Journal of Proteome Research最新文献

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Exosomes, as carriers of cell-to-cell communication, can serve as promising biomarkers for probing the early diagnosis of cancer. Pancreatic cancer is a common malignant tumor of the pancreas with an insidious onset and difficult early diagnosis. The aim of this study was to capture exosomes in urine samples by phosphatidylserine-molecularly imprinted polymers (PS-MIPs). Transmission electron microscopy and nanoparticle tracking analysis as well as Western blot showed that our molecularly imprinted material can effectively capture urinary exosomes. Three parallel tests verified the reproducibility of the mass spectrometry assay and the stability of the material capture efficiency. Mass Spectrometry with nontargeted proteomics was combined to show differentially expressed proteins in exosomes between 5 pancreatic cancer patients and 5 healthy controls. The most significant changes in the proteomic profile in pancreatic cancer patients compared to healthy controls were the overexpression of SLC9A3R1, SPAG9, and ferritin light chain (FTL) These proteins may have an important role in diagnosis and prognostic assessment, supporting further scientific and clinical studies on pancreatic cancer.

Intracerebral hemorrhage (ICH) could trigger inflammatory responses. However, the specific role of inflammatory proteins in the pathological mechanism, complications, and prognosis of ICH remains unclear. In this study, we investigated the expression of 92 plasma inflammation-related proteins in patients with ICH (n = 55) and healthy controls (n = 20) using an Olink inflammation panel and discussed the relation to the severity of stroke, clinical complications, 30-day mortality, and 90-day outcomes. Our result showed that six proteins were upregulated in ICH patients compared with healthy controls, while seventy-four proteins were downregulated. In patients with ICH, seven proteins were increased in the severe stroke group compared with the moderate stroke group. In terms of complications, two proteins were downregulated in patients with pneumonia, while nine proteins were upregulated in patients with sepsis. Compared with the survival group, three proteins were upregulated, and one protein was downregulated in the death group. Compared with the good outcome group, eight proteins were upregulated, and four proteins were downregulated in the poor outcome group. In summary, an in-depth exploration of the differential inflammatory factors in the early stages of ICH could deepen our understanding of the pathogenesis of ICH, predict patient prognosis, and explore new treatment strategies.

The rapid expansion of biological sequence databases due to high-throughput genomic and proteomic sequencing methods has left a considerable number of identified protein sequences with unclear or incomplete functional annotations. Domains of unknown function (DUFs) are protein domains that lack functional annotations but are present in numerous proteins. To address the challenge of finding functional annotations for DUFs, we have developed a computational method that efficiently identifies and annotates these enigmatic protein domains by utilizing the position-specific iterative basic local alignment search tool (PSI-BLAST) and data mining techniques. Our pipeline identifies putative potential functionalities of DUFs, thereby decreasing the gap between known sequences and functions. The tool can also take user input sequences to annotate. We executed our pipeline on 5111 unique DUF sequences obtained from Pfam, resulting in putative annotations for 2007 of these. These annotations were subsequently incorporated into a comprehensive database and interfaced with a web-based server named "AnnoDUF". AnnoDUF is freely accessible to both academic and industrial users, via the World Wide Web at the link http://bts.ibab.ac.in/annoduf.php. All scripts used in this study are uploaded to the GitHub repository, and these can be accessed from https://github.com/BioToolSuite/AnnoDUF.

Aromatic caninurine formamase (AFMID) is an enzyme involved in the tryptophan pathway, metabolizing N-formylkynurenine to kynurenine. AFMID had been found significantly downregulated in clear cell renal cell carcinoma (ccRCC) in both tissue and urine samples. Although ccRCC is characterized by a typical Warburg-like phenotype, mitochondrial dysfunction, and elevated fat deposition, it is unknown whether AFMID plays a role in tumorigenesis and the development of ccRCC. In the present study, AFMID overexpression had inhibitory effects for ccRCC cells, decreasing the rate of cell proliferation. Quantitative proteomics showed that AFMID overexpression altered cellular signaling pathways involved in cell growth and cellular metabolism pathways, including lipid metabolism and inositol phosphate metabolism. Further urine proteomic analysis indicated that cellular function dysfunction with AFMID overexpression could be reflected in the urine. The activity of predicted upregulators DDX58, TREX1, TGFB1, SMARCA4, and TNF in ccRCC cells and urine showed opposing change trends. Potential urinary biomarkers were tentatively discovered and further validated using an independent cohort. The protein panel of APOC3, UMOD, and CILP achieved an AUC value of 0.862 for the training cohort and 0.883 for the validation cohort. The present study is of significance in terms of highlighting various aspects of pathway changes associated with AFMID enzymes, discovering potential specific biomarkers for potential patient diagnosis, and therapeutic targeting.

N-Glycan-dependent endoplasmic reticulum quality control (ERQC) primarily mediates protein folding, which determines the fate of the polypeptide. Monoglucose residues on N-glycans determine whether the nascent N-glycosylated proteins enter into and escape from the calnexin (CANX)/calreticulin (CALR) cycle, which is a central system of the ERQC. To reveal the impact of ERQC on glycosylation and protein fate, we performed comprehensive quantitative proteomic and glycoproteomic analyses using cells defective in N-glycan-dependent ERQC. Deficiency of MOGS encoding the ER α-glucosidase I, CANX, or/and CALR broadly affected protein expression and glycosylation. Among the altered glycoproteins, the occupancy of oligomannosidic N-glycans was significantly affected. Besides the expected ER stress, proteins and glycoproteins involved in pathways for lysosome and viral infection are differentially changed in those deficient cells. We demonstrated that lysosomal hydrolases were not correctly modified with mannose-6-phosphates on the N-glycans and were directly secreted to the culture medium in N-glycan-dependent ERQC mutant cells. Overall, the CANX/CALR cycle promotes the correct folding of glycosylated peptides and influences the transport of lysosomal hydrolases.

Quality control and system suitability testing are vital protocols implemented to ensure the repeatability and reproducibility of data in mass spectrometry investigations. However, mass spectrometry imaging (MSI) analyses present added complexity since both chemical and spatial information are measured. Herein, we employ various machine learning algorithms and a novel quality control mixture to classify the working conditions of an MSI platform. Each algorithm was evaluated in terms of its performance on unseen data, validated with negative control data sets to rule out confounding variables or chance agreement, and utilized to determine the necessary sample size to achieve a high level of accurate classifications. In this work, a robust machine learning workflow was established where models could accurately classify the instrument condition as clean or compromised based on data metrics extracted from the analyzed quality control sample. This work highlights the power of machine learning to recognize complex patterns in MSI data and use those relationships to perform a system suitability test for MSI platforms.

This investigation aims to employ Olink proteomics in analyzing the distinct serum proteins associated with postmenopausal osteoporosis (PMOP) and identifying prognostic markers for early detection of PMOP via molecular mechanism research on postmenopausal osteoporosis. Postmenopausal women admitted to Beijing Jishuitan Hospital were randomly selected and categorized into three groups based on their dual-energy X-ray absorptiometry (DXA) T-scores: osteoporosis group (n = 24), osteopenia group (n = 20), and normal bone mass group (n = 16). Serum samples from all participants were collected for clinical and bone metabolism marker measurements. Olink proteomics was utilized to identify differentially expressed proteins (DEPs) that are highly associated with postmenopausal osteoporosis. The functional analysis of DEPs was performed using Gene Ontology and Kyto Encyclopedia Genes and Genomes (KEGG). The biological characteristics of these proteins and their correlation with PMOP were subsequently analyzed. ROC curve analysis was performed to identify potential biomarkers with the highest diagnostic accuracy for early stage PMOP. Through Olink proteomics, we identified five DEPs highly associated with PMOP, including two upregulated and three downregulated proteins. TWEAK and CDCP1 markers exhibited the highest area under the curve (0.8188 and 0.8031, respectively). TWEAK and CDCP1 have the potential to serve as biomarkers for early prediction of postmenopausal osteoporosis.

Proximity labeling (PL) has given researchers the tools to explore protein-protein interactions (PPIs) in living systems; however, most PL studies are performed on intracellular targets. We have adapted the original PL method to investigate PPIs within the extracellular compartment, which we term extracellular PL (ePL). To demonstrate the utility of this modified technique, we investigated the interactome of the matrisome protein TIMP2. TIMPs are a family of multifunctional proteins that were initially defined by their ability to inhibit metalloproteinases, the major mediators of extracellular matrix (ECM) turnover. TIMP2 exhibits broad expression and is often abundant in both normal and diseased tissues. Understanding the functional transformation of matrisome regulators, such as TIMP2, during disease progression is essential for the development of ECM-targeted therapeutics. Using dual orientation fusion proteins of TIMP2 with BioID2/TurboID, we describe the TIMP2 proximal interactome (MassIVE MSV000095637). We also illustrate how the TIMP2 interactome changes in the presence of different stimuli, in different cell types, in unique culture conditions (2D vs 3D), and with different reaction kinetics, demonstrating the power of this technique versus classical PPI methods. We propose that screening of matrisome targets in disease models using ePL will reveal new therapeutic targets for further comprehensive studies.

Staphylococcus aureus is a leading cause of severe pneumonia. Our recent proteomic investigations into S. aureus invasion of human lung epithelial cells revealed three key adaptive responses: activation of the SigB and CodY regulons and upregulation of the hibernation-promoting factor SaHPF. Therefore, our present study aimed at a functional and proteomic dissection of the contributions of CodY, SigB and SaHPF to host invasion using transposon mutants of the methicillin-resistant S. aureus USA300. Interestingly, disruption of codY resulted in a "small colony variant" phenotype and redirected the bacteria from (phago)lysosomes into the host cell cytoplasm. Furthermore, we show that CodY, SigB and SaHPF contribute differentially to host cell adhesion, invasion, intracellular survival and cytotoxicity. CodY- or SigB-deficient bacteria experienced faster intracellular clearance than the parental strain, underscoring the importance of these regulators for intracellular persistence. We also show an unprecedented role of SaHPF in host cell adhesion and invasion. Proteomic analysis of the different mutants focuses attention on the CodY-perceived metabolic state of the bacteria and the SigB-perceived environmental cues in bacterial decision-making prior and during infection. Additionally, it underscores the impact of the nutritional status and bacterial stress on the initiation and progression of staphylococcal lung infections.