Interpretation of Shotgun Proteomic Data

Elucidating the enzymatic response of the white rot basidiomycete Abortiporus biennis for the downgrade of polystyrene

Plastic pollution is a growing global environmental concern, with polyolefins such as polyethylene and polypropylene, as well as polystyrene (PS) constituting a significant amount of plastic waste. Both polyolefins and PS, when inappropriately disposed of in the environment, contribute to environmental contamination since they degrade slowly, with both abiotic and biotic factors contributing to their downgrade. In terms of the microbial effect on plastics, in recent decades, several studies have focused on the biodeterioration and assimilation of polyolefins, while more comprehensive degradation of PS by diverse organisms, including bacteria, fungi, and even insect larvae, has been documented. The present study investigates the biocatalytic potential of the white-rot basidiomycete Abortiporus biennis LGAM 436 for PS degradation. Building on prior research, we examined the ability of this fungal strain to modify the structure of different PS forms, including commercial expanded polystyrene (EPS) foam and amorphous PS film. In addition, we explored the impact of olive oil mill wastewater (OOMW) effluent as an enzymatic inducer to enhance the degradation process. Through gel permeation chromatography (GPC), surface morphology changes, and FTIR-ATR analysis, we assessed the extent of PS degradation and identified relevant enzymatic activities via proteomics. The findings offer insights into the discovery of novel fungal biocatalysts for addressing plastic pollution, particularly through the action of high-redox oxidative enzymes.

Compositional profiling of protein hydrolysates by high resolution liquid chromatography-mass spectrometry and chemometric analysis

Protein hydrolysates have attracted growing research and commercial attention due to their numerous nutritional, functional, and biological activities. However, only a limited range of proximate properties are determined routinely due to their substantial structural complexity and compositional variability. From both a manufacturing and functional perspective, it is of critical importance to monitor the compositional variations and identify potential similar or disparate features between different protein hydrolysates. In the current study, a single-approached method employing reverse phase ultra-high performance liquid chromatography coupled to high resolution electrospray ionization tandem mass spectrometry (RP-UHPLC-HR-ESI-MS/MS) was developed, optimized, and cross-validated for comprehensive structural and compositional profiling of a range of protein hydrolysates of varying raw materials, including soy, cotton, wheat, rice, and meat. Untargeted chemometric analysis and feature-based molecular network demonstrated potential for large-scale compositional assessment of protein hydrolysates without the need of prior component annotation. Signature features were identified to differentiate soy hydrolysates prepared from different batches of raw material and by different manufacturing processes. A hybrid approach combining de novo sequencing and target-decoy database homology search for peptide annotation is also described. Short peptides of 2 to 5 amino acids represented the most abundant components in soy protein hydrolysates (SPHs). A simple yet reliable integrated workflow for comprehensive structural and compositional profiling of protein hydrolysates was developed to enable an eventual correlation between their structure and function.

Operational Taxon-Function Framework in MetaX: Unveiling Taxonomic and Functional Associations in Metaproteomics

Metaproteomics analyzes the functional dynamics of microbial communities by identifying peptides and mapping them to the most likely proteins and taxa. One challenge in this field lies in seamlessly integrating taxonomic and functional annotations to accurately represent the contributions of individual microbial taxa to functional diversity. We introduce MetaX, a comprehensive tool for analyzing taxon-function relationships in metaproteomics by mapping peptides to their lowest common ancestors and assigning functions based on proportional thresholds, ensuring accurate peptide-level mappings. Importantly, MetaX introduces the Operational Taxon-Function (OTF), a new conceptual unit for exploring microbial roles and interactions within ecosystems. Additionally, MetaX includes extensive statistical and visualization tools, establishing it as a robust platform for metaproteomics analysis. We validated MetaX by reanalyzing ex vivo gut microbiome metaproteomic data exposed to various sweeteners, yielding more detailed results than traditional protein analysis. Furthermore, using the peptide-centric approach and OTF, we observed that Parabacteroides distasonis significantly responds to certain sweeteners, highlighting its role in modifying specific metabolic functions. With its intuitive, user-friendly interface, MetaX facilitates a detailed study of the complex interactions between microbial taxa and their functions in metaproteomics. It enhances our understanding of microbial roles in ecosystems and health.

Capillary Electrophoresis-Mass Spectrometry for Top-Down Proteomics

Mass spectrometry (MS)-based top-down proteomics (TDP) characterizes proteoforms in cells, tissues, and biological fluids (e.g., human plasma) to better our understanding of protein function and to discover new protein biomarkers for disease diagnosis and therapeutic development. Separations of proteoforms with high peak capacity are needed due to the high complexity of biological samples. Capillary electrophoresis (CE)-MS has been recognized as a powerful analytical tool for protein analysis since the 1980s owing to its high separation efficiency and sensitivity of CE-MS for proteoforms. Here, we review benefits of CE-MS for advancing TDP, challenges and solutions of the method, and the main research areas in which CE-MS-based TDP can make significant contributions. We provide a brief perspective of CE-MS-based TDP moving forward.

Grape-Pi: graph-based neural networks for enhanced protein identification in proteomics pipelines

Motivation

Protein identification via mass spectrometry (MS) is the primary method for untargeted protein detection. However, the identification process is challenging due to data complexity and the need to control false discovery rates (FDR) of protein identification. To address these challenges, we developed a graph neural network (GNN)-based model, Graph Neural Network using Protein-Protein Interaction for Enhancing Protein Identification (Grape-Pi), which is applicable to all proteomics pipelines. This model leverages protein-protein interaction (PPI) data and employs two types of message-passing layers to integrate evidence from both the target protein and its interactors, thereby improving identification accuracy.

Results

Grape-Pi achieved significant improvements in area under receiver-operating characteristic curve (AUC) in differentiating present and absent proteins: 18% and 7% in two yeast samples and 9% in gastric samples over traditional methods in the test dataset. Additionally, proteins identified via Grape-Pi in gastric samples demonstrated a high correlation with mRNA data and identified gastric cancer proteins, like MAP4K4, missed by conventional methods.

Availability and Implementation

Grape-Pi is freely available at https://zenodo.org/records/11310518 and https://github.com/FDUguchunhui/GrapePi.

Comprehensive Protein Inference Analysis with PyProteinInference Elucidates Biological Understanding of Tandem Mass Spectrometry Data

Selection and application of protein inference algorithms can have a significant impact on the data output from tandem mass spectrometry (MS/MS) experiments. However, this critical step is often taken for granted, with many studies simply utilizing the inference method embedded within the end-to-end software pipeline employed for analysis without consideration of the particular algorithm's suitability for the experiment at hand or its effects on the resulting data. Although many individual inference algorithms have been demonstrated, few unified tools are available that allow the researcher to quickly apply a variety of different inference algorithms to meet the needs of their analysis, are agnostic of other tools in the analysis pipeline, and are easy to use for the bench biologist. PyProteinInference provides a comprehensive suite of tools that enable researchers to apply different inference algorithms and compute protein-level set-based false discovery rates (FDR) from MS/MS data through a unified interface. Here, we describe the software and its application to a traditional protein inference benchmarking data set and to a K562 whole-cell lysate to demonstrate its utility in facilitating conclusions about underlying biological mechanisms in proteomic data.

Methodological Aspects of μLC-MS/MS for Wide-Scale Proteomic Analysis of Anthracycline-Induced Cardiomyopathy

The efforts to utilize microflow liquid chromatography hyphenated to tandem mass spectrometry (μLC-MS/MS) for deep-scale proteomic analysis are still growing. In this work, two-dimensional LC separation and peptide derivatization by a tandem mass tag (TMT) were used to assess the capability of μLC-MS/MS to reveal protein changes associated with the severe chronic anthracycline cardiotoxicity phenotype in comparison with nanoflow liquid chromatography (nLC-MS/MS). The analysis of the control and anthracycline-treated rabbit myocardium by μLC-MS/MS and nLC-MS/MS allowed quantification of 3956 and 4549 proteins, respectively, with 84% of these proteins shared in both data sets. Both nLC-MS/MS and μLC-MS/MS revealed marked global proteome dysregulation in severe anthracycline cardiotoxicity, with a significant change in approximately 55% of all detected proteins. The μLC-MS/MS analysis allowed less compressed and more precise determination of the TMT channel ratio and correspondingly broader fold-change protein distribution than nLC-MS/MS. The total number of significantly changed proteins was higher in nLC-MS/MS (2498 vs 2183, 1900 proteins shared), whereas the opposite was true for a number of significantly changed proteins with a fold-change cutoff ≥ 2 (535 vs 820). The profound changes concerned mainly proteins of cardiomyocyte sarcomeres, costameres, intercalated discs, mitochondria, and extracellular matrix. In addition, distinct alterations in immune and defense response were found with a remarkable involvement of type I interferon signaling that has been recently hypothesized to be essential for anthracycline cardiotoxicity pathogenesis. Hence, μLC-MS/MS was found to be a sound alternative to nLC-MS/MS that can be useful for comprehensive mapping of global myocardial proteome alterations such as those associated with severe anthracycline cardiotoxicity.

Relative quantification of proteins and post-translational modifications in proteomic experiments with shared peptides: a weight-based approach

MOTIVATION

Bottom-up mass spectrometry-based proteomics studies changes in protein abundance and structure across conditions. Since the currency of these experiments are peptides, i.e. subsets of protein sequences that carry the quantitative information, conclusions at a different level must be computationally inferred. The inference is particularly challenging in situations where the peptides are shared by multiple proteins or post-translational modifications. While many approaches infer the underlying abundances from unique peptides, there is a need to distinguish the quantitative patterns when peptides are shared.

RESULTS

We propose a statistical approach for estimating protein abundances, as well as site occupancies of post-translational modifications, based on quantitative information from shared peptides. The approach treats the quantitative patterns of shared peptides as convex combinations of abundances of individual proteins or modification sites, and estimates the abundance of each source in a sample together with the weights of the combination. In simulation-based evaluations, the proposed approach improved the precision of estimated fold changes between conditions. We further demonstrated the practical utility of the approach in experiments with diverse biological objectives, ranging from protein degradation and thermal proteome stability, to changes in protein post-translational modifications.

AVAILABILITY AND IMPLEMENTATION

The approach is implemented in an open-source R package MSstatsWeightedSummary. The package is currently available at https://github.com/Vitek-Lab/MSstatsWeightedSummary (doi: 10.5281/zenodo.14662989). Code required to reproduce the results presented in this article can be found in a repository https://github.com/mstaniak/MWS_reproduction (doi: 10.5281/zenodo.14656053).

Emerging opportunities for intact and native protein analysis using chemical proteomics

Chemical proteomics has advanced small molecule ligand discovery by providing insights into protein-ligand binding mechanism and enabling medicinal chemistry optimization of protein selectivity on a global scale. Mass spectrometry is the predominant analytical method for chemoproteomics, and various approaches have been deployed to investigate and target a rapidly growing number of protein classes and biological systems. Two methods, intact mass analysis (IMA) and top-down proteomics (TDMS), have gained interest in recent years due to advancements in high resolution mass spectrometry instrumentation. Both methods apply mass spectrometry analysis at the proteoform level, as opposed to the peptide level of bottom-up proteomics (BUMS), thus addressing some of the challenges of protein inference and incomplete information on modification stoichiometry. This Review covers recent research progress utilizing MS-based proteomics methods, discussing in detail the capabilities and opportunities for improvement of each method. Further, heightened attention is given to IMA and TDMS, highlighting these methods' strengths and considerations when utilized in chemoproteomic studies. Finally, we discuss the capabilities of native mass spectrometry (nMS) and ion mobility mass spectrometry (IM-MS) and how these methods can be used in chemoproteomics research to complement existing approaches to further advance the field of functional proteomics.

Defining proteoform-specific interactions for drug targeting in a native cell signalling environment

Understanding the dynamics of membrane protein-ligand interactions within a native lipid bilayer is a major goal for drug discovery. Typically, cell-based assays are used, however, they are often blind to the effects of protein modifications. In this study, using the archetypal G protein-coupled receptor rhodopsin, we found that the receptor and its effectors can be released directly from retina rod disc membranes using infrared irradiation in a mass spectrometer. Subsequent isolation and dissociation by infrared multiphoton dissociation enabled the sequencing of individual retina proteoforms. Specifically, we categorized distinct proteoforms of rhodopsin, localized labile palmitoylations, discovered a Gβγ proteoform that abolishes membrane association and defined lipid modifications on G proteins that influence their assembly. Given reports of undesirable side-effects involving vision, we characterized the off-target drug binding of two phosphodiesterase 5 inhibitors, vardenafil and sildenafil, to the retina rod phosphodiesterase 6 (PDE6). The results demonstrate differential off-target reactivity with PDE6 and an interaction preference for lipidated proteoforms of G proteins. In summary, this study highlights the opportunities for probing proteoform-ligand interactions within natural membrane environments.

Shotgun Sequencing of 512-mer Copolyester Allows Random Access to Stored Information

Digital information encoded in polymers has been exclusively decoded by mass spectrometry. However, the size limit of analytes in mass spectrometry restricts the storage capacity per chain. In addition, sequential decoding hinders random access to the bits of interest without full-chain sequencing. Here we report the shotgun sequencing of a 512-mer sequence-defined polymer whose molecular weight (57.3 kDa) far exceeds the analytical limit of mass spectrometry. A 4-bit fragmentation code was implemented at aperiodic positions during the synthetic encoding of 512-bit information without affecting storage capacity per chain. Upon activating the fragmentation code, the polymer chain splits into 18 oligomers, which could be individually decoded by tandem-mass sequencing. These sequences were computationally reconstructed into a full sequence using an error-detection method. The proposed sequencing method eliminates the storage limit of a single polymer chain and allows random access to the bits of interest without full-chain sequencing.

Moving Toward Metaproteogenomics: A Computational Perspective on Analyzing Microbial Samples via Proteogenomics

Microbial sample analysis has received growing attention within the last decade, driven by important findings in microbiome research and promising applications in the biotechnological field. Modern mass spectrometry-based methodology has been established in this context, providing sufficient sensitivity, resolution, dynamic range, and throughput to analyze the so-called metaproteome of complex microbial mixtures from clinical or environmental samples. While proteomic analyses were previously restricted to common model organisms, next-generation sequencing technologies nowadays allow for the rapid and cost-efficient characterization of whole metagenomes of microbial consortia and specific genomes from non-model organisms to which microbes contribute by significant amounts. This proteogenomic approach, meaning the combined application of genomic and proteomic methods, enables researchers to create a protein database that presents a tailored blueprint of the microbial sample under investigation. This contribution provides an overview of the computational challenges and opportunities in proteogenomics and metaproteomics as of January 2018. For practical application, we first showcase an integrative proteogenomic method that circumvents existing reference databases by creating sample-specific transcripts. The underlying algorithm uses a graph network approach that combines RNA-Seq and peptide information. As a second example, we provide a tutorial for a simulation tool that estimates the computational limits of detecting microbial non-model organisms. This method evaluates the potential influence of error-tolerant searches and proteogenomic approaches on databases of interest. Finally, we discuss recommendations for developing future strategies that may help overcome present limitations by combining the strengths of genome- and proteome-based methods and moving toward an integrated metaproteogenomics approach.

Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics-driven proteomics - A systematic review with meta-analysis

AIM

The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses.

METHODS

An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale.

RESULTS

The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases.

CONCLUSION

The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.

Top-Down Stability of Proteins from Rates of Oxidation (TD-SPROX) Approach for Measuring Proteoform-Specific Folding Stability

The crucial roles of proteoforms in biological processes and disease mechanisms have been increasingly recognized. However, the rate at which new proteoforms are being discovered using top-down proteomics has far outpaced the rate at which the functional significance of different proteoforms can be determined. Because of the close connection between protein folding and protein function, protein folding stability measurements on proteoforms have the potential to identify functionally significant proteoforms of a given protein. While a number of mass spectrometry-based proteomics methods for making protein folding stability measurements on the proteomic scale have been reported over the past decade, none have been interfaced with top-down proteomics. Described here is a top-down (TD) stability of proteins from the rates of oxidation (SPROX) approach for making proteoform specific folding stability measurements. This approach is validated using a mixture of three model proteins with well-characterized protein folding behavior by conventional SPROX as well as other more conventional biophysical techniques. The method is also used to evaluate the relative folding stabilities of the <30 kDa protein fraction isolated from an MCF-7 cell lysate. The relative folding stabilities of 150 proteoforms from 83 proteins were successfully characterized in the cell lysate analysis using the TD-SPROX approach.

Leveraging Ion-Ion and Ion-Photon Activation to Improve the Sequencing of Proteins Carrying Multiple Disulfide Bonds: The Human Serum Albumin Case Study

Gas-phase sequencing of large intact proteins (>30 kDa) via tandem mass spectrometry is an inherently challenging process that is further complicated by the extensive overlap of multiply charged product ion peaks, often characterized by a low signal-to-noise ratio. Disulfide bonds exacerbate this issue because of the need to cleave both the S-S and backbone bonds to liberate sequence informative fragments. Although electron-based ion activation techniques such as electron transfer dissociation (ETD) have been proven to rupture disulfide bonds in whole protein ions, they still struggle to produce extensive sequencing when multiple, concatenated S-S bonds are present on the same large polypeptide chain. Here, we evaluate the increase in sequence coverage obtained by combining activated-ion ETD (AI-ETD) and proton transfer charge reduction (PTCR) in the analysis of 66 kDa human serum albumin, which holds 17 disulfide bridges. We also describe the combination of AI-ETD with supplemental postactivation of the ETD reaction products via higher-energy collisional dissociation─a hybrid fragmentation method termed AI-EThcD. AI-EThcD leads to a further improvement compared to AI-ETD in both the global number of cleaved backbone bonds and the number of ruptured backbone bonds from disulfide-protected regions. Our results also demonstrate that the full potential of AI-ETD and AI-EThcD is unveiled only when combined with PTCR: reduction in overlap of ion signals leads to a sequence coverage as high as 39% in a single experiment, highlighting the relevance of spectral simplification in top-down mass spectrometry of large proteins.

Functional Analysis of MS-Based Proteomics Data: From Protein Groups to Networks

Mass spectrometry-based proteomics allows the quantification of thousands of proteins, protein variants, and their modifications, in many biological samples. These are derived from the measurement of peptide relative quantities, and it is not always possible to distinguish proteins with similar sequences due to the absence of protein-specific peptides. In such cases, peptide signals are reported in protein groups that can correspond to several genes. Here, we show that multi-gene protein groups have a limited impact on GO-term enrichment, but selecting only one gene per group affects network analysis. We thus present the Cytoscape app Proteo Visualizer (https://apps.cytoscape.org/apps/ProteoVisualizer) that is designed for retrieving protein interaction networks from STRING using protein groups as input and thus allows visualization and network analysis of bottom-up MS-based proteomics data sets.

Functional annotation of the Hippo pathway somatic mutations in human cancers

The Hippo pathway is commonly altered in cancer initiation and progression; however, exactly how this pathway becomes dysregulated to promote human cancer development remains unclear. Here we analyze the Hippo somatic mutations in the human cancer genome and functionally annotate their roles in targeting the Hippo pathway. We identify a total of 85 loss-of-function (LOF) missense mutations for Hippo pathway genes and elucidate their underlying mechanisms. Interestingly, we reveal zinc-finger domain as an integral structure for MOB1 function, whose LOF mutations in head and neck cancer promote tumor growth. Moreover, the schwannoma/meningioma-derived NF2 LOF mutations not only inhibit its tumor suppressive function in the Hippo pathway, but also gain an oncogenic role for NF2 by activating the VANGL-JNK pathway. Collectively, our study not only offers a rich somatic mutation resource for investigating the Hippo pathway in human cancers, but also provides a molecular basis for Hippo-based cancer therapy.

Differences in Uniquely Identified Peptides Between ddaPASEF and diaPASEF

Recent advancements in mass spectrometry-based proteomics have made it possible to conduct comprehensive protein analysis. In particular, the emergence of the data-independent acquisition (DIA) method powered by machine learning has significantly improved protein identification efficiency. However, compared with the conventional data-dependent acquisition (DDA) method, the degree to which peptides are uniquely identified by DIA and DDA has not been thoroughly examined. In this study, we identified over 10,000 proteins using the DDA and DIA methods and analyzed the characteristics of unique peptides identified by each method. Results showed that the number of peptides uniquely identified by DDA and DIA using the same column type was 19% and 32%, respectively, with shorter peptides preferentially detected by the DIA method. In addition, more DIA-specific peptides were identified, especially during the first 10% of elution time, and the overall 1/K0 and m/z shifted toward smaller values than in the DDA method. Furthermore, comparing the phosphorylation and ubiquitination proteome profiles with those of whole-cell lysates by DDA showed that the enrichment of post-translationally modified peptides resulted in wider m/z and 1/K0 ranges. Notably, the ubiquitin peptide-enriched samples displayed lower m/z values than the phospho-proteome. These findings suggest a bias in the types of peptides identified by the acquisition method and the importance of setting appropriate ranges for DIA based on the post-translational modification of peptide characteristics.

CBFA2T3-GLIS2 mediates transcriptional regulation of developmental pathways through a gene regulatory network

CBFA2T3-GLIS2 is a fusion oncogene found in pediatric acute megakaryoblastic leukemia that is associated with a poor prognosis. We establish a model of CBFA2T3-GLIS2 driven acute megakaryoblastic leukemia that allows the distinction of fusion specific changes from those that reflect the megakaryoblast lineage of this leukemia. Using this model, we map fusion genome wide binding that in turn imparts the characteristic transcriptional signature. A network of transcription factor genes bound and upregulated by the fusion are found to have downstream effects that result in dysregulated signaling of developmental pathways including NOTCH, Hedgehog, TGFβ, and WNT. Transcriptional regulation is mediated by homo-dimerization and binding of the ETO transcription factor through the nervy homology region 2. Loss of nerve homology region 2 abrogated the development of leukemia, leading to downregulation of JAK/STAT, Hedgehog, and NOTCH transcriptional signatures. These data contribute to the understanding of CBFA2T3-GLIS2 mediated leukemogenesis and identify potential therapeutic vulnerabilities for future studies.

Influence of Software Settings on the Identification Rate, Quantification Results, and Reproducibility in Profiling Post-Translational Modifications by Microflow Liquid Chromatography-Ion Mobility-Quadrupole Time-Of-Flight Analysis Using PEAKS Software

The influence of data evaluation parameters on qualitative and quantitative results of untargeted shotgun profiling of enzymatic and nonenzymatic post-translational modifications (PTMs) was investigated in a model of bovine whey protein α-lactalbumin heated with lactose. Based on the same raw data, individual adjustments to the protein database and enzyme settings of PEAKS studio software increased the identification rate from 27 unmodified peptides to 48 and from 322 peptides in total to 535. The qualitative and quantitative reproducibility was also assessed based on 18 measurements of one sample across three batches. A total of 570 peptides were detected. While 89 peptides were identified in all measurements, the majority of peptides (161) were detected only once and mostly based on nonindicative spectra. The reproducibility of label-free quantification (LFQ) in six measurements of the same sample was similar after processing the data by either the PTM algorithm or the LFQ algorithm. In both cases, about one-third of the peptides showed a coefficient of variation of above 20%. However, the LFQ algorithm increased the number of quantified peptides from 75 to 179. Data are available at the PRIDE Archive with the data set identifier PXD050363.

Exploring Top-Down Mass Spectrometric Approaches To Probe Forest Cobra (Naja melanoleuca) Venom Proteoforms

Snake venoms are comprised of bioactive proteins and peptides that facilitate severe snakebite envenomation symptoms. A comprehensive understanding of venom compositions and the subtle heterogeneity therein is important. While bottom-up proteomics has been the well-established approach to catalogue venom compositions, top-down proteomics has emerged as a complementary strategy to characterize venom heterogeneity at the intact protein level. However, top-down proteomics has not been as widely implemented in the snake venom field as bottom-up proteomics, with various emerging top-down methods yet to be developed for venom systems. Here, we have explored three main top-down mass spectrometry methodologies in a proof-of-concept study to characterize selected three-finger toxin and phospholipase A2 proteoforms from the forest cobra (Naja melanoleuca) venom. We demonstrated the utility of a data-independent acquisition mode "MSE" for untargeted fragmentation on a chromatographic time scale and its improvement in protein sequence coverage compared to conventional targeted tandem mass spectrometry analysis. We also showed that protein identification can be further improved using a hybrid fragmentation approach, combining electron-capture dissociation and collision-induced dissociation. Lastly, we reported the promising application of multifunctional cyclic ion mobility separation and post-ion mobility fragmentation on snake venom proteins for the first time.

Mass Spectrometry-Based Top-Down Proteomics in Nanomedicine: Proteoform-Specific Measurement of Protein Corona

Conventional mass spectrometry (MS)-based bottom-up proteomics (BUP) analysis of the protein corona [i.e., an evolving layer of biomolecules, mostly proteins, formed on the surface of nanoparticles (NPs) during their interactions with biomolecular fluids] enabled the nanomedicine community to partly identify the biological identity of NPs. Such an approach, however, fails to pinpoint the specific proteoforms─distinct molecular variants of proteins in the protein corona. The proteoform-level information could potentially advance the prediction of the biological fate and pharmacokinetics of nanomedicines. Recognizing this limitation, this study pioneers a robust and reproducible MS-based top-down proteomics (TDP) technique for characterizing proteoforms in the protein corona. Our TDP approach has successfully identified about 900 proteoforms in the protein corona of polystyrene NPs, ranging from 2 to 70 kDa, revealing proteoforms of 48 protein biomarkers with combinations of post-translational modifications, signal peptide cleavages, and/or truncations─details that BUP could not fully discern. This advancement in MS-based TDP offers a more advanced approach to characterize NP protein coronas, deepening our understanding of NPs' biological identities. We, therefore, propose using both TDP and BUP strategies to obtain more comprehensive information about the protein corona, which, in turn, can further enhance the diagnostic and therapeutic efficacy of nanomedicine technologies.

Exploring the unmapped cysteine redox proteoform landscape

Cysteine redox proteoforms define the diverse molecular states that proteins with cysteine residues can adopt. A protein with one cysteine residue must adopt one of two binary proteoforms: reduced or oxidized. Their numbers scale: a protein with 10 cysteine residues must assume one of 1,024 proteoforms. Although they play pivotal biological roles, the vast cysteine redox proteoform landscape comprising vast numbers of theoretical proteoforms remains largely uncharted. Progress is hampered by a general underappreciation of cysteine redox proteoforms, their intricate complexity, and the formidable challenges that they pose to existing methods. The present review advances cysteine redox proteoform theory, scrutinizes methodological barriers, and elaborates innovative technologies for detecting unique residue-defined cysteine redox proteoforms. For example, chemistry-enabled hybrid approaches combining the strengths of top-down mass spectrometry (TD-MS) and bottom-up mass spectrometry (BU-MS) for systematically cataloguing cysteine redox proteoforms are delineated. These methods provide the technological means to map uncharted redox terrain. To unravel hidden redox regulatory mechanisms, discover new biomarkers, and pinpoint therapeutic targets by mining the theoretical cysteine redox proteoform space, a community-wide initiative termed the "Human Cysteine Redox Proteoform Project" is proposed. Exploring the cysteine redox proteoform landscape could transform current understanding of redox biology.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Evidence for widespread translation of 5' untranslated regions

Ribosome profiling experiments support the translation of a range of novel human open reading frames. By contrast, most peptides from large-scale proteomics experiments derive from just one source, 5' untranslated regions. Across the human genome we find evidence for 192 translated upstream regions, most of which would produce protein isoforms with extended N-terminal ends. Almost all of these N-terminal extensions are from highly abundant genes, which suggests that the novel regions we detect are just the tip of the iceberg. These upstream regions have characteristics that are not typical of coding exons. Their GC-content is remarkably high, even higher than 5' regions in other genes, and a large majority have non-canonical start codons. Although some novel upstream regions have cross-species conservation - five have orthologues in invertebrates for example - the reading frames of two thirds are not conserved beyond simians. These non-conserved regions also have no evidence of purifying selection, which suggests that much of this translation is not functional. In addition, non-conserved upstream regions have significantly more peptides in cancer cell lines than would be expected, a strong indication that an aberrant or noisy translation initiation process may play an important role in translation from upstream regions.

A practical introduction to holo-omics

Holo-omics refers to the joint study of non-targeted molecular data layers from host-microbiota systems or holobionts, which is increasingly employed to disentangle the complex interactions between the elements that compose them. We navigate through the generation, analysis, and integration of omics data, focusing on the commonalities and main differences to generate and analyze the various types of omics, with a special focus on optimizing data generation and integration. We advocate for careful generation and distillation of data, followed by independent exploration and analyses of the single omic layers to obtain a better understanding of the study system, before the integration of multiple omic layers in a final model is attempted. We highlight critical decision points to achieve this aim and flag the main challenges to address complex biological questions regarding the integrative study of host-microbiota relationships.

IsoBayes: a Bayesian approach for single-isoform proteomics inference

Studying protein isoforms is an essential step in biomedical research; at present, the main approach for analyzing proteins is via bottom-up mass spectrometry proteomics, which return peptide identifications, that are indirectly used to infer the presence of protein isoforms. However, the detection and quantification processes are noisy; in particular, peptides may be erroneously detected, and most peptides, known as shared peptides, are associated to multiple protein isoforms. As a consequence, studying individual protein isoforms is challenging, and inferred protein results are often abstracted to the gene-level or to groups of protein isoforms. Here, we introduce IsoBayes, a novel statistical method to perform inference at the isoform level. Our method enhances the information available, by integrating mass spectrometry proteomics and transcriptomics data in a Bayesian probabilistic framework. To account for the uncertainty in the measurement process, we propose a two-layer latent variable approach: first, we sample if a peptide has been correctly detected (or, alternatively filter peptides); second, we allocate the abundance of such selected peptides across the protein(s) they are compatible with. This enables us, starting from peptide-level data, to recover protein-level data; in particular, we: i) infer the presence/absence of each protein isoform (via a posterior probability), ii) estimate its abundance (and credible interval), and iii) target isoforms where transcript and protein relative abundances significantly differ. We benchmarked our approach in simulations, and in two multi-protease real datasets: our method displays good sensitivity and specificity when detecting protein isoforms, its estimated abundances highly correlate with the ground truth, and can detect changes between protein and transcript relative abundances. IsoBayes is freely distributed as a Bioconductor R package, and is accompanied by an example usage vignette.

Critical steps in an environmental metaproteomics workflow

Environmental metaproteomics is a rapidly advancing field that provides insights into the structure, dynamics, and metabolic activity of microbial communities. As the field is still maturing, it lacks consistent workflows, making it challenging for non-expert researchers to navigate. This review aims to introduce the workflow of environmental metaproteomics. It outlines the standard practices for sample collection, processing, and analysis, and offers strategies to overcome the unique challenges presented by common environmental matrices such as soil, freshwater, marine environments, biofilms, sludge, and symbionts. The review also highlights the bottlenecks in data analysis that are specific to metaproteomics samples and provides suggestions for researchers to obtain high-quality datasets. It includes recent benchmarking studies and descriptions of software packages specifically built for metaproteomics analysis. The article is written without assuming the reader's familiarity with single-organism proteomic workflows, making it accessible to those new to proteomics or mass spectrometry in general. This primer for environmental metaproteomics aims to improve accessibility to this exciting technology and empower researchers to tackle challenging and ambitious research questions. While it is primarily a resource for those new to the field, it should also be useful for established researchers looking to streamline or troubleshoot their metaproteomics experiments.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Fitness Effects of Phenotypic Mutations at Proteome-Scale Reveal Optimality of Translation Machinery

Errors in protein translation can lead to non-genetic, phenotypic mutations, including amino acid misincorporations. While phenotypic mutations can increase protein diversity, the systematic characterization of their proteome-wide frequencies and their evolutionary impact has been lacking. Here, we developed a mechanistic model of translation errors to investigate how selection acts on protein populations produced by amino acid misincorporations. We fitted the model to empirical observations of misincorporations obtained from over a hundred mass spectrometry datasets of E. coli and S. cerevisiae. We found that on average 20% to 23% of proteins synthesized in the cell are expected to harbor at least one amino acid misincorporation, and that deleterious misincorporations are less likely to occur. Combining misincorporation probabilities and the estimated fitness effects of amino acid substitutions in a population genetics framework, we found 74% of mistranslation events in E. coli and 94% in S. cerevisiae to be neutral. We further show that the set of available synonymous tRNAs is subject to evolutionary pressure, as the presence of missing tRNAs would increase codon-anticodon cross-reactivity and misincorporation error rates. Overall, we find that the translation machinery is likely optimal in E. coli and S. cerevisiae and that both local solutions at the level of codons and a global solution such as the tRNA pool can mitigate the impact of translation errors. We provide a framework to study the evolutionary impact of codon-specific translation errors and a method for their proteome-wide detection across organisms and conditions.

Targeted Quantification of Proteoforms in Complex Samples by Proteoform Reaction Monitoring

Existing mass spectrometric assays used for sensitive and specific measurements of target proteins across multiple samples, such as selected/multiple reaction monitoring (SRM/MRM) or parallel reaction monitoring (PRM), are peptide-based methods for bottom-up proteomics. Here, we describe an approach based on the principle of PRM for the measurement of intact proteoforms by targeted top-down proteomics, termed proteoform reaction monitoring (PfRM). We explore the ability of our method to circumvent traditional limitations of top-down proteomics, such as sensitivity and reproducibility. We also introduce a new software program, Proteoform Finder (part of ProSight Native), specifically designed for the easy analysis of PfRM data. PfRM was initially benchmarked by quantifying three standard proteins. The linearity of the assay was shown over almost 3 orders of magnitude in the femtomole range, with limits of detection and quantification in the low femtomolar range. We later applied our multiplexed PfRM assay to complex samples to quantify biomarker candidates in peripheral blood mononuclear cells (PBMCs) from liver-transplanted patients, suggesting their possible translational applications. These results demonstrate that PfRM has the potential to contribute to the accurate quantification of protein biomarkers for diagnostic purposes and to improve our understanding of disease etiology at the proteoform level.

β-1,3-Glucan recognition by Acanthamoeba castellanii as a putative mechanism of amoeba-fungal interactions

In this study, we conducted an in-depth analysis to characterize potential Acanthamoeba castellanii (Ac) proteins capable of recognizing fungal β-1,3-glucans. Ac specifically anchors curdlan or laminarin, indicating the presence of surface β-1,3-glucan-binding molecules. Using optical tweezers, strong adhesion of laminarin- or curdlan-coated beads to Ac was observed, highlighting their adhesive properties compared to controls (characteristic time τ of 46.9 and 43.9 s, respectively). Furthermore, Histoplasma capsulatum (Hc) G217B, possessing a β-1,3-glucan outer layer, showed significant adhesion to Ac compared to a Hc G186 strain with an α-1,3-glucan outer layer (τ of 5.3 s vs τ 83.6 s). The addition of soluble β-1,3-glucan substantially inhibited this adhesion, indicating the involvement of β-1,3-glucan recognition. Biotinylated β-1,3-glucan-binding proteins from Ac exhibited higher binding to Hc G217B, suggesting distinct recognition mechanisms for laminarin and curdlan, akin to macrophages. These observations hinted at the β-1,3-glucan recognition pathway's role in fungal entrance and survival within phagocytes, supported by decreased fungal viability upon laminarin or curdlan addition in both phagocytes. Proteomic analysis identified several Ac proteins capable of binding β-1,3-glucans, including those with lectin/glucanase superfamily domains, carbohydrate-binding domains, and glycosyl transferase and glycosyl hydrolase domains. Notably, some identified proteins were overexpressed upon curdlan/laminarin challenge and also demonstrated high affinity to β-1,3-glucans. These findings underscore the complexity of binding via β-1,3-glucan and suggest the existence of alternative fungal recognition pathways in Ac.IMPORTANCEAcanthamoeba castellanii (Ac) and macrophages both exhibit the remarkable ability to phagocytose various extracellular microorganisms in their respective environments. While substantial knowledge exists on this phenomenon for macrophages, the understanding of Ac's phagocytic mechanisms remains elusive. Recently, our group identified mannose-binding receptors on the surface of Ac that exhibit the capacity to bind/recognize fungi. However, the process was not entirely inhibited by soluble mannose, suggesting the possibility of other interactions. Herein, we describe the mechanism of β-1,3-glucan binding by A. castellanii and its role in fungal phagocytosis and survival within trophozoites, also using macrophages as a model for comparison, as they possess a well-established mechanism involving the Dectin-1 receptor for β-1,3-glucan recognition. These shed light on a potential parallel evolution of pathways involved in the recognition of fungal surface polysaccharides.

Characterisation of the forest cobra (Naja melanoleuca) venom using a multifaceted mass spectrometric-based approach

Snake venoms consist of highly biologically active proteins and peptides that are responsible for the lethal physiological effects of snakebite envenomation. In order to guide the development of targeted antivenom strategies, comprehensive understanding of venom compositions and in-depth characterisation of various proteoforms, often not captured by traditional bottom-up proteomic workflows, is necessary. Here, we employ an integrated 'omics' and intact mass spectrometry (MS)-based approach to profile the heterogeneity within the venom of the forest cobra (Naja melanoleuca), adopting different analytical strategies to accommodate for the dynamic molecular mass range of venom proteins present. The venom proteome of N. melanoleuca was catalogued using a venom gland transcriptome-guided bottom-up proteomics approach, revealing a venom consisting of six toxin superfamilies. The subtle diversity present in the venom components was further explored using reversed phase-ultra performance liquid chromatography (RP-UPLC) coupled to intact MS. This approach showed a significant increase in the number of venom proteoforms within various toxin families that were not captured in previous studies. Furthermore, we probed at the higher-order structures of the larger venom proteins using a combination of native MS and mass photometry and revealed significant structural heterogeneity along with extensive post-translational modifications in the form of glycosylation in these larger toxins. Here, we show the diverse structural heterogeneity of snake venom proteins in the venom of N. melanoleuca using an integrated workflow that incorporates analytical strategies that profile snake venom at the proteoform level, complementing traditional venom characterisation approaches.

SpliceProt 2.0: A Sequence Repository of Human, Mouse, and Rat Proteoforms

SpliceProt 2.0 is a public proteogenomics database that aims to list the sequence of known proteins and potential new proteoforms in human, mouse, and rat proteomes. This updated repository provides an even broader range of computationally translated proteins and serves, for example, to aid with proteomic validation of splice variants absent from the reference UniProtKB/SwissProt database. We demonstrate the value of SpliceProt 2.0 to predict orthologous proteins between humans and murines based on transcript reconstruction, sequence annotation and detection at the transcriptome and proteome levels. In this release, the annotation data used in the reconstruction of transcripts based on the methodology of ternary matrices were acquired from new databases such as Ensembl, UniProt, and APPRIS. Another innovation implemented in the pipeline is the exclusion of transcripts predicted to be susceptible to degradation through the NMD pathway. Taken together, our repository and its applications represent a valuable resource for the proteogenomics community.

Integrative Proteogenomics for Differential Expression and Splicing Variation in a DM1 Mouse Model

Dysregulated mRNA splicing is involved in the pathogenesis of many diseases including cancer, neurodegenerative diseases, and muscular dystrophies such as myotonic dystrophy type 1 (DM1). Comprehensive assessment of dysregulated splicing on the transcriptome and proteome level has been methodologically challenging, and thus investigations have often been targeting only few genes. Here, we performed a large-scale coordinated transcriptomic and proteomic analysis to characterize a DM1 mouse model (HSALR) in comparison to wild type. Our integrative proteogenomics approach comprised gene- and splicing-level assessments for mRNAs and proteins. It recapitulated many known instances of aberrant mRNA splicing in DM1 and identified new ones. It enabled the design and targeting of splicing-specific peptides and confirmed the translation of known instances of aberrantly spliced disease-related genes (e.g., Atp2a1, Bin1, Ryr1), complemented by novel findings (Flnc and Ywhae). Comparative analysis of large-scale mRNA and protein expression data showed quantitative agreement of differentially expressed genes and splicing patterns between disease and wild type. We hence propose this work as a suitable blueprint for a robust and scalable integrative proteogenomic strategy geared toward advancing our understanding of splicing-based disorders. With such a strategy, splicing-based biomarker candidates emerge as an attractive and accessible option, as they can be efficiently asserted on the mRNA and protein level in coordinated fashion.

NME4 mediates metabolic reprogramming and promotes nonalcoholic fatty liver disease progression

Nonalcoholic fatty liver disease (NAFLD) is mainly characterized by excessive fat accumulation in the liver, and it is associated with liver-related complications and adverse systemic diseases. NAFLD has become the most prevalent liver disease; however, effective therapeutic agents for NAFLD are still lacking. We combined clinical data with proteomics and metabolomics data, and found that the mitochondrial nucleoside diphosphate kinase NME4 plays a central role in mitochondrial lipid metabolism. Nme4 is markedly upregulated in mice fed with high-fat diet, and its expression is positively correlated with the level of steatosis. Hepatic deletion of Nme4 suppresses the progression of hepatic steatosis. Further studies demonstrated that NME4 interacts with several key enzymes in coenzyme A (CoA) metabolism and increases the level of acetyl-CoA and malonyl-CoA, which are the major lipid components of the liver in NAFLD. Increased level of acetyl-CoA and malonyl-CoA  lead to increased triglyceride levels and lipid accumulation in the liver. Taken together, these findings reveal that NME4 is a critical regulator of NAFLD progression and a potential therapeutic target for NAFLD.

Orbitrap Mass Spectrometry and High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) Enable the in-Depth Analysis of Human Serum Proteoforms

Blood serum and plasma are arguably the most commonly analyzed clinical samples, with dozens of proteins serving as validated biomarkers for various human diseases. Top-down proteomics may provide additional insights into disease etiopathogenesis since this approach focuses on protein forms, or proteoforms, originally circulating in blood, potentially providing access to information about relevant post-translational modifications, truncations, single amino acid substitutions, and many other sources of protein variation. However, the vast majority of proteomic studies on serum and plasma are carried out using peptide-centric, bottom-up approaches that cannot recapitulate the original proteoform content of samples. Clinical laboratories have been slow to adopt top-down analysis, also due to higher sample handling requirements. In this study, we describe a straightforward protocol for intact proteoform sample preparation based on the depletion of albumin and immunoglobulins, followed by simplified protein fractionation via polyacrylamide gel electrophoresis. After molecular weight-based fractionation, we supplemented the traditional liquid chromatography-tandem mass spectrometry (LC-MS2) data acquisition with high-field asymmetric waveform ion mobility spectrometry (FAIMS) to further simplify serum proteoform mixtures. This LC-FAIMS-MS2 method led to the identification of over 1000 serum proteoforms < 30 kDa, outperforming traditional LC-MS2 data acquisition and more than doubling the number of proteoforms identified in previous studies.

Quantifying the impact of dynamic plant-environment interactions on metabolic regulation

A plant's genome encodes enzymes, transporters and many other proteins which constitute metabolism. Interactions of plants with their environment shape their growth, development and resilience towards adverse conditions. Although genome sequencing technologies and applications have experienced triumphantly rapid development during the last decades, enabling nowadays a fast and cheap sequencing of full genomes, prediction of metabolic phenotypes from genotype × environment interactions remains, at best, very incomplete. The main reasons are a lack of understanding of how different levels of molecular organisation depend on each other, and how they are constituted and expressed within a setup of growth conditions. Phenotypic plasticity, e.g., of the genetic model plant Arabidopsis thaliana, has provided important insights into plant-environment interactions and the resulting genotype x phenotype relationships. Here, we summarize previous and current findings about plant development in a changing environment and how this might be shaped and reflected in metabolism and its regulation. We identify current challenges in the study of plant development and metabolic regulation and provide an outlook of how methodological workflows might support the application of findings made in model systems to crops and their cultivation.

Mass spectrometry characterization of antibodies at the intact and subunit levels: from targeted to large-scale analysis

Antibodies are one of the most formidable molecular weapons available to our immune system. Their high specificity against a target (antigen) and capability of triggering different immune responses (e.g., complement system activation and antibody-dependent cell-mediated cytotoxicity) make them ideal drugs to fight many different human diseases. Currently, both monoclonal antibodies and more complex molecules based on the antibody scaffold are used as biologics. Naturally, such highly heterogeneous molecules require dedicated analytical methodologies for their accurate characterization. Mass spectrometry (MS) can define the presence and relative abundance of multiple features of antibodies, including critical quality attributes. The combination of small and large variations within a single molecule can only be determined by analyzing intact antibodies or their large (25 to 100 kDa) subunits. Hence, top-down (TD) and middle-down (MD) MS approaches have gained popularity over the last decade. In this Young Scientist Feature we discuss the evolution of TD and MD MS analysis of antibodies, including the new frontiers that go beyond biopharma applications. We will show how this field is now moving from the "quality control" analysis of a known, single antibody to the high-throughput investigation of complex antibody repertoires isolated from clinical samples, where the ultimate goal is represented by the complete gas-phase sequencing of antibody molecules without the need of any a priori knowledge.

Proteomics: Its Promise and Pitfalls in Shaping Precision Medicine in Solid Organ Transplantation

Solid organ transplantation is an established treatment of choice for end-stage organ failure. However, all transplant patients are at risk of developing complications, including allograft rejection and death. Histological analysis of graft biopsy is still the gold standard for evaluation of allograft injury, but it is an invasive procedure and prone to sampling errors. The past decade has seen an increased number of efforts to develop minimally invasive procedures for monitoring allograft injury. Despite the recent progress, limitations such as the complexity of proteomics-based technology, the lack of standardization, and the heterogeneity of populations that have been included in different studies have hindered proteomic tools from reaching clinical transplantation. This review focuses on the role of proteomics-based platforms in biomarker discovery and validation in solid organ transplantation. We also emphasize the value of biomarkers that provide potential mechanistic insights into the pathophysiology of allograft injury, dysfunction, or rejection. Additionally, we forecast that the growth of publicly available data sets, combined with computational methods that effectively integrate them, will facilitate a generation of more informed hypotheses for potential subsequent evaluation in preclinical and clinical studies. Finally, we illustrate the value of combining data sets through the integration of 2 independent data sets that pinpointed hub proteins in antibody-mediated rejection.

SEPepQuant enhances the detection of possible isoform regulations in shotgun proteomics

Shotgun proteomics is essential for protein identification and quantification in biomedical research, but protein isoform characterization is challenging due to the extensive number of peptides shared across proteins, hindering our understanding of protein isoform regulation and their roles in normal and disease biology. We systematically assess the challenge and opportunities of shotgun proteomics-based protein isoform characterization using in silico and experimental data, and then present SEPepQuant, a graph theory-based approach to maximize isoform characterization. Using published data from one induced pluripotent stem cell study and two human hepatocellular carcinoma studies, we demonstrate the ability of SEPepQuant in addressing the key limitations of existing methods, providing more comprehensive isoform-level characterization, identifying hundreds of isoform-level regulation events, and facilitating streamlined cross-study comparisons. Our analysis provides solid evidence to support a widespread role of protein isoform regulation in normal and disease processes, and SEPepQuant has broad applications to biological and translational research.

The Hippo pathway noncanonically drives autophagy and cell survival in response to energy stress

The Hippo pathway is known for its crucial involvement in development, regeneration, organ size control, and cancer. While energy stress is known to activate the Hippo pathway and inhibit its effector YAP, the precise role of the Hippo pathway in energy stress response remains unclear. Here, we report a YAP-independent function of the Hippo pathway in facilitating autophagy and cell survival in response to energy stress, a process mediated by its upstream components MAP4K2 and STRIPAK. Mechanistically, energy stress disrupts the MAP4K2-STRIPAK association, leading to the activation of MAP4K2. Subsequently, MAP4K2 phosphorylates ATG8-family member LC3, thereby facilitating autophagic flux. MAP4K2 is highly expressed in head and neck cancer, and its mediated autophagy is required for head and neck tumor growth in mice. Altogether, our study unveils a noncanonical role of the Hippo pathway in energy stress response, shedding light on this key growth-related pathway in tissue homeostasis and cancer.

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

Top-down proteomics of myosin light chain isoforms define chamber-specific expression in the human heart

Myosin functions as the "molecular motor" of the sarcomere and generates the contractile force necessary for cardiac muscle contraction. Myosin light chains 1 and 2 (MLC-1 and -2) play important functional roles in regulating the structure of the hexameric myosin molecule. Each of these light chains has an 'atrial' and 'ventricular' isoform, so called because they are believed to exhibit chamber-restricted expression in the heart. However, recently the chamber-specific expression of MLC isoforms in the human heart has been questioned. Herein, we analyzed the expression of MLC-1 and -2 atrial and ventricular isoforms in each of the four cardiac chambers in adult non-failing donor hearts using top-down mass spectrometry (MS)-based proteomics. Strikingly, we detected an isoform thought to be ventricular, MLC-2v (gene: MYL2), in the atria and confirmed the protein sequence using tandem MS (MS/MS). For the first time, a putative deamidation post-translation modification (PTM) located on MLC-2v in atrial tissue was localized to amino acid N13. MLC-1v (MYL3) and MLC-2a (MYL7) were the only MLC isoforms exhibiting chamber-restricted expression patterns across all donor hearts. Importantly, our results unambiguously show that MLC-1v, not MLC-2v, is ventricle-specific in adult human hearts. Moreover, we found elevated MLC-2 phosphorylation in male hearts compared to female hearts across each cardiac chamber. Overall, top-down proteomics allowed an unbiased analysis of MLC isoform expression throughout the human heart, uncovering previously unexpected isoform expression patterns and PTMs.

Single-cell omic molecular profiling using capillary electrophoresis-mass spectrometry

Tissues and other cell populations are highly heterogeneous at the cellular level, owing to differences in expression and modifications of proteins, polynucleotides, metabolites, and lipids. The ability to assess this heterogeneity is crucial in understanding numerous biological phenomena, including various pathologies. Traditional analyses apply bulk-cell sampling, which masks the potentially subtle differences between cells that can be important in understanding of biological processes. These limitations due to cell heterogeneity inspired significant efforts and interest toward the analysis of smaller sample sizes, down to the level of individual cells. Among the emerging techniques, the unique capabilities of capillary electrophoresis coupled with mass spectrometry (CE-MS) made it a prominent technique for proteomics and metabolomics analysis at the single-cell level. In this review, we focus on the application of CE-MS in the proteomic and metabolomic profiling of single cells and highlight the recent advances in sample preparation, separation, MS acquisition, and data analysis.

A spatially defined human Notch receptor interaction network reveals Notch intracellular storage and Ataxin-2-mediated fast recycling

The Notch signaling pathway controls cell growth, differentiation, and fate decisions. Dysregulation of Notch signaling has been linked to various human diseases. Notch receptor resides in multiple cellular compartments, and its translocation plays a central role in pathway activation. However, the spatial regulation of Notch receptor functions remains largely elusive. Using TurboID-based proximity labeling followed by affinity purification and mass spectrometry, we establish a spatially defined human Notch receptor interaction network. Notch receptors interact with different proteins in distinct subcellular compartments to perform specific cellular functions. This spatially defined interaction network also reveals that a large fraction of NOTCH is stored at the endoplasmic reticulum (ER)-Golgi intermediate compartment and recruits Ataxin-2-dependent recycling machinery for rapid recycling, Notch signaling activation, and leukemogenesis. Our work provides insights into dynamic Notch receptor complexes with exquisite spatial resolution, which will help in elucidating the detailed regulation of Notch receptors and highlight potential therapeutic targets for Notch-related pathogenesis.

Proteolytic signaling in cancer

INTRODUCTION

Cancer is a disease of (altered) biological pathways, often driven by somatic mutations and with several implications. Therefore, the identification of potential markers of disease is challenging. Given the large amount of biological data generated with omics approaches, oncology has experienced significant contributions. Proteomics mapping of protein fragments, derived from proteolytic processing events during oncogenesis, may shed light on (i) the role of active proteases and (ii) the functional implications of processed substrates in biological signaling circuits. Both outcomes have the potential for predicting diagnosis/prognosis in diseases like cancer. Therefore, understanding proteolytic processing events and their downstream implications may contribute to advances in the understanding of tumor biology and targeted therapies in precision medicine.

AREAS COVERED

Proteolytic events associated with some hallmarks of cancer (cell migration and proliferation, angiogenesis, metastasis, as well as extracellular matrix degradation) will be discussed. Moreover, biomarker discovery and the use of proteomics approaches to uncover proteolytic signaling events will also be covered.

EXPERT OPINION

Proteolytic processing is an irreversible protein post-translational modification and the deconvolution of biological data resulting from the study of proteolytic signaling events may be used in both patient diagnosis/prognosis and targeted therapies in cancer.

Proteomic profiling of retina and retinal pigment epithelium combined embryonic tissue to facilitate ocular disease gene discovery

To expedite gene discovery in eye development and its associated defects, we previously developed a bioinformatics resource-tool iSyTE (integrated Systems Tool for Eye gene discovery). However, iSyTE is presently limited to lens tissue and is predominantly based on transcriptomics datasets. Therefore, to extend iSyTE to other eye tissues on the proteome level, we performed high-throughput tandem mass spectrometry (MS/MS) on mouse embryonic day (E)14.5 retina and retinal pigment epithelium combined tissue and identified an average of 3300 proteins per sample (n = 5). High-throughput expression profiling-based gene discovery approaches-involving either transcriptomics or proteomics-pose a key challenge of prioritizing candidates from thousands of RNA/proteins expressed. To address this, we used MS/MS proteome data from mouse whole embryonic body (WB) as a reference dataset and performed comparative analysis-termed "in silico WB-subtraction"-with the retina proteome dataset. In silico WB-subtraction identified 90 high-priority proteins with retina-enriched expression at stringency criteria of ≥ 2.5 average spectral counts, ≥ 2.0 fold-enrichment, false discovery rate < 0.01. These top candidates represent a pool of retina-enriched proteins, several of which are associated with retinal biology and/or defects (e.g., Aldh1a1, Ank2, Ank3, Dcn, Dync2h1, Egfr, Ephb2, Fbln5, Fbn2, Hras, Igf2bp1, Msi1, Rbp1, Rlbp1, Tenm3, Yap1, etc.), indicating the effectiveness of this approach. Importantly, in silico WB-subtraction also identified several new high-priority candidates with potential regulatory function in retina development. Finally, proteins exhibiting expression or enriched-expression in the retina are made accessible in a user-friendly manner at iSyTE ( https://research.bioinformatics.udel.edu/iSyTE/ ), to allow effective visualization of this information and facilitate eye gene discovery.

Performance benchmarking microplate-immunoassays for quantifying target-specific cysteine oxidation reveals their potential for understanding redox-regulation and oxidative stress

The antibody-linked oxi-state assay (ALISA) for quantifying target-specific cysteine oxidation can benefit specialist and non-specialist users. Specialists can benefit from time-efficient analysis and high-throughput target and/or sample n-plex capacities. The simple and accessible "off-the-shelf" nature of ALISA brings the benefits of oxidative damage assays to non-specialists studying redox-regulation. Until performance benchmarking establishes confidence in the "unseen" microplate results, ALISA is unlikely to be widely adopted. Here, we implemented pre-set pass/fail criteria to benchmark ALISA by evaluating immunoassay performance in diverse contexts. ELISA-mode ALISA assays were accurate, reliable, and sensitive. For example, the average inter-assay CV for detecting 20%- and 40%-oxidised PRDX2 or GAPDH standards was 4.6% (range: 3.6-7.4%). ALISA displayed target-specificity. Immunodepleting the target decreased the signal by ∼75%. Single-antibody formatted ALISA failed to quantify the matrix-facing alpha subunit of the mitochondrial ATP synthase. However, RedoxiFluor quantified the alpha subunit displaying exceptional performance in the single-antibody format. ALISA discovered that (1) monocyte-to-macrophage differentiation amplified PRDX2-oxidation in THP-1 cells and (2) exercise increased GAPDH-specific oxidation in human erythrocytes. The "unseen" microplate data were "seen-to-be-believed" via orthogonal visually displayed immunoassays like the dimer method. Finally, we established target (n = 3) and sample (n = 100) n-plex capacities in ∼4 h with 50-70 min hands-on time. Our work showcases the potential of ALISA to advance our understanding of redox-regulation and oxidative stress.