MétaCan
Menu
Back to cohort
Record W3088340986 · doi:10.3390/v12101079

Regulation of Expression and Latency in BLV and HTLV

2020· review· en· W3088340986 on OpenAlex
Aneta Pluta, Juan Pablo Jaworski, Renée N. Douville

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueViruses · 2020
Typereview
Languageen
FieldImmunology and Microbiology
TopicT-cell and Retrovirus Studies
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsLatency (audio)VirologyBiologyComputational biologyComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

Human T-lymphotrophic virus type 1 (HTLV-1) and Bovine leukemia virus (BLV) belong to the Deltaretrovirus genus. HTLV-1 is the etiologic agent of the highly aggressive and currently incurable cancer adult T-cell leukemia (ATL) and a neurological disease HTLV-1-associated myelopathy (HAM)/tropical spastic paraparesis (TSP). BLV causes neoplastic proliferation of B cells in cattle: enzootic bovine leucosis (EBL). Despite the severity of these conditions, infection by HTLV-1 and BLV appear in most cases clinically asymptomatic. These viruses can undergo latency in their hosts. The silencing of proviral gene expression and maintenance of latency are central for the establishment of persistent infection, as well as for pathogenesis in vivo. In this review, we will present the mechanisms that control proviral activation and retroviral latency in deltaretroviruses, in comparison with other exogenous retroviruses. The 5′ long terminal repeats (5′-LTRs) play a main role in controlling viral gene expression. While the regulation of transcription initiation is a major mechanism of silencing, we discuss topics that include (i) the epigenetic control of the provirus, (ii) the cis-elements present in the LTR, (iii) enhancers with cell-type specific regulatory functions, (iv) the role of virally-encoded transactivator proteins, (v) the role of repressors in transcription and silencing, (vi) the effect of hormonal signaling, (vii) implications of LTR variability on transcription and latency, and (viii) the regulatory role of non-coding RNAs. Finally, we discuss how a better understanding of these mechanisms may allow for the development of more effective treatments against Deltaretroviruses.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.295
Teacher spread0.250 · 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