Proteomics最新文献

Recent advancements in omics techniques have revolutionised the study of biological systems, enabling the generation of high-throughput biomolecular data. These innovations have found diverse applications, ranging from personalised medicine to forensic sciences. While the investigation of multiple aspects of cells, tissues or entire organisms through the integration of various omics approaches (such as genomics, epigenomics, metagenomics, transcriptomics, proteomics and metabolomics) has already been established in fields like biomedicine and cancer biology, its full potential in forensic sciences remains only partially explored. In this review, we have presented a comprehensive overview of state-of-the-art analytical platforms employed in omics research, with specific emphasis on their application in the forensic field for the identification of the cadaver and the cause of death. Moreover, we have conducted a critical analysis of the computational integration of omics approaches, and highlighted the latest advancements in employing multi-omics techniques for forensic investigations.

The cuticles of arthropods provide an interface between the organism and its environment. Thus, the cuticle's structure influences how the organism responds to and interacts with its surroundings. Here, we used label-free quantification proteomics to provide a proteome of the moulted cuticle of the aquatic crustacean Daphnia magna, which has long been a prominent subject of studies on ecology, evolution, and developmental biology. We detected a total of 278 high-confidence proteins. Using protein sequence domain and functional enrichment analyses, we identified chitin-binding structural proteins and chitin-modifying enzymes as the most abundant protein groups in the cuticle proteome. Structural cuticular protein families showed a similar distribution to those found in other arthropods and indicated proteins responsible for the soft and flexible structure of the Daphnia cuticle. Finally, cuticle protein genes were also clustered as tandem gene arrays in the D. magna genome. The cuticle proteome presented here will be a valuable resource to the Daphnia research community, informing genome annotations and investigations on diverse topics such as the genetic basis of interactions with predators and parasites.

Extracellular vesicles (EVs) are taking a central stage in intercellular communication, as conserved signaling mediators across species and kingdoms. In fact, there has been an emergence in the understanding and expansion of EVs in diverse fields including cell biology, biomedical sciences, immune regulation and vaccine development, biomarker discovery, and disease diagnosis/monitoring. To enhance and modify their form, function, and therapeutic utility, the field has expanded to modify EVs using various bioengineering strategies, which collectively have garnered significant clinical interest given their potential for drug delivery and therapeutic intervention. As heterogeneous, phospholipid membrane-enclosed structures. EVs affect the functions of other cells through their surface proteins, complex encapsulated cargo molecules (including proteins and RNAs), and select lipids and glycans. Moreover, EVs are a potential source of disease-associated biomarkers for diagnosis, composed of a molecular fingerprint of the releasing cell type (i.e., tumor-specific molecules), enabling a molecular analysis of practically all organs in the body.

The form and function of EVs is marked by their proteome. Proteomic studies have generated new knowledge in the EV field, with exceptional insights into cargo sorting mechanisms [1, 2], EV heterogeneity and the subtypes and sub-populations of EVs and particles [3-7], genesis [8], surfaceome [9, 10], interaction network [11], intracellular trafficking pathways [12], release (including organs [13, 14]/tissues [15]), targeting/localization [16], uptake [17] and function [18, 19], to identifying specific marker proteins [20]. Their interrogation of EVs in biofluids has also highlighted their diagnostic potential [21-23] and therapeutic targets [24], establishing the role of EVs in health and disease.

This issue reveals a new understanding of EVs that influences their diverse signaling functions. The studies outlined in this issue (26 articles, covering 16 research studies) shed light on new potential players in intercellular signaling, from cells, tissues, bacteria, and even platelets, and uncovers the functional tasks and diagnostic potential accomplished by the cargo of these extracellular membranous structures.

Platelet-derived EVs (pEVs) represent the most abundant EV type in the circulation in healthy humans. Moon et al., [25] examined the proteome dynamic of pEVs in the context of different physiological platelet agonist to induce platelet activation, uncovering the mode of platelet activation as a direct impact on the proteome landscape. Using agonists representative of the varied activation states of platelets within a thrombus, the correlation study revealed an upregulation of various classes

Unconventional secretory proteins (USPs) are vital for cell-to-cell communication and are necessary for proper physiological processes. Unlike classical proteins that follow the conventional secretory pathway via the Golgi apparatus, these proteins are released using unconventional pathways. The primary modes of secretion for USPs are exosomes and ectosomes, which originate from the endoplasmic reticulum. Accurate and rapid identification of exosome-mediated secretory proteins is crucial for gaining valuable insights into the regulation of non-classical protein secretion and intercellular communication, as well as for the advancement of novel therapeutic approaches. Although computational methods based on amino acid sequence prediction exist for predicting unconventional proteins secreted by exosomes (UPSEs), they suffer from significant limitations in terms of algorithmic accuracy. In this study, we propose a novel approach to predict UPSEs by combining multiple deep learning models that incorporate both protein sequences and evolutionary information. Our approach utilizes a convolutional neural network (CNN) to extract protein sequence information, while various densely connected neural networks (DNNs) are employed to capture evolutionary conservation patterns.By combining six distinct deep learning models, we have created a superior framework that surpasses previous approaches, achieving an ACC score of 77.46% and an MCC score of 0.5406 on an independent test dataset.

Endometrial cancer, the most common gynaecological cancer worldwide, is closely linked to obesity and metabolic diseases, particularly in younger women. New circulating biomarkers have the potential to improve diagnosis and treatment selections, which could significantly improve outcomes. Our approach focuses on extracellular vesicle (EV) biomarker discovery by directly profiling the proteome of EVs enriched from frozen biobanked endometrial tumours. We analysed nine tissue samples to compare three clinical subgroups—low BMI (Body Mass Index) Endometrioid, high BMI Endometrioid, and Serous (any BMI)—identifying proteins related to histological subtype, BMI, and shared secreted proteins. Using collagenase digestion and size exclusion chromatography, we successfully enriched generous quantities of EVs (range 204.8–1291.0 µg protein: 1.38 × 10¹¹–1.10 × 10¹² particles), characterised by their size (∼150 nm), expression of EV markers (CD63/81), and proposed endometrial cancer markers (L1CAM, ANXA2). Mass spectrometry-based proteomic profiling identified 2075 proteins present in at least one of the 18 samples. Compared to cell lysates, EVs were successfully depleted for mitochondrial and blood proteins and enriched for common EV markers and large secreted proteins. Further analysis highlighted significant differences in EV protein profiles between the high BMI subgroup and others, underlining the impact of comorbidities on the EV secretome. Interestingly, proteins differentially abundant in tissue subgroups were largely not also differential in matched EVs. This research identified secreted proteins known to be involved in endometrial cancer pathophysiology and proposed novel diagnostic biomarkers (EIF6, MUC16, PROM1, SLC26A2).

Forecasting alterations in protein stability caused by variations holds immense importance. Improving the thermal stability of proteins is important for biomedical and industrial applications. This review discusses the latest methods for predicting the effects of mutations on protein stability, databases containing protein mutations and thermodynamic parameters, and experimental techniques for efficiently assessing protein stability in high-throughput settings. Various publicly available databases for protein stability prediction are introduced. Furthermore, state-of-the-art computational approaches for anticipating protein stability changes due to variants are reviewed. Each method's types of features, base algorithm, and prediction results are also detailed. Additionally, some experimental approaches for verifying the prediction results of computational methods are introduced. Finally, the review summarizes the progress and challenges of protein stability prediction and discusses potential models for future research directions.

Microorganisms which are resistant to antibiotics are a global threat to the health of humans and animals. Wastewater treatment plants are known hotspots for the dissemination of antibiotic resistances. Therefore, novel methods for the inactivation of pathogens, and in particular antibiotic-resistant microorganisms (ARM), are of increasing interest. An especially promising method could be a water treatment by physical plasma which provides charged particles, electric fields, UV-radiation, and reactive species. The latter are foremost responsible for the antimicrobial properties of plasma. Thus, with plasma it might be possible to reduce the amount of ARM and to establish this technology as additional treatment stage for wastewater remediation. However, the impact of plasma on microorganisms beyond a mere inactivation was analyzed in more detail by a proteomic approach. Therefore, Escherichia coli GW-AmxH19, isolated from hospital wastewater in Germany, was used. The bacterial solution was treated by a plasma discharge ignited between each of four pins and the liquid surface. The growth of E. coli and the pH-value decreased during plasma treatment in comparison with the untreated control. Proteome and antibiotic resistance profile were analyzed. Concentrations of nitrite and nitrate were determined as long-lived indicative products of a transient chemistry associated with reactive nitrogen species (RNS). Conversely, hydrogen peroxide served as indicator for reactive oxygen species (ROS). Proteome analyses revealed an oxidative stress response as a result of plasma-generated RNS and ROS as well as a pH-balancing reaction as key responses to plasma treatment. Both, the generation of reactive species and a decreased pH-value is characteristic for plasma-treated solutions. The plasma-mediated changes of the proteome are discussed also in comparison with the Gram-positive bacterium Bacillus subtilis. Furthermore, no effect of the plasma treatment, on the antibiotic resistance of E. coli, was determined under the chosen conditions. The knowledge about the physiological changes of ARM in response to plasma is of fundamental interest to understand the molecular basis for the inactivation. This will be important for the further development and implementation of plasma in wastewater remediation.

In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.