The mimivirus 1.2 Mb genome was shown to be organized into a nucleocapsid-like genomic fiber encased in the nucleoid compartment inside the icosahedral capsid. The genomic fiber protein shell is composed of a mixture of two GMC-oxidoreductase paralogs, one of them being the main component of the glycosylated layer of fibrils at the surface of the virion. In this study, we determined the effect of the deletion of each of the corresponding genes on the genomic fiber and the layer of surface fibrils. First, we deleted the GMC-oxidoreductase, the most abundant in the genomic fiber, and determined its structure and composition in the mutant. As expected, it was composed of the second GMC-oxidoreductase and contained 5- and 6-start helices similar to the wild-type fiber. This result led us to propose a model explaining their coexistence. Then we deleted the GMC-oxidoreductase, the most abundant in the layer of fibrils, to analyze its protein composition in the mutant. Second, we showed that the fitness of single mutants and the double mutant were not decreased compared with the wild-type viruses under laboratory conditions. Third, we determined that deleting the GMC-oxidoreductase genes did not impact the glycosylation or the glycan composition of the layer of surface fibrils, despite modifying their protein composition. Because the glycosylation machinery and glycan composition of members of different clades are different, we expanded the analysis of the protein composition of the layer of fibrils to members of the B and C clades and showed that it was different among the three clades and even among isolates within the same clade. Taken together, the results obtained on two distinct central processes (genome packaging and virion coating) illustrate an unexpected functional redundancy in members of the family Mimiviridae, suggesting this may be the major evolutionary force behind their giant genomes.

Glycoproteins located on the cell surface play a pivotal role in nearly every extracellular activity. N-glycosylation is one of the most common and important protein modifications in eukaryotic cells, and it often regulates protein folding and trafficking. Glycosylation of cell-surface proteins undergoes meticulous regulation by various enzymes in the endoplasmic reticulum (ER) and the Golgi, ensuring their proper folding and trafficking to the cell surface. However, the impacts of protein N-glycosylation, N-glycan maturity, and protein folding status on the trafficking of cell-surface glycoproteins remain to be explored. In this work, we comprehensively and site-specifically studied the trafficking of cell-surface glycoproteins in human cells. Integrating metabolic labeling, bioorthogonal chemistry, and multiplexed proteomics, we investigated 706 N-glycosylation sites on 396 cell-surface glycoproteins in monocytes, either by inhibiting protein N-glycosylation, disturbing N-glycan maturation, or perturbing protein folding in the ER. The current results reveal their distinct impacts on the trafficking of surface glycoproteins. The inhibition of protein N-glycosylation dramatically suppresses the trafficking of many cell-surface glycoproteins. The N-glycan immaturity has more substantial effects on proteins with high N-glycosylation site densities, while the perturbation of protein folding in the ER exerts a more pronounced impact on surface glycoproteins with larger sizes. Furthermore, for N-glycosylated proteins, their trafficking to the cell surface is related to the secondary structures and adjacent amino acid residues of glycosylation sites. Systematic analysis of surface glycoprotein trafficking advances our understanding of the mechanisms underlying protein secretion and surface presentation.

BACKGROUND: Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization.
METHODS: Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients.
RESULTS: We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods.
CONCLUSIONS: Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.

Over 400 different types of post-translational modifications (PTMs) have been reported and over 200 various types of PTMs have been discovered using mass spectrometry (MS)-based proteomics. MS-based proteomics has proven to be a powerful method capable of global PTM mapping with the identification of modified proteins/peptides, the localization of PTM sites and PTM quantitation. PTMs play regulatory roles in protein functions, activities and interactions in various heart related diseases, such as ischemia/reperfusion injury, cardiomyopathy and heart failure. The recognition of PTMs that are specific to cardiovascular pathology and the clarification of the mechanisms underlying these PTMs at molecular levels are crucial for discovery of novel biomarkers and application in a clinical setting. With sensitive MS instrumentation and novel biostatistical methods for precise processing of the data, low-abundance PTMs can be successfully detected and the beneficial or unfavorable effects of specific PTMs on cardiac function can be determined. Moreover, computational proteomic strategies that can predict PTM sites based on MS data have gained an increasing interest and can contribute to characterization of PTM profiles in cardiovascular disorders. More recently, machine learning- and deep learning-based methods have been employed to predict the locations of PTMs and explore PTM crosstalk. In this review article, the types of PTMs are briefly overviewed, approaches for PTM identification/quantitation in MS-based proteomics are discussed and recently published proteomic studies on PTMs associated with cardiovascular diseases are included.

Cell-surface proteins are extremely important for many cellular events, such as regulating cell-cell communication and cell-matrix interactions. Aberrant alterations in surface protein expression, modification (especially glycosylation), and interactions are directly related to human diseases. Systematic investigation of surface proteins advances our understanding of protein functions, cellular activities, and disease mechanisms, which will lead to identifying surface proteins as disease biomarkers and drug targets.
In this review, we summarize mass spectrometry (MS)-based proteomics methods for global analysis of cell-surface proteins. Then, investigations of the dynamics of surface proteins are discussed. Furthermore, we summarize the studies for the surfaceome interaction networks. Additionally, biological applications of MS-based surfaceome analysis are included, particularly highlighting the significance in biomarker identification, drug development, and immunotherapies.
Modern MS-based proteomics provides an opportunity to systematically characterize proteins. However, due to the complexity of cell-surface proteins, the labor-intensive workflow, and the limit of clinical samples, comprehensive characterization of the surfaceome remains extraordinarily challenging, especially in clinical studies. Developing and optimizing surfaceome enrichment methods and utilizing automated sample preparation workflow can expand the applications of surfaceome analysis and deepen our understanding of the functions of cell-surface proteins.
The cell surface contains many important proteins such as receptors and transporters. These proteins are responsible for cells to communicate with each other, take nutrients from outside, and interact with their surroundings. Aberrant changes in surface protein expression, modifications, and interactions with other molecules directly result in various diseases, including infections, immune disorders, and cancer. Currently, mass spectrometry (MS)-based proteomics is very powerful to study proteins on a large scale, and there has been a strong interest in employing MS to investigate cell-surface proteins. In this review, we discuss different methods combining mass spectrometry with other approaches to systematically characterize protein abundance, dynamics, modification, and interaction on the cell surface. These methods help uncover protein functions and specific cell-surface proteins related to human diseases. A better understanding of the functions and properties of cell-surface proteins can facilitate the discovery of surface proteins as effective biomarkers for disease early detection and the identification of drug targets for disease treatment.

The aim of the present study was to elucidate the potential diagnostic value of urinary N-glycoprotein in patients with IgA nephropathy (IgAN) using mass spectrometry (MS). All procedures were performed between June 2021 and June 2023 at Guangan People\'s Hospital (Guangan, China). Fresh mid-morning fasting midstream urine samples were collected from a total of 30 patients with IgAN and 30 sex- and age-matched healthy volunteers. Data acquired from 6 participants are available through ProteomeXchange with the identifier PXD041151. By comparison between the IgAN group (n=3) and healthy controls (n=3) and selection criteria of P<0.05 and |log fold-change|>2, a total of 11 upregulated and 22 downregulated glycoproteins in patients with IgAN were identified. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that glycoproteins are involved in various functions, such as the regulation of cell growth, cell adhesion, cellular component organization and protein binding, as well as multiple pathways, including p53, Notch and mTOR signaling pathways. The urine levels of afamin were further measured by ELISA in a validation cohort to assess the diagnostic performance of the single indicator model. In conclusion, MS-based proteomics of urinary glycoproteins may be an alternative option for diagnosing patients with IgAN. Biomarkers of IgAN may include, but are not limited to, CCL25, PD-L1, HLA-DRB1, IL7RD and WDR82. In addition, the levels of urinary AFM indicators are of diagnostic value for IgAN.

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In a currently 13-year-old girl of consanguineous Turkish parents, who developed unsteady gait and polyneuropathy at the ages of 3 and 6 years, respectively, we performed whole genome sequencing and identified a biallelic missense variant c.424C>T, p.R142W in glypican 1 (GPC1) as a putative disease-associated variant. Up to date, GPC1 has not been associated with a neuromuscular disorder, and we hypothesized that this variant, predicted as deleterious, may be causative for the disease. Using mass spectrometry-based proteomics, we investigated the interactome of GPC1 WT and the missense variant. We identified 198 proteins interacting with GPC1, of which 16 were altered for the missense variant. This included CANX as well as vacuolar ATPase (V-ATPase) and the mammalian target of rapamycin complex 1 (mTORC1) complex members, whose dysregulation could have a potential impact on disease severity in the patient. Importantly, these proteins are novel interaction partners of GPC1. At 10.5 years, the patient developed dilated cardiomyopathy and kyphoscoliosis, and Friedreich\'s ataxia (FRDA) was suspected. Given the unusually severe phenotype in a patient with FRDA carrying only 104 biallelic GAA repeat expansions in FXN, we currently speculate that disturbed GPC1 function may have exacerbated the disease phenotype. LC-MS/MS data are accessible in the ProteomeXchange Consortium (PXD040023).

Milk is a rich source of biologically important proteins and peptides. In addition, milk contains a variety of extracellular vesicles (EVs), including exosomes, that carry their own proteome cargo. EVs are essential for cell-cell communication and modulation of biological processes. They act as nature carriers of bioactive proteins/peptides in targeted delivery during various physiological and pathological conditions. Identification of the proteins and protein-derived peptides in milk and EVs and recognition of their biological activities and functions had a tremendous impact on food industry, medicine research, and clinical applications. Advanced separation methods, mass spectrometry (MS)-based proteomic approaches and innovative biostatistical procedures allowed for characterization of milk protein isoforms, genetic/splice variants, posttranslational modifications and their key roles, and contributed to novel discoveries. This review article discusses recently published developments in separation and identification of bioactive proteins/peptides from milk and milk EVs, including MS-based proteomic approaches.

Early and accurate identification of pathogens is essential for improved outcomes in patients with viral encephalitis (VE) and/or viral meningitis (VM).
In our research, Metagenomic next-generation sequencing (mNGS) which can identify viral pathogens unbiasedly was performed on RNA and DNA to identify potential pathogens in cerebrospinal fluid (CSF) samples from 50 pediatric patients with suspected VEs and/or VMs. Then we performed proteomics analysis on the 14 HEV-positive CSF samples and another 12 CSF samples from health controls (HCs). A supervised partial least squaresdiscriminant analysis (PLS-DA) and orthogonal PLS-DA (O-PLS-DA) model was performed using proteomics data.
Ten viruses in 48% patients were identified and the most common pathogen was human enterovirus (HEV) Echo18. 11 proteins overlapping between the top 20 DEPs in terms of P value and FC and the top 20 proteins in PLS-DA VIP lists were acquired.
Our result showed mNGS has certain advantages on pathogens identification in VE and VM and our research established a foundation to identify diagnosis biomarker candidates of HEV-positive meningitis based on MS-based proteomics analysis, which could also contribute toward investigating the HEV-specific host response patterns.