Top-Down Proteomic Characterization of Truncated Proteoforms

HCM-Associated MuRF1 Variants Compromise Ubiquitylation and Are Predicted to Alter Protein Structure

MuRF1 [muscle RING (Really Interesting New Gene)-finger protein-1] is an ubiquitin-protein ligase (E3), which encode by TRIM63 (tripartite motif containing 63) gene, playing a crucial role in regulating cardiac muscle size and function through ubiquitylation. Among hypertrophic cardiomyopathy (HCM) patients, 24 TRIM63 variants have been identified, with 1 additional variant linked to restrictive cardiomyopathy. However, only three variants have been previously investigated for their functional effects. The structural impacts of the 25 variants remain unexplored. This study investigated the effects of 25 MuRF1 variants on ubiquitylation activity using in vitro ubiquitylation assays and structural predictions using computational approaches. The variants were generated using site-directed PCR (Polymerase Chain Reaction) mutagenesis and subsequently purified with amylose affinity chromatography. In vitro ubiquitylation assays demonstrated that all 25 variants compromised the ability of MuRF1 to monoubiquitylate a titin fragment (A168-A170), while 17 variants significantly impaired or completely abolished auto-monoubiquitylation. Structural modelling predicted that 10 MuRF1 variants disrupted zinc binding or key stabilising interactions, compromising structural integrity. In contrast, three variants were predicted to enhance the structural stability of MuRF1, while six others were predicted to have no discernible impact on the structure. This study underscores the importance of functional assays and structural predictions in evaluating MuRF1 variant pathogenicity and provides novel insights into mechanisms by which these variants contribute to HCM and related cardiomyopathies.

Native top-down proteomics enables discovery in endocrine-resistant breast cancer

Oligomerization of proteoforms produces functional protein complexes. Characterization of these assemblies within cells is critical to understanding the molecular mechanisms involved in disease and to designing effective drugs. Here we present a native top-down proteomics (nTDP) strategy to identify protein assemblies (≤70 kDa) in breast cancer cells and in cells that overexpress epidermal growth factor receptor (EGFR), which serves as a resistance model of estrogen receptor-alpha (ER)-targeted therapies. This nTDP approach identified ~104 complexoforms from 17 protein complexes, which revealed several molecular features of the breast cancer proteome, including EGFR-induced dissociation of nuclear transport factor 2 (NUTF2) assemblies that modulate ER activity. We found that the K4 and K55 post-translational modification sites discovered with nTDP differentially impact the effects of NUTF2 on the inhibition of the ER signaling pathway. The characterization of endogenous proteoform-proteoform/ligand interactions revealed the molecular diversity of complexoforms and their role in breast cancer growth.

Full Window Data-Independent Acquisition Method for Deeper Top-Down Proteomics

Top-down proteomics (TDP) is emerging as a vital tool for the comprehensive characterization of proteoforms. However, as its core technology, top-down mass spectrometry (TDMS) still faces significant analytical challenges. While data-independent acquisition (DIA) has revolutionized bottom-up proteomics and metabolomics, they are rarely employed in TDP. The unique feature of protein ions in an electrospray mass spectrum as well as the data complexity require the development of new DIA strategies. This study introduces a machine learning-assisted Full Window DIA (FW-DIA) method that eliminates precursor ion isolation, making it compatible with a wide range of commercial mass spectrometers. Moreover, FW-DIA leverages all precursor protein ions to generate high-quality tandem mass spectra, enhancing signal intensities by ∼50-fold and protein sequence coverage by 3-fold in a modular protein analysis. The method was successfully applied to the analysis of a five-protein mixture under native conditions and Escherichia coli ribosomal proteoform characterization.

Native top-down proteomics reveals EGFR-ERα signaling crosstalk in breast cancer cells dissociates NUTF2 dimers to modulate ERα signaling and cell growth

Oligomerization of proteins and their modified forms (proteoforms) produces functional protein complexes 1,2. Complexoforms are complexes that consist of the same set of proteins with different proteoforms 3. The ability to characterize these assemblies within cells is critical to understanding the molecular mechanisms involved in disease and to designing effective drugs. An outstanding biological question is how proteoforms drive function and oligomerization of complexoforms. However, tools to define endogenous proteoform-proteoform/ligand interactions are scarce 4. Here, we present a native top-down proteomics (nTDP) strategy that combines size-exclusion chromatography, nano liquid-chromatography in direct infusion mode, field asymmetric ion mobility spectrometry, and multistage mass spectrometry to identify protein assemblies (≤70 kDa) in breast cancer cells and in cells that overexpress EGFR, a resistance model of estrogen receptor-α (ER-α) targeted therapies. By identifying ~104 complexoforms from 17 protein complexes, our nTDP approach revealed several molecular features of the breast cancer proteome, including EGFR-induced dissociation of nuclear transport factor 2 (NUTF2) assemblies that modulate ER activity. Our findings show that the K4 and K55 posttranslational modification sites discovered with nTDP differentially impact the effects of NUTF2 on the inhibition of the ER signaling pathway. By characterizing endogenous proteoform-proteoform/ligand interactions, we reveal the molecular diversity of complexoforms, which allows us to propose a model for ER drug discovery in the context of designing effective inhibitors to selectively bind and disrupt the actions of targeted ER complexoforms.

Enhanced Characterization of Histones Using 193 nm Ultraviolet Photodissociation and Proton Transfer Charge Reduction

Top-down characterization of histones, proteins that are critical participants in an array of DNA-dependent processes, offers the potential to examine the relationship between histone structure and mechanisms of genetic regulation. Mapping patterns of post-translational modifications (PTMs) of histones requires extensive backbone cleavages to bracket the sites of mass shifts corresponding to specific PTMs. Ultraviolet photodissociation (UVPD) causes substantial fragmentation of proteins, which is well-suited for PTM localization, but the resulting spectra are congested with fragment ions that may have overlapping isotopic distributions that confound deconvolution. Gas-phase proton transfer charge reduction (PTCR) decreases the charge states of highly charged ions, thus alleviating this congestion and facilitating the identification of additional sequence-determining and PTM-localizing fragment ions. By integrating UVPD with PTCR for histone proteoform analyses, sequence coverages up to 91% were achieved for calf thymus histone H4 containing acetylation marks at the N-terminus and Lys12 as well as a dimethylation at Arg3. UVPD-PTCR exhibited large gains in characterization for other histones, such as histone H2A, increasing the sequence coverage from 59 to 77% for monoacetylated H2A.

A guide to mass spectrometric analysis of extracellular vesicle proteins for biomarker discovery

Exosomes (small extracellular vesicles) in living organisms play an important role in processes such as cell proliferation or intercellular communication. Recently, exosomes have been extensively investigated for biomarker discoveries for various diseases. An important aspect of exosome analysis involves the development of enrichment methods that have been introduced for successful isolation of exosomes. These methods include ultracentrifugation, size exclusion chromatography, polyethylene glycol-based precipitation, immunoaffinity-based enrichment, ultrafiltration, and asymmetric flow field-flow fractionation among others. To confirm the presence of exosomes, various characterization methods have been utilized such as Western blot analysis, atomic force microscopy, electron microscopy, optical methods, zeta potential, visual inspection, and mass spectrometry. Recent advances in high-resolution separations, high-performance mass spectrometry and comprehensive proteome databases have all contributed to the successful analysis of exosomes from patient samples. Herein we review various exosome enrichment methods, characterization methods, and recent trends of exosome investigations using mass spectrometry-based approaches for biomarker discovery.

Human exhaled air diagnostic markers for respiratory tract infections in subjects receiving mechanical ventilation

Diagnosing respiratory tract infections (RTIs) in critical care settings is essential for appropriate antibiotic treatment and lowering mortality. The current diagnostic method, which primarily relies on clinical symptoms, lacks sensitivity and specificity, resulting in incorrect or delayed diagnoses, putting patients at a heightened risk. In this study we developed a noninvasive diagnosis method based on collecting non-volatile compounds in human exhaled air. We hypothesized that non-volatile compound profiles could be effectively used for bacterial RTI diagnosis. Exhaled air samples were collected from subjects receiving mechanical ventilation diagnosed with or without bacterial RTI in intensive care units at the Johns Hopkins Hospital. Truncated proteoforms, a class of non-volatile compounds, were characterized by top-down proteomics, and significant features associated with RTI were identified using feature selection algorithms. The results showed that three truncated proteoforms, collagen type VI alpha three chain protein, matrix metalloproteinase-9, and putative homeodomain transcription factor II were independently associated with RTI with thep-values of 2.0 × 10^-5, 1.1 × 10^-4, and 1.7 × 10^-3, respectively, using multiple logistic regression. Furthermore, a score system named 'TrunScore' was constructed by combining the three truncated proteoforms, and the diagnostic accuracy was significantly improved compared to that of individual truncated proteoforms, with an area under the receiver operator characteristic curve of 96.9%. This study supports the ability of this noninvasive breath analysis method to provide an accurate diagnosis for RTIs in subjects receiving mechanical ventilation. The results of this study open the doors to be able to potentially diagnose a broad range of diseases using this non-volatile breath analysis technique.

Validation of Top-Down Proteomics Data by Bottom-Up-Based N-Terminomics Reveals Pitfalls in Top-Down-Based Terminomics Workflows

Bottom-up proteomics (BUP)-based N-terminomics techniques have become standard to identify protein N-termini. While these methods rely on the identification of N-terminal peptides only, top-down proteomics (TDP) comes with the promise to provide additional information about post-translational modifications and the respective C-termini. To evaluate the potential of TDP for terminomics, two established TDP workflows were employed for the proteome analysis of the nematode Caenorhabditis elegans. The N-termini of the identified proteoforms were validated using a BUP-based N-terminomics approach. The TDP workflows used here identified 1658 proteoforms, the N-termini of which were verified by BUP in 25% of entities only. Caveats in both the BUP- and TDP-based workflows were shown to contribute to this low overlap. In BUP, the use of trypsin prohibits the detection of arginine-rich or arginine-deficient N-termini, while in TDP, the formation of artificially generated termini was observed in particular in a workflow encompassing sample treatment with high acid concentrations. Furthermore, we demonstrate the applicability of reductive dimethylation in TDP to confirm biological N-termini. Overall, our study shows not only the potential but also current limitations of TDP for terminomics studies and also presents suggestions for future developments, for example, for data quality control, allowing improvement of the detection of protein termini by TDP.

Accurate Proteoform Identification and Quantitation Using pTop 2.0

The remarkable advancement of top-down proteomics in the past decade is driven by the technological development in separation, mass spectrometry (MS) instrumentation, novel fragmentation, and bioinformatics. However, the accurate identification and quantification of proteoforms, all clearly-defined molecular forms of protein products from a single gene, remain a challenging computational task. This is in part due to the complicated mass spectra from intact proteoforms when compared to those from the digested peptides. Herein, pTop 2.0 is developed to fill in the gap between the large-scale complex top-down MS data and the shortage of high-accuracy bioinformatic tools. Compared with pTop 1.0, the first version, pTop 2.0 concentrates mainly on the identification of the proteoforms with unexpected modifications or a terminal truncation. The quantitation based on isotopic labeling is also a new function, which can be carried out by the convenient and user-friendly "one-key operation," integrated together with the qualitative identifications. The accuracy and running speed of pTop 2.0 is significantly improved on the test data sets. This chapter will introduce the main features, step-by-step running operations, and algorithmic developments of pTop 2.0 in order to push the identification and quantitation of intact proteoforms to a higher-accuracy level in top-down proteomics.

Shedding light on both ends: An update on analytical approaches for N- and C-terminomics

Though proteases were long regarded as nonspecific degradative enzymes, over time, it was recognized that they also hydrolyze peptide bonds very specifically with a limited substrate pool. This irreversible posttranslational modification modulates the fate and activity of many proteins, making proteolytic processing a master switch in the regulation of e.g., the immune system, apoptosis and cancer progression. N- and C-terminomics, the identification of protein termini, has become indispensable in elucidating protease substrates and therefore protease function. Further, terminomics has the potential to identify yet unknown proteoforms, e.g. formed by alternative splicing or the recently discovered alternative ORFs. Different strategies and workflows have been developed that achieve higher sensitivity, a greater depth of coverage or higher throughput. In this review, we summarize recent developments in both N- and C-terminomics and include the potential of top-down proteomics which inherently delivers information on both ends of analytes in a single analysis.

Multi-protease Approach for the Improved Identification and Molecular Characterization of Small Proteins and Short Open Reading Frame-Encoded Peptides

The identification of proteins below approximately 70-100 amino acids in bottom-up proteomics is still a challenging task due to the limited number of peptides generated by proteolytic digestion. This includes the short open reading frame-encoded peptides (SEPs), which are a subset of the small proteins that were not previously annotated or that are alternatively encoded. Here, we systematically investigated the use of multiple proteases (trypsin, chymotrypsin, LysC, LysargiNase, and GluC) in GeLC-MS/MS analysis to improve the sequence coverage and the number of identified peptides for small proteins, with a focus on SEPs, in the archaeon Methanosarcina mazei. Combining the data of all proteases, we identified 63 small proteins and additional 28 SEPs with at least two unique peptides, while only 55 small proteins and 22 SEP could be identified using trypsin only. For 27 small proteins and 12 SEPs, a complete sequence coverage was achieved. Moreover, for five SEPs, incorrectly predicted translation start points or potential in vivo proteolytic processing were identified, confirming the data of a previous top-down proteomics study of this organism. The results show clearly that a multi-protease approach allows to improve the identification and molecular characterization of small proteins and SEPs. LC-MS data: ProteomeXchange PXD023921.

Molecular cargo in myeloid-derived suppressor cells and their exosomes

Collaborative research is reviewed in which mass spectrometry-based proteomics and next generation sequencing were used qualitatively and quantitatively to interrogate proteins and RNAs carried in intact myeloid-derived suppressor cells (MDSC) and exosomes shed in vitro by MDSC. In aggregate exosomes more than 4000 proteins were identified, including annexins and immunosuppressive mediators. Bioassays showed that exosomes induce MDSC chemotaxis dependent on S100A8 and S100A9 in their cargo. Surface selective chemistry identified glycoproteins on MDSC and exosome surfaces, including CD47 and thrombospondin 1, which both facilitate exosome-catalyzed chemotaxis. Large numbers of mRNAs and microRNAs were identified in aggregate exosomes, whose potential functions in receptor cells include angiogenesis, and proinflammatory and immunosuppressive activities. Inflammation was found to have asymmetric effects on MDSC and exosomal cargos. Collectively, our findings indicate that the exosomes shed by MDSC provide divergent and complementary functions that support the immunosuppression and tumor promotion activities of MDSC.

Diagenetiforms: A new term to explain protein changes as a result of diagenesis in paleoproteomics

The term proteoform describes all combinations of change in a protein, as elucidated through intact mass proteomics. Paleoproteomic studies have begun using digestion-free and top-down techniques to access information from ancient and historical remains. However, to discuss protein changes that uniquely occur to archaeological and paleontological proteomes as the result of diagenesis (i.e., physical and chemical change imparted by burial), a novel term is needed that both addresses issues of combinatorics and distinguishes diagenetic-specific alteration. SIGNIFICANCE: The term diagenetiform provides the opportunity to communicate clearly the sets of diagenetic changes found on preserved proteins. The diagenetiform nomenclature will allow for top-down paleoproteomic studies to accurately describe the total changes detected on ancient proteins.

Detection of Tuberculosis by The Analysis of Exhaled Breath Particles with High-resolution Mass Spectrometry

Tuberculosis remains a global health threat killing over 1 million people per year. Current sputum-based diagnostics are specific but lack sensitivity resulting in treatment of many sputum negative cases. In this proof-of-concept study, we used high-resolution mass spectrometry to identify specific lipids in peripheral lung fluid samples of TB patients and controls, captured using a novel non-invasive sampling system. Exhaled respiratory particles were collected in liquid and after concentration and lipid extraction directly infused into a high-resolution mass spectrometer. High-resolution mass spectrometric data collection was conducted in a dual ion mode and chemical compositions were constructed using accurate mass measurement. Over 400 features with high segregating capacity were extracted and optimized using feature selection algorithm and machine learning, from which the accuracy of detection of positive tuberculosis patients was estimated. This current strategy provides sensitivity offered by high-resolution mass spectrometry and can be readily susceptible for developing a novel clinical assay exploring peripheral lung fluid for the detection of active TB cases.