Viral Diversity by Deep Sequencing: Approaches to Analyzing Effects of Anti-HIV Treatments

HIV is a deadly virus responsible for the AIDS pandemic, which has claimed countless lives since its origins in the early 1980s. A cure for HIV is still elusive - HIV can exist as a diverse and dynamic population that adapts quickly to immune and drug pressures, making elimination of infection difficult. Advances in antiretroviral (ARV) therapy have resulted in effective control of HIV for some but not all patients. This dissertation reports case studies of the response of viral populations to selection pressures exerted by emerging anti-HIV therapies. Deep sequencing technology was used to probe viral swarms at high-resolution, which helped make clinically relevant conclusions. Further, novel computational approaches were implemented to control procedural noise and carefully interpret signal. In one study, we examine HIV integrase inhibitors (INIs), which are among the latest ARV drugs. INIs act at a pre-integration level by aborting viral integration, which would normally lead to lasting infection. Raltegravir (RAL) is the only FDA-approved INI to date. Investigating drug resistance is crucial to informing future course of ARV therapy. We describe evolving HIV swarms in patients exhibiting a switch in RAL-resistance profiles. To understand implications of RAL administration, we analyzed the pre-therapy or treatment-naïve context for the viral populations in-depth. Our findings suggest that predominant mutations arise only in presence of RAL - in its absence, they do not constitute fit polymorphisms. For all their effectiveness, drugs have not eradicated HIV. A recent clinical case, however, involving transfer of HIV-resistant cells to an infected patient, resulted for the first time in possible cure. This emphasized the importance of gene-modification and cell-based therapies to treat HIV. One such strategy showing promise uses an antisense to target HIV. The approach has been safe although clinical efficacy has not been fully determined. In support of one such study, we deep-sequenced viral swarms in the presence of antisense-modified cells. Encouragingly, we observed minority strains harboring evidence of antisense pressure in vivo, demonstrating the potential of alternative therapy. Finally, this dissertation underscores the significance of rare signatures in HIV populations, and outlines methods to investigate them

Correlated evolution of positions within mammalian cis elements.

Transcriptional regulation critically depends on proper interactions between transcription factors (TF) and their cognate DNA binding sites. The widely used model of TF-DNA binding--the positional weight matrix (PWM)--presumes independence between positions within the binding site. However, there is evidence to show that the independence assumption may not always hold, and the extent of interposition dependence is not completely known. We hypothesize that the interposition dependence should partly be manifested as correlated evolution at the positions. We report a maximum-likelihood (ML) approach to infer correlated evolution at any two positions within a PWM, based on a multiple alignment of 5 mammalian genomes. Application to a genome-wide set of putative cis elements in human promoters reveals a prevalence of correlated evolution within cis elements. We found that the interdependence between two positions decreases with increasing distance between the positions. The interdependent positions tend to be evolutionarily more constrained and moreover, the dependence patterns are relatively similar across structurally related transcription factors. Although some of the detected mutational dependencies may be due to context-dependent genomic hyper-mutation, notably CG to TG, the majority is likely due to context-dependent preferences for specific nucleotide combinations within the cis elements. Patterns of evolution at individual nucleotide positions within mammalian TF binding sites are often significantly correlated, suggesting interposition dependence. The proposed methodology is also applicable to other classes of non-coding functional elements. A detailed investigation of mutational dependencies within specific motifs could reveal preferred nucleotide combinations that may help refine the DNA binding models

Example to illustrate the concept of normalized weight (or probability) of ancestral assignments.

<p>We present a simplified situation of two species related through a common ancestor, where the evolutionary tree has just one internal node representing the ancestor, with four possible ancestral assignments. For a sample PWM with 5 sites aligned over the two species, we provide representative values (in blue) for the probability of the tree corresponding to each site given a particular ancestral assignment. From these we work out the overall probability of an ancestral assignment given the data (last column). For details, see the text in the Materials and Methods section that references this table.</p

The distribution of <i>CoEvol</i> values for scope 1.

<p>Shown are <i>CoEvol</i> values for <i>Foreground</i>, <i>Random</i>, <i>RandomContext</i> and <i>Shuffle</i> controls. Values for <i>RandomContext</i> are included after correcting for a shift in the distribution of tree likelihoods (see Methods for details). Relative to the most stringent control – <i>Shuffle</i>, the <i>Foreground CoEvol</i> values are significantly greater (Mann-Whitney U test p-value = 0.02, Kolmogorov-Smirnov test p-value = 1.1e-12).</p

Overview of the approach.

<p>The top left panel depicts positions <i>i</i> and <i>j</i> within a binding site for a PWM <i>M</i> having <i>N</i> genome-wide matches. The binding site in human is shown in the context of a 5-species multiple alignment. The top left panel also shows the phylogenetic trees for the two positions. The phylogenetic parameters are estimated from the genome-wide set of promoter alignments (top right panel). The likelihoods of ancestral nodes for a specific PWM and position (or position-pair) are then estimated from the <i>N</i> instances and the phylogenetic parameters. The likelihoods of individual trees and tree-pairs and ultimately the <i>CoEvol</i> for a pair of PWM positions are then estimated as detailed in the text. The lower right panel illustrates the procedure to generate <i>Shuffle</i> control. The figure depicts <i>N</i> instances of position pair (<i>i,j</i>) in the central row. A random <i>j</i>-position is paired with each of the <i>i</i>-positions (lower row).</p

HIV Sequence Variation Associated With env Antisense Adoptive T-cell Therapy in the hNSG Mouse Model

The first use of lentiviral vectors in humans involved transduction of mature T-cells with an human immunodeficiency virus (HIV)–derived env antisense (envAS) vector to protect cells from HIV infection. In that study, only a minority of the patient T-cell population could be gene-modified, raising the question of whether the altered cells could affect replicating HIV populations. We investigated this using humanized NOD/SCID IL-2Rγnull (hNSG) mice reconstituted with ~4–11% envAS-modified human T-cells. Mice were challenged with HIV-1NL4-3, which has an env perfectly complementary to envAS, or with HIV-1BaL, which has a divergent env. No differences were seen in viral titer between mice that received envAS-modified cells and control mice that did not. Using 454/Roche pyrosequencing, we analyzed the mutational spectrum in HIV populations in serum—from 33 mice we recovered 84,074 total reads comprising 31,290 unique sequence variants. We found enrichment of A-to-G transitions and deletions in envAS-treated mice, paralleling a previous tissue culture study where most target cells contained envAS, even though minority of cells were envAS-modified here. Unexpectedly, this enrichment was only detected after the challenge with HIV-1BaL, where the viral genome would form an imperfect duplex with envAS, and not HIV-1NL4-3, where a perfectly matched duplex would form