Journal of Proteome Research最新文献

Acidithiobacillus spp. have traditionally been utilized to extract metals from mineral ores through bioleaching. This process has recently expanded to include artificial ores, such as those derived from municipal solid waste incineration (MSWI) residues. Previous studies have indicated that microbial adaptation enhances bioleaching efficiency, prompting this study to identify proteins involved in the adaptation of A. ferridurans to MSWI residues. We employed data-independent acquisition-parallel accumulation serial fragmentation to determine the proteomic response of A. ferridurans DSM 583 to three distinct materials: bottom ash (BA), kettle ash (KA), and filter ash (FA), which represent typical MSWI residues. Our findings indicate that, irrespective of the residue type, a suite of membrane transporters, porins, efflux pumps, and specific electron and cation transfer proteins was notably upregulated. The upregulation of certain proteins involved in anaerobic pathways suggested the development of a spontaneous microaerobic environment, which minimally impacted the bioleaching efficiency. Additionally, the adaptation was most efficient at half the target FA concentration, marked by a significant increase in the detoxification and efflux systems required by microorganisms to tolerate high heavy metal concentrations. Given that metal recovery peaked at lower FA concentrations for most metals of interest, further adaptation at the level of protein expression may not be warranted for improved bioleaching outcomes.

Exercise has been shown to promote wound healing, including tendon repair. Myokines released from the exercised muscles are believed to play a significant role in this process. In our previous study, we used an in vitro coculture and loading model to demonstrate that 2% static loading of myoblasts increased the migration and proliferation of cocultured tenocytes─two crucial aspects of wound healing. IGF-1, released from myoblasts in response to 2% static loading, was identified as a contributor to the increased proliferation. However, the factors responsible for the enhanced migration remained unknown. In the current study, we subjected myoblasts in single culture conditions to 2, 5, and 10% static loading and performed proteomic analysis of the cell supernatants. Gene Ontology (GO) analysis revealed that 2% static loading induced the secretion of NBL1, C5, and EFEMP1, which is associated with cell migration and motility. Further investigation by adding exogenous recombinant proteins to human tenocytes showed that NBL1 increased tenocyte migration but not proliferation. This effect was not observed with treatments using C5 and EFEMP1.

Mass spectrometry imaging (MSI) has gained popularity in clinical analyses due to its high sensitivity, specificity, and throughput. However, global profiling experiments are often still restricted to LC-MS/MS analyses that lack spatial localization due to low-throughput methods for on-tissue peptide identification and confirmation. Additionally, the integration of parallel LC-MS/MS peptide confirmation, as well as histological stains for accurate mapping of identifications, presents a large bottleneck for data analysis, limiting throughput for untargeted profiling experiments. Here, we present a novel platform, termed MSIght, which automates the integration of these multiple modalities into an accessible and modular platform. Histological stains of tissue sections are coregistered to their respective MSI data sets to improve spatial localization and resolution of identified peptides. MS/MS peptide identifications via untargeted LC-MS/MS are used to confirm putative MSI identifications, thus generating MS images with greater confidence in a high-throughput, global manner. This platform has the potential to enable large-scale clinical cohorts to utilize MSI in the future for global proteomic profiling that uncovers novel biomarkers in a spatially resolved manner, thus widely expanding the utility of MSI in clinical discovery.

Sodium transporters maintain cellular homeostasis by transporting ions, minerals, and nutrients across the membrane, and Na+/K+ ATPases facilitate the cotransport of solutes in neurons, muscle cells, and epithelial cells. Sodium transporters are important for many physiological processes, and their dysfunction leads to diseases such as hypertension, diabetes, neurological disorders, and cancer. The NA_mCNN computational method highlights the functional diversity and significance of sodium transporters in membrane proteins using protein language model embeddings (PLMs) and multiple-window scanning deep learning models. This work investigates PLMs that include Tape, ProtTrans, ESM-1b-1280, and ESM-2-128 to achieve more accuracy in sodium transporter classification. Five-fold cross-validation and independent testing demonstrate ProtTrans embedding robustness. In cross-validation, ProtTrans achieved an AUC of 0.9939, a sensitivity of 0.9829, and a specificity of 0.9889, demonstrating its ability to distinguish positive and negative samples. In independent testing, ProtTrans maintained a sensitivity of 0.9765, a specificity of 0.9991, and an AUC of 0.9975, which indicates its high level of discrimination. This study advances the understanding of sodium transporter diversity and function, as well as their role in human pathophysiology. Our goal is to use deep learning techniques and protein language models for identifying sodium transporters to accelerate identification and develop new therapeutic interventions.

Previous studies reported that preserving the anterior cruciate ligament (ACL) remnants following ACL rupture during reconstruction surgery could promote graft healing. However, the temporal proteomic expression of ACL remnants remains unclear. Based on previous reports, we have redefined the initial 6 weeks following ACL rupture as the acute phase and the subsequent 6 weeks to 6 months as the subacute phase. High-throughput proteomic sequencing on ACL remnants from the two groups was utilized. Our study unveiled a total of 381 differential expression proteins (DEPs), with 136 upregulated and 245 downregulated proteins in the acute phase. By intersecting these findings with secretory protein databases, we identified 26 upregulated secretory proteins and 19 downregulated in the acute phase. The upregulation of MMP9 and VTN and the downregulation of COL1A1 and POSTN in the acute phase were further confirmed by immunohistochemistry. These findings suggest that the elevated expression of secretory proteins in the acute phase may play crucial roles in promoting cell proliferation, angiogenesis, and tissue repair of the graft. This study not only enhances our understanding of repair mechanisms in ACL remnant preservation but also provides a theoretical foundation for guiding rational clinical surgical timing.

SARS-CoV-1 and MERS-CoV were the infective agents of the 2002 and 2012 coronavirus outbreaks, respectively. Here, we report a comparative liquid chromatography/mass spectrometry (LC/MS) Orbitrap N- and O-glycosylation glycoproteomics study of the recombinant S1 spike derived from these two viruses. The former was produced in HEK293 cells and the latter in both HEK293 and insect cells. Both proteins were highly glycosylated, with SARS-CoV-1 S1 having 13 and MERS-CoV S1 having 12 N-glycosites. Nearly all were occupied at 85% or more. Between 2 and 113 unique N-glycan compositions were detected at each N-glycosite across the three proteins. Complex N-glycans dominated in HEK293 cell-derived spike S1 proteins. While glycosylation differs between HEK293 and insect cells, the extent of glycan processing at glycosites was similar for the two MERS-CoV S1 forms. The HEK293-derived SARS-CoV-1 S1 N-glycans were more highly sialylated and fucosylated compared to MERS S1, while the latter had more high-mannose glycosides, particularly in the N-terminus and near the RBD. Seven and 8 O-glycosites were identified in SARS-CoV-1 S1 and MERS-CoV S1, respectively. Mapping of predicted antigenic and glycosylation sites reveals colocalization consistent with a role for glycosylation in immune system avoidance. Glycosylation patterns of these S1 proteins differ from those of other SARS-CoV-1 and MERS-CoV spike reported forms such as recombinant trimeric and virus-propagated forms, which has implications for virus research, including vaccine development, as glycosylation plays a role in spike function and epitope structure.

Neuroblastoma (NB) remains associated with high mortality and low initial response rate, especially for high-risk patients, thus warranting exploration of molecular markers for precision risk classifiers. Through integrating multiomics profiling, we identified a range of hub genes involved in cell cycle and associated with dismal prognosis and malignant cells. Single-cell transcriptome sequencing revealed that a subset of malignant cells, subcluster 1, characterized by high proliferation and dedifferentiation, was strongly correlated with the hub gene signature and orchestrated an immunosuppressive tumor microenvironment (TME). Furthermore, we constructed a robust malignant subcluster 1 related signature (MSRS), which was an independent prognostic factor and superior to other clinical characteristics and published signatures. Besides, TME differences conferred remarkably distinct therapeutic responses between high and low MSRS groups. Notably, polo-like kinase-1 (PLK1) was one of the most crucial contributors to MSRS and remarkably correlated with malignant subcluster 1, and PLK1 inhibition was effective for NB treatment as demonstrated by in silico analysis and in vitro experiments. Overall, our study constructs a novel molecular model to further guide the clinical classification and individualized treatment of NB.

Mass spectrometry data visualization is essential for a wide range of applications, such as validation of workflows and results, benchmarking new algorithms, and creating comprehensive quality control reports. Python offers a popular and powerful framework for analyzing and visualizing multidimensional data; however, generating commonly used mass spectrometry plots in Python can be cumbersome. Here we present pyOpenMS-viz, a versatile, unified framework for generating mass spectrometry plots. pyOpenMS-viz directly extends pandas DataFrame plotting for generating figures in a single line of code. This implementation enables easy integration across various Python-based mass spectrometry tools that already use pandas DataFrames to store MS data. pyOpenMS-viz is open-source under a BSD 3-Clause license and freely available at https://github.com/OpenMS/pyopenms_viz.

Stable isotope techniques serve as invaluable tools for kinetic measurements in metabolic research. In particular, deuterated water (D₂O) administration is increasingly being applied in human health research. For use in protein kinetic studies, this includes measurements on gas chromatography-mass spectrometry (GC-MS) analysis of alanine (ALA) and deuterium-labeled alanines (d-ALAs) coming from D₂O administration. However, the choice of the derivative of ALA and d-ALAs used in such analyses has not been evaluated thoroughly. Hence, we conducted a comprehensive head-to-head comparison to determine the most effective and reliable derivative. Two derivatization reagents, N,N-dimethylformamide dimethyl acetal (methyl-8 reagent) and N-methyl-N-tert-butyldimethylsilyltrifluoroacetamide (MtBSTFA), were considered as candidates. Using chemical standards and available rodent muscle tissue, both reagents underwent testing, including the standard curve linear regression fit, sensitivity, reproducibility, and, importantly, column effectiveness. Our findings indicate that both reagents were suitable for ALA/d-ALAs analyses. However, the MtBSTFA derivative exhibited a better linear regression fit, higher sensitivity, and greater reproducibility than methyl-8. More importantly, the methyl-8 derivative resulted in severe column damage. In conclusion, our study highlights the MtBSTFA derivative as a preferred choice for ALA and d-ALAs GC-MS analysis, contributing to a reliable and sensitive analytical method for D₂O administration studies for measurements of in vivo metabolic rates.

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.