Association between specific HIV-1 Env traits and virologic control in vivo

Abstract

HIV RNA levels are influenced by genetic characteristics of both the host and the virus. Here we applied machine learning techniques to determine if plasma-derived HIV-1 amino acid sequences can be used to predict spontaneous virologic control. We studied the relationship between HIV-1 env genotype and viral load in 20 chronically infected patients undergoing treatment interruptions (SSITT, Swiss-Spanish Intermittent Treatment Trial) and in 104 primary HIV infected (PHI) patients before antiretroviral therapy (cART) and where applicable also after treatment stop. Extensive longitudinal sampling during the interruptions was performed in nine SSITT patients. Sequences obtained from these nine patients during the first virus rebound were used as a training data set and revealed a strong genetic signature (accuracy 98.6% in cross-validation) associated with control of viremia at levels below 5000copies/mL of viral RNA maintained for at least 2 months after the final cART stop. The simple sequence pattern at gp120 positions 268E/358T was confirmed to be predictive of control in the clonal sequences originating from these patients during all subsequent rebounds. Sequences from the remaining 11 SSITT patients with less frequent sampling and from the PHI patients were used for external validation. High sensitivities (71-100%) and negative predictive values (80-100%) but low positive predictive values (12-40%) were achieved in the patient-wise analysis which was based on presence of the genetic pattern in all clones. These results suggest that presence of virus lacking the amino acid pattern 268E/358T is associated with VL >5000 at baseline of PHI and with low probability of spontaneous virologic control after treatment stop. Conversely, however, presence of 268E/358T does not predict control of viremia. These residues in HIV gp120 might affect in vivo HIV-1 fitness either at the level of Env function or influence susceptibility to adaptive or innate immune response.

Figure 1. Phylogenetic Tree

Inferred neighbor-joining phylogenetic tree of clonal HIV-1 env C2-V3-C3 sequences derived from 20 patients. Elongated triangles represent the compressed subtrees containing 31-190 clones isolated from plasma of each patient before antiretroviral therapy and after treatment interruptions. The length of the triangle corresponds to the respective intrapatient diversity, thickness is proportional to the number of taxa. The bar denotes 2% nucleotide divergence and diversity (determined using the MEGA software), bootstrap values corresponding to 1000 replications are indicated below the branches.

Figure 2. Diversity

Longitudinal changes in viral diversity of clonal C2-V3-C3 sequences from 9 patients before treatment and following structured treatment interruptions starting at time 0. The dark line indicates the median time course, patients have the same colour coding in Figures 2-4. Distances were estimated by Tamura-Nei 6-parameter model.

Figure 3. Divergence

Longitudinal changes of average genetic distances (net divergence) from pre-treatment C2-V3-C3 sequences in 9 patients following structured treatment interruptions starting at time 0. The dark line indicates the median time course, patients have the same colour coding in Figures 2-4. Distances were estimated by Tamura-Nei model.

Figure 4. Distance from MRCA

Longitudinal changes in average branch lengths to the most recent common ancestor (MRCA) of clonal C2-V3-C3 sequences from 9 patients before treatment and following structured treatment interruptions starting at time 0. The dark line indicates the median time course, patients have the same colour coding in Figures 2-4. Distances were estimated by Tamura-Nei model.

Figure 5. Decision Tree Learning

Genetic characteristics of rebounding virus in 9 patients with frequent longitudinal sampling. Signature associated with control of viremia after treatment stop (yes or no) in C2-V3-C3 sequences from 1^st rebound (A, n=140) and those from 1^st to 5^th rebound (B, n=614). J48 decision tree learning algorithm and stratified 10-fold cross-validation by Weka. Residues are numbered according to HXB2 gp160 positions. Minus sign (-) denotes an amino acid deletion in the sequence. Numbers in parentheses indicate the total number of instances classified by the node and, if a second value is present, how many instances are incorrectly classified. Controlling patients are preferentially characterized by glutamic acid (E) at position 268 in combination with threonine (T) at 358.

Figure 6. Structure of the HIV envelope protein gp120

Ribbon representation of gp120 JR-FL (Protein Data Bank accession code 2B4C) (Huang et al., 2005) The structure was derived from truncated gp120 crystallized in the CD4-bound conformation. The two amino acid residues 268 and 358 are located at solvent-exposed exterior positions in the regions flanking the V3 loop. The V3 loop itself comprises residues 297 – 330). The figure was generated by use of the program PyMOL (available at http://www.pymol.org).