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Record W2966737673 · doi:10.3390/cells8080787

An Omics Approach to Extracellular Vesicles from HIV-1 Infected Cells

2019· article· en· W2966737673 on OpenAlexaff
Robert A. Barclay, Pooja Khatkar, Gifty Mensah, Catherine DeMarino, Jeffery S. C. Chu, Benjamin Lepene, Weidong Zhou, Patrick M. Gillevet, Bahareh Torkzaban, Kamel Khalili, Lance A. Liotta, Fatah Kashanchi

Bibliographic record

VenueCells · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsApplied Biological Materials (Canada)
FundersNational Institute of Neurological Disorders and StrokeNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteNational Institutes of Health
KeywordsMicrovesiclesBiologyExtracellular vesiclemicroRNAKinaseRNAHuman immunodeficiency virus (HIV)VirologyExtracellularCell biologyExtracellular vesiclesGeneGenetics

Abstract

fetched live from OpenAlex

Human Immunodeficiency Virus-1 (HIV-1) is the causative agent of Acquired Immunodeficiency Syndrome (AIDS), infecting nearly 37 million people worldwide. Currently, there is no definitive cure, mainly due to HIV-1's ability to enact latency. Our previous work has shown that exosomes, a small extracellular vesicle, from uninfected cells can activate HIV-1 in latent cells, leading to increased mostly short and some long HIV-1 RNA transcripts. This is consistent with the notion that none of the FDA-approved antiretroviral drugs used today in the clinic are transcription inhibitors. Furthermore, these HIV-1 transcripts can be packaged into exosomes and released from the infected cell. Here, we examined the differences in protein and nucleic acid content between exosomes from uninfected and HIV-1-infected cells. We found increased cyclin-dependent kinases, among other kinases, in exosomes from infected T-cells while other kinases were present in exosomes from infected monocytes. Additionally, we found a series of short antisense HIV-1 RNA from the 3' LTR that appears heavily mutated in exosomes from HIV-1-infected cells along with the presence of cellular noncoding RNAs and cellular miRNAs. Both physical and functional validations were performed on some of the key findings. Collectively, our data indicate distinct differences in protein and RNA content between exosomes from uninfected and HIV-1-infected cells, which can lead to different functional outcomes in recipient cells.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.007
GPT teacher head0.217
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2019
Admission routes1
Has abstractyes

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