HIV Sequence Analysis

The plasma pol region of the HIV genome (Figure 2) is variable and encodes drug resistance mutations.99 _{Knowing the infection phenotype is valuable for clinical care decisions,}

as it conveys which combination of ARV drugs would be most effective for the patient. Thus, obtaining this information is now standard care in most of the US.100 _{In NC, blood samples are}

routinely collected at entry into care for drug resistance testing to determine whether the patient’s virus has encoded drug resistance.

The pol gene sequences collected during drug resistance testing can also be used to identify transmission clusters of persons with genetically similar virus and assess transmission chains, which has public health utility. The potential to link infections through phylogenetics may help link to101 _{or identify anonymous individuals in the network who aren’t identified by}

during partner notification.

Acute/recent infections are more likely to be identified in clusters defined by short genetic distances because less time has elapsed between transmission and sampling, so there is less genetic divergence.102 _{Coalescent models were used to demonstrate that some of the}

excess clustering of sequences obtained during acute infection is due to this and not solely excess transmission during acute infection. A study of publicly available pol sequences (n=84,527) representing 141 countries was undertaken with the intent to build a “global transmission network” by looking for similarities worldwide.103 _{Interestingly, the investigators}

found an inverse relationship between the number of linked sequences and the amount of drug resistance mutations (DRM) encoded in the sequences, demonstrating that we do not have a clear picture yet of how drug resistance circulates in populations because it appears as though having a lot of transmission is associated with having less risk drug resistance. An alternate explanation, however, not presented by the investigators is that groups with more DRM are those who have more access to ARV which explains both the lower transmission and the higher circulating DRM.

a. HIV transmission cluster analysis applied to population-based research

A recent investigation of an increase in acute HIV diagnoses in the area around

Charlotte, NC and in Western NC was unable to phylogenetically link all of the acute cases with the infections acquired from either chronically-infected individuals in a single cluster, or from anonymous partners who could not be located for testing.104 _{However, sexual networks were}

constructed, with two distinct groups noted. Although both locations are nearby, they are geographically distinct and differ by rurality. In the metropolitan area around Charlotte, young Black MSM accounted for most of the diagnoses while older white MSM accounted for most of the diagnoses in rural Western NC. No significant overlap between the two groups was found using partner trace back or HIV sequence analysis, so the increase in acute diagnoses was likely due to better case finding and diagnosis rather than an outbreak of acute HIV.

partner finding was not very successful for these cases as each case must have had at least 1 recent HIV-positive partner to be acutely infected.

A study of 1671 HIV-positive persons enrolled at two spatially-near, university-based HIV clinical cohorts in NC was able to link 557 of the patients, the largest cluster including 36

patients.49 _{Clustering was largely seen along racial lines, although not by ethnicity as Latinos}

were significantly less likely to cluster than non-Latinos. There were MSM and heterosexual clusters, although there were mixed clusters as well. 49 _{Phylogenetic analysis is a powerful tool}

for examining transmission patterns and delineating trends, although the likelihood of finding clusters can be reduced if time has passed between samples102 _{or one of the patients has been}

exposed to ART. The ability to construct a large network of individuals using partner data obtained via interview supplements the linkages identified using phylogenetic analysis. b. Limitations

Due to the limitations of HIV sequence analysis, phylogenetic data alone is not as powerful as the combination of phylogenetic and partnership data. First, neither first-degree partnerships nor directionality can be inferred from HIV sequence analysis. Second, observed cluster size may not represent actual transmission if there is a high proportion of missing data, which can occur at any of the first steps along the HIV care cascade. Third, cluster size is affected by cluster definition; if percent difference is used then cluster size changes with the cut- off selected. Fourth, sequences obtained for clinical care are a consensus sequence, where the sequence returned represents the most frequent base pair observed at any position after

sampling multiple viruses within the host. Therefore, minor variants are not captured and intrahost variability is unknown. A consensus sequence may also have ambiguous sites if a position has undergone mutations and is not clearly represented by any single base pair. Having many ambiguous sites may affect clustering.