Despite substantial advances in the use of proteomic technologies, their widespread application in fruit tissues of non-model and recalcitrant species remains limited. This hampers the understanding of critical molecular events during the postharvest period of fleshy tropical fruits. Therefore, we evaluated label-free quantitation (LFQ) and TMT-SPS-MS3 (TMT) approaches to analyse changes in the protein profile of mango peels during postharvest period. We compared two extraction methods (phenol and chloroform/methanol) and two peptide fractionation schemes (SCX and HPRP). We accurately identified 3065 proteins, of which, 1492 were differentially accumulated over at 6 days after harvesting (DAH). Both LFQ and TMT approaches share 210 differential proteins including cell wall proteins associated with fruit softening, as well as aroma and flavour-related proteins, which were increased during postharvest period. The phenolic protein extraction and the high-pH reverse-phase peptide fractionation was the most effective pipeline for relative quantification. Nevertheless, the information provided by the other tested strategies was significantly complementary. Besides, LFQ spectra allowed us to track down intact N-glycopeptides corroborating N-glycosylations on the surface of a desiccation-related protein. This work represents the largest proteomic comparison of mango peels during postharvest period made so far, shedding light on the molecular foundation of edible fruit during ripening.

Contaminants derived from consumables, reagents, and sample handling often negatively affect LC-MS data acquisition. In proteomics experiments, they can markedly reduce identification performance, reproducibility, and quantitative robustness. Here, we introduce a data analysis workflow combining MS1 feature extraction in Skyline with HowDirty, an R-markdown-based tool, that automatically generates an interactive report on the molecular contaminant level in LC-MS data sets. To facilitate the interpretation of the results, the HTML report is self-contained and self-explanatory, including plots that can be easily interpreted. The R package HowDirty is available from https://github.com/DavidGZ1/HowDirty. To demonstrate a showcase scenario for the application of HowDirty, we assessed the impact of ultrafiltration units from different providers on sample purity after filter-assisted sample preparation (FASP) digestion. This allowed us to select the filter units with the lowest contamination risk. Notably, the filter units with the lowest contaminant levels showed higher reproducibility regarding the number of peptides and proteins identified. Overall, HowDirty enables the efficient evaluation of sample quality covering a wide range of common contaminant groups that typically impair LC-MS analyses, facilitating corrective or preventive actions to minimize instrument downtime.

This study aimed to clarify the role of glutamine in atherosclerosis and its participating mechanism. Forty C57BL/6J mice were divided into wild control (wild Con), ApoE- /- control (ApoE- /- Con), glutamine + ApoE- /- control (Glut + ApoE- /- Con), ApoE- /- high fat diet (ApoE- /- HFD), and glutamine + ApoE- /- HFD (Glut + ApoE- /- HFD) groups. The degree of atherosclerosis, western blotting, and multiomics were detected at 18 weeks. An in vitro study was also performed. Glutamine treatment significantly decreased the degree of aortic atherosclerosis (p = 0.03). O-GlcNAcylation (O-GlcNAc), IL-1β, IL-1α, and pyruvate kinase M2 (PKM2) in the ApoE- /- HFD group were significantly higher than those in the ApoE- /- Con group (p < 0.05). These differences were attenuated by glutamine treatment (p < 0.05), and aggravated by O-GlcNA transferase (OGT) overexpression in the in vitro study (p < 0.05). Multiomics showed that the ApoE- /- HFD group had higher levels of oxidative stress regulatory molecules (guanine deaminase [GUAD], xanthine dehydrogenase [XDH]), proinflammatory regulatory molecules (myristic acid and myristoleic acid), and stress granules regulatory molecules (caprin-1 and deoxyribose-phosphate aldolase [DERA]) (p < 0.05). These differences were attenuated by glutamine treatment (p < 0.05). We conclude that glutamine supplementation might alleviate atherosclerosis through downregulation of O-GlcNAc, glycolysis, oxidative stress, and proinflammatory pathway.

Trans-activation response DNA binding protein of 43 kDa (TDP-43) regulates a great variety of cellular processes in the nucleus and cytosol. In addition, a defined subset of neurodegenerative diseases is characterized by nuclear depletion of TDP-43 as well as cytosolic mislocalization and aggregation. To perform its diverse functions TDP-43 can associate with different ribonucleoprotein complexes. Combined with transcriptomics, MS interactome studies have unveiled associations between TDP-43 and the spliceosome machinery, polysomes and RNA granules. Moreover, the highly dynamic, low-valency interactions regulated by its low-complexity domain calls for innovative proximity labeling methodologies. In addition to protein partners, the analysis of post-translational modifications showed that they may play a role in the nucleocytoplasmic shuttling, RNA binding, liquid-liquid phase separation and protein aggregation of TDP-43. Here we review the various TDP-43 ribonucleoprotein complexes characterized so far, how they contribute to the diverse functions of TDP-43, and roles of post-translational modifications. Further understanding of the fluid dynamic properties of TDP-43 in ribonucleoprotein complexes, RNA granules, and self-assemblies will advance the understanding of RNA processing in cells and perhaps help to develop novel therapeutic approaches for TDPopathies.

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Metabolomics, the systematic measurement of small molecules (<1000 Da) in a given biological sample, is a fast-growing field with many different applications. In contrast to transcriptomics and proteomics, sharing of data is not as widespread in metabolomics, though more scientists are sharing their data nowadays. However, to improve data analysis tools and develop new data analytical approaches and to improve metabolite annotation and identification, sharing of reference data is crucial. Here, different possibilities to share (metabolomics) data are reviewed and some recent approaches and applications regarding the (re-)use and (re-)analysis are highlighted.

Extracellular vesicles (EVs) are important mediators of embryo attachment and outgrowth critical for successful implantation. While EVs have garnered immense interest in their therapeutic potential in assisted reproductive technology by improving implantation success, their large-scale generation remains a major challenge. Here, we report a rapid and scalable production of nanovesicles (NVs) directly from human trophectoderm cells (hTSCs) via serial mechanical extrusion of cells; these NVs can be generated in approximately 6 h with a 20-fold higher yield than EVs isolated from culture medium of the same number of cells. NVs display similar biophysical traits (morphologically intact, spherical, 90-130 nm) to EVs, and are laden with hallmark players of implantation that include cell-matrix adhesion and extracellular matrix organisation proteins (ITGA2/V, ITGB1, MFGE8) and antioxidative regulators (PRDX1, SOD2). Functionally, NVs are readily taken up by low-receptive endometrial HEC1A cells and reprogram their proteome towards a receptive phenotype that support hTSC spheroid attachment. Moreover, a single dose treatment with NVs significantly enhanced adhesion and spreading of mouse embryo trophoblast on fibronectin matrix. Thus, we demonstrate the functional potential of NVs in enhancing embryo implantation and highlight their rapid and scalable generation, amenable to clinical utility.

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

Food allergens are molecules, mainly proteins, that trigger immune responses in susceptible individuals upon consumption even when they would otherwise be harmless. Symptoms of a food allergy can range from mild to acute; this last effect is a severe and potentially life-threatening reaction. The European Union (EU) has identified 14 common food allergens, but new allergens are likely to emerge with constantly changing food habits. Mass spectrometry (MS) is a promising alternative to traditional antibody-based assays for quantifying multiple allergenic proteins in complex matrices with high sensitivity and selectivity. Here, the main allergenic proteins and the advantages and drawbacks of some MS acquisition protocols, such as multiple reaction monitoring (MRM) and data-dependent analysis (DDA) for identifying and quantifying common allergenic proteins in processed foodstuffs are summarized. Sections dedicated to novel foods like microalgae and insects as new sources of allergenic proteins are included, emphasizing the significance of establishing stable marker peptides and validated methods using database searches. The discussion involves the in-silico digestion of allergenic proteins, providing insights into their potential impact on immunogenicity. Finally, case studies focussing on microalgae highlight the value of MS as an effective analytical tool for ensuring regulatory compliance throughout the food control chain.

Proteomics technologies are continually advancing, providing opportunities to develop stronger and more robust protein interaction networks (PINs). In part, this is due to the ever-growing number of high-throughput proteomics methods that are available. This review discusses how data-independent acquisition (DIA) and co-fractionation mass spectrometry (CF-MS) can be integrated to enhance interactome mapping abilities. Furthermore, integrating these two techniques can improve data quality and network generation through extended protein coverage, less missing data, and reduced noise. CF-DIA-MS shows promise in expanding our knowledge of interactomes, notably for non-model organisms (NMOs). CF-MS is a valuable technique on its own, but upon the integration of DIA, the potential to develop robust PINs increases, offering a unique approach for researchers to gain an in-depth understanding into the dynamics of numerous biological processes.

Proteins play an essential role in the vital biological processes governing cellular functions. Most proteins function as members of macromolecular machines, with the network of interacting proteins revealing the molecular mechanisms driving the formation of these complexes. Profiling the physiology-driven remodeling of these interactions within different contexts constitutes a crucial component to achieving a comprehensive systems-level understanding of interactome dynamics. Here, we apply co-fractionation mass spectrometry and computational modeling to quantify and profile the interactions of ∼2000 proteins in the bacterium Escherichia coli cultured under 10 distinct culture conditions. The resulting quantitative co-elution patterns revealed large-scale condition-dependent interaction remodeling among protein complexes involved in diverse biochemical pathways in response to the unique environmental challenges. The network-level analysis highlighted interactome-wide biophysical properties and structural patterns governing interaction remodeling. Our results provide evidence of the local and global plasticity of the E. coli interactome along with a rigorous generalizable framework to define protein interaction specificity. We provide an accompanying interactive web application to facilitate the exploration of these rewired networks.

Cellular communication is essential for cell-cell interactions, maintaining homeostasis and progression of certain disease states. While many studies examine extracellular proteins, the holistic extracellular proteome is often left uncaptured, leaving gaps in our understanding of how all extracellular proteins may impact communication and interaction. We used a cellular-based proteomics approach to more holistically profile both the intracellular and extracellular proteome of prostate cancer. Our workflow was generated in such a manner that multiple experimental conditions can be observed with the opportunity for high throughput integration. Additionally, this workflow is not limited to a proteomic aspect, as metabolomic and lipidomic studies can be integrated for a multi-omics workflow. Our analysis showed coverage of over 8000 proteins while also garnering insights into cellular communication in the context of prostate cancer development and progression. Identified proteins covered a variety of cellular processes and pathways, allowing for the investigation of multiple aspects into cellular biology. This workflow demonstrates advantages for integrating intra- and extracellular proteomic analyses as well as potential for multi-omics researchers. This approach possesses great value for future investigations into the systems biology aspects of disease development and progression.

Blood serum is arguably the most analyzed biofluid for disease prediction and diagnosis. Herein, we benchmarked five different serum abundant protein depletion (SAPD) kits with regard to the identification of disease-specific biomarkers in human serum using bottom-up proteomics. As expected, the IgG removal efficiency among the SAPD kits is highly variable, ranging from 70% to 93%. A pairwise comparison of database search results showed a 10%-19% variation in protein identification among the kits. Immunocapturing-based SAPD kits against IgG and albumin outperformed the others in the removal of these two abundant proteins. Conversely, non-antibody-based methods (i.e., kits using ion exchange resins) and kits leveraging a multi-antibody approach were proven to be less efficient in depleting IgG/albumin from samples but led to the highest number of identified peptides. Notably, our results indicate that different cancer biomarkers could be enriched up to 10% depending on the utilized SAPD kit compared with the undepleted sample. Additionally, functional analysis of the bottom-up proteomic results revealed that different SAPD kits enrich distinct disease- and pathway-specific protein sets. Overall, our study emphasizes that a careful selection of the appropriate commercial SAPD kit is crucial for the analysis of disease biomarkers in serum by shotgun proteomics.

The plant vascular system is a key element for long-distance communication. Understanding its composition may provide valuable information on how plants grow and develop themselves. In this study, a quantitative proteome dataset of the vascular sap proteome of three commercially important Eucalyptus species was shown. Protein extraction was carried out using a pressure bomb, whereas only in silico predicted extracellular proteins were considered as part of the sap proteome. A total of 132 different proteins were identified in all three Eucalyptus species and the most abundant proteome subset within all three species was comprised of proteins involved in the carbohydrate metabolic process, proteolysis, components of membrane, and defense response. The sap proteome of the species E. grandis and E. urophylla revealed the highest similarities. Functional classification indicated that the sap proteome of E. grandis and E. urophylla are mostly comprised of proteins involved in defense response and proteolysis; whereas no prominent functional class was observed for the E. camaldulensis species. Quantitative comparison highlighted characteristic sap proteins in each of the Eucalyptus species. The results that could be found in this study can be used as a reference for the proteome sap analysis of Eucalyptus plants grown under different conditions.

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.

Complete coverage of all N-glycosylation sites on the SARS-CoV2 spike protein would require the use of multiple proteases in addition to trypsin. Subsequent identification of the resulting glycopeptides by searching against database often introduces assignment errors due to similar mass differences between different permutations of amino acids and glycosyl residues. By manually interpreting the individual MS2 spectra, we report here the common sources of errors in assignment, especially those introduced by the use of chymotrypsin. We show that by applying a stringent threshold of acceptance, erroneous assignment by the commonly used Byonic software can be controlled within 15%, which can be reduced further if only those also confidently identified by a different search engine, pGlyco3, were considered. A representative site-specific N-glycosylation pattern could be constructed based on quantifying only the overlapping subset of N-glycopeptides identified at higher confidence. Applying the two complimentary glycoproteomic software in a concerted data analysis workflow, we found and confirmed that glycosylation at several sites of an unstable Omicron spike protein differed significantly from those of the stable trimeric product of the parental D614G variant.

Lipid droplets (LDs) are multifunctional organelles consisting of a central compartment of non-polar lipids shielded from the cytoplasm by a phospholipid monolayer. The excessive accumulation of LDs in cells is closely related to the development and progression of many diseases in humans and animals, such as liver-related and cardiovascular diseases. Thus, regulating the LDs size and abundance is necessary to maintain metabolic homeostasis. This study found that lipopolysaccharide (LPS) stimulation reduced the LDs content in the mouse liver. We tried to explain the possible molecular mechanisms at the broad protein and mRNA levels, finding that inhibition of the peroxisome proliferator-activated receptors (PPAR) signalling pathway by LPS may be a critical factor in reducing LDs content.

In recent years, the rapid growth of biological data has increased interest in using bioinformatics to analyze and interpret this data. Proteomics, which studies the structure, function, and interactions of proteins, is a crucial area of bioinformatics. Using natural language processing (NLP) techniques in proteomics is an emerging field that combines machine learning and text mining to analyze biological data. Recently, transformer-based NLP models have gained significant attention for their ability to process variable-length input sequences in parallel, using self-attention mechanisms to capture long-range dependencies. In this review paper, we discuss the recent advancements in transformer-based NLP models in proteome bioinformatics and examine their advantages, limitations, and potential applications to improve the accuracy and efficiency of various tasks. Additionally, we highlight the challenges and future directions of using these models in proteome bioinformatics research. Overall, this review provides valuable insights into the potential of transformer-based NLP models to revolutionize proteome bioinformatics.

Lysine 2-hydroxyisobutyrylation (Khib) is a novel protein post-translational modifications (PTMs) observed in both eukaryotes and prokaryotes. Recent studies suggested that this novel PTM has the potential to regulate different proteins in various pathways. Khib is regulated by lysine acyltransferases and deacylases. This novel PTM reveals interesting connections between modifications and protein physiological functions, including gene transcription, glycolysis and cell growth, enzymic activity, sperm motility, and aging. Here, we review the discovery and the current understanding of this PTM. Then, we outline the networks of complexity of interactions among PTMs in plants, and raise possible directions of this novel PTM for future investigations in plants.

Sulfated N- and O-glycans exist in trace levels which are challenging to detect, especially when abundant neutral and sialylated glycans are present. Current matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS)-based sulfoglycomics approaches effectively utilize permethylation to discriminate sulfated glycans from sialyl-glycans. And a charge-based separation to isolate the sulfated glycans from the rest of the permethylated neutral and sialyl-glycans. However, these approaches suffer from concomitant sample losses during cleanup steps. Herein, we describe Glycoblotting as a straightforward complementary method with seamless glycan purification, enrichment, methylation, and labeling on a single platform to address sulfated glycan enrichment, sialic acid methylation, and sample loss. Glycoblottings' on-bead chemoselective ligation of reducing sugars with hydrazide showed excellent recovery of sulfated glycans, allowing the detection of more sulfated glycan species. On-bead methyl esterification of sialic acid using 3-methyl-1-p-tolyltriazene (MTT) effectively discriminates sulfated glycans from sialyl-glycans. Furthermore, we have shown that using MTT as a methylating agent allowed us to simultaneously detect and differentiate sulfate from phosphate groups in isobaric N-glycan species. We believe that Glycoblotting will contribute significantly to the MALDI-TOF MS-based Sulphoglycomics workflow.