectrometric analysis of neuronal peptide content and peptide modifications related to neuromodulatory function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".