Journal of Proteome Research最新文献

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.

The daily light–dark cycle is a recurrent and predictable environmental phenomenon to which many organisms, including cyanobacteria, have evolved to adapt. Understanding how cyanobacteria alter their metabolic attributes in response to subjective light or dark growth may provide key features for developing strains with improved photosynthetic efficiency and applications in enhanced carbon sequestration and renewable energy. Here, we undertook a label-free proteomic approach to investigate the effect of extended light (LL) or extended dark (DD) conditions on the unicellular cyanobacterium Crocosphaera subtropica ATCC 51142. We quantified 2287 proteins, of which 603 proteins, were significantly different between the two growth conditions. These proteins represent several biological processes, including photosynthetic electron transport, carbon fixation, stress responses, translation, and protein degradation. One significant observation is the regulation of over two dozen proteases, including ATP-dependent Clp-proteases (endopeptidases) and metalloproteases, the majority of which were upregulated in LL compared to DD. This suggests that proteases play a crucial role in the regulation and maintenance of photosynthesis, especially the PSI and PSII components. The higher protease activity in LL indicates a need for more frequent degradation and repair of certain photosynthetic components, highlighting the dynamic nature of protein turnover and quality control mechanisms in response to prolonged light exposure. The results enhance our understanding of how Crocosphaera subtropica ATCC 51142 adjusts its molecular machinery in response to extended light or dark growth conditions.

Liquid chromatography–mass spectrometry (LC-MS) is an indispensable analytical technique in proteomics, metabolomics, and other life sciences. While OpenMS provides advanced open-source software for MS data analysis, its complexity can be challenging for nonexperts. To address this, we have developed OpenMS WebApps, a framework for creating user-friendly MS web applications based on the Streamlit Python package. OpenMS WebApps simplifies MS data analysis through an intuitive graphical user interface, interactive result visualizations, and support for both local and online execution. Key features include workspace management, automatic generation of input widgets, and parallel execution of tools, resulting in high performance and ready-to-use solutions for online and local deployment. This framework benefits both researchers and developers: scientists can focus on their research without the burden of complex software setups, and developers can rapidly create and distribute custom WebApps with novel algorithms. Several applications built on the OpenMS WebApps template demonstrate its utility across diverse MS-related fields, enhancing the OpenMS ecosystem for developers and a wider range of users. Furthermore, it integrates seamlessly with third-party software, extending its benefits to developers beyond the OpenMS community.

Extracellular vesicles (EVs) are emerging as crucial biomarkers in cancer diagnostics and therapeutics with their heterogeneity presenting both challenges and opportunities in prostate cancer research. However, existing methods for isolating and characterizing EV subtypes have been limited by inefficient separation and inadequate proteomic analysis. Here we show an optimized centrifugal microfluidic device, Exodisc, that efficiently isolates large quantities of EV subtypes from particle-enriched medium, enabling comprehensive proteomic analysis of small (EV-S, 20–200 nm) and large (EV-L, >200 nm) EVs. Using this device, we successfully separated EV-S and EV-L from prostate cancer cell lines LNCaP and PC3. Mass spectrometry-based proteomics revealed that EV proteins reflect parental cell characteristics more than EV size, with EV-L demonstrating increased expression of PSMA-correlated proteins. Our optimized protocol addresses challenges in EV isolation and characterization, providing a more effective method for studying cellular and molecular mechanisms of specific EV subtypes. This study extends the potential use of EVs as a liquid biopsy for cancer theranostics, paving the way for more precise isolation of EV subtypes and potentially leading to improved biomarker discovery and the development of personalized treatments.

This study aimed to elucidate the complexity of the humoral immune response in COVID-19 patients with varying disease trajectories using a SARS-CoV-2 whole proteome peptide microarray chip. The microarray, containing 5347 peptides spanning the entire SARS-CoV-2 proteome and key variants of concern, was used to analyze IgG responses in 10 severe-to-recovered, 9 nonsevere-to-severe cases, and 10 control case (5 pre-pandemic and 5 SARS-CoV-2-negative) plasma samples. We identified 1151 IgG-reactive peptides corresponding to 647 epitopes, with 207 peptides being cross-reactive across 124 epitopes. Nonstructural protein 3 (nsp3) exhibited the highest number of total and unique epitopes, followed by the spike protein. nsp12 had the most number of cross-reactive epitopes. Peptides from the spike protein and nsps 2, 3, 5, and 13 were notably associated with recovery. Additionally, specific mutations in SARS-CoV-2 variants were found to alter peptide immunoreactivity, with some mutations (e.g., G142D, L452R, and N501Y) enhancing and others (e.g., R190S and E484 K) reducing immune recognition. These findings have critical implications for the development of diagnostics, vaccines, and therapeutics. Understanding the distribution of epitopes and the impact of viral mutations on antigenicity provides insights into immune evasion mechanisms, informing strategies for controlling COVID-19 and future coronavirus outbreaks.

The E3 ubiquitin ligase neural precursor cell-expressed developmentally down-regulated 4 (NEDD4) is involved in various cancer signaling pathways, including PTEN/AKT. However, its role in promoting gastric cancer (GC) progression is unclear. This study was conducted to elucidate the role of NEDD4 in GC progression. We found that the inhibition of NEDD4 expression significantly reduced the migratory and proliferative abilities of GC cells, with minimal impact on the PTEN expression or p-AKT activation, suggesting that NEDD4 may exert its GC-promoting effects through alternative pathways. To gain novel insights into the role of NEDD4 in GC, we performed a comprehensive proteomic analysis to search for proteins with altered expression levels following NEDD4 gene knockdown, identifying a total of 3916 proteins. Pathway analysis of differentially expressed proteins (DEPs) indicated the potential involvement of NEDD4 in cancer-related metabolic pathways. Furthermore, the protein–protein interaction network of the DEPs revealed enriched core modules, highlighting key cellular processes and signaling pathways regulated by NEDD4 in GC. Additionally, we identified proteins whose expression was altered by NEDD4 inhibition, some of which were associated with poor prognosis in GC. These findings suggest that these proteins may act as downstream effectors that contribute to NEDD4-mediated GC progression.

Affinity capture (AC) combined with mass spectrometry (MS)-based proteomics is highly utilized throughout the drug discovery pipeline to determine small-molecule target selectivity and engagement. However, the tedious sample preparation steps and time-consuming MS acquisition process have limited its use in a high-throughput format. Here, we report an automated workflow employing biotinylated probes and streptavidin magnetic beads for small-molecule target enrichment in the 96-well plate format, ending with direct sampling from EvoSep Solid Phase Extraction tips for liquid chromatography (LC)-tandem mass spectrometry (MS/MS) analysis. The streamlined process significantly reduced both the overall and hands-on time needed for sample preparation. Additionally, we developed a data-independent acquisition-mass spectrometry (DIA-MS) method to establish an efficient label-free quantitative chemical proteomic kinome profiling workflow. DIA-MS yielded a coverage of ∼380 kinases, a > 60% increase compared to using a data-dependent acquisition (DDA)-MS method, and provided reproducible target profiling of the kinase inhibitor dasatinib. We further showcased the applicability of this AC-MS workflow for assessing the selectivity of two clinical-stage CDK9 inhibitors against ∼250 probe-enriched kinases. Our study here provides a roadmap for efficient target engagement and selectivity profiling in native cell or tissue lysates using AC-MS.

Sea anemones play a crucial role in marine ecosystems. Recent studies have highlighted their physiological and ecological responses to thermal stress. Therefore, our objective was to perform a proteomic analysis of Bunodosoma cavernatum sea anemones in the Gulf of Mexico, subjected to thermal stress, to understand whether these organisms activate specific processes to resist increased temperature. We submitted one group of sea anemones to variable temperatures (26 to 32 °C) and another group to a constant temperature (28 °C) for 1.5 months. Then we subjected them to thermal stress (32 °C) for 2 weeks. We evaluated the enzymatic activity and proteome in the columns and tentacles. The main effect of the temperature regime change is a reduction in mass. Also, sea anemones synthesized proteins related to the activation of the immune system and protection against temperature. We observed decreased peroxidase activity, while superoxide dismutase activity was higher only in the constant temperature group. On the basis of these data, we deduce that B. cavernatum sea anemones are vulnerable to climate change because they stop producing toxins in their tentacles when faced with thermal stress and activate cellular responses that make them susceptible to pathogens. These responses are not sufficient to guarantee an optimal health state.

The carboxyl-terminus of Hsp70-interacting protein (CHIP) plays crucial roles in tumorigenesis and immunity, with previous studies suggesting a double-edged sword in thyroid cancer. However, its precise functions and underlying molecular mechanisms in thyroid cancer remained unclear. Here, we demonstrate through immunohistochemistry (IHC) that CHIP expression progressively increases from normal thyroid tissue to primary papillary thyroid carcinoma (PTC) and lymph node metastases, with CHIP levels positively correlating with lymph node metastasis (P = 0.006). Moreover, CHIP overexpression enhanced thyroid cancer cell migration and invasion without significantly affecting cell viability. Tandem mass tag (TMT)-based LC-MS/MS analysis revealed that CHIP-regulated differentially expressed proteins, notably transgelin, were predominantly associated with metastasis-related pathways. Western blot, qPCR, and TCGA-THCA cohort data confirmed that CHIP regulates transgelin expression at the protein but not the genetic level. Mechanistically, CHIP promotes extracellular matrix degradation through the transgelin–matrix metalloproteinase-9 (MMP-9) axis, thereby facilitating PTC progression. Collectively, our findings indicate that CHIP expression was closely related to the progression and metastasis of PTC, suggesting that CHIP functions as a novel tumor oncoprotein in PTC via the transgelin–MMP-9 signaling axis.