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Record W4405707085 · doi:10.22456/1679-9216.129530

Phylogenetic Classification of Feline Immunodeficiency Virus

2023· article· en· W4405707085 on OpenAlexaboutno aff
Diezza Biondo, Diéssy Kipper, Juliana de Paula Maciel, Weslei de Oliveira Santana, André Felipe Streck, Vagner Ricardo Lunge

Bibliographic record

VenueACTA SCIENTIAE VETERINARIAE · 2023
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsFeline immunodeficiency virusVirologyPhylogenetic treeBiologyPhylogeneticsHuman immunodeficiency virus (HIV)MedicineGeneticsViral diseaseLentivirusGene

Abstract

fetched live from OpenAlex

Background: The feline immunodeficiency virus (FIV) is responsible for a retroviral disease that affects domestic and wild cats worldwide, causing Feline Acquired Immunodeficiency Syndrome (FAIDS). FIV is a lentivirus from the family Retroviridae and its genome has 3 main structural genes: gag, pol and env. Phylogenetic studies have classified FIV into 7 subtypes according to the diversity among strains from the World, mainly in the env gene. Epidemiological analyses have demonstrated the high predominance of FIV-A and FIV-B. This in silico study aimed to perform a phylogenetic analysis to study FIV diversity worldwide. Materials, Methods & Results: A total of 60 whole genome sequences (WGS) and 122 FIV env gene sequences were included in 2 datasets, which were aligned using MAFFT version 7. Recombination among genomes and/or env genes was analyzed with RDP5 software. Phylogenetic analyses with both datasets were performed, after removing the recombinant sequences, by the W-IQ-TREE and constructed and edited by the FigTree. A total of 12 recombination events involving 19 WGS were detected. In addition, 27 recombination events involving 49 sequences were observed in the env gene. A high rate of recombinants was observed inter-subtypes (A/B and B/D) and intra-subtypes (A/A). All recombinants were removed from the subsequent phylogenetic analyses. Phylogenies demonstrated 6 distinct main clades, 5 from domestic cats (A, B, C, E, U) and 1 from wild cat sequences (W) in the WGS, as well as in the specific env gene analyses. Most clustered with subtype B sequences. In the WGS analysis, clade B had a prevalence of 65.9% Brazilian sequences (27/41) and 2.4% Japanese sequences (1/41). In the env gene analyses, clade B showed a prevalence of 43.8% of Brazilian sequences (32/73) and 20.5% of USA sequences (15/73). The results of both analyses also confirm the FIV-wide geographical distribution around the world. In the phylogenetic analyses carried out with WGS, sequences from China (1/41; 2.4%), Colombia (1/41; 2.4%) and the USA (1/41; 2.4%) were identified in clade A; sequence from Canada in clade C (1/41; 2.4%); sequence from Botswana belonged to clade E (1/41; 2.4%); sequences from Brazil clustered into clade U (2/41; 5% - data not yet published); and sequences belonging to the clade W were from Canada (1/41; 2.4%) and the USA (5/41; 12.3%). Specific env gene phylogenetic analyses showed sequences from Colombia (1/73; 1.4%), France (2/73; 2.7%), the Netherlands (3/73; 4.1%), Switzerland (2/73; 2.7%), EUA (6/73; 8.3%), belonging to clade A; sequence from Canada belonging to clade C (1/73; 1.4%); sequences from Brazil belonging to clade U (2/73; 5% - data not yet published); and sequences belonging to clade W from the USA (6/73; 8.3%). Discussion: The results presented here demonstrate that FIV has a rapid viral evolution due to recombination and mutation events, more specifically in the env gene, which is highly variable. Currently, this retrovirus is classified into 7 subtypes (A, B, C, D, E, F and U-NZenv) according to their high genomic diversity. It also highlighted the importance of in silico sequence and phylogeny studies to demonstrate evolutionary processes. This was the first study to address the WGS FIV diversity with a phylogenetic approach. Keywords: FIV, in silico, phylogeny, subtypes, recombination.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.046
GPT teacher head0.308
Teacher spread0.262 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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