Profiling changes in cortical astroglial cells following chronic stress
Bibliographic record
Abstract
Recent studies have suggested that cortical astroglia play an important role in depressive-like behaviors. Potential astroglial contributions have been proposed based on their known neuroplastic functions, such as glutamate recycling and synaptic plasticity. However, the specific mechanisms by which astroglial cells may contribute or protect against a depressive phenotype remain unknown. To delineate astroglial changes that accompany depressive-like behavior, we used astroglial-specific bacTRAP mice exposed to chronic variable stress (CVS) and profiled the astroglial translatome using translating ribosome affinity purification (TRAP) in conjunction with RNAseq. As expected, CVS significantly increased anxiety- and depressive-like behaviors and corticosterone levels and decreased GFAP expression in astroglia, although this did not reflect a change in the total number of astroglial cells. TRAPseq results showed that CVS decreased genes associated with astroglial plasticity: RhoGTPases, growth factor signaling, and transcription regulation, and increased genes associated with the formation of extracellular matrices such as perineuronal nets (PNNs). PNNs inhibit neuroplasticity and astroglia contribute to the formation, organization, and maintenance of PNNs. To validate our TRAPseq findings, we showed an increase in PNNs following CVS. Degradation of PNNs in the prefrontal cortex of mice exposed to CVS reversed the CVS-induced behavioral phenotype in the forced swim test. These data lend further support to the neuroplasticity hypothesis of depressive behaviors and, in particular, extend this hypothesis beyond neuronal plasticity to include an overall decrease in genes associated with cortical astroglial plasticity following CVS. Further studies will be needed to assess the antidepressant potential of directly targeting astroglial cell function in models of depression.
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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 | 0.000 |
| 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".