Molecular & Cellular Proteomics最新文献

Understanding the integrated regulation of cellular processes during viral infection is crucial for developing host-targeted approaches. We have previously reported that an optimal in vitro infection by influenza A virus (IAV) requires three components of Cullin 4-RING E3 ubiquitin ligases (CRL4) complexes, namely the DDB1 adaptor and two substrate recognition factors, DCAF11 and DCAF12L1, which mediate non-degradative poly-ubiquitination of the PB2 subunit of the viral polymerase. However, the impact of IAV infection on the CRL4 interactome remains elusive. Here, using Affinity Purification coupled with Mass Spectrometry (AP-MS) approaches, we identified cellular proteins interacting with these CRL4 components in IAV-infected and non-infected contexts. IAV infection induces significant modulations in protein interactions, resulting in a global loss of DDB1 and DCAF11 interactions, and an increase in DCAF12L1-associated proteins. The distinct rewiring of CRL4's associations upon infection impacted cellular proteins involved in protein folding, ubiquitination, translation, splicing, and stress responses. Using a split-nanoluciferase-based assay, we identified direct partners of CRL4 components and via siRNA-mediated silencing validated their role in IAV infection, representing potential substrates or regulators of CRL4 complexes. Our findings unravel the dynamic remodeling of the proteomic landscape of CRL4's E3 ubiquitin ligases during IAV infection, likely involved in shaping a cellular environment conducive to viral replication and offer potential for the exploration of future host-targeted antiviral therapeutic strategies.

At the plasma membrane, in response to biotic and abiotic cues, specific ligands initiate the formation of receptor kinase heterodimers, which regulate the activities of plasma membrane proteins and initiate signaling cascades to the nucleus. In this study, we utilized affinity enrichment mass spectrometry to investigate the stimulus-dependent interactomes of LRR receptor kinases in response to their respective ligands, with an emphasis on exploring structural influences and potential cross-talk events at the plasma membrane. BRI1 and SIRK1 were chosen as receptor kinases with distinct coreceptor preference. By using interactome characteristic of domain-swap chimera following a gradient boosting learning algorithm trained on SIRK1 and BRI1 interactomes, we attribute contributions of extracellular domain, transmembrane domain, juxtamembrane domain, and kinase domain of respective ligand-binding receptors to their interaction with their coreceptors and substrates. Our results revealed juxtamembrane domain as major structural element defining the specific substrate recruitment for BRI1 and extracellular domain for SIRK1. Furthermore, the learning algorithm enabled us to predict the phenotypic outcomes of chimeric receptors based on different domain combinations, which was verified by dedicated experiments. As a result, our work reveals a tightly controlled balance of signaling cascade activation dependent on ligand-binding receptors domains and the internal ligand status of the plant. Moreover, our study shows the robust utility of machine learning classification as a quantitative metric for studying dynamic interactomes, dissecting the contribution of specific domains and predicting their phenotypic outcome.

Ubiquitination is crucial for maintaining protein homeostasis and plays a vital role in diverse biological processes. Ubiquitinome profiling and quantification are of great scientific significance. Artificial ubiquitin-binding domains (UBDs) have been widely employed to capture ubiquitinated proteins. The success of this enrichment relies on recognizing native spatial structures of ubiquitin and ubiquitin chains by UBDs under native conditions. However, the use of native lysis conditions presents significant challenges, including insufficient protein extraction, heightened activity of deubiquitinating enzymes and proteasomes in removing the ubiquitin signal, and purification of a substantial number of contaminant proteins, all of which undermine the robustness and reproducibility of ubiquitinomics. In this study, we introduced a novel approach that combines denatured-refolded ubiquitinated sample preparation (DRUSP) with a tandem hybrid UBD for ubiquitinomic analysis. The samples were effectively extracted using strongly denatured buffers and subsequently refolded using filters. DRUSP yielded a significantly stronger ubiquitin signal, nearly three times greater than that of the Control method. Then, eight types of ubiquitin chains were quickly and accurately restored; therefore, they were recognized and enriched by tandem hybrid UBD with high efficiency and no biases. Compared with the Control method, DRUSP showed extremely high efficiency in enriching ubiquitinated proteins, improving overall ubiquitin signal enrichment by approximately 10-fold. Moreover, when combined with ubiquitin chain-specific UBDs, DRUSP had also been proven to be a versatile approach. This new method significantly enhanced the stability and reproducibility of ubiquitinomics research. Finally, DRUSP was successfully applied to deep ubiquitinome profiling of early mouse liver fibrosis with increased accuracy, revealing novel insights for liver fibrosis research.

The bacillus Calmette-Guérin BCG vaccine (Mycobacterium bovis) is primarily used to prevent tuberculosis (TB) infections but has wide-ranging immunogenic effects. One of its most notable properties is its ability to induce trained immunity, a memory-like response in innate immune cells such as macrophages. Through targeted analyses of well-established histone marks, prior research has shown that these changes are generated through epigenetic modification. Mass spectrometry-based proteomic approaches provide a way to globally profile various aspects of the proteome, providing data to further identify unexplored mechanisms of BCG-mediated immunomodulation. Here we use multi-level proteomics (total, histone, and phospho to identify networks and potential mechanisms that mediate BCG-induced immunomodulation in macrophages. Histone-focused proteomics and total proteomics were performed at the University of Cape Town (data available via ProteomeXchange with identifier PXD051187), while phosphoproteomics data was retrieved from the ProteomeXchange Repository (identifier PXD013171). We identify several epigenetic mechanisms that may drive BCG-induced training phenotypes. Evidence across the proteomics and histone-focused proteomics data set pair 6 epigenetic effectors (NuA4, NuRD, NSL, Sin3A, SIRT2, SIRT6) and their substrates.

Dopaminergic neurons participate in fundamental physiological processes and are the cell type primarily affected in Parkinson's disease. Their analysis is challenging due to the intricate nature of their function, involvement in diverse neurological processes, and heterogeneity and localization in deep brain regions. Consequently, most of the research on the protein dynamics of dopaminergic neurons has been performed in animal cells ex vivo. Here we use iPSC-derived human mid-brain-specific dopaminergic neurons to study general features of their proteome biology and provide datasets for protein turnover and dynamics, including a human axonal translatome. We cover the proteome to a depth of 9409 proteins and use dynamic SILAC to measure the half-life of more than 4300 proteins. We report uniform turnover rates of conserved cytosolic protein complexes such as the proteasome and map the variable rates of turnover of the respiratory chain complexes in these cells. We use differential dynamic SILAC labeling in combination with microfluidic devices to analyze local protein synthesis and transport between axons and soma. We report 105 potentially novel axonal markers and detect translocation of 269 proteins between axons and the soma in the time frame of our analysis (120 h). Importantly, we provide evidence for local synthesis of 154 proteins in the axon and their retrograde transport to the soma, among them several proteins involved in RNA editing such as ADAR1 and the RNA helicase DHX30, involved in the assembly of mitochondrial ribosomes. Our study provides a workflow and resource for the future applications of quantitative proteomics in iPSC-derived human neurons.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Nitrogen (N) is of utmost importance for plant growth and development. Multiple studies have shown that N signaling is tightly coupled with carbon (C) levels, but the interplay between C/N metabolism and growth remains largely an enigma. Nonetheless, the protein kinases Sucrose Non-fermenting 1 (SNF1)-Related Kinase 1 (SnRK1) and Target Of Rapamycin (TOR), two ancient central metabolic regulators, are emerging as key integrators that link C/N status with growth. Despite their pivotal importance, the exact mechanisms behind the sensing of N status and its integration with C availability to drive metabolic decisions are largely unknown. Especially for SnRK1, it is not clear how this kinase responds to altered N levels. Therefore, we first monitored N-dependent SnRK1 kinase activity with an in vivo Separation of Phase-based Activity Reporter of Kinase (SPARK) sensor, revealing a contrasting N-dependency in Arabidopsis thaliana (Arabidopsis) shoot and root tissues. Next, using affinity purification (AP) and proximity labeling (PL) coupled to mass spectrometry (MS) experiments, we constructed a comprehensive SnRK1 and TOR interactome in Arabidopsis cell cultures during N-starved and N-repleted growth conditions. To broaden our understanding of the N-specificity of the TOR/SnRK1 signaling events, the resulting network was compared to corresponding C-related networks, identifying a large number of novel, N-specific interactors. Moreover, through integration of N-dependent transcriptome and phosphoproteome data, we were able to pinpoint additional N-dependent network components, highlighting for instance SnRK1 regulatory proteins that might function at the crosstalk of C/N signaling. Finally, confirmation of known and identification of novel SnRK1 interactors, such as Inositol-Requiring 1 (IRE1A) and the RAB GTPase RAB18, indicate that SnRK1, present at the ER, is involved in N signaling and autophagy induction.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Gastric cancer (GC) is a highly heterogeneous disease regarding histologic features, genotypes, and molecular phenotypes. Here, we investigate extracellular matrix (ECM)-centric analysis, examining its association with histologic subtypes and patient prognosis in human GC. We performed quantitative proteomic analysis of decellularized GC tissues that characterizes tumorous ECM, highlighting proteomic heterogeneity in ECM components. We identified 20 tumor-enriched proteins including four glycoproteins, serpin family H member 1 (SERPINH1), annexin family (ANXA3/4/5/13), S100A family (S100A6/8/9), MMP14, and other matrisome-associated proteins. In addition, histopathological characteristics of GC reveals differential expression in ECM composition, with the poorly cohesive carcinoma-not otherwise specified (PCC-NOS) subtype being distinctly demarcated from other histologic subtypes. Integrating ECM proteomics with single-cell RNA sequencing, we identified crucial molecular markers in the PCC-NOS-specific stroma. PCC-NOS-enriched matrisome proteins and gene expression signatures of adipogenic cancer-associated fibroblasts (CAF_adi) are closely linked, both associated with adverse outcomes in GC. Using tumor microarray analysis, we confirmed the CAF_adi surface marker, ATP binding cassette subfamily A member 8 (ABCA8), predominantly present in PCC-NOS tumors. Our ECM-focused analysis paves the way for studies to determine their utility as biomarkers for patient stratification, offering valuable insights for linking molecular and histologic features in GC.

Accurate and rapid identification of viruses is crucial for an effective medical diagnosis when dealing with infections. Conventional methods, including DNA amplification techniques or lateral-flow assays, are constrained to a specific set of targets to search for. In this study, we introduce a novel tandem mass spectrometry proteotyping-based method that offers a universal approach for the identification of pathogenic viruses and other components, eliminating the need for a priori knowledge of the sample composition. Our protocol relies on a time and cost-efficient peptide sample preparation, followed by an analysis with liquid chromatography coupled to high-resolution tandem mass spectrometry. As a proof of concept, we first assessed our method on publicly available shotgun proteomics datasets obtained from virus preparations and fecal samples of infected individuals. Successful virus identification was achieved with 53 public datasets, spanning 23 distinct viral species. Furthermore, we illustrated the method's capability to discriminate closely related viruses within the same sample, using alphaviruses as an example. The clinical applicability of our method was demonstrated by the accurate detection of the vaccinia virus in spiked saliva, a matrix of paramount clinical significance due to its non-invasive and easily obtainable nature. This innovative approach represents a significant advancement in pathogen detection and paves the way for enhanced diagnostic capabilities.