Journal of Proteome Research最新文献

Early detection of pancreatic ductal adenocarcinoma (PDAC) can improve survival but is hampered by the absence of early disease symptoms. Imaging remains key for surveillance but is cumbersome and may lack sensitivity to detect small tumors. CA19-9, the only FDA-approved blood biomarker for PDAC, is insufficiently sensitive and specific to be recommended for surveillance. We aimed to discover a blood-based protein signature to improve PDAC detection in our main target population consisting of stage I or II PDAC patients (n = 75) and various controls including healthy controls (n = 50), individuals at high risk (genetic and familial) for PDAC (n = 47), or those under surveillance for an intraductal papillary mucinous neoplasm (n = 36). Roughly 3000 proteins were measured using Olink multiplex technology and conventional immunoassays. Machine learning combined biomarker candidates into 4- to 6-plex signatures. These signatures significantly (p < 0.001) outperformed CA19-9 with 84% sensitivity at 95% specificity, compared to CA19-9's sensitivity of 53% in the target population. Exploratory analysis was performed in new-onset diabetes (n = 81) and chronic pancreatitis (n = 50) patients. In conclusion, 41 promising biomarker candidates across multiple signatures were identified using proteomics technology and will be further tested in an independent cohort.

The outbreak of COVID-19, led to an ongoing pandemic with devastating consequences for the global economy and human health. With the global spread of SARS-CoV-2, multidisciplinary initiatives were launched to explore new diagnostic, therapeutic, and vaccination strategies. From this perspective, proteomics could help to understand the mechanisms associated with SARS-CoV-2 infection and to identify new therapeutic options. A TMT-based quantitative proteomics and phosphoproteomics analysis was performed to study the proteome remodeling of human lung alveolar cells expressing human ACE2 (A549-ACE2) after infection with SARS-CoV-2. Detectability and the prognostic value of selected proteins was analyzed by targeted PRM. A total of 6802 proteins and 6428 phospho-sites were identified in A549-ACE2 cells after infection with SARS-CoV-2. The differential proteins here identified revealed that A549-ACE2 cells undergo a time-dependent regulation of essential processes, delineating the precise intervention of the cellular machinery by the viral proteins. From this mechanistic background and by applying machine learning modeling, 29 differential proteins were selected and detected in the serum of COVID-19 patients, 14 of which showed promising prognostic capacity. Targeting these proteins and the protein kinases responsible for the reported phosphorylation changes may provide efficient alternative strategies for the clinical management of COVID-19.

Textiles provide a valuable source of information regarding past cultures and their artistic practices. Understanding ancient textiles requires identifying the raw materials used, since the origin of dyes and fibers may be from plants or animals, with the specific species used varying based on geography, trade routes and cultural significance. A selection of nine Chancay textile fragments attributed to 800-1200 CE were studied with liquid chromatography mass spectrometry (LC-MS) and direct analysis in real time mass spectrometry (DART-MS) to identify the chemical compounds in extracts of natural dyes used to create green, blue, red, yellow and black colors. From the identified molecular markers, the green colors involved the overdyeing of indigo and flavonoid dyes, the blue colors were achieved using an indigo dye, the yellows came from a flavonoid dye, the reds from anthraquinone dyes of both plant and animal origin, and the black from a mixture of flavonoid, anthraquinone and indigo dyes. A subset of the textiles was identified as containing proteinaceous fibers based on ATR-FTIR. These textiles were further studied using a mass spectrometry-based proteomics approach to identify the species used, with the peptide sequences measured confirming the presence of South American camelids, most likely llama or alpaca.

Crossbreeding of zebu cattle (Bos indicus) with European breeds (Bos taurus) producing crossbred cattle was performed to overcome the low growth rates and milk production of indigenous tropical cattle breeds. However, zebu cattle fertility is higher than those of crossbred cattle and European breeds under warm conditions. Combination study of proteomics and metabolomics toward Malaysian indigenous breed Kedah × Kelantan-KK (B. indicus) and crossbreed Mafriwal-M (B. taurus × B. indicus) to understand physiological reasons for higher thermotolerance and fertility in Zebu cattle sperm. 161 regulated metabolites and 96 regulated proteins in KK and M (p < 0.05) showed more efficient carbohydrate and energy metabolism, higher integrity of the DNA and plasma membrane, a lower level of reactive oxygen species, and higher levels of phospholipids, which confirmed higher sperm plasma membrane integrity in KK. A stronger antioxidant system and lower polyunsaturated fatty acids help KK sperm cope with oxidative stress under warm conditions. The higher abundance of flagella structural proteins in KK provides a stronger structure that supports sperm motility. Abnormality of flagella, plasma membrane disruption, and DNA fragmentation were higher in M. These findings provide selective molecular markers for developing high-producing and more thermotolerant cattle breeds in tropical areas (197 words).

Proteo-SAFARI is a shiny application for fragment assignment by relative isotopes, an R-based software application designed for identification of protein fragment ions directly in the m/z domain. This program provides an open-source, user-friendly application for identification of fragment ions from a candidate protein sequence with support for custom covalent modifications and various visualizations of identified fragments. Additionally, Proteo-SAFARI includes a nonnegative least-squares fitting approach to determine the contributions of various hydrogen shifted fragment ions (a + 1, x + 1, y - 1, y - 2) observed in UVPD mass spectra which exhibit overlapping isotopic distributions. To show its utility, Proteo-SAFARI is applied to various MS/MS spectra of intact proteins, including proteins exhibiting dynamic hydrogen shifts in y ions, ubiquitin charge-reduced to the 1+ charge state, and a large protein recorded in full profile mode. Proteo-SAFARI is available at: github.com/mblanzillotti/Proteo-SAFARI.

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set's suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named 'lfproQC' and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

Atherosclerotic plaque rupture is a major cause of cardiovascular events. Plaque destabilization is associated with extracellular matrix (ECM) modification involving proteases which generate protein fragments with new N-termini. We hypothesized that rupture-prone plaques would contain elevated fragment levels, and their sequences would allow identification of active proteases and target proteins. Plaques from 21 patients who underwent surgery for symptomatic carotid artery stenosis were examined in an observational/cross-sectional study. Plaques were analyzed by liquid chromatography-mass spectrometry for the presence of N-terminal fragments. 33920 peptides were identified, with 17814 being N-terminal species. 5735 distinct N-terminal peptides were quantified and subjected to multidimensional scaling analysis and consensus clustering. These analyses indicated three clusters, which correlate with gross macroscopic plaque morphology (soft/mixed/hard), ultrasound classification (echolucent/echogenic), and the presence of hemorrhage/ulceration. Differences in the fragment complements are consistent with plaque-type-dependent turnover and degradation pathways. Identified peptides include signal and pro-peptides from synthesis and those from protein fragmentation. Sequence analysis indicates that targeted proteins include ECM species and responsible proteases (meprins, cathepsins, matrix metalloproteinases, elastase, and kallikreins). This study provides a large data set of peptide fragments and proteases present in plaques of differing stability. These species may have potential as biomarkers for improved atherosclerosis risk profiling.

The present study aims to summarize the current biomarker landscape in gynecological cancers (GCs) and incorporate bioinformatics analysis to highlight specific biological processes. The literature was retrieved from PubMed, Web of Science, Embase, Scopus, Ovid Medline, and Cochrane Library. The final search was conducted on December 7, 2022. Prospective registration was completed with the PROSPERO with registration number CRD42023477145. This systematic review covered proteomic research on biomarkers for cervical, endometrial, and ovarian cancers. The PANTHER classification system was used to classify the shortlisted candidate biomarkers (CBs), and the STRING database was utilized to visualize protein-protein interaction networks. A total of 23 articles were included in this systematic review. Consistently regulated CBs in the GCs include collagen alpha-2(I) chain, collagen alpha-1(III) chain, collagen alpha-2(V) chain, calreticulin, protein disulfide-isomerase A3, heat shock protein family A (Hsp70) member 5, prolyl 4-hydroxylase, beta polypeptide, fibrinogen alpha chain, fibrinogen gamma chain, apolipoprotein B-100, apolipoprotein C-IV, and apolipoprotein M. In conclusion, collagens, fibrinogens, chaperones, and apolipoproteins were revealed to be replicated in GCs and to be regulated consistently. These CBs contribute to GC etiology and physiology by participating in collagen fibril organization, blood coagulation, protein folding in endoplasmic reticulum, and lipid transporter activity.

Quantitative analysis of proteins and their post-translational modifications (PTMs) in complex biological samples is critical to understanding cellular biology as well as disease detection and treatment. Top-down proteomics methods provide a "bird's eye" view of the proteome by directly detecting and quantifying intact proteoforms. Here, we developed a high-throughput quantitative top-down proteomics platform to probe intact proteoform and phosphoproteoform abundance changes in HeLa cells as a result of treatment with staurosporine (STS), a broad-spectrum kinase inhibitor. In total, we identified and quantified 1187 proteoforms from 215 proteoform families. Among them, 55 proteoforms from 37 proteoform families were significantly changed upon STS treatment. These proteoforms were primarily related to catabolic, metabolic, and apoptotic pathways that are expected to be impacted as a result of kinase inhibition. In addition, we manually evaluated 25 proteoform families that expressed one or more phosphorylated proteoforms. We observed that phosphorylated proteoforms in the same proteoform family, such as eukaryotic initiation factor 4E binding protein 1 (4EBP1), were differentially regulated relative to the unphosphorylated proteoforms. Combining relative profiling of proteoforms within these proteoform families with individual proteoform profiling results in a more comprehensive picture of STS treatment-induced proteoform abundance changes that cannot be achieved using bottom-up methods.

Humoral immunity plays a critical role in clearing SARS-CoV-2 during viral invasion. However, the proteome-wide characteristics of antibody responses in individuals infected with Omicron variant, both asymptomatic and symptomatic, remain poorly understood. We profiled the serum antibodies from 108 individuals, including healthy controls and those infected with Omicron BA.2, using a SARS-CoV-2 proteome microarray at the amino acid resolution. We constructed a landscape of B-cell epitopes across the SARS-CoV-2 proteome in symptomatic and asymptomatic individuals. Immunodominant epitopes were mainly derived from S, N, Nsp3, M, and ORF3a proteins, with some epitopes overlapping with T-cell epitopes. Using machine learning, we identified a proteomic signature capable of distinguishing asymptomatic individuals from healthy controls in both training and validation cohorts, achieving AUCs of 0.988 and 0.857, respectively. These findings provide crucial immunological insights into BA.2 infections of the Omicron and have implications for future COVID-19 diagnostics and therapeutics.