Mapping Viral Transmission: A Network Analysis of In-Host HIV-1 Evolution
Notice bibliographique
Résumé
The Human Immunodeficiency Virus type 1 (HIV-1) is a globally prevalent retrovirus that causes acquired immunodeficiency syndrome (HIV/AIDS). Combination antiretroviral therapy (cART) has turned this deadly disease into a treatable chronic infection, but neither a cure nor vaccine is available. The ability of HIV to reverse-transcribe and integrate its genetic material into host cell DNA facilitates immune evasion, even with cART treatment. This is evidenced by the persistence of viral reservoirs in peripheral blood mononuclear cells (PBMCs) as well as other tissues including the esophagus, stomach, duodenum, and colon. Within these reservoirs, continuous viral replication and mutation gives rise to a plethora of genetic variants known as quasispecies. As viral reservoirs are not isolated compartments, they often infect and re-infect one another, which can lead to dramatic compositional changes in quasispecies over time. Viral quasispecies evolution is a complex process represented by extensive bioinformatic datasets, requiring a battery of different software tools to analyze viral phylogenetic characteristics, generate data models, and visualize transmission dynamics. Data analysis was performed on HIV-1 genetic sequences of various tissue reservoirs samples from HIV-1 infected individuals from the Southern Alberta HIV Clinic. Viral sequences isolated from tissue samples were subjected to phylogenetic and transmission analyses using BEAST 2.0 and TransPhylo. These software tools assessed viral evolution, infection timeframe, and transmission directionality between the tissue samples coming from a single individual. Gephi 0.10 was then used to visualize the resulting within-host phylogenetic transmission data for each individual, partitioning data points by tissue type and sampling timepoints. Network maps were then generated from each dataset with a force-directed algorithm (Force Atlas), with each network containing an “origin” node as determined by the previous TransPhylo analyses. Viral transmission patterns between tissues were highly dynamic and variable between individuals. However, some common patterns were observed, suggesting a PBMC role in influencing quasispecies evolution in other tissue compartments. Pattern definition may have been further influenced by differences in antiretroviral therapy that each individual may have received. A larger dataset with clinical information such as antiretroviral therapy and immune status would help further assess whether patterns are specific to- or consistent between individuals Optimizing data presentation factors within Gephi will also be necessary to generate a more comprehensive understanding of transmission dynamics between tissues using visual network analysis. Further analyses of within-host viral evolution will be useful for informing HIV-1 tissue targets and therapeutic approaches to better control or even stop viral transmission.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».