Molecular & Cellular Proteomics最新文献

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.

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.

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.

Universal sample preparation for proteomic analysis that enables unbiased protein manipulation, flexible reagent use, and low protein loss is required to ensure the highest sensitivity of downstream liquid chromatography-mass spectrometry (LC-MS) analysis. To address these needs, we developed a ZnCl₂ precipitation-assisted sample preparation method (ZASP) that depletes harsh detergents and impurities in protein solutions prior to trypsin digestion via 10 min of ZnCl₂ and methanol-induced protein precipitation at room temperature (RT). ZASP can remove trypsin digestion and LC-MS incompatible detergents such as SDS, Triton X-100, and urea at high concentrations in solution and unbiasedly recover proteins independent of the amount of protein input. We demonstrated the sensitivity and reproducibility of ZASP in an analysis of samples with 1 μg to 1000 μg of proteins. Compared to commonly used sample preparation methods such as SDC-based in-solution digestion, acetone precipitation, FASP, and SP3, ZASP has proven to be an efficient approach. Here, we present ZASP, a practical, robust, and cost-effective proteomic sample preparation method that can be applied to profile different types of samples.

Analysis of large-scale data-independent acquisition mass spectrometry metaproteomics data remains a computational challenge. Here, we present a computational pipeline called metaExpertPro for metaproteomics data analysis. This pipeline encompasses spectral library generation using data-dependent acquisition MS, protein identification and quantification using data-independent acquisition mass spectrometry, functional and taxonomic annotation, as well as quantitative matrix generation for both microbiota and hosts. By integrating FragPipe and DIA-NN, metaExpertPro offers compatibility with both Orbitrap and timsTOF MS instruments. To evaluate the depth and accuracy of identification and quantification, we conducted extensive assessments using human fecal samples and benchmark tests. Performance tests conducted on human fecal samples indicated that metaExpertPro quantified an average of 45,000 peptides in a 60-min diaPASEF injection. Notably, metaExpertPro outperformed three existing software tools by characterizing a higher number of peptides and proteins. Importantly, metaExpertPro maintained a low factual false discovery rate of approximately 5% for protein groups across four benchmark tests. Applying a filter of five peptides per genus, metaExpertPro achieved relatively high accuracy (F-score = 0.67-0.90) in genus diversity and showed a high correlation (r_Spearman = 0.73-0.82) between the measured and true genus relative abundance in benchmark tests. Additionally, the quantitative results at the protein, taxonomy, and function levels exhibited high reproducibility and consistency across the commonly adopted public human gut microbial protein databases IGC and UHGP. In a metaproteomic analysis of dyslipidemia patients, metaExpertPro revealed characteristic alterations in microbial functions and potential interactions between the microbiota and the host.

Protein N-acetylation is one of the most abundant co- and post-translational modifications in eukaryotes, extending its occurrence to chloroplasts within vascular plants. Recently, a novel plastidial enzyme family comprising eight acetyltransferases that exhibit dual lysine and N-terminus acetylation activities was unveiled in Arabidopsis. Among these, GNAT1, GNAT2, and GNAT3 reveal notable phylogenetic proximity, forming a subgroup termed NAA90. Our study focused on characterizing GNAT1, closely related to the state transition acetyltransferase GNAT2. In contrast to GNAT2, GNAT1 did not prove essential for state transitions and displayed no discernible phenotypic difference compared to the wild type under high light conditions, while gnat2 mutants were severely affected. However, gnat1 mutants exhibited a tighter packing of the thylakoid membranes akin to gnat2 mutants. In vitro studies with recombinant GNAT1 demonstrated robust N-terminus acetylation activity on synthetic substrate peptides. This activity was confirmed in vivo through N-terminal acetylome profiling in two independent gnat1 knockout lines. This attributed several acetylation sites on plastidial proteins to GNAT1, reflecting a subset of GNAT2's substrate spectrum. Moreover, co-immunoprecipitation coupled with mass spectrometry revealed a robust interaction between GNAT1 and GNAT2, as well as a significant association of GNAT2 with GNAT3 - the third acetyltransferase within the NAA90 subfamily. This study unveils the existence of at least two acetyltransferase complexes within chloroplasts, whereby complex formation might have a critical effect on the fine-tuning of the overall acetyltransferase activities. These findings introduce a novel layer of regulation in acetylation-dependent adjustments in plastidial metabolism.

De novo peptide sequencing is one of the most fundamental research areas in mass spectrometry-based proteomics. Many methods have often been evaluated using a couple of simple metrics that do not fully reflect their overall performance. Moreover, there has not been an established method to estimate the false discovery rate (FDR) of de novo peptide-spectrum matches. Here we propose NovoBoard, a comprehensive framework to evaluate the performance of de novo peptide-sequencing methods. The framework consists of diverse benchmark datasets (including tryptic, nontryptic, immunopeptidomics, and different species) and a standard set of accuracy metrics to evaluate the fragment ions, amino acids, and peptides of the de novo results. More importantly, a new approach is designed to evaluate de novo peptide-sequencing methods on target-decoy spectra and to estimate and validate their FDRs. Our FDR estimation provides valuable information to assess the reliability of new peptides identified by de novo sequencing tools, especially when no ground-truth information is available to evaluate their accuracy. The FDR estimation can also be used to evaluate the capability of de novo peptide sequencing tools to distinguish between de novo peptide-spectrum matches and random matches. Our results thoroughly reveal the strengths and weaknesses of different de novo peptide-sequencing methods and how their performances depend on specific applications and the types of data.

Early pregnancy loss (EPL) is a common event in human reproduction and is classified into histological subtypes such as hydropic abortion (HA) and hydatidiform moles, including complete hydatidiform moles (CHMs) and partial hydatidiform moles (PHMs). However, accurate diagnosis and improved patient management remain challenging due to high rates of misdiagnosis and diverse prognostic risks. Therefore, diagnostic biomarkers for EPL are urgently needed. Our study aimed to identify biomarkers for EPL through comprehensive proteomic analysis. Ten CHMs, six PHMs, ten HAs, and 10 normal control products of conception were used to obtain a proteomic portrait. Parallel reaction monitoring-targeted proteomic and regression analyses were used to verify and select the diagnostic signatures. Finally, 14 proteins were selected and a panel of diagnostic classifiers (DLK1, SPTB/COL21A1, and SAR1A) was built to represent the CHM, PHM, and normal control groups (area under the receiver operating characteristic curve = 0.900, 0.804/0.885, and 0.991, respectively). This high diagnostic power was further validated in another independent cohort (n = 148) by immunohistochemistry (n = 120) and Western blot analyses (n = 28). The protein SPTB was selected for further biological behavior experiments in vitro. Our data suggest that SPTB maintains trophoblast cell proliferation, angiogenesis, cell motility, and the cytoskeleton network. This study provides a comprehensive proteomic portrait and identifies potential diagnostic biomarkers. These findings enhance our understanding of EPL pathogenesis and offer novel targets for diagnosis and therapeutic interventions.