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Record W3008338337 · doi:10.1101/2020.02.23.959932

Specialized DNA structures act as genomic beacons for integration by evolutionarily diverse retroviruses

2020· preprint· en· W3008338337 on OpenAlexafffund
Hinissan P. Kohio, Hannah O. Ajoge, Macon D. Coleman, Emmanuel Ndashimye, Richard M. Gibson, Eric J. Arts, Stephen D. Barr

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCRISPR and Genetic Engineering
Canadian institutionsWestern University
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsBiologyProvirusGenomeGeneticsDNARetrovirusIntegraseGeneComputational biology

Abstract

fetched live from OpenAlex

ABSTRACT Retroviral integration site targeting is not random and plays a critical role in expression and long-term survival of the integrated provirus. To better understand the genomic environment surrounding retroviral integration sites, we performed an extensive comparative analysis of new and previously published integration site data from evolutionarily diverse retroviruses from seven genera, including different HIV-1 subtypes. We showed that evolutionarily divergent retroviruses exhibited distinct integration site profiles with strong preferences for non-canonical B-form DNA (non-B DNA). Whereas all lentiviruses and most retroviruses integrate within or near genes and non-B DNA, MMTV and ERV integration sites were highly enriched in heterochromatin and transcription-silencing non-B DNA features (e.g. G4, triplex and Z-DNA). Compared to in vitro -derived HIV-1 integration sites, in vivo -derived sites are significantly more enriched in transcriptionally silent regions of the genome and transcription-silencing non-B DNA features. Integration sites from individuals infected with HIV-1 subtype A, C or D viruses exhibited different preferences for non-B DNA and were more enriched in transcriptionally active regions of the genome compared to subtype B virus. In addition, we identified several integration site hotspots shared between different HIV-1 subtypes with specific non-B DNA sequence motifs present at these hotspots. Together, these data highlight important similarities and differences in retroviral integration site targeting and provides new insight into how retroviruses integrate into genomes for long-term survival. Graphical Abstract Schematic comparing integration site profiles from evolutionarily diverse retroviruses. Upper left, heatmaps showing the fold-enrichment (blue) and fold-depletion (red) of integration sites near non-B DNA features (lower left). Lower right, circa plot showing integration site hotspots shared between HIV-1 subtype A, B, C and D virus.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.261
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2020
Admission routes2
Has abstractyes

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