Disruption of Neuronal Autophagy by Infected Microglia Results in Neurodegeneration
Bibliographic record
Abstract
There is compelling evidence to support the idea that autophagy has a protective function in neurons and its disruption results in neurodegenerative disorders. Neuronal damage is well-documented in the brains of HIV-infected individuals, and evidence of inflammation, oxidative stress, damage to synaptic and dendritic structures, and neuronal loss are present in the brains of those with HIV-associated dementia. We investigated the role of autophagy in microglia-induced neurotoxicity in primary rodent neurons, primate and human models. We demonstrate here that products of simian immunodeficiency virus (SIV)-infected microglia inhibit neuronal autophagy, resulting in decreased neuronal survival. Quantitative analysis of autophagy vacuole numbers in rat primary neurons revealed a striking loss from the processes. Assessment of multiple biochemical markers of autophagic activity confirmed the inhibition of autophagy in neurons. Importantly, autophagy could be induced in neurons through rapamycin treatment, and such treatment conferred significant protection to neurons. Two major mediators of HIV-induced neurotoxicity, tumor necrosis factor-alpha and glutamate, had similar effects on reducing autophagy in neurons. The mRNA level of p62 was increased in the brain in SIV encephalitis and as well as in brains from individuals with HIV dementia, and abnormal neuronal p62 dot structures immunoreactivity was present and had a similar pattern with abnormal ubiquitinylated proteins. Taken together, these results identify that induction of deficits in autophagy is a significant mechanism for neurodegenerative processes that arise from glial, as opposed to neuronal, sources, and that the maintenance of autophagy may have a pivotal role in neuroprotection in the setting of HIV infection.
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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".