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Abstract Motoneurons critically depend on precise spatial and temporal control of translation for axon growth and the establishment and maintenance of neuromuscular connections. While defects in local translation have been implicated in the pathogenesis of motoneuron disorders, little is known about the mechanisms regulating axonal protein synthesis. Here, we report that motoneurons derived from Hnrnpr knockout mice show reduced axon growth accompanied by lowered synthesis of cytoskeletal and synaptic components in axons. Mutant mice display denervated neuromuscular junctions and impaired motor behavior. In axons, hnRNP R is a component of translation initiation complexes and, through interaction with O-linked β-N-acetylglucosamine (O-GlcNAc) transferase (Ogt), modulates O-GlcNAcylation of eIF4G. Restoring axonal O-GlcNAc levels rescued local protein synthesis and axon growth defects of hnRNP R knockout motoneurons. Together, these findings demonstrate a function of hnRNP R in controlling the local production of key factors required for axon growth and formation of neuromuscular innervations.

Abstract Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Abstract Skeletal muscle undergoes atrophy and loss of force during long space missions, when astronauts are persistently exposed to altered gravity and increased ionizing radiation. We previously carried out mass spectrometry-based proteomics from skeletal muscle biopsies of two astronauts, taken before and after a mission on the International Space Station. The experiments were part of an effort to find similarities between spaceflight and bed rest, a ground-based model of unloading, focused on proteins located at the costameres. We here extend the data analysis of the astronaut dataset and show compartment-resolved changes in the mitochondrial proteome, remodeling of the extracellular matrix and of the antioxidant response. The astronauts differed in their level of onboard physical exercise, which correlated with their respective preservation of muscle mass and force at landing in previous analyses. We show that the mitochondrial proteome downregulation during spaceflight, particularly the inner membrane and matrix, was dramatic for both astronauts. The expression of autophagy regulators and reactive oxygen species scavengers, however, showed partially opposite expression trends in the two subjects, possibly correlating with their level of onboard exercise. As mitochondria are primarily affected in many different tissues during spaceflight, we hypothesize that reactive oxygen species (ROS) rather than mechanical unloading per se could be the primary cause of skeletal muscle mitochondrial damage in space. Onboard physical exercise might have a strong direct effect on the prevention of muscle atrophy through mechanotransduction and a subsidiary effect on mitochondrial quality control, possibly through upregulation of autophagy and anti-oxidant responses.

Abstract Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.

Abstract In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Abstract Sporadic Parkinson’s Disease (sPD) is a progressive neurodegenerative disorder caused by multiple genetic and environmental factors. Mitochondrial dysfunction is one contributing factor, but its role at different stages of disease progression is not fully understood. Here, we showed that neural precursor cells and dopaminergic neurons derived from induced pluripotent stem cells (hiPSCs) from sPD patients exhibited a hypometabolism. Further analysis based on transcriptomics, proteomics, and metabolomics identified the citric acid cycle, specifically the α-ketoglutarate dehydrogenase complex (OGDHC), as bottleneck in sPD metabolism. A follow-up study of the patients approximately 10 years after initial biopsy demonstrated a correlation between OGDHC activity in our cellular model and the disease progression. In addition, the alterations in cellular metabolism observed in our cellular model were restored by interfering with the enhanced SHH signal transduction in sPD. Thus, inhibiting overactive SHH signaling may have potential as neuroprotective therapy during early stages of sPD.

Abstract 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.

Abstract The neuronal RNA-binding protein Ptbp2 regulates neuronal differentiation by modulating alternative splicing programs in the nucleus. Such programs contribute to axonogenesis by adjusting the levels of protein isoforms involved in axon growth and branching. While its functions in alternative splicing have been described in detail, cytosolic roles of Ptbp2 for axon growth have remained elusive. Here, we show that Ptbp2 is located in the cytosol including axons and growth cones of motoneurons, and that depletion of cytosolic Ptbp2 affects axon growth. We identify Ptbp2 as a major interactor of the 3’ UTR of Hnrnpr mRNA encoding the RNA-binding protein hnRNP R. Axonal localization of Hnrnpr mRNA and local synthesis of hnRNP R protein are strongly reduced when Ptbp2 is depleted, leading to defective axon growth. Ptbp2 regulates hnRNP R translation by mediating the association of Hnrnpr with ribosomes in a manner dependent on the translation factor eIF5A2. Our data thus suggest a mechanism whereby cytosolic Ptbp2 modulates axon growth by fine-tuning the mRNA transport and local synthesis of an RNA-binding protein.

How the histone variant H2A.Z controls cell fate remains unclear. Here, the authors reveal that the H2A.Z interacting partner HMG20A plays a key role in regulating transcription during early head and heart development.

Nagao et al. show that the insulin receptor can mediate receptor-dependent, but ligand- and tyrosine kinase-independent, events. These are associated with regulation of extracellular matrix, cell cycle, ATM signaling, senescence and apoptosis.

Deep learning (DL) has been frequently used in mass spectrometry-based proteomics but there is still a lot of potential. Here, the authors develop a framework that enables building DL models to predict arbitrary peptide properties with only a few lines of code.

Reusswig et al. use engineered systems to force DNA replication in the G1 phase of the cell cycle. This unscheduled G1 replication shows hallmarks of S phase replication, but leads to over-replication and DNA breaks from replication collisions.

The GID E3 ligase regulates glucose-induced degradation in yeast, and key physiology. This study unveils E3 ligase regulation by reshaping the substrate binding site, blocking substrate access to ubiquitination active sites, and a Cage-like assembly.

N-terminal protein acetylation is required for plant viability. Here the authors show that reducing N-terminal acetylation by NatA leads to an increase in global protein turnover that is facilitated by absent masking of a novel N-degron

Many interactions between viral and host proteins are mediated by short peptide motifs. Here, using a phage-based viral peptide library, the authors identify 269 peptide-based interactions for 18 coronaviruses, including an interaction between SARS-CoV-2 N and G3BP1/2 that affects stress granules.

Tumor necrosis factor (TNF) has various effects on phosphorylation-mediated cellular signaling. Combining phosphoproteomics, subcellular localization analyses and kinase inhibitor assays, the authors provide systems level insights into TNF signaling and identify modulators of TNF-induced cell death.

An extensive RNA binding protein atlas (RBPome) for primary T cells would be a useful resource. Here the authors use two different methods to characterise the mouse and human T cell RBPome and show regulation of Roquin-1/2 dependent and independent pathways.

Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.

The noncoding RNA 7SK controls the transcription of mRNAs. Here, the authors show that the 7SK complex interacts with the Smn complex, suggesting crosstalk between transcription and snRNP assembly.

Protein ubiquitylation is often studied by proteomics but how data independent acquisition (DIA) may advance these studies remains to be explored. Here, the authors show that DIA improves ubiquitylation site identification and quantification, enabling them to characterize the circadian ubiquitinome in human cells.

The human skin is a highly complex organ comprising multiple tissue layers and diverse cell types. Here, the authors present a spatially-resolved quantitative proteomic atlas of the healthy human skin, characterizing the protein profiles of four skin layers and nine cell types.

Cross-linking mass spectrometry (MS) is an important tool in structural biology, but its application to protein-DNA complexes has been hampered by low cross-linking efficiency. Here, the authors develop a femtosecond UV-laser induced cross-linking MS workflow to map protein-DNA interactions in vitro and in cells.

Trapped ion mobility (TIMS)-mass spectrometry with parallel accumulation-serial fragmentation (PASEF) facilitates high-sensitivity proteomics experiments. Here, the authors expand TIMS and PASEF to small molecules and demonstrate fast and comprehensive lipidomics of low biological sample amounts.

Insulin signaling represses Forkhead transcription factor FoxO activity, which contributes to organismal metabolism. Here, the authors use proteomics to identify positively regulated insulin signaling targets FoxK1/K2 and demonstrate their role in lipid metabolism and mitochondrial regulation.

Aging impacts lung functionality and makes it more susceptible to chronic diseases. Combining proteomics and single cell transcriptomics, the authors chart molecular and cellular changes in the aging mouse lung, discover aging hallmarks, and predict the cellular sources of regulated proteins.

PWWP2A is a chromatin-binding transcriptional regulator that mediates mitosis-progression. Here, the authors provide evidence that PWWP2A directly interacts with H2A.Z nucleosomes, DNA and H3K36me3, binds to an MTA1-specific subcomplex of the NuRD complex (M1HR) and promotes changes to histone acetylation.

Exosomes mediate cell-to-cell communication by transporting proteins, mRNAs, and miRNAs but the mechanisms of protein sorting to exosomes are poorly understood. Here, the authors uncover that ubiquitin-like 3 (UBL3) regulates protein sorting to exosomes by acting as a posttranslational modification.

John Fenn’s electrospray mass spectrometry (ESMS) was awarded the chemistry Nobel Prize in 2002 and is now the basis of the entire field of MS-based proteomics. Technological progress continues unabated, enabling single cell sensitivity and clinical applications.

The human heart is composed of distinct regions and cell types, but relatively little is known about their specific protein composition. Here, the authors present a region- and cell type-specific proteomic map of the healthy human heart, revealing functional differences and potential cell type markers.

Since protein interactions can be sensitive to small sequence changes, phylogenomics alone gives limited insight into protein functional evolution. Here, the authors illustrate how the combination of phylogenomics and interaction proteomics helps elucidate protein functional evolution with a study of the shelterin complex evolution.

Neoantigens determine anti-cancer immunoreactivity and are important functional targets for immunotherapy. Here, the authors use deep mass spectrometry to characterize neoepitopes from human melanoma tissue and show the presence of tumour-reactive T cells with specificity for selected neoantigens.

Dysfunction in insulin secretion is a main driver of type 2 diabetes development. Here the authors monitor phosphoproteome modulation in cells stimulated with glucose and treated with drugs affecting glucose-mediated insulin secretion to reveal phosphorylation sites implicated in insulin secretion control and gene expression regulation.

Circular RNAs are widely expressed in eukaryotic cells but their functions and mechanisms of action are still being elucidated. Here the authors show that circANRILmodulates rRNA maturation and confers protection again atherosclerosis.

Breast cancers have been extensively studied at the genomic and transcriptomic levels in the hope of tailoring therapeutic regimens. Here the authors generate deep coverage proteomes from several clinical breast cancer samples, and use machine learning techniques to uncover biological processes altered in specific cancer subtypes.