Characterizing Human and Viral Proteins in the HIV-1 Envelope Using Novel Methods in Flow Virometry
Bibliographic record
Abstract
The human immunodeficiency virus (HIV) presents a global public health challenge, with estimates of 38 million people worldwide living with HIV in 2021. While highly effective HIV treatments have been developed to prolong the lives of people living with HIV, continued research on cure and vaccine strategies remain critical. The viral envelope glycoprotein (Env) is the sole viral protein present on the surface of virions and is the primary target of vaccine designs. However, human proteins are also abundantly present on the HIV surface and can impact viral phenotypes including homing and infectivity. Thus, developing experimental tools to characterize proteins on the surface of HIV may help identify new antiviral targets or improve vaccine designs. Despite the importance of characterizing viral surface proteins, current techniques available for this purpose do not support high throughput analysis of individual virions, typically only offering semi-quantitative assessments of virus-associated proteins. To address this gap, we adapted flow cytometry, a technique traditionally used for rapid, high sensitivity characterization of single cells, for use on individual virus particles. Using a flow cytometer with nanoscale detection sensitivity, we applied flow cytometry to viruses, termed ‘flow virometry’ (FV), by developing quantitative protocols to stain cellular and viral glycoproteins on the surface of viruses directly in cell culture supernatants. Using FV and complementary techniques, we demonstrated that the virion-incorporated protein PSGL-1 remains functionally active and can facilitate HIV infection through a mechanism of viral capture and transfer to permissive cells, expanding known roles for this restriction factor. Next, we used FV methodology to discover a panel of novel human proteins present on the HIV surface, among which we found CD38 to be the most intriguing. Finally, we used FV as a new tool to study changes in HIV Env conformations, highlighting the use of FV for vaccine and antiviral studies. Taken together, this thesis demonstrates new advances in calibrated FV as a tool to provide sensitive, high throughput characterization of HIV envelope proteins in a quantitative manner. It also showcases the versatility of FV for protein screening, discovery, and for evaluating subtle changes in protein conformation on single virions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".