Mitigation of off-target toxicity in CRISPR-Cas9 screens for essential non-coding elements.

Pooled CRISPR-Cas9 screens are a powerful method for functionally characterizing regulatory elements in the non-coding genome, but off-target effects in these experiments have not been systematically evaluated. Here, we investigate Cas9, dCas9, and CRISPRi/a off-target activity in screens for essential regulatory elements. The sgRNAs with the largest effects in genome-scale screens for essential CTCF loop anchors in K562 cells were not single guide RNAs (sgRNAs) that disrupted gene expression near the on-target CTCF anchor. Rather, these sgRNAs had high off-target activity that, while only weakly correlated with absolute off-target site number, could be predicted by the recently developed GuideScan specificity score. Screens conducted in parallel with CRISPRi/a, which do not induce double-stranded DNA breaks, revealed that a distinct set of off-targets also cause strong confounding fitness effects with these epigenome-editing tools. Promisingly, filtering of CRISPRi libraries using GuideScan specificity scores removed these confounded sgRNAs and enabled identification of essential regulatory elements

Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map

The application of RNA interference (RNAi) to mammalian cells has provided the means to perform phenotypic screens to determine the functions of genes. Although RNAi has revolutionized loss-of-function genetic experiments, it has been difficult to systematically assess the prevalence and consequences of off-target effects. The Connectivity Map (CMAP) represents an unprecedented resource to study the gene expression consequences of expressing short hairpin RNAs (shRNAs). Analysis of signatures for over 13,000 shRNAs applied in 9 cell lines revealed that microRNA (miRNA)-like off-target effects of RNAi are far stronger and more pervasive than generally appreciated. We show that mitigating off-target effects is feasible in these datasets via computational methodologies to produce a consensus gene signature (CGS). In addition, we compared RNAi technology to clustered regularly interspaced short palindromic repeat (CRISPR)-based knockout by analysis of 373 single guide RNAs (sgRNAs) in 6 cells lines and show that the on-target efficacies are comparable, but CRISPR technology is far less susceptible to systematic off-target effects. These results will help guide the proper use and analysis of loss-of-function reagents for the determination of gene function

Discovery of common and rare genetic risk variants for colorectal cancer.

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.Goncalo R Abecasis has received compensation from 23andMe and Helix. He is currently an employee of Regeneron Pharmaceuticals. Heather Hampel performs collaborative research with Ambry Genetics, InVitae Genetics, and Myriad Genetic Laboratories, Inc., is on the scientific advisory board for InVitae Genetics and Genome Medical, and has stock in Genome Medical. Rachel Pearlman has participated in collaborative funded research with Myriad Genetics Laboratories and Invitae Genetics but has no financial competitive interest

An Arntl2-Driven Secretome Enables Lung Adenocarcinoma Metastatic Self-Sufficiency.

The ability of cancer cells to establish lethal metastatic lesions requires the survival and expansion of single cancer cells at distant sites. The factors controlling the clonal growth ability of individual cancer cells remain poorly understood. Here, we show that high expression of the transcription factor ARNTL2 predicts poor lung adenocarcinoma patient outcome. Arntl2 is required for metastatic ability in&nbsp;vivo and clonal growth in cell culture. Arntl2 drives metastatic self-sufficiency by orchestrating the expression of a complex pro-metastatic secretome. We identify Clock as an Arntl2 partner and functionally validate the matricellular protein Smoc2 as a pro-metastatic secreted factor. These findings shed light on the molecular mechanisms that enable single cancer cells to form allochthonous tumors in foreign tissue environments

An Arntl2-Driven Secretome Enables Lung Adenocarcinoma Metastatic Self-Sufficiency

The ability of cancer cells to establish lethal metastatic lesions requires the survival and expansion of single cancer cells at distant sites. The factors controlling the clonal growth ability of individual cancer cells remain poorly understood. Here, we show that high expression of the transcription factor ARNTL2 predicts poor lung adenocarcinoma patient outcome. Arntl2 is required for metastatic ability in vivo and clonal growth in cell culture. Arntl2 drives metastatic self-sufficiency by orchestrating the expression of a complex pro-metastatic secretome. We identify Clock as an Arntl2 partner and functionally validate the matricellular protein Smoc2 as a pro-metastatic secreted factor. These findings shed light on the molecular mechanisms that enable single cancer cells to form allochthonous tumors in foreign tissue environments

Decomposition by projection.

<p>(A) For shRNAs, the magnitude of the off-target effect comparing the leave-one-out CSS to projection. Pearson correlation coefficient = 0.43. (B) For individual shRNAs (top) and sgRNAs (bottom), in which the on-target magnitude passes FDR < 25%, distribution of on- and off-target magnitudes, as assessed by projection decomposition. (C) Scatter plots of on-target and off-target projection magnitudes for RNAi (top) and CRISPR (bottom) for all of the signatures of reagents in the dataset. While the 2 technologies show similar on-target activities, RNAi shows large off-target effects. CRISPR, clustered regularly interspaced short palindromic repeat; CSS, consensus seed signature; FDR, false discovery rate; RNAi, RNA interference; sgRNA, single guide RNA; shRNA, short hairpin RNA.</p

RNAi reagents have widespread off-target effects.

<p>(A) Heat map of Spearman correlations among pairs of shRNAs targeting control genes. Correlation on the diagonal reveals a gene expression signal that is reproducible and specific to each shRNA, despite the absence of a target. Control genes are labeled as follows: GFP, LUC, RFP, and LAC. Additional control treatments are grouped under Ctrl; 1: pgw, a lentivirus with no U6 promoter and no shRNA; 2: empty_vector, a lentivirus with a run of 5 thymidines immediately after the U6 promoter, to terminate transcription; 3: UnTrt, wells that did not receive any lentivirus. (B) Distribution of pairwise correlations of shRNA signatures with the same gene target, the same 6- and 7-mer seed sequence, and all pairs of shRNAs. Data shown are from HT29 cells. Pairs of shRNAs with the same seed correlate much better than those with the same gene, which correlate only marginally better than random pairs. (C) The fraction of pairs of shRNA signatures with the same target gene (red) or the same 6-mer seed (blue) that are statistically significant (<i>q</i> < 0.25) in each cell line. In all cell lines, correlation due to seed is more often significant than correlation due to gene. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.s006" target="_blank">S2 Data</a>. Ctrl, control; GFP, green fluorescent protein; LAC, beta-galactosidase; LUC, firefly luciferase; pgw, puromycin-GFP-WPRE; RFP, red fluorescence protein; RNAi, RNA interference; shRNA, short hairpin RNA; U6, human U6 polymerase III promoter; UnTrt, untreated.</p

CGSs and investigation of PC1.

<p>(A) Schematic of the weighted average procedure for combining individual shRNA signatures targeting the same gene into a CGS. The shRNAs are weighted by the sum of their correlations to other same-gene shRNAs and then averaged. (B) CGSs made from random groups of shRNAs show increasing variance of Spearman correlation with larger numbers of component shRNAs. Because these are random groups, there should not be a consistent signal; the increasing probability of very large correlations reveals a spurious signal that we attribute to the PC1 of the data. (C) Comparison of the fraction of variance explained by PC1 for either CMAP build 02, which used Affymetrix arrays to profile small molecules [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.ref001" target="_blank">1</a>], or the expansion of CMAP, which uses L1000 technology [<a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.ref005" target="_blank">5</a>] with different types of perturbation. Level 5 data were used. The shRNA CGS has a notably larger PC1. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.s007" target="_blank">S3 Data</a>. (D) Pearson correlation of PC1 across RNA measurement platforms and perturbation types in level 5 data. (E) For genes with 6 or more shRNAs, a fraction of statistically significant holdout results at different <i>q</i>-value-corrected false discovery rate thresholds, comparing PC1 retained or PC1 removed. Analysis was performed separately for each cell line and data for all cell lines are shown as a single distribution. Because holdout analysis combines multiple shRNA signatures, removal of PC1 decreases the background caused by the general increase in correlations shown in panel (B) and thus improves the performance of this particular analysis. (F) Removal of PC1 does not diminish the magnitude of the seed effect. After removal of PC1, distribution of pairwise Spearman correlations in HT29 (as a representative cell line) for pairs of shRNAs with the same gene target, the same 6- and 7-mer seed sequence, and all pairs of shRNAs. Compare to <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.g001" target="_blank">Fig 1C</a>. (G) Effect of PC1 of CMAP queries. For small molecules previously profiled in CMAP build 02 by Affymetrix technology, the rank of the matched compound when queried against small molecule L1000 data, with either PC1 retained or removed. CGS, consensus gene signature; CMAP, Connectivity Map; PC1, first principal component; shRNA, short hairpin RNA.</p

Gene expression analysis of CRISPR-Cas9 reagents.

<p>(A) Analysis of landmark transcript reduction, comparing CRISPR and RNAi for genes targeted by both technologies. The dotted line (blue) is a null distribution of the set of all z-scores. Both technologies show significant down-regulation of directly measured target transcripts. (B) As in <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.g002" target="_blank">Fig 2E</a>, comparison of holdout results either retaining or removing PC1 for the CRISPR dataset. (C) Holdout analysis for genes assayed by CRISPR (left) and RNAi (right). Genes are shown for RNAi only if they were also assayed by CRISPR; furthermore, because holdout analysis requires at least 6 independent reagents, not all of the genes assayed by CRISPR have sufficient coverage by RNAi; missing values in some cell lines are indicated by black boxes. Smaller <i>q</i>-values (green) indicate greater statistical significance, i.e., that the CGS is valid. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.s010" target="_blank">S6 Data</a>. Cas9, CRISPR-associated 9; CGS, consensus gene signature; CRISPR, clustered regularly interspaced short palindromic repeat; PC1, first principal component; RNAi, RNA interference.</p

CMAP queries for RNAi and CRISPR reagents.

<p>(A) For all genes assessed by both CRISPR and RNAi technologies, the <i>q</i>-values for querying CMAP with the CRISPR CGS and its resulting connectivity to the RNAi CGS. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.s011" target="_blank">S7 Data</a>. (B) Same as in (A), but only for genes passing holdout analysis (<i>q</i>-values < 0.25) by both technologies individually in a cell line, the <i>q</i>-values for connectivity. Holdout analysis <i>q</i>-values are plotted for each technology in the first 2 columns; connectivity <i>q</i>-values are plotted in the third column. See <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.2003213#pbio.2003213.s011" target="_blank">S7 Data</a>. CGS, consensus gene signature; CMAP, Connectivity Map; CRISPR, clustered regularly interspaced short palindromic repeat; RNAi, RNA interference.</p