Here we describe a proteomic data resource for the NCI-60 cell lines generated by pressure cycling technology and SWATH mass spectrometry. We developed the DIA-expert software to curate and visualize the SWATH data, leading to reproducible detection of over 3,100 SwissProt proteotypic proteins and systematic quantification of pathway activities. Stoichiometric relationships of interacting proteins for DNA replication, repair, the chromatin remodeling NuRD complex, β-catenin, RNA metabolism, and prefoldins are more evident than that at the mRNA level. The data are available in CellMiner (discover.nci.nih.gov/cellminercdb and discover.nci.nih.gov/cellminer), allowing casual users to test hypotheses and perform integrative, cross-database analyses of multi-omic drug response correlations for over 20,000 drugs. We demonstrate the value of proteome data in predicting drug response for over 240 clinically relevant chemotherapeutic and targeted therapies. In summary, we present a novel proteome resource for the NCI-60, together with relevant software tools, and demonstrate the benefit of proteome analyses.

Data on bioactivities of drug-like chemicals is rapidly accumulating in public repositories, creating new opportunities for research in computational systems pharmacology. However, integrative analysis of these data sets is difficult due to prevailing ambiguity between chemical names and identifiers and a lack of cross-references between databases.

Recent large-scale studies revealed cell-type specific proteomes. However, protein complexes, the basic functional modules of a cell, have been so far mostly considered as static entities with well-defined structures. The co-expression of their members has not been systematically charted at the protein level.

Stimulation of cells with epidermal growth factor (EGF) induces internalization and partial degradation of the EGF receptor (EGFR) by the endo-lysosomal pathway. For continuous cell functioning, EGFR plasma membrane levels are maintained by transporting newly synthesized EGFRs to the cell surface. The regulation of this process is largely unknown. In this study, we find that EGF stimulation specifically increases the transport efficiency of newly synthesized EGFRs from the endoplasmic reticulum to the plasma membrane. This coincides with an up-regulation of the inner coat protein complex II (COPII) components SEC23B, SEC24B, and SEC24D, which we show to be specifically required for EGFR transport. Up-regulation of these COPII components requires the transcriptional regulator RNF11, which localizes to early endosomes and appears additionally in the cell nucleus upon continuous EGF stimulation. Collectively, our work identifies a new regulatory mechanism that integrates the degradation and transport of EGFR in order to maintain its physiological levels at the plasma membrane.

Inverse gene expression profiling was recently shown to help drug repositioning. We showed that this approach works best for cancer and predicted novel drug candidates that may reduce metastasis in colorectal cancer. Antimetastatic activity of our predicted candidate, citalopram, was validated in an orthotopic mouse model of metastatic colorectal cancer.

Histone deacetylases (HDACs) and acetyltransferases control the epigenetic regulation of gene expression through modification of histone marks. Histone deacetylase inhibitors (HDACi) are small molecules that interfere with histone tail modification thus altering chromatin structure and epigenetically controlled pathways. They promote apoptosis in proliferating cells and are promising anti-cancer drugs. While some HDACis have already been approved for therapy and others are in different phases of clinical trials, the exact mechanism of action of this drug class remains elusive. Previous studies have shown that HDACis cause massive changes in chromatin structure but only moderate changes in gene expression. To which extent these changes manifest at the protein level has never been investigated on a proteome-wide scale. Here, we have studied HDACi-treated cells by large-scale mass spectrometry based proteomics. We show that HDACi treatment affects primarily the nuclear proteome and induces a selective decrease of bromodomain containing proteins (BCPs), the main readers of acetylated histone marks. By combining time-resolved proteome and transcriptome profiling, we show that BCPs are affected at the protein level as early as 12 hours after HDACi treatment and that their abundance is regulated by a combination of transcriptional and post-transcriptional mechanisms. Using gene silencing, we demonstrate that the decreased abundance of BCPs is sufficient to mediate important transcriptional changes induced by HDACi. Our data reveals a new aspect of the mechanism of action of HDACi that is mediated by an interplay between histone acetylation and the abundance of BCPs. Data are available via ProteomeXchange with identifier PXD001660 and NCBI Gene Expression Omnibus with identifier GSE64689.

Aging is associated with the decline of protein, cell, and organ function. Here, we use an integrated approach to characterize gene expression, bulk translation, and cell biology in the brains and livers of young and old rats. We identify 468 differences in protein abundance between young and old animals. The majority are a consequence of altered translation output, that is, the combined effect of changes in transcript abundance and translation efficiency. In addition, we identify 130 proteins whose overall abundance remains unchanged but whose sub-cellular localization, phosphorylation state, or splice-form varies. While some protein-level differences appear to be a generic property of the rats' chronological age, the majority are specific to one organ. These may be a consequence of the organ's physiology or the chronological age of the cells within the tissue. Taken together, our study provides an initial view of the proteome at the molecular, sub-cellular, and organ level in young and old rats.

Morphogenesis is the process whereby cell collectives are shaped into differentiated tissues and organs. The self-organizing nature of morphogenesis has been recently demonstrated by studies showing that stem cells in three-dimensional culture can generate complex organoids, such as mini-guts, optic-cups and even mini-brains. To achieve this, cell collectives must regulate the activity of secreted signalling molecules that control cell differentiation, presumably through the self-assembly of microenvironments or niches. However, mechanisms that allow changes in tissue architecture to feedback directly on the activity of extracellular signals have not been described. Here we investigate how the process of tissue assembly controls signalling activity during organogenesis in vivo, using the migrating zebrafish lateral line primordium. We show that fibroblast growth factor (FGF) activity within the tissue controls the frequency at which it deposits rosette-like mechanosensory organs. Live imaging reveals that FGF becomes specifically concentrated in microluminal structures that assemble at the centre of these organs and spatially constrain its signalling activity. Genetic inhibition of microlumen assembly and laser micropuncture experiments demonstrate that microlumina increase signalling responses in participating cells, thus allowing FGF to coordinate the migratory behaviour of cell groups at the tissue rear. As the formation of a central lumen is a self-organizing property of many cell types, such as epithelia and embryonic stem cells, luminal signalling provides a potentially general mechanism to locally restrict, coordinate and enhance cell communication within tissues.

Drug-induced gene expression profiles that invert disease profiles have recently been illustrated to be a starting point for drug-repositioning. In this study, we validate this approach and focus on prediction of novel drugs for colorectal cancer (CRC), for which there is a pressing need to find novel anti-metastatic compounds. We computationally predicted three novel and still unknown compounds against CRC; citalopram (an anti-depressant), troglitazone (an anti-diabetic) and enilconazole (a fungicide). We verified the compounds by in vitro assays of clonogenic survival, proliferation and migration and in a subcutaneous mouse model. We found evidence that the mode-of-action of these compounds may be through inhibition of TGF-β signaling. Furthermore, one compound, citalopram, reduced tumor size as well as the number of circulating tumor cells and metastases in an orthotopic mouse model of CRC. This study proposes citalopram as a potential therapeutic option for CRC patients illustrating the potential of systems pharmacology.

In pharmacology, it is crucial to understand the complex biological responses that drugs elicit in the human organism and how well they can be inferred from model organisms. We therefore identified a large set of drug-induced transcriptional modules from genome-wide microarray data of drug-treated human cell lines and rat liver, and first characterized their conservation. Over 70% of these modules were common for multiple cell lines and 15% were conserved between the human in vitro and the rat in vivo system. We then illustrate the utility of conserved and cell-type-specific drug-induced modules by predicting and experimentally validating (i) gene functions, e.g., 10 novel regulators of cellular cholesterol homeostasis and (ii) new mechanisms of action for existing drugs, thereby providing a starting point for drug repositioning, e.g., novel cell cycle inhibitors and new modulators of α-adrenergic receptor, peroxisome proliferator-activated receptor and estrogen receptor. Taken together, the identified modules reveal the conservation of transcriptional responses towards drugs across cell types and organisms, and improve our understanding of both the molecular basis of drug action and human biology.

To understand the structure and function of large molecular machines, accurate knowledge of their stoichiometry is essential. In this study, we developed an integrated targeted proteomics and super-resolution microscopy approach to determine the absolute stoichiometry of the human nuclear pore complex (NPC), possibly the largest eukaryotic protein complex. We show that the human NPC has a previously unanticipated stoichiometry that varies across cancer cell types, tissues and in disease. Using large-scale proteomics, we provide evidence that more than one third of the known, well-defined nuclear protein complexes display a similar cell type-specific variation of their subunit stoichiometry. Our data point to compositional rearrangement as a widespread mechanism for adapting the functions of molecular machines toward cell type-specific constraints and context-dependent needs, and highlight the need of deeper investigation of such structural variants.

SUMMARY: Drug versus Disease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories. Negatively correlated profiles can be used to generate hypotheses of drug-repurposing whilst positively correlated profiles may be used to infer side-effects of drugs. DvD allows users to compare drug and disease signatures with dynamic access to databases Array Express, GEO and data from the Connectivity Map.Availability and Implementation: R package (submitted to Bioconductor) under GPL 3 and Cytoscape plug-in freely available for download at www.ebi.ac.uk/saezrodriguez/DVD/ CONTACT: saezrodriguez@ebi.ac.uk SUPPLEMENTARY INFORMATION: Accompanies this paper.

The increased availability of large-scale open-access resources on bioactivities of small molecules has a significant impact on pharmacology facilitated mainly by computational approaches that digest the vast amounts of data. We discuss here how computational data integration enables systemic views on a drug's action and allows to tackle complex problems such as the large-scale prediction of drug targets, drug repurposing, the molecular mechanisms, cellular responses or side effects. We particularly focus on computational methods that leverage various cell-based transcriptional, proteomic and phenotypic profiles of drug response in order to gain a systemic view of drug action at the molecular, cellular and whole-organism scale.

In pharmacology, it is essential to identify the molecular mechanisms of drug action in order to understand adverse side effects. These adverse side effects have been used to infer whether two drugs share a target protein. However, side-effect similarity of drugs could also be caused by their target proteins being close in a molecular network, which as such could cause similar downstream effects. In this study, we investigated the proportion of side-effect similarities that is due to targets that are close in the network compared to shared drug targets. We found that only a minor fraction of side-effect similarities (5.8 %) are caused by drugs targeting proteins close in the network, compared to side-effect similarities caused by overlapping drug targets (64%). Moreover, these targets that cause similar side effects are more often in a linear part of the network, having two or less interactions, than drug targets in general. Based on the examples, we gained novel insight into the molecular mechanisms of side effects associated with several drug targets. Looking forward, such analyses will be extremely useful in the process of drug development to better understand adverse side effects.

Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes.

Drug perturbations of human cells lead to complex responses upon target binding. One of the known mechanisms is a (positive or negative) feedback loop that adjusts the expression level of the respective target protein. To quantify this mechanism systems-wide in an unbiased way, drug-induced differential expression of drug target mRNA was examined in three cell lines using the Connectivity Map. To overcome various biases in this valuable resource, we have developed a computational normalization and scoring procedure that is applicable to gene expression recording upon heterogeneous drug treatments. In 1290 drug-target relations, corresponding to 466 drugs acting on 167 drug targets studied, 8% of the targets are subject to regulation at the mRNA level. We confirmed systematically that in particular G-protein coupled receptors, when serving as known targets, are regulated upon drug treatment. We further newly identified drug-induced differential regulation of Lanosterol 14-alpha demethylase, Endoplasmin, DNA topoisomerase 2-alpha and Calmodulin 1. The feedback regulation in these and other targets is likely to be relevant for the success or failure of the molecular intervention.