Comparative profiling by data-independent acquisition mass spectrometry reveals featured plasma proteins in breast cancer: a pilot study.

Purpose: Breast cancer is known to be influenced by genetic and environmental factors, and several susceptibility genes have been discovered. Still, the majority of genetic contributors remain unknown. We aimed to analyze the plasma proteome of breast cancer patients in comparison to healthy individuals to identify differences in protein expression profiles and discover novel biomarkers. Methods: This pilot study was conducted using bioresources from Seoul National University Bundang Hospital's Human Bioresource Center. Serum samples from 10 breast cancer patients and 10 healthy controls were obtained. Liquid chromatography-mass spectrometry analysis was performed to identify differentially expressed proteins. Results: We identified 891 proteins; 805 were expressed in the breast cancer group and 882 in the control group. Gene set enrichment and differential expression analysis identified 30 upregulated and 100 downregulated proteins in breast cancer. Among these, 10 proteins were selected as potential biomarkers. Three proteins were upregulated in breast cancer patients, including cluster of differentiation 44, eukaryotic translation initiation factor 2-α kinase 3, and fibronectin 1. Seven proteins downregulated in breast cancer patients were also selected: glyceraldehyde-3-phosphate dehydrogenase, α-enolase, heat shock protein member 8, integrin-linked kinase, tissue inhibitor of metalloproteinases-1, vasodilator-stimulated phosphoprotein, and 14-3-3 protein gamma. All proteins had been previously reported to be related to tumor development and progression. Conclusion: The findings suggest that plasma proteome profiling can reveal potential diagnostic biomarkers for breast cancer and may contribute to early detection and personalized treatment strategies. A further validation study with a larger sample cohort of breast cancer patients is planned.

Parallel accumulation-serial fragmentation method for in-depth proteomic analysis of bronchoalveolar lavage fluid collected from patients with nonsmall cell lung cancer.

PURPOSE: Advances in mass spectrometry-based quantitative proteomic analysis have successfully demonstrated the in-depth detection of protein biomarkers in bronchoalveolar lavage fluid (BALF) from patients with lung cancers. Recently, ion mobility technology was incorporated into the mass spectrometers escalating the sensitivity and throughput. Utilizing these advantages, herein, we employed the parallel accumulation-serial fragmentation (PASEF) implanted in a timsTOF Pro mass spectrometer to examine the alteration of BALF proteomes in patients with nonsmall cell lung cancers (NSCLCs). EXPERIMENTAL DESIGN: BALF proteins were processed from patients with NSCLC and analyzed in a timsTOF Pro mass spectrometer with the PASEF method using a peptide input of 100 ng. Label-free mass spectrometry data were analyzed in the FragPipe platform. RESULTS: We quantitated over 1400 proteins from a single injection of 100 ng of peptides per sample with a median of ∼2000 proteins. We were able to find a few potential biomarker proteins upregulated in NSCLC. CONCLUSIONS AND CLINICAL RELEVANCE: The alterations of the BALF proteome landscape vary among patients with NSCLC as previously observed in patients with small-cell lung cancers. The PASEF method has significantly enhanced the sensitivity and throughput, demonstrating its effectiveness in clinical research and application.

Novel Online Three-Dimensional Separation Expands the Detectable Functional Landscape of Cellular Phosphoproteome.

Protein phosphorylation is a prevalent post-translational modification that regulates essentially every aspect of cellular processes. Currently, liquid chromatography-tandem mass spectrometry (LC-MS/MS) with an extensive offline sample fractionation and a phosphopeptide enrichment method is a best practice for deep phosphoproteome profiling, but balancing throughput and profiling depth remains a practical challenge. We present an online three-dimensional separation method for ultradeep phosphoproteome profiling that combines an online two-dimensional liquid chromatography separation and an additional gas-phase separation. This method identified over 100,000 phosphopeptides (>60,000 phosphosites) in HeLa cells during 1.5 days of data acquisition, and the largest HeLa cell phosphoproteome significantly expanded the detectable functional landscape of cellular phosphoproteome.

Novel Diagnostic Biomarkers for High-Grade Serous Ovarian Cancer Uncovered by Data-Independent Acquisition Mass Spectrometry.

High-grade serous ovarian cancer (HGSOC) represents the major histological type of ovarian cancer, and the lack of effective screening tools and early detection methods significantly contributes to the poor prognosis of HGSOC. Currently, there are no reliable diagnostic biomarkers for HGSOC. In this study, we performed liquid chromatography data-independent acquisition tandem mass spectrometry (MS) on depleted serum samples from 26 HGSOC cases and 24 healthy controls (HCs) to discover potential HGSOC diagnostic biomarkers. A total of 1,847 proteins were identified across all samples, among which 116 proteins showed differential expressions between HGSOC patients and HCs. Network modeling showed activations of coagulation and complement cascades, platelet activation and aggregation, neutrophil extracellular trap formation, toll-like receptor 4, insulin-like growth factor, and transforming growth factor ß signaling, as well as suppression of lipoprotein assembly and Fc gamma receptor activation in HGSOC. Based on the network model, we prioritized 28 biomarker candidates and validated 18 of them using targeted MS assays in an independent cohort. Predictive modeling showed a sensitivity of 1 and a specificity of 0.91 in the validation cohort. Finally, in vitro functional assays on four potential biomarkers (FGA, VWF, ARHGDIB, and SERPINF2) suggested that they may play an important role in cancer cell proliferation and migration in HGSOC. All raw data were deposited in PRIDE (PXD033169).

2-Undecanone derived from Pseudomonas aeruginosa modulates the neutrophil activity.

Pseudomonas aeruginosa (P. aeruginosa) is a well-known Gramnegative opportunistic pathogen. Neutrophils play key roles in mediating host defense against P. aeruginosa infection. In this study, we identified a metabolite derived from P. aeruginosa that regulates neutrophil activities. Using gas chromatography-mass spectrometry, a markedly increased level of 2-undecanone was identified in the peritoneal fluid of P. aeruginosa-infected mice. 2-Undecanone elicited the activation of neutrophils in a Gαi-phospholipase C pathway. However, 2-undecanone strongly inhibited responses to lipopolysaccharide and bactericidal activity of neutrophils against P. aeruginosa by inducing apoptosis. Our results demonstrate that 2-undecanone from P. aeruginosa limits the innate defense activity of neutrophils, suggesting that the production of inhibitory metabolites is a strategy of P. aeruginosa for escaping the host immune system. [BMB Reports 2022; 55(8): 395-400].

Statistical Modeling for Enhancing the Discovery Power of Citrullination from Tandem Mass Spectrometry Data.

Citrullination is a post-translational modification implicated in various human diseases including rheumatoid arthritis, Alzheimer's disease, multiple sclerosis, and cancers. Due to a relatively low concentration of citrullinated proteins in the total proteome, confident identification of citrullinated proteome is challenging in mass spectrometry (MS)-based proteomic analysis. From these MS-based analyses, MS features that characterize citrullination, such as immonium ions (IMs) and neutral losses (NLs), called diagnostic ions, have been reported. However, there has been a lack of systematic approaches to comprehensively search for diagnostic ions and no statistical methods for the identification of citrullinated proteome based on these diagnostic ions. Here, we present a systematic approach to identify diagnostic IMs, internal ions (INTs), and NLs for citrullination from tandem mass (MS/MS) spectra. Diagnostic INTs mainly consisted of internal fragment ions for di- and tripeptides that contained two and three amino acids with at least one citrullinated arginine, respectively. A statistical logistic regression model was built for a confident assessment of citrullinated peptides that database searches identified (true positives) and prediction of citrullinated peptides that database searches failed to identify (false negatives) using the diagnostic IMs, INTs, and NLs. Applications of our model to complex global proteome data sets demonstrated the increased accuracy in the identification of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes.

A User-Friendly Visualization Tool for Multi-Omics Data.

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated large multi-omic datasets for various cancers. Each dataset consists of common and differential data types, including genomics, epigenomics, transcriptomics, proteomics, and post-translational modifications data. They together make up a rich resource for researchers and clinicians interested in understanding cancer biology to draw from. Nevertheless, the complexity of these multi-omic datasets and a lack of an easily accessible analytical and visualization tool for exploring them continue to be a hurdle for those who are not trained in bioinformatics. In this issue, Calinawan et al. describe a user-friendly, web-based visualization platform named ProTrack for exploring the CPTAC clear cell renal cell carcinoma (ccRCC) dataset. Compared to other available visualization tools, ProTrack offers an easy yet powerful customization interface, solely dedicated to the CPTAC ccRCC dataset. Their tool enables ready inspection of potential associations between different data types within a single gene or across multiple genes without any need to code. Specific mutation types or phosphosites can also be easily looked up for any gene of interest. Calinawan et al. aim to extend their work into other CPTAC datasets, which will greatly contribute to the CPTAC as well as cancer biology community in general.