Loss of the psychiatric risk factor SLC6A15 is associated with increased metabolic functions in primary hippocampal neurons
Bibliographic record
Abstract
Major depressive disorder (MDD) is one of the most severe global health problems with millions of people affected, however, the mechanisms underlying this disorder is still poorly understood. Genome-wide association studies have highlighted a link between the neutral amino acid transporter SLC6A15 and MDD. Additionally, a number of preclinical studies support the function of this transporter in modulating levels of brain neurotransmitters, stress system regulation and behavioural phenotypes related to MDD. However, the molecular and functional mechanisms involved in this interaction are still unresolved. Therefore, to investigate the effects of the SLC6A15 transporter, we used hippocampal tissue from Slc6a15-KO and wild-type mice, together with several in-vitro assays in primary hippocampal neurons. Utilizing a proteomics approach we identified differentially regulated proteins that formed a regulatory network and pathway analysis indicated significantly affected cellular domains, including metabolic, mitochondrial and structural functions. Furthermore, we observed reduced release probability at glutamatergic synapses, increased mitochondrial function, higher GSH/GSSG redox ratio and an improved neurite outgrowth in primary neurons lacking SLC6A15. In summary, we hypothesize that by controlling the intracellular concentrations of neutral amino acids, SLC6A15 affects mitochondrial activity, which could lead to alterations in neuronal structure and activity. These data provide further indication that a pharmacological or genetic reduction of SLC6A15 activity may indeed be a promising approach for antidepressant therapy.
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".