Purpose: Evidence suggests that consuming epicatechin-rich green tea can increase metabolism in the body, and this metabolic effect might be linked to weight loss in obese subjects. The precise mechanism by which epicatechin influences weight loss is still unclear. Our goal was to identify a specific signature in the plasma proteins of obese individuals, categorized or not by gender (men and women), and to investigate how epicatechin (EC) supplementation affected them. Additionally, we analyzed anthropometric data to assess the potential anti-obesity effects of EC and to identify any gender-related differences that may have emerged.
Methods: In our clinical trial, we provided pure EC (90%) at a daily dosage of 200 mg, administered before the main meal, for three months. The participants were obese men and women with a body mass index (BMI) of 30 kg/m2 or higher. We conducted measurements of body dimensions and performed biochemical blood tests before and after the supplementation with EC, also analyzing the proteome in the plasma samples.
Findings: EC supplementation did not alter anthropometric parameters in obese subjects, but it did cause significant molecular changes in their plasma proteome, which varied between men and women. Key proteins like RPL30 were consistently regulated, indicating that EC might activate translational remodeling to adapt to metabolic stress in obesity.
Conclusions: Proteomic profiling reveals early biomarkers of therapeutic efficacy, and future research should examine EC's time-dependent effects on ribosomal biogenesis and metabolic regulation.
Purpose: Ubiquitylation is a vital post-translational modification involved in various biological processes, yet its role in the human hypothalamus remains largely unexplored. This study aims to profile the ubiquitinome of the human hypothalamus, uncovering the ubiquitylation landscape and its potential implications in hypothalamic function.
Experimental design: We employed LC‒MS/MS to analyze hypothalamic tissues from six healthy elderly individuals, focusing on identifying and characterizing ubiquitinated sites and proteins. Motif analysis, functional enrichment, and protein-protein interaction (PPI) network were conducted to profile the landscape.
Results: Our analysis identified 21,815 ubiquitinated sites across 5314 proteins and five types of modification motifs in the normal human hypothalamus. Ubiquitinated proteins were predominantly localized to the cell membrane. Functional enrichment related to neuronal and endocrine pathways, especially with MAPK signaling. PPI network analysis focused on five ubiquitinated proteins, including PRKACA, PRKACB, PRKCA, PRKCB, and PRKCG. Additionally, we analyzed their relationship with E3 ligases using UbiBrowser.
Conclusions and clinical relevance: This study offers the first comprehensive analysis of the human hypothalamic ubiquitinome. Our work provides a reliable foundation for future research into the implications of ubiquitylation in neuroendocrine-related disorders.
Summary: The human hypothalamus is crucial in regulating metabolism, stress response, and circadian rhythms. Dysfunctions in these processes are linked to various disorders, including Alzheimer's disease, obesity, and sleep disorders. Although ubiquitylation is a key protein modification that affects cellular function, its specific role within the hypothalamus remains poorly understood. This study provides the first detailed profile of lysine ubiquitylation in the human hypothalamus, identifying over 21,000 ubiquitinated sites on more than 5000 proteins. The findings provide valuable insights into the ubiquitylation landscape, highlighting key ubiquitinated proteins and pathways that may be involved in neuroendocrine diseases. This research provides a foundation for future research and highlights the potential of ubiquitylation as a therapeutic target for neurological and endocrine disorders.
Objective: The "blend sign" is a critical CT imaging marker for predicting hematoma expansion in intracerebral hemorrhage (ICH). This study aimed to elucidate its underlying pathological mechanisms by comparing proteomic profiles between hyperdense and hypodense regions within the hematoma.
Methods: Hematoma samples from nine ICH patients exhibiting the blend sign were obtained via minimally invasive puncture. Isotope-labeled proteomics and bioinformatic analyses were performed to identify differentially expressed proteins (DEPs), which were further validated by Western blotting and ELISA.
Results: A total of 77 DEPs were identified, including 66 upregulated and 11 downregulated in hyperdense regions compared to hypodense areas. Functional enrichment analysis revealed significant involvement of inflammatory responses, apoptosis, oxidative stress, and metabolic dysregulation. Notably, cytochrome C, growth-associated protein 43 (GAP43), and tau were markedly upregulated in hyperdense regions.
Conclusions: The blend sign is associated with region-specific molecular changes involving inflammation, apoptosis, and metabolic alterations. These findings provide mechanistic insights into hematoma heterogeneity and lay a foundation for future studies exploring their role in hematoma expansion and clinical outcomes.
Proteomic analysis of biofluids is central for identifying disease biomarkers. Tears have become popular targets for biomarker discovery and biosensor development, largely because they can be collected noninvasively and are rich sources of biomarkers for ocular and systemic diseases. Although basal and reflex tears have been well characterized, the proteome of psycho-emotionally stimulated tears remains largely unexplored, hindering their applicability in biomarker discovery studies and the advancement of tear-based biosensors. Comprehensive proteomic analysis across different tear types is crucial for identifying novel biomarkers and improving disease diagnosis and monitoring. In this pilot study, tears collected via conventional stimulation techniques ("standard") versus those elicited through emotional stimuli ("emotional") were purchased from single donors through two vendors. We compared the proteomic profiles of emotional (n = 6) and standard (n = 14) single donor human tears to better understand the biochemical composition and functional roles of different tear types. In total, 907 proteins were identified from all tear samples. Fifty-two tear proteins were significantly enriched in emotionally stimulated tears. Functional characterization of enriched proteins revealed that most were extracellular or secreted. Many were also involved in host defense or immune responses, including members of the S100A and neutrophil defensin protein families. SUMMARY: Although emotional tears are known to differ from basal and reflex tears in both composition and function, the specific biochemical characteristics and functional roles of emotional tears remain poorly understood. This gap in knowledge is largely due to the limited research conducted on emotional tears, despite their distinct origins. A more complete understanding of all tear types is necessary for the continuation of biomarker discovery and the development of tear fluid-based biosensors. In this exploratory study, tears collected via a conventional/standard protocol and those collected from emotional stimulus were obtained from two vendors and subjected to proteomic profiling and comparison. A bottom-up proteomic approach was utilized to analyze tear samples, facilitating a comparison between tear types and contributing to the characterization of psycho-emotional tears.
Purpose: Rugby players experience high-impact collisions, potentially increasing their risk of neurodegenerative conditions. This study investigates whether the plasma proteome of extracellular vesicles (EV) provides biomarkers to indicate differential risk associated with a rugby career.
Experimental design: Twenty-four males were recruited: eight academy rugby players (18 ± 1 years), eight professional rugby players (33 ± 5 years; >10-year career), and eight CrossFit athletes (32 ± 5 years; no history of collision-related injuries). EV were enriched from plasma using strong-anion exchange magnetic microparticles and digested proteins were analyzed by LC-MS/MS for label-free quantitation.
Results: A total of 449 proteins were identified (false discovery rate <1%). Statistical analysis on 403 proteins quantified in at least n = 3 participants in each group highlighted 52 significant (p < 0.05, q < 0.01) differences, including 44 proteins that had abundance profiles unique to professional rugby players. Eight proteins which were depleted and three proteins which were elevated have previously recognized roles in neurodegenerative processes.
Conclusions and clinical relevance: Proteins associated with neuroprotection were specifically depleted in the plasma EV proteome of long-serving professional rugby players. The proteins highlighted in professional rugby players could be used to develop biomarker panels for predicting at-risk athletes or for guiding treatment interventions.
Summary: Repetitive high-impact collisions experienced by rugby players may predispose them to neurodegenerative conditions, yet the biological processes underpinning this risk remain poorly understood. This study investigates whether the proteome of plasma extracellular vesicles (EV) could serve as early, minimally invasive biomarkers of neurodegenerative risk in athletes exposed to repeated head impacts. By comparing the EV proteome of professional rugby players, younger academy athletes, and non-collision sport controls, we identified specific proteins with known neuroprotective roles that were depleted in long-serving rugby professionals. These alterations suggest systemic biological changes related to prolonged exposure to collisions. Our findings provide novel insight by highlighting the potential of EV-based proteomic profiling as a tool for early detection and monitoring of neurodegeneration-related processes in at-risk athletic populations. This approach could ultimately inform strategies for risk stratification, early intervention, and tailored clinical monitoring in collision sport athletes.
Background: Polycystic ovary syndrome (PCOS) is a metabolic disorder affecting women of reproductive age, and its etiology remains unclear. Therefore, it is crucial to identify biomarkers of the metabolic disturbances in PCOS.
Methods: A total of eight clinical PCOS samples and control group samples were analyzed using data-independent acquisition (DIA) proteomics. Clinical data were used to identify protein biomarkers, and enzyme-linked immunosorbent assay (ELISA) validation was performed on 27 PCOS and 23 control samples.
Results: In the PCOS samples, a total of 114 differentially expressed proteins were identified, with 37 upregulated and 77 downregulated. Further biofunctional analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed two differentially expressed proteins, lactate dehydrogenase A (LDHA) and triosephosphate isomerase 1 (TPI1), both of which were significantly upregulated in clinical PCOS samples. LDHA and TPI1 are involved in the glycolysis/gluconeogenesis metabolic pathway. Finally, ELISA was used to validate the high expression of LDHA and TPI1 in PCOS patients.
Conclusion: DIA proteomics effectively identifies PCOS biomarkers. LDHA and TPI1 may serve as diagnostic biomarkers and could exert effects through glycolytic pathways.
Severe acute pancreatitis (SAP) involves dynamic shifts from inflammation to immunosuppression, where peptidomic profile evolution may reveal prognostic biomarkers. Here, the plasma peptidome of rats with taurocholate-induced SAP at 1, 3, 6, 12, and 24 h compared with controls was characterized using nLC-MS/MS. Ten peptides derived from eight precursor proteins were differentially regulated across time points. The 12-h period showed eight differentially regulated peptides, while the 6- and 24-h periods had four differentially regulated peptides, and one peptide was regulated between 1 and 3 h. Peptides derived from alpha-1-microglobulin (A1M) increased from 3 h onward, while peptides from actin showed major alterations at 12-24 h, coinciding with peak mortality (46%). Bioinformatic enrichment analyses revealed transient activation of mTOR, JAK/STAT, and cell adhesion pathways at 6 h, followed by bacterial invasion and actin cytoskeleton regulation pathways at later stages. These temporal profiles suggest an early antioxidant response and subsequent structural and infection-related remodeling. These findings suggest that A1M-derived peptides could represent potential early indicators of disease severity, although further validation in human clinical settings is required. These findings highlight the plasma peptidome as a promising tool for clinical diagnostics, providing a better understanding of SAP progression and identification of phase-specific biomarkers in pancreatitis. SUMMARY: This study reveals dynamic changes in the plasma peptidome during the progression of severe acute pancreatitis in rats. Through the identification of differentially regulated peptides, bioinformatic analysis was performed to define the altered pathways and genes, demonstrating the relationship between peptide alterations and disease progression. The 6-h time point after pancreatitis induction showed the highest number of signaling terms/pathways that characterize the inflammatory phase of the disease. In subsequent moments, the enrichment of pathways related to infection and the regulation of the actin cytoskeleton at 12- and 24-h post-pancreatitis induction suggests that this period is associated with the process of bacterial translocation and pancreatic necrosis infection. Therefore, the peptide profile and pathways may have implications for defining prognosis and early diagnosis of infection.
In label-free mass spectrometry experiments, the data output is typically a proteome table that requires further processing, quality testing, and visualization to fully interpret the captured proteomic signals. Currently, post-quantification analysis of these tables often relies on complex programmatic pipelines, which can become challenging to use. Here, we introduce the Proteomics Eye (ProtE), a single-function R package designed to streamline the analysis of proteome tables generated by commonly used software tools (DIA-NN, ProteomeDiscoverer, and MaxQuant). ProtE provides a broad range of options for data processing, preparation, and statistical testing. It also performs gene set enrichment analysis and offers a comprehensive suite of visualization plots to assess data quality and facilitate biological interpretation. Given a categorical variable with two or more groups, ProtE enables group-wide and pairwise statistical comparisons across all group combinations, using both traditional statistical tests and linear models for differential expression analysis. By integrating all these features into a single, user-friendly R function, ProtE simplifies the analysis of large-scale label-free DDA and DIA datasets, making advanced proteomic analysis accessible to both experienced researchers and beginners.
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