Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise its adoption. In this context, there are numerous improvements that are possible in the area of open science including the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include e.g. increased data sharing practices and availability of tailored open data standards. Additionally, the field would benefit from the development of open analysis workflows that can enable e.g. data reuse of public datasets, something that is increasingly common in other proteomics fields. We present an open and modular platform for the analysis and visualisation of TD proteomics data called TopDownApp. It can be used as a flexible analysis platform, through the use of a common workflow engine, common data formats for input/output, and software containerisation. It can also serve as a tool for visual inspection through its simple setup. As a key point, it can also be used as a development platform for new tools through the use of Python, a modular design, software containerisation and common data formats. TopDownApp is open source and freely available at: https://github.com/mwalzer/TopDownApp.

We propose an updated approach for approximating the isotope distribution of average peptides given their monoisotopic mass. Our methodology involves in-silico cleavage of the entire UNIPROT database of Human reviewed proteins using Trypsin, generating a theoretical peptide dataset. The isotope distribution is computed using BRAIN. We apply a compositional data modelling strategy that utilizes an additive log-ratio transformation for the isotope probabilities followed by a penalized spline regression. Furthermore, due to the impact of the number of Sulphur atoms on the course of the isotope distribution, we develop separate models for peptides containing zero up to five Sulphur atoms. Additionally, we propose three methods to estimate the number of Sulphur atoms based on an observed isotope distribution. The performance of the spline models and the Sulphur prediction approaches is evaluated using a mean squared error and a modified Pearson’s χ² goodness-of-fit measure on an experimental UPS2 data set. Our analysis reveals that the variability in spectral accuracy contributes more to the errors than the approximation of the theoretical isotope distribution by our proposed average peptide model. Moreover, we find that the accuracy of predicting the number of Sulphur atoms based on the observed isotope distribution is limited by measurement accuracy.

Previous studies have established the association of sex with gene and protein expression. This study investigated the association of sex with the abundance of endogenous urinary peptides, using capillary electrophoresis-coupled to mass spectrometry datasets from 2008 healthy individuals and patients with type II diabetes, divided in one discovery and two validation cohorts. Statistical analysis using the Mann-Whitney test, adjusted for multiple testing, revealed 143 sex-associated peptides in the discovery cohort. Of these, 90 peptides were associated with sex in at least one of the validation cohorts and showed agreement in their regulation trends across all cohorts. The 90 sex-associated peptides were fragments of 29 parental proteins. Comparison with previously published transcriptomics data demonstrated that the genes encoding 16 of these parental proteins had sex-biased expression. The 143 sex-associated peptides were combined into a support vector machine-based classifier that could discriminate males from females in two independent sets of healthy individuals and patients with type II diabetes, with an AUC of 89% and 81%, respectively. Collectively, the urinary peptidome contains multiple sex-associated differences, which may enable a better understanding of sex-biased molecular mechanisms and the development of more accurate diagnostic, prognostic or predictive classifiers for each individual sex.

Trans-activation response DNA binding protein of 43kDa (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 posttranslational 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.

Most proteins function by forming complexes within a dynamic interconnected network that underlies various biological mechanisms. To systematically investigate such interactomes, high-throughput techniques including CF-MS have been developed to capture, identify, and quantify protein-protein interactions (PPIs) in large-scale. Compared to other techniques, CF-MS allows the global identification and quantification of native protein complexes in one setting, without genetic manipulation and overexpression. Furthermore, quantitative CF-MS can potentially elucidate the distribution of a protein in multiple co-elution features, informing the stoichiometries and dynamics of a target protein complex. In this issue, Youssef et al. (Proteomics 2023, XX, XXXX-XXXX) combined multiplex CF-MS and an in-house algorithm to study the dynamics of the PPI network for Escherichia coli grown under ten different conditions. While the results demonstrated that while most proteins remained stable, the authors were able to detect disrupted interactions that were growth condition-specific. Further bioinformatics analyses also revealed biophysical properties and structural patterns that govern such a response.

The group 2 σ factor for RNA polymerase SigE plays important role in regulating central carbon metabolism in cyanobacteria. However, the regulation of SigE for these pathways at a proteome level remains unknown. Using a sigE-deficient strain (ΔsigE) of Synechocystis sp. PCC 6803 and quantitative proteomics, we found that SigE depletion induces differential protein expression for sugar catabolic pathways including glycolysis, oxidative pentose phosphate (OPP) pathway, and glycogen catabolism. Two glycogen debranching enzyme homologues Slr1857 and Slr0237 are found differentially expressed in ΔsigE. Glycogen determination indicated that Δslr0237 accumulated glycogen under photomixotrophic conditions but was unable to utilize these reserves in the dark, whereas Δslr1857 accumulates and utilize glycogen in a similar way as the WT strain does in the same conditions. These results suggest that Slr0237 plays the major role as the glycogen debranching enzyme in Synechocystis. To our knowledge, this is the first study to report the functional difference of two glycogen debranching enzyme in Synechocystis and the research highlights the intricate regulation of glycogen breakdown.

Over the past two decades, there has been increasing research into the molecular composition and function of small extracellular vesicles in the central nervous system. This is due in part to the recognition that small extracellular vesicles likely contribute to the pathogenesis of neurological diseases such as Alzheimer's disease, but also an understanding that small extracellular vesicles are a source of potential biomarkers. Small extracellular vesicles carry specific cargo that reflects their biogenesis and cellular origins, including protein, RNA and lipid. While the protein and RNA content of small extracellular vesicles in the central nervous system diseases and have been studied extensively, our understanding of the lipidome of small extracellular vesicles in the central nervous system is still in its infancy. Lipids play a significant role in maintaining central nervous system structure and function, and the dysregulation of lipid metabolism is known to occur in many neurological disorders, including Alzheimer's disease. Here we review what is currently known about lipid dyshomeostasis in Alzheimer's disease. We propose that small extracellular vesicle lipids may provide insight into the pathophysiology and progression of Alzheimer's disease and other neurological disorders, and, in the future perhaps, aid in disease monitoring and detection.

Cell-derived extracellular vesicles (EVs) are evolutionary-conserved secretory organelles that, based on their molecular composition, are important intercellular signaling regulators. At least three classes of circulating EVs are known based on mechanism of biogenesis: exosomes (sEVs/Exos), microparticles (lEVs/MPs) and shed midbody remnants (sMB-Rs). sEVs/Exos are of endosomal pathway origin, microparticles (lEVs/MPs) from plasma membrane blebbing, and shed midbody remnants (sMB-Rs) arise from symmetric cytokinetic abscission. Here, we isolate sEVs/Exos, lEVs/MPs and sMB-Rs secreted from human isogenic primary (SW480) and metastatic (SW620) colorectal cancer (CRC) cell lines in milligram quantities for label-free MS/MS-based proteomic profiling. Purified EVs revealed selective composition packaging of exosomal protein markers in SW480/SW620-sEVs/Exos, metabolic enzymes in SW480/SW620-lEVs/MPs, while centralspindlin complex proteins, nucleoproteins, splicing factors, RNA granule proteins, translation-initiation factors, and mitochondrial proteins selectively traffic to SW480/SW620-sMB-Rs. Collectively, we identify 39 human cancer-associated genes in EVs; 17 associated with SW480-EVs, 22 with SW620-EVs. We highlight oncogenic receptors/transporters selectively enriched in sEVs/Exos (EGFR/ FAS in SW480-Exos and MET, TGFBR2, ABCB1 in SW620-sEVs/Exos). Interestingly, MDK, STAT1, and TGM2 are selectively enriched in SW480-sMB-Rs, and ADAM15 to SW620-sMB-Rs. Our study reveals sEVs/Exos, lEVs/MPs and sMB-Rs have distinct protein signatures that open potential diagnostic avenues of distinct types of EVs for clinical utility.

Top-down proteomics (TDP) directly analyzes intact proteins and thus provides more comprehensive qualitative and quantitative proteoform-level information than conventional bottom-up proteomics that relies on digested peptides and protein inference. While significant advancements have been made in TDP in sample preparation, separation, instrumentation, and data analysis, reliable and reproducible data analysis still remains one of the major bottlenecks in TDP. A key step for robust data analysis is the establishment of an objective estimation of proteoform-level false discovery rate (FDR) in proteoform identification. The most widely used FDR estimation scheme is based on the target-decoy approach (TDA), which has primarily been established for bottom-up proteomics. We present evidence that the TDA-based FDR estimation may not work at the proteoform-level due to an overlooked factor, namely the erroneous deconvolution of precursor masses, which leads to incorrect FDR estimation. We argue that the conventional TDA-based FDR in proteoform identification is in fact protein-level FDR rather than proteoform-level FDR unless precursor deconvolution error rate is taken into account. To address this issue, we propose a formula to correct for proteoform-level FDR bias by combining TDA-based FDR and precursor deconvolution error rate.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases, and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity, and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation, and quantitation workflows.

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 ~2,000 proteins in the bacterium Escherichia coli cultured under ten 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. 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 exploration of these rewired networks.

Native mass spectrometry is a rapidly emerging technique for fast and sensitive structural analysis of protein constructs, maintaining the protein higher order structure. The coupling with electromigrative separation techniques under native conditions enables the characterization of proteoforms and highly complex protein mixtures. In this review, we present an overview of current native CE-MS technology. First, the status of native separation conditions is described for capillary zone electrophoresis (CZE), affinity capillary electrophoresis (ACE), and capillary isoelectric focusing (CIEF), as well as their chip-based formats, including essential parameters such as electrolyte composition and capillary coatings. Further, conditions required for native ESI-MS of (large) protein constructs, including instrumental parameters of QTOF and Orbitrap systems, as well as requirements for native CE-MS interfacing are presented. On this basis, methods and applications of the different modes of native CE-MS are summarized and discussed in the context of biological, medical, and biopharmaceutical questions. Finally, key achievements are highlighted and concluded, while remaining challenges are pointed out.

Fractionation of proteoforms is currently the most challenging topic in the field of protein purification. The need for considering the existence of proteoforms into experimental approaches is not only important in Life Science research in general but especially in the manufacturing of therapeutic proteins (TPs) like recombinant therapeutic antibodies (mAbs). Some of the proteoforms of TPs have significantly decreased actions or even cause side effects. The identification and removal of proteoforms differing from the main species, having the desired action, is challenging because the difference in the composition of atoms often is very small and their concentration in comparison to the main proteoform can be small. In this study we demonstrate that sample displacement batch chromatography (SDBC) is an easy to handle, economic and efficient method for fractionating proteoforms. As a model sample a commercial ovalbumin fraction was used, containing many ovalbumin proteoforms. The most promising parameters for the SDBC were determined by a screening approach and applied for a 10-segment fractionation of the ovalbumin with cation exchange chromatography resin. Mass spectrometry of intact proteoforms was used for characterizing the SDBC fractionation process. By SDBC a significant separation of different proteoforms was obtained.

Acclimations of Oreochromis mossambicus to hypersalinity were conducted with multiple rates of salinity increase and durations of exposure to determine the rate-independent maximum salinity limit and the incipient lethal salinity. Quantitative proteomics of over 3000 gill proteins simultaneously was performed to analyze molecular phenotypes associated with hypersalinity. For this purpose, a species- and tissue-specific data-independent acquisition (DIA) assay library of MSMS spectra was created. From these DIA data, protein networks representing complex molecular phenotypes associated with salinity acclimation were generated. O. mossambicus was determined to have a wide “zone of resistance” from approximately 75g/kg salinity to 120g/kg, which is tolerated for a limited period with eventual loss of organismal function. Crossing the critical threshold salinity into the zone of resistance corresponds with blood osmolality increasing beyond 400 mOsm/kg, significantly reduced body condition factor, and cessation of feeding. Gill protein networks impacted by hypersalinity include increased energy metabolism, especially upregulation of electron transport chain proteins, and regulation of specific osmoregulatory proteins. Cytoskeletal, cell adhesion, and extracellular matrix proteins are enriched in networks that are sensitive to the critical salinity threshold. Network analysis of these patterns provides deep insight into specific mechanisms of energy homeostasis during salinity stress.

Multiomics approaches to studying systems biology are very powerful tools that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a particular cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis, and specifically how the immune system responds to being challenged. With the emergence of the COVID-019 pandemic, now more than ever, the importance and utility of these tools has become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review we focus on the various state of the art “omics” technologies used within the scope of innate immunity.

Reliably scoring and ranking candidate models of protein complexes and assigning their oligomeric state from the structure of the crystal lattice represent outstanding challenges. A community-wide effort was launched to tackle these challenges. The latest resources on protein complexes and interfaces were exploited to derive a benchmark dataset consisting of 1677 homodimer protein crystal structures, including a balanced mix of physiological and non-physiological complexes. The non-physiological complexes in the benchmark were selected to bury a similar or larger interface area than their physiological counterparts, making it more difficult for scoring functions to differentiate between them. Next, 252 functions for scoring protein-protein interfaces previously developed by 13 groups were collected and evaluated for their ability to discriminate between physiological and non-physiological complexes. A simple consensus score generated using the best performing score of each of the 13 groups, and a cross-validated Random Forest (RF) classifier were created. Both approaches showed excellent performance, with an area under the Receiver Operating Characteristic (ROC) curve of 0.93 and 0.94 respectively, outperforming individual scores developed by different groups. Additionally, AlphaFold2 engines were shown to recall the physiological dimers with significantly higher accuracy than the non-physiological set, lending support for the pertinence of our benchmark dataset. Optimizing the combined power of interface scoring functions and evaluating it on challenging benchmark datasets appears to be a promising strategy.

HNF4α is a master regulator gene belonging to the nuclear receptor superfamily involved in regulating a wide range of critical biological processes in different organs. Structurally, the HNF4A locus is organized with two independent promoters and is subjected to alternative splicing with the production of twelve distinct isoforms. Little is known about the mechanisms each isoform uses to regulate transcription and their biological impact, with some reports addressing these aspects. Proteomic analyses have led to identifying proteins that interact with specific HNF4α isoforms. The identification and validation of these interactions and their role in co-regulating targeted gene expression are essential to understand better the role of this transcription factor in different biological processes and pathologies. This review addresses the historical origin of HNF4α isoforms, some of the main functions of the P1 and P2 isoform subgroups and provide information on the most recent hot topic research on the nature and function of proteins associated with each of the isoforms in some biological contexts.