Nicolo Fusi

Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9

CRISPR-Cas9–based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), one can reprogram Cas9 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.

A genome-to-genome analysis of associations between human genetic variation, HIV-1 sequence diversity, and viral control

HIV-1 sequence diversity is affected by selection pressures arising from host genomic factors. Using paired human and viral data from 1071 individuals, we ran >3000 genome-wide scans, testing for associations between host DNA polymorphisms, HIV-1 sequence variation and plasma viral load (VL), while considering human and viral population structure. We observed significant human SNP associations to a total of 48 HIV-1 amino acid variants (p<2.4 × 10−12). All associated SNPs mapped to the HLA class I region. Clinical relevance of host and pathogen variation was assessed using VL results. We identified two critical advantages to the use of viral variation for identifying host factors: (1) association signals are much stronger for HIV-1 sequence variants than VL, reflecting the ‘intermediate phenotype’ nature of viral variation; (2) association testing can be run without any clinical data. The proposed genome-to-genome approach highlights sites of genomic conflict and is a strategy generally applicable to studies of host–pathogen interaction. DOI: http://dx.doi.org/10.7554/eLife.01123.001

Joint Modelling of Confounding Factors and Prominent Genetic Regulators Provides Increased Accuracy in Genetical Genomics Studies

Expression quantitative trait loci (eQTL) studies are an integral tool to investigate the genetic component of gene expression variation. A major challenge in the analysis of such studies are hidden confounding factors, such as unobserved covariates or unknown subtle environmental perturbations. These factors can induce a pronounced artifactual correlation structure in the expression profiles, which may create spurious false associations or mask real genetic association signals. Here, we report PANAMA (Probabilistic ANAlysis of genoMic dAta), a novel probabilistic model to account for confounding factors within an eQTL analysis. In contrast to previous methods, PANAMA learns hidden factors jointly with the effect of prominent genetic regulators. As a result, this new model can more accurately distinguish true genetic association signals from confounding variation. We applied our model and compared it to existing methods on different datasets and biological systems. PANAMA consistently performs better than alternative methods, and finds in particular substantially more trans regulators. Importantly, our approach not only identifies a greater number of associations, but also yields hits that are biologically more plausible and can be better reproduced between independent studies. A software implementation of PANAMA is freely available online at http://ml.sheffield.ac.uk/qtl/.

Transcriptomic indices of fast and slow disease progression in two mouse models of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis is heterogeneous with high variability in the speed of progression even in cases with a defined genetic cause such as superoxide dismutase 1 (SOD1) mutations. We reported that SOD1(G93A) mice on distinct genetic backgrounds (C57 and 129Sv) show consistent phenotypic differences in speed of disease progression and life-span that are not explained by differences in human SOD1 transgene copy number or the burden of mutant SOD1 protein within the nervous system. We aimed to compare the gene expression profiles of motor neurons from these two SOD1(G93A) mouse strains to discover the molecular mechanisms contributing to the distinct phenotypes and to identify factors underlying fast and slow disease progression. Lumbar spinal motor neurons from the two SOD1(G93A) mouse strains were isolated by laser capture microdissection and transcriptome analysis was conducted at four stages of disease. We identified marked differences in the motor neuron transcriptome between the two mice strains at disease onset, with a dramatic reduction of gene expression in the rapidly progressive (129Sv-SOD1(G93A)) compared with the slowly progressing mutant SOD1 mice (C57-SOD1(G93A)) (1276 versus 346; Q-value ≤ 0.01). Gene ontology pathway analysis of the transcriptional profile from 129Sv-SOD1(G93A) mice showed marked downregulation of specific pathways involved in mitochondrial function, as well as predicted deficiencies in protein degradation and axonal transport mechanisms. In contrast, the transcriptional profile from C57-SOD1(G93A) mice with the more benign disease course, revealed strong gene enrichment relating to immune system processes compared with 129Sv-SOD1(G93A) mice. Motor neurons from the more benign mutant strain demonstrated striking complement activation, over-expressing genes normally involved in immune cell function. We validated through immunohistochemistry increased expression of the C3 complement subunit and major histocompatibility complex I within motor neurons. In addition, we demonstrated that motor neurons from the slowly progressing mice activate a series of genes with neuroprotective properties such as angiogenin and the nuclear factor (erythroid-derived 2)-like 2 transcriptional regulator. In contrast, the faster progressing mice show dramatically reduced expression at disease onset of cell pathways involved in neuroprotection. This study highlights a set of key gene and molecular pathway indices of fast or slow disease progression which may prove useful in identifying potential disease modifiers responsible for the heterogeneity of human amyotrophic lateral sclerosis and which may represent valid therapeutic targets for ameliorating the disease course in humans.

Orthologous CRISPR–Cas9 enzymes for combinatorial genetic screens

Combinatorial genetic screening using CRISPR–Cas9 is a useful approach to uncover redundant genes and to explore complex gene networks. However, current methods 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 Staphylococcus aureus and Streptococcus 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 also enabled a screen combining gene knockouts with transcriptional activation, which revealed genetic interactions with TP53. The “Big Papi” (paired aureus and pyogenes for interactions) approach described here will be widely applicable for the study of combinatorial phenotypes.

Further Improvements to Linear Mixed Models for Genome-Wide Association Studies

We examine improvements to the linear mixed model (LMM) that better correct for population structure and family relatedness in genome-wide association studies (GWAS). LMMs rely on the estimation of a genetic similarity matrix (GSM), which encodes the pairwise similarity between every two individuals in a cohort. These similarities are estimated from single nucleotide polymorphisms (SNPs) or other genetic variants. Traditionally, all available SNPs are used to estimate the GSM. In empirical studies across a wide range of synthetic and real data, we find that modifications to this approach improve GWAS performance as measured by type I error control and power. Specifically, when only population structure is present, a GSM constructed from SNPs that well predict the phenotype in combination with principal components as covariates controls type I error and yields more power than the traditional LMM. In any setting, with or without population structure or family relatedness, a GSM consisting of a mixture of two component GSMs, one constructed from all SNPs and another constructed from SNPs that well predict the phenotype again controls type I error and yields more power than the traditional LMM. Software implementing these improvements and the experimental comparisons are available at http://microsoft.com/science.

Warped linear mixed models for the genetic analysis of transformed phenotypes

Linear mixed models (LMMs) are a powerful and established tool for studying genotype–phenotype relationships. A limitation of the LMM is that the model assumes Gaussian distributed residuals, a requirement that rarely holds in practice. Violations of this assumption can lead to false conclusions and loss in power. To mitigate this problem, it is common practice to pre-process the phenotypic values to make them as Gaussian as possible, for instance by applying logarithmic or other nonlinear transformations. Unfortunately, different phenotypes require different transformations, and choosing an appropriate transformation is challenging and subjective. Here we present an extension of the LMM that estimates an optimal transformation from the observed data. In simulations and applications to real data from human, mouse and yeast, we show that using transformations inferred by our model increases power in genome-wide association studies and increases the accuracy of heritability estimation and phenotype prediction.

In Silico Predictive Modeling of CRISPR/Cas9 guide efficiency

The CRISPR/Cas9 system provides unprecedented genome editing capabilities; however, several facets of this system are under investigation for further characterization and optimization, including the choice of guide RNA that directs Cas9 to target DNA. In particular, given that one would like to target the protein-coding region of a gene, hundreds of guides satisfy the basic constraints of the CRISPR/Cas9 Protospacer Adjacent Motif sequence (PAM); however, not all of these guides actually generate gene knockouts with equal efficiency. Leveraging a broad set of experimental measurements of guide knockout efficiency, we introduce a state-of-the art in silico modeling approach to identify guides that will lead to more effective gene knockout. We first investigated which guide and gene features are critical for prediction (e.g., single- and di-nucleotide identity of the gene target), which are helpful (e.g., thermodynamics), and which are predictive but redundant (e.g., microhomology). We also investigated evaluation measures for comparing predictive models in the present context, suggesting that Area Under the Receiver Operating Curve is not ideal. Finally, we explored a variety of different model classes and found that use of gradient-boosted regression trees produced the best predictive performance. Pointers to our open-source software, code, and prediction server will be available at http://research.microsoft.com/en-us/projects/azimuth.

Prediction of off-target activities for the end-to-end design of CRISPR guide RNAs

Off-target effects of the CRISPR–Cas9 system can lead to suboptimal gene-editing outcomes and are a bottleneck in its development. Here, we introduce two interdependent machine-learning models for the prediction of off-target effects of CRISPR–Cas9. The approach, which we named Elevation, scores individual guide–target pairs, and also aggregates them into a single, overall summary guide score. We demonstrate that Elevation consistently outperforms competing approaches on both tasks. We also introduce an evaluation method that balances errors between active and inactive guides, thereby encapsulating a range of practical use cases. Because of the large-scale and computational demands of the prediction of off-target activities, we have developed a fast cloud-based service (https://crispr.ml) for end-to-end guide-RNA design. The service makes use of pre-computed on-target and off-target activity prediction for every genic region in the human genome.A cloud-based machine-learning software that scores individual guide–target pairs and provides an overall summary score for a given guide that outperforms competing algorithms for the prediction of CRISPR–Cas9 off-target effects.

Detecting regulatory gene–environment interactions with unmeasured environmental factors

MOTIVATION Genomic studies have revealed a substantial heritable component of the transcriptional state of the cell. To fully understand the genetic regulation of gene expression variability, it is important to study the effect of genotype in the context of external factors such as alternative environmental conditions. In model systems, explicit environmental perturbations have been considered for this purpose, allowing to directly test for environment-specific genetic effects. However, such experiments are limited to species that can be profiled in controlled environments, hampering their use in important systems such as human. Moreover, even in seemingly tightly regulated experimental conditions, subtle environmental perturbations cannot be ruled out, and hence unknown environmental influences are frequent. Here, we propose a model-based approach to simultaneously infer unmeasured environmental factors from gene expression profiles and use them in genetic analyses, identifying environment-specific associations between polymorphic loci and individual gene expression traits. RESULTS In extensive simulation studies, we show that our method is able to accurately reconstruct environmental factors and their interactions with genotype in a variety of settings. We further illustrate the use of our model in a real-world dataset in which one environmental factor has been explicitly experimentally controlled. Our method is able to accurately reconstruct the true underlying environmental factor even if it is not given as an input, allowing to detect genuine genotype-environment interactions. In addition to the known environmental factor, we find unmeasured factors involved in novel genotype-environment interactions. Our results suggest that interactions with both known and unknown environmental factors significantly contribute to gene expression variability. AVAILABILITY and implementation: Software available at http://pmbio.github.io/envGPLVM/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.