Protein Signatures of Parathyroid Adenoma according to Tumor Volume and Functionality

Background@#Parathyroid adenoma (PA) is a common endocrine disease linked to multiple complications, but the pathophysiology of the disease remains incompletely understood. The study aimed to identify the key regulator proteins and pathways of PA according to functionality and volume through quantitative proteomic analyses. @*Methods@#We conducted a retrospective study of 15 formalin-fixed, paraffin-embedded PA samples from tertiary hospitals in South Korea. Proteins were extracted, digested, and the resulting peptides were analyzed using liquid chromatography-tandem mass spectrometry. Pearson correlation analysis was employed to identify proteins significantly correlated with clinical variables. Canonical pathways and transcription factors were analyzed using Ingenuity Pathway Analysis. @*Results@#The median age of the participants was 52 years, and 60.0% were female. Among the 8,153 protein groups analyzed, 496 showed significant positive correlations with adenoma volume, while 431 proteins were significantly correlated with parathyroid hormone (PTH) levels. The proteins SLC12A9, LGALS3, and CARM1 were positively correlated with adenoma volume, while HSP90AB2P, HLA-DRA, and SCD5 showed negative correlations. DCPS, IRF2BPL, and FAM98A were the main proteins that exhibited positive correlations with PTH levels, and SLITRK4, LAP3, and AP4E1 had negative correlations. Canonical pathway analysis demonstrated that the RAN and sirtuin signaling pathways were positively correlated with both PTH levels and adenoma volume, while epithelial adherence junction pathways had negative correlations. @*Conclusion@#Our study identified pivotal proteins and pathways associated with PA, offering potential therapeutic targets. These findings accentuate the importance of proteomics in understanding disease pathophysiology and the need for further research.

Machine Learning-Based Proteomics Reveals Ferroptosis in COPD PatientDerived Airway Epithelial Cells Upon Smoking Exposure

Background@#Proteomics and genomics studies have contributed to understanding the pathogenesis of chronic obstructive pulmonary disease (COPD), but previous studies have limitations. Here, using a machine learning (ML) algorithm, we attempted to identify pathways in cultured bronchial epithelial cells of COPD patients that were significantly affected when the cells were exposed to a cigarette smoke extract (CSE). @*Methods@#Small airway epithelial cells were collected from patients with COPD and those without COPD who underwent bronchoscopy. After expansion through primary cell culture, the cells were treated with or without CSEs, and the proteomics of the cells were analyzed by mass spectrometry. ML-based feature selection was used to determine the most distinctive patterns in the proteomes of COPD and non-COPD cells after exposure to smoke extract.Publicly available single-cell RNA sequencing data from patients with COPD (GSE136831) were used to analyze and validate our findings. @*Results@#Five patients with COPD and five without COPD were enrolled, and 7,953 proteins were detected. Ferroptosis was enriched in both COPD and non-COPD epithelial cells after their exposure to smoke extract. However, the ML-based analysis identified ferroptosis as the most dramatically different response between COPD and non-COPD epithelial cells, adjusted P value = 4.172 × 10−6 , showing that epithelial cells from COPD patients are particularly vulnerable to the effects of smoke. Single-cell RNA sequencing data showed that in cells from COPD patients, ferroptosis is enriched in basal, goblet, and club cells in COPD but not in other cell types. @*Conclusion@#Our ML-based feature selection from proteomic data reveals ferroptosis to be the most distinctive feature of cultured COPD epithelial cells compared to non-COPD epithelial cells upon exposure to smoke extract.

The proteomic landscape shows oncologic relevance in cystitis glandularis

Background@#The relationship between cystitis glandularis (CG) and bladder malignancy remains unclear. @*Methods@#We identified the oncologic significance of CG at the molecular level using liquid chromatography-tandem mass spectrometry-based proteomic analysis of 10 CG, 12 urothelial carcinoma (UC), and nine normal urothelium (NU) specimens. Differentially expressed proteins (DEPs) were identified based on an analysis of variance false discovery rate < 0.05, and their functional enrichment was analyzed using a network model, Gene Set Enrichment Analysis, and Gene Ontology annotation. @*Results@#We identified 9,890 proteins across all samples and 1,139 DEPs among the three entities. A substantial number of DEPs overlapped in CG/NU, distinct from UC. Interestingly, we found that a subset of DEP clusters (n = 53, 5%) was differentially expressed in NU but similarly between CG and UC. This “UC-like signature” was enriched for reactive oxygen species (ROS) and energy metabolism, growth and DNA repair, transport, motility, epithelial-mesenchymal transition, and cell survival. Using the top 10 shortlisted DEPs, including SOD2, PRKCD, CYCS, and HCLS1, we identified functional elements related to ROS metabolism, development, and transport using network analysis. The abundance of these four molecules in UC/CG than in NU was consistent with the oncologic functions in CG. @*Conclusions@#Using a proteomic approach, we identified a predominantly non-neoplastic landscape of CG, which was closer to NU than to UC. We also confirmed a small subset of common DEPs in UC and CG, suggesting that altered ROS metabolism might imply potential cancerous risks in CG.

In-Depth, Proteomic Analysis of Nasal Secretions from Patients With Chronic Rhinosinusitis and Nasal Polyps

PURPOSE: Chronic rhinosinusitis (CRS) is a complex immunological condition, and novel experimental modalities are required to explore various clinical and pathophysiological endotypes; mere evaluation of nasal polyp (NP) status is inadequate. Therefore, we collected patient nasal secretions on filter paper and characterized the proteomes. METHODS: We performed liquid chromatography-mass spectrometry (MS)/MS in the data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes. Nasal secretions were collected from 10 controls, 10 CRS without NPs (CRSsNP) and 10 CRS with NPs (CRSwNP). We performed Orbitrap MS-based proteomic analysis in the DDA (5 controls, 5 CRSsNP and 5 CRSwNP) and the DIA (5 controls, 5 CRSsNP and 5 CRSwNP) modes, followed by a statistical analysis and a hierarchical clustering to identify differentially expressed proteins in the 3 groups. RESULTS: We identified 2,020 proteins in nasal secretions. Canonical pathway analysis and gene ontology (GO) evaluation revealed that interleukin (IL)-7, IL-9, IL-17A and IL-22 signaling and neutrophil-mediated immune responses like neutrophil degranulation and activation were significantly increased in CRSwNP compared to control. The GO terms related to the iron ion metabolism that may be associated with CRS and NP development. CONCLUSIONS: Collection of nasal secretions on the filter paper is a practical and non-invasive method for in-depth study of nasal proteomics. Our proteomic signatures also support that Asian NPs could be characterized as non-eosinophilic inflammation features. Therefore, the proteomic profiling of nasal secretions from CRS patients may enhance our understanding of CRS endotypes.

Current State and Prospects of Development of Blood-based Biomarkers for Mild Traumatic Brain Injury

The current understanding of the pathophysiology of mild traumatic brain injury (mTBI) is, without doubt, incomplete. Nevertheless, we tried to summarize the state-of-the-art explanation of how the brain is continuously injured even after a single impact. We also reviewed the real struggle of diagnosing mTBI, which culminated in showing the potential of blood-based biomarkers as an alternative or complementary way to overcome this difficulty. Pathophysiology of mTBI is subdivided into primary and secondary injuries. Primary injury is caused by a direct impact on the head and brain. Secondary injury refers to the changes in energy metabolism and protein synthesis/degradation resulting from the biochemical cascades as follows; calcium influx, mitochondrial dysfunction, fractured microtubules, and Wallerian degeneration, neuroinflammation, and toxic proteinopathy. Since the diagnosis of mTBI is made through the initial clinical information, it is difficult and inaccurate to diagnose mTBI without the absence of a witness or sign of head trauma. Blood-based biomarkers are expected to play an important role in diagnosing mTBI and predicting functional outcomes, due to their feasibility and the recent progress of targeted proteomics techniques (i.e., liquid chromatography tandem mass spectrometry [LC-MS/MS]).