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

Hierarchical clustering of image-based profiles.

<p>Details are shown for three of the clusters that were highly enriched for annotation terms. These enriched clusters contain compounds with similar mechanisms of action, some with similar and some with distinct chemical structure. The presence of these enriched clusters indicates that the assay can identify subtle, physiologically relevant effects of compounds on cultured cells. U2OS cells labeled for nuclei (blue), ER (green), nucleoli (grey), actin and Golgi (yellow), and mitochondria (red). Scale bars 50 µm.</p

Single Diastereomer of a Macrolactam Core Binds Specifically to Myeloid Cell Leukemia 1 (MCL1)

A direct binding screen of 100 000 sp³-rich molecules identified a single diastereomer of a macrolactam core that binds specifically to myeloid cell leukemia 1 (MCL1). A comprehensive toolbox of biophysical methods was applied to validate the original hit and subsequent analogues and also established a binding mode competitive with NOXA BH3 peptide. X-ray crystallography of ligand bound to MCL1 reveals a remarkable ligand/protein shape complementarity that diverges from previously disclosed MCL1 inhibitor costructures

A Maltose-Binding Protein Fusion Construct Yields a Robust Crystallography Platform for MCL1

<div><p>Crystallization of a maltose-binding protein MCL1 fusion has yielded a robust crystallography platform that generated the first apo MCL1 crystal structure, as well as five ligand-bound structures. The ability to obtain fragment-bound structures advances structure-based drug design efforts that, despite considerable effort, had previously been intractable by crystallography. In the ligand-independent crystal form we identify inhibitor binding modes not observed in earlier crystallographic systems. This MBP-MCL1 construct dramatically improves the structural understanding of well-validated MCL1 ligands, and will likely catalyze the structure-based optimization of high affinity MCL1 inhibitors.</p></div

The structure of Apo MBP-MCL1 determined at 1.90 Å.

<p>(A) The MBP domain (red) is connected by a short GS linker (orange) to MCL1 173–321 (blue). A portion of alpha helix four is not ordered in the structure (red dashed-line). Maltose ligand is shown in yellow. (B) The MCL1 domain is structurally very similar to the NMR structure of Apo-MCL1 (gray).</p

Comparison of PDB 4HW3 and MBP-MCL1 with fragment 4.

<p>The structure of MBP-MCL1 with fragment <b>4</b> (yellow) determined to 2.4 Å (blue) overlaid with the structure of MCL1 171–323 determined at 2.4 Å (PDB ID 4HW3, gray). The carboxylic acid of 4HW3 adopts multiple conformations depending on the chain; only chain A is shown for clarity.</p