The brain-specific factor FEZ1 is a determinant of neuronal susceptibility to HIV-1 infection
Bibliographic record
Abstract
Neurons are one of the few cell types in the human body that do not support HIV type-1 (HIV-1) replication. Although the lack of key receptors is a major obstacle to infection, studies suggest that additional functions inhibit virus replication to explain the exquisite resistance of neurons to HIV-1. However, specific neuronal factors that may explain this resistance remain to be discovered. In a screen for antiviral factors using a fibroblast line chemically mutagenized and selected for resistance to retroviral infection, we recently identified induction of rat FEZ1 (fasciculation and elongation protein zeta-1), a brain-specific protein, as the cause of this resistance. When exogenously expressed in nonneuronal cell lines rat FEZ1 blocked nuclear entry of retroviral DNA. Here, we demonstrate that among human brain cells, neurons naturally express high levels of FEZ1 compared to astrocytes or microglia cells and are correspondingly less susceptible to infection with pseudotyped HIV-1 that bypasses receptor-mediated viral entry. Demonstrating that endogenous FEZ1 was functionally important in the resistance of neurons to HIV-1 infection, siRNA-mediated knockdown of endogenous FEZ1 increased the infectivity of neurons while sensitive brain cell types like microglia became more resistant upon FEZ1 overexpression. In addition, FEZ1 expression was not induced in response to IFN treatment. As such, in contrast to other widely expressed, IFN-inducible antiviral factors, FEZ1 appears to represent a unique neuron-specific determinant of cellular susceptibility to infection in a cell type that is naturally resistant to HIV-1.
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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".