Journal of Proteome Research最新文献

Alteration of glycosylation in cancer cells leads to the expression of tumor-associated glycans, which can be used as biomarkers for diagnosis and prognostic prediction of diseases. In this study, we used nano-LC-QToF to identify serum N-glycan biomarkers for the detection of brain tumors. We observed an increase in sialylated N-glycans and a decrease in fucosylated N-glycans in the serum of patients with glioblastoma (GBM) and meningioma (MG) compared to healthy individuals. In GBM, a combination of increased serum sialylated N-glycan (6_4_0_2 compound) and decreased fucosylated N-glycan (4_4_1_0 compound) was identified as the most appropriate panel, with an area under the curve (AUC) of 0.8660, 78.95% sensitivity, 84.21% specificity, and 82.89% accuracy. For MG, a combination of decreased 6_6_2_0 and 5_5_2_0 compounds and increased 4_4_1_1 compound achieved an AUC of 0.9260, 82.35% sensitivity, 78.57% specificity, and 80.26% accuracy for diagnosis of MG. Additionally, an increase in 5_5_1_0 and 4_3_0_0 compounds combined with a decrease in 7_7_4_3 was associated with high-grade MG (WHO grades II-III). In conclusion, we identified serum N-glycan profiles associated with brain tumors, highlighting their potential as biomarkers for the diagnosis and prognosis of these diseases.

Protein sulfation can be crucial in regulating protein-protein interactions but remains largely underexplored. Sulfation is nearly isobaric to phosphorylation, making it particularly challenging to investigate using mass spectrometry. The degree to which tyrosine sulfation (sY) is misidentified as phosphorylation (pY) is, thus, an unresolved concern. This study explores the extent of sY misidentification within the human phosphoproteome by distinguishing between sulfation and phosphorylation based on their mass difference. Using Gaussian mixture models (GMMs), we screened ∼45 M peptide-spectrum matches (PSMs) from the PeptideAtlas human phosphoproteome build for peptidoforms with mass error shifts indicative of sulfation. This analysis pinpointed 104 candidate sulfated peptidoforms, backed up by Gene Ontology (GO) terms and custom terms linked to sulfation. False positive filtering by manual annotation resulted in 31 convincing peptidoforms spanning 7 known and 7 novel sY sites. Y47 in calumenin was particularly intriguing since mass error shifts, acidic motif conservation, and MS² neutral loss patterns characteristic of sulfation provided strong evidence that this site is sulfated rather than phosphorylated. Overall, although misidentification of sulfation in phosphoproteomics data sets derived from cell and tissue intracellular extracts can occur, it appears relatively rare and should not be considered a substantive confounding factor in high-quality phosphoproteomics data sets.

Estimating the false discovery rate (FDR) is one of the key steps in ensuring appropriate error control in the analysis of shotgun proteomics data. Traditional estimation methods typically rely on decoy sequence databases or spectral libraries, which may not always provide satisfactory results due to limitations of decoy construction methods. This study introduces the query mix-max (QMM) method, a decoy-free alternative for FDR estimation in proteomics. The QMM framework builds upon the existing mix-max procedure but replaces decoy matches with entrapment queries to estimate the number of false positive discoveries. Through simulations and real data set analyses, the QMM method was demonstrated to provide reasonably accurate FDR estimation across various scenarios, particularly when smaller sample-to-entrapment spectra ratios were achieved. The QMM method tends to be conservatively biased, particularly at higher FDR values, which can ensure stringent FDR control. While flexible, the protocol's effectiveness may vary depending on the evolutionary distance between the sample and entrapment organisms. It also requires a sufficient number of entrapment queries to provide stable FDR estimates, especially for low FDR values. Despite these limitations, the QMM method is a promising alternative as one of the first query-based FDR estimation approaches in shotgun proteomics.

The utilization of organophosphate pesticides (OPs) has escalated in response to the growing global food demand driven by a rapidly increasing population and the environmental disruptions caused by climate change. While acute exposure leads to cholinergic poisoning, chronic OP exposure has been linked to organ dysfunction, inflammation, and carcinogenesis. Serum samples from healthy individuals (n = 11), patients with acute OP exposure (n = 12), and those with chronic OP exposure (n = 31) were analyzed to discern the differentially expressed pathways after acute and chronic OP exposure. Differential expression analysis identified 132 proteins altered in chronic exposure vs control, 86 in acute exposure vs control, and 124 in chronic vs acute exposure. Pathway analysis revealed increased blood coagulation and reduced LXR/RXR activation and DCHR24 signaling in both acute and chronic exposures. Elevated levels of pro-inflammatory proteins, such as S100A8, VWF, and GPIBA, were observed, particularly in chronic exposure, highlighting significant inflammatory effects of OP exposure. These findings provide insights into the pathological mechanisms underlying chronic OP exposure and its contribution to inflammation and long-term health risks.

Global campaign against COVID-19 have vaccinated a significant portion of the world population in recent years. Combating the COVID-19 pandemic with mRNA vaccines played a pivotal role in the global immunization effort. However, individual responses to a vaccine are diverse and lead to varying vaccination efficacy. Despite significant progress, a complete understanding of the molecular mechanisms driving the individual immune response to the COVID-19 vaccine remains elusive. To address this gap, we combined a novel nanoparticle-based proteomic workflow with tandem mass tag (TMT) labeling, to quantitatively assess the proteomic changes in a cohort of 12 volunteers following two doses of the Pfizer-BioNTech mRNA COVID-19 vaccine. This optimized protocol seamlessly integrates comprehensive proteome analysis with enhanced throughput by leveraging the enrichment of low-abundant plasma proteins by engineered nanoparticles. Our data demonstrate the ability of this workflow to quantify over 3,000 proteins, providing the deepest view into COVID-19 vaccine-related plasma proteome study. We identified 69 proteins with boosted responses post-second dose and 74 proteins differentially regulated between individuals who contracted COVID-19 despite vaccination and those who did not. These findings offer valuable insights into individual variability in response to vaccination, demonstrating the potential of personalized medicine approaches in vaccine development.

Mass spectrometry-based single-cell proteomics (SCP) is gaining momentum but remains limited to a few laboratories due to the high costs and specialized expertise required. The ability to send samples to specialized core facilities would benefit nonspecialist laboratories and popularize SCP for biological applications. However, no methods have been tested in SCP to "freeze" the proteome state while maintaining cell integrity for transfer between laboratories or prolonged sorting using fluorescence-activated cell sorting (FACS). This study evaluates whether short-term formaldehyde (FA) fixation can maintain the cell states. We demonstrate that short-term FA fixation does not substantially affect protein recovery, even without heating and strong detergents, and maintains analytical depth compared with classical workflows. Fixation also preserves drug-induced specific perturbations of the protein abundance during cell sorting and sample preparation for SCP analysis. Our findings suggest that FA fixation can facilitate SCP by enabling sample shipping and prolonged sorting, potentially democratizing access to SCP technology and expanding its application in biological research, thereby accelerating discoveries in cell biology and personalized medicine.

General proteomics research for fundamental science typically addresses laboratory- or patient-derived samples of known origin and composition. However, in a few research areas, such as environmental proteomics, clinical identification of infectious organisms, archeology, art/cultural history, and forensics, attributing the origin of a protein-containing sample to the organisms that produced it is a central focus. A small number of groups have approached this problem and developed software tools for taxonomic characterization and/or identification using bottom-up proteomics. Most such tools identify peptides via database search, and many rely on organism-specific peptides as markers. Our group recently introduced MARLOWE, a software tool for taxonomic characterization of unknown samples based on de novo peptide identification and signal-erosion-resistant strong peptides, which are shared peptides distributed in a taxonomy-dependent manner. In the current work, we further characterize the utility of MARLOWE using publicly available proteomics data from forensically-relevant samples. MARLOWE characterizes samples based on their protein profile, and returns ranked organism lists of potential contributors and taxonomic scores based on shared strong peptides between organisms. Overall, the correct characterization rate ranges between 44 and 100%, depending on the sample type and data acquisition parameters (with lower numbers associated with lower-quality data sets). MARLOWE demonstrates successful characterization of true contributors and close relatives, and provides sufficient specificity to distinguish certain microbial species. MARLOWE demonstrates its ability to provide insight into potential taxonomic sources for a wide range of sample types without prior assumptions about sample contents. This approach can find utility in forensic science and also broadly in bioanalytical applications that utilize proteomics approaches for taxonomic characterization.

The immune response plays a crucial role in the treatment of ischemic stroke (IS). Our primary objective was to explore immune proteins related to stroke and to develop a noninvasive diagnostic panel. We used the high-throughput Olink immunoassay platform to quantitatively measure 92 proteins in the serum of 88 patients with IS and 88 controls. We first selected feature proteins using least absolute shrinkage and selection operator (LASSO), random forest (RF), and support vector machine (SVM), and then modeled them for external validation of IS. In this study, we found that 53 proteins exhibited significant differences in the IS compared to the control group. We selected GLB1, PRDX5, DDX58, and CLEC4C as potential protein biomarkers to differentiate IS from the control group using LASSO, RF, and SVM. The diagnostic model, which included these four proteins, demonstrated excellent performance in validation data sets, achieving an AUC value of 0.899 (95%CI: 0.848-0.950). Our findings offer valuable insights into both the immune response and the diagnosis of IS. These results offer a novel approach to clinical decision-making in the diagnosis and treatment of IS.

Ionizing radiation exposure from a potential nuclear energy plant leak or detonation of a nuclear weapon can cause massive casualties to both warfighters and civilians. RNA, proteins, and metabolite biomarkers in biological specimens like blood and tissue have shown potential to determine radiation dose levels. However, these biomarkers in blood and urine are short-lived, typically detectable within hours or a few days. To address the need for stable, long-term radiation exposure biomarkers, we developed two mass spectrometry-based methods using noninvasive hair samples to identify radiation-exposure biomarkers. Our results show that hippuric acid and 5-methoxy-3-indoleacetate significantly increase after higher (4 gray) doses of gamma irradiation compared to lower (1 and 2 Gy) doses or nonexposed hair samples. While 2-aminooctadec-4-ene-1,3-diol, oleoyl ethanolamide, palmitoylcarnitine, 25-hydroxy vitamin D3, vernolic acid, and azelaic acid significantly increased over time after exposure. Trimethylamine N-oxide (TMAO) was found in higher concentrations in female specimens across all time points. Further validation using a machine learning model suggested that these biomarkers can predict differences in the exposure dose and time point. Our findings highlight the potential of noninvasive hair sample analysis for assessing radiation exposure, offering a viable alternative to address critical public health concerns of unexpected radiation exposure.

The quantification of proteoforms, i.e., all molecular forms in which proteins can be present, by top-down proteomics provides essential insights into biological processes at the molecular level. Isobaric labeling-based quantification strategies are suitable for multidimensional separation strategies and allow for multiplexing of the samples. Here, we investigated cysteine-directed isobaric labeling by iodoTMT in combination with a gel- and gas-phase fractionation (GeLC-FAIMS-MS) for in-depth quantitative proteoform analysis. We optimized the acquisition workflow (i.e., the FAIMS compensation voltages, isolation windows, acquisition strategy, and fragmentation method) using a two-proteome mix to increase the number of quantified proteoforms and reduce ratio compression. Additionally, we implemented a mass feature-based quantification strategy in the widely used deconvolution algorithm FLASHDeconv, which improves and facilitates data analysis. The optimized iodoTMT GeLC-FAIMS-MS workflow was applied to quantitatively analyze the proteome of Escherichia coli grown under glucose or acetate as the sole carbon source, resulting in the identification of 726 differentially abundant proteoforms.