Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics.

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App. Analysis of the collective data provided molecular explanations for known phenotypic drug effects and uncovered new aspects of the MoA of human medicines. We found that histone deacetylase inhibitors potently and strongly down-regulated the T cell receptor complex resulting in impaired human T cell activation in vitro and ex vivo. This offers a rational explanation for the efficacy of histone deacetylase inhibitors in certain lymphomas and autoimmune diseases and explains their poor performance in treating solid tumors.

Integrating multi-omics data reveals energy and stress signaling activated by abscisic acid in Arabidopsis.

Phytohormones are essential signaling molecules regulating various processes in growth, development, and stress responses. Genetic and molecular studies, especially using Arabidopsis thaliana (Arabidopsis), have discovered many important players involved in hormone perception, signal transduction, transport, and metabolism. Phytohormone signaling pathways are extensively interconnected with other endogenous and environmental stimuli. However, our knowledge of the huge and complex molecular network governed by a hormone remains limited. Here we report a global overview of downstream events of an abscisic acid (ABA) receptor, REGULATORY COMPONENTS OF ABA RECEPTOR (RCAR) 6 (also known as PYRABACTIN RESISTANCE 1 [PYR1]-LIKE [PYL] 12), by integrating phosphoproteomic, proteomic and metabolite profiles. Our data suggest that the RCAR6 overexpression constitutively decreases the protein levels of its coreceptors, namely clade A protein phosphatases of type 2C, and activates sucrose non-fermenting-1 (SNF1)-related protein kinase 1 (SnRK1) and SnRK2, the central regulators of energy and ABA signaling pathways. Furthermore, several enzymes in sugar metabolism were differentially phosphorylated and expressed in the RCAR6 line, and the metabolite profile revealed altered accumulations of several organic acids and amino acids. These results indicate that energy- and water-saving mechanisms mediated by the SnRK1 and SnRK2 kinases, respectively, are under the control of the ABA receptor-coreceptor complexes.

Inhibiting Hippo pathway kinases releases WWC1 to promote AMPAR-dependent synaptic plasticity and long-term memory in mice.

The localization, number, and function of postsynaptic AMPA-type glutamate receptors (AMPARs) are crucial for synaptic plasticity, a cellular correlate for learning and memory. The Hippo pathway member WWC1 is an important component of AMPAR-containing protein complexes. However, the availability of WWC1 is constrained by its interaction with the Hippo pathway kinases LATS1 and LATS2 (LATS1/2). Here, we explored the biochemical regulation of this interaction and found that it is pharmacologically targetable in vivo. In primary hippocampal neurons, phosphorylation of LATS1/2 by the upstream kinases MST1 and MST2 (MST1/2) enhanced the interaction between WWC1 and LATS1/2, which sequestered WWC1. Pharmacologically inhibiting MST1/2 in male mice and in human brain-derived organoids promoted the dissociation of WWC1 from LATS1/2, leading to an increase in WWC1 in AMPAR-containing complexes. MST1/2 inhibition enhanced synaptic transmission in mouse hippocampal brain slices and improved cognition in healthy male mice and in male mouse models of Alzheimer's disease and aging. Thus, compounds that disrupt the interaction between WWC1 and LATS1/2 might be explored for development as cognitive enhancers.

D6PK plasma membrane polarity requires a repeated CXX(X)P motif and PDK1-dependent phosphorylation.

D6 PROTEIN KINASE (D6PK) is a polarly localized plasma-membrane-associated kinase from Arabidopsis thaliana that activates polarly distributed PIN-FORMED auxin transporters. D6PK moves rapidly to and from the plasma membrane, independent of its PIN-FORMED targets. The middle D6PK domain, an insertion between kinase subdomains VII and VIII, is required and sufficient for association and polarity of the D6PK plasma membrane. How D6PK polarity is established and maintained remains to be shown. Here we show that cysteines from repeated middle domain CXX(X)P motifs are S-acylated and required for D6PK membrane association. While D6PK S-acylation is not detectably regulated during intracellular transport, phosphorylation of adjacent serine residues, in part in dependence on the upstream 3-PHOSPHOINOSITIDE-DEPENDENT PROTEIN KINASE, promotes D6PK transport, controls D6PK residence time at the plasma membrane and prevents its lateral diffusion. We thus identify new mechanisms for the regulation of D6PK plasma membrane interaction and polarity.

Illuminating phenotypic drug responses of sarcoma cells to kinase inhibitors by phosphoproteomics.

Kinase inhibitors (KIs) are important cancer drugs but often feature polypharmacology that is molecularly not understood. This disconnect is particularly apparent in cancer entities such as sarcomas for which the oncogenic drivers are often not clear. To investigate more systematically how the cellular proteotypes of sarcoma cells shape their response to molecularly targeted drugs, we profiled the proteomes and phosphoproteomes of 17 sarcoma cell lines and screened the same against 150 cancer drugs. The resulting 2550 phenotypic profiles revealed distinct drug responses and the cellular activity landscapes derived from deep (phospho)proteomes (9-10,000 proteins and 10-27,000 phosphorylation sites per cell line) enabled several lines of analysis. For instance, connecting the (phospho)proteomic data with drug responses revealed known and novel mechanisms of action (MoAs) of KIs and identified markers of drug sensitivity or resistance. All data is publicly accessible via an interactive web application that enables exploration of this rich molecular resource for a better understanding of active signalling pathways in sarcoma cells, identifying treatment response predictors and revealing novel MoA of clinical KIs.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens.

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

The RALF signaling pathway regulates cell wall integrity during pollen tube growth in maize.

Autocrine signaling pathways regulated by RAPID ALKALINIZATION FACTORs (RALFs) control cell wall integrity during pollen tube germination and growth in Arabidopsis (Arabidopsis thaliana). To investigate the role of pollen-specific RALFs in another plant species, we combined gene expression data with phylogenetic and biochemical studies to identify candidate orthologs in maize (Zea mays). We show that Clade IB ZmRALF2/3 mutations, but not Clade III ZmRALF1/5 mutations, cause cell wall instability in the sub-apical region of the growing pollen tube. ZmRALF2/3 are mainly located in the cell wall and are partially able to complement the pollen germination defect of their Arabidopsis orthologs AtRALF4/19. Mutations in ZmRALF2/3 compromise pectin distribution patterns leading to altered cell wall organization and thickness culminating in pollen tube burst. Clade IB, but not Clade III ZmRALFs, strongly interact as ligands with the pollen-specific Catharanthus roseus RLK1-like (CrRLK1L) receptor kinases Z. mays FERONIA-like (ZmFERL) 4/7/9, LORELEI-like glycosylphosphatidylinositol-anchor (LLG) proteins Z. mays LLG 1 and 2 (ZmLLG1/2), and Z. mays pollen extension-like (PEX) cell wall proteins ZmPEX2/4. Notably, ZmFERL4 outcompetes ZmLLG2 and ZmPEX2 outcompetes ZmFERL4 for ZmRALF2 binding. Based on these data, we suggest that Clade IB RALFs act in a dual role as cell wall components and extracellular sensors to regulate cell wall integrity and thickness during pollen tube growth in maize and probably other plants.

CurveCurator: a recalibrated F-statistic to assess, classify, and explore significance of dose-response curves.

Dose-response curves are key metrics in pharmacology and biology to assess phenotypic or molecular actions of bioactive compounds in a quantitative fashion. Yet, it is often unclear whether or not a measured response significantly differs from a curve without regulation, particularly in high-throughput applications or unstable assays. Treating potency and effect size estimates from random and true curves with the same level of confidence can lead to incorrect hypotheses and issues in training machine learning models. Here, we present CurveCurator, an open-source software that provides reliable dose-response characteristics by computing p-values and false discovery rates based on a recalibrated F-statistic and a target-decoy procedure that considers dataset-specific effect size distributions. The application of CurveCurator to three large-scale datasets enables a systematic drug mode of action analysis and demonstrates its scalable utility across several application areas, facilitated by a performant, interactive dashboard for fast data exploration.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics.

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

Structural Basis for Highly Selective Class II Alpha Phosphoinositide-3-Kinase Inhibition.

Class II phosphoinositide-3-kinases (PI3Ks) play central roles in cell signaling, division, migration, and survival. Despite evidence that all PI3K class II isoforms serve unique cellular functions, the lack of isoform-selective inhibitors severely hampers the systematic investigation of their potential relevance as pharmacological targets. Here, we report the structural evaluation and molecular determinants for selective PI3K-C2α inhibition by a structure-activity relationship study based on a pteridinone scaffold, leading to the discovery of selective PI3K-C2α inhibitors called PITCOINs. Cocrystal structures and docking experiments supported the rationalization of the structural determinants essential for inhibitor activity and high selectivity. Profiling of PITCOINs in a panel of more than 118 diverse kinases showed no off-target kinase inhibition. Notably, by addressing a selectivity pocket, PITCOIN4 showed nanomolar inhibition of PI3K-C2α and >100-fold selectivity in a general kinase panel. Our study paves the way for the development of novel therapies for diseases related to PI3K-C2α function.

Negative feedback regulation of MAPK signaling is an important driver of chronic lymphocytic leukemia progression.

Despite available targeted treatments for the disease, drug-resistant chronic lymphocytic leukemia (CLL) poses a clinical challenge. The objective of this study is to examine whether the dual-specific phosphatases DUSP1 and DUSP6 are required to negatively regulate mitogen-activated protein kinases (MAPKs) and thus counterbalance excessive MAPK activity. We show that high expression of DUSP6 in CLL correlates with poor clinical prognosis. Importantly, genetic deletion of the inhibitory phosphatase DUSP1 or DUSP6 and blocking DUSP1/6 function using a small-molecule inhibitor reduces CLL cell survival in vitro and in vivo. Using global phospho-proteome approaches, we observe acute activation of MAPK signaling by DUSP1/6 inhibition. This promotes accumulation of mitochondrial reactive oxygen species and, thereby, DNA damage and apoptotic cell death in CLL cells. Finally, we observe that DUSP1/6 inhibition is particularly effective against treatment-resistant CLL and therefore suggest transient DUSP1/6 inhibition as a promising treatment concept to eliminate drug-resistant CLL cells.

Proteomic meta-study harmonization, mechanotyping and drug repurposing candidate prediction with ProHarMeD.

Proteomics technologies, which include a diverse range of approaches such as mass spectrometry-based, array-based, and others, are key technologies for the identification of biomarkers and disease mechanisms, referred to as mechanotyping. Despite over 15,000 published studies in 2022 alone, leveraging publicly available proteomics data for biomarker identification, mechanotyping and drug target identification is not readily possible. Proteomic data addressing similar biological/biomedical questions are made available by multiple research groups in different locations using different model organisms. Furthermore, not only various organisms are employed but different assay systems, such as in vitro and in vivo systems, are used. Finally, even though proteomics data are deposited in public databases, such as ProteomeXchange, they are provided at different levels of detail. Thus, data integration is hampered by non-harmonized usage of identifiers when reviewing the literature or performing meta-analyses to consolidate existing publications into a joint picture. To address this problem, we present ProHarMeD, a tool for harmonizing and comparing proteomics data gathered in multiple studies and for the extraction of disease mechanisms and putative drug repurposing candidates. It is available as a website, Python library and R package. ProHarMeD facilitates ID and name conversions between protein and gene levels, or organisms via ortholog mapping, and provides detailed logs on the loss and gain of IDs after each step. The web tool further determines IDs shared by different studies, proposes potential disease mechanisms as well as drug repurposing candidates automatically, and visualizes these results interactively. We apply ProHarMeD to a set of four studies on bone regeneration. First, we demonstrate the benefit of ID harmonization which increases the number of shared genes between studies by 50%. Second, we identify a potential disease mechanism, with five corresponding drug targets, and the top 20 putative drug repurposing candidates, of which Fondaparinux, the candidate with the highest score, and multiple others are known to have an impact on bone regeneration. Hence, ProHarMeD allows users to harmonize multi-centric proteomics research data in meta-analyses, evaluates the success of the ID conversions and remappings, and finally, it closes the gaps between proteomics, disease mechanism mining and drug repurposing. It is publicly available at https://apps.cosy.bio/proharmed/ .

Chemical proteomics reveals the target landscape of 1,000 kinase inhibitors.

Medicinal chemistry has discovered thousands of potent protein and lipid kinase inhibitors. These may be developed into therapeutic drugs or chemical probes to study kinase biology. Because of polypharmacology, a large part of the human kinome currently lacks selective chemical probes. To discover such probes, we profiled 1,183 compounds from drug discovery projects in lysates of cancer cell lines using Kinobeads. The resulting 500,000 compound-target interactions are available in ProteomicsDB and we exemplify how this molecular resource may be used. For instance, the data revealed several hundred reasonably selective compounds for 72 kinases. Cellular assays validated GSK986310C as a candidate SYK (spleen tyrosine kinase) probe and X-ray crystallography uncovered the structural basis for the observed selectivity of the CK2 inhibitor GW869516X. Compounds targeting PKN3 were discovered and phosphoproteomics identified substrates that indicate target engagement in cells. We anticipate that this molecular resource will aid research in drug discovery and chemical biology.

RBPome identification in egg-cell like callus of Arabidopsis.

RNA binding proteins (RBPs) have multiple and essential roles in transcriptional and posttranscriptional regulation of gene expression in all living organisms. Their biochemical identification in the proteome of a given cell or tissue requires significant protein amounts, which limits studies in rare and highly specialized cells. As a consequence, we know almost nothing about the role(s) of RBPs in reproductive processes such as egg cell development, fertilization and early embryogenesis in flowering plants. To systematically identify the RBPome of egg cells in the model plant Arabidopsis, we performed RNA interactome capture (RIC) experiments using the egg cell-like RKD2-callus and were able to identify 728 proteins associated with poly(A+)-RNA. Transcripts for 97 % of identified proteins could be verified in the egg cell transcriptome. 46 % of identified proteins can be associated with the RNA life cycle. Proteins involved in mRNA binding, RNA processing and metabolism are highly enriched. Compared with the few available RBPome datasets of vegetative plant tissues, we identified 475 egg cell-enriched RBPs, which will now serve as a resource to study RBP function(s) during egg cell development, fertilization and early embryogenesis. First candidates were already identified showing an egg cell-specific expression pattern in ovules.

Proteogenomic analysis reveals RNA as a source for tumor-agnostic neoantigen identification.

Systemic pan-tumor analyses may reveal the significance of common features implicated in cancer immunogenicity and patient survival. Here, we provide a comprehensive multi-omics data set for 32 patients across 25 tumor types for proteogenomic-based discovery of neoantigens. By using an optimized computational approach, we discover a large number of tumor-specific and tumor-associated antigens. To create a pipeline for the identification of neoantigens in our cohort, we combine DNA and RNA sequencing with MS-based immunopeptidomics of tumor specimens, followed by the assessment of their immunogenicity and an in-depth validation process. We detect a broad variety of non-canonical HLA-binding peptides in the majority of patients demonstrating partially immunogenicity. Our validation process allows for the selection of 32 potential neoantigen candidates. The majority of neoantigen candidates originates from variants identified in the RNA data set, illustrating the relevance of RNA as a still understudied source of cancer antigens. This study underlines the importance of RNA-centered variant detection for the identification of shared biomarkers and potentially relevant neoantigen candidates.

An Introduction to Analytical Challenges, Approaches, and Applications in Mass Spectrometry-Based Secretomics.

The active release of proteins into the extracellular space and the proteolytic cleavage of cell surface proteins are key processes that coordinate and fine-tune a multitude of physiological functions. The entirety of proteins that fulfill these extracellular tasks are referred to as the secretome and are of special interest for the investigation of biomarkers of disease states and physiological processes related to cell-cell communication. LC-MS-based proteomics approaches are a valuable tool for the comprehensive and unbiased characterization of this important subproteome. This review discusses procedures, opportunities, and limitations of mass spectrometry-based secretomics to better understand and navigate the complex analytical landscape for studying protein secretion in biomedical science.