Proteomes最新文献

Trophoblast migration and invasion play crucial roles in placental development. However, the effects of (-)-epigallocatechin-3-gallate (EGCG) on trophoblast cell functions remain largely unexplored. In this study, we investigated the impact of EGCG on the survival of trophoblast cells and employed a proteomics analysis to evaluate its influence on trophoblast cell migration and invasion. Be-Wo trophoblast cells were treated with EGCG, and a zone closure assay was conducted to assess the cell migration and invasion. Subsequently, a proteomics analysis was performed on the treated and control groups, followed by a bioinformatics analysis to evaluate the affected biological pathways and protein networks. A quantitative real-time PCR and Western blot analysis were carried out to validate the proteomics findings. Our results showed that EGCG significantly suppressed the trophoblast migration and invasion at a concentration not affecting cell survival. The proteomics analysis revealed notable differences in the protein expression between the EGCG-treated and control groups. Specifically, EGCG downregulated the signaling pathways related to EIF2, mTOR, and estrogen response, as well as the processes associated with the cytoskeleton, extracellular matrix, and protein translation. Conversely, EGCG upregulated the pathways linked to lipid degradation and oxidative metabolism. The quantitative PCR showed that EGCG modulated protein expression by regulating gene transcription, and the Western blot analysis confirmed its impact on cytoskeleton and extracellular matrix reorganization. These findings suggest EGCG may inhibit trophoblast migration and invasion through multiple signaling pathways, highlighting the potential risks associated with consuming EGCG-containing products during pregnancy. Future research should investigate the impact of EGCG intake on maternal and fetal proteoforms.

The yeast Saccharomyces cerevisiae is a powerful model system that is often used to expand our understanding of cellular processes and biological functions. Although many genetically well-characterized laboratory strains of S. cerevisiae are available, they may have different genetic backgrounds which can confound data interpretation. Here, we report a comparative whole-proteome analysis of two common laboratory yeast background strains, W303 and BY4742, in both exponential and stationary growth phases using isobaric-tag-based mass spectrometry to highlight differences in proteome complexity. We quantified over 4400 proteins, hundreds of which showed differences in abundance between strains and/or growth phases. Moreover, we used proteome-wide protein abundance to profile the mating type of the strains used in the experiment, the auxotrophic markers, and associated metabolic pathways, as well as to investigate differences in particular classes of proteins, such as the pleiotropic drug resistance (PDR) proteins. This study is a valuable resource that offers insight into mechanistic differences between two common yeast background strains and can be used as a guide to select a background that is best suited for addressing a particular biological question.

Urine provides a diverse source of information related to a patient's health status and is ideal for clinical proteomics due to its ease of collection. To date, most methods for the preparation of urine samples lack the throughput required to analyze large clinical cohorts. To this end, we developed a novel workflow, urine-HILIC (uHLC), based on an on-bead protein capture, clean-up, and digestion without the need for bottleneck processing steps such as protein precipitation or centrifugation. The workflow was applied to an acute kidney injury (AKI) pilot study. Urine from clinical samples and a pooled sample was subjected to automated sample preparation in a KingFisher™ Flex magnetic handling station using the novel approach based on MagReSyn^® HILIC microspheres. For benchmarking, the pooled sample was also prepared using a published protocol based on an on-membrane (OM) protein capture and digestion workflow. Peptides were analyzed by LCMS in data-independent acquisition (DIA) mode using a Dionex Ultimate 3000 UPLC coupled to a Sciex 5600 mass spectrometer. The data were searched in Spectronaut™ 17. Both workflows showed similar peptide and protein identifications in the pooled sample. The uHLC workflow was easier to set up and complete, having less hands-on time than the OM method, with fewer manual processing steps. Lower peptide and protein coefficient of variation was observed in the uHLC technical replicates. Following statistical analysis, candidate protein markers were filtered, at ≥8.35-fold change in abundance, ≥2 unique peptides and ≤1% false discovery rate, and revealed 121 significant, differentially abundant proteins, some of which have known associations with kidney injury. The pilot data derived using this novel workflow provide information on the urinary proteome of patients with AKI. Further exploration in a larger cohort using this novel high-throughput method is warranted.

The budding yeast Saccharomyces cerevisiae is a powerful model system that is widely used to investigate many cellular processes. The harvesting of yeast cells is the first step in almost every experimental procedure. Here, yeast cells are isolated from their growth medium, collected, and used for successive experiments or analysis. The two most common methods to harvest S. cerevisiae are centrifugation and filtration. Understanding if and how centrifugation and filtration affect yeast physiology is essential with respect to downstream data interpretation. Here, we profile and compare the proteomes and the phosphoproteomes, using isobaric label-based quantitative mass spectrometry, of three common methods used to harvest S. cerevisiae cells: low-speed centrifugation, high-speed centrifugation, and filtration. Our data suggest that, while the proteome was stable across the tested conditions, hundreds of phosphorylation events were different between centrifugation and filtration. Our analysis shows that, under our experimental conditions, filtration may cause both cell wall and osmotic stress at higher levels compared to centrifugation, implying harvesting-method-specific stresses. Thus, considering that the basal activation levels of specific stresses may differ under certain harvesting conditions is an important, but often overlooked, aspect of experimental design.

The value of crops such as perennial wheat (PW) for grain and grazing compared to conventional wheat (W), or the addition of lucerne to PW (PWL) is still being determined. This research sought to determine if these diets were associated with changes in the membranebound proteins that transport nutrients in the rumen epithelium (RE). Crossbred ewes (Poll Dorset × Merino) were fed W, PW, or PWL (50:50) fresh-cut forage ad libitum for 4 weeks. Average daily gain (ADG; p < 0.001) was highest in the W-fed lambs compared to the PW and PWL. Metabolisable energy intake (MEI) was higher in lambs fed W (p < 0.001) compared to PW and PWL. In pairwise comparisons of the PW and PWL diet group we found protein abundance was significantly (p < 0.05, FDR < 0.05, Benjamini p < 0.05) different in fatty acid metabolism, oxidative phosphorylation, and biosynthesis of cofactors pathways. There were not any differences in protein abundance related to nutrient transport or energy metabolism in the RE between W- vs. PW- and W- vs. PWL-fed lambs. However, in the PW- vs. PWL-fed lambs, there was a difference in the level of proteins regulating the metabolism of fatty acids and energy production in the mitochondria of the rumen epithelium.

Individual gene expression and molecular pathway activation profiles were shown to be effective biomarkers in many cancers. Here, we used the human interactome model to algorithmically build 7470 molecular pathways centered around individual gene products. We assessed their associations with tumor type and survival in comparison with the previous generation of molecular pathway biomarkers (3022 "classical" pathways) and with the RNA transcripts or proteomic profiles of individual genes, for 8141 and 1117 samples, respectively. For all analytes in RNA and proteomic data, respectively, we found a total of 7441 and 7343 potential biomarker associations for gene-centric pathways, 3020 and 2950 for classical pathways, and 24,349 and 6742 for individual genes. Overall, the percentage of RNA biomarkers was statistically significantly higher for both types of pathways than for individual genes (p < 0.05). In turn, both types of pathways showed comparable performance. The percentage of cancer-type-specific biomarkers was comparable between proteomic and transcriptomic levels, but the proportion of survival biomarkers was dramatically lower for proteomic data. Thus, we conclude that pathway activation level is the advanced type of biomarker for RNA and proteomic data, and momentary algorithmic computer building of pathways is a new credible alternative to time-consuming hypothesis-driven manual pathway curation and reconstruction.

Given the pathophysiological continuum of chronic kidney disease (CKD), different molecular determinants affecting progression may be associated with distinct disease phases; thus, identification of these players are crucial for guiding therapeutic decisions, ideally in a non-invasive, repeatable setting. Analyzing the urinary peptidome has been proven an efficient method for biomarker determination in CKD, among other diseases. In this work, after applying several selection criteria, urine samples from 317 early (stage 2) and advanced (stage 3b-5) CKD patients were analyzed using capillary electrophoresis coupled to mass spectrometry (CE-MS). The entire two groups were initially compared to highlight the respective pathophysiology between initial and late disease phases. Subsequently, slow and fast progressors were compared within each group in an attempt to distinguish phase-specific disease progression molecules. The early vs. late-stage CKD comparison revealed 929 significantly different peptides, most of which were downregulated and 268 with collagen origins. When comparing slow vs. fast progressors in early stage CKD, 42 peptides were significantly altered, 30 of which were collagen peptide fragments. This association suggests the development of structural changes may be reversible at an early stage. The study confirms previous findings, based on its multivariable-matched progression groups derived from a large initial cohort. However, only four peptide fragments differed between slow vs. fast progressors in late-stage CKD, indicating different pathogenic processes occur in fast and slow progressors in different stages of CKD. The defined peptides associated with CKD progression at early stage might potentially constitute a non-invasive approach to improve patient management by guiding (personalized) intervention.

Pancreatic cancer is a devastating disease that has a grim prognosis, highlighting the need for improved screening, diagnosis, and treatment strategies. Currently, the sole biomarker for pancreatic ductal adenocarcinoma (PDAC) authorized by the U.S. Food and Drug Administration is CA 19-9, which proves to be the most beneficial in tracking treatment response rather than in early detection. In recent years, proteomics has emerged as a powerful tool for advancing our understanding of pancreatic cancer biology and identifying potential biomarkers and therapeutic targets. This review aims to offer a comprehensive survey of proteomics' current status in pancreatic cancer research, specifically accentuating its applications and its potential to drastically enhance screening, diagnosis, and treatment response. With respect to screening and diagnostic precision, proteomics carries the capacity to augment the sensitivity and specificity of extant screening and diagnostic methodologies. Nonetheless, more research is imperative for validating potential biomarkers and establishing standard procedures for sample preparation and data analysis. Furthermore, proteomics presents opportunities for unveiling new biomarkers and therapeutic targets, as well as fostering the development of personalized treatment strategies based on protein expression patterns associated with treatment response. In conclusion, proteomics holds great promise for advancing our understanding of pancreatic cancer biology and improving patient outcomes. It is essential to maintain momentum in investment and innovation in this arena to unearth more groundbreaking discoveries and transmute them into practical diagnostic and therapeutic strategies in the clinical context.

In recent decades, the role played by extracellular vesicles in physiological and pathological processes has attracted attention. Extracellular vesicles are released by different types of cells and carry molecules that could become biomarkers for the diagnosis of diseases. Extracellular vesicles are also moldable tools for the controlled release of bioactive substances in clinical and therapeutic applications. However, one of the significant challenges when studying these exciting and versatile vesicles is the purification process, which presents significant difficulties in terms of lack of purity, yield, and reproducibility, reflected in unreliable data. Therefore, our objective in the present study was to compare the proteomic profile of serum-derived EVs purified using ExoQuick™ (Systems Biosciences), Total Isolation Kit (Life Technologies), Ultracentrifugation, and Ultrafiltration. Each technique utilized for purification has shown different concentrations and populations of purified particles. The results showed marked differences in distribution, size, and protein content, demonstrating the need to develop reproducible and reliable protocols to isolate extracellular vesicles for their clinical application.

Recent advances in the field of proteomics have allowed extensive insights into the molecular regulations of the cell proteome. Specifically, this allows researchers to dissect a multitude of signaling arrays while targeting for the discovery of novel protein signatures. These approaches based on data mining are becoming increasingly powerful for identifying both potential disease mechanisms as well as indicators for disease progression and overall survival predictive and prognostic molecular markers for cancer. Furthermore, mass spectrometry (MS) integrations satisfy the ongoing demand for in-depth biomarker validation. For the purpose of this review, we will highlight the current developments based on MS sensitivity, to place quantitative proteomics into clinical settings and provide a perspective to integrate proteomics data for future applications in cancer precision medicine. We will also discuss malignancies associated with oncogenic viruses such as Acquire Immunodeficiency Syndrome (AIDS) and suggest novel mechanisms behind this phenomenon. Human Immunodeficiency Virus type-1 (HIV-1) proteins are known to be oncogenic per se, to induce oxidative and endoplasmic reticulum stresses, and to be released from the infected or expressing cells. HIV-1 proteins can act alone or in collaboration with other known oncoproteins, which cause the bulk of malignancies in people living with HIV-1 on ART.