Mapping Viral Transmission: A Network Analysis of In-Host HIV-1 Evolution
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".