CRISPR-Cas9-based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), Cas9 can be reprogrammed to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently-devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.

We describe factored spectrally transformed linear mixed models (FaST-LMM), an algorithm for genome-wide association studies (GWAS) that scales linearly with cohort size in both run time and memory use. On Wellcome Trust data for 15,000 individuals, FaST-LMM ran an order of magnitude faster than current efficient algorithms. Our algorithm can analyze data for 120,000 individuals in just a few hours, whereas current algorithms fail on data for even 20,000 individuals (http://mscompbio.codeplex.com/).

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Perspective │2 Artificial intelligence (AI) tools are increasingly being applied in drug discovery. Whilst some protagonists point to vast opportunities potentially offered by such tools, others remain skeptical, waiting for a clear impact to be shown in drug discovery projects. The truth is probably somewhere between these extremes, but it is clear that AI is providing new challenges not only for the scientists involved but also for the biopharma industry and its established processes for discovering and developing new medicines. This article presents the views of a diverse group of international experts on the 'grand challenges' for small-molecule drug discovery with AI and approaches to address them.

i HLA class I-associated polymorphisms identified at the population level mark viral sites under immune pressure by individual HLA alleles. As such, analysis of their distribution, frequency, location, statistical strength, sequence conservation, and other properties offers a unique perspective from which to identify correlates of protective cellular immunity. We analyzed HLA-associated HIV-1 subtype B polymorphisms in 1,888 treatment-naïve, chronically infected individuals using phylogenetically informed methods and identified characteristics of HLA-associated immune pressures that differentiate protective and nonprotective alleles. Over 2,100 HLA-associated HIV-1 polymorphisms were identified, approximately one-third of which occurred inside or within 3 residues of an optimally defined cytotoxic T-lymphocyte (CTL) epitope. Differential CTL escape patterns between closely related HLA alleles were common and increased with greater evolutionary distance between allele group members. Among 9-mer epitopes, mutations at HLA-specific anchor residues represented the most frequently detected escape type: these occurred nearly 2-fold more frequently than expected by chance and were computationally predicted to reduce peptide-HLA binding nearly 10-fold on average. Characteristics associated with protective HLA alleles (defined using hazard ratios for progression to AIDS from natural history cohorts) included the potential to mount broad immune selection pressures across all HIV-1 proteins except Nef, the tendency to drive multisite and/or anchor residue escape mutations within known CTL epitopes, and the ability to strongly select mutations in conserved regions within HIV's structural and functional proteins. Thus, the factors defining protective cellular immune responses may be more complex than simply targeting conserved viral regions. The results provide new information to guide vaccine design and immunogenicity studies. HIV-1 is notorious for its genetic diversity and its ability to adapt to selection pressures (44,87,116). Despite this, within-host HIV-1 evolution in response to antiretroviral (61, 72), host cellular immune (15,50,71,94,95), antibody (46), and vaccine-induced (103) selection pressures occurs along generally predictable mutational pathways (3, 84). Studying these evolutionary pathways can offer insight into the immunopathogenesis of HIV-1 and may help inform the design of immune-based interventions and vaccines.Substantial progress has been made in our understanding of HIV-1's ability to evade human leukocyte antigen (HLA) class I-restricted CD8 ϩ cytotoxic T-lymphocytes (CTL). In particular, application of novel statistical methods (13,25,84) to large population-based data sets of linked host and viral genetic information has facilitated the systematic identification of HLA-associated immune escape and covarying mutations in 20,60,81,99,104), revealing important insights into HIV-1 adaptation to its host. We now appreciate that immune selection represents a major force shaping HIV-1 diversity (3,80,...

Combinatorial genetic screening using CRISPR-Cas9 is a useful approach to uncover redundant genes and to explore complex gene networks. However, current approaches suffer from interference between the single-guide RNAs (sgRNAs) and from limited gene targeting activity. To increase the efficiency of combinatorial screening, we employ orthogonal Cas9 enzymes from S. aureus and S. pyogenes. We used machine learning to establish S. aureus Cas9 sgRNA design rules and paired S. aureus Cas9 with S. pyogenes Cas9 to achieve dual targeting in a high fraction of cells. We also developed a lentiviral vector and cloning strategy to generate high-complexity pooled dual-knockout libraries to identify synthetic lethal and buffering gene pairs across multiple cell types, including MAPK pathway genes and apoptotic genes. Our orthologous approach enabled a screen combining gene knockouts with transcriptional activation, which revealed genetic interactions with TP53. The “Big Papi” (Paired aureus and pyogenes for intereactions) approach described here will be widely applicable for the study of combinatorial phenotypes.

jThe promiscuous presentation of epitopes by similar HLA class I alleles holds promise for a universal T-cell-based HIV-1 vaccine. However, in some instances, cytotoxic T lymphocytes (CTL) restricted by HLA alleles with similar or identical binding motifs are known to target epitopes at different frequencies, with different functional avidities and with different apparent clinical outcomes. Such differences may be illuminated by the association of similar HLA alleles with distinctive escape pathways. Using a novel computational method featuring phylogenetically corrected odds ratios, we systematically analyzed differential patterns of immune escape across all optimally defined epitopes in Gag, Pol, and Nef in 2,126 HIV-1 clade C-infected adults. Overall, we identified 301 polymorphisms in 90 epitopes associated with HLA alleles belonging to shared supertypes. We detected differential escape in 37 of 38 epitopes restricted by more than one allele, which included 278 instances of differential escape at the polymorphism level. The majority (66 to 97%) of these resulted from the selection of unique HLA-specific polymorphisms rather than differential epitope targeting rates, as confirmed by gamma interferon (IFN-␥) enzyme-linked immunosorbent spot assay (ELISPOT) data. Discordant associations between HLA alleles and viral load were frequently observed between allele pairs that selected for differential escape. Furthermore, the total number of associated polymorphisms strongly correlated with average viral load. These studies confirm that differential escape is a widespread phenomenon and may be the norm when two alleles present the same epitope. Given the clinical correlates of immune escape, such heterogeneity suggests that certain epitopes will lead to discordant outcomes if applied universally in a vaccine.

Previous studies have identified a central role for HLA-B alleles in influencing control of HIV infection. An alternative possibility is that a small number of HLA-B alleles may have a very strong impact on HIV disease outcome, dominating the contribution of other HLA alleles. Here, we find that even following the exclusion of subjects expressing any of the HLA-B class I alleles (B*57, B*58, and B*18) identified to have the strongest influence on control, the dominant impact of HLA-B alleles on virus set point and absolute CD4 count variation remains significant. However, we also find that the influence of HLA on HIV control in this C-clade-infected cohort from South Africa extends beyond HLA-B as HLA-Cw type remains a significant predictor of virus and CD4 count following exclusion of the strongest HLA-B associations. Furthermore, there is evidence of interdependent protective effects of the HLA-Cw*0401-B*8101, HLA-Cw*1203-B*3910, and HLA-A*7401-B*5703 haplotypes that cannot be explained solely by linkage to a protective HLA-B allele. Analysis of individuals expressing both protective and detrimental alleles shows that even the strongest HLA alleles appear to have an additive rather than dominant effect on HIV control at the individual level. Finally, weak but significant frequency-dependent effects in this cohort can be detected only by looking at an individual's combined HLA allele frequencies. Taken together, these data suggest that although individual HLA alleles, particularly HLA-B, can have a strong impact, HIV control overall is likely to be influenced by the additive effect of some or all of the other HLA alleles present.