Fig. S1. Gradient improvement for high pH SCX enrichment.A) Workflow of high pH SCX enrichment (1). LN229 cells were lysed in 8M urea and digested with trypsin. From 1 mg of protein, five fractions are produced by the SCX procedure and were subsequently run on a Q Exactive Plus MS. The data was searched on Proteome Discoverer 2.2 and methyl peptides were subject to a strict 1% methyl FDR. The number of PSMs for two gradients tested is shown in the table in E). B) Density plots of PSM retention times for methyl and nonmethyl PSMs run on the "Long" SCX gradient used from Wang et al.. PSMs from all 5 fractions were combined and their retention times plotted. The average density of nonmethyl PSMs and methyl PSMs was 275 PSM / min and 15 PSMs / min, respectively. C) Original "Long" gradient and a new proposed "Short" gradient for SCX. Wang et al. did not collect spectra for the first 20 min during sample loading, but because we found that methyl PSMs were eluting during this sample loading phase, we did collect spectra during this phase of the LC gradient. D) Density plot of methyl PSM retention time for methyl PSMs captured by the "Long" and "Short" SCX gradients. PSMs from all 5 fractions were combined and their retention times plotted for each gradient. The average density of "Long" and "Short" gradient methyl PSMs was 15 PSM / min and 19 PSMs / min, respectively. E) Summary of spectra identified by each gradient. Each of the five SCX fractions are shown individually. The numbers of methyl and nonmethyl PSMs were used to calculate the percent enrichment for each fraction. The number of MMA, Kme1, DMA, Kme2, PSMs, and mixed methyl PSMs are shown. Mixed PSMs contained a mixture of methyl marks on the same peptide (e.g.,MMA and DMA). The percolator q-value cutoff used to estimate the methyl FDR is also shown for each technique.

The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such that cancer cells are vulnerable to inhibition of one metabolic pathway only when activity of another metabolic pathway is altered. Notably, we also found that the no single metabolic pathway was universally essential, suggesting that cancer cells are not invariably dependent on any metabolic pathway. In addition, we confirmed that cell culture medium is a major confounding factor for the analysis of metabolic pathway vulnerabilities. Nevertheless, we found robust associations between metabolic pathway activity and sensitivity to clinically approved drugs that were independent of cell culture medium. Lastly, we used parallel integration of pharmacological and genetic dependency data to confidently identify metabolic pathway vulnerabilities. Taken together, this study serves as a comprehensive characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines.

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Running title: Deep methylation profiling reveals novel PRMT1 targetsDeep methylation profiling reveals novel PRMT1 targets 2 ABBREVIATIONS ADMA, asymmetric dimethyl arginine; DMA, dimethyl arginine; HILIC, hydrophilic interaction chromatography; IAP, immunoaffinity purification; Kme1, monomethyl lysine; Kme2, dimethyl lysine; Kme3, trimethyl lysine; KMT, lysine methyltransferase; LC, liquid chromatography; LFQ, label-free quantitation; MS, mass spectrometry; MMA, monomethyl arginine; PTM, posttranslational modification; PRMT, protein arginine methyltransferase; SCX, strong cation exchange; SDMA, symmetric dimethyl arginine;Deep methylation profiling reveals novel PRMT1 targets 3 ABSTRACT Protein methylation has been implicated in many important biological contexts including signaling, metabolism, and transcriptional control. Despite the importance of this post-translational modification, the global analysis of protein methylation by mass spectrometry-based proteomics has not been extensively studied due to the lack of robust, well-characterized techniques for methyl peptide enrichment. Here, to better investigate protein methylation, we compared two methods for methyl peptide enrichment: immunoaffinity purification (IAP) and high pH strong cation exchange (SCX). Using both methods, we identified 1,720 methylation sites on 778 proteins. Comparison of these methods revealed that they are largely orthogonal, suggesting that the usage of both techniques is required to provide a global view of protein methylation. Using both IAP and SCX, we then investigated changes in protein methylation downstream of protein arginine methyltransferase 1 (PRMT1). PRMT1 knockdown resulted in significant changes to 127 arginine methylation sites on 78 proteins. In contrast, only a single lysine methylation site was significantly changed upon PRMT1 knockdown. In PRMT1 knockdown cells, we found 114 MMA sites that were either significantly downregulated or upregulated on proteins enriched for mRNA metabolic processes. PRMT1 knockdown also induced significant changes in both asymmetric dimethyl arginine (ADMA) and symmetric dimethyl arginine (SDMA). Using characteristic neutral loss fragmentation ions, we annotated dimethylarginines as either ADMA or SDMA. Through integrative analysis of methyl forms, we identified 18 high confidence PRMT1 substrates and 12 methylation sites that are scavenged by other non-PRMT1 arginine methyltransferases in the absence of PRMT1 activity. We also identified one methylation site, HNRNPA1 R206, which switched from ADMA to SDMA upon PRMT1 knockdown. Taken together, our results suggest that deep protein methylation profiling by mass spectrometry requires orthogonal enrichment techniques to identify novel PRMT1 methylation targets and highlight the dynamic interplay between methyltransferases in mammalian cells. Deep methylation profiling reveals novel PRMT1 targets

The metabolic reprogramming of cancer cells creates metabolic vulnerabilities that can be therapeutically targeted. However, our understanding of metabolic dependencies and the pathway crosstalk that creates these vulnerabilities in cancer cells remains incomplete. Here, by integrating gene expression data with genetic loss-of-function and pharmacological screening data from hundreds of cancer cell lines, we identified metabolic vulnerabilities at the level of pathways rather than individual genes. This approach revealed that metabolic pathway dependencies are highly context-specific such cancer cells are vulnerable to inhibition of one metabolic pathway only when activity of another metabolic pathway is altered. Notably, we also found that the no single metabolic pathway was universally essential, suggesting that cancer cells are not invariably dependent on any metabolic pathway. In addition, we confirmed that cell culture medium is a major confounding factor for the analysis of metabolic pathway vulnerabilities. Nevertheless, we found robust associations between metabolic pathway activity and sensitivity to clinically approved drugs that were independent of cell culture medium. Lastly, we used parallel integration of pharmacological and genetic dependency data to confidently identify metabolic pathway vulnerabilities. Taken together, this study serves as a comprehensive characterization of the landscape of metabolic pathway vulnerabilities in cancer cell lines.