Intestinal stem cells (ISCs) face the challenge of integrating metabolic demands with unique regenerative functions. Studies have shown an intricate interplay between metabolism and stem cell capacity; however, it is still not understood how this process is regulated. Combining ribosome profiling and CRISPR screening in intestinal organoids, we identify the nascent polypeptide–associated complex (NAC) as a key mediator of this process. Our findings suggest that NAC is responsible for relocalizing ribosomes to the mitochondria and regulating ISC metabolism. Upon NAC inhibition, intestinal cells show decreased import of mitochondrial proteins, which are needed for oxidative phosphorylation, and, consequently, enable the cell to maintain a stem cell identity. Furthermore, we show that overexpression of NACα is sufficient to drive mitochondrial respiration and promote ISC identity. Ultimately, our results reveal the pivotal role of NAC in regulating ribosome localization, mitochondrial metabolism, and ISC function, providing insights into the potential mechanism behind it.

Intestinal stem cells (ISCs) face the challenge of integrating metabolic demands with unique regenerative functions. Studies have shown an intricate interplay between metabolism and stem cell capacity, however it is still not understood how this process is regulated. Combining ribosome profiling and CRISPR screening in intestinal organoids, we show that RNA translation is at the root of this interplay. We identify the nascent polypeptide-associated complex (NAC) as a key mediator of this process, and show that it regulates ISC metabolism by relocalizing ribosomes to the mitochondria. Upon NAC inhibition, intestinal cells show decreased import of mitochondrial proteins, which are needed for oxidative phosphorylation, and, consequently, enable the cell to maintain a stem cell identity. Furthermore, we show that overexpression of NACα is sufficient to drive mitochondrial respiration and promote ISC identity. Ultimately, our results reveal the pivotal role of ribosome localization in regulating mitochondrial metabolism and ISC function.Teaser The location of ribosomes in cells is regulated, and defines the fate of intestinal stem cells.### Competing Interest StatementThe authors have declared no competing interest.

This file contains Supplementary Tables 2-8, 13 and 14: Supplementary Table 2: Cell line doubling times estimated from growth curves of confluence measurements. Supplementary Table 3: Normalized dose response data for each of the seven kinase inhibitors. Supplementary Table 4: Absolute IC50 values estimated from the dose response data. Supplementary Table 5: Normalized and log-transformed read counts obtained from RNA sequencing. Supplementary Table 6: Mutations called from the DNA sequencing data. Supplementary Table 7: Copy number estimates obtained from the off-target DNA sequencing reads using CopywriteR. Supplementary Table 8: Protein and phosphorylation levels obtained from the RPPA assay. Supplementary Table 13: Comparison of mutation frequencies in the cell line panel with mutation frequencies observed for breast cancer in The Cancer Genome Atlas. Supplementary Table 14: Differential expression analysis of RPPA and RNAseq data between sensitive and resistant cell lines for each of the kinase inhibitors.

RNAseq read counts of IGF1R related genes in the TNBC cell line panel

The correlation between Î"AUC and IGF1R mRNA and protein

<p>This document contains Supplementary Materials & Methods including a mathematical specification of the model, as well as Supplementary Note 1 (Inferred signaling estimates agree with on-treatment phosphorylation measurements), Supplementary Note 2 (A simplified model can describe the response to seven drugs simultaneously), Supplementary Note 3 (Inclusion of feedback signaling from MTORC1 to PI3K does not improve the fit for drug response to AZD8055 and lapatinib), Supplementary Tables 1 (cell line panel), 9-10 (literature references for model nodes and interactions) and 11-12 (prior distributions), and Supplementary Figure 1-19.</p>

<p>This archive contains all files necessary for running the inference in BCM, including the models in BCM model specification format, processed data and configuration files. The archive also contains graphs of each of the inference results, including posterior distributions, posterior predictive distributions and sample traces.</p>

Supplementary Figure 1 | IGF1R inhibition by NVP-AEW541 increases sensitivity to PI3K inhibition by GDC-0941 in HCC1806 and CAL-51 cells Supplementary Figure 2 | GDC-0941 dose-response curves of the TNBC cell line panel at different concentrations of OSI-906 Supplementary Figure 3 | IGF1R, pIGF1R and IGF2BP3 levels in the 18 cell lines of the panel Supplementary Figure 4 | Sensitivity of the TNBC cell line panel to MEK-inhibitor PD-0325901 Supplementary Figure 5 | Dynamic quantification of apoptosis in CAL-51 cells after drug exposure Supplementary Figure 6 | HCC1806 cells become resistant to GDC-0941 after prolonged exposure but are still sensitive to combination treatment Supplementary Figure 7 | Overexpression of IGF1R has no significant effect on sensitivity to GDC-0941 in HCC1806, CAL-51, CAL-148 and MFM-223 cells Supplementary Figure 8 | Colony outgrowth of wild type and IGF1R-overexpressing cell lines Supplementary Figure 9 | Antibodies raised against IGF2 do not modulate the response of CAL-148 and MFM-223 cells to GDC-0941

Manually curated set of IGF1R- and PI3K-related genes

Oligonucleotide sequences used in PCR reactions

Legends Supplemental Figures 1-9 and Tables 1-5

Normalized counts of shRNA constructs in GDC-0941-exposed and control populations

CRISPR technology is an invaluable tool for large-scale functional genomic screening. Genome editing efficiency and timing are important parameters impacting the performance of pooled CRISPR screens. Here we show that by optimizing Cas9 expression levels, the time necessary for gene editing can be reduced contributing to improved performance of CRISPR based screening.

ABSTRACT Background The widespread application of CRISPR/Cas9 technology has yielded numerous findings in biomedical research in recent years, making it an invaluable tool for gene knockout and for high-throughput screening studies. In (low-throughput) gene knockout studies, editing efficiency is not a major concern because only a few edited clones are necessary for a successful assay. However, in large scale pooled screening studies, editing efficiency is a major concern because each sgRNA has to knockout its target gene in a large cell population in a short period of time. Therefore, a thorough understanding of the role that key factors play in determining CRISPR knockout efficiency is essential to improve the performance of pooled CRISPR screening. Methods In this study, cell lines with different expression levels of CAS9 were generated and used to determine gene-editing efficiency. Collections of sgRNAs targeting essential genes were used to study their depletion in the different cell line models. Results Using cell lines with variable expression of Cas9, we confirmed that editing efficiency and speed are mostly dependent on the sgRNA sequence and Cas9 expression, respectively. Importantly, we show that the strategy employed for delivering sgRNAs and Cas9 to cells impacts the performance of high-throughput screens, which is improved in conditions with higher Cas9 expression. Conclusions Our findings highlight the importance of optimizing Cas9 expression levels when performing gene editing experiments and provide guidance on the necessary decisions for implementing optimal pooled CRISPR screening strategies.

Abstract Cancer cell lines differ greatly in their sensitivity to anticancer drugs as a result of different oncogenic drivers and drug resistance mechanisms operating in each cell line. Although many of these mechanisms have been discovered, it remains a challenge to understand how they interact to render an individual cell line sensitive or resistant to a particular drug. To better understand this variability, we profiled a panel of 30 breast cancer cell lines in the absence of drugs for their mutations, copy number aberrations, mRNA, protein expression and protein phosphorylation, and for response to seven different kinase inhibitors. We then constructed a knowledge-based, Bayesian computational model that integrates these data types and estimates the relative contribution of various drug sensitivity mechanisms. The resulting model of regulatory signaling explained the majority of the variability observed in drug response. The model also identified cell lines with an unexplained response, and for these we searched for novel explanatory factors. Among others, we found that 4E-BP1 protein expression, and not just the extent of phosphorylation, was a determinant of mTOR inhibitor sensitivity. We validated this finding experimentally and found that overexpression of 4E-BP1 in cell lines that normally possess low levels of this protein is sufficient to increase mTOR inhibitor sensitivity. Taken together, our work demonstrates that combining experimental characterization with integrative modeling can be used to systematically test and extend our understanding of the variability in anticancer drug response. Significance: By estimating how different oncogenic mutations and drug resistance mechanisms affect the response of cancer cells to kinase inhibitors, we can better understand and ultimately predict response to these anticancer drugs. Graphical Abstract: http://cancerres.aacrjournals.org/content/canres/78/15/4396/F1.large.jpg. Cancer Res; 78(15); 4396–410. ©2018 AACR.

Introduction Response to cancer therapeutics, whether in cell lines or patients, is variable and often difficult to predict. While many oncogenic drivers and drug resistance mechanisms have been described, understanding of how the interplay of these factors impacts response is still limited, precluding implementation in clinical practice. Material and methods To better understand the mechanisms behind the variability in drug response in breast cancer, we profiled both cell lines and tumour samples, in the absence of drug treatment, for the presence of mutations, copy number aberrations, mRNA and protein expression as well as protein phosphorylation. Response of the cell lines to several drugs in the RTK/PI3K/MAPK pathways was also determined. The molecular characteristics together with response data were used to construct a knowledge-based, Bayesian computational model that integrates all data types and estimates the relative contribution of the various drug sensitivity mechanisms. Using a set of patient samples, with known response to a combination treatment of trastuzumab, paclitaxel and carboplatin in a neoadjuvant setting (TRAIN trial), we have constructed a preliminary model to explain the observed variability in pathological complete response (pCR). Upon further refinement, the predictive ability of this model will be tested using an independent validation set of patient samples. Results and discussions Our model of regulatory signalling is able to explain most of the variability observed in drug response in cell lines. It also identified cell lines with an unexplained response, and provided an opportunity for us to search for novel explanatory factors. Among others, we found that the 4E-BP1 protein expression level – and not just the extent of its phosphorylation – is a determinant of mTOR inhibitor sensitivity, which we validated experimentally. Extending the modelling approach to focus on a predictive clinical application, specifically response to chemotherapy combined with trastuzumab treatment in a neoadjuvant setting for HER2 +breast cancer, we have constructed a preliminary model able to explain part of the variability in pCR, the refinement and validation of which will be presented. Conclusion Combining molecular characterisation with integrative modelling can be used to systematically test and extend our understanding of the variability in anticancer drug response. Such approaches pave the way for establishing effective personalised treatments.

An important aspect of cellular signaling networks is the existence of feedback mechanisms. However, due to the complexity of signaling networks, as well as the presence of multiple interrelated feedback events, it can be difficult to identify which signaling routes are active in any particular context. We have previously shown that Inference of Signaling Activity (ISA) can be a useful method to study steady-state oncogenic signaling across different cell lines and inhibitor treatments. However, ISA did not explicitly include feedback signaling events. Incorporating feedback will increase the complexity and computational cost of the model, and more data is likely to be needed to infer feedback activities. Here, we developed feedback-ISA (f-ISA), an extension of the ISA modeling approach which incorporates feedback signaling events. It also includes integrated batch correction in order to fit the models to multiple, independent datasets simultaneously. We find that the identifiability of feedback activities can be counter-intuitive, which shows the importance of analyzing the full, joint uncertainty in model parameters. By iteratively adapting the model and including multiple datasets, including both steady state and intervention data, we constructed a model that can explain a large part of the phosphorylation levels of several signaling molecules in the MAPK and AKT pathways, across many breast cancer cell lines and across various conditions. The resulting model delineates which routes in the signaling network are likely to be active in each cell line and condition, given all of the data. Additionally, such models can indicate whether datasets agree with each other, and identify which parts of the data cannot be explained, thereby highlighting gaps in the current knowledge. We conclude that this modeling approach can be useful to quantitatively understand how complex cellular signaling networks behave across different cell lines and conditions.

High-throughput genetic screens have become essential tools for studying a wide variety of biological processes. Here we experimentally compare systems based on clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated protein 9 (Cas9) or its transcriptionally repressive variant, CRISPR-interference (CRISPRi), with a traditional short hairpin RNA (shRNA)-based system for performing lethality screens. We find that the CRISPR technology performed best, with low noise, minimal off-target effects and consistent activity across reagents.

Abstract Targeted therapies have proven invaluable in the treatment of breast cancer, as exemplified by tamoxifen treatment for hormone receptor–positive tumors and trastuzumab treatment for HER2-positive tumors. In contrast, a subset of breast cancer negative for these markers, triple-negative breast cancer (TNBC), has met limited success with pathway-targeted therapies. A large fraction of TNBCs depend on the PI3K pathway for proliferation and survival, but inhibition of PI3K alone generally has limited clinical benefit. We performed an RNAi-based genetic screen in a human TNBC cell line to identify kinases whose knockdown synergizes with the PI3K inhibitor GDC-0941 (pictilisib). We discovered that knockdown of insulin-like growth factor-1 receptor (IGF1R) expression potently increased sensitivity of these cells to GDC-0941. Pharmacologic inhibition of IGF1R using OSI-906 (linsitinib) showed a strong synergy with PI3K inhibition. Furthermore, we found that the combination of GDC-0941 and OSI-906 is synergistic in 8 lines from a panel of 18 TNBC cell lines. In these cell lines, inhibition of IGF1R further decreases the activity of downstream PI3K pathway components when PI3K is inhibited. Expression analysis of the panel of TNBC cell lines indicates that the expression levels of IGF2BP3 can be used as a potential predictor for sensitivity to the PI3K/IGF1R inhibitor combination. Our data show that combination therapy consisting of PI3K and IGF1R inhibitors could be beneficial in a subset of TNBCs. Mol Cancer Ther; 15(7); 1545–56. ©2016 AACR.

Transcription of the ribosomal RNA genes (rDNA) by RNA polymerase I (Pol I) is a major control step for ribosome synthesis and is tightly linked to cellular growth. However, the question of whether this process is modulated primarily at the level of transcription initiation or elongation is controversial. Studies in markedly different cell types have identified either initiation or elongation as the major control point. In this study, we have re-examined this question in NIH3T3 fibroblasts using a combination of metabolic labeling of the 47S rRNA, chromatin immunoprecipitation analysis of Pol I and overexpression of the transcription initiation factor Rrn3. Acute manipulation of growth factor levels altered rRNA synthesis rates over 8-fold without changing Pol I loading onto the rDNA. In fact, robust changes in Pol I loading were only observed under conditions where inhibition of rDNA transcription was associated with chronic serum starvation or cell cycle arrest. Overexpression of the transcription initiation factor Rrn3 increased loading of Pol I on the rDNA but failed to enhance rRNA synthesis in either serum starved, serum treated or G0/G1 arrested cells. Together these data suggest that transcription elongation is rate limiting for rRNA synthesis. We propose that transcription initiation is required for rDNA transcription in response to cell cycle cues, whereas elongation controls the dynamic range of rRNA synthesis output in response to acute growth factor modulation.