A Bayesian semi-parametric model for thermal proteome profiling.

Funder: Wellcome TrustThe thermal stability of proteins can be altered when they interact with small molecules, other biomolecules or are subject to post-translation modifications. Thus monitoring the thermal stability of proteins under various cellular perturbations can provide insights into protein function, as well as potentially determine drug targets and off-targets. Thermal proteome profiling is a highly multiplexed mass-spectrommetry method for monitoring the melting behaviour of thousands of proteins in a single experiment. In essence, thermal proteome profiling assumes that proteins denature upon heating and hence become insoluble. Thus, by tracking the relative solubility of proteins at sequentially increasing temperatures, one can report on the thermal stability of a protein. Standard thermodynamics predicts a sigmoidal relationship between temperature and relative solubility and this is the basis of current robust statistical procedures. However, current methods do not model deviations from this behaviour and they do not quantify uncertainty in the melting profiles. To overcome these challenges, we propose the application of Bayesian functional data analysis tools which allow complex temperature-solubility behaviours. Our methods have improved sensitivity over the state-of-the art, identify new drug-protein associations and have less restrictive assumptions than current approaches. Our methods allows for comprehensive analysis of proteins that deviate from the predicted sigmoid behaviour and we uncover potentially biphasic phenomena with a series of published datasets

Whole cell proteome regulation by microRNAs captured in a pulsed SILAC mass spectrometry approach

Since gene expression is controlled on many different levels in a cell, capturing a comprehensive snapshot of all regulatory processes is a difficult task. One possibility to monitor effective changes within a cell is to directly quantify changes in protein synthesis, which reflects the accumulative impact of regulatory mechanisms on gene expression. Pulsed stable isotope labeling by amino acids in cell culture (pSILAC) has been shown to be a viable method to investigate de novo protein synthesis on a proteome-wide scale (Schwanhausser et al., Proteomics 9:205-209, 2009; Selbach et al., Nature 455:58-63, 2008). One application of pSILAC is to study the regulation of protein expression by microRNAs. Here, we describe how pSILAC in conjunction with shotgun mass spectrometry can assess differences in the protein profile between cells transfected with a microRNA and non-transfected cells

Multi-omics Prediction from High-content Cellular Imaging with Deep Learning

High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function. However, the biological determinants through which changes in multi-omics measurements influence cellular morphology have not yet been systematically explored, and the degree to which cell imaging could potentially enable the prediction of multi-omics directly from cell imaging data is therefore currently unclear. Here, we address the question of whether it is possible to predict bulk multi-omics measurements directly from cell images using Image2Omics -- a deep learning approach that predicts multi-omics in a cell population directly from high-content images stained with multiplexed fluorescent dyes. We perform an experimental evaluation in gene-edited macrophages derived from human induced pluripotent stem cell (hiPSC) under multiple stimulation conditions and demonstrate that Image2Omics achieves significantly better performance in predicting transcriptomics and proteomics measurements directly from cell images than predictors based on the mean observed training set abundance. We observed significant predictability of abundances for 5903 (22.43%; 95% CI: 8.77%, 38.88%) and 5819 (22.11%; 95% CI: 10.40%, 38.08%) transcripts out of 26137 in M1 and M2-stimulated macrophages respectively and for 1933 (38.77%; 95% CI: 36.94%, 39.85%) and 2055 (41.22%; 95% CI: 39.31%, 42.42%) proteins out of 4986 in M1 and M2-stimulated macrophages respectively. Our results show that some transcript and protein abundances are predictable from cell imaging and that cell imaging may potentially, in some settings and depending on the mechanisms of interest and desired performance threshold, even be a scalable and resource-efficient substitute for multi-omics measurements

Identification of Plasmodium PI4 kinase as target of MMV390048 by chemoproteomics

Most antimalarial drugs face decreased efficacy due to the emergence of resistant parasites. Therefore, the discovery of new antimalarial medicines is focused on new drugs that act by novel mechanisms and are active against different P. falciparum development stages. Screening of a focused compound library for antiparasitic activity, lead to identification of a novel class of compounds with activity against P. falciparum, 2-aminopyridines. The selected hits were validated and subjected to a lead optimization program resulting in the pre-clinical candidate MMV390048. Here we report an unbiased chemoproteomics strategy for the identification of targets of MMV390048

PROTAC-mediated degradation of Bruton's tyrosine kinase is inhibited by covalent binding

The impact of covalent binding on PROTAC-Mediated degradation of BTK was investigated through the preparation of both covalent binding and reversible binding PROTACs derived from the covalent BTK inhibitor ibrutinib. It was determined that a covalent binding PROTAC inhibited BTK degradation despite evidence of target engagement, while BTK degradation was observed with a reversible binding PROTAC. These observations were consistently found when PROTACs that were able to recruit either IAP or cereblon E3 ligases were employed. Proteomics analysis determined that the use of a covalently bound PROTAC did not result in the degradation of covalently bound targets, while degradation was observed for some reversibly bound targets. This observation highlights the importance of catalysis for successful PROTAC-Mediated degradation and highlights a potential caveat for the use of covalent target binders in PROTAC design

NQO2 is a reactive oxygen species generating off-target for acetaminophen

[Image: see text] The analgesic and antipyretic compound acetaminophen (paracetamol) is one of the most used drugs worldwide. Acetaminophen overdose is also the most common cause for acute liver toxicity. Here we show that acetaminophen and many structurally related compounds bind quinone reductase 2 (NQO2) in vitro and in live cells, establishing NQO2 as a novel off-target. NQO2 modulates the levels of acetaminophen derived reactive oxygen species, more specifically superoxide anions, in cultured cells. In humans, NQO2 is highly expressed in liver and kidney, the main sites of acetaminophen toxicity. We suggest that NQO2 mediated superoxide production may function as a novel mechanism augmenting acetaminophen toxicity

Kinome rewiring reveals AURKA limits PI3K-pathway inhibitor efficacy in breast cancer.

Dysregulation of the PI3K-AKT-mTOR signaling network is a prominent feature of breast cancers. However, clinical responses to drugs targeting this pathway have been modest, possibly because of dynamic changes in cellular signaling that drive resistance and limit drug efficacy. Using a quantitative chemoproteomics approach, we mapped kinome dynamics in response to inhibitors of this pathway and identified signaling changes that correlate with drug sensitivity. Maintenance of AURKA after drug treatment was associated with resistance in breast cancer models. Incomplete inhibition of AURKA was a common source of therapy failure, and combinations of PI3K, AKT or mTOR inhibitors with the AURKA inhibitor MLN8237 were highly synergistic and durably suppressed mTOR signaling, resulting in apoptosis and tumor regression in vivo. This signaling map identifies survival factors whose presence limits the efficacy of targeted therapies and reveals new drug combinations that may unlock the full potential of PI3K-AKT-mTOR pathway inhibitors in breast cancer

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

Genetic and Proteomic Approaches to Identify Cancer Drug Targets

While target-based small-molecule discovery has taken centre-stage in the pharmaceutical industry, there are many cancer-promoting proteins not easily addressed with a traditional target-based screening approach. In order to address this problem, as well as to identify modulators of biological states in the absence of knowing the protein target of the state switch, alternative phenotypic screening approaches, such as gene expression-based and high-content imaging, have been developed. With this renewed interest in phenotypic screening, however, comes the challenge of identifying the binding protein target(s) of small-molecule hits. Emerging technologies have the potential to improve the process of target identification. In this review, we discuss the application of genomic (gene expression-based), genetic (short hairpin RNA and open reading frame screening), and proteomic approaches to protein target identification