Wastewater-based surveillance enables population-level monitoring of chemical exposures, pathogens, and antimicrobial resistance. Proteomics could extend this framework by providing direct information on biological function through proteins derived from human excreta and microbial communities. Although only a few untargeted wastewater proteomics studies have been reported, they show that characterizing the wastewater proteome is technically feasible and that clinically relevant human proteins are consistently detected despite the complexity of the matrix. In this review, we examine the current state of wastewater proteomics and evaluate its potential for One Health surveillance. We summarize insights on the composition of the wastewater proteome and discuss methodological considerations spanning sampling, protein recovery, mass spectrometry, and bioinformatics, drawing on established practices from wastewater-based surveillance, clinical proteomics, and environmental metaproteomics. We then discuss applications, including biomarker discovery for human disease surveillance, microbial community function and antimicrobial resistance, and outline key challenges and future directions. The available evidence, though still limited, indicates that wastewater proteomics is moving from proof of concept toward methodological optimization and biological validation. Future progress will depend on standardized analytical workflows, longitudinal validation studies, dedicated wastewater protein reference databases, and governance frameworks that develop in parallel with technical advances.
Radioresistance limits durable control of nasopharyngeal carcinoma (NPC), but pretreatment tumor-region metabolic patterns associated with subsequent radiotherapy response remain incompletely characterized. We applied pathology-guided matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to pretreatment fresh-frozen NPC tissues from three radiosensitive (RS) and two radioresistant (RR) patients. Histologically annotated tumor regions of interest (ROIs) were registered to MSI data, and LC-MS/MS profiles from the corresponding residual tissue blocks supported molecular annotation. Independent proteomic and transcriptomic cohorts were used for cross-omics contextualization. Exploratory analysis of the pathology-defined tumor ROIs highlighted a glycerophospholipid-associated pattern involving phosphatidic acid, phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, phosphatidylcholine, and phosphatidylglycerol species. Preranked pathway analysis further highlighted glycerophospholipid metabolism together with membrane- and stress-related processes. In the broader transcriptome- and proteome-driven network analyses, glycerophospholipid metabolism was the only shared human-relevant pathway that also appeared in the pathology-guided MSI enrichment. In this exploratory five-patient cohort, pooled-pixel analysis described a pathology-localized glycerophospholipid-associated pattern in the RR-versus-RS comparison. No MSI feature achieved patient-level significance after multiple-testing correction, and the direction of the highlighted lipid pattern was sensitive to aggregation. These findings are hypothesis-generating and warrant evaluation in larger, independently accrued cohorts.
Modern imaging mass spectrometry (MSI) technologies, such as matrix-assisted laser desorption/ionization (MALDI), provide unprecedented spatial resolution for mapping tissue biomolecules. However, experimental variations frequently cause mass-to-charge (m/z) shifts across different data batches, misaligning identical biological substances. Existing alignment algorithms often fail to project matched spectra onto the exact same m/z coordinates as the reference data, hindering downstream comparative analyses. Here, we propose PGAlign (Peak-Group Alignment), a novel peak-group-based algorithm that aligns unknown spectra directly to a reference, producing matched data sets defined on a shared m/z grid. Operating on representative mean spectra, PGAlign executes four discrete steps: peak calling, peak grouping, peak-group pairing, and fine alignment assessment. Using simulated mouse pancreatic MALDI data and metabolomics data from a macrophage efferocytosis study, we show that PGAlign achieves lower alignment error than other methods. In canine sarcoma MALDI data, PGAlign preserves biologically meaningful differences between cancer and normal tissues and supports downstream classification, suggesting that alignment onto a common m/z grid can improve the reliability of MS-based biomarker analyses.
Abstract Increasing temperatures are a concern for global agriculture because they inhibit the activity of the nitrogenase enzyme, which is crucial for N2 fixation. The study reveals how the nitrogenase Mo–Fe α subunit adapts to high temperatures by comparing thermophilic and mesophilic bacteria. The molecular dynamics simulation study of Acetivibrio thermocellus and Endomicrobium proavitum explores how specific amino acids contribute to their physicochemical shifts, such as increased aliphatic index, hydrogen bonds, secondary-structure content, and decreased RMSD and RMSF, which reflect constrained conformational dynamics that confer thermal resilience. A. thermocellus maintains a more compact, stable structure under heat stress. These structural features enable the engineering of heat-tolerant nitrogenase, boosting sustainable agriculture in warm-climate regions.
Abstract Ovarian cancer (OC) is one of the deadliest cancers in women, with high mortality rates and a lack of effective screening tools. Among all OC subtypes, high-grade serous carcinoma (HGSC), also known as high-grade serous ovarian cancer, is the most common, making up about 75% of epithelial OC cases. N-Glycans may play essential roles in OC development, as they are involved in cell signaling, recognition, adhesion, and differentiation. In this study, we present spatially resolved N-glycan profiling of HGSC reproductive tissues from a triple mutant (TKO) mouse model (n = 6) using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI). By comparing levels of various N-glycan classes between OC tissues and control tissues (n = 6), we identified potential OC N-glycan biomarkers and developed multivariate statistical models to distinguish HGSC from healthy tissues. Endoglycosidase F3 digestion, followed by a two-step amidation reaction for sialic acid, confirmed that core-fucosylation and α2,6-sialylation were the two most important N-glycan modifications observed in HGSC regions of TKO mice that displayed the most significant differences between controls and TKO. The notable increase in bisecting core-fucosylated N-glycans further supports the hypothesis that protein transport and metastasis are elevated in the later stages of OC. Conversely, the decrease in certain α2,6-sialylated N-glycans suggests potential roles in OC cell signaling, migration, and metastasis, which warrant further validation. Additionally, the rise in high-mannose N-glycans and N-glycan degradation products offered early insights into how abnormal N-glycan levels may contribute to OC progression and disrupt normal cell functions.
