HLA-driven optimization of an HIV vaccine immunogen

Background: HIV diversity has been driven in large part by the intense selective pressure of HLA-restricted immune responses and is a significant challenge in HIV vaccine design. Sites of HLA-associated polymorphisms indicate potential immunogenic peptides that should be incorporated into an HIV vaccine. Method: Full-length (pretreatment) HIV sequencing and high-resolution HLA-A, -B, and -C genotyping was undertaken on 245 individuals in the Western Australian HIV Cohort Study. We determined statistically significant associations between polymorphisms in HIV sequences and HLA genotypes. Given these HLA associations we consider alternative measures of protection on the basis of the match between a viral peptide sequence and a corresponding segment of the vaccine. The measure is defined for all overlapping HIV peptides in the dataset. Each peptide contains a putative epitope and its associated flanking region. The vaccine is said to protect against a peptide sequence if the sites of HLA association in both the peptide sequence and the corresponding segment of the vaccine have nonescaped amino acids, and one of the following three criteria hold: (1), "no play"— the remaining sites in the peptide sequence and corresponding segment of the vaccine match exactly, (2), "mid-play"— the remaining sites in the sequence and vaccine differ only by conservative amino-acid substitutions, and (3) "full-play"—the remaining sites in the sequence and vaccine need have no relationship.The three criteria represent different assumptions about the degree to which T cells cross-react. An optimal vaccine immunogen of a given length is the one that contains the largest number of (possibly overlapping) protected against peptides. We provide a general machine-learning approach to optimization of such immunogens. Results: We optimized vaccines of length up to 2000 aa. The predicted efficacy of the optimized vaccine immunogens depends considerably on which criterion is used. For instance, an optimized vaccine immunogen of length 1300aa can protect against all peptides in the data under the full-play assumption, compared with 80% of all peptides under the mid-play assumption and 65% under the no-play assumption. Conclusion: These data demonstrate a novel, rational approach to optimizing the immunogenicity of an HIV vaccine against diverse circulating viruses in a human population, guided by knowledge of the population HLA

POPULATION SEQUENCING FROM CHROMATOGRAM DATA *

One of the key components of sequencing technologies is proper separation of a single species/strain/allele of the targeted sequence from a sample. In traditional techniques, this has been achieved chemically (e.g., using specific primer sequences), however, multiple different but related species can still possibly be picked up with the same primer. This is especially problematic in sequencing RNA or proviral DNA, when the virus in question is highly variable and each individual is infected with a different swarm of viral strains. In case of HIV, for example, when the dominant sequences in the population differ by one or more insertions and deletions, the standard sequencing techniques fail to recover any of the components strains sufficiently well. We show that the chromatograms of mixed sequences can be used to accurately infer the individual strains, removing the need for additional sequencing steps, e.g. new primer synthesis or cloning of individual viral variants. To this purpose, we have developed a statistical generative model of raw chromatogram data and an appropriate inference algorithm based on maximizing the likelihood of an observed chromatogram. To illustrate this technique, we used an automated ABI 3730XL sequencer to capture mixed samples of pro-viral DNA of HIV-infected patients. The chromatograms of the mixed samples were analyzed by the presented algorithm, providing the inferred individual strains for each mixture as output. The mixture components were then compared with the sequences of the original clones. In many cases, the separated components had fewer than 1% differences to the ground truth which compares favorably to the output of the basic sequencer, whose errors went up to 40%

ShoRAH: estimating the genetic diversity of a mixed sample from next-generation sequencing data

<p>Abstract</p> <p>Background</p> <p>With next-generation sequencing technologies, experiments that were considered prohibitive only a few years ago are now possible. However, while these technologies have the ability to produce enormous volumes of data, the sequence reads are prone to error. This poses fundamental hurdles when genetic diversity is investigated.</p> <p>Results</p> <p>We developed ShoRAH, a computational method for quantifying genetic diversity in a mixed sample and for identifying the individual clones in the population, while accounting for sequencing errors. The software was run on simulated data and on real data obtained in wet lab experiments to assess its reliability.</p> <p>Conclusions</p> <p>ShoRAH is implemented in C++, Python, and Perl and has been tested under Linux and Mac OS X. Source code is available under the GNU General Public License at <url>http://www.cbg.ethz.ch/software/shorah</url>.</p

Increased Breadth and Depth of Cytotoxic T Lymphocytes Responses against HIV-1-B Nef by Inclusion of Epitope Variant Sequences

Different vaccine approaches cope with HIV-1 diversity, ranging from centralized1–4 to variability-encompassing5–7 antigens. For all these strategies, a concern remains: how does HIV-1 diversity impact epitope recognition by the immune system? We studied the relationship between HIV-1 diversity and CD8+ T Lymphocytes (CTL) targeting of HIV-1 subtype B Nef using 944 peptides (10-mers overlapping by nine amino acids (AA)) that corresponded to consensus peptides and their most common variants in the HIV-1-B virus population. IFN-γ ELISpot assays were performed using freshly isolated PBMC from 26 HIV-1-infected persons. Three hundred and fifty peptides elicited a response in at least one individual. Individuals targeted a median of 7 discrete regions. Overall, 33% of responses were directed against viral variants but not elicited against consensus-based test peptides. However, there was no significant relationship between the frequency of a 10-mer in the viral population and either its frequency of recognition (Spearman's correlation coefficient ρ = 0.24) or the magnitude of the responses (ρ = 0.16). We found that peptides with a single mutation compared to the consensus were likely to be recognized (especially if the change was conservative) and to elicit responses of similar magnitude as the consensus peptide. Our results indicate that cross-reactivity between rare and frequent variants is likely to play a role in the expansion of CTL responses, and that maximizing antigenic diversity in a vaccine may increase the breadth and depth of CTL responses. However, since there are few obvious preferred pathways to virologic escape, the diversity that may be required to block all potential escape pathways may be too large for a realistic vaccine to accommodate. Furthermore, since peptides were not recognized based on their frequency in the population, it remains unclear by which mechanisms variability-inclusive antigens (i.e., constructs enriched with frequent variants) expand CTL recognition

Terminal NK cell maturation is controlled by concerted actions of T-bet and Zeb2 and is essential for melanoma rejection

Natural killer (NK) cell maturation is a tightly controlled process that endows NK cells with functional competence and the capacity to recognize target cells. Here, we found that the transcription factor (TF) Zeb2 was the most highly induced TF during NK cell maturation. Zeb2 is known to control epithelial to mesenchymal transition, but its role in immune cells is mostly undefined. Targeted deletion of Zeb2 resulted in impaired NK cell maturation, survival, and exit from the bone marrow. NK cell function was preserved, but mice lacking Zeb2 in NK cells were more susceptible to B16 melanoma lung metastases. Reciprocally, ectopic expression of Zeb2 resulted in a higher frequency of mature NK cells in all organs. Moreover, the immature phenotype of Zeb2(-/-) NK cells closely resembled that of Tbx21(-/-) NK cells. This was caused by both a dependence of Zeb2 expression on T-bet and a probable cooperation of these factors in gene regulation. Transgenic expression of Zeb2 in Tbx21(-/-) NK cells partially restored a normal maturation, establishing that timely induction of Zeb2 by T-bet is an essential event during NK cell differentiation. Finally, this novel transcriptional cascade could also operate in human as T-bet and Zeb2 are similarly regulated in mouse and human NK cells

RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease

To facilitate precision medicine and whole genome annotation, we developed a machine learning technique that scores how strongly genetic variants affect RNA splicing, whose alteration contributes to many diseases. Analysis of over 650,000 intronic and exonic variants reveals widespread patterns of mutation-driven aberrant splicing. Intronic disease mutations alter splicing nine times more often than common variants, and missense exonic disease mutations that least impact protein function are five times more likely to alter splicing than others. Tens of thousands of disease-causing mutations are detected, including those involved in cancers and spinal muscular atrophy. Examination of intronic and exonic variants found using whole genome sequencing of individuals with autism reveals mis-spliced genes with neurodevelopmental phenotypes. Our approach provides evidence for causal variants and should enable new discoveries in precision medicine