Journal of Proteome Research最新文献

Biological sex is key information for archeological and forensic studies, which can be determined by proteomics. However, the lack of a standardized approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for the determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to a high-resolution parallel reaction monitoring (PRM) assay allowing for a throughput of 200 samples per day (SPD) with high quantitative performance enabling confident identification of both males and females. Additionally, we developed a streamlined data analysis pipeline and integrated it into a Shiny interface for ease of use. The method was first developed and optimized using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by 10-fold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens. Data are available via ProteomeXchange with the identifier PXD049326.

Antibody-drug conjugate (ADC) consists of engineered antibodies and cytotoxic drugs linked via a chemical linker, and the stability of ADC plays a crucial role in ensuring its safety and efficacy. The stability of ADC is closely related to the conjugation site; however, no method has been developed to assess the stability of different conjugation sites due to the low response of conjugated peptides. In this study, an integrated strategy was developed and validated to assess the stability of different conjugation sites on ADC in serum. Initial identification of the conjugated peptides of the model drug ado-trastuzumab emtansine (T-DM1) was achieved by the proteomic method. Subsequently, a semiquantitative method for conjugated peptides was established in liquid chromatography-hybrid linear ion trap triple quadrupole mass spectrometry (LC-QTRAP-MS/MS) based on the qualitative information. The pretreatment method of the serum sample was optimized to reduce matrix interference. The method was then validated and applied to evaluate the stability of the conjugation sites on T-DM1. The results highlighted differences in stability among the different conjugation sites on T-DM1. This is the first study to assess the stability of different conjugation sites on the ADC in serum, which will be helpful for the design and screening of ADCs in the early stages of development.

Peptide mapping requires cleavage of proteins in a predictable fashion so that target protein-specific peptides can be reliably identified and quantified. Trypsin, a commonly used protease in this process, can also undergo self-cleavage or autolysis, thereby reducing the effectivity and even cleavage specificity at lysine and arginine residues. Here, we report highly efficient and reproducible peptide mapping of biotherapeutic monoclonal antibodies. We highlight the properties of a homogeneous chemically modified trypsin on thermal stability, a 54% increase in melting temperature with an 84% increase in energy required for unfolding, an indication of more thermally stable trypsin, >90% retained intact mass peak area after exposure to digestion conditions confirming autolysis resistance, 10× more intensity for intact enzyme compared to trypsin of similar source and narrower molecular weight distribution with LC-MS indicative of low degradation compared to 3 other types of trypsin. Finally, we show the utility of this autolysis-resistant trypsin in characterizing biotherapeutic monoclonal antibodies consistently and reliably showing a >30% reduction in missed cleavage for a short-duration protein digestion time of 30 min compared to heterogeneously modified trypsin of a similar source.

Photoaffinity labeling (PAL) methodologies have proven to be instrumental for the unbiased deconvolution of protein-ligand binding events in physiologically relevant systems. However, like other chemical proteomic workflows, they are limited in many ways by time-intensive sample manipulations and data acquisition techniques. Here, we describe an approach to address this challenge through the innovation of a carboxylate bead-based protein cleanup procedure to remove excess small-molecule contaminants and couple it to plate-based, proteomic sample processing as a semiautomated solution. The analysis of samples via label-free, data-independent acquisition (DIA) techniques led to significant improvements on a workflow time per sample basis over current standard practices. Experiments utilizing three established PAL ligands with known targets, (+)-JQ-1, lenalidomide, and dasatinib, demonstrated the utility of having the flexibility to design experiments with a myriad of variables. Data revealed that this workflow can enable the confident identification and rank ordering of known and putative targets with outstanding protein signal-to-background enrichment sensitivity. This unified end-to-end throughput strategy for processing and analyzing these complex samples could greatly facilitate efficient drug discovery efforts and open up new opportunities in the chemical proteomics field.

The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled data sets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a graph neural network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein data sets with pseudolabels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating data set-specific training, our research illustrates that GraphPI, due to the well-normalized nature of Percolator features, exhibits universal applicability without data set-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test data sets and deliver significantly reduced computation times compared to common protein inference algorithms.

Integrating cross-linking mass spectrometry (XL-MS) into structural biology workflows provides valuable information about the spatial arrangement of amino acid stretches, which can guide elucidation of protein assembly architecture. Additionally, the combination of XL-MS with peptide quantitation techniques is a powerful approach to delineate protein interface dynamics across diverse conditions. While XL-MS is increasingly effective with isolated proteins or small complexes, its application to whole-cell samples poses technical challenges related to analysis depth and throughput. The use of enrichable cross-linkers has greatly improved the detectability of protein interfaces in a proteome-wide scale, facilitating global protein-protein interaction mapping. Therefore, bringing together enrichable cross-linking and multiplexed peptide quantification is an appealing approach to enable comparative characterization of structural attributes of proteins and protein interactions. Here, we combined phospho-enrichable cross-linking with TMT labeling to develop a streamline workflow (PhoXplex) for the detection of differential structural features across a panel of cell lines in a global scale. We achieved deep coverage with quantification of over 9000 cross-links and long loop-links in total including potentially novel interactions. Overlaying AlphaFold predictions and disorder protein annotations enables exploration of the quantitative cross-linking data set, to reveal possible associations between mutations and protein structures. Lastly, we discuss current shortcomings and perspectives for deep whole-cell profiling of protein interfaces at large-scale.

A deeper understanding of colorectal cancer (CRC) biology would help to identify specific early diagnostic markers. Here, we conducted quantitative proteomics on FFPE healthy, adenoma, and adenocarcinoma tissue samples from six stage I sporadic CRC patients to identify dysregulated proteins during early CRC development. Two independent quantitative 10-plex TMT experiments were separately performed. After protein extraction, trypsin digestion, and labeling, proteins were identified and quantified by using a Q Exactive mass spectrometer. A total of 2681 proteins were identified and quantified after data analysis and bioinformatics with MaxQuant and the R program. Among them, 284 and 280 proteins showed significant upregulation and downregulation (expression ratio ≥1.5 or ≤0.67, p-value ≤0.05), respectively, in adenoma and/or adenocarcinoma compared to healthy tissue. Ten dysregulated proteins were selected to study their role in CRC by WB, IHC, TMA, and ELISA using tissue and plasma samples from CRC patients, individuals with premalignant colorectal lesions (adenomas), and healthy individuals. In vitro loss-of-function cell-based assays and in vivo experiments using three CRC cell lines with different metastatic properties assessed the important roles of SLC8A1 and TXNDC17 in CRC and liver metastasis. Additionally, SLC8A1 and TXNDC17 protein levels in plasma possessed the diagnostic ability of early CRC stages.

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and address challenges of complete annotation of molecules in untargeted metabolomics. "Molecular networks" (MNs), as used in the Global Natural Products Social Molecular Networking platform, are a prominent strategy for exploring and visualizing molecular relationships and improving annotation. MNs are mathematical graphs showing the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated with a single molecular identity. This paper introduces "molecular hypernetworks" (MHNs) as more complex MN models able to natively represent multiway relationships among observations. Compared to MNs, MHNs can more parsimoniously represent the inherent complexity present among groups of observations, initially supporting improved exploratory data analysis and visualization. MHNs also promise to increase confidence in annotation propagation, for both human and analytical processing. We first illustrate MHNs with simple examples, and build them from liquid chromatography- and ion mobility spectrometry-separated MS data. We then describe a method to construct MHNs directly from existing MNs as their "clique reconstructions", demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

Circulating proteomes provide a snapshot of the physiological state of a human organism responding to pathogenic challenges and drug interventions. The outcomes of patients with COVID-19 and acute respiratory distress syndrome triggered by the SARS-CoV2 virus remain uncertain. Tocilizumab is an anti-interleukin-6 treatment that exerts encouraging clinical activity by controlling the cytokine storm and improving respiratory distress in patients with COVID-19. We investigate the biological determinants of therapeutic outcomes after tocilizumab treatment. Overall, 28 patients hospitalized due to severe COVID-19 who were treated with tocilizumab intravenously were included in this study. Sera were collected before and after tocilizumab, and the patient's outcome was evaluated until day 30 post-tocilizumab infusion for favorable therapeutic response to tocilizumab and mortality. Hyperreaction monitoring measurements by liquid chromatography-mass spectrometry-based proteomic analysis with data-independent acquisition quantified 510 proteins and 7019 peptides in the serum of patients. Alterations in the serum proteome reflect COVID-19 outcomes in patients treated with tocilizumab. Our results suggested that circulating proteins associated with the most significant prognostic impact belonged to the complement system, platelet degranulation, acute-phase proteins, and the Fc-epsilon receptor signaling pathway. Among these, upregulation of the complement system by activation of the classical pathway was associated with poor response to tocilizumab, and upregulation of Fc-epsilon receptor signaling was associated with lower mortality.

The transition from planktonic to biofilm growth in bacteria is often accompanied by greater resistance to antibiotics and other stressors, as well as distinct alterations in physical traits, genetic activity, and metabolic restructuring. In many species, the heme nitric oxide/oxygen binding proteins (H-NOX) play an important role in this process, although the signaling mechanisms and pathways in which they participate are quite diverse and largely unknown. In Paracoccus denitrificans, deletion of the hnox gene results in a severe biofilm-deficient phenotype. Quantitative proteomics was used to assemble a comprehensive data set of P. denitrificans proteins showing altered abundance of those involved in several important metabolic pathways. Further, decreased levels of pyruvate and elevated levels of C₁₆ homoserine lactone were detected for the Δhnox strain, associating the biofilm deficiency with altered central carbon metabolism and quorum sensing, respectively. These results expand our knowledge of the important role of H-NOX signaling in biofilm formation.