Journal of Proteome Research最新文献

Mass spectrometry (MS) generates large data sets that are stored in increasingly optimized and complex file types, demanding technical expertise to extract information rapidly and easily. We wondered whether a simple structured query language (SQL) database could hold raw MS data and allow for easily readable queries without incurring major penalties in the read time or disk space relative to other popular MS formats. Here, we describe a basic MS schema with intuitive database tables and fields that can outperform other formats for exploratory and interactive analysis according to six data subsets commonly extracted: single scans (both MS¹ and MS²), ion chromatograms, retention time ranges, and fragmentation searches (both precursor and fragment search). Additionally, we compare SQLite, DuckDB, and Parquet implementations and find that they can perform these tasks in under a second, even when the files occupy over a gigabyte of data on the disk. We believe that this tidy data schema expands nicely to most forms of MS data and offers a way to transparently query data sets while preserving computational performance.

Identifying effective compounds to restore the polarity of absorptive enterocytes (AEs) holds promise for mitigating the severity and duration of small intestinal disorders. Spermidine (SPD) is a natural polyamine; whether it can repair inflammation-induced loss of AE polarity remains unclear. In this study, we employed lipopolysaccharide (LPS)-challenged mice models combined with 4D data-independent acquisition (DIA) proteomics to investigate the mechanisms by which SPD alleviates polarity loss in AEs. Our results demonstrated that SPD supplementation enhanced the antioxidant capacity and improved the villus/crypt ratio in the jejunum of LPS-treated mice. Proteomic analysis revealed that LPS induced acute phase and inflammatory responses, significantly downregulating the expression of cytoskeletal proteins (Pdlim3, Pdlim7) essential for epithelial morphology as well as proteins involved in apical–basal polarity (Pard6b, Pard3, Prkcz, LLGL2), apical membrane integrity (Vil1, Pdims, Akp3, Tjps, Pards), and apical SLC transporters. Conversely, SPD attenuated mucosal- and tissue-specific immune responses and reversed the downregulation of these protein groups. Furthermore, using a Caco-2 cell model, we confirmed the anti-inflammatory effect of SPD and elucidated its role in suppressing AE polarity loss via the regulation of HDAC4 signaling. These findings indicate that SPD effectively alleviates the inflammation-induced loss of AE polarity in the jejunum of LPS-challenged mice.

Recent advances in high-throughput molecular analysis have significantly enhanced our understanding of the molecular mechanisms underlying adrenocortical diseases. To identify differences in protein signatures that may reveal insights into disease-specific pathogenesis, we used LC–MS/MS and bioinformatics to compare proteomic profiles of normal human adrenal (NHA) tissue, adrenocortical adenomas (ACA), adrenocortical carcinomas (ACC), and primary macronodular adrenocortical hyperplasia (PMAH) tumors, with and without ARMC5 mutations. In total, 7350 proteins were identified, and 3976 were quantified across all samples. Differentially expressed proteins (DEPs) were found in ACA vs NHA (27 DEPs), ACC vs NHA (49 DEPs), and PMAH vs NHA (81 DEPs). Comparing ACC and ACA revealed 64 upregulated and 48 downregulated DEPs. PMAH with ARMC5 mutations (PMAHw) vs without ARMC5 mutations (PMAHwt) had the fewest DEPs, 12 upregulated and 4 downregulated proteins in PMAHw. These findings were validated using an independent ACC cohort from Seoul National University Hospital, which showed 99.8% overall similarity and with no significant disparities. This comprehensive profiling of NHA, ACA, ACC, and PMAH offers insights into normal adrenal function and tumor-associated changes. Our study presents a high-quality proteomic data set, highlighting potential biomarkers and therapeutic targets, and makes a significant contribution to the understanding of adrenocortical disease mechanisms.

Nasopharyngeal carcinoma (NPC) represents a malignant tumor linked to Epstein–Barr virus (EBV) that is characterized by distinctive, clinically relevant biological features. As protein N-glycosylation plays critical roles in cancer progression, we applied quantitative glycoproteomics to characterize NPC-specific glycosylation patterns. Using label-free proteomics and intact N-glycoproteomics, we analyzed 17 NPC tissues and 7 normal mucosal epithelia. Integrated analysis with PET/CT imaging and bioinformatics revealed correlations among differentially expressed glycoproteins, glycosyltransferases, and immunophenotypes. Functional validation demonstrated that MANEA, an enzyme linked to high-mannose glycosylation, influences NPC cell proliferation and migration. Furthermore, MANEA likely modulates PD-L1 expression through high-mannose glycans. Our findings indicate that high-mannose glycoproteins are predominant in NPC and may promote tumor progression not only by enhancing malignant behavior but also by facilitating immune escape via PD-L1 regulation, thereby impairing antitumor immunity.

Different software and algorithms are available for peak picking in nontargeted metabolomics, and each may have its own strengths and limitations. The choice of the peak picking method can significantly influence the results obtained, including the number and identity of metabolites detected, their quantification, and subsequent biomarker analysis. The impact of peak picking by different tools in an untargeted metabolomics-based biomarker study is largely understated. This study compares two popular open-source software tools for peak picking in untargeted metabolomics of cancer cells, tissues, and biofluids: XCMS and MZmine 2. The investigation evaluates the impact of these peak picking algorithms on biomarker identification after careful noise filtering by blank feature filtering (BFF). We found significant discrepancy between the results obtained from XCMS and MZmine 2, regardless of the sample types, solvent gradient phases, retention time, or mass-to-charge ratio (m/z) tolerances used. Notably, this study revealed significant disagreement between peak picking tools in the context of metabolite-based biomarker study after BFF and highlighted the importance of carefully evaluating and selecting appropriate peak picking tools to ensure reliable and accurate results in untargeted metabolomics research.

Heavy carbon labeling has emerged as a popular way to study metabolic diseases. However, most carbon labeling techniques use untargeted mass spectrometry, which typically requires dependence on a research core and specialized software. By combining published ¹³C labeling patterns and known enzyme reactions, an optimized targeted mass spectrometry method was generated to measure stable isotope labeling with carbon-13 through glycolysis, the tricarboxylic acid cycle, the hexosamine biosynthetic pathway, and glutaminolysis using uniformly labeled glucose or glutamine. This method provides a novel and adaptable approach to investigate pointed hypotheses on the utilization of glucose or glutamine in disease states and models.

People with type 2 diabetes mellitus (T2DM) are at high risk of cardiovascular disease (CVD), which is often not explained by traditional risk factors such as low-density lipoprotein (LDL) cholesterol. Proton nuclear magnetic resonance (¹H NMR) metabolomics is a promising tool to help explain this residual risk. This study aimed to evaluate the incidence of CVD in people with T2DM via untargeted ¹H NMR data. The ¹H NMR raw spectra of 24 cases and 24 controls (with basal T2DM and with/without CVD at follow-up) matched by age, sex, body mass index, and LDL cholesterol from the [email protected] cohort were processed, and the peak-picked features (p = 269) were used in a partial least-squares discriminant analysis classification with repeated double cross-validation and validated against permuted data sets (AUC = 0.758; p-value = 0.011). For each feature, a stringent variable selection method analyzing the distributions of variable importance in projection scores and beta coefficients across all the repeated models was used, yielding a metabolomic signature composed of 16 selected features related to inflammation, triglycerides, muscular function, and HDL particles, together with features putatively arising from albumin, although further validation of the annotations is needed. In summary, untargeted ¹H NMR metabolomics can help assess cardiovascular risk in people with T2DM beyond LDL cholesterol.

Myocardial hypertrophy is an adaptive response in the initial stage of heart failure (HF), which exacerbates HF by causing cardiac decompensation and impaired contractility. In proteomic analysis, 216 differentially expressed proteins were obtained in the RHD patients relative to normal controls, including 141 upregulated and 75 downregulated proteins. Among these candidate proteins, protein phosphatase 3 catalytic subunit alpha (PPP3CA), indolethylamine N-methyltransferase (INMT), a disintegrin and metalloproteinase 9 (ADAM9), and myosin light chain-2 (MYL2) exhibited significantly higher expression in the myocardial tissues from patients compared with controls. Moreover, bioinformatic analysis demonstrated that dysregulation of PPP3CA, INMT, ADAM9, and MYL2 may alter the expression of proteins involved in cell adhesion, gap junction coupling, and tight junction stability, weakening cell-cell contacts and disrupting intercellular homeostasis, ultimately facilitating myocardial hypertrophy. In the Angiotensin (Ang) II-induced myocardial hypertrophy model in AC16 cardiomyocytes, the protein expression of PPP3CA, ADAM9, and INMT was elevated. Furthermore, PPP3CA, ADAM9, and INMT were involved in Ang II-induced myocardial hypertrophy by upregulating the expression of smooth muscle α-actin, atrial natriuretic factor, and connective tissue growth factor. Our study identifies molecular alterations associated with the development of myocardial hypertrophy, which may provide insights into potential therapeutic strategies for RHD and subsequent heart failure.

Preterm premature rupture of membranes (PPROM) is a significant obstetric complication, often associated with infection-related processes. However, the underlying mechanisms remain poorly understood, particularly regarding the interplay between microbial dysbiosis and host metabolism. To address this, urine samples from women with PPROM and healthy controls were analyzed using 16S rRNA gene sequencing and ¹H NMR-based metabolomics. Microbiota analysis revealed increased abundance of Hoylesella, Escherichia, Pseudomonas, and Enterococcus in the PPROM group, whereas Lactobacillus and Limosilactobacillus dominated in controls. Metabolomics identified key metabolites with diagnostic potential. Receiver operating characteristic (ROC) analysis showed that hippurate, formate, citrate, glycolate, serine, valine, and isoleucine had high discriminatory accuracy (AUC > 0.7), while β-glucose, asparagine, pyroglutarate, 2-hydroxyglutarate, tyrosine, creatinine, and imidazole had moderate predictive power. The PPROM group exhibited increased valine, isoleucine, asparagine, and β-glucose, while others decreased compared to controls. Multiomics integration revealed robust correlations between specific bacterial species and urinary metabolites, suggesting interactions between microbial dysbiosis and host metabolic pathways. These findings demonstrate distinct microbial and metabolic signatures in the urine of women with PPROM and support the utility of urinary multiomics analysis in advancing understanding of PPROM pathophysiology.

Aberrant glycosylation in meningioma (MG) suggests the potential use of glycobiomarkers for diagnosis, monitoring, and prognosis. Using lectin-based glycomics, Dolichos biflorus agglutinin (DBA) is an effective tool for detecting the glyco-biomarker DBA-binding glycan (DBAG) in patient sera. Using an in-house enzyme-linked lectin assay, the level of serum DBAG in MG patients (44.10 AU/mL) was significantly higher than that of healthy controls (10.21 AU/mL), with 86.4% sensitivity and 90.0% specificity. The MG patients with WHO grade II–III (77.35 AU/mL) had higher serum DBAG levels than those with grade I (35.92 AU/mL). Lectin histochemistry staining of MG tissues showed that tumor cells were positive for DBAG in 149 of 150 cases, indicating the tumor origin of serum DBAG. Functional analyses using MG cell lines, IOMM-Lee and HKBMM, suggested the involvement of DBAG in MG progression. The serum DBAG levels in other brain tumors (BT) were also measured. Data showed that serum DBAG levels were higher in malignant BTs (67.44 AU/mL) compared to benign BTs (50.43 AU/mL). In conclusion, DBA can be used to detect a serum glycobiomarker for MG and brain tumors. DBAG was involved in MG cell viability and migration, suggesting its potential as a target for future MG therapy.