Journal of Proteome Research最新文献

TP53 mutation-driven gene expression programs define oncogenic phenotypes. While extensive studies have concentrated on the transcriptome and proteome, post-transcriptional processes, particularly translational variation, remain underexplored. This study presents a comprehensive analysis of the transcriptomics, translatiomics, and proteomics dynamics in the ovarian cancer cell line SKOV3, with a focus on the effects of p53 missense mutations (R175H, R273H, and Y220C) on gene dosage fluctuations. Despite clear transcriptional differences between wild-type and mutant p53, we find that extensive translational and post-translational buffering processes attenuate these discrepancies, yielding comparatively stable protein abundances. Moreover, we delineate that the relative contributions of transcription output, translation engagement, and protein stability collectively shape the final protein abundance in the context of p53 mutations. Clinical proteomic analysis of platinum-resistant ovarian cancer tissues reveals tumor-specific factors and acquired resistance pathways linked to p53 mutations. Our findings elucidate the multilayered regulatory landscape of p53 mutations and identify potential risk factors for platinum resistance associated with these mutations.

Plasma proteomics has been explored extensively for biomarker discovery for both diagnostic and therapeutic purposes. The gold standard for plasma proteomics sample preparation requires immediate plasma isolation following blood collection, which poses logistical challenges in many clinical settings. To evaluate the feasibility of delayed plasma processing, we investigated the impact of prolonged on-ice storage (i.e., 0/4/8 h postcollection) on plasma proteomic profiles using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) workflow. Each sample was analyzed by a label-free quantitative data-independent MS/MS method against a project-specific spectral library in technical replicates without fractionation, identifying 752 to 1,000 (903 ± 58) protein groups within each sample. While 72–86% of protein groups were qualitatively identified in samples of all three preparation time conditions from the same donor, quantitative analysis revealed significant alterations in the plasma proteome with as little as a 4-h delay in plasma preparation. Notably, von Willebrand factor (VWF), a plasma biomarker currently used in several clinical tests, was found to have decreased levels in samples with delayed processing. Peptide-level analysis revealed that several VWF-derived peptides were less susceptible to degradation during storage. These findings suggest that monitoring stable peptide markers, rather than intact proteins, may provide a more accurate reflection of the in vivo proteomic state and enhance biomarker reliability. Advancing the development of robust, disease-specific peptide panels will be key to realizing the full potential of clinical proteomics for precise and predictive diagnostics.

Data-independent acquisition (DIA) mass spectrometry systematically fragments all precursor ions within predefined isolation windows of a predefined mass-to-charge (m/z) range. Unlike data-dependent acquisition (DDA), which selects precursor ions based on intensity, DIA enhances identification and quantification opportunities for lower-intensity peptides, significantly improving proteome coverage. Nevertheless, standard DIA methodologies have limited application for isobaric-labeled peptides, primarily due to challenges in accurately quantifying reporter ions arising from coisolation interference from coeluting peptides, degrading quantitative precision and accuracy. Here, an ultra-narrow-window DIA workflow compatible with 18-plex TMTpro labeling is presented, a novel strategy overcoming a major limitation in conventional pipelines for isobaric labeling-based DIA analysis. Acquisition with an Orbitrap Astral mass spectrometer operating at 200 Hz MS/MS scan speed and 80,000 resolving power (m/z 200) enabled 0.6 Th isolation windows approaching DDA-level precursor specificity. Leveraging mPE-MMR, precursor masses were accurately assigned to multiplexed DIA spectra prior to conventional spectrum-centric database searching, permitting routine peptide-to-spectrum matching. Applied to ovarian cancer tissue digests, the method identified substantially more peptides and protein groups than did DDA analyses while sustaining reporter ion precision and accuracy. These gains translate into deeper proteomic coverage without compromising quantitative robustness for multiplexed proteomics, thereby holding significant potential for clinical and population-scale studies.

Urinary proteins offer multifaceted insights into tissue repair, dysfunction, and renal health, with significant implications for both human and veterinary medicine. However, marine mammal medicine lacks comprehensive studies on urine protein composition. This study aimed to describe the urine proteome of wild common bottlenose dolphins (Tursiops truncatus) at two Gulf of Mexico sites (Sarasota Bay, FL, and Barataria Bay, LA) and to compare urine proteins by sex. Ten urine samples (Barataria Bay, LA: N = 6; Sarasota Bay, FL: N = 4) were analyzed by nano LC-MS/MS. Peptide spectral matching identified 1872 protein families across all individuals (FDR < 0.01). Cystatin 11 was notably present in males (median rank abundance: 8.1%) and absent in females (median: 0.0%), with semen contamination elevating protein diversity in male urine. Two putative antimicrobial proteins, cathelicidin and lysozyme, accounted for 2.66% of the urine proteome, suggesting an innate immune defense mechanism. In total, 27 proteins that are recognized as acute kidney injury markers in humans, and 12 putative stone formation proteins were detected in dolphin urine. This research provides a reference database of urinary proteins that can be used to develop advanced methods for investigating dolphin renal health. Data are available via ProteomeXchange with identifier PXD054283.

Peptaibols are nonribosomal peptides produced by Trichoderma species and are rich in nonproteinogenic amino acids such as α-aminoisobutyric acid (Aib). Their pronounced structural diversity, and underrepresentation in spectral libraries pose significant challenges for dereplication in high-throughput liquid chromatography coupled with tandem high resolution mass spectrometry (LC-HRMS/MS) workflows. This study introduces a novel strategy that integrates theoretical MS/MS fragmentation prediction, intensity modeling based on experimental data, and dimensionality reduction via t-distributed stochastic neighbor embedding (t-SNE) to improve peptaibol dereplication and classification. A custom spectral database was generated using predicted b- and y-type ions, with fragment intensities calibrated to match observed fragmentation patterns. The method demonstrated strong robustness across a diverse range of peptaibols, allowing clearer sequence assignments. One limitation was noted with Alamethicin F-50, where missing sequence motifs in the intensity model slightly reduced specificity. Nonetheless, the approach supports fast and reliable classification of unknown peptaibols through fragment-based matching, offering a powerful tool for prioritizing bioactive molecular families. To our knowledge, this is the first implementation of a t-SNE–guided theoretical spectral database for dereplication and classification of peptide-like natural products. Ongoing enrichment of the database is expected to further expand its specificity and applicability across broader peptides families.

Proteomics has become a transformative tool in oncology, offering unique opportunities for early detection, diagnosis, and cancer stratification. By enabling large-scale analysis of protein expression, interactions, and post-translational modifications, proteomics facilitates the discovery of clinically relevant biomarkers that reflect the dynamic molecular state of disease. These biomarkers, often detectable in easily accessible body fluids like blood, urine, or saliva, serve as minimally invasive “liquid biopsies” capable of identifying malignancies at asymptomatic stages. The integration of high-resolution mass spectrometry with advanced computational methods, including machine learning and network-based analytics, has accelerated the identification of reliable biomarker panels for cancer prediction, monitoring, and treatment response. Proteomics not only complements genomic and transcriptomic data but also provides a functional view of cellular states, bridging the gap between genotype and phenotype. This is especially important for customizing personalized treatment plans based on tumor-specific protein profiles, thus reducing the off-target effects of traditional therapies. Furthermore, proteomics plays a crucial role in finding cancer-related mechanisms, such as immune evasion, angiogenesis, and metastatic progression. Although proteomics-based biomarker discovery holds critical promise for cancer diagnosis and precision medicine, substantial translational challenges remain. These include assay standardization, sample accessibility from a regional biobank, regulatory obstacles, cross-population validation, and the integration of discoveries into clinical workflows. In this perspective, we offer a comprehensive overview of the latest progress in proteomics-driven cancer biomarker discovery, with a particular focus on its translational potential in early detection and precision oncology. The increasing incidence of cancer in the Middle East is highlighted, necessitating an urgent expansion of research into population-specific biomarkers.

Pathological changes in the knee joint are reflected in the protein composition of synovial fluid (SF), which is altered in osteoarthritis (OA) and may serve as a source of biomarkers. This study used label-free quantification proteomics to analyze SF protein profiles from individuals with varying grades of cartilage damage and healthy controls. SF samples (n = 61) from healthy knees (grade 0) and OA-affected joints (grades I–IV, Outerbridge score) were analyzed using LC–MS/MS. Feature selection was performed with the Jonckheere-Terpstra nonparametric test. Candidate biomarkers were validated by ELISA in an independent cohort (n = 51), with OA severity graded according to the Kellgren–Lawrence (K/L) scale. Using this approach, nine proteins were significantly differentially expressed between OA and control samples (p < 0.01), showing higher levels in early OA stages compared to moderate and late disease (p < 0.05). Among these, aldo-keto reductase family 1 member C1 (AKR1C1), a protein involved in oxidative stress and autophagy, positively correlated with OA severity in both cohorts. These findings highlight several protein biomarkers with potential utility in early OA diagnosis and monitoring disease progression. Notably, AKR1C1 emerges as a promising diagnostic and prognostic biomarker, warranting further investigation.

Weight loss induced by bariatric surgery (BS) has a profound impact on several biological systems. This study aimed to identify urinary proteins reflecting kidney and systemic adaptations to weight loss in patients with obesity before and after BS. Urine samples from individuals with obesity (n = 16) were collected before and two years after BS. Untargeted high-resolution LC-MS with label-free quantification was used to assess urinary proteome changes. Among the 2347 identified proteins, 1016 depicted a significantly different abundance postsurgery (p < 0.05). In particular, 54 proteins were either upregulated (n = 42) or downregulated (n = 12) by at least 50% (≥1.5-fold). Protein functional classification revealed associations with immune function (n = 17; e.g., protein S100-A9, α-1-acid glycoproteins); cytoskeleton/cell adhesion (n = 11; e.g., supervillin, ezrin, periplakin), and kidney adaptation (n = 11; e.g., elongation factor 1-α 1, megalin, cubilin). A decrease in inflammation protein markers (α-1-acid glycoproteins), alongside an increase in proteins associated with immune modulation and oxidative stress protection (dipeptidase 1, heat shock cognate 71 kDa protein) were observed. Overall, the urinary proteome suggests changes in inflammation and oxidative stress status, as well as in kidney function and cellular organization succeeding BS. Our results reveal potential novel pathways contributing to systemic modifications and nephroprotective effects of BS-induced weight loss.

Epithelial ovarian cancer (EOC) remains the most lethal form of cancer despite improvements in surgical techniques and therapeutic interventions over recent decades. The high mortality rate is largely associated with a lack of sensitive and specific early diagnostic biomarkers to allow timely intervention. Hence, the identification and validation of novel noninvasive biomarkers for primary diagnosis and for disease monitoring is of high importance. Malignant transformations of N-glycosylation have been reported across various cancer types including EOC, but little is known about the N-glycome of African populations. In this work, we investigated aberrant N-glycosylation for the first time in an African EOC cohort comprising primary patients and those undergoing chemotherapy. In this pilot study, the N-glycome of African EOC and controls was comparable to those previously found in European cohorts. Of importance, high-mannose N-glycans increased with response to treatment in early chemotherapy cycles, and complex-type sialylated fucosylated N-glycans decreased, especially in the late chemotherapy cycles. Interestingly, the glycan-based index that we previously developed to detect primary EOC was more sensitive and specific than the routine diagnostic biomarker to identify primary EOC and to monitor chemoresponse in the early phase of the treatment.

Asymptomatic Plasmodium falciparum infections are prevalent in malaria-endemic regions, yet their metabolic consequences remain poorly understood. In this exploratory study, we investigated the serum metabolomic profiles of 48 asymptomatic school-aged children from a high-transmission area in Tanzania (27 malaria-positive, 21 malaria-negative), using proton nuclear magnetic resonance (¹H NMR) spectroscopy and multivariate statistical analyses. Participants were matched by age, sex, and place of residence to control for confounding factors. Group-level comparisons revealed only modest and heterogeneous metabolic alterations in malaria-positive children, including lower concentrations of lactate, pyruvate, branched-chain amino acids, and threonine. However, KODAMA algorithm identified a metabolically distinct subgroup (18.5%) within the malaria-positive cohort. This subgroup displayed specific shifts in amino acid and lipid metabolism, such as reduced phenylalanine and alanine and increased glycerophospholipids, resembling profiles seen in symptomatic malaria, suggesting that a subset of asymptomatic individuals may exhibit biologically meaningful responses. In contrast, urinary tract infections (UTIs), detected in a subset of children, were associated with more consistent and malaria-independent metabolic signatures, particularly involving mannose and carbohydrate metabolism. These findings highlight the complexity of host responses to asymptomatic infections and emphasize the importance of accounting for coinfections and intragroup variability when interpreting metabolomic data in endemic settings.