Proteomics of urinary small extracellular vesicles in early diagnosis of kidney diseases in children-expectations and limitations.

The primary function of the kidneys is to maintain systemic homeostasis (disruption of renal structure and function results in multilevel impairment of body function). Kidney diseases are characterized by a chronic, progressive course and may result in the development of chronic kidney disease (CKD). Evaluation of the composition of the proteome of urinary small extracellular vesicles (sEVs) as a so-called liquid biopsy is a promising new research direction. Knowing the composition of sEV could allow localization of cellular changes in specific sections of the nephron or the interstitial tissue before fixed changes, detectable only at an advanced stage of the disease, occur. Research is currently underway on the role of sEVs in the diagnosis and monitoring of many disease entities. Reports in the literature on the subject include: diabetic nephropathy, focal glomerulosclerosis in the course of glomerulopathies, renal fibrosis of various etiologies. Studies on pediatric patients are still few, involving piloting if small groups of patients without validation studies. Here, we review the literature addressing the use of sEV for diagnosis of the most common urinary disorders in children. We evaluate the clinical utility and define limitations of markers present in sEV as potential liquid biopsy.

Trauma diagnostic-related target proteins and their detection techniques.

Trauma is a significant health issue that not only leads to immediate death in many cases but also causes severe complications, such as sepsis, thrombosis, haemorrhage, acute respiratory distress syndrome and traumatic brain injury, among trauma patients. Target protein identification technology is a vital technique in the field of biomedical research, enabling the study of biomolecular interactions, drug discovery and disease treatment. It plays a crucial role in identifying key protein targets associated with specific diseases or biological processes, facilitating further research, drug design and the development of treatment strategies. The application of target protein technology in biomarker detection enables the timely identification of newly emerging infections and complications in trauma patients, facilitating expeditious medical interventions and leading to reduced post-trauma mortality rates and improved patient prognoses. This review provides an overview of the current applications of target protein identification technology in trauma-related complications and provides a brief overview of the current target protein identification technology, with the aim of reducing post-trauma mortality, improving diagnostic efficiency and prognostic outcomes for patients.

Prediction of thermophilic protein using 2-D general series correlation pseudo amino acid features.

The demand for thermophilic protein has been increasing in protein engineering recently. Many machine-learning methods for identifying thermophilic proteins have emerged during this period. However, most machine learning-based thermophilic protein identification studies have only focused on accuracy. The relationship between the features' meaning and the proteins' physicochemical properties has yet to be studied in depth. In this article, we focused on the relationship between the features and the thermal stability of thermophilic proteins. This method used 2-D general series correlation pseudo amino acid (SC-PseAAC-General) features and realized accuracy of 82.76% using the J48 classifier. In addition, this research found the presence of higher frequencies of glutamic acid in thermophilic proteins, which help thermophilic proteins maintain their thermal stability by forming hydrogen bonds and salt bridges that prevent denaturation at high temperatures.

Identity verification of monoclonal antibodies by triple injection capillary zone electrophoresis.

This paper presents an approach based on triple injection capillary zone electrophoresis for identification of monoclonal antibodies. The analyte to be identified is injected between two zones of a known reference. The distances between the reference zones (plug I and III) and the target zone (plug II) are adjusted by partial electrophoresis of the first and second injection plugs. The full migration time of the target analyte is calculated from the observed migration time by considering the migration times of the reference in the first and third injection plugs. The relative migration time, that is, the ratio between the full migration time of the analyte and the migration time of the reference in the third injection plug provides the basis for identification. Here, eight monoclonal antibodies, including a pair of biosimilars, were used interchangeably as both analyte and reference to investigate potential of the method. The relative migration time for a preliminary positive identification were found to vary between 0.994 and 1.006 (1.000 ± 0.006, p = 95%). Beside the relative migration time, isoform distribution, peak profiles, and early migrating peaks, originating from components in the pharmaceutical formulations, were successfully used to verify the identity of all tested monoclonal antibodies.

Probing Protein Complexes Composition, Stoichiometry, and Interactions by Peptide-Based Mass Spectrometry.

The characterization of a protein complex by mass spectrometry can be conducted at different levels. Initial steps regard the qualitative composition of the complex and subunit identification. After that, quantitative information such as stoichiometric ratios and copy numbers for each subunit in a complex or super-complex is acquired. Peptide-based LC-MS/MS offers a wide number of methods and protocols for the characterization of protein complexes. This chapter concentrates on the applications of peptide-based LC-MS/MS for the qualitative, quantitative, and structural characterization of protein complexes focusing on subunit identification, determination of stoichiometric ratio and number of subunits per complex as well as on cross-linking mass spectrometry and hydrogen/deuterium exchange as methods for the structural investigation of the biological assemblies.

Small proteins in bacteria - Big challenges in prediction and identification.

Proteins with up to 100 amino acids have been largely overlooked due to the challenges associated with predicting and identifying them using traditional methods. Recent advances in bioinformatics and machine learning, DNA sequencing, RNA and Ribo-seq technologies, and mass spectrometry (MS) have greatly facilitated the detection and characterisation of these elusive proteins in recent years. This has revealed their crucial role in various cellular processes including regulation, signalling and transport, as toxins and as folding helpers for protein complexes. Consequently, the systematic identification and characterisation of these proteins in bacteria have emerged as a prominent field of interest within the microbial research community. This review provides an overview of different strategies for predicting and identifying these proteins on a large scale, leveraging the power of these advanced technologies. Furthermore, the review offers insights into the future developments that may be expected in this field.

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data.

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

Machine learning-based antioxidant protein identification model: Progress and evaluation.

Efficient and accurate identification of antioxidant proteins is of great significance. In recent years, many models for identifying antioxidant proteins have been proposed, but the low sensitivity and high dimensionality of the models are common problems. The generalization ability of the model needs to be improved. Researchers have tried different feature extraction algorithms and feature selection algorithms to obtain the most effective feature combination and have chosen more appropriate classification algorithms and tools to improve model performance. In this article, we systematically reviewed the data set of the most frequently used antioxidant proteins and the method selection for each step of model establishment and discussed the characteristics of each method. We have conducted a detailed analysis of recent research and believe that the practical ability and efficiency of model application can be improved by reducing model dimensions. The key to improving the performance of antioxidant protein recognition models in the future may lie in feature selection, so this paper also focuses on the combination of feature extraction and selection steps in the analysis of the model building process.

Mass spectrometry-based protein identification in proteomics-a review.

Statistically, accurate protein identification is a fundamental cornerstone of proteomics and underpins the understanding and application of this technology across all elements of medicine and biology. Proteomics, as a branch of biochemistry, has in recent years played a pivotal role in extending and developing the science of accurately identifying the biology and interactions of groups of proteins or proteomes. Proteomics has primarily used mass spectrometry (MS)-based techniques for identifying proteins, although other techniques including affinity-based identifications still play significant roles. Here, we outline the basics of MS to understand how data are generated and parameters used to inform computational tools used in protein identification. We then outline a comprehensive analysis of the bioinformatics and computational methodologies used in protein identification in proteomics including discussing the most current communally acceptable metrics to validate any identification.

Recent progress in analytical strategies of arsenic-binding proteomes in living systems.

Arsenic (As) is one of the most concerning elements due to its high exposure risks to organisms and ecosystems. The interaction between arsenicals and proteins plays a pivotal role in inducing their biological effects on living systems, e.g., arsenicosis. In this review article, the recent advances in analytical techniques and methods of As-binding proteomes were well summarized and discussed, including chromatographic separation and purification, biotin-streptavidin pull-down probes, in situ imaging using novel fluorescent probes, and protein identification. These analytical technologies could provide a growing body of knowledge regarding the composition, level, and distribution of As-binding proteomes in both cells and biological samples, even at the organellar level. The perspectives on analysis of As-binding proteomes are also proposed, e.g., isolation and identification of minor proteins, in vivo targeted protein degradation (TPD) technologies, and spatial As-binding proteomics. The application and development of sensitive, accurate, and high-throughput methodologies of As-binding proteomics would enable us to address the key molecular mechanisms underlying the adverse health effects of arsenicals.

Different chemical proteomic approaches to identify the targets of lapatinib.

The process of identifying the protein targets and off-targets of a biologically active compound is of great importance in modern drug discovery. Various chemical proteomics approaches have been established for this purpose. To compare the different approaches, and to understand which method would provide the best results, we have chosen the EGFR inhibitor lapatinib as an example molecule. Lapatinib derivatives were designed using linkers with motifs, including amino (amidation), alkyne (click chemistry) and the diazirine group (photo-affinity). These modified lapatinib analogues were validated for their ability to inhibit EGFR activity in vitro and were shown to pull down purified recombinant EGFR protein. In all of the approaches evaluated here, we identified EGFR as the main protein target from the lysate of immortalised cell line expressing EGFR, thus validating its potential use to identify unknown protein targets. Taken together, the results reported here give insight into the cellular activities of lapatinib.

Endogenous TOM20 Proximity Labeling: A Swiss-Knife for the Study of Mitochondrial Proteins in Human Cells.

Biotin-based proximity labeling approaches, such as BioID, have demonstrated their use for the study of mitochondria proteomes in living cells. The use of genetically engineered BioID cell lines enables the detailed characterization of poorly characterized processes such as mitochondrial co-translational import. In this process, translation is coupled to the translocation of the mitochondrial proteins, alleviating the energy cost typically associated with the post-translational import relying on chaperone systems. However, the mechanisms are still unclear with only few actors identified but none that have been described in mammals yet. We thus profiled the TOM20 proxisome using BioID, assuming that some of the identified proteins could be molecular actors of the co-translational import in human cells. The obtained results showed a high enrichment of RNA binding proteins close to the TOM complex. However, for the few selected candidates, we could not demonstrate a role in the mitochondrial co-translational import process. Nonetheless, we were able to demonstrate additional uses of our BioID cell line. Indeed, the experimental approach used in this study is thus proposed for the identification of mitochondrial co-translational import effectors and for the monitoring of protein entry inside mitochondria with a potential application in the prediction of mitochondrial protein half-life.

Deciphering the Kidney Matrisome: Identification and Quantification of Renal Extracellular Matrix Proteins in Healthy Mice.

Precise characterization of a tissue's extracellular matrix (ECM) protein composition (matrisome) is essential for biomedicine. However, ECM protein extraction that requires organ-specific optimization is still a major limiting factor in matrisome studies. In particular, the matrisome of mouse kidneys is still understudied, despite mouse models being crucial for renal research. Here, we comprehensively characterized the matrisome of kidneys in healthy C57BL/6 mice using two ECM extraction methods in combination with liquid chromatography tandem mass spectrometry (LC-MS/MS), protein identification, and label-free quantification (LFQ) using MaxQuant. We identified 113 matrisome proteins, including 22 proteins that have not been previously listed in the Matrisome Database. Depending on the extraction approach, the core matrisome (structural proteins) comprised 45% or 73% of kidney ECM proteins, and was dominated by glycoproteins, followed by collagens and proteoglycans. Among matrisome-associated proteins, ECM regulators had the highest LFQ intensities, followed by ECM-affiliated proteins and secreted factors. The identified kidney ECM proteins were primarily involved in cellular, developmental and metabolic processes, as well as in molecular binding and regulation of catalytic and structural molecules' activity. We also performed in silico comparative analysis of the kidney matrisome composition in humans and mice based on publicly available data. These results contribute to the first reference database for the mouse renal matrisome.

The Introduction of Detergents in Thermal Proteome Profiling Requires Lowering the Applied Temperatures for Efficient Target Protein Identification.

Although the use of detergents in thermal proteome profiling (TPP) has become a common practice to identify membrane protein targets in complex biological samples, surprisingly, there is no proteome-wide investigation into the impacts of detergent introduction on the target identification performance of TPP. In this study, we assessed the target identification performance of TPP in the presence of a commonly used non-ionic detergent or a zwitterionic detergent using a pan-kinase inhibitor staurosporine, our results showed that the addition of either of these detergents significantly impaired the identification performance of TPP at the optimal temperature for soluble target protein identification. Further investigation showed that detergents destabilized the proteome and increased protein precipitation. By lowering the applied temperature point, the target identification performance of TPP with detergents is significantly improved and is comparable to that in the absence of detergents. Our findings provide valuable insight into how to select the appropriate temperature range when detergents are used in TPP. In addition, our results also suggest that the combination of detergent and heat may serve as a novel precipitation-inducing force that can be applied for target protein identification.

Ethrel-induced release of fresh seed dormancy causes remodelling of amylase activity, proteomics, phytohormone and fatty acid profile of groundnut (Arachis hypogaea L.).

It is important to have a short period of fresh seed dormancy in some of the groundnut species to counter pre-harvest sprouting (PHS). One of the main causes of PHS is the activation of ethylene-mediated pathways. To determine the effect of ethylene, the study was conducted and alterations in amylase, proteins and fatty acids were observed at the 0, 6, 12, and 24 h stages after ethrel administration. The result showed an increase in amylase activity, and the fatty acids profile showed a unique alteration pattern at different germination stages. Two-dimensional gel electrophoresis (2DGE) revealed differential expression of proteins at each stage. The trypsin digestion following spectral development through UPLC-MS/MS enabled identification of number of differentially expressed proteins. A total of 49 proteins were identified from 2DGE excised spots. The majority were belonged to seed storage-related proteins like Arah1, Arah2, AAI- domain containing protein, conglutin, Arah3/4, arachin, glycinin. Expression of lipoxygenase1, lipoxygenase9 and Arah2 genes were further confirmed by qRT-PCR which showed its involvement at transcript level. Up-regulation of lipoxygenase9 is correlated with decreased content of fatty acids during germination. Phytohormone detection revealed decrease in ABA, SA and JA content which are generally inhibitor of seed germination while GA, IAA and kinetin concentration increased revealing positive regulation of seed germination. We present an integrated view of proteomics, phytohormone profile, carbohydrate and lipid metabolism to unravel mechanism of fresh seed dormancy. Supplementary Information: The online version contains supplementary material available at 10.1007/s12298-023-01332-6.

Collision Cross Sections for Native Proteomics: Challenges and Opportunities.

Recent advancements place a comprehensive catalog of protein structure, oligomeric state, sequence, and modification status tentatively within reach, thus providing an unprecedented roadmap to therapies for many human diseases. To achieve this goal, revolutionary technologies capable of bridging key gaps in our ability to simultaneously measure protein composition and structure must be developed. Much of the current progress in this area has been catalyzed by mass spectrometry (MS) tools, which have become an indispensable resource for interrogating the structural proteome. For example, methods associated with native proteomics seek to comprehensively capture and quantify the endogenous assembly states for all proteins within an organism. Such technologies have often been partnered with ion mobility (IM) separation, from which collision cross section (CCS) information can be rapidly extracted to provide protein size information. IM technologies are also being developed that utilize CCS values to enhance the confidence of protein identification workflows derived from liquid chromatography-IM-MS analyses of enzymatically produced peptide mixtures. Such parallel advancements in technology beg the question: can CCS values prove similarly useful for the identification of intact proteins and their complexes in native proteomics? In this perspective, I examine current evidence and technology trends to explore the promise and limitations of such CCS information for the comprehensive analysis of multiprotein complexes from cellular mixtures.

Characterization of the effect of histone deacetylation inhibitors on CD8+ T cells in the context of aging.

BACKGROUND: Posttranslational protein modifications regulate essential cellular processes, including the immune cell activation. Despite known age-related alterations of the phenotype, composition and cytokine profiles of immune cells, the role of acetylation in the aging process of the immune system was not broadly investigated. Therefore, in the current study the effect of acetylation on the protein expression profiles and function of CD8+ T cells from donors of distinct age was analyzed using histone deacetylase inhibitors (HDACi). METHODS: CD8+ T cells isolated from peripheral blood mononuclear cells of 30 young (< 30 years) and 30 old (> 60 years) healthy donors were activated with anti-CD3/anti-CD28 antibodies in the presence and absence of a cocktail of HDACi. The protein expression profiles of untreated and HDACi-treated CD8+ T cells were analyzed using two-dimensional gel electrophoresis. Proteins with a differential expression level (less than 0.66-fold decrease or more than 1.5-fold increase) between CD8+ T cells of young and old donors were identified by matrix-associated laser desorption ionization-time of flight mass spectrometry. Functional enrichment analysis of proteins identified was performed using the online tool STRING. The function of CD8+ T cells was assessed by analyses of cytokine secretion, surface expression of activation markers, proliferative capacity and apoptosis rate. RESULTS: The HDACi treatment of CD8+ T cells increased in an age-independent manner the intracellular acetylation of proteins, in particular cytoskeleton components and chaperones. Despite a strong similarity between the protein expression profiles of both age groups, the functional activity of CD8+ T cells significantly differed with an age-dependent increase in cytokine secretion and expression of activation markers for CD8+ T cells from old donors, which was maintained after HDACi treatment. The proliferation and apoptosis rate of CD8+ T cells after HDACi treatment was equal between both age groups. CONCLUSIONS: Despite a comparable effect of HDACi treatment on the protein signature of CD8+ T cells from donors of different ages, an initial higher functionality of CD8+ T cells from old donors when compared to CD8+ T cells from young donors was detected, which might have clinical relevance.

On the potential of micro-flow LC-MS/MS in proteomics.

INTRODUCTION: Due to its excellent sensitivity, nano-flow liquid chromatography tandem mass spectrometry (LC-MS/MS) is the mainstay in proteome research; however, this comes at the expense of limited throughput and robustness. In contrast, micro-flow LC-MS/MS enables high-throughput, robustness, quantitative reproducibility, and precision while retaining a moderate degree of sensitivity. Such features make it an attractive technology for a wide range of proteomic applications. In particular, large-scale projects involving the analysis of hundreds to thousands of samples. AREAS COVERED: This review summarizes the history of chromatographic separation in discovery proteomics with a focus on micro-flow LC-MS/MS, discusses the current state-of-the-art, highlights advances in column development and instrumentation, and provides guidance on which LC flow best supports different types of proteomic applications. EXPERT OPINION: Micro-flow LC-MS/MS will replace nano-flow LC-MS/MS in many proteomic applications, particularly when sample quantities are not limited and sample cohorts are large. Examples include clinical analyses of body fluids, tissues, drug discovery and chemical biology investigations, plus systems biology projects across all kingdoms of life. When combined with rapid and sensitive MS, intelligent data acquisition, and informatics approaches, it will soon become possible to analyze large cohorts of more than 10,000 samples in a comprehensive and fully quantitative fashion.

A protein identification method for proteomics using amino acid composition analysis with IoT-based remote control.

In the present study, we developed a protein identification method using low-cost and easy-to-operate amino acid composition analysis. The identification program automatically compares the quantitative result for each amino acid concentration obtained from the amino acid analysis to the amino acid composition data retrieved from the UniProt protein database. We found that the accuracy of protein identification using amino acid composition analysis was comparable to that of mass spectrometry analysis. The method was able to distinguish and identify differences in amino acid substitutions of several residues between proteins with high sequence homology. The identification accuracy of proteins was also improved by correcting the concentrations in the program for Cys, Trp, and Ile residues, which cannot be quantified by general sample preparation for amino acid analysis. Moreover, the amino acid analyzer was remotely controlled in accordance with the growing demand for remote work. The measured amino acid data were automatically uploaded to the IoT portal within a few minutes of each measurement, allowing researchers to download data and analyze them using the identification program anywhere and at any time by connecting to a network. The results indicated that the present method is useful for protein identification.

Fast Quantitative Analysis of timsTOF PASEF Data with MSFragger and IonQuant.

Ion mobility brings an additional dimension of separation to LC-MS, improving identification of peptides and proteins in complex mixtures. A recently introduced timsTOF mass spectrometer (Bruker) couples trapped ion mobility separation to TOF mass analysis. With the parallel accumulation serial fragmentation (PASEF) method, the timsTOF platform achieves promising results, yet analysis of the data generated on this platform represents a major bottleneck. Currently, MaxQuant and PEAKS are most used to analyze these data. However, because of the high complexity of timsTOF PASEF data, both require substantial time to perform even standard tryptic searches. Advanced searches (e.g. with many variable modifications, semi- or non-enzymatic searches, or open searches for post-translational modification discovery) are practically impossible. We have extended our fast peptide identification tool MSFragger to support timsTOF PASEF data, and developed a label-free quantification tool, IonQuant, for fast and accurate 4-D feature extraction and quantification. Using a HeLa data set published by Meier et al. (2018), we demonstrate that MSFragger identifies significantly (∼30%) more unique peptides than MaxQuant (1.6.10.43), and performs comparably or better than PEAKS X+ (∼10% more peptides). IonQuant outperforms both in terms of number of quantified proteins while maintaining good quantification precision and accuracy. Runtime tests show that MSFragger and IonQuant can fully process a typical two-hour PASEF run in under 70 min on a typical desktop (6 CPU cores, 32 GB RAM), significantly faster than other tools. Finally, through semi-enzymatic searching, we significantly increase the number of identified peptides. Within these semi-tryptic identifications, we report evidence of gas-phase fragmentation before MS/MS analysis.