Juvenile-type granulosa cell tumors (JGCTs) manifest during the prepubertal period as precocious pseudo-puberty and/or dysmenorrhea. We have previously identified pathogenic variants in AKT1 in JGCTs. This study aims to understand how these variants affect cellular function at the phenotypic and molecular levels using a Drosophila model.

Pathogenic variants in the CACNA1F gene are linked to congenital stationary night blindness type 2 though their specific molecular effects remain elusive. This study examines the retinal impact of two variants: a truncation (RX) and a gain-of-function (IT) to explore variant-specific retinal proteome changes. Electroretinography showed that RX primarily affects rod pathways, while IT disrupts both rod and cone signaling, consistent with morphological findings. Comprehensive quantitative proteomic analysis using mass spectrometry identified approximately 4000 proteins across wild-type control and mutant retinas, including also low-abundant membrane proteins. IT retinas exhibited widespread proteomic remodeling suggesting broad cellular responses and also compensatory molecular adaptations. In contrast, RX retinas displayed a more restricted profile. Similar to IT retinas, we found reduced Cav1.4 protein levels but without transcriptional downregulation in RX, alongside selective changes in synaptic proteins such as Erc1, Lrfn2, vGlut1 and Rab3a. These findings suggest selective molecular changes in synaptic organization and calcium-related pathways in RX retinas, offering insights into the mechanisms of Cav1.4 dysfunction in retinal disease. Deep proteomic analysis demonstrates how retinal cells reorganize their molecular architecture in response to calcium channel defects and highlights the utility of comprehensive proteomics to characterize adaptive cellular responses to genetic perturbations in retinal synaptic organization.

Invasive candidiasis (IC) is a serious, life-threatening, and costly fungal infection if not diagnosed early and treated appropriately. However, this healthcare-associated mycosis caused by Candida spp. is difficult to diagnose because of its nonspecific clinical signs and symptoms, and the lack of early and accurate detection methods. IC is also difficult to treat due to its late diagnosis, the limited antifungal arsenal, and the rapid emergence and spread of (multi)drug-resistant Candida strains. Therefore, early and accurate innovative methods for species and resistance identification in IC (candidemia and deep-seated candidiasis) are urgently needed to initiate timely and appropriate antifungal therapy, and reduce its high morbidity, mortality, and healthcare costs in hospitalized patients (in particular, severely immunocompromised or critically ill patients). The availability of the complete genome sequences of the most clinically relevant Candida species coupled with recent advances in high-throughput omics technologies have spurred an unprecedented era in the discovery and development of IC diagnostics at different levels of molecular complexity. Here we review the contribution of current and emerging omics technologies, including genomics, transcriptomics, proteomics, peptidomics, metabolomics, lipidomics, glycomics, immunomics (immunoproteomics, immunopeptidomics, and immunoglycomics), imiomics (imaging-omics), and microbiomics (metagenomics, metatranscriptomics, metaproteomics, and metabonomics), to the process of biomarker development for early diagnosis, antifungal susceptibility, prognosis, follow-up, and therapeutic monitoring in IC. We highlight the potential of integrating multiple omic data (through integromics, multiomics or panomics, together with systems biology and artificial intelligence) for the discovery of multidimensional biomarker signatures and computational algorithms for IC diagnosis. Finally, we discuss future challenges and prospects for their clinical implementation. These next-generation IC diagnostics promise to revolutionize medical practice by unraveling the complexity of biological systems at multiple levels. In addition, these could help clinicians make more precise and personalized clinical decisions through multiomics or panomics-based precision medicine approaches, rather than traditional one-size-fits-all approaches.

Protein truncation is a common modification that can alter protein localization, interaction, activity, and function. Top-down proteomics (TDP) targets the identification of all molecular forms in which a protein can exist (termed "proteoforms") and is thus well-suited for termini analysis. To examine the properties, origin, and consistency of truncated proteoforms, we performed a meta-analysis of 50 TDP datasets published over the past decade, covering 140,000 proteoforms derived from 14,500 proteins across various species. On average across all datasets, approximately 71% of proteoforms were truncated, with the vast majority not yet being documented in protein databases. Our analysis was able to distinguish between artificial truncations (e.g., sample preparation effects on labile peptide bonds) and endogenous truncations, enabling the identification of novel signal peptides and truncations between structured domains. This study highlights the importance of a common yet understudied mechanism for generating protein diversity and provides a valuable resource for future studies, targeting truncated proteoform functions or aiming to reduce artefacts in proteomics sample preparation.

Essential tremor (ET) stands as one of the most prevalent movement disorders originating from cerebellar dysfunction. However, effective treatment remains limited, largely due to a poor understanding of its molecular pathology. The harmaline-induced tremor in mice is a well-established model for ET research, though its mechanisms remain unclear. This study aimed to get insight into the molecular intricacies underlying cerebellar dysfunction in this model. Combining LC-MS/MS and RNA-Seq approach, we delved into the cerebellar alterations in harmaline-induced tremor in mouse. Multi-omics profiling identified 5194 correlated coding molecules, among which 19 were significantly dysregulated. Further KEGG enrichment analysis identified cerebellar serotonin transporter (SERT) as the key molecule in harmaline-induced tremor. We validated the upregulation of SERT in the cerebellar cortex following harmaline induction, particularly within Purkinje cells, and demonstrated that pharmacological inhibition or genetical knockdown of SERT significantly attenuated tremor severity and neuronal hyperexcitability. Further mechanistic studies revealed that harmaline-induced SERT upregulation leads to depleted serotonin levels in the cerebellum, contributing to tremor pathogenesis. In general, our study unveils crucial insights that could pave the way for molecular target identification and effective therapeutic interventions for ET.

Single-cell omics technologies, such as single-cell RNA sequencing (scRNA-seq) and single-cell proteomics (scp-MS), offer unprecedented insights into cellular heterogeneity and dynamic regulatory processes. However, integrating these data types to construct comprehensive transcription-translation profiles remains challenging due to their distinct and complex behaviors. This study presents a novel approach using pseudo-temporal cell ordering to integrate scRNA-seq and scp-MS data, facilitating the analysis of transcription-translation expression dynamics. We collected longitudinal single-cell samples following hypoxia. By leveraging key markers, we constructed pseudo-temporal trajectories for each data type, revealing transcriptional and translational responses to hypoxia. This profile of unified single-cell mRNA and protein expression uncovers distinct regulatory mechanisms, including an immediate transcriptomic response, followed by delayed proteomic expression. It illustrates the use of pseudo-temporal integration to integrate single-cell transcriptomic and proteomic datasets to understand the cellular phenotypes under hypoxic stress and provides a framework for future investigations into transcription-translation dynamics.

Missing values are a major challenge in the analysis of mass spectrometry proteomics data. Missing values hinder reproducibility, decrease statistical power for identifying differentially abundant proteins and make it challenging to analyze low-abundance proteins. We present Lupine, a deep learning-based method for imputing, or estimating, missing values in quantitative proteomics data. Lupine is, to our knowledge, the first imputation method that is designed to learn jointly from many datasets, and we provide evidence that this approach leads to more accurate predictions. We validated Lupine by applying it to tandem mass tag data from >1,000 cancer patient samples spanning ten cancer types from the Clinical Proteomics Tumor Atlas Consortium. Lupine outperforms the state-of-the-art for proteomics imputation, uniquely identifies differentially abundant proteins and Gene Ontology terms and learns a meaningful representation of proteins and patient samples. Lupine is implemented as an open-source Python package.

Biophysical proteomics assays allow for proteome-wide, label-free monitoring of ligand-induced changes in protein structure and stability, offering insights into protein-ligand interactions and modulation of biophysical properties of cellular proteins. These assays exploit the principle that compound-induced alterations in structure or stability of proteins can be detected through changes in their susceptibility to denaturation. Here we introduce SPICE (Solvent Proteome Profiling In Cells), which employs solvent-based denaturation of proteins under otherwise physiological conditions in intact cells. We characterized solvent-induced denaturation of proteins inside cells as distinct from that in cell extracts and validated SPICE by detecting known drug-target interactions for multiple compound classes. Our results indicate that SPICE, unlike experiments in cell extracts, also detects secondary compound-induced effects such as target profiles of drug metabolites, modulation of protein-protein interactions, and downstream signalling events. We further demonstrate complementarity of SPICE and CETSA, which both robustly detect the designated targets of well-characterized drugs and individually provide biologically meaningful and interpretable results. Finally, we show that SPICE can detect covalent drug-targets, compound-induced target-destabilization and stabilization of degrader drug targets despite their concurrent degradation.

Septic shock, the excessive immune response to pathogen infection, accounts globally for ∼20% of all deaths. Current methods to establish disease severity are unacceptably slow, unspecific and insensitive, hindering timely and effective treatment. Aiming to establish easy-to-measure glyco-signatures that may identify the most critically unwell patients, we applied comparative glycomics and glycoproteomics to sera longitudinally collected from septic shock survivors (n=29) and non-survivors (n=8). Glycomics of all 134 serum samples (sampled daily until recovery/death) revealed significant N-glycome dynamics across both patient groups. Unsupervised clustering of the serum N-glycome upon ICU admission (day 1) indicated survivorship-specific glyco-signatures. We therefore employed machine learning to train a random forest model using the serum N-glycome data. The model accurately classified survivorship outcomes of 35 of 37 patients (accuracy 94.6%) and correctly predicted 29 of 29 survivors (specificity 100%) and 6 of 8 non-survivors (sensitivity 75%). Interrogation of the serum N-glycome data revealed that Lewis x (Le)-type N-glycans are elevated in non-survivors relative to survivors at ICU admission, a finding recapitulated by glycoproteomics. Amongst the 58 other Le-containing serum glycoproteins that were strongly associated with acute phase response and stress pathways, alpha-1-acid-glycoprotein (AGP-1) was identified as a principal carrier of Le glycoepitopes with a potential to stratify septic shock survivors from non-survivors (AUC 0.90). This study lays a foundation for risk stratification of septic shock patients by uncovering easy-to-assay AGP-1-Le glycoforms that identify individuals experiencing poor survival outcomes already upon ICU admission, with the potential to translate to early individualised clinical care at the bedside.

The E3 ligase substrate receptor ankyrin and SOCS box protein 9 (ASB9) was shown to bind over 10 different proteins including metabolic enzymes such as creatine kinase, filament proteins such as vimentin, and histones. In previous work, we characterized the ASB9-Cullin 5 E3 ligase (ASB9-CRL5) ubiquitylation of creatine kinase and showed that ubiquitylation required the ring-between-ring ligase, ARIH2. Here, we characterized the ASB9-CRL5 ubiquitylation of histones and show that histones H3 and H4 are polyubiquitylated by the ASB9-CRL5 whereas histones H2A and H2B are much poorer substrates. Many, but not all lysines in the histones are ubiquitylated suggesting some substrate specificity. Binding experiments show that the ligase-histone interaction is highly electrostatic and the neddylated ASB9-CRL5 binds with the highest affinity. Histones in nucleosomes or in complex with the chaperone Asf1, are not ubiquitylated. Only K48 and K63 polyubiquitin chains were observed, suggesting that the ubiquitylation probably drives histone degradation. The presence of ASB9 in specific cell types correlates with situations in which free histones H3 and H4 need to be degraded. In this work, we demonstrate that the ASB9-CRL5 is the ligase that facilitates degradation of histones H3 and H4. In addition, this work represents the first example of Cullin-5 mediated ubiquitylation that does not require a ring-between-ring "helper" ligase.

Melanoma is an aggressive form of skin cancer that often metastasizes through lymph nodes (LNs). Lymphatic small extracellular vesicles (sEVs) derived from melanoma play a crucial role in establishing a premetastatic niche (PMN) within the sentinel-LN (SLN). Therefore, analyzing the proteomic content of tumor-draining lymphatic sEVs that deliver oncogenic signals to the SLN is vital in understanding the PMN. To investigate this, we performed multiplexing (18-samples) using tandem mass tag labeling to profile the lymphatic sEVs proteomes obtained from afferent lymphatic channels leading to the SLN of melanoma patients (n=6), non-cancer-associated afferent lymphatic channels (n=3), and postoperative lymphatic fluid after LN dissection (n=9). We identified 595 new proteomic cargoes compared to those reported in ExoCarta, and 1003 new cargoes relative to three previously reported lymphatic EVs datasets. The analysis revealed 145 differentially expressed proteins (DEPs) of melanoma sEVs that link to increased cellular stress and injury pathways and a decrease in extracellular matrix organization [-log(p-value)>7.0]. Analysis of the top 50 DEPs included expressions of normal, primary, and metastatic samples across multiple omics datasets. Hierarchical clustering with postoperative samples demonstrated 9 upregulated and 2 downregulated proteins specific to melanoma sEVs, which are associated with melanoma progression (p < 0.05). Notably, several common proteins associated with melanoma and postoperative samples were related to the wound healing mechanism. The multiplex immunofluorescence analysis of selected proteins reveals significantly increased expression levels of CD38, galectin 9 (LGALS9), and tenascin C (TNC) in the lymphatic sinuses of SLN (-) compared to the control LN sinuses. Moreover, higher levels of LGALS9 in LN tissue are associated with poor overall survival of melanoma patients (p= 0.0018). In summary, this study reveals an altered landscape of sEV proteome in the afferent lymphatic fluid of melanoma, highlighting distinct sEV proteins that are uniquely present in the SLN during PMN development.

The MIRAGE (Minimum Information Required for A Glycomics Experiment) guidelines for mass spectrometry (MS) data were initially developed to standardize the reporting of instrumentation, data acquisition and analytical details of the MS-based identification of released glycans. However, the growing interest in the study of intact glycoproteins and recent advances in MS-based glycoproteomics now necessitate a revision and expansion of these guidelines. This update includes an enhanced section focused on glycan structure analysis (glycomics) and introduces a new component tailored to the specific requirements of glycoproteomics. It addresses both shared and unique aspects of each approach and highlights glycoinformatics resources designed to facilitate data submission in compliance with the updated standards.

Nitric oxide (NO) is a crucial signaling molecule involved in various developmental processes and stress responses through post-translational protein modification and modulation of gene expression. Despite significant advances in understanding the mechanism of NO-mediated protein modifications, how NO regulates gene expression remains largely unclear. Here, we show that the energy sensor KIN10, a catalytic α-subunit of sucrose non-fermenting 1-related kinase 1 (SnRK1), plays a vital role in NO-mediated regulation of gene expression in Arabidopsis. NO-mediated S-nitrosylation at Cys-177 of KIN10 inhibits its degradation, leading to protein stabilization. A non-nitrosylatable mutation of Cys-177 to serine results in NO insensitivity and functional deficiencies. Quantitative phosphoproteomic analysis reveals that S-nitrosylation at Cys-177 of KIN10 modulates the phosphorylation of splicing factors within the spliceosome. We propose that NO regulates RNA splicing through the enhancement of KIN10 activity via S-nitrosylation, thereby establishing a molecular link between NO signaling and gene expression.

The extracellular matrix (ECM) plays a crucial role in tissue structure and function and serves as an integral component of diverse biological systems. However, the comprehensive characterization of ECM components is challenging because of the insoluble nature of highly cross-linked and glycosylated ECM proteins. This study introduces a chemical digestion-assisted proteomic approach to overcome challenges in ECM profiling, offering an in-depth analysis of the human periodontal ligament (PDL), a tissue critical for oral function and regeneration. Furthermore, we investigated alterations in the ECM composition of cultured PDL cells to provide insights relevant to tissue engineering applications. Our protocol combined chemical digestion and deglycosylation to enhance the identification of ECM proteins. Chemical digestion by hydroxylamine improved protein extraction efficiency by approximately two-fold compared to conventional chaotropic extraction, and deglycosylation increased the number of identified ECM proteins without substantially altering the ECM profile, offering robust quantification of ECM components. A sequential protein extraction approach revealed that proteins insoluble by conventional methods are primarily composed of highly cross-linked fibrillar collagen. Through the application of this technique to human PDL tissue, we present a comprehensive ECM profile for the first time, revealing a high collagen content (> 80%) and identifying dominant non-collagenous ECM proteins, such as periostin, dermatopontin, and lumican. Our findings highlight the significant differences between the native ECM of PDL tissue and that produced by cultured PDL cells, emphasizing the importance of considering these variations in regenerative strategies. This study offers a robust tool for analyzing the ECM and its dynamics across diverse tissues and under various physiological and pathological conditions. The results enhance our understanding of periodontal tissue and will inform future approaches for periodontal tissue regeneration.

Bottom-up proteomics is a powerful technique for comprehensive analysis of proteins by proteolytic cleavage followed by liquid chromatography/tandem mass spectrometry to identify the resulting peptides. Trypsin is the gold-standard protease for bottom-up proteomics, though its cleavage specificity limits peptide identification, depending on the protein sequence. In addition, its optimal pH is weakly alkaline, which can cause modification artifacts such as deamidation. We hypothesized that these limitations might be overcome by using protease type XIII (P13ase) from Aspergillus saitoi, which is active at low pH. P13ase has been used for protein structural analysis by hydrogen-deuterium exchange mass spectrometry, but its cleavage preferences have not been clarified. Here, we demonstrated that P13ase exhibits a preference for cleaving the C-terminal sides of leucine (21%), lysine (19%) and arginine (15%) residues, and that the optimal conditions for P13ase digestion in bottom-up proteomics are pH 3.5 and 37 °C for 60 minutes. Under these conditions, sequence coverage of more than 90% was achieved for several proteins in HeLa cell extracts, which is unachievable with trypsin. In addition, P13ase digestion reduced artifacts such as deamidation products generated by cyclization reactions and subsequent hydrolysis. These results indicate that P13ase is a promising new tool for precision proteomics.

In Alzheimer's disease (AD) and other tauopathies, tau dissociates from microtubules and forms toxic aggregates that contribute to neurodegeneration. Although some of the pathological interactions of tau have been identified from postmortem brain tissue, these studies are limited by their inability to capture transient interactions. To investigate the interactome of aggregate-prone fragments of tau, we applied an in vitro proximity labeling technique using split TurboID biotin ligase (sTurbo) fused with the tau microtubule repeat domain (TauRD), a core region implicated in tau aggregation. We characterized this sTurbo TauRD tagging interactors with the requirement for both ligase fragment co-expression for robust enzyme activity and nuclear and cytoplasmic localization of the recombinant proteins. Following enrichment of biotinylated proteins and mass spectrometry, we identified over 700 TauRD interactors. Gene ontology analysis of enriched TauRD interactors highlighted processes often dysregulated in tauopathies, including spliceosome complexes, RNA-binding proteins (RBPs), and nuclear speckles. The disease relevance of these interactors was supported by integrating recombinant TauRD interactome data with human AD tau interactome datasets and protein co-expression networks from individuals with AD and related tauopathies. This revealed an overlap with the TauRD interactome and several modules enriched with RBPs and increased in AD and progressive supranuclear palsy. These findings emphasize the importance of nuclear pathways in tau pathology, such as mRNA surveillance and RNA splicing, establishing the sTurbo TauRD system as a valuable tool for exploring the tau interactome.

The overarching symptom of myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is post-exertional malaise (PEM), an exacerbation of symptoms following physical or mental exertion. To investigate the molecular underpinnings of PEM, we performed longitudinal plasma proteomics using the Somascan® 7K aptamer-based assay to monitor 6,361 unique plasma proteins in 132 individuals (96 females and 36 males) subjected to two maximal cardiopulmonary exercise tests separated by a 24-hour recovery period. The cohort included 79 ME/CFS cases compared to 53 age- and BMI-matched sedentary controls, allowing us to distinguish disease-specific molecular alterations from those due to physical deconditioning. Longitudinal profiling revealed widespread proteomic changes following exertion, with the most pronounced alterations observed in ME/CFS participants during the recovery phase, coinciding with the onset of PEM. Compared to controls, ME/CFS subjects showed persistent dysregulation of immune, metabolic, and neuromuscular pathways. Key findings included suppression of T and B cell signaling, downregulation of IL-17 and cell-cell communication pathways, and upregulation of glycolysis/gluconeogenesis, suggestive of mitochondrial stress and impaired immune recovery from exercise. Proteomic associations with physiological performance (VOmax, anaerobic threshold) revealed disruptions between protein abundance and exercise capacity in ME/CFS versus controls. Correlations with symptom severity linked changes in immune-related proteins and ME/CFS symptoms including muscle pain, recurrent sore throat, and lymph node tenderness. Sex-stratified analyses revealed distinct molecular responses between females and males, emphasizing the importance of considering sex as a biological variable in ME/CFS research. Finally, our analysis of sedentary controls contributes new data of molecular responses to acute exertion in a predominantly female sedentary cohort, a population historically underrepresented in exercise physiology studies. Together, these findings underscore the value of dynamic, proteomic profiling over time for characterizing maladaptive responses to exertion in ME/CFS and provide a foundation for deeper mechanistic investigation into PEM.

Dynamic environments require cyanobacteria to rapidly respond to fluctuating light conditions on timescales faster than transcription-translation processes allow, which is possible through immediate regulation of protein function via molecular and conformational adjustments. Traditional abundance-based proteomics cannot capture these rapid structural changes, creating a critical gap in understanding cellular adaptation mechanisms. We hypothesized that application of alternative structural proteomics approaches would enable identification of immediate and extensive structural remodeling across the cyanobacterial proteome triggered by environmental perturbations, potentially driving functional adaptations invisible to conventional abundance-based methods. We interrogated three complementary techniques-limited proteolysis mass spectrometry (LiP-MS), thermal proteome profiling (TPP-MS), and redox proteomics-for their capacity to unveil structural reorganization within the model cyanobacterium Synechococcus elongatus PCC 7942 during physiologically relevant light transitions. Within 30 minutes of increased light exposure, we detected structural changes in 753 proteins (LiP-MS), thermal stability shifts in 600 proteins (TPP-MS), and cysteine oxidation in 1,887 sites, while only 145 proteins changed in abundance. All three techniques consistently revealed coordinated remodeling of photosynthetic machinery, ribosomal complexes, and carbon metabolism, exemplified by cytochrome f stabilization modulating electron transport efficiency. Remarkably, <10% of proteins overlapped between methods, demonstrating that each technique captures distinct molecular dimensions of environmental adaptation. This structural proteomics framework demonstrates how alternative techniques can reveal hidden facets of proteome dynamics underlying cellular processes, offering new methodological approaches for understanding environmental responses and informing biotechnological applications.

The involvement of the oral microbiome (OM) in the pathophysiology of Alzheimer's disease (AD) and vascular dementia (VaD) has been recognized epidemiologically, but the molecular mechanisms remain elusive. In this study, we uncovered the presence of oral microbiome-derived proteins (OMdPs) in brain extracellular vesicles (bEVs) from post-mortem AD and VaD subjects using unbiased metaproteomics. OMdPs circulation in blood extracellular vesicles was also confirmed in an independent cohort. Our findings also reveal that specific OMdPs are present in bEVs, with their levels varying with disease progression. Peptidome-wide correlation analyses further explored their exchange dynamics and composition within bEVs. Additionally, we validated the ability of OM-derived EVs (OM-EVs) to cross the blood-brain barrier (BBB) using a BBB-on-a-chip model, confirming a potential route for bacterial-derived molecules to reach the CNS. Bioinformatics-driven interaction analyses indicated that OMdPs engage with key neuropathological proteins, including amyloid-beta and tau, suggesting a novel mechanism linking dysbiotic OM to dementia. These results provide new insights into the role of the OM in neurodegeneration and highlight OMdPs as potential biomarkers and therapeutic targets.

The mechanism underlying asthenozoospermia in male infertility has been a prominent topic in reproductive medicine research. Human sperm function is modified by various protein post-translational modifications (PTMs). Among these, lactylation modification, a relatively novel PTM, has not been previously reported in the context of the male reproductive system. Comparative analyses between asthenozoospermic and normal sperm have revealed a significant down-regulation in the level of lysine lactylation (Kla) in proteins from asthenozoospermic sperm. Based on proteomic studies of protein Kla, 220 lactylated proteins were identified in sperm. Bioinformatics results showed that these lactylated proteins were highly enriched in the glycolytic pathway. Phosphoglycerate kinase 2 (PGK2), a key glycolytic enzyme and testis-specific protein, has been found to have 10 lactylated sites (K6, K11, K31, K41, K141, K192, K220, K272, K322, and K353). In asthenozoospermic sperm, both the lactylation level of PGK2 and its enzyme activity were reduced, while exogenous supplementation with PGK2 downstream products ameliorated sperm motility dysfunction. Mutation experiments at the K220 site confirmed that PGK2 (K220) lactylation affects glycolysis by regulating its enzyme activity. This study provides the first evidence of the regulatory role of proteins lactylation in sperm function.

Obesity remains a worldwide health issue, with visceral adipose tissue as a leading driver of this pathology. As the executors of biological functions in living cells, proteins have their activity regulated by diverse post-translational modifications, including ubiquitination. However, obesity-related changes in ubiquitination of VAT proteins are still poorly understood. Here, we obtained the global proteomic and ubiquitylomic data of epididymal VAT from lean and obese male mice by mass spectrometry. Our proteomic analyses revealed significant changes of metabolic pathways involved in fatty acid, acyl-CoA and branched chain amino acids metabolism in obese VAT. Intriguingly, a comparative analysis of proteomic and ubiquitylomic data highlighted a discordance in the quantity changes of certain proteins and their ubiquitination levels. Notably, STEAP4 exhibited a markedly reduced protein level coupled with an enhanced K48-linked ubiquitination, suggesting a potential role for ubiquitination-mediated proteasome degradation in VAT dysfunction. Further in vitro experiments revealed that knockdown of STEAP4 in adipocytes impaired mitochondrial function of 3T3-L1 adipocytes. Collectively, this study introduces the first combined proteomic and ubiquitylomic examination of murine VAT, offering novel insights and potential therapeutic targets for obesity.