Exploring the Relationship Between Extracellular Vesicles, the Dendritic Cell Immunoreceptor and MicroRNA-155 in an In Vivo Model of HIV-1 Infection to Understand the Disease and Develop New Treatments
Bibliographic record
Abstract
Abstract HIV-1 infection induces persistent immune system activation despite antiretroviral therapy. New immunomodulatory targets might be required to restore immune competence. The dendritic cells immunoreceptor (DCIR) can bind HIV-1 and regulate immune functions and extracellular vesicles (EVs) production. EVs have emerged as biomarkers and a non-invasive tool to monitor HIV-1 progression. In people living with HIV-1, an increase in the size and abundance of EVs is associated with a decline in the CD4/CD8 T cells ratio, a key marker of immune dysfunction. Analysis of host nucleic acids within EVs has revealed an enrichment of microRNA-155 (miR-155) during HIV-1 infection. Experiments have demonstrated that miR-155-rich EVs enhance HIV-1 infection in vitro. A humanized NSG-mice model was established to assess the in vivo impact of miR-155-rich EVs. Co-production of virus with miR-155-rich EVs heightened the viral load and lowered the CD4/CD8 ratio in the mice. Upon euthanasia, EVs were isolated from plasma for size and quantity assessment. Consistent with findings in individuals with HIV-1, increased EVs size and abundance were inversely correlated with the CD4/CD8 ratio. Next, by using the more closely related physiological virus co-product with EV-miR-155, we tested a DCIR inhibitor to limit infection and immune damage in a humanized mouse model. DCIR inhibition reduced infection and partially restored immune functions. Finally, viral particles and various EV subtypes can convey HIV-1 RNA. HIV-1 RNA was predominantly associated with large EVs (200-1000nm) rather than small EVs (50-200nm). Viral loads in large EVs strongly correlated with blood and tissue markers of immune activation. The humanized mice model has proven its applicability to studying the roles of EVs on HIV-1 infection and investigating the impact of DCIR inhibition. Author Summary Despite more than 40 years of research, HIV remains a threat to public health around the world. People living with HIV are efficiently treated with antiretroviral therapy, but damage to the immune system persists and the causes remain unknown. Extracellular vesicles allow material, such as microRNA, to transfer between cells. Here, we evaluated the impact of one microRNA, microRNA-155, transported by extracellular vesicles, on HIV infection. Mice were grafted with a human immune system to allow infection by HIV. We showed that extracellular vesicles carrying microRNA-155 amplified mice infection. Extracellular vesicles also reflect the state of their cell of origin. Their analysis can reveal biomarkers to monitor HIV infection. Thus, HIV viral load was quantified in purified extracellular vesicles. We found that the measurement of HIV viral load in purified EVs is a more precise biomarker of disease progression than the traditional plasma viral load. Additionally, potential treatments like DCIR inhibitors improve our ability to manage HIV-1 by restoring the CD4/CD8 ratio, a critical element of the infection process. Overall, our study highlighted the importance of extracellular vesicle cargo in a humanized mouse model of HIV-1 infection, as well as the potential of targeting DCIR to restore the immune response. Highlights MicroRNA-155 promotes HIV-1 infection of humanized NSG mice Abundance and size of total plasmatic EVs are biomarkers of immune dysfunction associated with HIV-1 infection DCIR inhibition limits HIV-1 infection of humanized NSG mice and attenuates immune impairment HIV-1 RNA enrichment in large EVs was associated with biomarkers of immune activation and dysfunction
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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 | 0.001 | 0.002 |
| 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".