Targeting cancer with small-molecule pan-KRAS degraders.

Mutations in the Kirsten rat sarcoma viral oncogene homolog (KRAS) protein are highly prevalent in cancer. However, small-molecule concepts that address oncogenic KRAS alleles remain elusive beyond replacing glycine at position 12 with cysteine (G12C), which is clinically drugged through covalent inhibitors. Guided by biophysical and structural studies of ternary complexes, we designed a heterobifunctional small molecule that potently degrades 13 out of 17 of the most prevalent oncogenic KRAS alleles. Compared with inhibition, KRAS degradation results in more profound and sustained pathway modulation across a broad range of KRAS mutant cell lines, killing cancer cells while sparing models without genetic KRAS aberrations. Pharmacological degradation of oncogenic KRAS was tolerated and led to tumor regression in vivo. Together, these findings unveil a new path toward addressing KRAS-driven cancers with small-molecule degraders.

AI-Assisted Processing Pipeline to Boost Protein Isoform Detection.

Proteomics, the study of proteins within biological systems, has seen remarkable advancements in recent years, with protein isoform detection emerging as one of the next major frontiers. One of the primary challenges is achieving the necessary peptide and protein coverage to confidently differentiate isoforms as a result of the protein inference problem and protein false discovery rate estimation challenge in large data. In this chapter, we describe the application of artificial intelligence-assisted peptide property prediction for database search engine rescoring by Oktoberfest, an approach that has proven effective, particularly for complex samples and extensive search spaces, which can greatly increase peptide coverage. Further, it illustrates a method for increasing isoform coverage by the PickedGroupFDR approach that is designed to excel when applied on large data. Real-world examples are provided to illustrate the utility of the tools in the context of rescoring, protein grouping, and false discovery rate estimation. By implementing these cutting-edge techniques, researchers can achieve a substantial increase in both peptide and isoform coverage, thus unlocking the potential of protein isoform detection in their studies and shedding light on their roles and functions in biological processes.

Exploring crop genomes: assembly features, gene prediction accuracy, and implications for proteomics studies.

Plant genomics plays a pivotal role in enhancing global food security and sustainability by offering innovative solutions for improving crop yield, disease resistance, and stress tolerance. As the number of sequenced genomes grows and the accuracy and contiguity of genome assemblies improve, structural annotation of plant genomes continues to be a significant challenge due to their large size, polyploidy, and rich repeat content. In this paper, we present an overview of the current landscape in crop genomics research, highlighting the diversity of genomic characteristics across various crop species. We also assessed the accuracy of popular gene prediction tools in identifying genes within crop genomes and examined the factors that impact their performance. Our findings highlight the strengths and limitations of BRAKER2 and Helixer as leading structural genome annotation tools and underscore the impact of genome complexity, fragmentation, and repeat content on their performance. Furthermore, we evaluated the suitability of the predicted proteins as a reliable search space in proteomics studies using mass spectrometry data. Our results provide valuable insights for future efforts to refine and advance the field of structural genome annotation.

Mitochondrial lipidomes are tissue specific - low cholesterol contents relate to UCP1 activity.

Lipid composition is conserved within sub-cellular compartments to maintain cell function. Lipidomic analyses of liver, muscle, white and brown adipose tissue (BAT) mitochondria revealed substantial differences in their glycerophospholipid (GPL) and free cholesterol (FC) contents. The GPL to FC ratio was 50-fold higher in brown than white adipose tissue mitochondria. Their purity was verified by comparison of proteomes with ER and mitochondria-associated membranes. A lipid signature containing PC and FC, calculated from the lipidomic profiles, allowed differentiation of mitochondria from BAT of mice housed at different temperatures. Elevating FC in BAT mitochondria prevented uncoupling protein (UCP) 1 function, whereas increasing GPL boosted it. Similarly, STARD3 overexpression facilitating mitochondrial FC import inhibited UCP1 function in primary brown adipocytes, whereas a knockdown promoted it. We conclude that the mitochondrial GPL/FC ratio is key for BAT function and propose that targeting it might be a promising strategy to promote UCP1 activity.

A Novel AMPK Inhibitor Sensitizes Pancreatic Cancer Cells to Ferroptosis Induction.

Cancer cells must develop strategies to adapt to the dynamically changing stresses caused by intrinsic or extrinsic processes, or therapeutic agents. Metabolic adaptability is crucial to mitigate such challenges. Considering metabolism as a central node of adaptability, it is focused on an energy sensor, the AMP-activated protein kinase (AMPK). In a subtype of pancreatic ductal adenocarcinoma (PDAC) elevated AMPK expression and phosphorylation is identified. Using drug repurposing that combined screening experiments and chemoproteomic affinity profiling, it is identified and characterized PF-3758309, initially developed as an inhibitor of PAK4, as an AMPK inhibitor. PF-3758309 shows activity in pre-clinical PDAC models, including primary patient-derived organoids. Genetic loss-of-function experiments showed that AMPK limits the induction of ferroptosis, and consequently, PF-3758309 treatment restores the sensitivity toward ferroptosis inducers. The work established a chemical scaffold for the development of specific AMPK-targeting compounds and deciphered the framework for the development of AMPK inhibitor-based combination therapies tailored for PDAC.

Decrypting lysine deacetylase inhibitor action and protein modifications by dose-resolved proteomics.

Lysine deacetylase inhibitors (KDACis) are approved drugs for cutaneous T cell lymphoma (CTCL), peripheral T cell lymphoma (PTCL), and multiple myeloma, but many aspects of their cellular mechanism of action (MoA) and substantial toxicity are not well understood. To shed more light on how KDACis elicit cellular responses, we systematically measured dose-dependent changes in acetylation, phosphorylation, and protein expression in response to 21 clinical and pre-clinical KDACis. The resulting 862,000 dose-response curves revealed, for instance, limited cellular specificity of histone deacetylase (HDAC) 1, 2, 3, and 6 inhibitors; strong cross-talk between acetylation and phosphorylation pathways; localization of most drug-responsive acetylation sites to intrinsically disordered regions (IDRs); an underappreciated role of acetylation in protein structure; and a shift in EP300 protein abundance between the cytoplasm and the nucleus. This comprehensive dataset serves as a resource for the investigation of the molecular mechanisms underlying KDACi action in cells and can be interactively explored online in ProteomicsDB.

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.