The biased nucleotide composition of HIV-1 triggers type I interferon response and correlates with subtype D increased pathogenicity

The genome of human immunodeficiency virus (HIV) has an average nucleotide composition strongly biased as compared to the human genome. The consequence of such nucleotide composition on HIV pathogenicity has not been investigated yet. To address this question, we analyzed the role of nucleotide bias of HIV-derived nucleic acids in stimulating type-I interferon response in vitro. We found that the biased nucleotide composition of HIV is detected in human cells as compared to humanized sequences, and triggers a strong innate immune response, suggesting the existence of cellular immune mechanisms able to discriminate RNA sequences according to their nucleotide composition or to detect specific secondary structures or linear motifs within biased RNA sequences. We then extended our analysis to the entire genome scale by testing more than 1300 HIV-1 complete genomes to look for an association between nucleotide composition of HIV-1 group M subtypes and their pathogenicity. We found that subtype D, which has an increased pathogenicity compared to the other subtypes, has the most divergent nucleotide composition relative to the human genome. These data support the hypothesis that the biased nucleotide composition of HIV-1 may be related to its pathogenicity.     

High-throughput profiling of point mutations across the HIV-1 genome

Background

The HIV-1 pandemic is not the result of a static pathogen but a large genetically diverse and dynamic viral population. The virus is characterized by a highly mutable genome rendering efforts to design a universal vaccine a significant challenge and drives the emergence of drug resistant variants upon antiviral pressure. Gaining a comprehensive understanding of the mutational tolerance of each HIV-1 genomic position is therefore of critical importance.

Results

Here we combine high-density mutagenesis with the power of next-generation sequencing to gauge the replication capacity and therefore mutational tolerability of single point mutations across the entire HIV-1 genome. We were able to achieve the evaluation of point mutational effects on viral replicative capacity for 5,553 individual HIV-1 nucleotide positions – representing 57% of the viral genome. Replicative capacity was assessed at 3,943 nucleotide positions for a single alternate base change, 1,459 nucleotide positions for two alternate base changes, and 151 nucleotide positions for all three possible alternate base changes. This resulted in the study of how a total of 7,314 individual point mutations impact HIV-1 replication on a single experimental platform. We further utilize the dataset for a focused structural analysis on a capsid inhibitor binding pocket.

Conclusion

The approach presented here can be applied to any pathogen that can be genetically manipulated in a laboratory setting. Furthermore, the methodology can be utilized under externally applied selection conditions, such as drug or immune pressure, to identify genetic elements that contribute to drug or host interactions, and therefore mutational routes of pathogen resistance and escape.

     

Mechanism of HIV-1 viral protein R-induced apoptosis

The paradigm of HIV-1 infection includes the diminution of CD4(+) T cells, loss of immune function, and eventual progression to AIDS. However, the mechanisms that drive host T cell depletion remain elusive. One HIV protein thought to participate in this destructive cascade is the Vpr gene product. Accordingly, we review the biology of the HIV-1 viral protein R (Vpr) an apoptogenic HIV-1 accessory protein that is packaged into the virus particle. In this review we focus specifically on Vpr's ability to induce host cell apoptosis. Recent evidence suggests that Vpr implements a unique mechanism to drive host cell apoptosis, by directly depolarizing the mitochondria membrane potential. Vpr's attack on the mitochondria results in release of cytochrome c resulting in activation of the caspase 9 pathway culminating in the activation of caspase 3 and the downstream events of apoptosis. Vpr may interact with the adenine nucleotide translocator (ANT) to prompt this cascade. The role of Vpr-induced apoptosis in HIV pathogenesis is considered.     

Excision of HIV-1 proviral DNA by recombinant cell permeable tre-recombinase

Over the previous years, comprehensive studies on antiretroviral drugs resulted in the successful introduction of highly active antiretroviral therapy (HAART) into clinical practice for treatment of HIV/AIDS. However, there is still need for new therapeutic approaches, since HAART cannot eradicate HIV-1 from the infected organism and, unfortunately, can be associated with long-term toxicity and the development of drug resistance. In contrast, novel gene therapy strategies may have the potential to reverse the infection by eradicating HIV-1. For example, expression of long terminal repeat (LTR)-specific recombinase (Tre-recombinase) has been shown to result in chromosomal excision of proviral DNA and, in consequence, in the eradication of HIV-1 from infected cell cultures. However, the delivery of Tre-recombinase currently depends on the genetic manipulation of target cells, a process that is complicating such therapeutic approaches and, thus, might be undesirable in a clinical setting. In this report we demonstrate that E.coli expressed Tre-recombinases, tagged either with the protein transduction domain (PTD) from the HIV-1 Tat trans-activator or the translocation motif (TLM) of the Hepatitis B virus PreS2 protein, were able to translocate efficiently into cells and showed significant recombination activity on HIV-1 LTR sequences. Tre activity was observed using episomal and stable integrated reporter constructs in transfected HeLa cells. Furthermore, the TLM-tagged enzyme was able to excise the full-length proviral DNA from chromosomal integration sites of HIV-1-infected HeLa and CEM-SS cells. The presented data confirm Tre-recombinase activity on integrated HIV-1 and provide the basis for the non-genetic transient application of engineered recombinases, which may be a valuable component of future HIV eradication strategies.     

Comparing mutational pathways to lopinavir resistance in HIV-1 subtypes B versus C

Although combination antiretroviral therapies seem to be effective at controlling HIV-1 infections regardless of the viral subtype, there is increasing evidence for subtype-specific drug resistance mutations. The order and rates at which resistance mutations accumulate in different subtypes also remain poorly understood. Most of this knowledge is derived from studies of subtype B genotypes, despite not being the most abundant subtype worldwide. Here, we present a methodology for the comparison of mutational networks in different HIV-1 subtypes, based on Hidden Conjunctive Bayesian Networks (H-CBN), a probabilistic model for inferring mutational networks from cross-sectional genotype data. We introduce a Monte Carlo sampling scheme for learning H-CBN models for a larger number of resistance mutations and develop a statistical test to assess differences in the inferred mutational networks between two groups. We apply this method to infer the temporal progression of mutations conferring resistance to the protease inhibitor lopinavir in a large cross-sectional cohort of HIV-1 subtype C genotypes from South Africa, as well as to a data set of subtype B genotypes obtained from the Stanford HIV Drug Resistance Database and the Swiss HIV Cohort Study. We find strong support for different initial mutational events in the protease, namely at residue 46 in subtype B and at residue 82 in subtype C. The inferred mutational networks for subtype B versus C are significantly different sharing only five constraints on the order of accumulating mutations with mutation at residue 54 as the parental event. The results also suggest that mutations can accumulate along various alternative paths within subtypes, as opposed to a unique total temporal ordering. Beyond HIV drug resistance, the statistical methodology is applicable more generally for the comparison of inferred mutational networks between any two groups.