Noncanonical assembly, neddylation and chimeric cullin-RING/RBR ubiquitylation by the 1.8 MDa CUL9 E3 ligase complex.

Ubiquitin ligation is typically executed by hallmark E3 catalytic domains. Two such domains, 'cullin-RING' and 'RBR', are individually found in several hundred human E3 ligases, and collaborate with E2 enzymes to catalyze ubiquitylation. However, the vertebrate-specific CUL9 complex with RBX1 (also called ROC1), of interest due to its tumor suppressive interaction with TP53, uniquely encompasses both cullin-RING and RBR domains. Here, cryo-EM, biochemistry and cellular assays elucidate a 1.8-MDa hexameric human CUL9-RBX1 assembly. Within one dimeric subcomplex, an E2-bound RBR domain is activated by neddylation of its own cullin domain and positioning from the adjacent CUL9-RBX1 in trans. Our data show CUL9 as unique among RBX1-bound cullins in dependence on the metazoan-specific UBE2F neddylation enzyme, while the RBR domain protects it from deneddylation. Substrates are recruited to various upstream domains, while ubiquitylation relies on both CUL9's neddylated cullin and RBR domains achieving self-assembled and chimeric cullin-RING/RBR E3 ligase activity.

Epigenetic control over cell-intrinsic immune response antagonizes self-renewal in acute myeloid leukemia.

Epigenetic modulation of the cell-intrinsic immune response holds promise as a therapeutic approach for leukemia. However, current strategies designed for transcriptional activation of endogenous transposons and subsequent interferon type-I (IFN-I) response, show limited clinical efficacy. Histone lysine methylation is an epigenetic signature in IFN-I response associated with suppression of IFN-I and IFN stimulated genes, suggesting histone demethylation as key mechanism of reactivation. In this study, we unveil the histone demethylase PHF8 as a direct initiator and regulator of cell-intrinsic immune response in acute myeloid leukemia (AML). Site-specific phosphorylation of PHF8 orchestrates epigenetic changes that upregulate cytosolic RNA sensors, particularly the TRIM25-RIG-I-IFIT5 axis, thereby triggering the cellular IFN-I response-differentiation-apoptosis network. This signaling cascade largely counteracts differentiation block and growth of human AML cells across various disease subtypes in vitro and in vivo. Through proteome analysis of over 200 primary AML bone marrow samples, we identify a distinct PHF8/IFN-I signature in half of the patient population, without significant associations with known clinically or genetically defined AML subgroups. This profile was absent in healthy CD34-positive hematopoietic progenitor cells, suggesting therapeutic applicability in a large fraction of AML patients. Pharmacological support of PHF8 phosphorylation significantly impairs growth of primary AML patient samples. These findings provide novel opportunities for harnessing the cell-intrinsic immune response in the development of immunotherapeutic strategies against AML.

Mustn1 is a smooth muscle cell-secreted microprotein that modulates skeletal muscle extracellular matrix composition.

OBJECTIVE: Skeletal muscle plasticity and remodeling are critical for adapting tissue function to use, disuse, and regeneration. The aim of this study was to identify genes and molecular pathways that regulate the transition from atrophy to compensatory hypertrophy or recovery from injury. Here, we have used a mouse model of hindlimb unloading and reloading, which causes skeletal muscle atrophy, and compensatory regeneration and hypertrophy, respectively. METHODS: We analyzed mouse skeletal muscle at the transition from hindlimb unloading to reloading for changes in transcriptome and extracellular fluid proteome. We then used qRT-PCR, immunohistochemistry, and bulk and single-cell RNA sequencing data to determine Mustn1 gene and protein expression, including changes in gene expression in mouse and human skeletal muscle with different challenges such as exercise and muscle injury. We generated Mustn1-deficient genetic mouse models and characterized them in vivo and ex vivo with regard to muscle function and whole-body metabolism. We isolated smooth muscle cells and functionally characterized them, and performed transcriptomics and proteomics analysis of skeletal muscle and aorta of Mustn1-deficient mice. RESULTS: We show that Mustn1 (Musculoskeletal embryonic nuclear protein 1, also known as Mustang) is highly expressed in skeletal muscle during the early stages of hindlimb reloading. Mustn1 expression is transiently elevated in mouse and human skeletal muscle in response to intense exercise, resistance exercise, or injury. We find that Mustn1 expression is highest in smooth muscle-rich tissues, followed by skeletal muscle fibers. Muscle from heterozygous Mustn1-deficient mice exhibit differences in gene expression related to extracellular matrix and cell adhesion, compared to wild-type littermates. Mustn1-deficient mice have normal muscle and aorta function and whole-body glucose metabolism. We show that Mustn1 is secreted from smooth muscle cells, and that it is present in arterioles of the muscle microvasculature and in muscle extracellular fluid, particularly during the hindlimb reloading phase. Proteomics analysis of muscle from Mustn1-deficient mice confirms differences in extracellular matrix composition, and female mice display higher collagen content after chemically induced muscle injury compared to wild-type littermates. CONCLUSIONS: We show that, in addition to its previously reported intracellular localization, Mustn1 is a microprotein secreted from smooth muscle cells into the muscle extracellular space. We explore its role in muscle ECM deposition and remodeling in homeostasis and upon muscle injury. The role of Mustn1 in fibrosis and immune infiltration upon muscle injury and dystrophies remains to be investigated, as does its potential for therapeutic interventions.

AlphaPept: a modern and open framework for MS-based proteomics.

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.

Purification of time-resolved insulin granules reveals proteomic and lipidomic changes during granule aging.

Endocrine cells employ regulated exocytosis of secretory granules to secrete hormones and neurotransmitters. Secretory granule exocytosis depends on spatiotemporal variables such as proximity to the plasma membrane and age, with newly generated granules being preferentially released. Despite recent advances, we lack a comprehensive view of the molecular composition of insulin granules and associated changes over their lifetime. Here, we report a strategy for the purification of insulin secretory granules of distinct age from insulinoma INS-1 cells. Tagging the granule-resident protein phogrin with a cleavable CLIP tag, we obtain intact fractions of age-distinct granules for proteomic and lipidomic analyses. We find that the lipid composition changes over time, along with the physical properties of the membrane, and that kinesin-1 heavy chain (KIF5b) as well as Ras-related protein 3a (RAB3a) associate preferentially with younger granules. Further, we identify the Rho GTPase-activating protein (ARHGAP1) as a cytosolic factor associated with insulin granules.

The NSP3 protein of SARS-CoV-2 binds fragile X mental retardation proteins to disrupt UBAP2L interactions.

Viruses interact with numerous host factors to facilitate viral replication and to dampen antiviral defense mechanisms. We currently have a limited mechanistic understanding of how SARS-CoV-2 binds host factors and the functional role of these interactions. Here, we uncover a novel interaction between the viral NSP3 protein and the fragile X mental retardation proteins (FMRPs: FMR1, FXR1-2). SARS-CoV-2 NSP3 mutant viruses preventing FMRP binding have attenuated replication in vitro and reduced levels of viral antigen in lungs during the early stages of infection. We show that a unique peptide motif in NSP3 binds directly to the two central KH domains of FMRPs and that this interaction is disrupted by the I304N mutation found in a patient with fragile X syndrome. NSP3 binding to FMRPs disrupts their interaction with the stress granule component UBAP2L through direct competition with a peptide motif in UBAP2L to prevent FMRP incorporation into stress granules. Collectively, our results provide novel insight into how SARS-CoV-2 hijacks host cell proteins and provides molecular insight into the possible underlying molecular defects in fragile X syndrome.

Comparative Quantitative Proteomic Analysis of Melanoma Subtypes, Nevus-Associated Melanoma, and Corresponding Nevi.

A substantial part of cutaneous malignant melanomas develops from benign nevi. However, the precise molecular events driving the transformation from benign to malignant melanoma are not well-understood. We used laser microdissection and mass spectrometry to analyze the proteomes of melanoma subtypes, including superficial spreading melanomas (n = 17), nodular melanomas (n = 17), and acral melanomas (n = 15). Furthermore, we compared the proteomes of nevi cells with those of melanoma cells within the same specimens (nevus-associated melanoma (n = 14)). In total, we quantified 7935 proteins. Despite the genomic and clinical differences of the melanoma subtypes, our analysis revealed relatively similar proteomes, except for the upregulation of proteins involved in immune activation in nodular melanomas versus acral melanomas. Examining nevus-associated melanoma versus nevi, we found 1725 differentially expressed proteins (false discovery rate < 0.05). Among these proteins were 140 that overlapped with cancer hallmarks, tumor suppressors, and regulators of metabolism and cell cycle. Pathway analysis indicated aberrant activation of the phosphoinositide 3-kinase-protein kinase B-mTOR pathways and the Hippo-YAP pathway. Using a classifier, we identified six proteins capable of distinguishing melanoma from nevi samples. Our study represents a comprehensive comparative analysis of the proteome in melanoma subtypes and associated nevi, offering insights into the biological behavior of these distinct entities.

Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis.

Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.

IMBAS-MS Discovers Organ-Specific HLA Peptide Patterns in Plasma.

Distinction of non-self from self is the major task of the immune system. Immunopeptidomics studies the peptide repertoire presented by the human leukocyte antigen (HLA) protein, usually on tissues. However, HLA peptides are also bound to plasma soluble HLA (sHLA), but little is known about their origin and potential for biomarker discovery in this readily available biofluid. Currently, immunopeptidomics is hampered by complex workflows and limited sensitivity, typically requiring several mL of plasma. Here, we take advantage of recent improvements in the throughput and sensitivity of mass spectrometry (MS)-based proteomics to develop a highly sensitive, automated, and economical workflow for HLA peptide analysis, termed Immunopeptidomics by Biotinylated Antibodies and Streptavidin (IMBAS). IMBAS-MS quantifies more than 5000 HLA class I peptides from only 200 µl of plasma, in just 30 min. Our technology revealed that the plasma immunopeptidome of healthy donors is remarkably stable throughout the year and strongly correlated between individuals with overlapping HLA types. Immunopeptides originating from diverse tissues, including the brain, are proportionately represented. We conclude that sHLAs are a promising avenue for immunology and potentially for precision oncology.

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.

In-Depth Proteomic Map of Innate Lymphoid Cells from Healthy Human Skin and Blood.

Innate lymphoid cells (ILCs) are essential players in the skin-associated immune system, nevertheless little is known about their proteomes and proteomic diversity. In this study, we describe about 6,600 proteins constitutively expressed by ILC2s and ILC3s from healthy human skin and blood using state-of-the-art proteomics. Although the vast majority of proteins was expressed by both ILC subsets and in both compartments, the skin ILC2s and ILC3s were more distinct than their counterparts in blood. Only skin ILC3s expressed uniquely detected proteins. Our in-depth proteomic dataset allowed us to define the cluster of differentiation marker profiles of the ILC subsets, explore distribution and abundance of proteins known to have immunological functions, as well as identify subset-specific proteins that have not previously been implicated in ILC biology. Taken together, our analyses substantially expand understanding of the protein expression signatures of ILC subsets. Going forward, these proteomic datasets will serve as valuable resources for future studies of ILC biology.

Mitochondrial phosphoproteomes are functionally specialized across tissues.

Mitochondria are essential organelles whose dysfunction causes human pathologies that often manifest in a tissue-specific manner. Accordingly, mitochondrial fitness depends on versatile proteomes specialized to meet diverse tissue-specific requirements. Increasing evidence suggests that phosphorylation may play an important role in regulating tissue-specific mitochondrial functions and pathophysiology. Building on recent advances in mass spectrometry (MS)-based proteomics, we here quantitatively profile mitochondrial tissue proteomes along with their matching phosphoproteomes. We isolated mitochondria from mouse heart, skeletal muscle, brown adipose tissue, kidney, liver, brain, and spleen by differential centrifugation followed by separation on Percoll gradients and performed high-resolution MS analysis of the proteomes and phosphoproteomes. This in-depth map substantially quantifies known and predicted mitochondrial proteins and provides a resource of core and tissue-specific mitochondrial proteins (mitophos.de). Predicting kinase substrate associations for different mitochondrial compartments indicates tissue-specific regulation at the phosphoproteome level. Illustrating the functional value of our resource, we reproduce mitochondrial phosphorylation events on dynamin-related protein 1 responsible for its mitochondrial recruitment and fission initiation and describe phosphorylation clusters on MIGA2 linked to mitochondrial fusion.

Multisite phosphorylation dictates selective E2-E3 pairing as revealed by Ubc8/UBE2H-GID/CTLH assemblies.

Ubiquitylation is catalyzed by coordinated actions of E3 and E2 enzymes. Molecular principles governing many important E3-E2 partnerships remain unknown, including those for RING-family GID/CTLH E3 ubiquitin ligases and their dedicated E2, Ubc8/UBE2H (yeast/human nomenclature). GID/CTLH-Ubc8/UBE2H-mediated ubiquitylation regulates biological processes ranging from yeast metabolic signaling to human development. Here, cryoelectron microscopy (cryo-EM), biochemistry, and cell biology reveal this exquisitely specific E3-E2 pairing through an unconventional catalytic assembly and auxiliary interactions 70-100 Å away, mediated by E2 multisite phosphorylation. Rather than dynamic polyelectrostatic interactions reported for other ubiquitylation complexes, multiple Ubc8/UBE2H phosphorylation sites within acidic CK2-targeted sequences specifically anchor the E2 C termini to E3 basic patches. Positions of phospho-dependent interactions relative to the catalytic domains correlate across evolution. Overall, our data show that phosphorylation-dependent multivalency establishes a specific E3-E2 partnership, is antagonistic with dephosphorylation, rigidifies the catalytic centers within a flexing GID E3-substrate assembly, and facilitates substrate collision with ubiquitylation active sites.

Immunoproteasome function maintains oncogenic gene expression in KMT2A-complex driven leukemia.

Pharmacologic targeting of chromatin-associated protein complexes has shown significant responses in KMT2A-rearranged (KMT2A-r) acute myeloid leukemia (AML) but resistance frequently develops to single agents. This points to a need for therapeutic combinations that target multiple mechanisms. To enhance our understanding of functional dependencies in KMT2A-r AML, we have used a proteomic approach to identify the catalytic immunoproteasome subunit PSMB8 as a specific vulnerability. Genetic and pharmacologic inactivation of PSMB8 results in impaired proliferation of murine and human leukemic cells while normal hematopoietic cells remain unaffected. Disruption of immunoproteasome function drives an increase in transcription factor BASP1 which in turn represses KMT2A-fusion protein target genes. Pharmacologic targeting of PSMB8 improves efficacy of Menin-inhibitors, synergistically reduces leukemia in human xenografts and shows preserved activity against Menin-inhibitor resistance mutations. This identifies and validates a cell-intrinsic mechanism whereby selective disruption of proteostasis results in altered transcription factor abundance and repression of oncogene-specific transcriptional networks. These data demonstrate that the immunoproteasome is a relevant therapeutic target in AML and that targeting the immunoproteasome in combination with Menin-inhibition could be a novel approach for treatment of KMT2A-r AML.

The social and structural architecture of the yeast protein interactome.

Cellular functions are mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome1. The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps2. This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset.

A reversible state of hypometabolism in a human cellular model of sporadic Parkinson's disease.

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.

Spatial proteo-transcriptomic profiling reveals the molecular landscape of borderline ovarian tumors and their invasive progression.

Serous borderline tumors (SBT) are epithelial neoplastic lesions of the ovaries that commonly have a good prognosis. In 10-15% of cases, however, SBT will recur as low-grade serous cancer (LGSC), which is deeply invasive and responds poorly to current standard chemotherapy1,2,3. While genetic alterations suggest a common origin, the transition from SBT to LGSC remains poorly understood4. Here, we integrate spatial proteomics5 with spatial transcriptomics to elucidate the evolution from SBT to LGSC and its corresponding metastasis at the molecular level in both the stroma and the tumor. We show that the transition of SBT to LGSC occurs in the epithelial compartment through an intermediary stage with micropapillary features (SBT-MP), which involves a gradual increase in MAPK signaling. A distinct subset of proteins and transcripts was associated with the transition to invasive tumor growth, including the neuronal splicing factor NOVA2, which was limited to expression in LGSC and its corresponding metastasis. An integrative pathway analysis exposed aberrant molecular signaling of tumor cells supported by alterations in angiogenesis and inflammation in the tumor microenvironment. Integration of spatial transcriptomics and proteomics followed by knockdown of the most altered genes or pharmaceutical inhibition of the most relevant targets confirmed their functional significance in regulating key features of invasiveness. Combining cell-type resolved spatial proteomics and transcriptomics allowed us to elucidate the sequence of tumorigenesis from SBT to LGSC. The approach presented here is a blueprint to systematically elucidate mechanisms of tumorigenesis and find novel treatment strategies.

Mass spectrometry-based autoimmune profiling reveals predictive autoantigens in idiopathic pulmonary fibrosis.

Autoimmunity plays a role in certain types of lung fibrosis, notably connective tissue disease-associated interstitial lung disease (CTD-ILD). In idiopathic pulmonary fibrosis (IPF), an incurable and fatal lung disease, diagnosis typically requires clinical exclusion of autoimmunity. However, autoantibodies of unknown significance have been detected in IPF patients. We conducted computational analysis of B cell transcriptomes in published transcriptomics datasets and developed a proteomic Differential Antigen Capture (DAC) assay that captures plasma antibodies followed by affinity purification of lung proteins coupled to mass spectrometry. We analyzed antibody capture in two independent cohorts of IPF and CTL-ILD patients over two disease progression time points. Our findings revealed significant upregulation of specific immunoglobulins with V-segment bias in IPF across multiple cohorts. We identified a predictive autoimmune signature linked to reduced transplant-free survival in IPF, persisting over time. Notably, autoantibodies against thrombospondin-1 were associated with decreased survival, suggesting their potential as predictive biomarkers.

Spatial single-cell mass spectrometry defines zonation of the hepatocyte proteome.

Single-cell proteomics by mass spectrometry is emerging as a powerful and unbiased method for the characterization of biological heterogeneity. So far, it has been limited to cultured cells, whereas an expansion of the method to complex tissues would greatly enhance biological insights. Here we describe single-cell Deep Visual Proteomics (scDVP), a technology that integrates high-content imaging, laser microdissection and multiplexed mass spectrometry. scDVP resolves the context-dependent, spatial proteome of murine hepatocytes at a current depth of 1,700 proteins from a cell slice. Half of the proteome was differentially regulated in a spatial manner, with protein levels changing dramatically in proximity to the central vein. We applied machine learning to proteome classes and images, which subsequently inferred the spatial proteome from imaging data alone. scDVP is applicable to healthy and diseased tissues and complements other spatial proteomics and spatial omics technologies.