How Viral Evolution Reveals Time Since Infection
When a person tests positive for HIV, one of the most challenging questions for public health officials and researchers is: How long ago did this infection occur? The answer holds the key to accurately tracking the spread of the virus, evaluating prevention programs, and ultimately controlling the epidemic.
Until recently, scientists relied primarily on serological assays that measure antibody maturity to distinguish recent from long-term infections. While helpful, these methods have limitations—they can be influenced by individual immune response variations and don't perform equally well across different HIV subtypes globally.
Now, an innovative approach is emerging from an unexpected source: the genetic material of the virus itself. Just as archaeologists can date fossils by examining radioactive decay, virologists are learning to read the genetic timeline hidden within HIV's rapid evolution. Two particular genetic markers—pairwise diversity and time to the most recent common ancestor (tMRCA)—are showing remarkable promise in pinpointing infection recency with unprecedented precision 1 3 .
This article explores how scientists are cracking HIV's genetic code to estimate infection timelines, detailing a groundbreaking study from Botswana that could revolutionize how we track and ultimately stop the spread of HIV.
of HIV transmissions start with a single viral variant
viral sequences analyzed in the Botswana study
To understand how genetic markers can reveal infection timing, we must first appreciate HIV's remarkable evolutionary pace. Unlike human DNA, which changes slowly over generations, HIV mutates at lightning speed. Once the virus enters a new host, it begins a continuous process of replication and mutation, creating a diverse family of viral variants known as a "quasispecies."
The story begins with what scientists call the transmission bottleneck. Approximately 80% of heterosexual HIV transmissions are established by just a single genetic variant of the virus, known as the transmitted/founder (T/F) virus 4 . This genetic bottleneck occurs because the new infection must overcome numerous barriers in the recipient's body, typically allowing only the most "fit" viral variants to successfully establish infection.
Single variant establishes infection
Low genetic diversity
Moderate diversity
Once established, the virus begins to diversify:
Viral sequences remain highly similar to the founder virus
Mutations accumulate, creating increasingly diverse viral populations
Complex "quasispecies" emerge with significant genetic variation
In 2017, a research team in Botswana conducted a landmark study to systematically evaluate whether genetic diversity markers could reliably estimate HIV infection recency 1 3 . Botswana represents an ideal natural laboratory for such research—it has one of the world's highest HIV prevalence rates, dominated by HIV-1 subtype C, which accounts for nearly half of global HIV infections.
The team generated 2,540 HIV-1 envelope gene sequences (V1C5 region of gp120) using single genome amplification and sequencing—a method that prevents artificial recombination during amplification. This yielded an average of 61 viral sequences per participant 3 .
To ensure clean analysis, the researchers excluded samples with evidence of superinfection (simultaneous infection with multiple HIV strains), those from participants who had started antiretroviral therapy, and samples with low viral load (<1000 copies/mL), as these could represent false recent infections 3 .
Using the ape package in R software, the team computed "raw pairwise distances"—a measure of genetic differences between all possible pairs of viral sequences from each participant at each time point 3 .
For the same samples, they applied Bayesian evolutionary analysis (using BEAST v1.8.2) to infer the time to the most recent common ancestor of the viral sequences—essentially estimating when all current viral variants in a person shared a common ancestor 3 .
Finally, they used mixed-effects models to account for multiple samples from the same individuals while testing associations between genetic markers and time since infection 3 .
| Participants | 42 |
|---|---|
| Female | 76.2% |
| Median age at enrollment | 27 years |
| HIV-1 subtype | C |
| Total viral sequences analyzed | 2,540 |
| Sequences per participant | 61 (average) |
| Sequences per time point | 11 (average) |
Both pairwise diversity and tMRCA showed statistically significant associations with estimated time since HIV infection (both with p < 0.001) 1 3 .
When accounting for multiplicity of infection, associations became even stronger 1 3 .
tMRCA estimates demonstrated no significant advantage over the simpler pairwise diversity method 1 3 .
Pairwise diversity calculations are computationally simpler and less resource-intensive than complex Bayesian analysis for tMRCA estimation.
| Genetic Marker | Definition | Relationship with Time | Advantages |
|---|---|---|---|
| Pairwise Diversity | Average genetic distance between all viral sequences in a host | Increases linearly over time | Computationally simple, strong correlation with time |
| tMRCA | Time to the Most Recent Common Ancestor | Increases over time | Provides evolutionary context, strong correlation with time |
| APD (Average Pairwise Diversity) | Similar to pairwise diversity, used as proxy for time since infection | Increases over time | Validated in multiple studies, high accuracy 9 |
Conducting this type of sophisticated genetic analysis requires specialized laboratory and computational tools. The Botswana study utilized a suite of advanced research reagents and software solutions that represent the state of the art in viral evolutionary studies.
BEAST (Bayesian Evolutionary Analysis) estimates evolutionary parameters including tMRCA, implementing molecular clock models to infer time scales 3 .
| Research Tool | Type | Primary Function | Application in HIV Recency Research |
|---|---|---|---|
| Single Genome Amplification & Sequencing | Laboratory technique | Generates authentic viral sequences without artificial recombination | Provides high-quality sequence data for diversity analysis |
| Limiting Antigen Avidity Assay (LAg) | Serological test | Measures antibody maturity to identify recent infections | Used as comparison for validating genetic markers 2 |
| BEAST (Bayesian Evolutionary Analysis) | Software package | Estimates evolutionary parameters including tMRCA | Implements molecular clock models to infer time scales |
| R Software with ape package | Statistical computing environment | Calculates pairwise distances and performs phylogenetic analyses | Computes genetic diversity metrics from sequence data |
| QIAamp Viral RNA Kit | Laboratory reagent | Extracts viral RNA from plasma samples | Obtains genetic material for sequencing |
| PhyML | Software tool | Performs maximum likelihood phylogenetic tree estimation | Reconstructs evolutionary relationships between viral sequences |
The ability to accurately determine HIV infection recency using genetic markers has far-reaching implications for public health surveillance and epidemic control. As research continues to refine these methods, we're moving closer to a future where public health officials can:
Target prevention resources efficiently by identifying areas with high rates of recent infections
Evaluate intervention programs with precise data on new infection rates
Disrupt chains of HIV spread by understanding transmission patterns
Recent studies have continued to validate and refine these approaches. A 2024 study from Switzerland demonstrated that average pairwise diversity (APD) could successfully serve as a proxy for time since infection in studying HLA-dependent selection pressures on HIV 9 . Meanwhile, ongoing research in Botswana continues to explore recency rates using both serological and genetic approaches, with a 2025 study reporting a 7.6% rate of recent infection among newly diagnosed individuals 2 .
Despite these promising developments, challenges remain. Genetic sequencing is still more expensive and technically demanding than standard serological tests, limiting its widespread use in resource-limited settings. Researchers are now working to simplify these methods and determine the minimum sequencing requirements for accurate recency classification.
As these techniques become more refined and accessible, the genetic clock hidden within HIV's rapid evolution may become one of our most powerful tools in finally ending the global HIV epidemic. The virus's greatest strength—its ability to mutate and evolve—may ultimately become its greatest weakness in the face of scientific ingenuity.